Cardiovascular Disease Risk Tests

Number: 0381

Policy
Applicable CPT / HCPCS / ICD-10 Codes
Background
References

Policy

Scope of Policy

This Clinical Policy Bulletin addresses cardiovascular disease risk tests.

Medical Necessity

Aetna considers the following cardiovascular disease risk tests medically necessary (unless otherwise specified) when criteria are met:
1. Apolipoprotein B (ApoB)
  
  For members receiving lipid‑lowering therapy who have met their low-density lipoprotein cholesterol (LDL‑C) and/or non–high-density lipoprotein cholesterol (non-HDL‑C) goals and the test will be used to guide further therapeutic intensification, and member meets all the following criteria:
  1. Adult aged 19 years and older; and
  2. Receiving stable lipid‑lowering therapy (e.g., statin, ezetimibe, PCSK9 inhibitor, inclisiran, bempedoic acid); and
  3. One or more of the following goals has been achieved based on guideline‑appropriate risk stratification:
    1. LDL‑C at or below the individualized treatment target,
    2. Non–HDL‑C at or below the individualized treatment target; and
  4. Member has one of the following high-risk indications:
    1. Established atherosclerotic cardiovascular disease (ASCVD) (secondary prevention)
    2. Cardiovascular‑kidney‑metabolic (CKM) syndrome (characterized by abdominal obesity, insulin-resistant glucose metabolism, dyslipidemia, increased blood pressure, and sometimes microalbuminuria, proteinuria, or chronic kidney disease [CKD])
    3. Type 2 diabetes mellitus,
    4. Elevated triglycerides, defined as:
      1. Fasting triglycerides greater than or equal to150 mg/dL, or
      2. Non‑fasting triglycerides greater than or equal to 175 mg/dL; or
2. High-Sensitivity C-Reactive Protein (hsCRP)
  
  When all of the following criteria are met:
  1. Adults aged 30 to 79 years; and
  2. No known ASCVD; and
  3. Has borderline 10-year ASCVD risk estimate (3% to less than 5%) by the PREVENT-ASCVD equations (see Appendix); and
  4. Not currently on statin therapy; and
  5. LDL-lowering therapy initiation or intensity remains uncertain after standard risk assessment and shared decision making; or
  Routine serial testing is considered not medically necessary outside confirmatory measurement.
3. Homocysteine Testing
  
  For medical necessity indications, see CPB 0763 - Homocysteine Testing;
4. Lipoprotein(a)
  
  One-time lipoprotein(a) [Lp(a)] test for all adults aged 19 years and older to assess ASCVD risk.
  
  Repeat Lp(a) testing is considered not medically necessary because Lp(a) levels are largely determined by genetics and remain relatively stable over a person's lifetime; a single measurement is considered sufficient.
Experimental, Investigational, or Unproven

The following cardiovascular disease risks tests are considered experimental, investigational, or unproven because their clinical value has not been established (not an all-inclusive list):
1. Apolipoprotein B (Apo B)
  
  Members who have not met medically necessary criteria (above) and for all other indications, including:
  1. As a replacement for LDL-C or non-HDL-C in initial primary prevention screening
  2. For general population risk assessment
  3. Screening individuals not receiving lipid‑lowering therapy
  4. Repeated or serial ApoB measurements without a documented change in therapy or clinical status;
2. Cardiac Stress Testing and Stress Echocardiography
  
  Cardiac stress testing and stress echocardiography for cardiovascular disease risk assessment in asymptomatic low risk individuals;
3. High-Sensitivity C-Reactive Protein (hsCRP)
  1. For general population risk assessment,
  2. Routine cardiovascular screening in asymptomatic individuals
  3. Use as a stand‑alone predictor of ASCVD risk
  4. Use as an isolated indication for statin therapy
  5. Monitoring response to therapy,
  6. Secondary prevention in members with known ASCVD,
  7. Evaluation of acute or chronic inflammatory or infectious conditions unrelated to ASCVD risk assessment,
4. Homocysteine Testing
  
  For experimental, investigational, or unproven indications, see CPB 0763 - Homocysteine Testing;
5. Measurement of Carotid Intima-Media Thickness
  
  Measurement of carotid intima-media thickness for assessing CHD risk;
6. Noninvasive Measurement of Arterial Elasticity
  
  Noninvasive measurements of arterial elasticity by means of blood pressure waveforms (e.g., CardioVision MS-2000, CVProfilor, Digital Pulse Analyzer (DPA), DSI Pulse Wave Velocity analysis, Max Pulse and HD/PulseWave CR-2000) and noninvasive calculation and analysis of central arterial pressure waveforms (SphygmoCor) for assessing CHD risk;
7. Peripheral Arterial Tonometry
  
  Peripheral arterial tonometry (e.g., the Endo-PAT2000/EndoPAT device) for assessing CHD;
8. Ultrasound of the Upper and Lower Extremity Arteries
  
  Ultrasound of the upper and lower extremity arteries for screening of persons without signs or symptoms of peripheral arterial disease;
9. Venous Ultrasound
  
  Venous ultrasound for screening persons without signs or symptoms of peripheral venous disease (PVD) and who are not at high risk for venous thromboembolic disorders.
10. Experimental, Investigational, or Unproven CHD Risk Tests
  
  Aetna considers the following tests / devices for assessing CHD risk experimental, investigational, or unproven because their clinical value has not been established:
  1. Acarix CADScor System
  2. Activated factor VII
  3. Adiponectin
  4. Algorithmically scored multi-protein biomarker panels (i.e., HART CADhs, HART CVE, HART KD)
  5. Allelica Multi-Ancestry CAD PRS
  6. Angiotensin gene (CardiaRisk AGT)
  7. Anti-thrombin III
  8. Apelin
  9. Apolipoprotein A-I (apo AI) (Boston Heart HDL Map panel)
  10. Apolipoprotein E (apo E)
  11. Apolipoprotein E genotyping
  12. ASCVD risk testing (individual or panel) (eg, c-peptide, islet cell antibodies, nonesterified fatty acids (free fatty acids), proinsulin and total insulin)
  13. B-type natriuretic peptides
  14. CADence System
  15. CARDIO inCode-Score
  16. CardioRisk+
  17. Carotid ultrasound screening of asymptomatic persons for carotid artery stenosis
  18. Cathepsin S
  19. Chromosome 9 polymorphism 9p21
  20. Circulating microRNAs (e.g., miR-1, miR-16, miR-26a, miR-27a, and miR-29a, miR-133a, and miR-199a-5p; not an all-inclusive list)
  21. Coenzyme Q10 (CoQ10)
  22. Coronary artery reactivity test
  23. Corus CAD Gene Expression Profile
  24. Cystatin-C
  25. Endothelin testing
  26. Epi+Gen CHD for prediction of coronary heart disease
  27. Factor II (thrombin) (F2 gene)
  28. Factor V Leiden (F5 gene)
  29. Fibrinogen
  30. 4q25 genotype testing (eg, 4q25-AF Risk Genotype Test, Cardio IQ 4q25-AF Risk Genotype Test)
  31. Galectin-3
  32. Genetic testing
  33. GlycA (glycosylated acute phase proteins)
  34. Growth stimulation expressed gene 2 (ST2)
  35. HDL Reverse Cholesterol Transport Panel with pCAD Score for evaluating risk of cardiovascular diseases
  36. HDL subspecies (LpAI, LpAI/AII and/or HDL3 and HDL2)
  37. Interleukin 6 (IL-6)
  38. Interleukin 6 -174 g/c promoter polymorphism
  39. Interleukin 17 gene polymorphism
  40. Interleukin 18 (IL-18)
  41. Kinesin-like protein 6 (KLP6)
  42. LDL gradient gel electrophoresis
  43. LDL subspecies (small and large LDL particles)
  44. Leptin
  45. Lipidomic and metabolomic risk markers
  46. Lipoprotein remnants: intermediate density lipoproteins (IDL) and small density lipoproteins
  47. Lipoprotein-associated phospholipase A2 (Lp-PLA2) (PLAC)
  48. Liposcale test
  49. Long chain omega-3 fatty acids composition in red blood cell
  50. LPA Intron-25 genotype testing (eg, Cardio IQ Intron-25 Genotype Test, LPA Intron-25 Genotype Test)
  51. MaxPulse testing
  52. Measurement of free fatty acids
  53. Methods to determine vascular age
  54. Mid-regional pro-atrial natriuretic peptide
  55. MIRISK VP test
  56. MTHFR genetic testing
  57. Myeloperoxidase (MPO)
  58. NMR Lipoprofile
  59. OmegaCheck Panel
  60. Osteoprotegerin
  61. Oxidized low-density lipoprotein as a biomarker for cardiovascular disease stratification
  62. Oxidized phospholipids
  63. Peroxisome proliferator-activated receptor
  64. Plasma ceramide
  65. Plasma levels of trimethylamine-N-oxide (TMAO) (Note: Measurement of urinary trimethylamine (TMA) and TMA-N-oxide (TMAO) is considered medically necessary for diagnosing individuals with foul smelling urine suspected of having trimethylaminuria (fish odor syndrome))
  66. Plasminogen activator inhibitor (PAI–1)
  67. PrecisionCHD for prediction of coronary heart disease
  68. Pregnancy-associated plasma protein-A (PAPP-A)
  69. Protein C
  70. Prothrombin gene mutation testing
  71. QuantaFlo System for evaluation of peripheral arterial disease
  72. Receptor for advanced glycosylation end products (RAGE) gene Gly82Ser polymorphism testing
  73. Resistin
  74. Retinol binding protein 4 (RBP4)
  75. Serum sterols (eg, Boston Heart Cholesterol Balance Test)
  76. Singulex SMC testing for risk of cardiac dysfunction and vascular inflammation (eg, SMC Endothelin, SMC IL-6, SMC IL 17A, SMC c TnI and SMC TNF-α)
  77. Skin cholesterol (eg, PREVU)
  78. SLCO1B1 (statin induced myopathy genetic testing)
  79. SmartVascular Dx (SmartHealth Vascular Dx)
  80. SNP-based testing (eg, Cardiac Healthy Weight DNA Insight, Healthy Woman DNA Insight Test, Heart Health Genetic Test)
  81. Soluble cell adhesion molecules (e.g., intercellular adhesion molecule-1 [ICAM-1], vascular cell adhesion molecule-1 [VCAM-1], E-selectin, and P-selectin)
  82. Thromboxane metabolite(s) testing
  83. Tissue plasminogen activator (tPA)
  84. Toll-like receptor 4 (TLR4) Asp299Gly (rs4986790) polymorphism
  85. Transforming growth factor beta
  86. Troponin I (e.g., PATHFAST cTnI-II)
  87. TruD MDS ASCVD (TruDiagnostic, Inc.)
  88. Tumor necrosis factor-alpha (TNF-a)
  89. Total cholesterol content in red blood cell membranes
  90. Vertical Auto Profile (VAP) with or without vertical lipoprotein particle (VLP) technology
  91. Vicorder device for non-invasive measurements of arterial elasticity
  92. Visfatin
  93. von Willebrand factor antigen level.
  The medical literature does not support the utility of the above tests for screening, diagnosis, or management of CHD.
Related Policies

Table:
CPT Codes / HCPCS Codes / ICD-10 Codes
Code	Code Description
*High-sensitivity C-reactive protein (hs-CRP)*:
CPT codes covered if selection criteria are met:
86141	C-reactive protein; high sensitivity (hsCRP) [2 or more major risk factors, LDL 100-300 mg/dl, and intermediate risk of CVD by global risk assessment - see criteria]
Other CPT codes related to the CPB:
81400 - 81408	Molecular pathology
Other HCPCS codes related to the CPB:
Ezetimibe, PCSK9 inhibitor, Bempedoic acid- No Specific Code
J1306	Injection, inclisiran, 1 mg
ICD-10 codes covered if selection criteria are met:
Z13.6	Encounter for screening for cardiovascular disorders [Borderline 10-year ASCVD risk estimate (3% to less than 5%) by the PREVENT-ASCVD equations]
ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):
I25.10	Atherosclerotic heart disease of native coronary artery without angina pectoris
I25.110 – I25.119	Atherosclerotic heart disease of native coronary artery with angina pectoris
Major risk factors [need at least 2]:
*Apolipoprotein B (apo B)*:
CPT codes covered if selection criteria are met:
82172	Apolipoprotein, each [covered for apoB - not apoA1 or apoE]
Other CPT codes related to the CPB:
80061	Lipid panel This panel must include the following: Cholesterol, serum, total (82465) Lipoprotein, direct measurement, high density cholesterol (HDL cholesterol) (83718) Triglycerides (84478)
83721	Lipoprotein, direct measurement; LDL cholesterol
Other HCPCS codes related to the CPB:
No Specific Code
J1306	Injection, inclisiran, 1 mg
ICD-10 codes covered if selection criteria are met:
E10.10 - E11.9	Diabetes mellitus
E66.01 - E66.9	Overweight and obesity
E78.00	Pure hypercholesterolemia
E78.1	Pure hyperglyceridemia
E78.2	Mixed hyperlipidemia
E78.5	Hyperlipidemia, unspecified [Dyslipidemia]
E88.810 - E88.819	Metabolic syndrome and other insulin resistance
I20.0 - I25.9	Ischemic heart diseases
I25.10	Atherosclerotic heart disease of native coronary artery without angina pectoris
I50.1 - I50.9	Heart failure
N18.1 - N18.9	Chronic kidney disease (CKD)
R03.0	Elevated blood-pressure reading, without diagnosis of hypertension
R80.0 - R80.9	Proteinuria
*Trimethylamine/trimethylamine N-oxide (TMA/TMAO) profile*:
CPT codes covered if selection criteria are met:
0256U	Trimethylamine/trimethylamine N-oxide (TMA/TMAO) profile, tandem mass spectrometry (MS/MS), urine, with algorithmic analysis and interpretive report
ICD-10 codes covered if selection criteria are met:
E72.52	Trimethylaminuria
R43.9	Unspecified disturbances of smell and taste
R82.998	Other abnormal findings in urine
*Carotid Ultrasound Screening*:
CPT codes covered for indications listed in the CPB:
93880	Duplex scan of extracranial arteries; complete bilateral study
93882	unilateral or limited study
Other HCPCS codes related to the CPB:
G0537	Administration of a standardized, evidence-based atherosclerotic cardiovascular disease (ascvd) risk assessment, 5-15 minutes, not more often than every 12 months
G0538	Atherosclerotic cardiovascular disease (ascvd) risk management services; clinical staff time; per calendar month
ICD-10 codes covered if selection criteria are met:
G04.1	Tropical spastic paraplegia
G45.0 – G45.9	Transient cerebral ischemic attacks and related syndromes
G46.0	Middle cerebral artery syndrome
G46.1	Anterior cerebral artery syndrome
G46.2	Posterior cerebral artery syndrome
G81.00 – G81.94	Hemiplegia and hemiparesis
G82.20 – G82.54	Paraplegia (paraparesis) and quadriplegia (quadriparesis)
G83.0 – G83.34	Other paralytic syndromes
G83.4	Cauda equina syndrome
G83.5	Locked-in state
G83.81 – G93.9	Other specified paralytic syndromes
G97.31 – G97.32	Intraoperative hemorrhage and hematoma of a nervous system organ or structure complicating a procedure
G97.41 – G97.49	Accidental puncture and laceration of a nervous system organ or structure during a procedure
G97.51 – G97.52	Postprocedural hemorrhage of a nervous system organ or structure following a procedure
H34.00 – H34.8132	Retinal vascular occlusions
H34.821 – H34.829	Venous engorgement
H34.8310 – H34.9	Tributary (branch) retinal vein occlusion
H35.061 – H35.069	Retinal vasculitis
H35.81 – H35.9	Other specified retinal disorders
H47.011 – H47.019	Disorders of optic nerve, not elsewhere classified
H53.10 – H53.11	Subjective visual disturbances
H53.121 – H53.129	Transient visual loss
H53.131 – H53.139	Sudden visual loss
H53.2	Diplopia
H53.40	Unspecified visual field defects
H53.411 – H53.489	Scotoma involving central area
H53.8	Other visual disturbances
H53.9	Unspecified visual disturbance
H54.7	Unspecified visual loss
H59.211 – H59.229	Accidental puncture and laceration of eye and adnexa during a procedure
H81.01 – H81.09	Benign paroxysmal vertigo
H81.4	Vertigo of central origin
H93.11 – H93.19	Tinnitus
H93.A1 – H93.A9	Pulsatile tinnitus
H95.31 – H95.32	Accidental puncture and laceration of ear and mastoid process during a procedure
I25.10	Atherosclerotic heart disease of native coronary artery without angina pectoris
I25.110 – I25.119	Atherosclerotic heart disease of native coronary artery with angina pectoris
I25.2	Old myocardial infarction
I25.5	Ischemic cardiomyopathy
I60.00 – I62.9	Nontraumatic subarachnoid hemorrhage
I63.00 – I63.9	Cerebral infarction
I65.01 – I65.9	Occlusion and stenosis of precerebral arteries, not resulting in cerebral infarction
I66.01 – I66. 9	Occlusion and stenosis of cerebral arteries, not resulting in cerebral infarction
I67.0 – I67.9	Other cerebrovascular diseases
I68.0	Cerebral arteritis in other diseases classified elsewhere
I68.8	Other cerebrovascular disorders in diseases classified elsewhere
I69.021	Dysphasia following nontraumatic subarachnoid hemorrhage
I69.022	Dysarthria following nontraumatic subarachnoid hemorrhage
I69.023	Fluency disorder following nontraumatic subarachnoid hemorrhage
I69.028	Other speech and language deficits following nontraumatic subarachnoid hemorrhage
I69.090 – I69.098	Other sequelae of nontraumatic subarachnoid hemorrhage
I69.120 – I69.128	Speech and language deficits following nontraumatic intracerebral hemorrhage
I69.190 – I69.198	Other sequelae of nontraumatic intracerebral hemorrhage
I69.220 – I69. 228	Speech and language deficits following other nontraumatic intracranial hemorrhage
I69.290 – I69.298	Other sequelae of other nontraumatic intracranial hemorrhage
I69.320 – I69.328	Speech and language deficits following cerebral infarction
I69.351 – I69.359	Hemiplegia and hemiparesis following cerebral infarction
I69.390 – I69.398	Other sequelae of cerebral infarction
I69.820 – I69.828	Speech and language deficits following other cerebrovascular disease
I69.890 – I69.898	Other sequelae of other cerebrovascular disease
I69.920 – I69.928	Speech and language deficits following unspecified cerebrovascular disease
I70.0 – I70.1	Atherosclerosis
I70.211 – I70.219	Atherosclerosis of native arteries of extremities with intermittent claudication
I70.8	Atherosclerosis of other arteries
I70.90 – I70.92	Other and unspecified atherosclerosis
I72.0 – I72.9	Other aneurysm
I75.011 – I75.89	Atheroembolism
I77.0 – I77.6	Other disorders of arteries and arterioles
I77.70 – I77.79	Other arterial dissection
I79.0 – I79.8	Disorders of arteries, arterioles and capillaries in diseases classified elsewhere
I97.51 – I97.52	Accidental puncture and laceration of a circulatory system organ or structure during a procedure
I97.810 – I97.821	Other intraoperative and postprocedural complications and disorders of the circulatory system, not elsewhere classified
J95.71 – J95.72	Accidental puncture and laceration of a respiratory system organ or structure during a procedure
K91.71 – K91.72	Accidental puncture and laceration of a digestive system organ or structure during a procedure
L76.11 – L76.12	Accidental puncture and laceration of skin and subcutaneous tissue during a procedure
M30.0 – M30.8	Polyarteritis nodosa and related conditions
M31.10 – M31.19	Thrombotic microangiopathy
M31.30 – M31.31	Wegener's granulomatosis
M31.4	Aortic arch syndrome [Takayasu]
M31.5	Giant cell arteritis with polymyalgia rheumatica
M31.6	Other giant cell arteritis
M31.7	Microscopic polyangiitis
M31.8	Other specified necrotizing vasculopathies
M31.9	Necrotizing vasculopathy, unspecified
M96.820 – M96.821	Accidental puncture and laceration of a musculoskeletal structure during a procedure
N99.71 – N99.72	Accidental puncture and laceration of a genitourinary system organ or structure during a procedure
R09.01 – R09.89	Other symptoms and signs involving the circulatory and respiratory system
R13.10 – R13.19	Dysphagia
R20.0 – R20.9	Disturbances of skin sensation
R22.0 - R22.2	Localized swelling, mass and lump of skin and subcutaneous tissue
R26.0 – R26.9	Abnormalities of gait and mobility
R27.0 – R27.9	Other lack of coordination
R29.5	Transient paralysis
R29.810 – R29.818	Other symptoms and signs involving the nervous system
R42	Dizziness and giddiness
R47.01 – R47.89	Speech disturbances, not elsewhere classified
R55	Syncope and collapse
S09.0XXA – S09.0XXS	Injury of blood vessels of head, not elsewhere classified
S15.001A – S15.001S	Unspecified injury of right carotid artery
S15.002A – S15.002S	Unspecified injury of left carotid artery
S15.009A – S15.009S	Unspecified injury of unspecified carotid artery
S15.011A – S15.011S	Minor laceration of right carotid artery
S15.012A – S15.012S	Minor laceration of left carotid artery
S15.019A – S15.019S	Minor laceration of unspecified carotid artery
S15.021A – S15.021S	Major laceration of right carotid artery
S15.022A – S15.022S	Major laceration of left carotid artery
S15.029A – S15.029S	Major laceration of unspecified carotid artery
S15.091A – S15.091S	Other specified injury of right carotid artery
S15.092A – S15.092S	Other specified injury of left carotid artery
S15.099A – S15.099S	Other specified injury of unspecified carotid artery
S15.101A – S15.101S	Unspecified injury of right vertebral artery
S15.102A – S15.102S	Unspecified injury of left vertebral artery
S15.109A – S15.109S	Unspecified injury of unspecified vertebral artery
S15.111A – S15.111S	Minor laceration of right vertebral artery
S15.112A – S15.112S	Minor laceration of left vertebral artery
S15.122A – S15.122S	Major laceration of left vertebral artery
S15.191A – S15.191S	Other specified injury of right vertebral artery
S15.192A – S15.192S	Other specified injury of left vertebral artery
S15.211A – S15.211S	Minor laceration of right external jugular vein
S15.212A – S15.212S	Minor laceration of left external jugular vein
S15.221A – S15.221D	Major laceration of right external jugular vein
S15.222A – S15.222S	Major laceration of left external jugular vein
S15.291A – S15.291S	Other specified injury of right external jugular vein
S15.292A – S15.292D	Other specified injury of left external jugular vein
S15.311A – S15.311S	Minor laceration of right internal jugular vein
S15.312A – S15.312S	Minor laceration of left internal jugular vein
S15.321A – S15.321S	Major laceration of right internal jugular vein
S15.322A – S15.322D	Major laceration of left internal jugular vein
S15.391A – S15.391S	Other specified injury of right internal jugular vein
S15.392A – S15.392S	Other specified injury of left internal jugular vein
S15.8XXA – S15.8XXS	Injury of other specified blood vessels at neck level
S25.111A – S25.111S	Minor laceration of right innominate or subclavian artery
S25.112A – S25.112S	Minor laceration of left innominate or subclavian artery
S25.119A – S25.119S	Minor laceration of unspecified innominate or subclavian artery
S25.121A – S25.121S	Major laceration of right innominate or subclavian artery
S25.122A – S25.122S	Major laceration of left innominate or subclavian artery
S25.129A – S25.129S	Major laceration of unspecified innominate or subclavian artery
S25.191A – S25.191S	Other specified injury of right innominate or subclavian artery
S25.192A – S25.192S	Other specified injury of left innominate or subclavian artery
S25.199A – S25.199S	Other specified injury of unspecified innominate or subclavian artery
T82.311A – T82.311S	Breakdown (mechanical) of carotid arterial graft (bypass)
T82.321A – T82.321S	Displacement of carotid arterial graft (bypass)
T82.322A – T82.322S	Displacement of femoral arterial graft (bypass)
T82.328A – T82.328S	Displacement of other vascular grafts
T82.329A – T82.329S	Displacement of unspecified vascular grafts
T82.330A – T82.330S	Leakage of aortic (bifurcation) graft (replacement)
T82.331A – T82.331S	Leakage of carotid arterial graft (bypass)
T82.391A – T82.392S	Other mechanical complication of carotid arterial graft (bypass)
Z01.810	Encounter for preprocedural cardiovascular examination
Z01.818	Encounter for other preprocedural examination
Z09	Encounter for follow-up examination after completed treatment for conditions other than malignant neoplasm
Z48.812	Encounter for surgical aftercare following surgery on the circulatory system
Z86.711	Personal history of pulmonary embolism
Z86.73	Personal history of transient ischemic attack (TIA), and cerebral infarction without residual deficits
ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):
Z00.00 - Z00.01	Encounter for general adult medical examination without or with abnormal findings
Z01.810	Encounter for preprocedural cardiovascular examination
Z01.818	Encounter for other preprocedural examination
Z03.89	Encounter for observation for other suspected diseases and conditions ruled out
Z04.9	Encounter for examination and observation for unspecified reason
Z09	Encounter for follow-up examination after completed treatment for conditions other than malignant neoplasm
Z13.220	Encounter for screening for lipoid disorders
Z13.6	Encounter for screening for cardiovascular disorders
Z48.812	Encounter for surgical aftercare following surgery on the circulatory system
Z82.49	Family history of ischemic heart disease and other diseases of the circulatory system
Z86.73	Personal history of transient ischemic attack (TIA), and cerebral infarction without residual deficits
*Lipoprotein(a) [Lp(a)]*:
CPT codes covered if selection criteria are met:
83695	Lipoprotein (a)
ICD-10 codes covered if selection criteria are met:
Z13.6	Encounter for screening for cardiovascular disorders [Atherosclerotic cardiovascular disease (ASCVD) risk]
*Tests considered experimental and investigational for assessing CHD risk*:
CPT codes not covered for indications listed in the CPB:
CADence System, QuantaFlo System, Vicorder device, Allelica Multi-Ancestry CAD PRS - no specific code
0024U	Glycosylated acute phase proteins (GlycA), nuclear magnetic resonance spectroscopy, quantitative
0052U	Lipoprotein, blood, high resolution fractionation and quantitation of lipoproteins, including all five major lipoprotein classes and subclasses of HDL, LDL, and VLDL by vertical auto profile ultracentrifugation
0119U	Cardiology, ceramides by liquid chromatography–tandem mass spectrometry, plasma, quantitative report with risk score for major cardiovascular events
0308U	Cardiology (coronary artery disease [CAD]), analysis of 3 proteins (high sensitivity [hs] troponin, adiponectin, and kidney injury molecule-1 [KIM-1]), plasma, algorithm reported as a risk score for obstructive CAD
0309U	Cardiology (cardiovascular disease), analysis of 4 proteins (NT-proBNP, osteopontin, tissue inhibitor of metalloproteinase-1 [TIMP-1], and kidney injury molecule-1 [KIM-1]), plasma, algorithm reported as a risk score for major adverse cardiac event
0310U	Pediatrics (vasculitis, Kawasaki disease [KD]), analysis of 3 biomarkers (NT- proBNP, C-reactive protein, and T-uptake), plasma, algorithm reported as a risk score for KD
0377U	Cardiovascular disease, quantification of advanced serum or plasma lipoprotein profile, by nuclear magnetic resonance (NMR) spectrometry with report of a lipoprotein profile (including 23 variables)
0401U	Cardiology (coronary heart disease [CAD]), 9 genes (12 variants), targeted variant genotyping, blood, saliva, or buccal swab, algorithm reported as a genetic risk score for a coronary event
0415U	Cardiovascular disease (acute coronary syndrome [ACS]), IL-16, FAS, FASLigand, HGF, CTACK, EOTAXIN, and MCP-3 by immunoassay combined with age, sex, family history, and personal history of diabetes, blood, algorithm reported as a 5-year (deleted risk) score for ACS [SmartVascular Dx]
0439U	Cardiology (coronary heart disease [CHD]), DNA, analysis of 5 single-nucleotide polymorphisms (SNPs) (rs11716050 [LOC105376934], rs6560711 [WDR37], rs3735222 [SCIN/LOC107986769], rs6820447 [intergenic], and rs9638144 [ESYT2]) and 3 DNA methylation markers (cg00300879 [transcription start site {TSS200} of CNKSR1], cg09552548 [intergenic], and cg14789911 [body of SPATC1L]), qPCR and digital PCR, whole blood, algorithm reported as a 4-tiered risk score for a 3-year risk of symptomatic CHD
0440U	Cardiology (coronary heart disease [CHD]), DNA, analysis of 10 single-nucleotide polymorphisms (SNPs) (rs710987 [LINC010019], rs1333048 [CDKN2B-AS1], rs12129789 [KCND3], rs942317 [KTN1-AS1], rs1441433 [PPP3CA], rs2869675 [PREX1], rs4639796 [ZBTB41], rs4376434 [LINC00972], rs12714414 [TMEM18], and rs7585056 [TMEM18]) and 6 DNA methylation markers (cg03725309 [SARS1], cg12586707 [CXCL1, cg04988978 [MPO], cg17901584 [DHCR24-DT], cg21161138 [AHRR], and cg12655112 [EHD4]), qPCR and digital PCR, whole blood, algorithm reported as detected or not detected for CHD
0466U	Cardiology (coronary artery disease [CAD]), DNA, genomewide association studies (564856 single-nucleotide polymorphisms [SNPs], targeted variant genotyping), patient lifestyle and clinical data, buccal swab, algorithm reported as polygenic risk to acquired heart disease
0541U	Cardiovascular disease (HDL reverse cholesterol transport), cholesterol efflux capacity, LC-MS/MS, quantitative measurement of 5 distinct HDL-bound apolipoproteins (apolipoproteins A1, C1, C2, C3, and C4), serum, algorithm reported as prediction of coronary artery disease (pCAD) score
0617U	Cardiovascular (atherosclerotic cardiovascular disease [ASCVD]), DNA methylation analysis of more than 20,000 sites, whole blood, algorithm reported as positive or negative risk
0716T	Cardiac acoustic waveform recording with automated analysis and generation of coronary artery disease risk score
81229	Cytogenomic constitutional (genome-wide) microarray analysis; interrogation of genomic regions for copy number and single nucleotide polymorphism (SNP) variants for chromosomal abnormalities [not covered for cardiovascular disease risk]
81240	F2 (prothrombin, coagulation factor II) (eg, hereditary hypercoagulability) gene analysis, 20210G>A variant
81241	F5 (coagulation Factor V) (eg, hereditary hypercoagulability) gene analysis, Leiden variant
81291	MTHFR (5,10-methylenetetrahydrofolate reductase) (eg, hereditary hypercoagulability) gene analysis, common variants (eg, 677T, 1298C)
81328	SLCO1B1 (solute carrier organic anion transporter family, member 1B1) (eg, adverse drug reaction), gene analysis, common variant(s) (eg, *5)
81400	Molecular pathology procedure, Level 1(eg, identification of single germline variant [eg, SNP] by techniques such as restriction enzyme digestion or melt curve analysis)
81401	Molecular pathology procedure, Level 2 (eg, 2-10 SNPs, 1 methylated variant, or 1 somatic variant [typically using nonsequencing target variant analysis], or detection of a dynamic mutation disorder/triplet repeat)
81405	Molecular pathology procedure, Level 6
81406	Molecular pathology procedure, Level 7
81493	Coronary artery disease, mRNA, gene expression profiling by real-time RT-PCR of 23 genes, utilizing whole peripheral blood, algorithm reported as a risk score
82163	Angiotensin II
82542	Column chromatography, includes mass spectrometry, if performed (eg, HPLC, LC, LC/MS, LC/MS-MS, GC, GC/MS-MS, GC/MS, HPLC/MS), non-drug analyte(s) not elsewhere specified, qualitative or quantitative, each specimen [not covered for cardiovascular disease risk]
82610	Cystatin C [not covered for cardiovascular disease risk]
82725	Fatty acids, nonesterified [not covered for cardiovascular disease risk]
82777	Galectin-3 [not covered for cardiovascular disease risk]
83006	Growth stimulation expressed gene 2 (ST2, Interleukin 1 receptor like-1)
83519	Immunoassay for analyte other than infectious agent antibody or infectious agent antigen; quantitative, by radioimmunoassay (eg, RIA)
83520	Immunoassay for analyte other than infectious agent antibody or infectious agent antigen; quantitative, not otherwise specified [adiponectin] [leptin] [interleukin-6 (IL-6)] [tumor necrosis factor alpha (TNF-a)] [Oxidized phospholipids] [interleukin 17] [toll-like receptor 4 (TLR4)] [Interleukin-18 (IL-18)] [soluble cell adhesion molecules (e.g., intercellular adhesion molecule-1 [ICAM-1], vascular cell adhesion molecule-1 [VCAM-1], E-selectin, P-selectin)] [transforming growth factor beta] [Oxidized low-density lipoprotein]
83525	Insulin, total [not covered for cardiovascular disease risk]
83695	Lipoprotein (a)
83698	Lipoprotein-associated phospholipase A2 (Lp-PLA2)
83700	Lipoprotein, blood; electorophoretic separation and quantitation
83701	high resolution fractionation and quantitation of lipoproteins including lipoprotein subclasses when performed (eg, electrophoresis, ultracentrifugation) [VAP cholesterol test]
83704	quantitation of lipoprotein particle numbers and lipoprotein particle subclasses (eg, by nuclear magnetic resonance spectroscopy)
83719	Lipoprotein, direct measurement; VLDL cholesterol
83722	Lipoprotein, direct measurement; small dense LDL cholesterol
83876	Myeloperoxidase (MPO)
83880	Natriuretic peptide
83883	Nephelometry, each analyte not elsewhere specified [retinol binding protein 4 (RBP4)]
84163	Pregnancy-associated plasma protein-A (PAPP-A)
84206	Proinsulin [not covered for cardiovascular disease risk]
84431	Thromboxane metabolite(s), including thromboxane if performed, urine [not covered for cardiovascular disease risk]
84484	Troponin, quantitative
84512	Troponin, qualitative
84681	C-peptide [not covered for cardiovascular disease risk]
85246	Factor VIII, VW factor antigen
85300	Clotting inhibitors or anticoagulants; antithrombin III, activity
85301	Clotting inhibitors or anticoagulants; antithrombin III, antigen assay
85302	Clotting inhibitors or anticoagulants; protein c, antigen
85303	Clotting inhibitors or anticoagulants; protein c, activity, and Activated Protein C (APC) resistance assay
85384	Fibrinogen; activity
85385	antigen
85415	Fibrinolytic factors and inhibitors; plasminogen activator
86341	Islet cell antibody [not covered for cardiovascular disease risk]
88271 - 88275	Molecular cytogenetics [genetic testing] [MIRISK VP test]
93050	Arterial pressure waveform analysis for assessment of central arterial pressures, includes obtaining waveform(s), digitization and application of nonlinear mathematical transformations to determine central arterial pressures and augmentation index, with interpretation and report, upper extremity artery, non-invasive
93350	Echocardiography, transthoracic, real-time with image documentation (2D), includes M-mode recording, when performed, during rest and cardiovascular stress test using treadmill, bicycle exercise and/or pharmacologically induced stress, with interpretation and report
93351	including performance of continuous electrocardiographic monitoring, with supervision by a physician or other qualified health care professional
+93352	Use of echocardiographic contrast agent during stress echocardiography (List separately in addition to code for primary procedure)
93895	Quantitative carotid intima media thickness and carotid atheroma evaluation, bilateral
93880	Duplex scan of extracranial arteries; complete bilateral study
93882	unilateral or limited study
93922	Limited bilateral noninvasive physiologic of upper or lower extremity arteries, (eg, for lower extremity: ankle/brachial indices at distal posterior tibial and anterior tibial/dorsalis pedis arteries plus bidirectional, Doppler waveform recording and analysis at 1-2 levels, or ankle/brachial indices at distal posterior tibial and anterior tibial/dorsalis pedis arteries plus volume plethysmography at 1-2 levels, or ankle/brachial indices at distal posterior tibial and anterior tibial/dorsalis pedis arteries with transcutaneous oxygen tension measurements at 1-2 levels) [Digital Pulse Analyzer (DPA)] [MaxPulse] [DSI Pulse Wave Velocity analysis]
93923	Complete bilateral noninvasive physiologic studies of upper or lower extremity arteries, 3 or more levels (eg, for lower extremity: ankle/brachial indices at distal posterior tibial and anterior tibial/dorsalis pedis arteries plus segmental blood pressure measurements with bidirectional Doppler waveform recording and analysis, at 3 or more levels, or ankle/brachial indices at distal posterior tibial and anterior tibial/dorsalis pedis arteries plus segmental volume plethysmography at 3 or more levels, or ankle/brachial indices at distal posterior tibial and anterior tibial/dorsalis pedis arteries plus segmental transcutaneous oxygen tension measurements at 3 or more level(s), or single level study with provocative functional maneuvers (eg, measurements with postural provocative tests, or measurements with reactive hyperemia [Digital Pulse Analyzer (DPA)]) [DSI Pulse Wave Velocity analysis]
93970	Duplex scan of extremity veins including responses to compression and other maneuvers; complete bilateral study
93971	unilateral or limited study
Other CPT codes related to the CPB:
93454 - 93461, 93563	Coronary Angiography [coronary artery reactivity test]
ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):
A40.0 - A41.9	Systemic infections
E10.10 - E13.9	Diabetes mellitus
E75.21 - E75.6, E78.00 - E78.9	Disorders of lipoid metabolism
F17.200 - F17.201, F17.210 - F17.211, F17.220 - F17.221, F17.290 - F17.291	Nicotine dependence
I10 - I15.9	Hypertensive disease
I21.01 - I22.9	ST elevation (STEMI) and non-ST elevation (STEMI) myocardial infarction
I25.10 - I25.119, I25.700 - I25.9	Coronary atherosclerosis
I25.2	Old myocardial infarction
I50.1 - I50.9	Heart failure
I73.00 - I73.9	Other peripheral vascular diseases
R65.10 - R65.11	Systemic inflammatory response syndrome (SIRS) of non-infectious origin without/with acute organ dysfunction
T86.21	Heart transplant rejection
T86.22	Heart transplant failure
Z13.6	Encounter for screening for cardiovascular disorders
Z79.51 - Z79.52	Long term (current) use of steroids
Z79.899	Other long term (current) drug therapy [immunosuppressive agents, chemotherapeutic agents]
Z82.49	Family history of ischemic heart disease and other diseases of the circulatory system
Z87.891	Personal history of nicotine dependence
Z94.1	Heart transplant status
Z95.1	Presence of aortocoronary bypass graft
*Cardiac Stress Testing*:
CPT codes covered if selection criteria are met:
93015	Cardiovascular stress test using maximal or submaximal treadmill or bicycle exercise, continuous electrocardiographic monitoring, and/or pharmacological stress; with supervision, interpretation and report
93016	supervision only, without interpretation and report
93017	tracing only, without interpretation and report
93018	interpretation and report only
Other CPT codes related to the CPB:
93000	Electrocardiogram, routine ECG with at least 12 leads; with interpretation and report
93005	tracing only, without interpretation and report
93010	interpretation and report only
ICD-10 codes covered if selection criteria are met:
A18.84	Tuberculosis of heart
D86.85	Sarcoid myocarditis
E10.10 – E10.9	Type 1 diabetes mellitus
E11.00 – E11.9	Type 2 diabetes mellitus
E13.00 – E13.9	Other specified diabetes mellitus
E78.010 - E79.019	Familial hypercholesterolemia
E78.49	Other hyperlipidemia
E78.5	Hyperlipidemia, unspecified
E85.4	Organ-limited amyloidosis
E85.81	Light chain (AL) amyloidosis
G45.0 – G45.9	Transient cerebral ischemic attacks and related syndromes
G46.4	Cerebellar stroke syndrome
G93.31 - G93.39	Postviral fatigue syndrome
H35.122	Retinopathy of prematurity, stage 1, left eye
I05.0 – I05.9	Rheumatic mitral valve diseases
I06.0 – I06.9	Rheumatic aortic valve diseases
I07.0 – I07.9	Rheumatic tricuspid stenosis
I08.0 – I08.9	Multiple valve diseases
I09.0	Rheumatic myocarditis
I09.81	Rheumatic heart failure
I09.89	Other specified rheumatic heart diseases
I09.9	Rheumatic heart disease, unspecified
I11.0	Hypertensive heart disease with heart failure
I13.0	Hypertensive heart and chronic kidney disease with heart failure and stage 1 through stage 4 chronic kidney disease, or unspecified chronic kidney disease
I13.2	Hypertensive heart and chronic kidney disease with heart failure and with stage 5 chronic kidney disease, or end stage renal disease
I16.0 – I16.9	Hypertensive crisis
I20.0 -I20.9	Angina pectoris
I21.01 – I21.09	ST elevation (STEMI) myocardial infarction of anterior wall
I21.11 – I21.29	ST elevation (STEMI) myocardial infarction of inferior wall
I21.3 – I21.9	ST elevation (STEMI) myocardial infarction of other sites
I21.A1 – I21.A9	Other type of myocardial infarction
I22.0 – I22.8	Subsequent ST elevation (STEMI) and non-ST elevation (NSTEMI) myocardial infarction
I23.1 – I23.8	Certain current complications following ST elevation (STEMI) and non-ST elevation (NSTEMI) myocardial infarction (within the 28 day period)
I24.1	Dressler's syndrome
I24.81- I24.89	Other forms of acute ischemic heart disease
I24.9	Acute ischemic heart disease, unspecified
I25.10	Atherosclerotic heart disease of native coronary artery without angina pectoris
I25.110 – I25.119	Atherosclerotic heart disease of native coronary artery with angina pectoris
I25.2	Old myocardial infarction
I25.3	Aneurysm of heart
I25.41 – I25.42	Coronary artery aneurysm and dissection
I25.5	Ischemic cardiomyopathy
I25.6	Silent myocardial ischemia
I25.700 – I25.719	Atherosclerosis of coronary artery bypass graft(s), unspecified, with angina pectoris
I25.720 – I25.729	Atherosclerosis of autologous artery coronary artery bypass graft(s) with angina pectoris
I25.730 – I25.739	Atherosclerosis of nonautologous biological coronary artery bypass graft(s) with angina pectoris
I25.750 – I25.759	Atherosclerosis of native coronary artery of transplanted heart with angina pectoris
I25.760 – I25.768	Atherosclerosis of bypass graft of coronary artery of transplanted heart with angina pectoris
I25.790 – I25.799	Atherosclerosis of other coronary artery bypass graft(s) with angina pectoris
I25.810 – I25.812	Atherosclerosis of other coronary vessels without angina pectoris
I25.83 – I25.89	Other forms of chronic ischemic heart disease
I25.9	Chronic ischemic heart disease, unspecified
I27.0	Primary pulmonary hypertension
I27.20 – I27.29	Other secondary pulmonary hypertension
I27.81 – I27.9	Other specified pulmonary heart diseases
I34.0 – I34.89	Nonrheumatic mitral valve disorders
I35.0 – I35.9	Nonrheumatic aortic valve disorders
I36.0 – I36.8	Nonrheumatic tricuspid valve disorders
I37.0 – I37.8	Nonrheumatic pulmonary valve disorders
I42.0 – I42.9	Cardiomyopathy
I43	Cardiomyopathy in diseases classified elsewhere
I44.0 – I44.2	Atrioventricular and left bundle-branch block, first degree, second degree
I44.39	Other atrioventricular block
I44.4	Left anterior fascicular block
I44.5	Left posterior fascicular block
I44.69	Other fascicular block
I44.7	Left bundle-branch block, unspecified
I45.0	Right fascicular block
I45.19	Other right bundle-branch block
I45.5	Other specified heart block
I45.6	Pre-excitation syndrome
I45.81 – I45.89	Other specified conduction disorders
I46.2	Cardiac arrest due to underlying cardiac condition
I46.8	Cardiac arrest due to other underlying condition
I47.0 – I47.29	Paroxysmal tachycardia
I48.0	Paroxysmal atrial fibrillation
I48.11	Longstanding persistent atrial fibrillation
I48.21	Permanent atrial fibrillation
I48.3	Typical atrial flutter
I48.4	Atypical atrial flutter
I48.91 – I48.92	Unspecified atrial fibrillation and atrial flutter
I49.01 – I49.02	Ventricular fibrillation and flutter
I49.1	Atrial premature depolarization
I49.3	Ventricular premature depolarization
I49.49	Other premature depolarization
I49.5	Sick sinus syndrome
I49.8	Other specified cardiac arrhythmias
I50.1	Left ventricular failure, unspecified
I50.20 – I50.23	Systolic (congestive) heart failure
I50.30 – I50.33	Diastolic (congestive) heart failure
I50.40 – I50.43	Combined systolic (congestive) and diastolic (congestive) heart failure
I50.810 – I50.9	Other heart failure
I51.0 – I51.3	Complications and ill-defined descriptions of heart disease
I51.5	Myocardial degeneration
I51.7	Cardiomegaly
I51.89	Other ill-defined heart diseases
I63.00	Cerebral infarction due to thrombosis of unspecified precerebral artery
I63.011 – I63.019	Cerebral infarction due to thrombosis of vertebral artery
I63.02	Cerebral infarction due to thrombosis of basilar artery
I63.031 – I63.039	Cerebral infarction due to thrombosis of carotid artery
I63.09	Cerebral infarction due to thrombosis of other precerebral artery
I63.10	Cerebral infarction due to embolism of unspecified precerebral artery
I63.111 – I63.119	Cerebral infarction due to embolism of vertebral artery
I63.12	Cerebral infarction due to embolism of basilar artery
I63.131 – I63.139	Cerebral infarction due to embolism of carotid artery
I63.19	Cerebral infarction due to embolism of other precerebral artery
I63.20	Cerebral infarction due to unspecified occlusion or stenosis of unspecified precerebral arteries
I63.211 – I63.219	Cerebral infarction due to unspecified occlusion or stenosis of vertebral arteries
I63.231 – I63. 239	Cerebral infarction due to unspecified occlusion or stenosis of carotid arteries
I63.29	Cerebral infarction due to unspecified occlusion or stenosis of other precerebral arteries
I63.30	Cerebral infarction due to thrombosis of unspecified cerebral artery
I63.311 – I63.319	Cerebral infarction due to thrombosis of middle cerebral artery
I63.321 – I63.329	Cerebral infarction due to thrombosis of anterior cerebral artery
I63.331 – I63.339	Cerebral infarction due to thrombosis of posterior cerebral artery
I63.341 – I63.349	Cerebral infarction due to thrombosis of cerebellar artery
I63.39	Cerebral infarction due to thrombosis of other cerebral artery
I63.40	Cerebral infarction due to embolism of unspecified cerebral artery
I63.411 – I63.419	Cerebral infarction due to embolism of middle cerebral artery
I63.421 – I63.429	Cerebral infarction due to embolism of anterior cerebral artery
I63.431 – I63.439	Cerebral infarction due to embolism of posterior cerebral artery
I63.441 – I63.449	Cerebral infarction due to embolism of cerebellar artery
I63.49	Cerebral infarction due to embolism of other cerebral artery
I63.511 – I63.519	Cerebral infarction due to unspecified occlusion or stenosis of middle cerebral artery
I63.521 – I63.529	Cerebral infarction due to unspecified occlusion or stenosis of anterior cerebral artery
I63.531 – I63.539	Cerebral infarction due to unspecified occlusion or stenosis of posterior cerebral artery
I63.541 – I63.549	Cerebral infarction due to unspecified occlusion or stenosis of cerebellar artery
I63.59	Cerebral infarction due to unspecified occlusion or stenosis of other cerebral artery
I63.6	Cerebral infarction due to cerebral venous thrombosis, nonpyogenic
I63.81 – I63.9	Other cerebral infarction
I65.01 -I65.09	Occlusion and stenosis of vertebral artery
I65.1	Occlusion and stenosis of basilar artery
I65.21 – I65.29	Occlusion and stenosis of carotid artery
I65.8	Occlusion and stenosis of other precerebral arteries
I65.9	Occlusion and stenosis of unspecified precerebral artery
I66.01 – I66.9	Occlusion and stenosis of middle cerebral artery
I70.0 – I70.1	Atherosclerosis
I70.201 – I70.209	Unspecified atherosclerosis of native arteries of extremities
I70.211 – I70.219	Atherosclerosis of native arteries of extremities with intermittent claudication
I70.221 – I70.229	Atherosclerosis of native arteries of extremities with rest pain
I70.231- I70.239	Atherosclerosis of native arteries of right leg with ulceration of heel and midfoot
I70.241 – I70.249	Atherosclerosis of native arteries of left leg with ulceration
I70.25	Atherosclerosis of native arteries of other extremities with ulceration
I70.261 – I70.269	Atherosclerosis of native arteries of extremities with gangrene
I70.291 – I70.299	Other atherosclerosis of native arteries of extremities
I70.301 – I70.309	Unspecified atherosclerosis of unspecified type of bypass graft(s) of the extremities
I70.311 – I70.319	Atherosclerosis of unspecified type of bypass graft(s) of the extremities with intermittent claudication
I70.321 – I70.329	Atherosclerosis of unspecified type of bypass graft(s) of the extremities with rest pain
I70.331 – I70.339	Atherosclerosis of unspecified type of bypass graft(s) of the right leg with ulceration
I70.341 – I70.349	Atherosclerosis of unspecified type of bypass graft(s) of the left leg with ulceration
I70.35	Atherosclerosis of unspecified type of bypass graft(s) of other extremity with ulceration
I70.361 – I70.369	Atherosclerosis of unspecified type of bypass graft(s) of the extremities with gangrene
I70.391 – I70.399	Other atherosclerosis of unspecified type of bypass graft(s) of the extremities
I70.401 – I70.409	Unspecified atherosclerosis of autologous vein bypass graft(s) of the extremities
I70.411 – I70.419	Atherosclerosis of autologous vein bypass graft(s) of the extremities with intermittent claudication
I70.421 – I70.729	Atherosclerosis of autologous vein bypass graft(s) of the extremities with rest pain
I70.431 – I70.439	Atherosclerosis of autologous vein bypass graft(s) of the right leg with ulceration
I70.441 – I70.449	Atherosclerosis of autologous vein bypass graft(s) of the left leg with ulceration
I70.45	Atherosclerosis of autologous vein bypass graft(s) of other extremity with ulceration
I70.461 – I70.469	Atherosclerosis of autologous vein bypass graft(s) of the extremities with gangrene
I70.491 - I70.499	Other atherosclerosis of autologous vein bypass graft(s) of the extremities
I70.501 – I70.509	Unspecified atherosclerosis of nonautologous biological bypass graft(s) of the extremities
I70.511 – I70.519	Atherosclerosis of nonautologous biological bypass graft(s) of the extremities intermittent claudication
I70.521 - I70.529	Atherosclerosis of nonautologous biological bypass graft(s) of the extremities with rest pain
I70.531 – I70.539	Atherosclerosis of nonautologous biological bypass graft(s) of the right leg with ulceration
I70.541 – I70.549	Atherosclerosis of nonautologous biological bypass graft(s) of the left leg with ulceration
I70.55	Atherosclerosis of nonautologous biological bypass graft(s) of other extremity with ulceration
I70.561 – I70.569	Atherosclerosis of nonautologous biological bypass graft(s) of the extremities with gangrene
I70.591 – I70.599	Other atherosclerosis of nonautologous biological bypass graft(s) of the extremities
I70.601 – I70.609	Unspecified atherosclerosis of nonbiological bypass graft(s) of the extremities
I70.611 – I70.619	Atherosclerosis of nonbiological bypass graft(s) of the extremities with intermittent claudication
I70.621 - I70.692	Atherosclerosis of nonbiological bypass graft(s) of the extremities with rest pain
I70.631 – I70.639	Atherosclerosis of nonbiological bypass graft(s) of the right leg with ulceration
I70.641 - I70.649	Atherosclerosis of nonbiological bypass graft(s) of the left leg with ulceration
I70.65	Atherosclerosis of nonbiological bypass graft(s) of other extremity with ulceration
I70.661 - I70.669	Atherosclerosis of nonbiological bypass graft(s) of the extremities with gangrene
I70.691 – I70.699	Other atherosclerosis of nonbiological bypass graft(s) of the extremities
I70.701 – I70.709	Unspecified atherosclerosis of other type of bypass graft(s) of the extremities
I70.711 - I70.719	Atherosclerosis of other type of bypass graft(s) of the extremities with intermittent claudication
I70.721 - I70.729	Atherosclerosis of other type of bypass graft(s) of the extremities with rest pain
I70.731 – I70.739	Atherosclerosis of other type of bypass graft(s) of the right leg with ulceration
I70.741 - I70.749	Atherosclerosis of other type of bypass graft(s) of the left leg with ulceration
I70.75	Atherosclerosis of other type of bypass graft(s) of other extremity with ulceration
I70.761 - I70.769	Atherosclerosis of other type of bypass graft(s) of the extremities with gangrene
I70.791 - I70.799	Other atherosclerosis of other type of bypass graft(s) of the extremities
I70.8	Atherosclerosis of other arteries
I70.90 – I70.92	Other and unspecified atherosclerosis
I71.00 – I71.03	Aortic aneurysm and dissection
I71.10 - I71.13	Thoracic aortic aneurysm, ruptured
I71.20 - I71.23	Thoracic aortic aneurysm, without rupture
I71.30 - I71.33	Abdominal aortic aneurysm, ruptured
I71.40 - I71.43	Abdominal aortic aneurysm, without rupture
I71.50 - I71.52	Thoracoabdominal aortic aneurysm, ruptured
I71.60 - I71.62	Thoracoabdominal aortic aneurysm, without rupture
I71.8	Aortic aneurysm of unspecified site, ruptured
I71.9	Aortic aneurysm of unspecified site, without rupture
I73.1	Thromboangiitis obliterans [Buerger's disease]
I74.01 - I74.09	Embolism and thrombosis of abdominal aorta
I74.10 – I74.9	Embolism and thrombosis of other and unspecified parts of aorta
I75.011 – I75.019	Atheroembolism of upper extremity
I75.021 – I75.029	Atheroembolism of lower extremity
I79.0	Aneurysm of aorta in diseases classified elsewhere
I97.0	Postcardiotomy syndrome
I97.110 – I97.111	Postprocedural cardiac insufficiency
I97.120 – I97.121	Postprocedural cardiac arrest
I97.130 – I97.131	Postprocedural heart failure
I97.190 – I97.191	Other postprocedural cardiac functional disturbances
M79.601	Pain in right arm
M79.602	Pain in left arm
M79.603	Pain in arm, unspecified
Q20.0 – Q20.8	Congenital malformations of cardiac chambers and connections
Q21.10 – Q21.8	Congenital malformations of cardiac septa
Q22.0 – Q22.8	Congenital malformations of pulmonary and tricuspid valves
Q23.0 – Q23.88	Congenital malformations of aortic and mitral valves
Q24.0 – Q24.8	Other congenital malformations of heart
R00.0	Tachycardia, unspecified
R00.1	Bradycardia, unspecified
R00.2	Palpitations
R06.00	Dyspnea, unspecified
R06.01	Orthopnea
R06.02	Shortness of breath
R06.09	Other forms of dyspnea
R06.89	Other abnormalities of breathing
R07.2	Precordial pain
R07.81 – R07.89	Other chest pain
R07.9	Chest pain, unspecified
R55	Syncope and collapse
R68.84	Jaw pain
R93.1	Abnormal findings on diagnostic imaging of heart and coronary circulation
R94.30 – R94.39	Abnormal results of cardiovascular function studies
T46.991A - T46.994S	Poisoning by other agents primarily affecting the cardiovascular system, accidental (unintentional)
T82.855A - T82.855S	Stenosis of coronary artery stent
T82.857A – T82.857S	Stenosis of other cardiac prosthetic devices, implants and grafts
T82.897A – T82.897S	Other specified complication of cardiac prosthetic devices, implants and grafts
T86.10 – T86.19	Complications of kidney transplant
T86.20 – T86.23	Complications of heart transplant
T86.290 – T86.298	Other complications of heart transplant
T86.30 – T86.39	Complications of heart-lung transplant
T86.40 – T86.49	Complications of liver transplant
T86.5	Complications of stem cell transplant
T86.810 – T86.819	Complications of lung transplant
T86.820 – T86.829	Complications of skin graft (allograft) (autograft)
T86.830 – T86.839	Complications of bone graft
T86.8401 – T86.8499	Complications of corneal transplant
T86.850 – T86.859	Complication of intestine transplant
T86.890 – T86.899	Complications of other transplanted tissue
T86.90 – T86.99	Complication of unspecified transplanted organ and tissue
Z08	Encounter for follow-up examination after completed treatment for malignant neoplasm
Z09	Encounter for follow-up examination after completed treatment for conditions other than malignant neoplasm
Z48.21	Encounter for aftercare following heart transplant
Z48.280	Encounter for aftercare following heart-lung transplant
Z79.899	Other long term (current) drug therapy
Z94.1	Heart transplant status
Z94.3	Heart and lungs transplant status

Background

Cardiovascular disease (CVD) risk testing is utilized to indicate the chances of having a coronary event. The most common tests to determine cardiac risk are high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, and triglycerides (often referred to as a basic or standard lipid panel).

Non-traditional risk factors for coronary heart disease (CHD) are increasingly used to determine patient risk, in part because of the assumption that many patients with CHD lack traditional risk factors (e.g., cigarette smoking, diabetes, hyperlipidemia, and hypertension).

Hackman and Anand (2003) summarized existing evidence about the connection between atherosclerotic vascular disease and certain non-traditional CHD risk factors (abnormal levels of C-reactive protein [CRP], fibrinogen, lipoprotein(a), and homocysteine [Hcy]). The authors concluded that current evidence does not support the notion that non-traditional risk assessment adds overall value to traditional risk assessment. They explained that “for each putative risk factor, there must be prospective controlled trials demonstrating that targeting individuals with elevated levels of these risk factors for proven risk-reducing interventions offers advantages over current methods of targeting therapy (e.g., by cholesterol, diabetes, and blood pressure screening); or selectively and specifically reducing the risk factor reduces hard cardiovascular endpoints, such as mortality, nonfatal myocardial infarction, and stroke.”

Large prospective studies support screening for traditional risk factors. In one study, Greenland et al. (2003) assessed major antecedent risk factors among patients who suffered fatal CHD or non-fatal myocardial infarction (MI) while enrolled in three prospective cohort studies involving nearly 400,000 patients (age range of 18 to 59). Follow-up ranged from 21 to 30 years. Major risk factors were defined as total cholesterol greater than or equal to 240 mg/dL (greater than or equal to 6.22 mmol/L), systolic blood pressure (BP) greater than or equal to 140 mm Hg, diastolic BP greater than or equal to 90 mm Hg, current cigarette smoking, and diabetes. Of patients aged 40 to 59 at baseline who died of CHD during the three studies, 90% to 94% of women and 87% to 93% of men had at least one major CHD risk factor. In the one study that assessed non-fatal MI, at least one major risk factor was present in 87% of women and 92% of men aged 40 to 59.

In another large study (Khot et al., 2003), researchers analyzed data from more than 120,000 patients enrolled in 14 randomized controlled trials (RCTs) to determine the prevalence of baseline conventional risk factors among CHD patients. Of patients with CHD, 85% of women and 81% of men had at least one conventional risk factor.

As Canto and Iskandrian (2003) note, these data challenge the assumption that "only 50%" of CHD is attributable to conventional risk factors and emphasize the importance of screening for these risk factors and aggressively treating patients who have them.

An assessment by the BlueCross BlueShield Association Technology Evaluation Center (BCBSA, 2005) provided a framework for the evaluation of the potential clinical utility of putative risk factors for cardiovascular disease. The assessment explained that the strongest evidence of the value of such a test is direct evidence that its measurement to assess cardiovascular disease risk results in improved patient outcomes. In the absence of such evidence, the assessment of the potential clinical utility of a test lies in understanding a chain of logic and the evidence supporting those links in the chain. The potential for clinical utility of a test for assessing cardiovascular disease risk lies in following a chain of logic that relies on evidence regarding the ability of a measurement to predict cardiovascular disease beyond that of current risk prediction methods or models, and evidence regarding the utility of risk prediction to treatment of cardiovascular disease. In order to assess the utility of a test in risk prediction, specific recommendations regarding patient management based on the test results should be stated. The assessment notes that another factor that would be important to consider is the availability and reliability of laboratory measurements.

In a report on the use of non-traditional risk factors in CHD risk assessment, the U.S. Preventive Services Task Force (USPSTF, 2009) stated that there is insufficient evidence to recommend the use of non-traditional risk factors to screen asymptomatic individuals with no history of CHD to prevent CHD events. Treatment to prevent CHD events by modifying risk factors is currently based on the Framingham risk model. Risk factors not currently part of the Framingham model (i.e., non-traditional risk factors) include high-sensitivity CRP (hsCRP), ankle-brachial index (ABI), leukocyte count, fasting blood glucose level, periodontal disease, carotid intima-media thickness, electron beam computed tomography, Hcy level, and lipoprotein(a) level.

To determine if non-traditional risk factors could play a role in determining those at high risk for CHD, the USPSTF reviewed the published literature and found the availability and validity of the evidence varied considerably (USPSTF, 2009). They stated there is insufficient evidence to determine the percentage of intermediate-risk individuals who would be reclassified by screening with non-traditional risk factors, other than hsCRP and ABI. For individuals reclassified as high-risk on the basis of hsCRP or ABI scores, data are not available to determine whether they benefit from additional treatments. In addition, there is not enough information available about the benefits and harms of using non-traditional risk factors in screening. Potential harms include lifelong use of medications without proven benefit and psychological and other harms from being misclassified in a higher risk category. The USPSTF stated that clinicians should continue to use the Framingham model to assess CHD risk and guide risk-based preventive therapy (USPSTF, 2009).

The 2025 American Association of Clinical Endocrinology (AACE) clinical practice guideline emphasizes that atherosclerotic cardiovascular disease (ASCVD) risk calculators are most valuable for adults at intermediate cardiovascular risk, where the decision to initiate pharmacologic lipid-lowering therapy is uncertain, and should be used to support shared decision-making between clinicians and patients. These tools are not intended for individuals who already meet clear criteria for treatment, such as those requiring secondary prevention, those at high risk for future ASCVD events, or those with genetic lipid disorders, nor are they particularly helpful in persons without cardiovascular risk factors. Risk calculators should be applied prior to treatment initiation and may be recalculated to assess changes in risk during lifestyle modification; however, many models underestimate risk in familial hypercholesterolemia and inadequately account for population diversity, social determinants of health, or comorbid conditions. While several calculators are available and considered interchangeable for routine care, growing concern exists regarding inappropriate use of race in risk algorithms, prompting development of newer models such as the American Heart Association PREVENT calculator, which substitutes ZIP code–based social deprivation indices for race and incorporates kidney function and A1C, though its comparative clinical accuracy remains uncertain. The guideline also recognizes the limitations and modest incremental value of additional markers such as Lp(a), ApoB, and coronary artery calcium (CAC) scoring, which may occasionally aid decision-making in intermediate-risk patients but lack robust supporting evidence; moreover, repeated CAC testing is discouraged due to radiation exposure, limited reliability in certain populations (e.g., younger individuals, those with diabetes or HIV, or nicotine users), and well-documented barriers to access related to cost and socioeconomic disparities (Patel et al., 2025).

The 2026 updated ACC/AHA/Multisociety Dyslipidemia Guideline emphasize earlier and more proactive management of dyslipidemia across the life course to reduce cumulative exposure to atherogenic lipoproteins and associated atherosclerotic cardiovascular disease (ASCVD) risk. Lifestyle optimization and health behavior counseling should begin in youth, with early consideration of pharmacotherapy in individuals with familial hypercholesterolemia and in young adults with LDL‑C ≥160 mg/dL or a strong family history of premature ASCVD. For primary prevention in adults aged 30 to 79 years, the guidelines recommend using the contemporary PREVENT‑ASCVD equations rather than older risk calculators, applying a “CPR” framework – Calculate risk, Personalize risk using factors not captured in equations, and Reclassify risk when appropriate using tools such as coronary artery calcium (CAC) scoring – to guide lipid‑lowering therapy decisions. LDL‑C–lowering therapy may be considered at borderline 10‑year risk (3% to <5%) and should be considered at intermediate risk (5% to <10%) following clinician–patient discussion, with a renewed emphasis on both absolute LDL‑C/non‑HDL‑C targets and percentage LDL‑C reduction based on risk category. The guidelines also highlight adjunctive testing to refine risk and guide therapy, including selective use of apolipoprotein B (ApoB) after LDL‑C and non‑HDL‑C goals are met, one‑time measurement of lipoprotein(a) to identify genetically mediated risk, and CAC scoring to improve risk stratification and treatment targeting. Statin therapy remains foundational for patients with diabetes, chronic kidney disease, HIV, and persistently elevated triglycerides, with intensified LDL‑C goals (<55 mg/dL) recommended for most patients in secondary prevention and for those at very high ASCVD risk (Blumenthal et al., 2026).

The 2026 ACC/AHA/Multisociety Dyslipidemia Guideline risk enhancers include:

History of premature ASCVD in a parent or sibling (onset age <55 y for men, <65 y for women)
Higher risk ancestry (e.g., South Asian, Filipino)
High polygenic risk (if measured)
Chronic inflammatory diseases (e.g., systemic lupus, rheumatoid arthritis, advanced psoriasis, inflammatory arthritis)
Lipoprotein(a) [Lp(a)] ≥125 nmol/L or ≥50 mg/dL
High-sensitivity C-reactive protein (hsCRP) ≥2 mg/L on >1 occasion (if measured)
Triglycerides persistently ≥175 mg/dL (2 mmol/L) (if nonfasting) and ≥150 mg/dL (1.7 mmol/L) (if fasting)
Cardiovascular‑kidney‑metabolic (CKM) syndrome; The ACC/AHA characterizes CKM by abdominal obesity (evidenced by a high body mass index [BMI] and/or large waist circumference), insulin-resistant glucose metabolism (including hyperinsulinemia, impaired fasting glucose, impaired glucose tolerance, and type 2 diabetes), dyslipidemia (marked by high serum triglycerides and low high-density lipoprotein cholesterol [HDL-C] levels), elevated blood pressure, and occasionally microalbuminuria, proteinuria, or chronic kidney disease (CKD).
Low-density lipoprotein cholesterol (LDL-C) persistently ≥160 - 189 mg/dL (4.1 - 4.9 mmol/L), non-HDL-C ≥190 - 219 mg/dL or apolipoprotein B (ApoB) ≥120 mg/dL; Note: Although LDL-C is not included in the PREVENT-ASCVD equations (total cholesterol and HDL-C are included), it is included because persistent elevation of LDL-C may be a useful factor to include in risk-benefit discussions about LDL-C–lowering therapy, given that it is the target of that therapy.
Reproductive risk markers (premature menopause, preeclampsia, gestational diabetes, gestational hypertension, preterm delivery.

Apo [Apolipoprotein] B Testing

An apolipoprotein is any of various proteins that combine with a lipid to form a lipoprotein, such as HDL or LDL. Apolipoproteins are important in the transport of cholesterol in the body and the regulation of the level of cholesterol in cells and blood. Apolipoprotein B (apo B) is the primary apolipoprotein of LDL, which is responsible for carrying cholesterol to tissues.

Each LDL particle has one molecule of apo B per particle. Therefore, the apo B concentration is an indirect measurement of the number of LDL particles, in contrast to LDL cholesterol, which is simply a measure of the cholesterol contained within the LDL. Because apo B is a marker for LDL particle number, a higher apo B level suggests an increased level of small, dense LDL particles, which are thought to be especially atherogenic.

Guidelines from the ACC and the ADA recommend the use of apo B in persons at elevated cardiometabolic risk to assess whether additional intense interventions are necessary when LDL cholesterol goals are reached (Brunzell et al., 2008). According to these guidelines, high-risk persons are those with known CVD, diabetes, or multiple CVD risk factors (i.e., smoking, hypertension, family history of premature CVD). The American Association of Clinical Chemistry has issued similar recommendations regarding the use of apo B (Contois et al., 2009).

The INTERHEART study found the apo B:apo A-1 ratio to be a stronger predictor of MI than their cholesterol counterparts (McQueen et al., 2008). In this study, 12,461 patients with acute MI from the world’s major regions and ethnic groups were compared with 14,637 age- and sex-matched controls to assess the contributions of various cardiovascular risk factors. Investigators obtained non-fasting blood samples from 9,345 cases and 12,120 controls and measured cholesterol fractions and apolipoproteins to determine their respective predictive values. Ratios were stronger predictors of MI than were individual components, and apolipoproteins were better predictors than their cholesterol counterparts. The apo B:apo A-1 ratio was the strongest predictor, with a population-attributable risk of 54%, compared with risks of 37% for LDL/HDL and 32% for total cholesterol/HDL. A 1-standard-deviation increase in apo B:apo A-1 was associated with an odds ratio of 1.59 for MI, compared with 1.17 for an equivalent increase in total cholesterol/HDL. The results were similar for both sexes and across all ethnic groups and ages.

Apo B testing has not been validated as a tool for risk assessment in the general population. A study found that measuring apo B and apo A-I, the main structural proteins of atherogenic and antiatherogenic lipoproteins and particles, adds little to existing measures of CAD risk assessment and discrimination in the general population. van der Steeg et al. (2007) measured apolipoprotein and lipid levels for 869 cases (individuals who developed fatal or nonfatal CAD) and 1,511 matched controls (individuals who remained CAD-free) over a mean follow-up of 6 years. Upon enrollment, participants were 45 to 79 years old and apparently healthy. Occurrence of CAD during follow-up was determined using a regional health authority database (hospitalizations) and U.K. Office of National Statistics records (deaths). The apo B:apo A-I ratio was associated with future CAD events independent of traditional lipid values, including total cholesterol:HDL cholesterol ratio (adjusted odds ratio, 1.85), and independent of the Framingham risk score (OR, 1.77). However, the apo B:apo A-I ratio did no better than lipid values in discriminating between individuals who would and would not develop CAD, and it added little to the predictive value of the Framingham risk score. In addition, this ratio incorrectly classified 41% of cases and 50% of controls.

A large, population-based cohort study suggests that the apo B:apo A-1 ratio has little clinical utility in predicting incident CHD in the general population, and that measuring total cholesterol and HDL appears to suffice to determine heart disease risk (Ingelsson et al., 2007). Investigators used a variety of techniques to evaluate the relative utility of apo B, apolipoprotein A-1 (apo A-1), serum total cholesterol, HDL cholesterol, LDL cholesterol, non-HDL cholesterol, and three lipid ratios in determining risk for CHD, as well as the relative ability of these measures to reclassify CHD risk. More than 3,300 middle-aged white participants in the Framingham Offspring Study without CVD were followed for a median of 15 years. A total of 291 first CHD events occurred, 198 of them in men. In men, elevations in non-HDL cholesterol, apo B, total cholesterol:HDL ratio, LDL:HDL ratio, and apo B:apo A-1 ratio were all significantly associated with increased CHD risk to a similar degree. Elevated apo A-1 and HDL were likewise associated with reduced CHD risk. Women had results similar to those in men, except that decreased apo A-1 was not significantly associated with incident CHD. In sex-specific analyses, elevated LDL and total cholesterol were not significantly associated with increased CHD risk in either men or women, perhaps owing to the lack of statistical power of these substudies. In men, total cholesterol:HDL and apo B:apo A-1 ratios both improved reclassification of 10-year risk for CHD; however, the difference between the two was not significant. In women, neither lipid ratio improved CHD risk reclassification.

Canadian Cardiovascular Society guidelines (2009, 2013) recommend apo B as the primary alternate target to LDL-C. The guidelines explain that, based on the available evidence, many experts have concluded that apo B is a better marker than LDL-C for the risk of vascular disease and a better index of the adequacy of LDL-lowering therapy than LDL-C. The guidelines also note that there now appears to be less laboratory error in the determination of apo B than LDL-C, particularly in patients with hypertriglyceridemia, and all clinical laboratories could easily and inexpensively provide standardized measurements of apo B. The guidelines state, however, that not all experts are fully convinced that apo B should be measured routinely and, in any case, apo B is not presently being measured in most clinical laboratories. Consequently, a substantial educational effort for patients and physicians would be required for the most effective introduction of apo B into widespread clinical practice. The guidelines conclude that, despite these reservations, all would agree that physicians who wish to use apo B in their clinical care should be encouraged to do so. Furthermore, the present compromise approach represents a positive transitional phase in the assessment of lipid parameters to improve the prevention of CVD through the clinical measurement of apo B. The guidelines state that the apo B target for high-risk subjects is less than 0.80 g/L.

Guidelines from the British Columbia Medical Services Commission (2008) state that apolipoprotein B (apo B) should be considered for follow-up testing in high-risk patients who are undergoing treatment for hypercholesterolemia (but not for other dyslipidemias). The guidelines state that other lipid tests are not required if using apo B for follow-up.

Guidelines from the American Association of Clinical Endocrinologists (2012) recommend apo B measurements to assess the success of LDL-C–lowering therapy. The guidelines note that LDL particle number, as reflected by apo B, is a more potent measure of cardiovascular disease (CVD) risk than LDL-C and LDL particle size (e.g., small, dense LDL).

A European consensus statement (2012) reported that, because apo B levels have so frequently been measured in outcome studies in parallel with LDL cholesterol, apo B can be substituted for LDL cholesterol, but it does not add further to the risk assessment. The guidelines found that, based on the available evidence, it appears that apo B is a similar risk marker to LDL cholesterol and a better index of the adequacy of LDL-lowering therapy. Also, there appears to be less laboratory error in the determination of apo B than LDL cholesterol, particularly in patients with hypertriglyceridemia, and laboratories could easily and inexpensively provide standardized measurements of apo B. The guideline stated, however, that apo B is not presently being measured in most laboratories, but if measured, it should be less than 80 and less than 100 mg/dL for subjects with very high or high CVD risk, respectively.

Further study is needed to determine the usefulness of apolipoprotein B measurement as an adjunct to risk evaluation by routine lipid measurements in the general population. An assessment prepared for the Agency for Healthcare Research and Quality (Helfand et al., 2009) concluded that "the contribution of Apo B ... to risk assessment for a first ASCVD event is uncertain at present."

There is emerging evidence of a relationship between apo B and stroke risk. Bhatia et al. (2006) assessed the relationships between various lipid subfractions and ischemic stroke risk in a cohort of 261 patients after transient ischemic attack (TIA). During 10 years of follow-up, 45 patients experienced ischemic stroke. Apolipoprotein B (Apo B) and the Apo B/Apo A1 ratio were the only predictors of stroke.

Standards of Care from the American Diabetes Association (2014) state that some experts recommend a greater focus on non–HDL cholesterol, apolipoprotein B (apo B), or lipoprotein particle measurements to assess residual CVD risk in statin-treated patients who are likely to have small LDL particles, such as people with diabetes, but it is unclear whether clinical management would change with these measurements.

A Working Group of the American Association for Clinical Chemistry (Cole et al., 2013) found that, in most studies, both apo B and LDL particle number were comparable in association with clinical outcomes and nearly equivalent in their ability to assess risk for cardiovascular disease. The Working Group stated that apo B appears to be the preferable biomarker for guideline adoption because of its availability, scalability, standardization, and relatively low cost.

The National Heart, Lung, and Blood Institute’s expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents (2011) stated that “In terms of other lipid measurements:

at this time, most but not all studies indicate that measurement of apolipoprotein B (ApoB) and apolipoprotein A-1 (apoA–1) for universal screening provides no additional advantage over measuring non-HDL–C, LDL–C, and HDL–C;
measurement of lipoprotein(a) (Lp[a]) is useful in the assessment of children with both hemorrhagic and ischemic stroke;
in offspring of a parent with premature CVD and no other identifiable risk factors, elevations of ApoB, apoA–1, and Lp(a) have been noted; and
measurement of lipoprotein subclasses and their sizes by advanced lipoprotein testing has not been shown to have sufficient clinical utility in children at this time (Grade B)”.

An Endocrine Society practice guideline (Berglund et al., 2012) states that "The Task Force suggests that measurement of apolipoprotein B (apo B) or lipoprotein(a) [Lp(a)] levels can be of value, whereas measurement of other apolipoprotein levels has little clinical value."

The Emerging Risk Factors Collaboration (Di Angelantonio et al., 2012) found, in a study of individuals without known CVD, that the addition of information on the combination of apolipoprotein B and A-I to risk scores containing total cholesterol and HDL-C led to a slight improvement in CVD prediction. The investigators estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. The investigators estimated that additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1% of people to a 20% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines.

Guidelines from the American College of Cardiology and the American Heart Association (Goff et al., 2014) state that "the contribution of Apo B ... to risk assessment for a first ASCVD event is uncertain at present."

A systematic review by Witt et al. (2025) explored the mismatch between traditional cholesterol markers and the actual number of atherogenic lipoprotein particles in individuals with metabolic diseases, such as metabolic syndrome and type 2 diabetes. The authors investigated whether low-density lipoprotein cholesterol (LDL-C), commonly used in cardiovascular risk assessment, accurately reflects the burden of atherogenic particles compared to apolipoprotein B-100 (ApoB) and non-high-density lipoprotein cholesterol (non-HDL-C). Through a comprehensive search of peer-reviewed observational studies published between 2000 and 2024, nine studies were selected that met strict inclusion criteria, including the use of fasted blood samples and exclusion of pharmacological or dietary interventions. The findings consistently revealed that individuals with metabolic disease often present with LDL-C levels within recommended targets while simultaneously exhibiting elevated ApoB and non-HDL-C levels. This discordance is attributed to the presence of small, dense LDL particles and triglyceride-rich lipoproteins (TRLs), which are more atherogenic and prevalent in insulin-resistant states. ApoB, which directly measures the number of atherogenic particles, was shown to be a more reliable marker of cardiovascular risk than LDL-C, especially in individuals with elevated triglycerides. The review highlights that relying solely on LDL-C may underestimate ASCVD risk in these populations and supports the integration of ApoB into clinical practice for more accurate risk stratification. However, the study has limitations, including the small number of included studies, heterogeneity in study populations and measurement techniques, and exclusion of interventional studies, which may limit generalizability and the ability to assess causality. Other limitations include its reliance on cross-sectional data, the inclusion of both metabolic syndrome and type 2 diabetes populations without distinguishing their unique clinical pathways, and potential bias due to single-reviewer assessment.

In 2026, the ACC/AHA, along with multiple national societies, updated the guidance for ApoB testing. The ACC/AHA acknowledge that LDL-C remains the traditional lipid marker for assessing atherosclerotic cardiovascular disease (ASCVD) risk and establishing treatment targets; however, it reflects the cholesterol mass within LDL particles rather than the actual number of atherogenic lipoproteins. In contrast, ApoB directly measures the number of atherogenic particles, with one molecule corresponding to each LDL, very-low-density lipoprotein (VLDL), and Lp(a) particle. The ACC/AHA state that ApoB has been shown to predict ASCVD risk more accurately than LDL-C, particularly in cases of discordance where LDL-C and ApoB provide differing risk estimates. Clinically significant discordance often occurs when LDL-C is at goal but ApoB remains elevated above the treatment target, indicating a persistent burden of atherogenic particles and a potential need for therapy intensification. ApoB testing can be useful to improve risk assessment and guide therapy once LDL-C and non–HDL-C goals are met, particularly in those with elevated triglycerides (TG) (>200 mg/dL), diabetes, or low achieved LDL-C (<70 mg/dL). Thus, the ACC/AHA now recommend obtaining an ApoB measurement in adults (19 years and older) who are on lipid-lowing therapy, particularly those with ASCVD, cardiovascular-kidney-metabolic (CKM) syndrome, type 2 diabetes, and/or elevated triglycerides. They state that measurement of ApoB is considered reasonable to guide decisions regarding further therapeutic intensification once LDL-C and/or non–HDL-C goals are achieved (Class of Recommendation 2a, Level of Evidence B-NR) (Blumenthal et al., 2026).

ApoB predicts ASCVD risk more accurately than LDL-C in cases of disagreement, or discordance, where LDL-C and ApoB provide differing risk estimates. Clinically significant discordance often occurs when LDL-C is at goal but apoB remains elevated above the treatment goal, indicating persistent atherogenic particle burden and potential need for therapy intensification. This pattern is most common in individuals with cardiometabolic disease, including ASCVD, CKM syndrome, diabetes, and/or triglycerides (TG) ≥150 mg/dL, although it may occur at lower TG levels. TG persistently ≥175 mg/dL (2 mmol/L) (if non-fasting) and ≥150 mg/dL (1.7 mmol/L) (if fasting) increase the risk of developing cardiovascular disease beyond what is estimated by traditional risk calculators. In such scenarios, ApoB serves as a more reliable marker for ASCVD risk and therapeutic guidance (Blumenthal et al., 2026).

High Sensitivity C-Reactive Protein (hsCRP)

C-reactive protein (CRP) is produced by the liver. An elevated CRP level may be indicative of inflammation (nonspecific location). High-sensitivity CRP (hsCRP) can detect the slight elevations in serum CRP that are associated with coronary artery disease (CAD), which can be within the normal range.

It has been theorized that certain markers of inflammation – both systemic and local – may play a role in the development of atherosclerosis. High-sensitivity CRP (hsCRP) is one systemic marker of inflammation that has been intensively studied and identified as an independent risk factor for coronary artery disease (CAD). Of current inflammatory markers identified, hsCRP has the analyte and assay characteristics most conducive for use in practice. A Writing Group convened by the American Heart Association and the Centers for Disease Control and Prevention (Pearson et al., 2003) endorsed the optional use of hsCRP to identify persons without known cardiovascular disease who are at intermediate risk (10% to 20% risk of coronary heart disease over the next 10 years). For these patients, the results of hsCRP testing may help guide considerations of further evaluation (e.g., imaging, exercise testing) or therapy (e.g., drug therapies with lipid-lowering, anti-platelet, or cardio-protective agents). The Writing Group noted, however, that the benefits of such therapy based on this strategy remain uncertain. High-sensitivity CRP testing is not necessary in high-risk patients who have a 10-year risk of greater than 20%, as these patients already qualify for intensive medical interventions. Individuals at low risk (less than 10% per 10 years) will be unlikely to have a high risk (greater than 20%) identified through hsCRP testing. The Writing Group recommended screening average-risk individuals (10-year risk less than 10%) for hsCRP for purposes of cardiovascular risk assessment. The Writing Group stated that hsCRP may also be useful in estimating prognosis in patients who need secondary preventive care, such as those with stable coronary disease or acute coronary syndromes and those who have undergone percutaneous coronary interventions. The Writing Group posited that this information may be useful in patient counseling because it offers motivation to comply with proven secondary preventive interventions. However, the Writing Group noted that the utility of hsCRP in secondary prevention is more limited because current guidelines for secondary prevention generally recommend, without measuring hsCRP, the aggressive application of secondary preventive interventions. The Writing Group recommends that measurement of hsCRP be performed twice (averaging results), optimally 2 weeks apart, fasting or non-fasting in metabolically stable patients. Patients with an average hsCRP level greater than 3.0 mg/dL are considered to be at high relative risk of CHD. Patients with an average hsCRP level less than 1 mg/L are at low relative risk, and patients with an hsCRP level between 1.0 and 3.0 mg/L are at average relative risk. If the hsCRP level is greater than 10 mg/dL, the Writing Group recommends that testing should be repeated and the patient examined for sources of infection or inflammation. The Writing Group recommended against the measurement of inflammatory markers other than hsCRP (cytokines, other acute-phase reactants) for determination of coronary risk in addition to hsCRP.

In an analysis of Women’s Health Study participants, including hsCRP in cardiovascular disease (CVD) risk prediction improved the predictive accuracy in non-diabetic women whose traditional 10-year CVD risk was at least 5%. Cook et al. (2006) compared risk-prediction models that include or do not include hsCRP. The models were applied to 15,048 Women’s Health Study participants who were age 45 or older and free of cardiovascular disease and cancer at baseline. During a mean follow-up of 10 years, 390 women developed CVD. For accurately predicting CVD events, hsCRP was outmatched only by older age, current smoking, and high blood pressure among traditional Framingham variables. Non-diabetic women were classified according to their 10-year risk for CVD in a model without CRP. Adding CRP to the model substantially improved predictive accuracy for women with an initial 10-year CVD risk of at least 5%. The gain in accuracy was greatest among women initially classified in the 5% to 9.9% risk range: 21.3% of those women were reclassified in a more accurate risk category when CRP was included in the risk-prediction model (11.9% moved down a risk category [to less than 5%] and 9.5% moved up a risk category [to 10% to 19.9%]). Accounting for the predictive value of older age, smoking, and high blood pressure lessened the predictive contribution of CRP but still left CRP ahead of any cholesterol parameter (total, LDL, or HDL).

In a nested, case-control study of 122 cases and 244 controls drawn from a cohort of Women's Health Study participants, Ridker et al. (2000) assessed the risk for CVD according to levels of four inflammatory markers: hsCRP, serum amyloid A, interleukin-6, and soluble intercellular adhesion molecule type-1 (sICAM-1). Homocysteine and several lipid and lipoprotein fractions (including apolipoprotein A-I, apolipoprotein B-100, lipoprotein(a), total cholesterol, and HDL cholesterol) were measured. Outcomes included fatal CHD, non-fatal MI, stroke, or coronary revascularization procedures. Overall, hsCRP showed the strongest univariate association with all markers studied. Although several other markers studied were univariate predictors of CVD, hsCRP was the only novel plasma marker that predicted risk in multivariate analysis. The total cholesterol-to-HDL ratio also predicted risk in multivariate analysis.

Yeh (2005) noted that as a clinical tool for assessment of cardiovascular risk, hsCRP testing enhances information provided by lipid screening or global risk assessment. While statin therapy and other interventions can reduce hsCRP, whether or not such reductions can actually prevent cardiovascular events is being investigated. This is in agreement with the observation of Nambi and Ballantyne (2005), who stated that studies are now underway to evaluate if targeting patients with high CRP and low LDL cholesterol will have any impact on future cardiovascular events and survival, and whether changes in CRP correlate to event reduction.

Evidence from the JUPITER trial suggests that, for people choosing to start statin therapy, reduction in both LDL cholesterol and hsCRP are indicators of successful treatment with statins (Ridker et al., 2009). In an analysis of 15,548 initially healthy men and women participating in the JUPITER trial (87% of the full cohort), investigators prospectively assessed the effects of rosuvastatin versus placebo on rates of non-fatal myocardial infarction, non-fatal stroke, admission for unstable angina, arterial revascularization, or cardiovascular death during a maximum follow-up of 5 years (median of 1.9 years). Compared with placebo, participants allocated to rosuvastatin who achieved LDL cholesterol less than 1.8 mmol/L had a 55% reduction in vascular events, and those achieving hsCRP less than 2 mg/L had a 62% reduction. Although LDL cholesterol and hsCRP reductions were only weakly correlated in individual patients (r values < 0.15), the investigators reported a 65% reduction in vascular events in participants allocated to rosuvastatin who achieved both LDL cholesterol less than 1.8 mmol/L and hsCRP less than 2 mg/L, versus a 33% reduction in those who achieved one or neither target. In participants who achieved LDL cholesterol less than 1.8 mmol/L and hsCRP less than 1 mg/L, the investigators found a 79% reduction. The investigators reported that achieved hsCRP concentrations were predictive of event rates irrespective of the lipid endpoint used, including the apolipoprotein B to apolipoprotein A-I ratio (Ridker et al., 2009).

A meta-analysis found that hsCRP concentration has continuous associations with the risk of coronary heart disease, ischemic stroke, and vascular mortality (Emerging Risk Factors Collaboration, 2010). Investigators assessed the associations of hsCRP concentration with the risk of vascular and non-vascular outcomes under different circumstances. Investigators meta-analyzed individual records of 160,309 people without a history of vascular disease (i.e., 1.31 million person-years at risk, 27,769 fatal or non-fatal disease outcomes) from 54 long-term prospective studies. Within-study regression analyses were adjusted for within-person variation in risk factor levels. The investigators found that log(e) hsCRP concentration was linearly associated with several conventional risk factors and inflammatory markers, and nearly log-linearly with the risk of ischemic vascular disease and non-vascular mortality. Risk ratios (RRs) for coronary heart disease per 1 standard deviation higher log(e) hsCRP concentration (3-fold higher) were 1.63 (95% confidence interval [CI]: 1.51 to 1.76) when initially adjusted for age and sex only, and 1.37 (1.27 to 1.48) when adjusted further for conventional risk factors; 1.44 (1.32 to 1.57) and 1.27 (1.15 to 1.40) for ischemic stroke; 1.71 (1.53 to 1.91) and 1.55 (1.37 to 1.76) for vascular mortality; and 1.55 (1.41 to 1.69) and 1.54 (1.40 to 1.68) for non-vascular mortality. The investigators noted that RRs were largely unchanged after exclusion of smokers or initial follow-up. After further adjustment for fibrinogen, the corresponding RRs were 1.23 (1.07 to 1.42) for coronary heart disease; 1.32 (1.18 to 1.49) for ischemic stroke; 1.34 (1.18 to 1.52) for vascular mortality; and 1.34 (1.20 to 1.50) for non-vascular mortality. The investigators concluded that hsCRP concentration has continuous associations with the risk of coronary heart disease, ischemic stroke, vascular mortality, and death from several cancers and lung disease that are each of broadly similar size. The investigators noted that the relevance of hsCRP to such a range of disorders is unclear. The investigators found that associations with ischemic vascular disease depend considerably on conventional risk factors and other markers of inflammation.

According to guidelines from the National Academy of Clinical Biochemistry (2009), if global risk is intermediate and uncertainty remains as to the use of preventive therapies, hsCRP measurement might be useful for further stratification into a higher or lower risk category. Guidelines from the American College of Cardiology/American Heart Association (2010) also address the selection of patients for statin therapy, stating it can be useful in men 50 years or older and women 60 years of age or older with LDL-C less than 130 mg/dL; not on lipid-lowering, hormone replacement, or immunosuppressant therapy; without clinical coronary heart disease, diabetes, chronic kidney disease, severe inflammatory conditions, or contraindications to statins.

Guidelines from the Canadian Cardiovascular Society (2009, 2013) state that the measurement of hsCRP is being recommended in men older than 50 years and women older than 60 years of age who are at intermediate risk (10% to 19%) according to their Framingham risk score and who do not otherwise qualify for lipid-lowering therapy (i.e., if their LDL-C is less than 3.5 mmol/L). The guidelines explain that the rationale for measuring hsCRP specifically in these individuals is that we now have class I evidence for the benefit of statin therapy in such individuals if their hsCRP is greater than 2.0 mg/L. The guidelines found that data from the JUPITER study show that statin therapy reduces cardiovascular events (hazard ratio 0.56 [95% CI 0.46 to 0.69]; P < 0.00001). The guidelines note that, because hsCRP can be elevated during acute illness, clinical judgment should be exercised in the interpretation of any single measurement of hsCRP. Canadian Cardiovascular Society guidelines (2013) state that those subjects who meet JUPITER criteria (men greater than 50 years and women greater than 60 years of age and CRP greater than or equal to 2 mg/L and LDL greater than 3.5 mmol/L) could be considered for treatment based on the results of that study.

An American Heart Association statement on nontraditional risk factors and biomarkers in cardiovascular disease in youth (Balagopal et al., 2011) stated: "There currently is no clinical role for measuring CRP routinely in children when assessing or considering therapy for CVD risk factors." The AHA statement explains that, although numerous studies suggest that CRP is elevated in children with higher CVD risk, correlates with the progression of atherosclerotic changes, and tracks, albeit weakly, over 21 years from childhood to adulthood independently of other metabolic and conventional cardiovascular risk factors, it is not yet clear whether high CRP levels during childhood and adolescence lead to an increased risk of CVD in later life. The AHA stated that lifestyle interventions have been shown to decrease CRP in children, and statins reduce CRP in adults. "However, minimal information is available on the effect of statins on CRP in children and youth and, importantly, whether lowering CRP in children per se would modify preclinical disease or CVD outcomes."

An assessment prepared for the Agency for Healthcare Research and Quality (Helfand et al., 2009) found that "across all of the criteria listed in the table, C-reactive protein and electron beam computed tomography scan had the strongest evidence for an independent effect in intermediate-risk individuals, and both reclassify some individuals as high-risk."

A National Heart, Lung, and Blood Institute (2012) guideline on cardiovascular disease risk in children and adolescents found insufficient evidence to recommend the measurement of inflammatory markers in youths.

The American Association of Clinical Endocrinologists (2012) has a 2b recommendation for the use of hsCRP to stratify CVD risk in patients with a standard risk assessment that is borderline, or in those with an LDL-C concentration less than 130 mg/dL.

A European consensus guideline (2012) included a strong recommendation that hsCRP should not be measured in asymptomatic low-risk individuals and high-risk patients to assess 10-year risk of CVD. The guideline included a weak recommendation that high-sensitivity CRP may be measured as part of refined risk assessment in patients with an unusual or moderate CVD risk profile.

In 2026, the ACC/AHA, along with multiple national societies, updated the guidance for hsCRP testing. The ACC/AHA guidance acknowledges that several clinical conditions and biomarkers not incorporated into the PREVENT‑ASCVD equations are independently associated with increased long‑term ASCVD risk and are termed risk enhancers. These risk enhancers – such as chronic inflammatory diseases, family history of premature ASCVD, elevated Lp(a), elevated triglycerides, and hsCRP ≥2 mg/L on more than one occasion – do not replace risk estimation but can help individualize decision‑making, particularly in adults at borderline estimated 10‑year ASCVD risk (3% to <5%) (Class of Recommendation: 2a; Level of Evidence: B-R). Evidence from randomized trials (notably JUPITER) and long‑term cohort studies demonstrates that individuals with elevated hsCRP benefit from statin therapy, with reductions in ASCVD events comparable to LDL‑C lowering alone. However, the presence of a risk enhancer does not necessarily improve statistical risk prediction beyond PREVENT‑ASCVD equations, and absence of risk enhancers should not be interpreted as reduced risk or used to defer lipid‑lowering therapy. Instead, risk enhancers are intended to support shared clinician–patient discussions about initiating or intensifying statin therapy when absolute risk estimates alone do not clearly justify action. LDL-lowering therapy is recommended for primary prevention in adults aged 40 to 75 years with diabetes, CKD stage 3 or 4, or human immunodeficiency virus (HIV), regardless of LDL-C level. After age 75 years, LDL-C–lowering pharmacotherapy can be considered in conjunction with lifestyle interventions to reduce ASCVD risk (Blumenthal et al., 2026).

The 2026 ACC/AHA guideline provides a COR 1 (strong recommendation) for the PREVENT-ASCVD Equations in adults aged 30 to 79 years without ASCVD or subclinical atherosclerosis and with an LDL-C level between 70 and 189 mg/dL (1.8-4.9 mmol/L). The PREVENT-ASCVD equations should be used to estimate 10-y ASCVD risk, with categorization as having low (<3%), borderline (3% to <5%), intermediate (5% to <10%), or high (≥10%) risk.

Lipoprotein (a) Enzyme Immunoassay

Lipoprotein(a) testing (Lp[a]) is an LDL cholesterol particle that is attached to a special protein called apo A. Elevated levels in the blood are purportedly linked to a greater likelihood of atherosclerosis and heart attacks.

The lipoprotein(a) (Lp[a]) enzyme immunoassay has been promoted as an important determinant of CHD risk and as a guide to drug and diet therapy in patients with established CAD.

Although there is evidence for an association of Lp(a) with cardiovascular disease, there are no data to suggest that more aggressive risk factor modification would improve patient-oriented health outcomes (Pejic and Jamieson, 2007). Furthermore, it is very difficult to modify Lp(a). Some studies suggest that it can be lowered using high doses of niacin, neomycin, or estrogen in women (e.g., Gurakar et al., 1985).

Braunwald et al. state, “because Lp(a) measurement is not a widely available laboratory determination and the clinical significance of alterations in Lp(a) is not known, the NCEP [National Cholesterol Education Program] does not recommend the routine measurement of this lipoprotein at this time.”

Prospective studies that evaluated Lp(a) as a predictor of cardiovascular events have had conflicting results. Some studies suggested that Lp(a) was an independent risk factor for CHD (Bostom et al., 1994; Bostom et al., 1996; Schaefer et al., 1994; Nguyen et al., 1997; Wald et al., 1994; Cremer et al., 1994; Schwartzman et al., 1998; Ariyo et al., 2003; Shai et al., 2003), while others showed no significant association (Coleman et al., 1992; Ridker et al., 1993; Jauhiainen et al., 1991; Cantin et al., 1998; Nishino et al., 2000). A meta-analysis of 5,436 patients followed for at least 1 year concluded that elevated Lp(a) is associated with increased cardiovascular risk (relative risk 1.6; 95% CI: 1.4 to 1.8) (Danesh et al., 2000).

Hackam and Anand (2003) systematically reviewed the evidence for Lp(a) and concluded that “the use of Lp(a) as a screening tool has some limitations.” Although they identified moderate evidence for its role as an independent risk factor, they found minimal information on its incremental risk and no prospective clinical outcome studies evaluating its role in management.

Although some studies have linked elevated serum levels of Lp(a) to cardiovascular risk, the clinical utility of this marker has not been established. Suk Danik et al. (2006) analyzed data available from a cohort of about 28,000 participants followed for 10 years in the Women’s Health Study. Blood samples that had been frozen at study entry were tested for lipoprotein(a), and incident cardiovascular events were documented during the follow-up period. A total of 26% of the women had lipoprotein(a) levels greater than 30 mg/dL, which is the level currently considered to confer increased cardiovascular risk. However, only the women in the highest quintile with respect to lipoprotein(a) level (greater than or equal to 44 mg/dL) were more likely to experience cardiovascular events than women in the lowest quintile (hazard ratio [HR], 1.47); thus, a threshold effect was seen. Overall, women with the highest rates of cardiovascular disease were those who had lipoprotein(a) levels at or above the 90th percentile and LDL-C levels at or above the median. These findings indicate that routinely measuring lipoprotein(a) is of little benefit for most women. However, lipoprotein(a) testing might be helpful in the clinical management of women who are at particularly high risk or who have already experienced a cardiovascular event despite having few or no traditional risk factors. Since lipoprotein(a) is not decreased by lipid-lowering therapies, the mainstay of therapy for cardiovascular risk is still aggressive control of LDL-C levels with a statin or niacin, regardless of a woman’s lipoprotein(a) level.

A study by Ariyo et al. (2003) of the predictive value of Lp(a) in the elderly (age greater than 65 years) found that lipoprotein(a) levels have prognostic value for stroke and death in men, but not for CHD in men or for any major vascular outcome in women. However, even the links for stroke and death in men were evident only in the highest compared with the lowest quintile, not in intermediate quintiles. Ariyo et al. (2003) prospectively studied 3,972 Cardiovascular Health Study participants (minimum age of 65) who had Lp(a) measurements taken at baseline and did not have vascular disease. Overall, mean baseline Lp(a) levels were slightly higher among women (4.4 mg/dL) than among men (3.9 mg/dL). Median follow-up was 7.4 years. Study participants were placed into quintiles of Lp(a) level (lowest, 0.1 to 1.2 mg/dL; highest, 8.2 to 47.5 mg/dL). In analyses adjusted for other vascular disease risk factors, elderly women in the highest Lp(a) quintile were no more likely to experience stroke, CHD, death from vascular causes, or death from any cause than were elderly women in the lowest quintile. However, compared with elderly men in the lowest Lp(a) quintile, elderly men in the highest quintile were significantly more likely to experience stroke (HR, 2.92), death from vascular causes (HR, 2.09), and death from any cause (HR, 1.60), but not CHD. The authors concluded that, overall, these results do not appear to support routine measurement of Lp(a) levels in elderly persons.

A meta-analysis found independent but modest associations of Lp(a) concentration with risk of CHD and stroke (Emerging Risk Factors Collaboration, 2009). To assess the relationship of Lp(a) concentration with risk of major vascular and non-vascular outcomes, the investigators examined long-term prospective studies that recorded Lp(a) concentration and subsequent major vascular morbidity and/or cause-specific mortality published between January 1970 and March 2009. Individual records were provided for each of 126,634 participants in 36 prospective studies. During 1.3 million person-years of follow-up, 22,076 first-ever fatal or non-fatal vascular disease outcomes or non-vascular deaths were recorded, including 9,336 CHD outcomes, 1,903 ischemic strokes, 338 hemorrhagic strokes, 751 unclassified strokes, 1,091 other vascular deaths, 8,114 non-vascular deaths, and 242 deaths of unknown cause. Within-study regression analyses were adjusted for within-person variation and combined using meta-analysis. Analyses excluded participants with known pre-existing CHD or stroke at baseline. The investigators reported that Lp(a) concentration was weakly correlated with several conventional vascular risk factors and it was highly consistent within individuals over several years. The investigators also found that associations of Lp(a) with CHD risk were broadly continuous in shape. In the 24 cohort studies, the rates of CHD in the top and bottom thirds of baseline Lp(a) distributions, respectively, were 5.6 (95% CI: 5.4 to 5.9) per 1,000 person-years and 4.4 (95% CI: 4.2 to 4.6) per 1,000 person-years. The risk ratio for CHD, adjusted for age and sex only, was 1.16 (95% CI: 1.11 to 1.22) per 3.5-fold higher usual Lp(a) concentration (i.e., per 1 standard deviation), and it was 1.13 (95% CI: 1.09 to 1.18) following further adjustment for lipids and other conventional risk factors. The corresponding adjusted risk ratios were 1.10 (95% CI: 1.02 to 1.18) for ischemic stroke, 1.01 (95% CI: 0.98 to 1.05) for the aggregate of non-vascular mortality, 1.00 (95% CI: 0.97 to 1.04) for cancer deaths, and 1.00 (95% CI: 0.95 to 1.06) for non-vascular deaths other than cancer.

A genetic association study identified two single nucleotide polymorphisms that were strongly associated with both an increased level of Lp(a) lipoprotein and an increased risk for coronary artery disease, providing support for a causal role of Lp(a) lipoprotein in CAD (Clarke et al., 2009). Investigators assessed 2,100 candidate genes in 3,145 case patients with CAD and 3,352 controls. Single-nucleotide polymorphisms (SNPs) mapped to three chromosomal regions (6q26-27, 9p21, and 1p13) associated with Lp(a) lipoprotein were significantly associated with CAD risk. An accompanying editorial (Katherisan, 2009) stated: "Although the appropriate role of plasma Lp(a) lipoprotein in risk assessment remains a subject of debate, there is likely to be increased enthusiasm for measuring plasma Lp(a) lipoprotein levels (and possibly LPA genetic variants) to assess the risk of coronary disease. Additional studies are needed to determine whether knowledge regarding Lp(a) lipoprotein will prove to be clinically useful with respect to risk discrimination, calibration, or reclassification." In particular, the editorialist stated: "To close the loop for plasma Lp(a) lipoprotein from a curiosity to a causal risk factor, a therapeutic intervention that selectively lowers the plasma Lp(a) lipoprotein level will need to be tested in a randomized clinical trial" (Katherisan, 2009).

In a nested case-control study, lipoprotein(a) was found to add little to standard lipid measures and CRP in predicting the development of peripheral arterial disease. Ridker et al. (2001) had access to baseline plasma samples from 14,916 healthy men from the Physicians' Health Study. Samples from 140 cases who developed symptomatic peripheral arterial disease (PAD) during 9 years of follow-up were compared with samples from 140 controls (matched by age, smoking status, and length of follow-up) who did not develop PAD. Eleven standard and novel biomarkers were analyzed. Most biomarkers were significant independent predictors of PAD. The ratio of total cholesterol (TC) to HDL cholesterol was the strongest lipid predictor (adjusted relative risk, 3.9; 95% CI: 1.7 to 8.6); CRP was the strongest non-lipid predictor (adjusted RR, 2.8; 95% CI: 1.3 to 5.9). In a separate analysis of which novel biomarkers would enhance the predictive power of standard lipid measures (TC and TC/HDL ratio), the inflammatory markers (fibrinogen and CRP) were the only ones to add to it significantly (CRP even more than fibrinogen). As expected, lipoprotein(a) and homocysteine added little, as did LDL cholesterol, apolipoprotein A-I, and apolipoprotein B-100.

No universally accepted, standardized method for determination of Lp(a) exists, although a working group of the International Federation of Clinical Chemistry demonstrated the inaccuracy of Lp(a) values determined by methods sensitive to apo(a) size and recommended the widespread implementation of a proposed reference material for those Lp(a) assays that are validated to be unaffected by apo(a) size heterogeneity (Tate et al., 1998; Tate et al., 1999; Marcovina et al., 2000). Lipoprotein(a) concentrations are unaffected by most available lipid-lowering therapies, with the exception of high-dose nicotinic acid, which is often poorly tolerated. This has made it difficult to demonstrate that Lp(a) plays a direct role in vascular disease, since large-scale controlled intervention studies examining the reduction of Lp(a) and hard cardiovascular endpoints have not been performed. Lastly, the incremental predictive value of Lp(a) measurement additive to that of traditional screening methods for global risk assessment has not been formally studied.

There is no uniform guideline recommendation for the use of Lp(a) in the assessment of cardiovascular disease risk. The U.S. Preventive Services Task Force (USPSTF, 2009) does not recommend the use of Lp(a) for cardiovascular screening. The USPSTF (2009) concluded that there is insufficient evidence to recommend the use of lipoprotein(a) level to screen asymptomatic individuals with no history of CHD to prevent CHD events.

An assessment prepared for the Agency for Healthcare Quality and Research (Helfand et al., 2009) concluded that "lipoprotein(a) probably provides independent information about coronary heart disease risk, but data about their prevalence and impact when added to the Framingham risk score in intermediate-risk individuals are limited."

An assessment by the National Academy of Clinical Biochemistry (Cooper et al., 2009) stated that lipoprotein(a) screening is not warranted for primary prevention and assessment of cardiovascular risk. However, if risk is intermediate (10% to 20%) and uncertainty remains as to the use of preventive therapies such as statins or aspirin, then lipoprotein(a) measurement "may be done at the physician’s discretion." The assessment also stated that, after global risk assessment, lipoprotein(a) measurements in patients with a strong family history of premature CVD "may be useful" for identifying individuals having a genetic predisposition to CVD. The assessment stated, however, that the benefits of therapies based on lipoprotein(a) concentrations are uncertain. If both lipoprotein(a) and LDL-C are highly increased, "an attempt can be made at the physician’s discretion to lower lipoprotein(a) level by lowering the elevated LDL-C." The assessment stated that there is insufficient evidence to support therapeutic monitoring of lipoprotein(a) levels for evaluating the effects of treatment. The assessment also stated that population routine testing for small size apolipoprotein(a) is not warranted.

A consensus statement by the American College of Cardiology (ACC) and the American Diabetes Association (ADA) (Brunzell et al., 2008) concluded that the clinical utility of routine measurement of Lp(a) is unclear, although more aggressive control of other lipoprotein parameters may be warranted in those with high concentrations of Lp(a).

A European consensus statement (2012) found that high concentrations of Lp(a) are associated with increased risk of CHD and ischemic stroke, although there is no randomized intervention showing that reducing Lp(a) decreases CVD risk. The guidelines concluded that there is no justification for screening the general population for Lp(a) at present, and no evidence that any value should be considered as a target.

Canadian Cardiovascular Society guidelines (2013) state that measurement of Lp(a) might be of value in additional risk assessment, particularly in individuals with a family history of premature vascular disease and familial hypercholesterolemia. The guidelines, however, make no recommendation for the use of Lp(a) in cardiovascular disease risk assessment.

Guidelines from the American Academy of Clinical Endocrinology (2012) state that testing for lipoprotein(a) is not generally recommended, although it may provide useful information to ascribe risk in white patients with CAD or in those with an unexplained family history of early CAD.

Guidelines from the National Heart, Lung, and Blood Institute (2012) on cardiovascular disease in children and adolescents state that there is currently no medication therapy specific for elevated Lp(a), and similar to isolated low HDL–C levels, management may focus on addressing other risk factors and on more aggressively managing concomitant elevations of LDL–C, triglycerides, and non-HDL–C. In adults, niacin will lower Lp(a) approximately 15 percent, but this has not been studied in children.

The Emerging Risk Factors Collaboration (Di Angelantonio et al., 2012) found, in a study of individuals without known CVD, that the addition of information on the combination of apolipoprotein B and A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 mass to risk scores containing total cholesterol and HDL-C led to slight improvement in CVD prediction. Individual records were available for 165,544 participants without baseline CVD in 37 prospective cohorts (calendar years of recruitment: 1968-2007) with up to 15,126 incident fatal or nonfatal CVD outcomes (10,132 CHD and 4,994 stroke outcomes) during a median follow-up of 10.4 years (interquartile range, 7.6-14 years). The investigators assessed discrimination of CVD outcomes and reclassification of participants across predicted 10-year risk categories of low (<10%), intermediate (10%-<20%), and high (≥20%) risk. The addition of information on various lipid-related markers to total cholesterol, HDL-C, and other conventional risk factors yielded improvement in the model's discrimination: C-index change, 0.0006 (95% CI, 0.0002-0.0009) for the combination of apolipoprotein B and A-I; 0.0016 (95% CI, 0.0009-0.0023) for lipoprotein(a); and 0.0018 (95% CI, 0.0010-0.0026) for lipoprotein-associated phospholipase A2 mass. Net reclassification improvements were less than 1% with the addition of each of these markers to risk scores containing conventional risk factors. The investigators estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1%; lipoprotein(a), 4.1%; and lipoprotein-associated phospholipase A2 mass, 2.7% of people to a 20% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines.

O'Donoghue et al. (2014) evaluated the prognostic utility of Lp(a) in individuals with CAD. Plasma Lp(a) was measured in 6,708 subjects with CAD from three studies; data were then combined with eight previously published studies for a total of 18,978 subjects. Across the three studies, increasing levels of Lp(a) were not associated with the risk of cardiovascular events when modeled as a continuous variable (odds ratio [OR]: 1.03 per log-transformed SD, 95% CI: 0.96 to 1.11) or by quintile (Q5:Q1 OR: 1.05, 95% CI: 0.83 to 1.34). When data were combined with previously published studies of Lp(a) in secondary prevention, subjects with Lp(a) levels in the highest quantile were at increased risk of cardiovascular events (OR: 1.40, 95% CI: 1.15 to 1.71), but with significant between-study heterogeneity (p = 0.001). When stratified on the basis of LDL cholesterol, the association between Lp(a) and cardiovascular events was significant in studies in which average LDL cholesterol was greater than or equal to 130 mg/dL (OR: 1.46, 95% CI: 1.23 to 1.73, p < 0.001), whereas this relationship did not achieve statistical significance for studies with an average LDL cholesterol less than 130 mg/dL (OR: 1.20, 95% CI: 0.90 to 1.60, p = 0.21). The authors concluded that Lp(a) is significantly associated with the risk of cardiovascular events in patients with established CAD; however, there exists marked heterogeneity across trials. In particular, the prognostic value of Lp(a) in patients with low cholesterol levels remains unclear. The authors stated that “although the current study demonstrates that patients with established CAD who have a high level of Lp(a) are at an increased risk of subsequent major adverse cardiovascular events (MACE), the marked heterogeneity between studies raises questions regarding the value of Lp(a) as a clinically useful biomarker for risk assessment, particularly among patients with well-controlled LDL cholesterol. Moreover, although Lp(a) may directly contribute to CHD, there is currently insufficient evidence to suggest that Lp(a) levels above a discrete cut point should be used to guide therapy or that treatment will translate into improved clinical outcomes.”

Shah et al. (2024) conducted a retrospective study using electronic medical record (EMR) data from five health systems between 2019 and 2021 to assess baseline characteristics and patterns of lipid-lowering therapy in patients with atherosclerotic cardiovascular disease (ASCVD) who underwent lipoprotein (a) (Lp(a)) testing compared to those who did not. Multivariable regression modeling was employed to identify factors associated with Lp(a) testing. Among 595,684 patients with ASCVD, only 2,587 (0.4%) were tested for Lp(a). Patients more likely to receive Lp(a) testing included those with familial hypercholesterolemia, ischemic stroke or transient ischemic attack, peripheral artery disease, prior lipid-lowering therapy, or low-density lipoprotein cholesterol levels of ≥130 mg/dL. Conversely, older age, Black race, higher body mass index, current smoking status, and diagnoses of hypertension or diabetes were associated with lower likelihoods of Lp(a) testing. Patients who received Lp(a) tests were more frequently initiated on statin therapy (30.3% vs. 10.6%, P < 0.001), ezetimibe (7.65% vs. 0.8%, P < 0.001), or proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors (6.7% vs. 0.3%, P < 0.001) compared to those who did not, regardless of their Lp(a) levels. Additionally, patients with elevated Lp(a) levels were more likely to start ezetimibe (11.5% vs. 5.9%, P < 0.001) or PCSK9 inhibitors (10.9% vs. 4.8%, P < 0.001) than those without elevated levels. The study acknowledged several limitations, including its retrospective nature, reliance on EMR data, and the impact of the COVID-19 pandemic, which may have led to data gaps and hindered the assessment of lipid-lowering therapy adherence and intolerance. Despite these limitations, the authors concluded that Lp(a) testing utilization among ASCVD patients is low, with disparities observed in older and Black individuals regarding testing rates, and that there is a notable increase in the initiation of lipid-lowering therapies, particularly ezetimibe and PCSK9 inhibitors, among those who underwent Lp(a) testing or had elevated Lp(a) levels. They emphasized the urgent need for multidisciplinary and inclusive strategies to enhance awareness of Lp(a) testing and promote guideline-driven care.

The 2025 American Association of Clinical Endocrinology (AACE) clinical practice guideline acknowledge that "Lp(a) is an independent risk factor for CVD, and while its potential for atherogenicity is much higher than for the LDL-C particle, given its relatively lower preponderance in the plasma, it is not clear whether targeting this lipoprotein will be an effective strategy for lowering future ASCVD events, and if select target populations are more suitable for this strategy given there are ethnic variations in this risk factor. The task force is aware of phase 3 clinical trials targeting this specific particle that may identify the specific population that will benefit from intervention" (Patel et al., 2025).

The 2026 ACC/AHA/Multisociety Dyslipidemia Guideline recommends measurement of lipoprotein(a) [Lp(a)] concentration in all adults (defined as 19 years and older) at least once to assess risk for atherosclerotic cardiovascular disease (ASCVD); in individuals with familial hypercholesterolemia (FH), premature ASCVD, or high Lp(a), cascade testing of first-degree family members for high Lp(a) concentration to identify those at increased ASCVD risk; and the use of laboratories that employ such assays to accurately measure Lp(a) to characterize ASCVD risk (Class of Recommendation: 1, Level of Evidence: B-NR) (Blumenthal et al., 2026).

Lp(a) is an LDL-like particle that carries a unique apoprotein(a) strand bound to its ApoB-100 component, with concentrations primarily determined by the LPA gene and varying by ancestry, particularly higher in individuals of African descent. Robust associations between high Lp(a) levels and nonfatal ASCVD outcomes, aortic valve disease, cardiovascular disease (CVD), and all-cause mortality have been established through prospective studies, with ASCVD risk increasing continuously with higher Lp(a) concentrations, independent of LDL-C levels. A single measurement of Lp(a) is generally sufficient due to its genetic stability, although secondary causes of elevation include kidney, liver, or thyroid disease, pregnancy, menopause transition, and certain medications. Cascade testing by measuring Lp(a) concentration in first-degree family members (parent, sibling, offspring) of individuals with high Lp(a) is beneficial to identify other affected family members at increased ASCVD risk, especially if there is a personal or family history of premature ASCVD, premature aortic stenosis, or familial hypercholesterolemia (FH) (Blumenthal et al., 2026).

Lp(a) is a genetically determined, LDL-like particle whose levels remain largely stable over time and are minimally influenced by lifestyle. Elevated Lp(a) is designated a risk-enhancing factor, with concentrations ≥125 nmol/L (50 mg/dL) associated with an approximately 40% (≈1.4-fold) increase in ASCVD risk, and levels ≥250 nmol/L (100 mg/dL) associated with an estimated ≥2-fold higher risk, independent of LDL-C and other traditional risk factors. Elevated Lp(a) levels indicate the need for intensified LDL-C lowering and management of other risk factors (Blumenthal et al., 2026).

9p21 and Other Genetic Tests

Some labs offer a variety of genetic tests to attempt to predict risk of cardiovascular diseases. These tests include the following:

4q25 genotype testing (e.g., 4q25-AF Risk Genotype Test and Cardio IQ 4q25-AF Risk Genotype Test) has been proposed to identify individuals at risk of atrial fibrillation (AF) and cardioembolic (CE) stroke.
9p21 genotype testing has been proposed to predict risk of early myocardial infarction (MI), abdominal aortic aneurysm (AAA) and MI/coronary heart disease (CHD).
LPA Intron-25 genotype testing (e.g., Cardio IQ LPA Intron-25 Genotype Test and LPA-Intron 25 Genotype Test) has been proposed to predict risk of CHD.

Single-nucleotide polymorphism (SNP)-based testing (e.g., Cardiac Healthy Weight DNA Insight, Heart Health Genetic Test) analyzes a variety of genes to identify risk factors purportedly associated with heart conditions including AF and coronary artery disease (CAD).

Labs may also offer genetic or SNP-based tests that can reportedly promote and influence general health and wellness by analyzing genes associated with response to diet, metabolism, and exercise. An example of this type of test is Healthy Woman DNA Insight.

Additionally, genetic studies to determine the risk of hypercoagulation or thrombosis have been proposed to assess risk for cardiovascular disease (CVD). Panels typically include factor II (i.e., F2 gene), factor V (i.e., F5 gene), or plasminogen activator inhibitor (PAI-1).

The Evaluation of Genomic Applications in Practice and Prevention Working Group (EWG, 2010) noted that prevention of CVD is a public health priority. Improvements in outcomes associated with genomic profiling may have important impacts. Traditional risk factors, such as those used in the Framingham Risk Scores, have an advantage in clinical screening and risk assessment strategies because they measure the actual targets for therapy (e.g., lipid levels and blood pressure). To add value, genomic testing may lead to better outcomes than those achievable by assessment and treatment of traditional risk factors alone. Some issues important for clinical utility remain unknown, such as the biological mechanism underlying the most convincing marker's (9p21) association with CVD; the level of risk that changes intervention; whether long-term disease outcomes will improve; how individuals ordering direct-to-consumer tests will understand/respond to test results and interact with the health care system; and whether direct-to-consumer testing will motivate behavior change or amplify potential harms. It has been suggested that an improvement in CVD risk classification (adjusting intermediate risk of CVD into high- or low-risk categories) might lead to management changes (e.g., earlier initiation or higher rates of medical interventions, or targeted recommendations for behavioral change) that improve CVD outcomes. In the absence of direct evidence to support this possibility, the EWG sought indirect evidence aimed at documenting the extent to which genomic profiling alters CVD risk estimation, alone and in combination with traditional risk factors, and the extent to which risk reclassification improves health outcomes. Assay-related evidence on available genomic profiling tests was deemed inadequate. However, based on existing technologies that have been or may be used and on data from two of the companies performing such testing, the analytic sensitivity and specificity of tests for individual gene variants might be at least satisfactory. A total of 29 gene candidates were evaluated, with 58 different gene variant/disease associations. Evidence on clinical validity was rated inadequate for 34 of these associations (59%) and adequate for 23 (40%). Inadequate grades were based on limited evidence, poor replication, existence of possible biases, or combinations of these factors. For heart disease (25 combined associations) and stroke (13 combined associations), profiling provided areas under the receiver operator characteristics curve of 66% and 57%, respectively. Only the association of 9p21 variants with heart disease had convincing evidence of a per-allele odds ratio of between 1.2 and 1.3; this was the highest effect size for any variant/disease combination with at least adequate evidence. Although the 9p21 association seems to be independent of traditional risk factors, there is adequate evidence that the improvement in risk prediction is, at best, small. Clinical utility was not formally evaluated in any of the studies reported to date, including for 9p21. As a result, no evidence was available on the balance of benefits and harms. Also, there was no direct evidence available to assess the health benefits and harms of adding these markers to traditional risk factors (e.g., Framingham Risk Score). However, the estimated additional benefit from adding genomic markers to traditional risk factors was found to be negligible. In summary, the EWG found insufficient evidence to recommend testing for the 9p21 genetic variant or 57 other variants in 28 genes to assess risk for CVD in the general population, specifically heart disease and stroke. The EWG found that the magnitude of net health benefit from the use of any of these tests alone or in combination is negligible. The EWG discourages clinical use unless further evidence supports improved clinical outcomes. Based on the available evidence, the overall certainty of net health benefit is deemed "low."

Guidelines from the American College of Cardiology and the American Heart Association (2010) state that genotype testing for coronary heart disease (CHD) risk assessment in asymptomatic adults is not recommended.

Guidelines from the Canadian Cardiovascular Society (2009) state that genetic testing for severe lipoprotein disorders is available in a few highly specialized centers. The guidelines state, however, that a molecular genetic diagnosis is not necessary for the majority of patients with severe dyslipidemia; the biochemical and clinical data usually suffice to make a diagnosis. As a research tool, however, the molecular study of extreme lipoprotein disorders has provided considerable scientific insight, including the identification of potential future therapeutic targets.

European consensus guidelines (2012) make a strong recommendation that DNA-based tests for common genetic polymorphisms do not presently add significantly to diagnosis, risk prediction, or patient management and cannot be recommended. The guidelines also make a strong recommendation that the added value of genotyping, as an alternative or in addition to phenotyping, for better management of risk and early prevention in relatives cannot be recommended.

European guidelines (2012) note that "a number of genetic polymorphisms (sequence variants that occur at a frequency >1%) appear to be associated with statistically significant effects on risk at the population level. Because of the polygenic and polyfactorial determinants of the most common CVDs, the impact of any single polymorphism remains rather modest. Genetic testing can identify variants associated with increased risk to individual CVD risk factors, CHD, or stroke. Commercial testing was recently made available to predict an individual's genetic risk, including direct-to-consumer testing. The clinical benefits of commercial testing have not yet been demonstrated."

A review of genomics of cardiovascular disease published in the New England Journal of Medicine (O'Donnell & Nabel, 2011) concluded: "Genetic risk prediction is at an early stage, and insufficient evidence exists at present to warrant the use of a genetic risk score on the basis of SNPs identified through genomic approaches. Additional research is needed to prospectively assess the utility of genetic risk scores in the prediction of cardiovascular disease, such as myocardial infarction and coronary artery disease, before clinical use."

A statement from the American Heart Association (Ashley et al., 2012) on genetics and cardiovascular disease states: "Although robust genome-wide association studies evidence exists linking common variants to complex CVD, studies are not yet available to inform the clinical benefit of providing such genetic information to patients."

Guidelines from the American Stroke Association and the American Heart Association (Goldstein et al., 2011) state: "Although commercially available tests exist for the 9p21 and 4q25 risk loci, studies have yet to show that knowledge of genotypes at these loci leads to an improvement in risk prediction or measurable and cost-effective improvements in patient care."

Guidelines from the Royal Australian College of General Practitioners (2012) state that there is limited evidence on the balance of benefits and harms of genomic profiling generally, as well as ethical issues and uncertain utility.

Algorithmically Scored Multi-Protein Biomarker Tests

Prevencio, Inc. offers artificial intelligence (AI)-driven, multiple protein, algorithmically scored blood tests for diagnosing and/or assessing risk for cardiovascular disease. The HART CADhs aims to diagnose the likelihood of coronary artery disease. The HART CVE (Cardiovascular Events) aims to identify individuals at risk for developing a major adverse cardiovascular event (myocardial infarction, stroke or cardiovascular death) within the next year. The HART KD (Kawasaki Disease) aims to diagnose Kawasaki disease in pediatric patients which can lead to damage to the coronary arteries, potentially requiring long-term management. Artificial Intelligence is employed to interrogate well-characterized clinical data sets to produce novel, multi-protein, algorithmically-scored tests (Prevencio, 2022).

Allelica Multi-Ancestry CAD PRS Test

The Allelica Multi-Ancestry CAD PRS test is a physician-ordered quantitative in-vitro diagnostic (IVD) test for individuals of any ethnicity who are 40 to 79 years old and have been identified as having either a positive family history of atherosclerotic cardiovascular disease (ASCVD) or borderline or intermediate 10-year ASCVD risk (5-20%). Ten-year ASCVD risk calculators are appropriate for individuals who are 40 years of age or older, do not already have ASCVD, and are not already on lipid-lowering medication. The result of the genetic test is an assessment of an individual’s genetic risk, which is reported as either “Not-elevated” or “High.” A “High” result indicates that an individual’s genetic risk of coronary artery disease (CAD) is at least twice that of the rest of the population, which is equivalent to a risk-enhancing factor for ASCVD.

Mujwara et al. (2022) aimed to evaluate the cost-effectiveness of incorporating a polygenic risk score for coronary artery disease (CAD-PRS) as a risk-enhancing factor into the Pooled Cohort Equations (PCE) for atherosclerotic cardiovascular disease (ASCVD) risk assessment. The goal was to determine whether using CAD-PRS to guide statin therapy initiation in primary prevention would improve health outcomes and be economically justified. The investigators conducted a cost-effectiveness analysis using a Markov model. The study involved a hypothetical cohort of US adults aged 40–75 years without prior ASCVD, eligible for primary prevention. The study modeled integrating CAD-PRS as a risk-enhancing factor in the PCE to guide statin initiation, compared to standard PCE-based risk assessment alone. The primary outcomes were quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER). The analysis was conducted from a US healthcare payer perspective over a lifetime horizon. Multiple sensitivity analyses were performed to test the robustness of results to variations in key parameters (e.g., statin efficacy, costs, adherence, and CAD-PRS performance). The investigators found that incorporating CAD-PRS into the PCE led to more individuals being recommended for statin therapy, particularly those at borderline or intermediate risk by traditional assessment. The CAD-PRS–guided strategy resulted in improved health outcomes (more QALYs) and reduced ASCVD events compared to standard care. The ICER for the CAD-PRS–guided approach was below commonly accepted willingness-to-pay thresholds, indicating that it was cost-effective. Sensitivity analyses showed the results were robust across a range of assumptions, especially when the cost of genotyping was low and statin adherence was high. The study had a number of limitations. The analysis was based on modeling and hypothetical cohorts, not real-world clinical trial data. Assumptions regarding the effect size of CAD-PRS, statin efficacy, and adherence may not fully reflect real-world practice. The model did not account for potential psychological or behavioral impacts of genetic risk disclosure. The investigators concluded that integrating a polygenic risk score for CAD as a risk-enhancing factor in the Pooled Cohort Equations is likely to be a cost-effective strategy for guiding statin therapy in primary prevention, improving health outcomes at an acceptable cost. However, real-world studies are needed to confirm these findings and address implementation challenges.

Petranovic et al. (2025) sought to determine whether the Allelica Multi-Ancestry coronary artery disease polygenic risk score (CAD-PRS) can predict the presence, burden, and severity of atherosclerotic plaque in patients undergoing coronary computed tomography angiography (CCTA). The goal was to assess if CAD-PRS could help estimate the clinical yield of CCTA, especially in identifying early atherosclerosis. The study included patients at Mass General Brigham (MGB) without prior coronary artery disease (CAD), who were genotyped in the MGB Biobank and had a CCTA between 2004 and 2021. High Polygenic Risk Score (PRS) was defined as the top decile, and low as the bottom decile. The presence and severity of CAD were categorized using CAD-RADS (0 = absent, 1–2 = non-obstructive, 3–5 = obstructive). High plaque burden was defined as involvement of ≥3 vessels. CCTA findings were compared across PRS categories, both overall and stratified by age (men <50/women <60 vs. older). Logistic regression adjusted for age, sex, and traditional cardiac risk factors. The study included 1,991 patients (48.7% female, mean age 58 years). Regarding plaque prevalence, 65.6% had plaque (41.0% non-obstructive, 24.6% obstructive). Plaque prevalence increased stepwise with PRS: 50.0% (low), 66.4% (intermediate), 75.4% (high) (p-trend <0.001). High CAD-PRS was associated with a 3.58-fold increased odds of any plaque, a 3.45-fold increased odds of high plaque burden, and a 3.91-fold increased odds of obstructive plaque (all p<0.001). In younger adults, plaque prevalence was 57.5% in the high PRS group vs. 28.0% in the low PRS group, effectively doubling the clinical yield of CCTA in this population. The investigators concluded that high CAD-PRS is strongly associated with greater presence, burden, and severity of atherosclerotic plaque on CCTA, independent of traditional risk factors. The investigators stated that the use of CAD-PRS to guide CCTA testing is particularly valuable in younger adults, where early detection of atherosclerosis may have significant clinical impact. The study had several limitations. It is observational and retrospective, which may introduce selection bias. All data were from a single health system (MGB), which may limit generalizability. Only patients who were genotyped and had CCTA were included, possibly introducing selection bias. Despite adjustment for traditional risk factors, residual confounding cannot be excluded.

The 2026 ACC/AHA/Multisociety Guideline on the Management of Dyslipidemia recognized polygenic risk scores (PRS) as an emerging tool that can inform a more personalized assessment of atherosclerotic cardiovascular disease (ASCVD) risk, but it does not recommend their routine use (Blumenthal, et al., 2026). Within the guideline, a high coronary artery disease (CAD) PRS, when measured, is listed as a risk enhancer, similar in magnitude to other nontraditional factors such as elevated lipoprotein(a) or chronic inflammatory disease. Evidence summarized by the writing committee indicates that individuals with high CAD PRS have approximately a twofold higher relative risk of coronary events, that this risk is greatest in younger adults, and that genetic risk is additive to family history. Post hoc analyses of randomized lipid-lowering trials further suggest that individuals with high PRS derive greater relative and absolute benefit from statins and other LDL-C–lowering therapies, and disclosure of genetic risk may modestly improve adherence and lipid control. Despite these signals, the guideline emphasizes important limitations of the current evidence base. Polygenic risk scores are heterogeneous, such that different scores use different underlying genome-wide association datasets and analytic methods, leading to variability in risk estimates for the same individual. Their performance is inconsistent across ancestries, with more robust validation in populations of European descent than in others. Crucially, there are no prospective randomized trials demonstrating that PRS-guided risk assessment or treatment decisions improve clinical outcomes beyond established approaches. It also remains uncertain whether PRS meaningfully improves risk prediction over the PREVENT-ASCVD equations, which are prioritized in the guideline because of their robust calibration and validation in contemporary, diverse U.S. populations. Accordingly, the guideline frames PRS as adjunctive information that may be considered during clinician–patient risk discussions, particularly in borderline-risk individuals or younger patients with a strong family history, but not as a standalone risk predictor or a substitute for traditional risk factors, imaging (such as coronary artery calcium scoring), or established clinical risk calculators. Further research, including prospective trials and broader validation across ancestries, is identified as a key evidence gap before PRS can be more fully integrated into routine cardiovascular prevention strategies.

Angiotensin Gene

Angiotensin gene polymorphisms have been associated with CVD risk and certain forms of hypertension. Certain AGT polymorphisms have been associated with responsiveness of BP to sodium restriction and ACE inhibitors, so that analysis of the AGT gene may have the potential to help individualize therapy by predicting patients' responsiveness to certain anti-hypertensive interventions. CardiaRisk AGT from Myriad Genetics Laboratories is a laboratory test that analyzes the angiotensinogen gene. The value of analyzing angiotensin gene polymorphisms in altering the management and improving outcomes of patients has not been demonstrated in prospective clinical studies.

Apolipoprotein A-1

Apolipoprotein A1 (Apo A1) is the major protein constituent of HDL cholesterol and a relatively abundant plasma protein. Apo A1 is instrumental in promoting the transfer of cholesterol into the liver, where it is metabolized and then excreted from the body via the intestine. Although most guidelines recommend cardiovascular risk assessment based on LDL, measurement of Apo A1 has not been established as a clinically useful test at this time. It has not been proven useful in determining therapy for patients with CAD or dyslipidemia.

Apolipoproteins are measured in routine clinical laboratories with the use of immunonephelometric or immunoturbidimetric assays. Importantly, international standards have been developed for apolipoprotein B100 (ApoB) and A1 (Mora, 2009). ApoB reflects the number of potentially atherogenic lipoprotein particles because each particle of very-low-density lipoprotein (VLDL), β-VLDL, intermediate-density lipoprotein (IDL), LDL, and lipoprotein(a) carries on its surface one ApoB100 protein. Most of plasma ApoB is found in LDL particles. HDL particles do not carry ApoB but instead carry apolipoprotein A-1 (ApoA-1). However, ApoA-1 does not correspond directly to the concentration of HDL particles in the one-to-one fashion seen for ApoB100 and LDL particles because an HDL particle may carry more than one ApoA-1 protein (Mora, 2009).

While Ridker et al. (2005) found that Apo A1 predicts cardiovascular disease, it has no more predictive value than more readily available markers, such as the non-HDL cholesterol level and the ratio of total to HDL cholesterol. In a secondary analysis of a large prospective cohort study involving 15,632 healthy women in the Women's Health Study, investigators assessed the value of several markers. Subjects were followed for at least 10 years, during which time 464 had first cardiovascular events (MI, ischemic stroke, coronary revascularization, or death). After adjustment for age, smoking status, blood pressure, diabetes, and BMI, the hazard ratios for a first cardiovascular event in the most extreme quintiles for each marker (compared with the most favorable quintiles) were as follows: LDL cholesterol level, 1.62; apolipoprotein A-I level, 1.75; total cholesterol level, 2.08; HDL cholesterol level, 2.32; apolipoprotein B level, 2.50; non-HDL cholesterol level, 2.51; CRP level, 2.98. For lipid ratios, the hazard ratios were: Apo B:Apo A-I, 3.01; LDL:HDL cholesterol, 3.18; Apo B:HDL cholesterol, 3.56; total:HDL cholesterol, 3.81.

A case-control study found that the ratio of apolipoprotein B to apolipoprotein A-I was associated with coronary artery disease but added little to existing measures of risk assessment (van der Steeg et al., 2007). United Kingdom researchers evaluated whether the ratio of apolipoprotein B to apolipoprotein A-I was associated with CAD among 869 adults with CAD and 1,511 controls matched for age, sex, and time of enrollment. The highest quartile of the apolipoprotein ratio was significantly associated with fatal and non-fatal CAD (odds ratio, 1.85) in analyses adjusted for cardiovascular risk factors (sex, diabetes, BMI, smoking, systolic blood pressure, CRP levels, and LDL and HDL cholesterol levels). The ratio was also associated with CAD (odds ratio [OR], 1.77) in analyses adjusted for the Framingham risk score (a well-established algorithm for combining risk factors to predict CAD). However, the total/HDL cholesterol ratio and the apolipoprotein ratio categorized cases and controls similarly. In addition, the proportion of people with CAD who were predicted to have a higher risk for CAD was similar when both ratios were used and when the apolipoprotein ratio was added to the Framingham risk score. An editorialist commented that "risk factor proliferation puts patients and clinicians at risk for confusion" (Berkwits and Guallar, 2007).

A report from the Framingham Offspring Study, a large, population-based cohort study, found that the Apo A-1 ratio has little clinical utility in predicting incident coronary heart disease and that measuring total cholesterol and HDL appears to suffice to determine heart disease risk (Ingelsson et al., 2007). More than 3,300 middle-aged white participants in the Framingham Offspring Study without CVD were followed for a median of 15 years. A total of 291 first CHD events occurred, 198 of them in men. In men, elevations in non-HDL cholesterol, Apo B, total cholesterol:HDL ratio, LDL:HDL ratio, and Apo B:Apo A-1 ratio were all significantly associated with increased CHD risk to a similar degree. Elevated Apo A-1 and HDL were likewise associated with reduced CHD risk. Women had results similar to those in men, except that decreased Apo A-1 was not significantly associated with incident CHD. In sex-specific analyses, elevated LDL and total cholesterol were not significantly associated with increased CHD risk in either men or women, perhaps owing to the lack of statistical power of these substudies. In men, total cholesterol:HDL and Apo B:Apo A-1 ratios both improved reclassification of 10-year risk for CHD; however, the difference between the two was not significant. In women, neither lipid ratio improved CHD risk reclassification.

A large observational study reported that apolipoproteins were better than HDL and LDL in cardiac disease risk assessment (McQueen et al., 2008). In the INTERHEART study, 12,461 patients with acute MI from the world’s major regions and ethnic groups were compared with 14,637 age- and sex-matched controls to assess the contributions of various cardiovascular risk factors. Investigators obtained non-fasting blood samples from 9,345 cases and 12,120 controls and measured cholesterol fractions and apolipoproteins to determine their respective predictive values. The investigators found that ratios were stronger predictors of MI than were individual components, and apolipoproteins were better predictors than their cholesterol counterparts. The Apo B/Apo A1 ratio was the strongest predictor, with a population-attributable risk of 54%, compared with risks of 37% for LDL/HDL and 32% for total cholesterol/HDL. A one-standard-deviation increase in Apo B/Apo A1 was associated with an odds ratio of 1.59 for MI, compared with 1.17 for an equivalent increase in total cholesterol/HDL. The results were similar for both sexes and across all ethnic groups and ages. The authors argued that Apo B and Apo A1 should be used in clinical practice worldwide for cardiovascular risk assessment. A commentator noted, however, that no prospective evidence indicates that such a change would improve clinical outcomes (Soloway, 2008).

A meta-analysis found no relationship between Apo A1 and Apo B and stroke risk (Emerging Risk Factors Collaboration, 2009). An individual-patient meta-analysis aimed at providing clear estimates of the vascular risks associated with lipid levels included 68 prospective studies with data on 302,430 people without vascular disease at baseline; of these, 32 studies provided data on ischemic stroke outcomes in more than 173,000 people. Non-HDL cholesterol level was modestly associated with ischemic stroke risk, but triglyceride and HDL cholesterol levels were not associated with either ischemic or hemorrhagic stroke risk. Both non-HDL and HDL cholesterol levels were associated with cardiac risk. Measurement of Apo B and Apo A-I did not add predictive value.

The NCEP report concludes that Apo A1 is not appropriate for routine cardiovascular risk screening. An ACC/ADA consensus statement (Brunzell et al., 2008) concluded that measurements of Apo A1 appear to provide little clinical value beyond measurements of HDL cholesterol.

A European consensus statement (2012) explains that Apo A1 is the major apoprotein of HDL. The consensus stated that "it is beyond doubt that the ApoB:ApoA1 ratio is one of the strongest risk markers." The guidelines note, however, that it is still not established whether this variable should be used as a treatment goal. "As the measurement of apolipoproteins is not available to all physicians in Europe, is more costly than currently used lipid variables, and does not add more information, its use is not as yet generally recommended."

Apolipoprotein E (apo E) Testing

Apolipoprotein E (Apo E) is a type of lipoprotein that is a major component of very low-density lipoproteins (VLDL). Apo E is essential for the normal catabolism (breaking down) of triglyceride-rich lipoprotein constituents (components). A major function of VLDL is to remove excess cholesterol from the blood and carry it to the liver for processing.

Apo E is essential in the metabolism of cholesterol and triglycerides and helps to clear chylomicrons and very-low-density lipoproteins. Apo E has been studied for many years for its involvement in cardiovascular disease (CVD). Apo E polymorphisms have functional effects on lipoprotein metabolism and have been studied in disorders associated with elevated cholesterol levels and lipid derangements. The common isoforms of apolipoprotein E (Apo E), E2, E3, and E4, have been found to be determinants of plasma lipid concentrations, and one allele of the Apo E gene, the epsilon4 (E4) allele, is associated with an increased risk of coronary heart disease. In addition, the Apo E4 allele is being investigated as a potential risk factor for Alzheimer's disease and stroke.

Several small studies and an earlier review have demonstrated variation in cholesterol levels and coronary disease risk associated with Apo E isoforms. The literature on Apo E and CVD was reviewed by Eichner et al. (2002); the investigators concluded that the Apo E genotype yields poor predictive values when screening for clinically defined atherosclerosis despite positive, but modest associations with plaque and coronary heart disease outcomes. The value of Apo E testing in the diagnosis and management of coronary heart disease (CHD) needs further evaluation.

One study found that smoking increases the risk of coronary heart disease in men of all Apo E genotypes, but particularly in men carrying the epsilon4 allele. Humphries et al. (2001) investigated whether the effect of smoking on coronary heart disease risk is affected by Apo E genotype. The investigators enrolled 3,052 middle-aged men who were free of coronary heart disease for prospective cardiovascular surveillance in the second Northwick Park Heart Study (NPHSII). Compared with never-smokers, the risk of coronary heart disease in ex-smokers was 1.34 (95% CI: 0.86 to 2.08), and in smokers, it was 1.94 (1.25 to 3.01). This risk was independent of other classic risk factors. In never-smokers, risk was closely similar in men with different genotypes. Risk in men homozygous for the epsilon3 allele was 1.74 (1.10 to 2.77) in ex-smokers and 1.68 (1.01 to 2.83) in smokers, whereas in men carrying the epsilon4 allele, risk was 0.84 (0.40 to 1.75) and 3.17 (1.82 to 5.50), respectively, with no significant differences in risk in the epsilon2 carriers. For the epsilon3 group, the genotype effect on risk was no longer significant after adjustment for classic risk factors (including plasma lipids). However, even after adjustment, smokers who were carriers of the epsilon4 allele showed significantly raised risk of coronary heart disease compared with the non-smoking group (2.79, 1.59 to 4.91, epsilon4-smoking interaction p = 0.007). An accompanying editorial pointed out that it is important to determine how much of the variation in risk for CHD is attributable to the effects of Apo E in order to evaluate the importance of screening for Apo E genotype (Wang and Mahaney, 2001).

Bennett et al. (2007) conducted a meta-analysis to assess the relation of Apo E genotypes to LDL cholesterol (LDL-C) and coronary disease risk. The researchers identified 82 studies of lipid levels (involving data on some 86,000 healthy participants) and 121 studies of coronary outcomes (involving data on some 38,000 cases and 83,000 controls) from both published and unreported sources. Pooling the lipid studies, researchers found a roughly linear relation toward increasing LDL-C levels when Apo E genotypes were ordered 2/2, 2/3, 2/4, 3/3, 3/4, and 4/4. Participants with the 2/2 genotype had LDL-C levels that were 31% lower than those with the 4/4 genotype. The associations were weaker between Apo E alleles and triglyceride levels or HDL cholesterol levels. Turning to the coronary outcome studies, when the researchers used patients with the most common allele – 3/3 – as a reference, they found that carriers of the 2 allele had a 20% lower risk for coronary disease, while those with the 4 allele had a 6% increase in risk. Compared with individuals with the most common allele, those with the 2/2 genotype appear to have a 20% lower risk for coronary heart disease, while those with the 4/4 genotype appear to have a slightly higher risk. A commentator stated that these results are interesting, but the low prevalence of the 2 allele (about 7% in Western populations) and its association with the development of Parkinson's disease make the consequences of these results – and the utility and feasibility of routine screening – uncertain (Foody, 2007).

Available evidence indicates that Apo E genotype is a poor predictor of ischemic stroke. Sturgeon and colleagues examined whether Apo E genotype alters the risk for ischemic stroke, as previous studies examining whether Apo E genotype alters the risk for stroke have yielded conflicting results. In this study, 14,679 individuals in the Atherosclerosis Risk in Communities (ARIC) study were genotyped for Apo E. During more than 183,569 person-years of follow-up, 498 participants had an ischemic stroke. After stratifications by sex and race and adjustments for non-lipid risk factors for stroke, no significant relation between Apo E genotype and stroke was identified, except for a lower risk associated with APOE-epsilon-2 compared with APOE-epsilon-3 in black women only. The investigators concluded that the Apo E genotype is at most a weak factor for ischemic stroke.

The American Association of Clinical Chemistry (AACC, 2009) has stated that the test for Apo E is not widely used and its clinical usefulness is still being researched. Guidelines from the American Association of Clinical Endocrinologists (2012) have a grade 2B recommendation that assessment of Apo A1 "may be useful in certain cases." The AACE guidelines state that a normal Apo A1 level in a patient with low HDL-C suggests the existence of an adequate number of HDL-C particles that contain less cholesterol and may be an indication of less risk.

B-Type Natriuretic Peptides

Brain natriuretic peptide (BNP) is a hormone produced in the body that, when elevated, may be an indication of congestive heart failure (CHF). BNP testing may be used to detect this hormone and aid in the diagnosis of CHF. N-terminal pro-BNP (NT-proBNP) is the precursor molecule for BNP. BNP or NT-proBNP testing has been proposed for the determination of cardiovascular disease (CVD) risk and may be included in CVD risk testing panels.

In a systematic review and meta-analysis on B-type natriuretic peptides (BNP) and cardiovascular risk, Di Angelantonio and colleagues (2009) stated that measurement of BNP concentration or its precursor (N-terminal fragment [NT-proBNP]) is recommended in patients with symptoms of left ventricular dysfunction and in other settings, but the relevance of these peptides to CVD in general populations or in patients with stable vascular disease is uncertain. These investigators collated data from 40 long-term prospective studies involving a total of 87,474 participants and 10,625 incident CVD outcomes. In a comparison of individuals in the top third with those in the bottom third of baseline values of natriuretic peptides, the combined relative risk (RR), adjusted for several conventional risk factors, was 2.82 (95% CI: 2.40 to 3.33) for CVD. Analysis of the six studies with at least 250 CVD outcomes (which should be less prone to selective reporting than smaller studies) yielded an adjusted RR of 1.94 (95% CI: 1.57 to 2.39). Risk ratios were broadly similar with BNP or NT-proBNP (RR, 2.89 [95% CI: 1.91 to 4.38] and 2.82 [95% CI: 2.35 to 3.38], respectively) and by different baseline vascular risk (RR, 2.68 [95% CI: 2.07 to 3.47] in approximately general populations; RR, 3.35 [95% CI: 2.38 to 4.72] in people with elevated vascular risk factors; RR, 2.60 [95% CI: 1.99 to 3.38] in patients with stable CVD). Assay of BNP or NT-proBNP in addition to measurement of conventional CVD risk factors yielded generally modest improvements in risk discrimination. The authors concluded that available prospective studies indicate strong associations between circulating concentrations of natriuretic peptides and CVD risk under a range of different circumstances. They stated that further investigation is warranted, particularly in large general population studies, to clarify any predictive utility of these markers and to better control for publication bias.

Melander and co-workers (2009) assessed the utility of contemporary biomarkers for predicting cardiovascular risk when added to conventional risk factors. A total of 5,067 participants (mean age of 58 years; 60% women) without CVD were included in this study. Participants underwent measurement of CRP, cystatin C, Lp-PLA2, mid-regional pro-adrenomedullin (MR-proADM), mid-regional pro-atrial natriuretic peptide, and N-terminal pro-B-type natriuretic peptide (N-BNP) and underwent follow-up using the Swedish national hospital discharge and cause-of-death registers and the Stroke in Malmo register for first cardiovascular events (e.g., MI, stroke, coronary death). Main outcome measures were incident cardiovascular and coronary events. During a median follow-up of 12.8 years, there were 418 cardiovascular and 230 coronary events. Models with conventional risk factors had C statistics of 0.758 (95% CI: 0.734 to 0.781) and 0.760 (0.730 to 0.789) for cardiovascular and coronary events, respectively. Biomarkers retained in backward-elimination models were CRP and N-BNP for cardiovascular events and MR-proADM and N-BNP for coronary events, which increased the C statistic by 0.007 (p = 0.04) and 0.009 (p = 0.08), respectively. The proportion of participants reclassified was modest (8% for cardiovascular risk, 5% for coronary risk). Net reclassification improvement was non-significant for cardiovascular events (0.0%; 95% CI: -4.3% to 4.3%) and coronary events (4.7%; 95% CI: -0.76% to 10.1%). Greater improvements were observed in analyses restricted to intermediate-risk individuals (cardiovascular events: 7.4%; 95% CI: 0.7% to 14.1%; p = 0.03; coronary events: 14.6%; 95% CI: 5.0% to 24.2%; p = 0.003). However, correct reclassification was almost entirely confined to down-classification of individuals without events rather than up-classification of those with events. The authors concluded that selected biomarkers may be used to predict future cardiovascular events, but the gains over conventional risk factors are minimal. Risk classification improved in intermediate-risk individuals, mainly through the identification of those unlikely to develop events. They stated that "[t]hese data do not exclude a future role for circulating biomarkers as adjuncts to conventional risk factors, nor do they minimize the potential for biomarkers to provide insight into underlying mechanisms of diseases. Several biomarkers studied did lead to shifts in predictive accuracy that were at least statistically significant. The challenge will be to find new cardiovascular biomarkers that alone or in combination with existing biomarkers can bring about improvements in risk assessment that are not just statistically but clinically significant as well." Commenting on this study, Schwenk (2009) concluded that this study "shows that several markers that are associated with CAD and other cardiovascular diseases in high-risk populations do not provide much incremental predictive value over known demographic and clinical risk factors in low-to-moderate risk community-based populations. For now, more precise personalized approaches to risk stratification and subsequent prevention of cardiovascular disease are not available."

An assessment by the National Academy of Clinical Biochemistry (Christenson et al., 2009) stated that measurement of B-type natriuretic peptide (BNP) or N-terminal proBNP (NT-proBNP) concentrations for CVD risk assessment in the primary prevention setting is unwarranted. Similarly, guidelines from the American College of Cardiology and the American Heart Association (2010) do not recommend measurement of natriuretic peptides for CVD risk assessment in asymptomatic adults.

Using specific immunoassay and tandem mass spectrometry, Siriwarden et al. (2010) showed that a fragment derived from the signal peptide of B-type natriuretic peptide (BNPsp) not only is detectable in cytosolic extracts of explant human heart tissue but also is secreted from the heart into the circulation of healthy individuals. Furthermore, plasma levels of BNPsp in patients with documented acute ST-elevation myocardial infarction (n = 25) rise to peak values (about three times higher than the 99th percentile of the normal range) significantly earlier than the currently used biomarkers myoglobin, creatine kinase-MB, and troponin. Preliminary receiver-operating characteristic curve analysis comparing BNPsp concentrations in ST-elevation MI patients and other patient groups was positive (AUC = 0.97; p < 0.001), suggesting that further, more rigorous studies in heterogeneous chest pain patient cohorts are warranted. The authors concluded that these findings demonstrated for the first time that BNPsp exists as a distinct entity in the human circulation and could serve as a new class of circulating biomarker with the potential to accelerate the clinical diagnosis of cardiac ischemia and myocardial infarction.

In an editorial that accompanied the aforementioned article, Ichiki and Burnett (2010) stated that the study was small (n = 25). If the current findings are confirmed, then BNPsp17-26 may markedly increase the armamentarium of cardiac biomarkers for myocardial ischemia and injury. They noted that further studies are needed.

Guidelines from the Royal Australian College of General Practitioners (2012) stated that the evidence for screening for heart failure using BNP is mixed despite its sensitivity and prognostic significance. The guidelines stated that BNP may be useful in excluding the condition in suspected heart failure.

Cardiac Stress Testing and Stress Echocardiography for Screening of Asymptomatic Patients

The American Society for Echocardiography (ASE) recommends avoiding the use of stress echocardiograms on asymptomatic patients who meet “low risk” scoring criteria for coronary disease (ASE, 2013). The ASE explains that stress echocardiography is mostly used in symptomatic patients to assist in the diagnosis of obstructive coronary artery disease. There is very little information on using stress echocardiography in asymptomatic individuals for the purposes of cardiovascular risk assessment, either as a stand-alone test or in addition to conventional risk factors. See also Douglas et al. (2011).

Per the "2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients with Stable Ischemic Heart Disease" (Fihn et al., 2012), "multiple ACCF/AHA guidelines and scientific statements have discouraged the use of ambulatory monitoring, treadmill testing, stress echocardiography, stress myocardial perfusion imaging (MPI), and computed tomography (CT) scoring of coronary calcium or coronary angiography as routine screening tests in asymptomatic individuals."

The U.S. Preventive Services Task Force (USPSTF, 2018) provides a grade D recommendation against screening with resting or exercise electrocardiography (ECG) to prevent cardiovascular disease (CVD) events in asymptomatic adults at low risk of CVD events.

Roth et al. (2018) state that, "Stress ECG (exercise stress tests), stress echocardiography, and myocardial perfusion imaging are commonly used to evaluate patients for CAD. However, it is unclear if these tests add any prognostic benefit beyond a careful evaluation of underlying cardiovascular risk factors in patients without cardiac symptoms." "Screening for CAD with stress tests has not been shown to affect clinical outcomes or further inform the use of risk-reducing therapies beyond a good clinical assessment."

CARDIO inCode-Score

CARDIO inCode-Score (GENinCode U.S. Inc.) is a genetic test used to evaluate the risk of having a cardiovascular event. The test analyzes nine genes (12 single nucleotide polymorphisms [SNPs]) obtained from a blood, saliva, or buccal swab sample using the quantitative polymerase chain reaction (qPCR) method. Using an algorithm, the results are reported as low, intermediate, or high risk.

Lluis-Ganella et al. (2012) assessed the association between a multi-locus genetic risk score (GRS) and incident coronary heart disease (CHD), as well as evaluated whether this GRS improves the predictive capacity of the Framingham risk function. The investigators generated a multi-locus GRS using eight genetic variants associated with CHD without including classical cardiovascular (CV) risk factors. The investigators found the multi-locus GRS to be linearly associated with CHD in two population-based cohorts: The REGICOR Study (n = 2,351) and The Framingham Heart Study (n = 3,537). Inclusion of the GRS in the Framingham risk function improved its discriminative capacity in the Framingham sample (p = 0.042) but not in the REGICOR sample. According to both the net reclassification improvement (NRI) index and the integrated discrimination index (IDI), the investigators state that the GRS improved reclassification among individuals with intermediate coronary risk (meta-analysis NRI [95% CI]: 17.44 [8.04; 26.83]), but not overall. The investigators concluded that these results indicate the potential value of including genetic information in classical functions for risk assessment in the intermediate risk population group. The investigators acknowledged that the main limitation of their study was that the size of the individual cohorts and the number of events observed was limited, and that this was especially true in the REGICOR sample because of the low incidence of disease in that population. Furthermore, four additional markers that fulfilled their selection criteria have been reported since performing their initial SNP selection in August 2010. Nonetheless, the investigators report that they obtained similar results in terms of the strength of the per-unit and per-quintile risk effects when they repeated the analyses in the Framingham cohort (genotype data for these SNPs were not available in the REGICOR cohort). An additional limitation in this study is that the findings may only be applicable to European Caucasians or their descendants.

The CARDIO inCode-Score website cites the Iribareen et al. (2016) study regarding the clinical utility of multimarker GRS for prediction of incident CHD. Iribareen et al. performed a cohort study among 51,954 European-ancestry members of a Northern California integrated healthcare system to evaluate whether including multilocus GRS into the Framingham Risk Equation improves the predictive capacity, discrimination, and reclassification of asymptomatic individuals with respect to CHD. The investigators report that between 8 and 51 previously identified genetic variants, four GRSs were constructed. After a mean follow-up of 5.9 years, 1,864 incident CHD events were documented. The investigators found that all GRSs were linearly associated with CHD in a model adjusted by individual risk factors, and that the net reclassification improvement was 5% for GRS_8, GRS_12, and GRS_36, and 4% for GRS_51 in the entire cohort. After correcting for bias, the improvement was 9% for GRS_8 and GRS_12, and 7% for GRS_36 and GRS_51 when analyzing those classified as intermediate Framingham risk (10%–20%). The number needed to treat to prevent one CHD event after selectively treating with statins based on genetic information was 36 for GRS_8 and GRS_12, 41 for GRS_36, and 43 for GRS_51. The investigators concluded that their results demonstrate significant and clinically relevant incremental discriminative/predictive capability of four multilocus GRSs for incident CHD among persons of European ancestry. However, the investigators acknowledge limitations in their study. There was a lack of inclusion of non-European populations, and the mean follow-up was less than six years when common risk functions estimate 10-year risk. In addition, the cohort represented the upper end of the educational spectrum, which could limit its ability to generalize for populations with lower educational backgrounds.

Rincón et al. (2020) conducted a prospective study to evaluate whether a genetic risk score (GRS) improves prediction of recurrent events in young nondiabetic patients presenting with an acute myocardial infarction (AMI) and identifies a more aggressive form of atherosclerosis. The investigators performed a genetic test, cardiac computed tomography, and analyzed several biomarkers on 81 consecutive nondiabetic patients aged less than 55 years presenting with AMI. The investigators studied the association of a GRS composed of 11 genetic variants and a primary composite endpoint (cardiovascular mortality, a recurrent event, and cardiac hospitalization). The median follow-up was 4.1 years. The investigators found that there were 24 recurrent cardiovascular events, and that compared with the general population, patients had a higher prevalence of 9 out of 11 risk alleles. The GRS was significantly associated with recurrent cardiovascular events, especially when baseline low-density lipoprotein cholesterol (LDL-C) levels were elevated. Compared with the low-risk GRS tertile, the multivariate-adjusted hazard ratio (HR) for recurrences was 10.2 (p = 0.04) for the intermediate-risk group and 20.7 (p = 0.006) for the high-risk group when LDL-C was 2.8 mmol/L (110 mg/dL). Inclusion of the GRS improved the C statistic (DC-statistic = 0.086), continuous net reclassification improvement (cNRI) (30%), and the integrated discrimination improvement (IDI) index (0.05). Cardiac computed tomography frequently detected coronary calcified atherosclerosis but had limited value for prediction of recurrences. No association was observed between metalloproteinases, GRS, and recurrences. The investigators concluded that a multilocus GRS may identify individuals at increased risk of long-term recurrences among young nondiabetic patients with AMI and improve clinical risk stratification models, particularly among patients with high baseline LDL-C levels. The investigators acknowledged limitations in their study. "First, because of the highly restrictive selection criteria, the number of patients is relatively low; selection was based on previous observations that younger individuals are more prone to have genetic contributors to their recurrence risk; therefore, the results may not be generalizable to the whole spectrum of ages, diabetic populations, or different ethnicities." In addition, the investigators used a composite endpoint to include events not genetically driven. Furthermore, the investigators stated that the high use of optimal medical therapy resulted in fewer than expected follow-up events.

CardioRisk+ for Evaluation of Cardiovascular Risk

Kruger et al. (2006) noted that the prediction of cardiovascular (CV) complications represents the Achilles' heel of end-stage renal disease (ESRD). Surrogate markers of endothelial dysfunction have been advocated as predictors of CV risk in this cohort of patients. These investigators adapted a non-invasive laser Doppler flowmetry (LDF) functional testing of endothelium-dependent microvascular reactivity and showed that patients with ESRD are characterized by profound alterations in thermal hyperemic responsiveness. These researchers hypothesized that such functional evaluation of the cutaneous microcirculation may offer a valid, non-invasive test of the severity of endothelial dysfunction and CV risk. To test this hypothesis, these investigators carried out a cross-sectional study in which they compared LDF measurements to conventional risk factors and conducted a longitudinal pilot study. LDF studies were carried out in 70 patients and 33 controls. Framingham and Cardiorisk scores were nearly equivalent for low-risk patients but more divergent as risk increased. CRP levels and LDF parameters (amplitude of thermal hyperemia [TH], area under the curve of TH) showed significant abnormality in high-risk versus low-risk patients calculated using either Framingham or Cardiorisk scores. Patients who had abnormal LDF parameters showed increased CV mortality; however, they had similar risk assessments (Framingham, Cardiorisk, CRP, and homocysteine) to those with unimpaired LDF tracings. The authors concluded that LDF parameters of microvascular reactivity offered a sensitive characterization of endothelial dysfunction, which may improve CV risk assessment through incorporation into the Framingham or Cardiorisk algorithm. Moreover, these researchers stated that further diagnostic and prognostic validation of this non-invasive test of microvascular endothelial dysfunction will require a prospective, longitudinal study of ESRD patients.

Dell'anna et al. (2010) stated that CV disease, which is one of the main causes of mortality in industrialized countries, is increasingly the focus of prevention. In this study, called "Cardiorisk," these researchers examined cardiovascular risk in the population of blood donors at the Service of Immunohematology and Transfusion Medicine in Parma. Between January 2007 and December 2008, a total of 6,172 consecutive blood donors (aged 35 to 65 years) were enrolled in this project that involved calculating each subject's CV risk score based on an evaluation of both unalterable risk factors (age and gender) and modifiable risk factors (total cholesterol, HDL, LDL, triglycerides, glycemia, smoking, hypertension) as well as anti-hypertensive and/or cholesterol-lowering therapy. Of the 6,172 donors enrolled in the study, 5,039 (81.7%) had a low CV risk (score from 0 to 10), 774 (12.5%) had a moderate CV risk (score from 11 to 19), and 359 (5.8%) donors had a high CV risk (score from 20 to 28). The authors concluded that the calculation of CV risk was an important instrument for preventive medicine in blood donors. Moreover, these investigators stated that this trial could not be considered an epidemiological investigation of the general population of Parma since it involved a selected population (i.e., a cohort of blood donors); thus, the data from this study could not be extrapolated to estimate the global CV risk of residents in the city of Parma. These researchers hoped that similar studies on selected populations will be performed in order to reduce the mortality and morbidity, and the associated high social costs, related to CV diseases.

The authors stated that the key drawback of this trial was the use of the Framingham algorithm in their population of donors, which overestimated CV risk when applied to Mediterranean populations, including the Italian population. In fact, the percentage of donors with a high CV risk (score of higher than 20) was higher in this trial than in that conducted by the group in Milan, which used the Italian “Progetto CUORE” algorithm (5.8% versus 3.0%, respectively). Given the methodological differences, the results of the two studies could not be compared.

Longo et al. (2010) noted that CV diseases remain the leading cause of mortality and disability in developed countries; thus, it is necessary to increase a policy of primary prevention. The most recent European guidelines recommended the use of the absolute risk profile as a tool to identify high-risk individuals but also underlined the need for interventions on the whole population. They also mentioned the concept of opportunistic screening for CV and cerebrovascular risk factors. From September 2004 to December 2008, a total of 13,619 consecutive blood donors were evaluated to determine the absolute risk profile by using the CUORE Project score. Inclusion criteria were age between 35 and 69 years, no evidence of CV disease, 12-hour fasting, and informed consent. All blood donors underwent physical examination and blood tests. The absolute risk profile system included eight variables: age, gender, diabetes, smoking habit, systolic blood pressure (SBP), total and HDL cholesterol, and anti-hypertensive therapy. The population was classified into five risk categories (less than 5%; 5% to 10%; 10% to less than 15%; 15% to 20%; 20% or higher). The results were analyzed according to age and gender. The mean risk score was 2.9 ± 3 in men and 0.8 ± 1.04 in women. In addition, the proportion of subjects at low risk was high even in the most advanced age groups in both sexes, differing from the general population. In particular, in young and female subjects, the risk score did not exceed 20%. The proportion of men at high risk increased in adulthood, varying between 0.5% in the 50 to 59 age range to 4% in subjects 60 years of age or older. The authors concluded that the findings of this study showed the feasibility of a primary CV prevention program in a new opportunistic setting not assessed previously. The implementation of this program was a valuable tool not only to identify high-risk subjects but also to maintain a favorable risk profile in low-risk subjects over time. These preliminary findings need to be validated by well-designed studies.

Capuzzo et al. (2016) stated that the ABO blood group exerts a profound influence on hemostasis; hence, it has been associated with the development of thrombotic CV adverse events. In this study, these researchers examined the relationship between the ABO blood group and the risk of CV disease assessed with the Cardiorisk score. All blood donors aged between 35 and 65 years were enrolled in the Cardiorisk program, which included the assessment of eight variables (sex, age, total cholesterol, HDL cholesterol, plasma glucose, arterial BP, anti-hypertensive therapy, and smoking) that were used to generate a score. Individuals with a resulting score of 20 or higher, considered at high CV risk, underwent additional instrumental tests (chest X-ray, stress electrocardiogram, and Doppler ultrasound of supra-aortic trunks) and were closely clinically monitored. Between January 2005 and December 2015, a total of 289 blood donors with a Cardiorisk score of 20 or higher were identified, 249 of whom were included in the study with at least two years of follow-up. Among these, 36 (14.5%) had instrumental abnormality tests and developed adverse CV events (10 acute coronary syndromes [ACS], 2 cerebral ischemia, 3 cardiac arrhythmia, 8 stenosis of supra-aortic trunks or iliac arteries) during a median follow-up of 5.3 years. In this group of 249 high-risk individuals, a statistically significant association (p = 0.02) was found between the non-O blood type and the risk of developing subclinical or clinical CV events (odds ratio [OR], 3.3; 95% CI: 1.1 to 10.1; p = 0.033). The authors concluded that the findings of this trial underlined the key role of the ABO blood group in the risk of developing arterial thrombotic events and the need for including such unmodifiable variables in the scores assessing thrombotic risk.

Reda et al. (2019) noted that Egypt is the most populous country in the Middle East and North Africa and has more than 15% of the CV deaths in the region; however, little is known regarding the prevalence of traditional risk factors and treatment strategies in ACS patients across Egypt. From November 2015 to August 2017, data were collected from 1,681 patients with ACS in 30 coronary care centers, covering 11 governorates across Egypt, spanning the Mediterranean coast, Nile Delta, and Upper Egypt, with a focus on risk factors and management strategies. Women constituted 25% of the patients. Premature ACS was common, with 43% of men aged less than 55 years and 67% of women under 65 years. Most men had STEMI (49%), while a larger percentage of women had unstable angina and non-STEMI (NSTEMI) (32% each; p < 0.001). Central obesity was present in 80% of men and 89% of women, with 32% of men and women having atherogenic dyslipidemia. Current smoking was reported by 62% of men and by 72% of men under 55 years. A larger proportion of women had type 2 diabetes mellitus (T2DM) (53% versus 34% of men), hypertension (69% versus 49%), dyslipidemia, and obesity (71% versus 41%) (p < 0.001 for all). There were no gender differences in most diagnostic and therapeutic procedures; however, among STEMI patients, 51% of men underwent primary percutaneous coronary intervention (PCI) compared to 46% of women (p = 0.064). The authors concluded that central obesity and smoking were extremely prevalent in Egypt, contributing to an increased burden of premature ACS that warranted tailored prevention strategies. The globally recognized tendency to treat men more aggressively was less pronounced than expected. Moreover, these researchers stated that this study may help provide a basis for age- and gender-specific national preventative guidelines and strategies to enhance adherence to global management protocols.

Reda et al. (2021) stated that little is known regarding the prevalence of atherosclerosis risk factors in Egyptian patients with ACS. In an observational study, these investigators described the prevalence of these risk factors with a focus on gender-specific data and patients with premature presentation. From November 2015 to August 2018, data were collected from 3,224 patients with ACS in 30 coronary care centers covering 11 governorates across Egypt, with a focus on premature ACS. The vast majority were males (74%), and the most prevalent age group was 56 to 65 years, representing 37% of the whole study population. Among female patients, 92% were post-menopausal. The prevalence of premature ACS was 51%. A total of 45% of males and 69.6% of females with ACS had premature presentation (p < 0.001). Abdominal obesity was the most prevalent risk factor (66%). Nearly 50% of the entire study cohort were current smokers (48%). These researchers showed a high prevalence of documented dyslipidemia (48%) as well. An early invasive management strategy was used in 65% of patients, with no significant gender disparity noticed. Vascular access for coronary angiography was most commonly femoral (80% of the time). Emergent PCI was attempted in 53% of patients. Thrombolytic therapy (using streptokinase) was used in 24% of included participants. The authors concluded that among Egyptian patients with ACS, premature presentation was common with greater male preponderance. Abdominal obesity was the most prevalent risk factor, followed by hypertension. Most traditional risk factors (apart from smoking) were more prevalent in women than in men. Moreover, these investigators stated that these findings may help establish age- and gender-specific preventive and management strategies to close the gap between international guidelines’ recommendations and the reality in Egypt.

The authors stated that this trial had several drawbacks. First, the observational nature of the study made it fall short of providing causal inferences between any CV risk factor and the occurrence of ACS. Second, the lack of reporting in-hospital outcomes was mainly due to two reasons: they aimed primarily through the current report to figure out the national picture of CV risk factors among a very high-risk group, and the absence of well-established in-hospital record systems for reporting outcomes. Third, these researchers did not report some biochemical markers such as cardiac troponins and NT-proBNP due to factors related to the lack of standardization of troponin assays among different laboratories in various coronary care units, in addition to the costs associated with ordering some of these tests (NT-proBNP).

Carotid Intima-Media Thickness

Carotid intima media thickness (IMT) testing measures the thickness of the inner two layers of the wall of the carotid artery. The intima is the innermost layer, and the media is the middle layer of the arterial wall. An ultrasound image is used to detect carotid IMT, which can purportedly diagnose early stages of atherosclerosis before symptoms occur and assess for drug efficacy. It is thought that a thickening of the carotid intima media confirms the likelihood of atherosclerosis in other arteries, including the coronary and carotid arteries. This led to the theory that carotid IMT could be used to identify persons at high risk for cardiovascular and cerebrovascular disease. Examples of U.S. Food and Drug Administration (FDA) approved IMT devices include ArterioVision and CardioHealth Station.

Carotid ultrasonography measurement of the intimal medial thickness of the carotid arteries has been used to assess the atherosclerotic plaque burden. Increased carotid intimal medial thickness has been correlated with a gradual, graded increase in the risk of future cardiovascular events, but the magnitude of the relationship lessened when traditional risk factors were taken into account (Chambless et al., 1997; Hodis et al., 1998; O'Leary et al., 1999; Simons et al., 1999; Touboul et al., 2000; Bots et al., 2007; Lorenz et al., 2007).

The Adult Treatment Panel III (ATPIII) reports that the extent of carotid atherosclerosis correlates positively with the severity of coronary atherosclerosis, and that some studies have shown that the severity of intimal medial thickness independently correlates with the risk for major coronary events. ATPIII states, however, that the predictive power of carotid medial intima thickness for persons without multiple risk factors has not been determined in prospective studies. ATPIII concluded that “its expense, lack of availability, and difficulties with standardization preclude a current recommendation for its use in routine risk assessment for the purpose of modifying the intensity of LDL-lowering therapy.”

A consensus statement from the American Diabetes Association (ADA) and the American College of Cardiology (ACC) observed that measurements of carotid intima media thickness, as well as measurements of coronary calcification and ankle-brachial index, can detect the presence of so-called subclinical vascular disease, and that patients with documented subclinical atherosclerosis are at increased cardiovascular disease (CVD) risk and may be considered candidates for more aggressive therapy. The consensus statement concluded, however, that it is unclear whether such tests improve prediction or clinical decision-making in patients with cardiometabolic risk (Brunzell et al., 2008).

The U.S. Preventive Services Task Force (USPSTF, 2009) stated that there is insufficient evidence to recommend the use of carotid intima-media thickness to screen asymptomatic individuals with no history of coronary heart disease (CHD) to prevent CHD events.

The American Association of Clinical Endocrinology (2012) guidelines state that carotid intima media thickness measurements should not be performed routinely but may be used in certain clinical situations as adjuncts to standard CVD risk factors in an attempt to refine risk stratification and the need for more aggressive preventive strategies. This is a grade 4 recommendation, based upon opinion (D level evidence).

An American Heart Association guideline on cardiovascular disease in women (Mosca et al., 2011) stated: "Although recent evidence suggests that using imaging modalities such as coronary calcium scoring and carotid ultrasound to demonstrate the presence of advanced atherosclerosis has the greatest utility for reclassifying risk in those (including women) predicted to be at intermediate risk on the basis of short-term risk equations such as the Framingham risk score, their value in improving clinical outcomes has not been established."

An assessment prepared for the Agency for Healthcare Research and Quality (Helfand et al., 2009) concluded that "carotid intima media thickness... probably provides independent information about coronary heart disease risk, but data about their prevalence and impact when added to the Framingham risk score in intermediate-risk individuals are limited."

Guidelines from the Canadian Cardiovascular Society (Anderson et al., 2013) noted that a recent meta-analysis found that carotid intima media measurements added only a little to risk reclassification after adjustment for conventional risk factors.

Van den Oord et al. (2013) conducted a systematic review and meta-analysis of the evidence on the association of carotid intima media thickness with future cardiovascular events and the additional value of carotid intima media thickness to traditional cardiovascular risk prediction models. The association of carotid intima media thickness with future cardiovascular events and the additional value of carotid intima media thickness were calculated using random effects analysis. The literature search yielded 1,196 articles, of which 15 provided sufficient data for the meta-analysis. A 1 standard deviation increase in carotid intima media thickness was predictive for myocardial infarction (HR 1.26, 95% CI 1.20-1.31) and for stroke (HR 1.31, 95% CI 1.26-1.36). A 0.1 mm increase in carotid intima media thickness was predictive for myocardial infarction (HR 1.15, 95% CI 1.12-1.18) and for stroke (HR 1.17, 95% CI 1.15-1.21). The overall performance of risk prediction models did not significantly increase after the addition of carotid intima media thickness data. The areas under the curve increased from 0.726 to 0.729 (p = 0.8). The authors concluded that carotid intima media thickness, as measured by B-mode ultrasound, is associated with future cardiovascular events. However, the addition of carotid intima media thickness to traditional cardiovascular risk prediction models does not lead to a statistically significant increase in the performance of those models.

Den Ruijter (2012) conducted a meta-analysis to determine whether common carotid intima media thickness has added value in 10-year risk prediction of first-time myocardial infarctions or strokes, above that of the Framingham Risk Score. The authors identified relevant studies through literature searches of databases (PubMed from 1950 to June 2012 and EMBASE from 1980 to June 2012) and expert opinion. The included studies had participants drawn from the general population, common carotid intima media thickness was measured at baseline, and individuals were followed up for first-time myocardial infarction or stroke. The authors combined individual data into one dataset and performed an individual participant data meta-analysis on individuals without existing cardiovascular disease. The authors included 14 population-based cohorts contributing data for 45,828 individuals. During a median follow-up of 11 years, 4,007 first-time myocardial infarctions or strokes occurred. The authors first refitted the risk factors of the Framingham Risk Score and then extended the model with common carotid intima media thickness measurements to estimate the absolute 10-year risks of developing a first-time myocardial infarction or stroke in both models. The C statistic of both models was similar (0.757; 95% CI, 0.749-0.764; and 0.759; 95% CI, 0.752-0.766). The authors found that the net reclassification improvement with the addition of common carotid intima media thickness was small (0.8%; 95% CI, 0.1%-1.6%). In those at intermediate risk, the net reclassification improvement was 3.6% in all individuals (95% CI, 2.7%-4.6%) with no differences between men and women. The authors concluded that the addition of common carotid intima media thickness measurements to the Framingham Risk Score was associated with a small improvement in 10-year risk prediction of first-time myocardial infarction or stroke, but this improvement is unlikely to be of clinical importance.

Guidelines from the American College of Cardiology and the American Heart Association (Goff et al., 2014) state that "routine measurement of CIMT is not recommended in clinical practice for risk assessment for a first atherosclerotic cardiovascular disease (ASCVD) event."

Carotid Ultrasound Screening

The United States Preventive Services Task Force (USPSTF, 2014) recommends against screening for asymptomatic carotid artery stenosis in the general population of adults without a history of transient ischemic attack, stroke, or other neurologic signs and symptoms. This is a D recommendation, meaning that the USPSTF recommends against this service because there is moderate or high certainty that the service has no net benefit or that the harms outweigh the benefits. The USPSTF observed that the most feasible screening test for carotid artery stenosis (defined as 60% to 99% stenosis) is ultrasonography. The USPSTF stated that, although adequate evidence indicates that this test has high sensitivity and specificity, in practice, ultrasonography yields many false-positive results in the general population, which has a low prevalence of carotid artery stenosis (approximately 0.5% to 1%). The USPSTF also found that there are are no externally validated, reliable tools that can determine who is at increased risk for carotid artery stenosis or for stroke when carotid artery stenosis is present. The USPSTF found that all screening strategies, including ultrasonography with or without confirmatory tests (digital subtraction or magnetic resonance angiography), have imperfect sensitivity and could lead to unnecessary surgery and result in serious harms, including death, stroke, and myocardial infarction. The USPSTF concluded with moderate certainty that the harms of screening for asymptomatic carotid artery stenosis outweigh the benefits.

Cholesterol Balance Test

Cholesterol Balance test is used to predicts response to therapy. Boston Heart Diagnostics is the only commercial laboratory that analyzes both the markers of cholesterol synthesis (lathosterol) and cholesterol absorption (beta-sitosterol and campesterol). The Cholesterol Balance™ test utilizes an advanced chromatography technique to measure these key production and absorption markers. This analysis determines the contribution to total blood cholesterol from the amount produced by the body versus the amount absorbed in the intestines. Boston Heart Diagnostics - Cholesterol Balance Test. However, there is a lack of evidence regarding the clinical value of the Cholesterol Balance test.

Coenzyme Q Testing

Coenzyme Q10 (CoQ10) is a fat-soluble, vitamin-like substance required for normal mitochondrial function that occurs naturally in the body. CoQ10 is used to produce energy to fuel cell growth and maintenance. CoQ10 is also an antioxidant sold in the United States (U.S.) as a dietary supplement. A deficiency of CoQ10 is associated with a number of diseases such as mitochondrial disease, heart failure and hypertension. Testing CoQ10 levels has been proposed for determining CVD risk and statin-related myopathy.

Corus CAD

The majority of first-time coronary angiography patients do not have obstructive coronary artery disease (CAD). The Corus CAD (CardioDx, Palo Alto, CA) is a peripheral blood gene expression score (GES), consisting of 23 genes, age, and sex, which can assess the likelihood of obstructive CAD (at least one vessel with ≥ 50% angiographic coronary artery stenosis) in non-diabetic patients.

Rosenberg et al. (2010) conducted a multicenter study at 30 U.S. centers to validate the Corus CAD for the diagnosis of obstructive CAD in nondiabetic patients. Blood samples were obtained prior to angiography in an independent validation cohort of 526 nondiabetic patients with a clinical indication for coronary angiography. Patients with chronic inflammatory disorders, elevated levels of leukocytes or cardiac protein markers, or diabetes were excluded from the study. Obstructive CAD was defined as 50% or greater stenosis in one or more major coronary arteries by quantitative coronary angiography. The area under the ROC curve (AUC) was 0.70 ± 0.02 (P < 0.001); the test added to clinical variables (Diamond-Forrester method) (AUC, 0.72 with the test vs. 0.66 without; P = 0.003) and added somewhat to an expanded clinical model (AUC, 0.745 with the test vs. 0.732 without; P = 0.089). The test improved net reclassification over both the Diamond-Forrester method and the expanded clinical model (P < 0.001). At a score threshold that corresponded to a 20% likelihood of obstructive CAD (14.75), the sensitivity and specificity were 85% and 43% (yielding a negative predictive value of 83% and a positive predictive value of 46%), with 33% of patient scores below this threshold. The authors concluded that the Corus CAD may be useful for assessing obstructive CAD in nondiabetic patients without known CAD.

In a prospective, multicenter study, Thomas et al. (2012) obtained peripheral blood samples for the Corus CAD before myocardial perfusion imaging (MPI) in 537 consecutive patients. Patients with abnormal MPI usually underwent invasive coronary angiography; all others had research coronary computed tomographic angiography, with core laboratories defining coronary anatomy. A total of 431 patients completed GES, coronary imaging (invasive coronary angiography or computed tomographic angiography), and MPI. Mean age was 56 ± 10 years (48% women). The prespecified primary endpoint was Corus CAD gene expression score (GES) receiver-operating characteristics analysis to discriminate ≥ 50% stenosis (15% prevalence by core laboratory analysis). The area under the receiver-operating characteristics curve for the Corus CAD GES was 0.79 (95% confidence interval, 0.73-0.84; P < 0.001), with sensitivity, specificity, and negative predictive value of 89%, 52%, and 96%, respectively, at a prespecified threshold of ≤ 15, with 46% of patients below this score. The Corus CAD GES outperformed clinical factors by receiver-operating characteristics and reclassification analysis and showed significant correlation with maximum percent stenosis. Six-month follow-up on 97% of patients showed that 27 of 28 patients with adverse cardiovascular events or revascularization had GES > 15. Site and core-laboratory MPI had areas under the curve of 0.59 and 0.63, respectively, significantly less than GES. The investigators concluded that the Corus CAD GES has high sensitivity and negative predictive value for obstructive coronary artery disease. In this population clinically referred for MPI, the Corus CAD outperformed clinical factors and MPI.

McPherson et al. (2013) evaluated the clinical utility of the Corus CAD in a cardiology practice. In this study, 171 patients presenting with stable chest pain and related symptoms without a history of CAD were referred to six cardiologists for evaluation. In the prospective cohort of 88 patients, the cardiologist's diagnostic strategy was evaluated before and after gene expression score (GES) testing. The objective of the study was to measure the effect of the Corus CAD on diagnostic testing using a pre/post study design. There were 83 prospective patients evaluable for study analysis, which included 57 (69%) women, mean age 53 ± 11 years, and mean Corus CAD gene expression score (GES) 12.5 ± 9. Presenting symptoms were classified as typical angina, atypical angina, and noncardiac chest pain in 33%, 60%, and 7% of patients (n = 27, 50, and 6), respectively. After the Corus CAD, changes in diagnostic testing occurred in 58% of patients (n = 48, P < 0.001). The investigators noted that 91% (29/32) of patients with decreased testing had low GES (≤ 15), whereas 100% (16/16) of patients with increased testing had elevated GES (P < 0.001). A historical cohort of 83 patients, matched to the prospective cohort by clinical factors, had higher diagnostic test use compared with the post-GES prospective cohort (P < 0.001). The investigators concluded that the GES showed clinical utility in the evaluation of patients with suspected obstructive CAD presenting to the cardiologist's office.

Herman et al. (2013) found that the use of the Corus CAD gene expression score (GES) led to a change in diagnostic evaluation. The investigators reported on the results of the Primary Care Providers Use of a Gene Expression Test in Coronary Artery Disease Diagnosis (IMPACT-PCP) trial, a prospective study of stable, nonacute, nondiabetic patients presenting with chest pain and related symptoms at four primary care practices. All patients underwent GES testing, with clinicians documenting their planned diagnostic strategy both before and after GES. Of the 251 study patients, 140 were women (56%); the participants had a mean age of 56 years (standard deviation, 13.0) and a mean body mass index of 30 mg/kg² (standard deviation, 6.7). The mean GES was 16 (range, 1-38), and 127 patients (51%) had a low GES (less than or equal to 15). The investigators noted a change in the diagnostic testing pattern before and after GES testing in 145 of 251 patients (58% observed vs. 10% predefined expected change; P < 0.001). The investigators concluded that incorporation of the GES into the diagnostic workup showed clinical utility above and beyond conventional clinical factors by optimizing the patient's diagnostic evaluation.

Ladapo et al. (2015) concluded that the results of a registry study demonstrated the clinical utility of the Corus CAD by guiding the decision-making of primary care providers during the assessment of symptomatic patients with suspected obstructive CAD. The REGISTRY I study measured the impact of the Corus CAD Gene Expression Score (GES) on subsequent cardiac referral decisions by primary care providers. Of the 342 stable, nonacute patients evaluated, the mean age was 55 years, 53% were female, and mean (SD) GES was 16 (±10) (range = 1-40). Low GES (≤ 15), indicating a low current likelihood of obstructive coronary artery disease (CAD), was observed in 49% of patients. The investigators reported that, after clinical covariate adjustment, each 10-point GES decrease was associated with a 14-fold decreased odds of cardiac referral (P < 0.0001). Low GES patients had 94% reduced odds of referral relative to elevated GES patients (P < 0.0001), with follow-up supporting a favorable safety profile.

Hochheiser et al. (2014) published the results of a decision analysis model to assess the economic utility of the Corus CAD gene expression score (GES) for the diagnosis of obstructive CAD. Within a representative commercial health plan's adult membership, current practice for obstructive CAD diagnosis (usual care) was compared to a strategy that incorporates the GES test (GES-directed care). The model projected the number of diagnostic tests and procedures performed, the number of patients receiving medical therapy, type I and type II errors for each strategy of obstructive CAD diagnosis, and the associated costs over a one-year time horizon. Results demonstrate that GES-directed care to exclude the diagnosis of obstructive CAD prior to myocardial perfusion imaging may yield savings to health plans relative to usual care by reducing the utilization of noninvasive and invasive cardiac imaging procedures and increasing diagnostic yield at invasive coronary angiography (ICA). At a 50% capture rate of eligible patients in GES-directed care, it is projected that a commercial health plan will realize savings of $0.77 per member per month; savings increase proportionally to the GES capture rate. The authors concluded that these findings illustrate the potential value of the Corus CAD for health plans and patients in an age of greater emphasis on personalized medicine.

On December 20, 2018, Noridian Healthcare Solutions, LLC rescinded coverage for the Corus CAD test (effective 12/26/2018). According to Noridian Healthcare Solutions, “the vendor has provided no evidence that use of the test results in improved patient outcomes (clinical utility). Thus, this test does not meet Medicare’s reasonable and necessary criteria for coverage. A number of the published papers have stressed that physician behavior has changed on the basis of the test. However, clinical utility is not established by clinician referrals to cardiology or for further cardiac evaluation. These articles provide no defined treatment protocol(s) to manage patients with a GES of any value. Furthermore, clinicians are left to interpret the test results as they see fit. The test is neither a ‘rule out’ nor a ‘rule in’ test and is marketed to primary care and cardiologists without providing value to the patient or physician management of the patient. Finally, the Corus CAD test is not included in any professional society management or treatment guidelines.”

Kunthara and Ho (2019) noted that “A 16-year-old Bay Area medical diagnostics company is shutting down after the federal government’s Medicare health insurance program – one of the largest purchasers of the company’s flagship product, a blood test for heart disease – rescinded coverage of the test in many states after the test was found to be unnecessary and of little use to patients. But that was not before the company, CardioDx in Redwood City, sold millions of dollars’ worth of the test over the course of six years until Medicare in November stopped paying for it. At least 175,000 patients have used the test, the company has said. CardioDx’s shutdown appears abrupt: The same day in December it publicly touted the value of its blood test, it submitted a notice to a state agency disclosing it was laying off 110 employees as it planned to ‘wind down its current business operations.’ But warning signs emerged in the fall when insurance companies that administer Medicare benefits began issuing public notices that they planned to stop covering the test in November. Around that time, a federal court unsealed whistle-blower complaints brought by former employees who accused the company of defrauding Medicare by knowingly selling an unnecessary test. Medicare stopped paying for the test, called Corus CAD, in November, according to three Local Coverage Determination notices posted by the U.S. Centers for Medicare and Medicaid Services in September and October. The coverage decisions were made by insurance companies that administer Medicare benefits regionally and help decide which services will be covered, though the payments for those services ultimately come from the federal government. The manufacturer has failed to demonstrate that testing resulted in improved patient outcomes or that testing changed physician management to result in improved patient outcomes, one notice said. Data regarding its clinical usefulness in elderly (Medicare-aged) patients, particularly males, is significantly lacking in all scientific articles. The notice also says the test is not included in any professional society management or treatment guidelines. The Corus CAD test is used to rule out whether a patient’s chest discomfort requires further testing for coronary artery disease. CardioDx-sponsored studies said the test would rule out 50% of patients for further testing, but it only ruled out further testing for 20% of Medicare-covered women and did not rule out further testing for any men over age 65, lawyers for one of the former employees claimed in a suit.”

Endothelin Testing

Camsarl et al. (2003) assessed endothelin-1 (ET-1) and nitric oxide (NO) concentrations in slow coronary flow (SCF) patients before and at the peak of the exercise stress test and compared these values with those from healthy controls. The study population consisted of 25 patients who underwent coronary angiography and were diagnosed with SCF (11 females [44%], aged 56.7 ± 9.8 years) and 20 normal subjects (9 females [45%], aged 54.3 ± 9.2 years). Mean thrombolysis in myocardial infarction (TIMI) frame count in the patients was 54.1 ± 13.4. Blood samples were drawn at rest and immediately at the end of exercise testing. The baseline ET-1 concentrations of the control subjects were lower than those of the patients (7.0 ± 4.5 pg/ml versus 11.1 ± 5.9 pg/ml, p < 0.0001), and this difference increased after exercise (6.2 ± 4.3 pg/ml versus 20.1 ± 10.4 pg/ml, p < 0.0001). Post-exercise ET-1 concentrations were significantly higher than baseline in patients with SCF (p < 0.0001), and a reduction in the ET-1 concentrations was observed in control subjects (p < 0.05). Baseline NO concentrations of the patients were lower than those of the control subjects (27 ± 5.1 micromol/L versus 31.2 ± 4.9 micromol/L, p = 0.0001). Although the NO concentrations in both groups were significantly increased after exercise (29.4 ± 5.9 micromol/L versus 33.3 ± 5.6 micromol/L, p < 0.05 for both), the difference was not significant. A significant negative correlation among post-exercise ET-1 concentrations and maximal heart rate, exercise duration, and exercise rate-pressure product, and a significant positive correlation among post-exercise NO concentrations and maximal heart rate and exercise duration were observed in both groups. The authors concluded that the results of this study showed that endothelial function (assessed by ET-1 and NO concentrations) and its response to exercise were abnormal in SCF patients compared with healthy subjects, and this may play some pathophysiologic role.

Pekdemir and colleagues (2004) noted that SCF is characterized by delayed opacification of coronary arteries in the absence of epicardial occlusive disease. In this study, these researchers determined ET-1 and NO levels and time domain heart rate variability (HRV) parameters in patients with SCF and the relationship among these parameters. A total of 33 patients with SCF detected in coronary angiography (17 females; mean age of 55 ± 7 years) and 19 patients with normal coronary flow (10 females; mean age of 54 ± 11 years) as a control group were enrolled in the study. Patients were divided into two groups according to exercise testing as if positive (group A, n = 8) or negative (group B, n = 25). Plasma ET-1 levels were higher in group A patients (28.7 ± 17.4 pg/ml) than in group B (15.9 ± 10.6 pg/ml) and the control group (6.0 ± 5.7 pg/ml); and higher in group B patients than in the control group (p < 0.05). Although groups A and B did not differ according to plasma NO levels (23.4 ± 13.5 micromol/L versus 32.8 ± 22.7 micromol/L, p > 0.05), NO levels in group A were lower than in the control group (23.4 ± 13.5 micromol/L versus 42.5 ± 15.9 micromol/L, p < 0.05). Time domain HRV parameters were decreased in all patient groups, with this being more prominent in group A. Additionally, HRV parameters were negatively correlated with ET-1 and TIMI frame counts. TIMI frame count was also significantly correlated with ET-1 and NO levels (r = 0.61, p < 0.0001; r = -0.30, p < 0.05). Upon intravascular ultrasonography investigation, the common finding was longitudinally extended massive calcification throughout the epicardial arteries. Mean intimal thickness was 0.50 ± 0.13 mm (group A: 0.58 ± 0.11 mm; group B: 0.47 ± 0.12 mm, p = 0.029). The authors concluded that the present study demonstrated that in patients with SCF, both increased plasma ET-1, decreased plasma NO, and diffuse atherosclerosis may cause the decrease in HRV by affecting myocardial blood flow.

Kurita et al. (2005) stated that vascular tone is regulated by vasodilators and vasoconstrictors. Endothelin-1 is the predominant vasoconstrictor peptide that constricts vascular smooth muscle, whereas NO is the primary vasodilator peptide that relaxes vascular smooth muscle. In this study, these researchers examined whether the NO/ET-1 ratio is a useful marker for detecting CAD, by comparison with evaluation based on vascular endothelial (VE) function. They measured plasma NO and ET-1 using ENO-200 and radioimmunoassay in 38 subjects with normal (NL) coronary arteries (NL group; mean age of 60 ± 12 years) and 25 subjects with CAD (CAD group; mean age of 69 ± 6 years). Vascular endothelial function (randomized endothelium-dependent [D] and endothelium-independent [I] VE function) was assessed by measuring brachial artery (BA) diameter using high-resolution ultrasound (7.5 MHz). Soon after these procedures, symptom-limited exercise testing was performed. There were no statistically significant differences in serum lipid concentrations or VE function between the groups. However, the CAD group had a significantly lower NO/ET-1 ratio (1.2 ± 1.1 versus 2.7 ± 2.2, p < 0.01) and BA diameter after sublingual nitroglycerin (VEID function: 6 ± 7% versus 10 ± 4%, p < 0.05). As expected, the ST segment and treadmill exercise duration were significantly lower in the CAD group. Sensitivity and specificity for detecting CAD by plasma NO/ET-1 ratio (greater than or equal to 2.0) were 90% and 85%, respectively; sensitivity and specificity for detecting CAD by ST depression (greater than or equal to 1 mm) were 80% and 78%, respectively. The authors concluded that the findings of this study suggested that the plasma NO/ET-1 ratio is a useful biological marker for predicting CAD.

Epi+Gen CHD / PrecisionCHD for Prediction of Coronary Heart Disease

Epi+Gen CHD is a patented next-generation test that examines near-term (3-year) risk for a heart attack. It is the first and only clinical test for coronary heart disease (CHD) primary prevention that provides personalized insights based on two types of DNA biomarkers – genetic and epigenetic. Epigenetics considers how environment and lifestyle can influence one’s heart health, providing a more holistic estimate of one’s 3-year risk for CHD. Epi+Gen CHD is designed for individuals over the age of 35 who are interested in determining their risk for CHD. It is not appropriate for those who are younger than 35 years of age, have already been diagnosed with CHD, have had a heart attack or any cardiac operation, have angina (especially with exertion), or have had an abnormal cardiac stress test.

PrecisionCHD is a blood test that combines genetics, epigenetics, and artificial intelligence (AI) to examine the presence of CHD, the most common type of heart disease and the major cause of heart attacks. PrecisionCHD is designed for individuals between the ages of 35 to 80 years who present to be evaluated for CHD and who have not had a bone marrow transplant. The test analyzes genetic and epigenetic DNA biomarkers that capture additional insights that can be used to personalize patient care.

Dogan et al. (2021) noted that the Framingham Risk Score (FRS) and atherosclerotic cardiovascular disease (ASCVD) Pooled Cohort Equation (PCE) for predicting risk for incident CHD work poorly. To improve risk stratification for CHD, these researchers developed a novel integrated genetic-epigenetic risk prediction model (Epi+Gen CHD). Epi+Gen CHD consisted of a total of eight biomarkers, three of which were DNA methylation biomarkers, and the remaining five were single nucleotide polymorphisms (SNPs). Using machine learning (ML) techniques and datasets from the Framingham Heart Study (FHS) and Intermountain Healthcare (IM), these investigators developed and validated an integrated genetic-epigenetic model for predicting 3-year incident CHD. Their approach was more sensitive than FRS and PCE and had high generalizability across cohorts. It performed with sensitivity/specificity of 79%/75% in the FHS test set and 75%/72% in the IM set. The sensitivity/specificity was 15%/93% in FHS and 31%/89% in IM for FRS, and sensitivity/specificity was 41%/74% in FHS and 69%/55% in IM for PCE. The authors concluded that the use of their tool in a clinical setting could better identify patients at high risk for a heart attack. These researchers stated that drawbacks of this study included that the test was developed and tested in subjects of European ancestry. They also stated that further investigations are needed to confirm and extend these findings into diverse populations.

Xia et al. (2021) stated that CHD is a type of cardiovascular disease (CVD) that affects the coronary arteries, which provide oxygenated blood to the heart. It is a major cause of mortality globally. Various prediction methods have been developed to examine the likelihood of developing CHD, including those based on clinical features and genetic variation. Recent epigenome-wide studies have identified DNA methylation signatures associated with the development of CHD, indicating that DNA methylation may play a role in predicting future CHD. These investigators examined recent findings from DNA methylation studies of incident CHD (iCHD) events from epigenome-wide association studies (EWASs). The results suggested that DNA methylation signatures may identify new mechanisms involved in CHD progression and could prove a useful adjunct for the prediction of future CHD. The authors concluded that the rapid development of epigenome-wide technologies has enabled research efforts that provide an opportunity to add an epigenetic layer into the prediction of iCHD. Multiple epigenetic signals have been identified in iCHD; however, a large-scale meta-analysis across these EWASs has yet to be performed. These investigators stated that further exploration of larger cohorts, ideally with a more detailed and homogeneous classification for iCHD cases, is needed. In addition to blood, DNA methylation profiling of heart tissue or vascular walls, which has been very limited to date, would provide highly relevant findings. Furthermore, more functional follow-ups need to be conducted to characterize the function of the identified iCHD DNA methylation signatures, as this may provide insights for the development of iCHD events and their prevention. Finally, studies that evaluate the predictive ability of DNA methylation signatures of iCHD should be performed in larger cohorts incorporating the identified DNA methylation signatures of iCHD. These efforts may also aid clinical interventions, for example, providing more accurate iCHD prediction that may result in informed decisions regarding the choice of intervention.

Zhang et al. (2022) noted that DNA methylation-regulated genes have been demonstrated as crucial participants in the occurrence of CHD. Machine learning based on DNA methylation-regulated genes has tremendous potential for mining non-invasive predictive biomarkers and examining underlying new mechanisms of CHD. First, the 2085 age-gender-matched individuals in FHS were randomly divided into a training set and a validation set. These researchers then integrated methylome and transcriptome data of peripheral blood leukocytes (PBLs) from the training set to probe into the methylation and expression patterns of CHD-related genes. A total of five hub DNA methylation-regulated genes were identified in CHD through dimensionality reduction, including ATG7, BACH2, CDKN1B, DHCR24, and MPO. Subsequently, methylation and expression features of the hub DNA methylation-regulated genes were used to construct ML models for CHD prediction by LightGBM, XGBoost, and Random Forest. The optimal model established by LightGBM exhibited favorable predictive capacity, with an AUC, sensitivity, and specificity of 0.834, 0.672, and 0.864 in the validation set, respectively. In addition, the methylation and expression statuses of the hub genes were verified in monocytes using methylation microarray and transcriptome sequencing. The methylation statuses of ATG7, DHCR24, and MPO and the expression statuses of ATG7, BACH2, and DHCR24 in monocytes of this study population were consistent with those in PBLs from FHS. The authors identified five DNA methylation-regulated genes based on a predictive model for CHD using ML, which may provide clues to the new epigenetic mechanism for CHD.

The authors stated that this study had two main drawbacks. First, in view of FHS mainly containing individuals of European descent, the results in Chinese or other populations might deviate, although DNA methylation-regulated genes were verified in a small Chinese population. Second, the sample size was relatively insufficient for the validation of DNA methylation-regulated genes in monocytes. Moreover, these researchers stated that further investigations are needed to examine the epigenetic regulation mechanisms of ATG7 and other hub genes in CHD in multi-ethnic populations.

Philibert et al. (2023) stated that CHD is the leading cause of death globally. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide post-diagnosis therapy. In prior work using data from the FHS, these researchers provided in-silico evidence that integrated genetic-epigenetic tools may provide a new avenue for assessing CHD. In this communication, these researchers used an improved ML approach and data from two additional cohorts, totaling 449 cases and 2,067 controls, to develop a better model for ascertaining symptomatic CHD. Using the DNA from the two new cohorts, these investigators translated and validated the in-silico findings into an AI-guided, clinically implementable method that uses input from six methylation-sensitive digital PCR (dPCR) and ten genotyping assays. Using this method, the overall average AUC, sensitivity, and specificity in the three test cohorts were 82%, 79%, and 76%, respectively. Analysis of targeted cytosine-phospho-guanine loci showed that they map to key risk pathways involved in atherosclerosis, suggesting specific therapeutic approaches. The authors concluded that this scalable integrated genetic-epigenetic approach was useful for the diagnosis of symptomatic CHD, performed favorably compared with many existing methods, and may provide personalized insight into CHD therapy. In addition, given the dynamic nature of DNA methylation and the ease of methylation-sensitive dPCR methodologies, these findings may pave a pathway for precision epigenetic approaches for monitoring CHD treatment response. These researchers stated that this study extended previous studies to show that an AI-guided method that simultaneously considers both genetic and DNA methylation information can be used to evaluate current CHD status in a clinically meaningful manner. They stated that this form of epigenetic testing may provide a rapid, scalable, and sensitive alternative to currently established methods for assessing current CHD status. Because the six methylation indices are both dynamic and map to molecular pathways of established clinical risk factors, it may be possible that the epigenetic information contained in the test could be used to inform treatment choice and guide the evaluation of intervention effectiveness. These researchers stated that future studies are needed to test this hypothesis.

The authors stated that this study had several drawbacks. First, the number of subjects with CHD in the FHS cohort was relatively small. Second, the methylation information used in these analyses was acquired from genome-wide arrays, which are time-consuming to process, costly, and relatively inaccurate. Third, the FHS study participants were all White individuals from the northeastern U.S. Fourth, the FHS CHD assessments represented only best estimates from the Framingham Endpoint Review Committee. Diagnostic testing was not carried out as part of the study protocol. It was uncertain whether this analysis and results would be applicable to subjects ascertained by other clinical methods, recruited in other regions of the country, or of another race or ethnicity.

Fibrinogen

Fibrinogen is a circulating glycoprotein in the blood that helps with blood clotting. Too much fibrinogen may promote excessive clumping of platelets, which can cause clots to form in an artery, potentially leading to a heart attack or stroke. Fibrinogen has been suggested as a possible indicator of inflammation that accompanies atherosclerosis.

Fibrinogen acts at the final step in the coagulation response to vascular and tissue injury, and epidemiological data support an independent association between elevated levels of fibrinogen and cardiovascular morbidity and mortality.

In a structured evidence review, Hackman and Anand (2003) found moderate evidence that fibrinogen is an independent risk predictor for atherosclerotic disease (coronary heart disease [CHD], cerebrovascular disease, and peripheral vascular disease). However, they found minimal evidence that fibrinogen is an incremental risk predictor. Hackam and Anand (2003) identified only one study that examined the additive yield of screening for fibrinogen. The authors noted that precise and validated tests are not available for fibrinogen. In addition, they concluded that it is unclear whether fibrinogen is causal or merely a marker of atherosclerotic vascular disease. The investigators found few, if any, controlled studies evaluating risk-reduction strategies for fibrinogen or any of the other novel risk factors that they evaluated. They concluded that “clinical trials are necessary before it can be determined whether fibrinogen has a causal role in atherothrombosis or is merely a marker of the degree of vascular damage taking place.”

A consensus statement from the American College of Cardiology (ACC) and the American Diabetes Association (ADA) (Brunzell et al., 2008) stated that the independent predictive power and clinical utility of fibrinogen measurement is unclear. A guideline from the National Academy of Clinical Biochemistry (Cushman et al., 2009) stated: "There are sufficient data that fibrinogen is an independent marker of cardiovascular disease (CVD) risk; however, because of analytical concerns, insufficient assay standardization, and uncertainty in identifying treatment strategies, measurement is not recommended for this application."

The American Heart Association (Balagopal et al., 2011) statement on nontraditional risk factors and biomarkers for cardiovascular disease in youth concluded: "Although studies in children suggest the presence of a prothrombotic state in obese children at an early age, the role of fibrinogen as a potential marker of CVD risk needs to be confirmed in longitudinal studies; a cause-and-effect relationship cannot be assigned at present in children."

Guidelines from the American Association of Clinical Endocrinologists (2012) state that fibrinogen screening in the general population is not recommended because fibrinogen levels can vary among ethnic groups. Furthermore, factors unrelated to coronary artery disease (CAD) may affect fibrinogen levels, and no standard measurement assay exists.

The Emerging Risk Factors Collaboration (Kaptoge et al., 2012) analyzed individual records of 52 prospective cohort studies with 246,669 participants without a history of CVD to investigate the value of adding fibrinogen levels to conventional risk factors for the prediction of cardiovascular risk. The analysis showed that adding information about an inflammation biomarker to the standard risk factors used to predict the 10-year risk of the first cardiovascular event leads to a very small but statistically significant increase in the C-statistics (0.0027 for fibrinogen).

A European consensus guideline (2012) included a strong recommendation that fibrinogen should not be measured in asymptomatic low-risk individuals and high-risk patients to assess 10-year risk of CVD. The guidelines included a weak recommendation that fibrinogen may be measured as part of refined risk assessment in patients with an unusual or moderate CVD risk profile.

European guidelines (2012) identified several issues with measurement of fibrinogen for cardiovascular disease risk, including:

multiplicity of confounders: dependence on other classical major risk factors;
lack of precision: narrow diagnostic window for fibrinogen level and risk of CVD;
lack of specificity: similar level of risk for other non-cardiovascular causes of morbidity and mortality (e.g. other low-grade inflammatory diseases);
lack of dose–effect or causality relationship between changes in fibrinogen level and risk of CVD; and
lack of specific therapeutic strategies or agents targeting circulating fibrinogen and showing reduction in CVD incidence.

The guideline noted that similar observations could be made for high-sensitivity C-reactive protein. The guidelines also noted their higher cost of test compared with classical biological risk factors (e.g. blood glucose and lipids).

GlycA (Glycosylated Acute Phase Proteins) Testing

Nuclear magnetic resonance (NMR) spectra of serum obtained under quantitative conditions for lipoprotein particle analyses contain additional signals that could potentially serve as useful clinical biomarkers (Otvos et al., 2015). GlycA, a protein glycosylation, is the name given to a particular inflammation-responsive signal in the NMR spectra of serum and plasma, which can be measured clinically. Connelly et al. (2017) evaluated recent studies of this novel biomarker concerning systemic inflammation and cardiovascular disease risk. GlycA is found to be positively correlated with body mass index (BMI), insulin resistance, markers of metabolic syndrome, and the ratio of leptin to adiponectin, suggesting that, in addition to being elevated in acute inflammation, levels are also raised along with adipose tissue-associated low-grade chronic inflammation. As such, GlycA may be a reliable biomarker of cardiometabolic risk. The authors state that recent cross-sectional, observational, and interventional studies have demonstrated that GlycA is elevated in acute and chronic inflammation, can predict death in healthy individuals, and is associated with disease severity in patients with chronic inflammatory diseases such as rheumatoid arthritis, lupus, and psoriasis. The authors conclude that GlycA test results may have clinical utility similar to or complementary to high-sensitivity C-reactive protein (hsCRP), fibrinogen, and other biomarkers; however, larger prospective studies and randomized trials are necessary to assess the impact of novel therapies on GlycA in patients with chronic inflammatory conditions, which may be concomitant with cardiovascular benefits.

Akinkuolie et al. (2016) conducted a post-hoc analysis from the JUPITER trial to evaluate GlycA in the setting of statin therapy to see if GlycA predicts cardiovascular disease (CVD) events. The JUPITER trial was a double-blind, placebo-controlled trial that evaluated rosuvastatin versus placebo in the primary prevention of major CVD events among 17,802 healthy subjects with low low-density lipoprotein cholesterol (LDL-C) but who were at increased risk of cardiovascular events based on elevated hsCRP. Akinkuolie and colleagues analyzed a total of 12,527 participants who provided a sufficient blood sample at baseline for NMR GlycA measurements, of which 10,039 participants had a sufficient blood sample at both baseline and at one year. The aim was to evaluate the effect of one year of rosuvastatin treatment on GlycA, the association of baseline and on-statin GlycA with incident CVD, and to see whether the CVD relative risk reduction attributable to rosuvastatin treatment in JUPITER was modified by GlycA levels. The authors found that a total of 310 first primary CVD events occurred during a maximum follow-up of five years. GlycA changed minimally after one year on study treatment: a 6.8% and 4.7% decrease in the rosuvastatin and placebo groups, respectively. Overall, baseline GlycA levels were associated with an increased risk of CVD (P=0.0006). After additionally adjusting for hsCRP, this was slightly attenuated (P=0.01). On-treatment GlycA levels were also associated with CVD (before and after additionally adjusting for hsCRP: 1.27 [P<0.0001] and 1.24 [P=0.004]). Tests for heterogeneity by treatment arm were not significant (P for interaction >0.20). The authors concluded that increased levels of GlycA were associated with an increased risk of CVD events independent of traditional risk factors and hsCRP. Limitations include a lack of generalizability given the entry criteria of JUPITER, which excluded participants with low hsCRP, high LDL-C, high triglycerides, known CVD, or diabetes, and a limited follow-up duration.

Duprez et al. (2016) studied the association of GlycA and inflammatory biomarkers with future death and disease. A total of 6,523 subjects in the Multi-Ethnic Study of Atherosclerosis who were free of overt cardiovascular disease (CVD) and in generally good health had baseline blood analyzed for high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and d-dimer. A spectral deconvolution algorithm was used to quantify GlycA signal amplitudes from automated NMR LipoProfile® test spectra. The median follow-up was 12.1 years. The authors found that the relative risk per standard deviation (SD) of GlycA, IL-6, and d-dimer for total death (n = 915); for total CVD (n = 922); and for chronic inflammatory-related severe hospitalization and death (ChrlRD) (n = 1,324) ranged from 1.05 to 1.20, independently of covariates. In contrast, prediction from hsCRP was statistically explained by adjustment for other inflammatory variables. Only GlycA was predictive for total cancer (n = 663). Women had 7% higher values of all inflammatory biomarkers than men and had a significantly lower GlycA prediction coefficient than men in predicting total cancer. The authors concluded that GlycA derived from NMR is associated with the risk for total death, CVD, chronic inflammatory-related severe hospitalization and death, and cancer. This novel biomarker reflecting the risk of death, CVD, ChrIRD, and cancer may have the potential to improve risk assessment.

HART CADhs

The HART CADhs panel and algorithm were developed using machine learning (a subset of AI) and a cohort of 927 subjects from Massachusetts General Hospital’s CASABLANCA prospective, single-center, investigator-initiated, observational cohort study. This included external validation using a cohort of 241 patients presenting to the Emergency Department with suspicion of acute myocardial infarction (MI) from the University of Hamburg’s Biomarkers in Acute Cardiac Care (BACC) prospective, observational study population.

From the trials, some summarized below, three final proteins were identified: adiponectin, kidney injury molecule-1 (KIM-1), and hsTroponin. These biomarkers represent pathophysiological pathways that affect glucose and fatty acid metabolism (adiponectin); cardiorenal syndrome and vascular inflammation (KIM-1); and cardiac ischemia or stress (troponin). Based on observational cohort studies, those with severe coronary artery disease (CAD) had lower concentrations of adiponectin and higher concentrations of KIM-1 and hsTroponin I at baseline.

The HART CADhs test is reported as a risk score, scaled from 1 to 5. The scores are divided into three risk ranges: lower risk (score 1 to 2), moderate risk (score of 3), and higher risk (scores 4 to 5). A score of 1 had a mean stenosis of approximately 20% and a 9% risk of having a greater than or equal to 70% obstruction in at least one epicardial coronary artery. A score of 2 had a mean stenosis of approximately 40% and a 21% risk of having a greater than or equal to 70% obstruction in at least one epicardial coronary artery. A score of 3 had a mean stenosis of approximately 55% and a 46% risk of having a greater than or equal to 70% obstruction in at least one epicardial coronary artery. A score of 4 had a mean stenosis of approximately 85% and an 85% risk of having a greater than or equal to 70% obstruction in at least one epicardial coronary artery. A score of 5 had a mean stenosis of 90% and a 93% risk of having a greater than or equal to 70% obstruction in at least one epicardial coronary artery. The HART CADhs risk scores are aimed at identifying those at higher risk who merit consideration for aggressive invasive or medical management therapy while considering less aggressive measures in lower-risk patients.

Patel et al. (2010) noted that guidelines for triaging patients for cardiac catheterization recommend a risk assessment and non-invasive testing. These investigators examined patterns of non-invasive testing and the diagnostic yield of catheterization among patients with suspected CAD in a contemporary national sample. From January 2004 through April 2008, at 663 hospitals in the American College of Cardiology National Cardiovascular Data Registry, these researchers identified patients without known CAD who were undergoing elective catheterization. Subjects’ demographic characteristics, risk factors, and symptoms, as well as the results of non-invasive testing, were correlated with the presence of obstructive CAD, which was defined as stenosis of 50% or more of the diameter of the left main coronary artery or stenosis of 70% or more of the diameter of a major epicardial vessel. A total of 398,978 patients were included in the study. The median age was 61 years; 52.7% of the patients were men, 26.0% had diabetes, and 69.6% had hypertension. Non-invasive testing was carried out in 83.9% of the patients. At catheterization, 149,739 patients (37.6%) had obstructive CAD. No CAD (defined as less than 20% stenosis in all vessels) was reported in 39.2% of the patients. Independent predictors of obstructive CAD included male sex (odds ratio [OR], 2.70; 95% confidence interval [CI]: 2.64 to 2.76), older age (OR per 5-year increment, 1.29; 95% CI: 1.28 to 1.30), presence of insulin-dependent diabetes (OR, 2.14; 95% CI: 2.07 to 2.21), and presence of dyslipidemia (OR, 1.62; 95% CI: 1.57 to 1.67). Patients with a positive result on a non-invasive test were moderately more likely to have obstructive CAD than those who did not undergo any testing (41.0% versus 35.0%; p < 0.001; adjusted OR, 1.28; 95% CI: 1.19 to 1.37). The authors concluded that in this study, slightly more than one-third of patients without known disease who underwent elective cardiac catheterization had obstructive CAD. These investigators stated that better strategies for risk stratification are needed to inform decisions and to increase the diagnostic yield of cardiac catheterization in routine clinical practice. This study did not provide any information on the use of a multi-biomarker panel for predicting the presence of obstructive CAD.

Douglas et al. (2015) stated that many patients have symptoms suggestive of CAD and are often evaluated using diagnostic testing, although there are limited data from randomized trials to guide care. These investigators randomly assigned 10,003 symptomatic patients to a strategy of initial anatomical testing using computed tomography angiography (CTA) or to functional testing (exercise electrocardiography, nuclear stress testing, or stress echocardiography). The composite primary endpoint was death, MI, hospitalization for unstable angina, or major procedural complication. Secondary endpoints included invasive cardiac catheterization that did not show obstructive CAD and radiation exposure. The mean age of the patients was 60.8 ± 8.3 years; 52.7% were women, and 87.7% had chest pain or dyspnea on exertion. The mean pre-test likelihood of obstructive CAD was 53.3 ± 21.4%. Over a median follow-up period of 25 months, a primary endpoint event occurred in 164 of 4,996 patients in the CTA group (3.3%) and in 151 of 5,007 (3.0%) in the functional-testing group (adjusted hazard ratio [HR], 1.04; 95% CI: 0.83 to 1.29; p = 0.75). CTA was associated with fewer catheterizations showing no obstructive CAD than functional testing (3.4% versus 4.3%, p = 0.02), although more patients in the CTA group underwent catheterization within 90 days after randomization (12.2% versus 8.1%). The median cumulative radiation exposure per patient was lower in the CTA group than in the functional-testing group (10.0 mSv versus 11.3 mSv); however, 32.6% of the patients in the functional-testing group had no exposure, so the overall exposure was higher in the CTA group (mean, 12.0 mSv versus 10.1 mSv; p < 0.001). The authors concluded that in symptomatic patients with suspected CAD who required non-invasive testing, a strategy of initial CTA, compared with functional testing, did not improve clinical outcomes over a median follow-up of 2 years. Again, this study did not provide any information on the use of a multi-biomarker panel for predicting the presence of obstructive CAD.

Ibrahim et al. (2017) state that noninvasive models to predict the presence of coronary artery disease (CAD) may help reduce the societal burden of CAD. The goal of their study was to identify clinical and biomarker predictors of clinically significant CAD in an at-risk population of subjects enrolled in the CASABLANCA (Catheter Sampled Blood Archive in Cardiovascular Diseases) prospective, single-center, investigator-initiated, observational cohort study undergoing coronary angiography for numerous indications. The authors hypothesized that the addition of plasma biomarkers to known clinical risk factors might increase the accuracy of predicting clinically significant CAD. The authors selected 927 patients from the CASABLANCA study for analysis. These patients were randomly split into a training set, which included predictors of greater than or equal to 70% stenosis in at least one major coronary vessel (n = 649), and a holdout validation set (n = 278). All studies for biomarker selection and the development of a diagnostic model were conducted exclusively on the training set. The scoring system consisted of clinical variables (male sex and previous percutaneous coronary intervention) and four biomarkers (midkine, adiponectin, apolipoprotein C-I, and kidney injury molecule–1). In the training cohort, elevated scores were predictive of greater than or equal to 70% stenosis in all subjects (p < 0.001), men (p < 0.001), women (p < 0.001), and those with no previous CAD (p < 0.001). In the validation cohort, the score had an area under the receiver-operating characteristic curve of 0.87 (p < 0.001) for coronary stenosis greater than or equal to 70%. Higher scores were associated with greater severity of angiographic stenosis. At the optimal cutoff, the score had 77% sensitivity, 84% specificity, and a positive predictive value of 90% for greater than or equal to 70% stenosis. Partitioning the score into five levels allowed for identifying or excluding CAD with greater than 90% predictive value in 42% of subjects. An elevated score predicted incident acute myocardial infarction during 3.6 years of follow-up (p < 0.001). The authors concluded that they developed a clinical and biomarker scoring strategy to reliably diagnose severe epicardial CAD. Advantages of such a reliable clinical and biomarker score include the fact that such a technology can be widely disseminated in a cost-effective manner, is easily interpreted, and might be associated with a well-defined sequence of therapeutic steps to reduce the risk for CAD-related complications, such as antiplatelet or lipid-lowering therapy. Limitations of the study include that the patient population was predominantly white males from a tertiary care referral center. In addition, the training and validation cohorts were taken from the same population, although they were randomly selected. Also, patients referred to angiography enrolled in their study had an a priori reason for the procedure; accordingly, due to Bayesian considerations, the pre-test probability for significant CAD was higher than if a community-based cohort without indications for invasive angiography was studied. Furthermore, many subjects did not have a stress test performed before referral for angiography; as such, the authors were unable to correlate the presence of ischemia with the degree of coronary stenosis. Further studies using the authors' scoring system are planned.

McCarthy et al. (2017) sought to develop a multiple biomarker approach for the prediction of incident major adverse cardiac events (MACE) in patients referred for coronary angiography. In a 649-participant training cohort from the CASABLANCA study, predictors of MACE within one year were identified using least-angle regression; over 50 clinical variables and 109 biomarkers were analyzed. Predictive models were generated using least absolute shrinkage and selection operator with logistic regression. A score derived from the final model was developed and evaluated with a 278-patient validation set during a median of 3.6 years of follow-up. The scoring system consisted of N-terminal pro B-type natriuretic peptide (NT-proBNP), kidney injury molecule-1, osteopontin, and tissue inhibitor of metalloproteinase-1; no clinical variables were retained in the predictive model. In the validation cohort, each biomarker improved model discrimination or calibration for MACE; the final model had an area under the curve (AUC) of 0.79 (p < 0.001), higher than the AUC for clinical variables alone (0.75). In net reclassification improvement analyses, the addition of other markers to NT-proBNP resulted in significant improvement (net reclassification improvement 0.45; p = 0.008). At the optimal score cutoff, the authors found 64% sensitivity, 76% specificity, 28% positive predictive value, and 93% negative predictive value for one-year MACE. Time-to-first MACE was shorter in those with an elevated score (p < 0.001); such risk extended to at least four years. In conclusion, in a cohort of patients who underwent coronary angiography, the authors describe a novel multiple biomarker score for incident MACE within one year. The authors noted study limitations such as the number of patients from which they derived and validated their findings was relatively small and with a low number of events. Further, they only measured biomarkers at a single point in time, which may not reflect levels at future time points. Their results need further validation and should not be extrapolated to the general population without suspected or known coronary artery disease, as these patients were not included in their study. Finally, whether aggressive treatment strategies in patients with an elevated risk score will modify their score, and whether that will reflect lower risk, remains unknown and will require further investigation.

McCarthy et al. (2020) sought to derive and externally validate a high-sensitivity cardiac troponin (hs-cTn)–based proteomic model to diagnose obstructive coronary artery disease. In a derivation cohort of 636 patients from the CASABLANCA study (a prospective, single-center, investigator-initiated, observational cohort study) referred for coronary angiography, predictors of ≥70% coronary stenosis were identified from six clinical variables and 109 biomarkers. The final model was first internally validated on a separate cohort (n = 275) and then externally validated on a cohort of 241 patients presenting to the emergency department (ED) (the Biomarkers in Acute Cardiac Care [BACC] prospective observational study) with suspected acute myocardial infarction, where ≥50% coronary stenosis was considered significant. The resulting model consisted of three clinical variables (male sex, age, and previous percutaneous coronary intervention) and three biomarkers (hs-cTnI [high-sensitivity cardiac troponin I], adiponectin, and kidney injury molecule–1). In the internal validation cohort, the model yielded an area under the receiver operating characteristic curve of 0.85 for coronary stenosis ≥70% (p < 0.001). The authors observed 80% sensitivity, 71% specificity, a positive predictive value of 83%, and a negative predictive value of 66% for ≥70% stenosis. Partitioning the score result into five levels resulted in a positive predictive value of 97% and a negative predictive value of 89% at the highest and lowest levels, respectively. In the external validation cohort, the score performed similarly well. Notably, in patients who had myocardial infarction neither ruled in nor ruled out via hs-cTnI testing (“indeterminate zone,” n = 65), the score had an area under the receiver operating characteristic curve of 0.88 (p < 0.001). Study limitations included that the biomarkers were measured at a single point in time, which may not reflect levels at future time periods. As the rates of stress testing and CT coronary angiography were variable in both cohorts (CASABLANCA and BACC studies), comparisons of their diagnostic performance cannot be made. However, the score performs well when compared with the gold standard, invasive coronary angiography. Lastly, the definition of obstructive CAD differed in each cohort as a result of differences in adjudication in the CASABLANCA and BACC study cohorts. The authors concluded that a model including hs-cTnI can predict the presence of obstructive coronary artery disease with high accuracy, including in those with indeterminate hs-cTnI concentrations.

Mohebi et al. (2022) sought to assess the added value of novel kidney biomarkers to a clinical score in the CASABLANCA study. The authors evaluated individuals undergoing coronary and/or peripheral angiography and added four candidate biomarkers for acute kidney injury (kidney injury molecule-1, interleukin-18, osteopontin, and cystatin C) to a previously described contrast-associated acute kidney injury (CA-AKI) risk score. Participants were categorized into integer score groups based on the risk assigned by the biomarker-enhanced CA-AKI model. Risk for incident cardiorenal outcomes during a median of 3.7 years of follow-up was assessed. Of 1,114 participants studied, 55 (4.94%) developed CA-AKI. In adjusted models, neither kidney injury molecule-1 nor interleukin-18 improved discrimination for CA-AKI; the addition of osteopontin and cystatin C to the CA-AKI clinical model significantly increased the c-statistic from 0.69 to 0.73 (p for change < 0.001) and resulted in a net reclassification index of 59.4. Considering those with the lowest CA-AKI integer score as a reference, the intermediate, high-risk, and very-high-risk groups were associated with adverse cardiorenal outcomes. The corresponding hazard ratios of the very-high-risk group were 3.39 (95% CI, 2.14-5.38) for nonprocedural acute kidney injury, 5.58 (95% CI, 3.23-9.63) for incident chronic kidney disease, 6.21 (95% CI, 3.67-10.47) for myocardial infarction, and 8.94 (95% CI, 4.83-16.53) for all-cause mortality. The authors concluded that a biomarker-enhanced risk model significantly improves the prediction of CA-AKI beyond clinical variables alone and may stratify the risk of future cardiorenal outcomes.

Cai et al. (2024) stated that a comprehensive overview of AI for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. In a systematic review, these investigators examined AI-Ms of CVD prediction in the general and special populations and developed a new independent validation score (IVS) for AI-Ms replicability evaluation. PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, these researchers designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. In 20,887 screened references, 79 articles (82.5% published between 2017 and 2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). These researchers identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once, and only 39.4% over three times. A large number of different predictors (range of 5 to 52,000, median of 21) and large-span sample sizes (range of 80 to 660,000, median of 4,466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. The authors concluded that AI has led the digital revolution in the field of CVD prediction; however, it is still in the early stage of development due to defects in research design, reporting, and evaluation systems. These researchers stated that the IVS they designed may provide a practical tool for evaluating model replicability. It is expected to contribute to independent external validation research and subsequent extensive clinical application. Moreover, these investigators stated that the development of AI CVD risk prediction may depend largely on the collaborative efforts of researchers, health policymakers, editors, reviewers, and quality controllers.

HART CVE Panel

The HART CVE panel and algorithm were developed using a subset of AI and data from the CASABLANCA study. The HART CVE analyzes 4 proteins: N-terminal pro-brain-type natriuretic peptide (NT-proBNP), osteopontin, tissue inhibitor of metalloproteinase-1 (TIMP-1), and kidney injury molecule-1 (KIM-1) in plasma. Risk scores are divided into 3 risk ranges: low, moderate, higher risk of having a heart attack, stroke, or cardiovascular death at 1 year.

Neumann et al. (2020) sought to validate a machine learning-driven, multi-biomarker panel for prediction of incident major adverse cardiovascular events (MACE). A previously described prognostic panel for MACE consisting of four biomarkers (NT-proBNP, KIM-1, osteopontin, and TIMP-a) was measured in 748 patients with suspected MI. The investigated end point was incident MACE within 1 year. The prognostic value of a continuous score and an optimal cut-off was investigated. The area under the curve was 0.86 for the overall model. Using the optimal cut-off resulted in a negative predictive value of 99.4% for incident MACE. Patients with an elevated prognostic score were at high risk for MACE. The authors concluded that among patients with suspected MI, they validated a multi-biomarker panel for predicting 1-year MACE. However, the authors do note some study limitations. The analyses was performed retrospectively, and the absolute number of observed cardiovascular events were small and the overall sample size was limited to 750 individuals, which could impact the significance of their findings.

HART KD Panel

The HART KD panel includes analysis of 3 biomarkers: NT-proBNP, C-reactive protein, and thyroid hormone uptake (T-uptake) on plasma. An algorithm is reported out as a risk score for Kawasaki disease. Per Prevencio Inc, using a protein/analyte-based approach and machine learning, Prevencio developed and internally validated a multiple blood assay panel for predicting the presence of KD. NT-pro BNP and CRP individually were no surprise but do not provide clinical specificity needed for KD diagnosis. There are no published peer-reviewed studies evaluating the efficacy of this panel.

HDL Reverse Cholesterol Transport Panel with pCAD Score for Evaluation of Risk of Cardiovascular Diseases

An "HDL Reverse Cholesterol Transport Panel + pCAD Score" refers to a blood test that measures not only the level of HDL cholesterol but also analyzes the specific proteins within the HDL particles to evaluate their functionality in the process of "reverse cholesterol transport," using a score called the "pCAD score," which indicates the potential risk for coronary artery disease (CAD) based on the quality of HDL particles, going beyond just the quantity of HDL cholesterol present.

Dastmalchi et al. (2023) noted that high-density lipoprotein cholesterol (HDL-C) is a known contributor to atherosclerotic cardiovascular disease (ASCVD) risk when HDL-C is less than 40 mg/dL in men and less than 50 mg/dL in women. There has been much interest in the potential cardioprotective properties of HDL-C, as it removes cholesterol from the periphery to the liver for excretion and holds inherent anti-thrombotic and anti-inflammatory properties. However, clinical trials raising HDL-C pharmacologically have not shown improvements in cardiovascular outcomes. In fact, observational studies have shown an increased risk of non-cardiovascular mortality and infection when HDL-C is greater than 90 mg/dL and greater than 70 mg/dL in women and men, respectively. The ability of the HDL particle to effectively transport cholesterol from the periphery for excretion in bile is more complex than illustrated on a standard cholesterol panel. There is variability in its function, size, density, subclass, reverse cholesterol transport, and cholesterol efflux capacity, which impact the particle's ability to effectively reduce cardiovascular disease (CVD) risk. Research has shown that HDL particles are prone to a reduction in their effectiveness in response to infection, diabetes mellitus, gestational hypertensive disorders, rheumatological disorders, as well as menopause. Furthermore, recent studies have demonstrated that low HDL-C may not adequately influence ASCVD risk in Black adults. The authors concluded that advanced lipid testing evaluating HDL particle number, size, and subclasses may prove beneficial in the future, given the heterogeneity of the lipoprotein particle and its impact on reducing serum cholesterol. However, subdividing HDL into its components currently has limited clinical utility, especially given that clinical trials have shown no reduction in CVD risk when pharmacologically increasing HDL-C.

Madaudo et al. (2024) stated that HDL cholesterol is traditionally viewed as protective against CVD. However, emerging evidence shows that dysfunctional HDL, characterized by impaired reverse cholesterol transport, reduced anti-inflammatory and antioxidant activities, and increased endothelial dysfunction, can contribute to CAD. Dysfunctional HDL, resulting from oxidative modifications of apolipoprotein A-1 (Apo A-1) and enzyme inactivation, fails to effectively remove cholesterol from peripheral tissues and may promote inflammation and atherosclerosis. Genetic mutations affecting HDL metabolism further complicate its role in cardiovascular health. Studies have demonstrated that conventional therapies aimed at raising HDL-C levels do not necessarily reduce cardiovascular events, highlighting the need for new approaches that improve HDL functionality. Therapeutic strategies such as Apo A-1 mimetic peptides, reconstituted HDL infusions, as well as drugs targeting specific HDL metabolic pathways are being explored. Furthermore, weight loss, statin therapy, and niacin have demonstrated potential in enhancing HDL function. The pathophysiology of dysfunctional HDL entails complex mechanisms, including oxidative stress, inflammation, and genetic mutations, which alter its structure and function, diminishing its cardioprotective effects. New functional assays (e.g., cholesterol efflux capacity [CEC] and HDL inflammatory index) provide more accurate predictions of cardiovascular risk by examining HDL quality rather than quantity. The authors concluded that as research progresses, the focus is shifting towards therapeutic strategies that enhance HDL function and address the root causes of its dysfunction, offering a more effective approach to reducing cardiovascular risk and preventing CAD.

Furthermore, an UpToDate review on “HDL cholesterol: Clinical aspects of abnormal values” (Rosenson and Durrington, 2025) states that “Cholesterol efflux capacity and CVD risk – The HDL particle has multiple potentially antiatherogenic properties. Much of its antiatherogenic effect is thought to be mediated by its participation in the removal of cholesterol from macrophages in atherosclerotic plaques during a process termed 'macrophage cholesterol efflux.' This is the first step in the process of reverse cholesterol transport in which excess cholesterol is removed from the body. Cholesterol efflux capacity is an ex vivo measure of HDL function. The potential value of assessing macrophage cholesterol efflux was evaluated in a study of 2,924 individuals free from cardiovascular disease (CVD). HDL cholesterol, HDL particle concentration, and cholesterol efflux capacity were measured (as were other lipoprotein variables) at baseline. The primary endpoint was atherosclerotic CVD, defined as a first nonfatal myocardial infarction, non-fatal stroke, or coronary revascularization or death from cardiovascular causes. After a median follow-up period of 9.4 years, there was a strong inverse relationship between cholesterol efflux capacity and the primary endpoint (adjusted hazard ratio 0.33, 95% CI: 0.19 to 0.55) comparing the highest quartile with the lowest. Baseline HDL cholesterol was not associated with cardiovascular events in this study. We believe this study helps to explain the limitations of using HDL cholesterol for risk prediction. Functional properties of the HDL particle (e.g., macrophage cholesterol efflux capacity) are likely to be better predictors of CVD events. Cholesterol efflux capacity has also been evaluated as a CVD risk predictor. In one study, it improved risk prediction beyond that of coronary artery calcification, family history, and high-sensitivity C-reactive protein. The utility of the macrophage cholesterol efflux assay has not been established in patients treated with high-intensity statins. In part, this may be due to reduced mobilization of HDL particles into arterial walls in the setting of very low LDL cholesterol levels.”

HDL Subspecies

Lipoprotein subfraction testing is testing that separates two of the commonly measured lipoprotein fractions, HDL and LDL, into subclasses based on their size, density and/or electrical charge. HDL subclass testing is suggested to provide information regarding CVD risk when utilized with standard lipoprotein tests, such as total cholesterol, HDL and LDL testing.

HDL comprises several components and subfractions that also have been related to CHD risk. While HDL cholesterol is the risk indicator most often used, HDL subfractions (lipoprotein AI (LpAI) and lipoprotein AI/AII (LpAI/AII) and/or HDL3 and HDL2) have also been used for risk prediction. ATPIII concluded, however, that the superiority of HDL subspecies over HDL cholesterol has not been demonstrated in large, prospective studies. Consequently, ATPIII did not recommend the routine measurement of HDL subspecies in CHD risk assessment. A consensus statement by the ACC and the ADA (Brunzell et al., 2008) state that measurements of HDL subfractions appear to provide little clinical value beyond measurements of HDL cholesterol.

Interleukin 6 -174 g/c Promoter Polymorphism

Inflammation plays an important role in the pathogenesis of atherosclerosis. Interleukin 6 (IL-6) has many inflammatory functions, and the IL-6 -174 g/c promoter polymorphism appears to influence IL-6 levels. Previous findings on the relation between this polymorphism and risk of CVD are inconsistent. Sie and colleagues (2006) examined this polymorphism in relation to risk of CHD in a population-based study and meta-analysis. Subjects (n = 6,434) of the Rotterdam Study were genotyped. Analyses on the relation between genotype and CHD were performed using Cox proportional hazards tests, and the association between genotype and plasma levels of IL-6 and CRP was investigated. All of the analyses were adjusted for age, sex, and common cardiovascular risk factors. A meta-analysis was performed, using a random effects model. No association between genotype and risk of CHD was observed. The polymorphism was not associated with IL-6 levels, but the C-allele was associated with higher CRP levels (p < 0.01). This meta-analysis did not show a significant association between the genotype and risk of CHD. The authors concluded that the polymorphism is not a suitable genetic marker for increased risk of CHD in persons aged 55 years or older.

In men, plasma interleukin-6 (IL-6) concentrations have been shown to be predictive of a future myocardial infarction (Ridker et al., 2000; Woods et al., 2000), but its contribution to risk of MI is attenuated significantly when other risk factors are taken into account (Pai et al., 2004).

Interleukin 17A Gene Polymorphism

Geng et al. (2015) performed a case-control study to examine the association between genetic variants of IL-17A rs2275913 and IL-17F rs763780 and the development of coronary artery disease (CAD) in a Chinese population. A total of 306 individuals with CAD and 306 unaffected individuals were enrolled from the Zhengzhou People's Hospital between May 2012 and May 2014. The IL-17A rs2275913 and IL-17F rs763780 genes were genotyped by PCR combined with a restriction fragment length polymorphism (PCR-RFLP). Logistic regression analysis revealed that individuals with the AA genotype of rs2275913 were associated with an increased risk of CAD compared to those with the GG genotype in a co-dominant model (adjusted odds ratio [OR] = 1.96; 95% confidence interval [CI]: 1.10 to 3.53). On the other hand, the AA genotype of rs2275913 was correlated with a moderately increased risk of CAD compared to the GG + GA genotype (adjusted OR = 1.76; 95% CI: 1.02 to 3.07) in a recessive model. However, no significant differences were observed between polymorphisms at the IL-17F rs763780 locus and CAD risk in co-dominant, dominant, and recessive models. The authors concluded that the findings of this study suggested that the IL-17A rs2275913 polymorphism may affect the development of CAD; however, no significant association was observed between the IL-17F rs763780 polymorphism and the risk of CAD.

Shuang et al. (2015) carried out a case-control study to estimate the association between IL-17A rs2275913, rs3819025, and rs3748067 polymorphisms and the development of CAD. A total of 415 patients with CAD and 448 healthy controls were recruited during the period from March 2013 to October 2014. Genotyping of IL-17A rs2275913, rs3819025, and rs3748067 was analyzed by PCR-RFLP. By logistic regression analysis, these researchers found that individuals with the AA genotype (OR, 2.18; 95% CI: 1.35 to 3.56) and the GA + AA genotype (OR, 1.39; 95% CI: 1.06 to 1.84) of rs2275913 were associated with an increased risk of CAD when compared with the GG genotype. Individuals carrying the GA + AA genotype of rs2275913 were more likely to have a higher risk of CAD in those with hypertension and a smoking habit, and the adjusted ORs (95% CI) were 3.92 (2.13 to 6.82) and 2.74 (1.71 to 4.40). The authors concluded that the findings of this study suggested that individuals with the AA genotype and the GA + AA genotype of rs2275913 are associated with an increased risk of CAD, especially in those with hypertension and a smoking habit. These findings need to be validated by well-designed studies.

Vargas-Alarcon et al. (2015) evaluated the role of IL-17A gene polymorphisms as susceptibility markers for CAD in the Mexican population. Four IL-17A gene polymorphisms (rs8193036, rs3819024, rs2275913, and rs8193037) were genotyped by 5' exonuclease TaqMan assays in a group of 900 patients with premature CAD and 667 healthy controls (with a negative calcium score by computed tomography), seeking associations with CAD and other metabolic and cardiovascular risk factors using logistic regression analyses. No single IL-17A polymorphism was associated with premature CAD; however, two haplotypes (CAGG and TAGA) were significantly associated with increased risk of premature CAD (OR = 1.35, 95% CI: 1.00 to 1.84, p = 0.018 and OR = 2.09, 95% CI: 1.16 to 3.76, p = 0.003, respectively). Moreover, rs3819024 was associated with increased levels of visceral abdominal fat (p = 0.002), and rs8193036 was significantly associated with the risk of central obesity (p = 0.020), hypertriglyceridemia (p = 0.027), and metabolic syndrome (p = 0.027) in the premature CAD group, under dominant models adjusted by age, gender, body mass index (BMI), smoking history, alcohol consumption, and treatment. The authors concluded that the findings of this study suggested that IL-17A haplotypes are involved in the risk of developing premature CAD and that some IL-17A polymorphisms are associated with cardiovascular risk factors in Mexican individuals with premature CAD.

Intermediate Density Lipoproteins

Lipoprotein remnants testing measures triglyceride-rich lipoproteins that include intermediate-density lipoproteins (IDL) and very-low-density lipoproteins (VLDL). It is proposed that lipoprotein remnants penetrate arterial walls more easily than larger lipoproteins and may be independent risk factors for cardiovascular disease (CVD).

Data from the Framingham Study have suggested that remnant-like particle cholesterol (RLP-C) (intermediate-density lipoproteins) is an independent risk factor for CVD in women, and studies have shown that hormone therapy can lower RLP-C levels in healthy post-menopausal women.

The Women's Angiographic Vitamin and Estrogen (WAVE) trial (Bittner et al., 2004) examined whether hormone therapy can reduce RLP-C and RLP-triglyceride (TG) levels in women with coronary artery disease and whether these factors predict disease progression. WAVE was a randomized, placebo-controlled clinical trial of hormone therapy (conjugated equine estrogen or estrogen plus medroxyprogesterone acetate) and antioxidants in 423 post-menopausal women with angiographic coronary disease. Follow-up angiography at 2.8 years showed no benefit with hormone therapy or antioxidants, and no interaction between the two. The WAVE investigators also measured RLP-C and RLP-TG levels in a subset of 397 women. Mean RLP values among the WAVE participants were very high, corresponding to the 90th percentiles in the Framingham cohort. In multivariate analyses, RLP-C and RLP-TG levels were not related to waist-hip ratio, body mass index (BMI), smoking status, or use of lipid-lowering agents. Compared with placebo, hormone therapy did not significantly reduce RLP levels. Neither baseline RLP levels nor changes in the levels predicted angiographic findings at the end of the study.

The National Cholesterol Education Program Adult Treatment Panel III (ATPIII) Guidelines (2002) state that lipoprotein remnants, including intermediate-density lipoproteins (IDLs), as well as very-low-density lipoproteins (VLDL) and small-density lipoproteins, have been shown to be atherogenic through several lines of evidence. According to ATPIII, “prospective studies relating various measures to coronary heart disease (CHD) risk are limited, and measurement with specific assays cannot be recommended for routine practice.” The ATPIII panel concluded, however, that the most readily available method of measuring atherogenic triglyceride-rich lipoproteins is measurement of VLDL. A consensus statement by the American College of Cardiology (ACC) and the American Diabetes Association (ADA) (Brunzell et al., 2008) noted that, although small dense LDL has been shown to be particularly atherogenic, the association of small LDL and cardiovascular disease may simply reflect the increased number of LDL particles in patients with small LDL.

Kinesin-Like Protein 6 (KLP6)

Kinesin family member 6 (KIF6) genotype testing has been proposed to aid in the assessment of individuals being considered for statin medication therapy.

Genome-wide association studies have revealed connections between a number of common single nucleotide polymorphisms (SNPs) and cardiovascular diseases. Kinesin-like protein 6 is a protein involved in intracellular transport, expressed in many tissues and cell types. A SNP located in the kinesin-like family 6 (KIF6) gene substitutes a thymidine (T) for a cytosine (C), resulting in the substitution of arginine for tryptophan at amino acid 719 (p.Trp719Arg) of the KIF6 protein. Prospective studies have suggested that carriers of the 719Arg allele in KIF6 are at increased risk of clinical coronary artery disease (CAD) compared with non-carriers. There have been claims that non-carriers of the KIF6 719Arg variant receive little benefit from statin therapy. Screening for this genetic variant is now being used to influence statin use. Celera Corporation (Alameda, CA) has marketed its KIF6 Trp719Arg variant assay (Statincheck) to cardiologists and primary care physicians. However, a recent study found that statin therapy significantly reduced the incidence of coronary and other major vascular events to a similar extent, irrespective of KIF6 genotype (Hopewell et al., 2011). Investigators sought to test the effects of the KIF6 Trp719Arg polymorphism (rs20455) on vascular risk and response to statin therapy in 18,348 participants from the Heart Protection Study. Study subjects received 40 mg simvastatin daily for 4 to 6 weeks before being randomly allocated to 40 mg simvastatin daily or placebo for 5 years. Major coronary events were pre-defined as coronary death or non-fatal myocardial infarction (MI), and major vascular events were pre-defined as major coronary events plus revascularization or stroke. The investigators found that the KIF6 genotype was not significantly associated, among placebo-allocated participants, with the risks of incident major vascular events, major coronary events, revascularizations, or strokes. Overall, 40 mg simvastatin daily produced a 42% reduction in low-density lipoprotein cholesterol, which did not differ significantly by KIF6 719Arg carrier status (p = 0.51). Proportional reductions in the risk of major vascular events with statin therapy were similar (interaction p = 0.70) and highly significant across KIF6 genotypes: 23% (95% CI: 16% to 29%; p = 5.3 × 10^-10) in carriers (Arg/Arg or Trp/Arg) and 24% (95% CI: 17% to 31%; p = 4.6 × 10^-9) in non-carriers (Trp/Trp). A similar lack of interaction was observed for major coronary events, revascularizations, and strokes considered separately. The authors concluded that the use of KIF6 genotyping to guide statin therapy is not warranted, as statin therapy significantly reduced the incidence of coronary and other major vascular events to a similar extent, irrespective of KIF6 genotype.

Previously, a large replication study found that the KIF6 Trp719Arg polymorphism was not associated with the risk of clinical CAD (Assimes et al., 2010). Investigators sought to replicate the association between the kinesin-like protein 6 (KIF6) Trp719Arg polymorphism (rs20455) and clinical CAD. The KIF6 Trp719Arg polymorphism (rs20455) was genotyped in 19 case-control studies of non-fatal CAD, either as part of a genome-wide association study or in a formal attempt to replicate the initial positive reports. A total of 17,000 cases and 39,369 controls of European descent, as well as a modest number of South Asians, African Americans, Hispanics, East Asians, and admixed cases and controls, were successfully genotyped. None of the 19 studies demonstrated an increased risk of CAD in carriers of the 719Arg allele compared with non-carriers. Regression analyses and fixed-effects meta-analyses ruled out, with a high degree of confidence, an increase of 2% in the risk of CAD among European 719Arg carriers. Investigators also observed no increase in the risk of CAD among 719Arg carriers in the subset of Europeans with early-onset disease (younger than 50 years of age for men and younger than 60 years of age for women) compared with similarly aged controls, as well as in all non-European subgroups. The investigators concluded that the KIF6 Trp719Arg polymorphism was not associated with the risk of clinical CAD in this large replication study. Accompanying editorialists commented on the lack of a plausible mechanism for the relationship between KIF6, statins, and heart disease (Topol and Damani, 2010). The editorialists stated that "the KIF6 story should serve as a valuable reminder of the potential pitfalls present in prematurely adopting a genomic test without sufficient evidence."

LDL Subspecies (LDL Particle Sizes) and LDL Particle Number

LDL subclass testing is suggested as part of an overall risk assessment for cardiovascular disease (CVD). This test measures the cholesterol content of lipoprotein particles in the blood and determines the LDL particle size and/or density pattern.

Density gradient ultracentrifugation (Vertical Autoprofile [VAP] test) measures the relative distribution of cholesterol within various lipoprotein subfractions, quantifying the cholesterol content of very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), LDL, lipoprotein(a), and HDL subclasses (Mora, 2009). The VAP test also determines the predominant LDL size distribution (e.g., A, AB, or B phenotype) but does not provide concentrations of the lipoprotein particles themselves. ApoB is also provided, although it is not measured directly. Some labs offer vertical lipoprotein particle (VLP) technology included with the VAP test to further analyze CVD risk. The VLP technology purportedly reports a true particle number (LDL-P), a proposed biomarker for increased risk of heart disease and stroke.

Nuclear Magnetic Resonance (NMR) Spectroscopy (Liposcience) is based on the concept that each lipoprotein particle in plasma of a given size has its own characteristic lipid methyl group nuclear magnetic resonance (NMR) signal (Mora, 2009). Particle concentrations of lipoprotein subfractions of different sizes are obtained from the measured amplitudes of their lipid methyl group NMR signals. Lipoprotein particle sizes are then derived from the sum of the diameter of each subclass multiplied by its relative mass percentage based on the amplitude of its methyl NMR signal. The NMR LipoProfile simultaneously quantifies lipoprotein concentrations of VLDL, IDL, LDL, and HDL particles and their subfractions, each expressed as a lipoprotein particle concentration (number of particles per liter) or as an average particle size for each of VLDL, LDL, and HDL.

The gradient gel electrophoresis method determines the distribution of LDL size phenotype by proprietary segmented polyacrylamide gradient gels, which separate lipoproteins in a gradient gel on the basis of their size and, to a lesser extent, their charge (Mora, 2009). Pattern A corresponds to large LDL particles; B to small, dense LDL particles; and AB to an intermediate phenotype. This method gives the relative, or predominant, distribution of lipoprotein particles as determined by the predominant peak particle size.

LDL gradient gel electrophoresis (GGE) has been promoted as an important determinant of coronary heart disease (CHD) risk and as a guide to drug and diet therapy in patients with established coronary artery disease (CAD). The measurement of LDL subclass patterns may be useful in elucidating possible atherogenic dyslipidemia in patients who have no abnormalities in conventional measurements (total cholesterol, HDL, LDL, and triglycerides). However, the therapeutic usefulness of discovering such subclass abnormalities has not been substantiated.

Ion mobility analysis measures both the size and concentrations of lipoprotein particle subclasses on the basis of gas-phase differential electric mobility.

A number of studies have reported that both larger low-density lipoprotein (LDL) particle size and smaller LDL particle sizes are more atherogenic than intermediate-sized particles, and these particles at the extremes of LDL size may be associated with coronary heart disease (CHD) risk. It is thought that LDL subspecies at both extremes of LDL size and density distribution have a reduced LDL receptor affinity.

Musunuru et al. (2009) tested whether combinations of lipoprotein subfractions independently predict cardiovascular disease in a prospective cohort of 4,594 initially healthy men and women (the Malmö Diet and Cancer Study, mean follow-up 12.2 years, 377 incident cardiovascular events). Plasma lipoproteins and lipoprotein subfractions were measured at baseline with a novel high-resolution ion mobility technique. Principal component analysis (PCA) of subfraction concentrations identified three major independent (i.e., zero correlation) components of CVD risk: one representing LDL-associated risk, a second representing HDL-associated protection, and the third representing a pattern of decreased large HDL, increased small/medium LDL, and increased triglycerides. The last corresponds to the previously described "atherogenic lipoprotein phenotype." Several genes that may underlie this phenotype – CETP, LIPC, GALNT2, MLXIPL, APOA1/A5, LPL – are suggested by SNPs associated with the combination of small/medium LDL and large HDL. The investigators concluded that principal component analysis on lipoprotein subfractions yielded three independent components of CVD risk. Genetic analyses suggest these components represent independent mechanistic pathways for the development of CVD.

The Adult Treatment Panel III (ATPIII) stated that although the presence of small LDL particles has been associated with an increased risk of CHD, the extent to which small LDL particles predict CHD independent of other risk factors is “controversial.” It has been argued by Campos et al. (2002), based on epidemiologic evidence, that the relationship between small LDL and CHD found in some studies is probably due to its correlation with other lipoprotein risk factors, and that small LDL is not an independent risk factor for CHD.

Campos et al. (2002) demonstrated in a prospective cohort study that large LDL size is a potential statistically significant predictor of coronary events. Large LDL particles are thought to be large because of high cholesterol ester content. However, Campos reported that the relationship between LDL particle size and coronary events was not present among members of the cohort who were treated with pravastatin, perhaps because pravastatin acts by reducing the size of LDL particles. The author concluded that identifying patients on the basis of LDL size may not be useful clinically, since effective treatment for elevated LDL cholesterol concentrations also effectively treats the risk associated with large LDL.

Commenting on LDL particle size, a consensus statement from the American College of Cardiology (ACC) and the American Diabetes Association (ADA) stated: "The size of LDL particles can also be measured. As small dense LDL particles seem to be particularly atherogenic, assessment of particle size has intuitive appeal. Both LDL particle concentration and LDL size are important predictors of CVD. However, the Multi-Ethnic Study of Atherosclerosis suggested that on multivariate analyses, both small and large LDL were strongly associated with carotid intima-media thickness (Mora et al., 2007), while the Veterans Affairs High-Density Lipoprotein Cholesterol Intervention Trial (VA-HIT) showed that both were significantly related to coronary heart disease (CHD) events (Otvos et al., 2006). The association of small LDL and CVD may simply reflect the increased number of LDL particles in patients with small LDL. Hence, it is unclear whether LDL particle size measurements add value to measurement of LDL particle concentration" (Brunzell et al., 2008).

The ACC/ADA consensus statement recommended ApoB measurement over measurement of particle number with NMR (Brunzell et al., 2008): "Limitations of the clinical utility of NMR measurement of LDL particle number or size include the facts that the technique is not widely available and that it is currently relatively expensive. In addition, there is a need for more independent data confirming the accuracy of the method and whether its CVD predictive power is consistent across various ethnicities, ages, and conditions that affect lipid metabolism."

An assessment by the California Technology Assessment Forum (CTAF) (Walsh, 2008) of LDL particle number as assessed by NMR spectroscopy concluded that this test did not meet CTAF's assessment criteria. The CTAF assessment stated that there were no studies addressing whether or not treated LDL particle levels affected clinical outcomes.

A systematic evidence review of LDL subfractions, including the methods of gradient gel electrophoresis, NMR spectroscopy, and ultracentrifugation, prepared for the Federal Agency for Healthcare Research and Quality (AHRQ), concluded that "the data do not adequately answer the question of how strongly LDL subfraction information is associated with CVD [cardiovascular disease], in relation to other known and putative risk factors. In summary, none of the LDL subfraction measurements have definitively been demonstrated to add to the ability to discriminate between individuals who are at higher versus lower risks of cardiovascular events compared to commonly used predictors, such as LDL and HDL cholesterol" (Balk et al., 2008). The AHRQ report stated that it has yet to be determined if cardiac disease risk assessment and treatment decisions would be improved by adding LDL subfraction (subclass) measurements (Balk et al., 2008).

An assessment by the National Academy of Clinical Biochemistry (Wilson et al., 2009) concluded that lipoprotein subclasses have been shown to be related to the development of initial CHD events, but the data analyses of existing studies are generally not adequate to show added benefit over standard risk assessment for primary prevention. The assessment found that there are also insufficient data that measurement of lipoprotein subclasses over time is useful to evaluate the effects of treatments. The assessment also noted that several methods are available to assess lipoprotein subclasses and that standardization is needed for this technology.

The NACB assessment on LDL particle concentration and subclasses (including measurement by gradient gel electrophoresis) (Wilson et al., 2009) concluded: "Lipoprotein subclasses, especially the number or concentration of small, dense LDL particles, have been shown to be related to the development of initial CHD events, but the data analyses of existing studies are generally not adequate to show added benefit over standard risk assessment for primary prevention."

There is inadequate evidence that LDL subclassification by electrophoresis improves outcomes of patients with cardiovascular disease. According to the guidelines of the National Cholesterol Education Program, electrophoretic methods “cannot be recommended as procedures of choice for measuring LDL cholesterol.”

Furthermore, guidelines from the National Academy of Clinical Biochemistry (Myers, 2009) do not support LDL subclass testing.

According to guidelines from the American College of Cardiology and the American Heart Association (2010), measurement of lipid parameters, including particle size and density, beyond a standard fasting lipid profile is not recommended for cardiovascular risk assessment in asymptomatic adults. Guidelines from the Canadian Cardiovascular Society (2013) recommend measurement of ApoB or non-HDL-C as alternative targets and make no recommendation for the use of other measures of lipid particle number.

Guidelines from the National Heart, Lung, and Blood Institute (2012) on cardiovascular disease in children and adolescents concluded that measurement of lipoprotein subclasses and their sizes by advanced lipoprotein testing has not been shown to have sufficient clinical utility in children at this time. The guidelines state that the plasma levels of VLDL-C, LDL-C, and HDL-C subclasses and their sizes have been determined in children and adolescents by nuclear magnetic resonance spectroscopy and by vertical-spin density-gradient ultracentrifugation in research studies, but cut points derived from these methods for the diagnosis and treatment of dyslipidemia in youths are not currently available.

Guidelines on the prevention of cardiovascular disease in women from the American Heart Association (Mosca et al., 2011) state that the role that novel CVD risk biomarkers, including advanced lipid testing, should play in risk assessment and in delineation of appropriate preventive interventions is not yet well defined.

A special report of an AACC Working Group on ApoB and NMR Lipoprofile for measuring particle number (Cole et al., 2013) concluded: "Currently, in the opinion of this Working Group on Best Practices, ApoB appears to be the preferred biomarker for guideline adoption because of its widespread availability, scalability, standardization, and relatively low cost."

Standards of Care from the American Diabetes Association (2013) state that some experts recommend a greater focus on non-HDL cholesterol, apolipoprotein B (ApoB), or lipoprotein particle measurements to assess residual CVD risk in statin-treated patients who are likely to have small LDL particles, such as people with diabetes, but it is unclear whether clinical management would change with these measurements.

An Endocrine Society Clinical Practice Guideline on hypertriglyceridemia (Brunzell et al., 2012) states that "The Task Force recommends against the routine measurement of lipoprotein particle heterogeneity in patients with hypertriglyceridemia."

Lipidomic and Metabolomic Markers

Laaksonen (2016) summarize published data on lipidomic and metabolomic risk markers of CAD. Studies were identified from a literature search of PubMed. Published data showed that analysis of metabolites and lipids offers an opportunity to increase the knowledge of the biological processes related to development and progression of atherosclerotic coronary disease. It is evident that advanced analytical technologies are able to detect and identify a large number of molecules that may have important structural and functional roles over and above currently used biomarkers in the cardiovascular field. It is suggested in a number of reports that the novel biomarkers can be used to improve risk stratification and patient selection for different treatments. In addition, monitoring treatment safety and effectiveness as well as lifestyle changes should be facilitated by such novel markers. The authors concluded that until now a plethora of biomarker candidates associated with cardiovascular event risk have been identified, but very few have passed through clinical and analytical validation and found their way into clinical use. Consequently, the appetite of physicians to use these novel tests in daily clinical routine has not yet been truly tested.

Lipoprotein-Associated Phospholipase A2 (Lp-PLA2) (PLAC)

Lipoprotein-associated phospholipase A2 (Lp-PLA2 or PLAC) testing is an enzyme immunoassay for the quantitative determination of Lp-PLA2 in plasma, used in conjunction with clinical evaluation and individual risk assessment as a suggested aid in predicting risk for coronary heart disease (CHD).

Lipoprotein-associated phospholipase A2 (Lp-PLA2) is an enzyme that can hydrolyze oxidized phospholipids to generate lysophosphatidylcholine and oxidized fatty acids, which have pro-inflammatory properties (Ballantyne et al., 2004). Based on a 510(k) premarket notification, the U.S. Food and Drug Administration has cleared for marketing the PLAC Test (diaDexus, Inc., South San Francisco, CA), an enzyme immunoassay for the quantitative determination of Lp-PLA2 in plasma.

Data regarding the association between Lp-PLA2 level and the incidence of cardiovascular events are conflicting (Persson et al., 2008). Some large prospective clinical studies have found lipoprotein-associated phospholipase A2 (Lp-PLA2) to be an independent risk factor for coronary artery disease (CAD) (e.g., Packard et al., 2000; Blake et al., 2001; Ballantyne et al., 2004), although another large study (Women's Health Study) found that the predictivity of Lp-PLA2 was no longer statistically significant after adjustment for other risk factors (Blake et al., 2001).

Other studies have failed to find an association between Lp-PLA2 and various cardiac disease endpoints (e.g., Kardys et al., 2006; Allison et al., 2006; Kardys et al., 2007; Rana et al., 2011; Oldgren et al., 2007). Rana et al. (2011) examined the contribution of physical activity and abdominal obesity to the variation in Lp-PLA2 and other inflammatory biomarkers and incident CHD. In a prospective case-control study nested in the European Prospective Investigation into Cancer and Nutrition-Norfolk cohort, they examined the associations between circulating levels or activity of lipoprotein-associated phospholipase A2 (Lp-PLA2) and other inflammatory markers and CHD risk over a 10-year period among healthy men and women (45 to 79 years of age). A total of 1,002 cases who developed fatal or non-fatal CHD were matched to 1,859 controls on the basis of age, sex, and enrollment period. After adjusting for waist circumference, physical activity, smoking, diabetes, systolic blood pressure, low-density lipoprotein, and high-density lipoprotein cholesterol levels, and further adjusted for hormone replacement therapy in women, Lp-PLA2 was not associated with an increased CHD risk.

A meta-analysis found Lp-PLA2 to be significantly associated with CVD (Garza et al., 2007). The researchers reported that the risk estimate appears to be relatively unaffected by adjustment for conventional CVD risk factors. They reported an unadjusted odds ratio of 1.51 (95% CI: 1.30 to 1.75) for the association between elevated Lp-PLA2 and CVD. When adjusted for traditional CVD risk factors and CRP, the odds ratio was 1.60 (95% CI: 1.36 to 1.89). An accompanying editorial noted: "Although meta-analytic confirmation of this association is notable, clinicians must not 'jump the gun.' Important questions should be answered before Lp-PLA2 is incorporated into clinical practice, and the authors acknowledge this fully in their discussion" (Steinberg and Mayer, 2007). The editorialist explained that one of these questions is whether measurement of Lp-PLA2 yields additional predictive power beyond that already provided by an assessment of traditional cardiovascular risk factors and by current scoring systems such as the Framingham Risk Score. The editorialist stated that, given the weak association between Lp-PLA2 and CVD, this seems unlikely. The editorialist explained that if a patient's baseline probability of CVD is 50%, plotting an odds ratio of 1.60 on a Bayesian nomogram results in a posterior probability of about 59%, a relatively small increase. "Such small changes in probability rarely translate into changes in patient management or reclassification of patients into different risk groups." The editorialist also stated that the operating characteristics of the FDA-cleared test for Lp-PLA2, the PLAC test (diaDexus Inc, San Francisco, CA), have not been adequately established (Steinberg and Mayer, 2007). The editorialist argued that decisions about the utility of a novel biomarker should not be based solely on measurements of association, such as odds ratios or relative risk. Instead, clinical decision-making should be guided by the performance characteristics of the diagnostic test that measures the biomarker. The editorialist stated that test characteristics can vary significantly between patient populations. The positive and negative likelihood ratios of the PLAC test for patients at low, intermediate, and high risk of various cardiovascular outcomes need to be clarified if the test is to be used in these populations. Furthermore, prospective studies need to be performed to determine whether the use of the PLAC test, or any other test of Lp-PLA2, leads to meaningful changes in patient management. "As mentioned previously, the weak association between Lp-PLA2 and CVD makes this unlikely." The editorialist also explained that the fact that Lp-PLA2 is associated with CVD does not mean it can be relied on as a surrogate marker of morbidity or mortality in clinical trials (Steinberg and Mayer, 2007). Clinical trials of drug therapy will surely track Lp-PLA2 levels, but they must also measure clinical outcomes. The editorialist also questioned whether widespread statin use, which has changed and grown considerably since many of the patients in previous studies were enrolled, is already offsetting the small increased risk of CVD that elevated Lp-PLA2 might confer. "This question highlights a critical goal for researchers of Lp-PLA2 drug therapy – randomized controlled trials must be performed against background therapy that reflects current practice." The editorialist explained that not until this work is done will we know if lowering Lp-PLA2 with targeted drug therapy is good for patients. The editorialist concluded that Lp-PLA2 should not be used for screening or risk stratification until further study. Regarding Lp-PLA2-specific drug therapy, "healthy skepticism is advised." "Responsible clinicians will resist the temptation to prescribe on the basis of pharmaceutical claims and inadequate information and wait for solid data instead."

In a prospective U.S. cohort study (Cook et al., 2006), researchers assessed whether adding measurements of Lp-PLA2 or any of 18 other novel risk factors to traditional risk factors (age, race, sex, HDL and total cholesterol levels, systolic BP, use of anti-hypertensive agents, and smoking and diabetes status) improved prediction of incident coronary heart disease among nearly 16,000 adults (age 45 years or older). The authors found that, although Lp-PLA2 showed a statistically significant increase in predictive value compared with traditional risk factors only, this increase was not clinically important. The accompanying editorialist commented that, given that only 1 in 3 people with elevated blood pressure or cholesterol levels achieves adequate control, clinicians should focus on treatment and control of traditional risk factors. The authors concluded that, for now, routine screening of Lp-PLA2 levels seems unwarranted.

An analysis of the Atherosclerosis Risk in Communities Study, which assessed the association of 19 novel risk factors with coronary heart disease in a cohort of 15,792 adults, found that measurement of Lp-PLA2 in that population added very little to the 5-year predicted risk of a coronary heart disease event based on assessment of traditional risk factors (Folsom et al., 2006). Although Lp-PLA2 was among the novel risk factors that added the most to the area under the receiver operating curve (AUC), Lp-PLA2 resulted in a very small increase in the AUC of only 0.006. The authors concluded that routine measurement of Lp-PLA2 and other novel markers is not warranted for risk assessment. The authors stated that, on the other hand, their findings reinforce the utility of major, modifiable risk factor assessment to identify individuals at risk for CHD for preventive action.

There is insufficient evidence that Lp-PLA2 is useful in reducing the risk of stroke. Ballantyne et al. (2005) evaluated the ability of Lp-PLA2 and C-reactive protein to predict stroke cases in a manner that is statistically independent from traditional risk factors. The authors used data from the Atherosclerosis Risk in Communities (ARIC) Study, a high-quality prospective follow-up of healthy U.S. adults with standardized risk factor measurements as well as stored blood samples that facilitated analysis of the potential new risk predictors. As expected from prior research on stroke risk, race, hypertension, diabetes, systolic and diastolic blood pressure, and triglyceride and HDL-C levels were each individually associated with higher stroke risk. The investigators reported an association of higher Lp-PLA2 and CRP levels with increased stroke risk in statistical models adjusted for the major traditional risk factors. In the highest tertile, CRP level was associated with higher stroke risk by about 2-fold, although confidence intervals were wide. For Lp-PLA2 levels in the top tertile, with adjustment for traditional risk factors and CRP, stroke risk was higher by about 2-fold as well. Thus, the investigators found that the Lp-PLA2 level was a moderately strong stroke risk predictor, and its association with stroke in this study was statistically independent of traditional risk factors as well as the inflammatory marker CRP. In unadjusted analyses, apparently healthy middle-aged people with high levels of both CRP and Lp-PLA2 (highest tertiles of both) had a stroke risk 11-fold higher than people with low levels of both. The authors speculated that Lp-PLA2 and CRP levels may be complementary to traditional risk factors for identifying middle-aged individuals at increased risk for stroke.

The accompanying editorialists explained, however, that from the Ballantyne et al. study, it is unclear how useful CRP or Lp-PLA2 levels will be for improving risk prediction versus traditional risk factors alone (Greenland and O'Malley, 2005). The editorialists explained that simply showing statistical independence is not adequate for demonstrating clinical utility for risk prediction. "Hazard ratios and p-values are useful for demonstrating statistical associations, but they fail to show whether the new marker is truly capable of making a major impact on risk prediction or risk discrimination." The editorialists explained that one helpful way to determine the additive utility of a new test is through the use of receiver operating characteristic (ROC) curves and AUC information. The editorialist noted that, unfortunately, Ballantyne et al. did not report AUC or ROC information. However, based on statistical analytic findings reported elsewhere, individual tests with relative risks of only 2.0 to 3.0 "are simply not capable of increasing the AUC to a clinically significant degree." The editorial concluded that "[t]o date, this search for new cardiovascular risk markers has not led to any test that can be recommended as a routine measurement beyond that of traditional risk factors."

A cohort study found no significant gain of Lp-PLA2 and minimal gains of other novel biomarkers over conventional biomarkers in predicting future cardiovascular events in a low-to-moderate risk community-based population. Melander et al. (2009) reported on a cohort study of 5,067 persons without cardiovascular disease from Malmö, Sweden, who attended a baseline examination between 1991 and 1994. Participants underwent measurement of Lp-PLA2, CRP, cystatin C, midregional proadrenomedullin (MR-proADM), mid-regional proatrial natriuretic peptide, and N-terminal pro-B-type natriuretic peptide (N-BNP) and underwent follow-up until 2006 using the Swedish national hospital discharge and cause-of-death registers and the Stroke in Malmö register for first cardiovascular events (MI, stroke, coronary death). During a median follow-up of 12.8 years, there were 418 cardiovascular and 230 coronary events. Lp-PLA2 did not have a statistically significant relationship to cardiovascular events or coronary events and was not retained in backward elimination models for cardiovascular events and coronary events. Models with conventional risk factors had C statistics of 0.758 (95% CI: 0.734 to 0.781) and 0.760 (0.730 to 0.789) for cardiovascular and coronary events, respectively. Biomarkers retained in backward-elimination models were CRP and N-BNP for cardiovascular events and MR-proADM and N-BNP for coronary events, which increased the C statistic by 0.007 (p = 0.04) and 0.009 (p = 0.08), respectively. The investigators reported that the proportion of participants reclassified was modest (8% for cardiovascular risk, 5% for coronary risk). Net reclassification improvement was non-significant for cardiovascular events (0.0%; 95% CI: -4.3% to 4.3%) and coronary events (4.7%; 95% CI: -0.76% to 10.1%). Greater improvements were observed in analyses restricted to intermediate-risk individuals (cardiovascular events: 7.4%; 95% CI: 0.7% to 14.1%; p = 0.03; coronary events: 14.6%; 95% CI: 5.0% to 24.2%; p = 0.003). However, correct reclassification was almost entirely confined to down-classification of individuals without events rather than up-classification of those with events. In this cohort of some 5,000 participants initially free of CVD and followed almost 13 years, the novel biomarkers improved prediction scores "only minimally," resulting in the re-assignment of only 1% of participants to a higher risk group (Melander et al., 2009).

A meta-analysis found associations of circulating Lp-PLA2 mass and activity with the risk of coronary heart disease, stroke, and mortality under different circumstances (Lp-PLA2 Studies Collaboration, 2010). The investigators conducted a meta-analysis of 39 studies to calculate risk ratios (RRs) per 1 standard deviation (SD) higher value of Lp-PLA2. The investigators found relative risks for coronary heart disease, adjusted for conventional risk factors, of 1.10 (95% CI: 1.05 to 1.16) with Lp-PLA2 activity and 1.11 (1.07 to 1.16) with Lp-PLA2 mass. Relative risks for ischemic stroke were 1.08 (0.97 to 1.20) for Lp-PLA2 activity and 1.14 (1.02 to 1.27) for Lp-PLA2 mass. Relative risks were 1.16 (1.09 to 1.24) and 1.13 (1.05 to 1.22) for vascular mortality; and 1.10 (1.04 to 1.17) and 1.10 (1.03 to 1.18) for non-vascular mortality, respectively. Although the researchers acknowledge that further research is required in this area, they suggest, “Randomized trials of potent reversible pharmacological inhibitors of Lp-PLA2 activity should help to establish whether modification of Lp-PLA2 can reverse vascular risk.” An accompanying editorial stated that these analyses suggest that increased Lp-PLA2 activity is associated with a higher risk of coronary heart disease (Rosenson, 2010). The editorialist noted, however, that the predictive value of Lp-PLA2 activity was weaker with higher apolipoprotein B concentrations; lower concentrations of apolipoprotein B (0.85 mg/L for the mean in the lowest tertile) were associated with higher risk (1.23 [95% CI: 1.14 to 1.33] per 1-SD change in Lp-PLA2 activity) than were apolipoprotein B concentrations in the higher two tertiles (1.09 [1.01 to 1.19] and 1.11 [1.03 to 1.19], respectively). The editorialist stated that future studies that evaluate the cardiovascular risks associated with Lp-PLA2 activity and/or mass should at least adjust for apolipoprotein B concentrations and small LDL-particle concentration. The editorialist stated that these analyses are important to fully understand the contribution of increased Lp-PLA2 activity and/or mass to future risk of cardiovascular events beyond the risk obtained from quantification of LDL particles. "Clinically, the independent contribution of Lp-PLA2 concentrations or activity for risk stratification beyond the association with small LDL-particle concentration awaits the results of randomized trials that are designed to investigate whether selective and reversible inhibition of this pathway reduces cardiovascular events."

Lp-PLA2 is also being investigated for predicting outcomes in acute ischemic stroke. Elkind et al. (2006) reported on a population-based study of stroke risk factors in 467 patients with first ischemic stroke. The study was undertaken to determine whether levels of hsCRP and Lp-PLA2 predict the risk of stroke recurrence, other vascular events, and death. The investigators found that levels of Lp-PLA2 and hsCRP were weakly correlated (r = 0.09; p = 0.045). High-sensitivity CRP, but not Lp-PLA2, was associated with stroke severity. After adjusting for age, sex, race and ethnicity, history of coronary artery disease, diabetes mellitus, hypertension, hyperlipidemia, atrial fibrillation, smoking, and hsCRP level, compared with the lowest quartile of Lp-PLA2, those in the highest quartile had an increased risk of recurrent stroke (adjusted HR, 2.08; 95% CI: 1.04 to 4.18) and of the combined outcome of recurrent stroke, MI, or vascular death (adjusted HR, 1.86; 95% CI: 1.01 to 3.42). The researchers reported that, after adjusting for confounders, hsCRP was not associated with the risk of recurrent stroke or recurrent stroke, MI, or vascular death but was associated with the risk of death (adjusted HR, 2.11; 95% CI: 1.18 to 3.75).

Whiteley et al. (2009) reported on a systematic review of the evidence relating Lp-PLA2 and other blood markers and prognosis in ischemic stroke. The investigators searched Medline and EMBASE from 1966 to January 2007 for studies of blood markers in patients with ischemic stroke and an assessment of outcome (death, disability, or handicap). The investigators found 82 studies of 41 blood markers that met inclusion criteria, including 1 study of Lp-PLA2 (citing Elkind et al., 2006). The researchers found that, although blood biomarkers might provide useful information to improve the prediction of outcomes after acute ischemic stroke, the review showed that many studies were subject to bias. The researchers found that although some markers had some predictive ability, none of the studies was able to demonstrate that the biomarker added predictive power to a validated clinical model. The researchers concluded that the clinical usefulness of blood biomarkers for predicting prognosis in the setting of ischemic stroke has yet to be established.

Few studies have investigated the role of elevated Lp-PLA2 with stroke risk (Wassertheil-Smoller et al., 2008). Wassertheil-Smoller and colleagues (2008) assessed the relationship between Lp-PLA2 and the risk of incident ischemic stroke in 929 stroke patients and 935 control subjects in the Hormones and Biomarkers Predicting Stroke Study, a nested case-control study from the Women's Health Initiative Observational Study. Mean (SD) levels of Lp-PLA2 were significantly higher among case subjects (309.0 [97.1]) than control subjects (296.3 [87.3]; p < 0.01). The odds ratio for ischemic stroke for the highest quartile of Lp-PLA2, compared with the lowest, controlling for multiple covariates, was 1.08 (95% CI: 0.75 to 1.55). However, among 1,137 non-users of hormone therapy at baseline, the corresponding odds ratio was 1.55 (95% CI: 1.05 to 2.28), whereas there was no significant association among 737 hormone users (odds ratio: 0.70; 95% CI: 0.42 to 1.17; p for interaction = 0.055). Moreover, among non-hormone users, women with high CRP and high Lp-PLA2 had more than twice the risk of stroke (odds ratio: 2.26; 95% CI: 1.55 to 3.35) compared with women with low levels in both biomarkers. Furthermore, different stroke cases were identified as high-risk by Lp-PLA2 rather than by CRP. The investigators concluded that Lp-PLA2 was associated with incident ischemic stroke independently of CRP and traditional cardiovascular risk factors among non-users of hormone therapy, with the highest risk in those who had both high CRP and high Lp-PLA2.

Persson et al. (2008) reported on a prospective population-based study exploring the relationship between baseline Lp-PLA2 activity and mass, respectively, on levels and incidence of first CHD and ischemic stroke. Lp-PLA2 activity and mass were assessed in 5,393 (60% women) subjects who participated in the Malmö Diet and Cancer Study cardiovascular program during 1991 to 1994. In all, 347 subjects had an event (195 CHD and 152 ischemic strokes) during the follow-up period (mean 10.6 ± 1.7 years). In an age-, sex-, and cardiovascular risk factors-adjusted Cox regression analysis, comparing the top to the bottom tertile of Lp-PLA2 activity, the relative risk (RR; 95% CI) for incident CHD and ischemic stroke events were 1.48 (0.92 to 2.37) and RR: 1.94 (1.15 to 3.26), respectively. The corresponding figures for Lp-PLA2 mass were 0.95 (0.65 to 1.40) and RR: 1.92 (1.20 to 3.10). The investigators concluded that elevated levels of Lp-PLA2 activity and mass, respectively, were in this study, independently of established risk factors related to the incidence of ischemic stroke, but after adjustment for lipids, not significantly related to incident CHD.

Nambi et al. (2009) reported on a prospective case-cohort (n = 949) study in 12,762 persons in the Atherosclerosis Risk in Communities (ARIC) study to determine whether Lp-PLA2 and hsCRP levels improved the AUC for 5-year ischemic stroke risk. The investigators also examined how Lp-PLA2 and hsCRP levels altered the classification of individuals into low-, intermediate-, or high-risk categories compared with traditional risk factors. In a model using traditional risk factors alone, the AUC was 0.732. The addition of the biomarkers increased the AUC modestly, by 0.011 for hsCRP alone, 0.020 for Lp-PLA2 alone, and 0.042 when hsCRP, Lp-PLA2, and its interaction term were added. The investigators reported that, with the use of traditional risk factors to assess 5-year risk for ischemic stroke, 86% of participants were categorized as low risk (less than 2%); 11% as intermediate risk (2% to 5%); and 3% as high risk (greater than 5%). The addition of hsCRP, Lp-PLA2, and their interaction to the model reclassified 4%, 39%, and 34% of the low-, intermediate-, and high-risk categories, respectively. The investigators stated that, based on their analysis, the addition of both hsCRP and Lp-PLA2 seems to satisfy the statistical requirements for a test to improve risk prediction. The investigators stated, however, that the more important question is whether the improvement conferred by the addition of the marker is clinically important and cost-effective. The investigators noted that the addition of hsCRP and Lp-PLA2 did change risk categories in approximately 13% of the study population. "It would be ideal to validate our findings in other cohorts, conduct studies to examine if changes in therapy secondary to such a risk stratification scheme will improve ischemic stroke prevention, and examine the cost-effectiveness of such a strategy."

Randomized clinical studies of statin therapy for hyperlipidemic persons have shown a lower incidence of stroke in the placebo group (Armarenco and Labreuche, 2009). Prospective randomized studies of statins for the prevention of recurrence in stroke and transient ischemic attack (TIA) have shown marginal effects (Manktelow and Potter, 2009). However, it is not known whether treatment with statins would reduce stroke risk in a subset of normolipidemic patients for whom statin therapy would otherwise not be indicated. In addition, a number of studies have also shown that certain drugs can have an impact on Lp-PLA2 levels; these studies, however, do not demonstrate whether changes in Lp-PLA2 can improve outcomes when used as a target of treatment.

There is a lack of evidence from prospective clinical studies that incorporation of Lp-PLA2 testing in cardiovascular risk assessment improves clinical outcomes. The Adult Treatment Panel III (ATPIII) guidelines do not include a recommendation for Lp-PLA2 testing in the assessment of coronary artery disease (CAD) risk. Guidelines from the American Heart Association and the American Stroke Association (Goldstein et al., 2006) on primary prevention of ischemic stroke state: "No recommendations about Lp-PLA2 modification can be made because of an absence of outcome studies showing clinical benefit with reduction in its blood levels." A consensus statement from the American College of Cardiology and the American Diabetes Association on the management of patients with cardiometabolic risk makes no mention of Lp-PLA2 (Brunzell et al., 2008). The American Association of Clinical Chemistry (AACC, 2009) has stated that Lp-PLA2 is not widely available, and, "while the findings from recent studies support the potential usefulness of Lp-PLA2 in coronary heart disease and ischemic stroke risk assessment, its ultimate clinical utility has yet to be established." Canadian Cardiovascular Society guidelines (Genest et al., 2009) do not recommend Lp-PLA2 for screening for heart disease risk. The American College of Cardiology and the American Heart Association (2010) examined Lp-PLA2 and concluded that it might be reasonable for assessment in intermediate-risk asymptomatic adults. This was a class IIb recommendation, indicating that the recommendation's usefulness/efficacy is less well established.

European consensus guidelines (2012) state that the magnitude of Lp-PLA2's effect on risk remains modest at the level of the general population; study limitations or bias are present. The guidelines state that Lp-PLA2 remains a "second-line" marker for cardiovascular disease (CVD) risk estimation. The guidelines suggest that Lp-PLA2 may be measured as part of a refined risk assessment in patients at high risk of a recurrent acute atherothrombotic event. This is a class IIb recommendation, indicating that the recommendation's usefulness/efficacy is less well established.

The American Stroke Association and the American Heart Association (Goldstein et al., 2011) also rendered a class IIb recommendation for the use of Lp-PLA2. "Measurement of inflammatory markers such as hsCRP or Lp-PLA2 in patients without CVD may be considered to identify patients who may be at increased risk of stroke, although their effectiveness (i.e., usefulness in routine clinical practice) is not well established."

Guidelines from the American College of Clinical Endocrinology (2012) have a grade 2B recommendation to use highly sensitive CRP to stratify CVD risk in patients with a standard risk assessment that is borderline, or in those with an LDL-C concentration less than 130 mg/dL, and to measure Lp-PLA2 when it is necessary to further stratify a patient’s CVD risk.

Other guidelines make no recommendation for the measurement of Lp-PLA2 (New Zealand Guidelines Group, 2009; National Vascular Disease Prevention Alliance, 2009; Lindsay et al., 2010; National Vascular Disease Prevention Alliance, 2012). A National Heart, Lung, and Blood Institute (2012) guideline on cardiovascular disease risk in children and adolescents found insufficient evidence to recommend the measurement of inflammatory markers in youths.

An ad-hoc panel of Lp-PLA2 investigators recommended consensus guidelines for Lp-PLA2 use in clinical practice (Davidson et al., 2008). The panel recommended Lp-PLA2 testing as an adjunct to traditional risk factors in determining the target goal for lipid treatment in correlation with absolute risk. The panel did not recommend Lp-PLA2 testing as a screening tool for low-risk patients. Commenting on these guidelines, Ali and Madjid (2009) stated that it is to be noted that these recommendations are based on consensus, and that more evidence is needed to determine the exact clinical approach for the use of Lp-PLA2 as a screening test and as part of a treatment regimen.

Bertoia et al. (2013) examined the prospective association between oxidation-specific biomarkers, primarily oxidized phospholipids (OxPL) on apolipoprotein B-100-containing lipoproteins (OxPL/ApoB) and lipoprotein (a) [Lp(a)], and risk of peripheral artery disease (PAD). These researchers examined, as secondary analyses, indirect measures of oxidized lipoproteins, including autoantibodies to malondialdehyde-modified low-density lipoprotein (MDA-LDL) and apolipoprotein B-100 immune complexes (ApoB-IC). The study population included two parallel nested case-control studies of 143 men within the Health Professionals Follow-up Study (1994 to 2008) and 144 women within the Nurses' Health Study (1990 to 2010) with incident confirmed cases of clinically significant PAD, matched 1:3 to control subjects. Levels of OxPL/ApoB were positively associated with the risk of PAD in men and women: pooled relative risk: 1.37 (95% CI: 1.19 to 1.58) for each 1-SD increase after adjusting for age, smoking, fasting status, month of blood draw, lipids, BMI, and other cardiovascular disease risk factors. Lipoprotein (a) was similarly associated with the risk of PAD (pooled adjusted relative risk: 1.36; 95% CI: 1.18 to 1.57 for each 1-SD increase). Autoantibodies to MDA-LDL and ApoB-IC were not consistently associated with the risk of PAD. The authors concluded that OxPL/ApoB were positively associated with the risk of PAD in men and women. The major lipoprotein carrier of OxPL, Lp(a), was also associated with the risk of PAD, reinforcing the key role of OxPL in the pathophysiology of atherosclerosis mediated by Lp(a).

The main drawbacks of this study included:

because the NHS and HPFS studies contain predominantly white subjects, it is unclear if these findings can be generalized to minority populations, some of whom are at increased risk for PAD,
it is possible that some control subjects have undiagnosed PAD, and
these finding alone cannot definitely separate OxPL and Lp(a) as individual determinants of PAD, given their inherent biological inter-relationship.

The authors stated that “Future research should continue to explore the mechanisms that link oxidation to risk of PAD and test whether modifiable risk factors, potentially including novel therapies that reduce levels of OxPL, might prevent the development of artherosclerotic diseases such as PAD”.

The Emerging Risk Factors Collaboration (Di Angelantonio et al., 2012) found, in a study of individuals without known CVD, the addition of information on Lp-PLA2 to risk scores containing total cholesterol and HDL-C led to slight improvement in CVD prediction. Individual The investigators estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with Lp-PLA2 would reclassify 2.7% of people to a 20% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines.

Holst-Albrechtsen et al. (2013) noted that studies indicate that elevated plasma concentrations of Lp-PLA2 is associated with increased risk of cardiovascular disease. Lp-PLA2 seems to play a crucial role in the formation of plaques and acute inflammation, and plasma Lp-PLA2 could therefore potentially be used as a predictor of long-term outcome in ACS patients. To evaluate this, data concerning Lp-PLA2 as a predictor in ACS patients was gathered through a systematic literature review, and studies on this issue were extracted from relevant databases, including PubMed and Cochrane. A total of 14 articles were retrieved, but after thorough evaluation and elimination of irrelevant articles only 7 studies were eligible for the literature review. All studies except 2 showed significant correlation between Lp-PLA2 and CV events in ACS patients. Only 1 study found an independent value to predict CV events 30 days after ACS. Altogether, there was inconsistency in the findings regarding the potential use of Lp-PLA2 and a lack of knowledge on several issues. These investigators stated that Lp-PLA2 seems to give valuable information on which ACS patients are prone to new events and also provides important information on plaque size. However, they stated that more focused studies concerning genetic variations, time-window impact, patients with and without CV risk factors (e.g., diabetes), and treatment effects are needed. The authors concluded that Lp-PLA2 offers new insight in the pathophysiological development of ACS, but until the aforementioned issues are addressed, the biomarker will mainly be of interest in a research setting, not as a predictive parameter in a clinical setting.

Mahmut and colleagues (2014) documented the presence and role of Lp-PLA2 in calcific aortic valve disease (CAVD). These researchers documented the expression of the phospholipase A2 family of genes in aortic valves by using a transcriptomic assay. Messenger ribonucleic acid and protein expression were confirmed in aortic valves explanted from 60 patients by quantitative polymerase chain reaction (qPCR) and immunohistochemistry, respectively. The effect of lysophosphatidylcholine, the product of Lp-PLA2 activity, was documented on the mineralization of valve interstitial cell cultures. Transcriptomic analyses of CAVD and control non-mineralized aortic valves revealed that Lp-PLA2 was increased by 4.2-fold in mineralized aortic valves. Higher expression of Lp-PLA2 in stenotic aortic valves was confirmed by qPCR, immunohistochemistry, and enzymatic Lp-PLA2 activity. The number of Lp-PLA2 transcripts correlated with several indexes of tissue remodeling. In-vitro, lysophosphatidylcholine increased the expression of alkaline phosphatase, the ectonucleotide pyrophosphatase/phosphodiesterase 1 enzyme, sodium-dependent phosphate cotransporter 1 (encoded by the SLC20A1 gene), and osteopontin. These investigators then showed that lysophosphatidylcholine-induced mineralization involved ectonucleotidase enzyme as well as apoptosis through a protein-kinase-A-dependent pathway. The authors concluded that these results demonstrated that Lp-PLA2 is highly expressed in CAVD, and it plays a role in the mineralization of valve interstitial cells. Moreover, they stated that further work is needed to document whether Lp-PLA2 could be considered as a novel target in CAVD. Krintus et al. (2014) stated that despite great progress in prevention strategies, pharmacotherapy and interventional treatment of coronary artery disease (CAD), cardiovascular events still constitute the leading cause of mortality and morbidity in the modern world. Traditional risk factors, including hypertension, diabetes mellitus, smoking, obesity, dyslipidemia, and positive family history account for the occurrence of the majority of these events, but not all of them. Adequate risk assessment remains the most challenging in individuals classified into low or intermediate risk categories. Inflammation plays a key role in the initiation and promotion of atherosclerosis and may lead to acute coronary syndrome (ACS) by the induction of plaque instability. For this reason, numerous inflammatory markers have been extensively investigated as potential candidates for the enhancement of cardiovascular risk assessment. These investigators assessed the clinical utility of well-established (C-reactive protein [CRP] and fibrinogen), newer (Lp-PLA2 and myeloperoxidase [MPO]) and novel (growth differentiation factor-15 [GDF-15]) inflammatory markers which, reflect different pathophysiological pathways underlying CAD. Although according to the traditional approach all discussed inflammatory markers were shown to be associated with the risk of future cardiovascular events in individuals with and without CAD, their clear clinical utility remains not fully elucidated. Current recommendations of numerous scientific societies predominantly advocate routine assessment of CRP in healthy people with intermediate cardiovascular risk. However, these recommendations substantially vary in their strength among particular societies. These discrepancies have a multi-factorial background, including:

the strong prognostic value of CRP supported by solid scientific evidence and proven to be comparable in magnitude with that of total and high-density lipoprotein cholesterol, or hypertension,
favorable analytical characteristics of commercially available CRP assays,
lack of CRP specificity and causal relationship between CRP concentration and cardiovascular risk, and
CRP dependence on other classical risk factors.

Of major importance, CRP measurement in healthy men greater than or equal to 50 years of age or healthy women greater than or equal to 60 years of age with low-density lipoprotein cholesterol less than 130 mg/dL may be helpful in the selection of patients for statin therapy. Additionally, evaluation of CRP and fibrinogen or Lp-PLA2 may be considered to facilitate risk stratification in ACS patients and in healthy individuals with intermediate cardiovascular risk, respectively. Nevertheless, the clinical utility of CRP requires further investigation in a broad spectrum of CAD patients, while other promising inflammatory markers, particularly GDF-15 and Lp-PLA2, should be tested in individuals both with and without established CAD. These researchers noted that further studies should also focus on novel performance metrics such as measures of discrimination, calibration and reclassification, in order to better address the clinical utility of investigated biomarkers and to avoid misleadingly optimistic results. It also has to be emphasized that, due to the multi-factorial pathogenesis of CAD, detailed risk stratification remains a complex process also involving, beyond assessment of inflammatory biomarkers, the patient's clinical characteristics, results of imaging examinations, electrocardiographic findings and other laboratory parameters (e.g. lipid profile, indices of renal function, markers of left ventricular over-load and fibrosis, and biomarkers of myocardial necrosis, preferably cardiac troponins).

Liposcale Test

The Liposcale Test (CIMA Sciences, LLC) is an advanced blood lipoprotein profile that includes an analysis based on two-dimensional (2D) nuclear magnetic resonance (NMR) spectrometry, which directly measures a patient's standard lipid profile, in addition to the particle number and size, and the cholesterol and triglyceride composition of the main lipoprotein classes and subclasses (VLDL, LDL, HDL). The test uses a Liposcale medical software algorithm (Biosfer Testlab) to generate a report that is divided into two sections. The first section includes information on the patient's standard lipid panel (total cholesterol, triglycerides, LDL, and HDL cholesterol), concentrations of large, intermediate, and small VLDL, LDL, and HDL particles, average particle sizes of VLDL, LDL, and HDL, as well as the lipid contour. The lipid contour is a graphical model that illustrates a global assessment of a patient's lipid metabolism situation beyond the traditional parameters. It combines information from the 10 variables associated with cardiovascular risk, which are represented in colors on the model (e.g., orange represents moderate/low risk compared to values of a general population of 6,000 men and women aged 15 to 85 years old). The patient’s contour delimits a smaller central area when the variables have values associated with an increased risk of developing cardiovascular diseases, and a higher central area otherwise. In addition, if the variables contribute to clearly reduce the area delimited by the curve, they appear in red on the panel; otherwise, they appear in green. If the value is similar to the recommended value, it is represented in yellow. The second section of the report includes information on the extended lipoprotein panel (including cholesterol and triglyceride content in VLDL, IDL, LDL, and HDL particles) and patient clinical outcomes.

Mallol et al. (2015) discussed their validation study on the use of the Liposcale test for the determination of lipoprotein particle size and number in improving cardiovascular risk prediction. The authors explored the degree of correlation between the VLDL-, LDL-, and HDL-Ps calculated using the Liposcale and the LipoProfile® tests (Liposcience), which is an FDA-cleared blood test that uses 1D NMR to measure the cholesterol content of lipoprotein particles in the blood and determine the LDL particle size. The authors also explored the degree of correlation between the apolipoprotein content of each lipoprotein class using these two testing methods. The authors obtained samples from the VITAGE project to develop the Liposcale test and then used a second cohort to validate the results provided by the test. Their method used diffusion coefficients to provide a direct measure of the mean particle sizes and numbers. The authors state that from 177 plasma samples obtained from healthy individuals, along with the concentration of ApoB and ApoA from isolated lipoprotein fractions, their test showed a stronger correlation between the NMR-derived lipoprotein particle numbers and apolipoprotein concentrations than the LipoProfile test. The authors also converted LDL particle numbers to ApoB equivalents (milligrams per deciliter), which the authors state yielded similar values of LDL-ApoB to the LipoProfile test. In addition, their HDL particle number values were reported as more concordant with the calibrated values determined using ion mobility. Furthermore, the authors report that principal component analysis distinguished type 2 diabetic patients with and without atherogenic dyslipidemia (AD) on a second cohort of 307 subjects characterized using the Liposcale test (area under the curve = 0.88) and showed concordant relationships between variables explaining AD. The authors state that they found very similar correlations between the Liposcale test and a reference NMR technique, although the derived particle numbers measured by the Liposcale test yielded higher correlations with external validations, such as the concentration of VLDL-ApoB, LDL-ApoB, and HDL-ApoAI. The authors concluded that their method provides reproducible and reliable characterization of lipoprotein particles and is applicable to pathological states such as AD. The authors acknowledge that "the characterization of IDL lipoproteins by NMR spectroscopy is not straightforward due to (i) its low concentration range compared with the other lipoproteins, and (ii) its NMR response arises between the small VLDL and large LDL lipoproteins in terms of chemical shift." Thus, the authors chose to be cautious and avoid the characterization of IDL, even in the event that this could appear as a limitation of their method. However, the authors state that "isolation of the IDL fractions to obtain pure VLDL and LDL fractions allowed for a good characterization of the latter, which in turn are the most clinically useful together with the HDL fraction. Moreover, although in subjects with normal lipid levels the concentrations of VLDL-ApoB and IDL-ApoB might be low and therefore below the limit of detection of the immunoturbidimetric assay, in this circumstance the clinical utility of these parameters is minimal."

Pintó and colleagues (2020) presented a consensus document created by an expert group of lipidologists from the Spanish Society of Arteriosclerosis (SEA) regarding the clinical use of 2D NMR to assess lipoprotein metabolism (Liposcale). 2D NMR is based on "studying particle mobility within a fluid (serum or plasma), which is associated with the size of the particles." This approach makes it possible to "directly measure the quantity, size, and composition of lipoprotein fractions and subfractions." Using the data from the Liposcale validation study by Mallol et al. (2015), the authors aimed to establish a series of recommendations for the use of this technique in clinical practice. The authors report that determining the number, size, and composition of lipoprotein particles may offer highly valuable information for a more precise assessment of cardiovascular risk in clinical practice. "The spectrum generated by Liposcale® is translated into information on the number and size of lipoprotein particles distributed arbitrarily in three fractions (VLDL, LDL, and HDL), each of which contains three subfractions. The cholesterol and triglyceride content of each type of lipoprotein is also determined. The c-LDL concentration, as well as the number of LDL particles, including Lp(a), cannot be quantified individually using this technique." Liposcale analysis produces more than 25 variables associated with lipoprotein particles. "The large number of data, although it underlines the enormous potential of the technique, hinders its clinical use, as this requires simplicity of interpretation." Thus, a graphical model of the main findings was developed to inform clinicians about the patient's lipoprotein profile in comparison with the standard profile of a general population. The Liposcale test offers more complete and detailed information about lipoprotein metabolism alterations and the associated cardiovascular risk, which may benefit those patients that: (a) have suspected mismatch between lipid concentrations and particles, a situation that frequently occurs in diabetes, obesity, and metabolic syndrome; (b) early atherothrombotic cardiovascular disease (ECVA); (c) rare or complex lipid disorders; and (d) clinical situations where traditional analytical techniques cannot be used, such as very low c-LDL values. The authors concluded that the lipid profile study using NMR may be highly useful in evaluating cardiovascular risk or dyslipidemia that has been poorly defined by standard clinical studies and laboratory tests.

MaxPulse Testing

Max Pulse testing is a non-invasive way to measure pulse waveform and heart rate by means of photoelectric plethysmography. There is a lack of reliable evidence regarding the clinical value of this approach. Alnaeb et al. (2007) described the potential uses of photoplethysmography for peripheral artery disease in research and clinical use. A number of studies have reported on measurements of arterial stiffness by a digital volume pulse analysis technique using photoplethysmography, looking at its correlation with known cardiovascular disease risk factors (Gunarathne et al., 2008; Otsuka et al., 2006). Hashimoto et al. (2001) reported on the relationship between photoplethysmography and pulse wave velocity measurements in hypertensive patients. Current evidence based guidelines from leading national medical professional organizations and Federal public health agencies have no recommendation for performing photoplethysmography for the screening or diagnosis of peripheral artery disease or other cardiovascular disease.

Measurement of Arterial Elasticity

Arterial elasticity has been shown to decrease with aging and with vascular disease. A number of studies have demonstrated a loss of arterial elasticity in persons with coronary artery disease (CAD), heart failure, hypertension, and diabetes.

Arterial stiffness, measured as aortic pulse wave velocity between the carotid and femoral arteries, appears to be a predictor of cardiovascular events (Mattace-Raso et al., 2006; Willum-Hansen et al., 2006). In the Rotterdam Study, the adjusted relative risk for coronary disease or stroke in the 2nd and 3rd tertiles was 1.72 and 2.45 compared to the lowest tertile (Mattace-Raso et al., 2006). The predictive value was independent of cardiovascular risk factors, carotid intima-media thickness, and pulse pressure. By contrast, carotid artery distensibility was not independently associated with cardiovascular disease (CVD).

Hypertension Diagnostics, Inc. (HDI, Eagan, MN) has developed a method of analyzing blood pressure waveforms to noninvasively measure the elasticity (compliance) of arteries and arterioles. The HDI CVProfilor and the HD/PulseWave CR-2000 graph the blood pressure waveform (“pulse contour analysis”) and calculate the elasticity (flexibility) of large and small arteries and arterioles. The CVProfilor obtains blood pressure and waveform data by using a blood pressure cuff placed on the left upper arm and a piezoelectric-based, direct contact, acoustical transducer placed over the right radial artery near the wrist. A computer performs a pulse contour analysis of blood pressure waveform data and generates a report that includes a large artery elasticity index (a measure of capacitative compliance) and a small artery elasticity index (a measurement of oscillatory or reflective compliance). The CVProfilor also provides measurements of standard blood pressure values (systolic, diastolic, and mean arterial pressure), heart rate, body surface area (BSA), and body mass index (BMI). Arterial elasticity has been investigated as an early marker of vascular disease in patients without standard risk factors for CVD. Several studies have examined the impact of various factors on arterial elasticity and have explored the question of whether arterial elasticity is an independent risk factor for cardiovascular disease. However, there is inadequate evidence from prospective clinical studies demonstrating that non-invasive measurements of arterial elasticity using the CVProfilor alter patient management and improve clinical outcomes. Current guidelines from leading medical professional organizations do not include a recommendation for the use of pulse waveform analysis in cardiovascular disease risk assessment.

In a clinical trial, Woodman et al. (2005) reported that large and small artery compliance, stroke volume/pulse pressure (measured by HDI/PulseWave CR-2000), and systemic arterial compliance show poor agreement with central pulse wave velocity, an established measure of central arterial stiffness.

Measurement of Long-Chain Omega-3 Fatty Acids in Red Blood Cell Membranes

Long-chain omega-3 fatty acids are a family of unsaturated fatty acids that have in common a carbon-carbon double bond in the third position from the methyl end of the fatty acid. Omega-3 fatty acids cannot be manufactured by the body and are obtained from foods such as fish (e.g., salmon, halibut), certain plants, and nut oils. Serum long-chain omega-3 fatty acids have been suggested as a cardiac risk factor for sudden cardiac death.

Higher levels of palmitic acid and lower levels of long-chain omega-3 fatty acids (e.g., alpha-linolenic, eicosapentaenoic, and docosahexaenoic acids) in serum are correlated with a higher incidence of coronary heart disease (CHD) in middle-aged men at high risk for cardiovascular disease (CVD) (Simon et al., 1995). Improvements in plasma fatty acids and vitamins E and C were the only factors found to be related to improvements in life expectancy and a 70% reduction in heart disease in a study population (Renaud et al., 1995).

Harris (2004) stated that consumption of between 450 and 1,000 mg/day of long-chain omega-3 fatty acids (from fish or fish oil) is recommended for those without and with known CHD, respectively. Based on animal and isolated cell studies, these fatty acids were presumed to have anti-arrhythmic effects. It has been proposed that red blood cell (RBC) fatty acid composition, which is an index of long-term intake of eicosapentaenoic and docosahexaenoic acids, can be considered a new, modifiable, and clinically relevant risk factor for death from CHD.

However, there is a lack of scientific evidence regarding how measurements of RBC omega-3 fatty acid composition would affect the management of individuals at risk for or patients with CHD. Large randomized controlled trials (RCTs) are needed to ascertain the clinical value of RBC omega-3 fatty acid composition in the management of CHD.

Measurement of Plasma Ceramide for Risk Stratification of Cardiovascular Events

Mu and colleagues (2009) noted that increased plasma levels of lactosylceramide (LacCer) have been associated with cardiovascular disease (CVD). However, it is largely unknown whether LacCer directly contributes to the dysfunction of smooth muscle cells (SMCs), a key event in vascular lesion formation. These researchers examined the effects and potential mechanisms of LacCer on cell migration and proliferation in human aortic SMCs (AoSMCs). Cell migration and proliferation were determined by a modified Boyden chamber assay and a non-radioactive colorimetric (MTS) assay, respectively. They found that LacCer significantly induced AoSMC migration and proliferation in a concentration- and time-dependent manner. Furthermore, LacCer significantly upregulated the expression of PDGFR-B, integrins (alpha(v) and beta(3)), and matrix metalloproteinases (matrix metalloproteinase-1 and -2) at both mRNA and protein levels, as determined by real-time PCR and Western blot analyses, respectively. Additionally, LacCer increased superoxide anion production and the transient phosphorylation of ERK1/2 in AoSMCs, as determined by dihydroethidium staining and immunoassay, respectively. Accordingly, LacCer-induced cell migration and proliferation were effectively blocked by antioxidants (seleno-l-methionine and Mn tetrakis porphyrin) and by a specific ERK1/2 inhibitor. Therefore, LacCer promoted cell migration and proliferation through oxidative stress and activation of ERK1/2 in AoSMCs. The authors concluded that the findings of this study provided a better understanding of the roles of LacCer in the vascular system and suggested new therapeutic strategies to control CVD.

Hilvo and associates (2020) stated that distinct ceramide lipids have been shown to predict the risk for CVD events, especially cardiovascular death. As phospholipids have also been linked with CVD risk, these investigators examined whether the combination of ceramides with phosphatidylcholines (PCs) would be synergistic in the prediction of CVD events in patients with atherosclerotic coronary heart disease (CHD) in three independent cohort studies. Ceramides and PCs were analyzed using liquid chromatography-mass spectrometry (LC-MS) in three studies: WECAC (The Western Norway Coronary Angiography Cohort) (n = 3,789), LIPID (Long-Term Intervention with Pravastatin in Ischemic Disease) trial (n = 5,991), and KAROLA (Langzeiterfolge der KARdiOLogischen Anschlussheilbehandlung) (n = 1,023). A simple risk score, based on the ceramides and PCs showing the best prognostic features, was developed in the WECAC study and validated in the other two cohorts. This score was highly significant in predicting CVD mortality [multi-adjusted hazard ratios (HRs) (95% CI) per standard deviation were 1.44 (1.28 to 1.63) in WECAC, 1.47 (1.34 to 1.61) in the LIPID trial, and 1.69 (1.31 to 2.17) in KAROLA]. In addition, a combination of the risk score with high-sensitivity troponin T increased the HRs to 1.63 (1.44 to 1.85) and 2.04 (1.57 to 2.64) in the WECAC and KAROLA cohorts, respectively. The C-statistics in WECAC for the risk score combined with sex and age was 0.76 for CVD death. The ceramide-phospholipid risk score showed comparable and synergistic predictive performance with previously published CVD risk models for secondary prevention. The authors concluded that a simple ceramide- and phospholipid-based risk score could efficiently predict residual CVD event risk in patients with CAD. Moreover, these researchers stated that this validated risk stratification tool may be used to further improve the development of personalized management of CHD, which should be tested in future prospective studies.

The authors noted that a drawback of the study was that the PCs, and partly ceramides, were analyzed with a slightly different analytical methodology in all three cohorts, which did not allow these investigators to compare the calibration of the score across the cohorts. Notably, however, within different cohorts, the results replicated well despite these different methodologies. Therefore, it appeared that the lipids described in this study were robust both statistically and analytically, although a fully validated mass-spectrometric laboratory method will be needed for the clinical application of CERT2 (the new ceramide test score). They stated that an interesting area for further studies is to examine if CERT2 could identify those subjects who will benefit from n-3 fatty acid supplementation.

Papazoglou and colleagues (2022) stated that accumulating evidence over the past 10 years suggests the promising role of ceramides as potential mediators of CAD or prognostic biomarkers of its clinical course. In a meta-analysis, these investigators examined the prognostic value of a ceramide- and phosphatidylcholine-based risk score, Coronary Event Risk Test 2 (CERT2) score, for the prediction of major adverse cardiovascular events (MACE) (cardiovascular death, myocardial infarction, or stroke) in 26,896 individuals with established CAD. Patients with CERT2 = 0 to 3 were used as a reference group. The pooled relative risk (RR) of MACE among patients with CERT2 = 4 to 6 was equal to 1.35 (95% CI: 1.11 to 1.64). Patients with CERT2 = 7 to 8 had an 81% increased risk of MACE (RR = 1.81, CI: 1.40 to 2.34), while those with CERT2 = 9 to 12 had a 165% increased risk of MACE (RR = 2.65, CI: 1.85 to 3.80). Subgroup analysis in patients with chronic coronary syndrome yielded an adjusted HR for MACE equal to 1.20 (CI: 1.09 to 1.32) per 1 standard deviation increase of CERT2 score. A summary C-statistic of the score combined with the classical risk assessment model was found to be equal to 0.68 (95% CI: 0.58 to 0.77; approximate 95% prediction interval of 0.38 to 0.88). The authors concluded that the CERT2 score appeared to emerge as a robust predictor of MACE; however, additional research is needed to establish the cost-effectiveness of CERT2 score calculation for the determination of residual risk in patients with CAD.

Methods to Determine Vascular Age

Groenewegen et al. (2016) stated that vascular age is an alternate means of representing an individual's cardiovascular risk. Little consensus exists on what vascular age represents and its clinical utility has not been determined. These investigators systematically reviewed the literature to provide a comprehensive overview of different methods that have been used to define vascular age, and to examine its potential clinical value in patient communication and risk prediction. These researchers identified 39 articles on vascular age, 20 proposed to use vascular age as a communication tool and 19 proposed to use vascular age as a means to improve cardiovascular risk prediction; 8 papers were methodological and 31 papers reported on vascular age in study populations. Of these 31 papers, vascular age was a direct translation of the absolute risk estimated by existing cardiovascular risk prediction models in 15 papers, 12 derived vascular age from the reference values of an additional test, and in 3 papers vascular age was defined as the age at which the estimated cardiovascular risk equals the risk from non-invasive imaging observed degree of atherosclerosis. One trial found a small effect on risk factor levels when vascular age was communicated instead of cardiovascular risk. The authors concluded that despite sharing a common name, various studies have proposed distinct ways to define and measure vascular age. They stated that studies into the effects of vascular age as a tool to improve cardiovascular risk prediction or patient communication are scarce but will be needed before its clinical use can be justified.

Mid-Regional Pro-Atrial Natriuretic Peptide

The rapid and reliable estimation of prognosis in acute ischemic stroke is pivotal to optimizing clinical care. Mid-regional pro-atrial natriuretic peptide (MR-proANP), a recently described stable fragment of the ANP precursor hormone, may be useful in this setting. In a prospective observational study, Katan and colleagues (2010) examined the prognostic value of MR-proANP in patients with acute ischemic stroke. These researchers measured MR-proANP on admission in the plasma of 362 consecutive patients presenting with acute ischemic stroke. The prognostic value of MR-proANP to predict mortality within 90 days and functional outcome (defined as a modified Rankin Scale of less than or equal to 2 or greater than or equal to 3) was evaluated and compared with the National Institutes of Health Stroke Scale (NIHSS) score. The discriminatory accuracy, calculated with the area under the curve (AUC) of the receiver operating characteristics curve, of MR-proANP to predict death was comparable to the NIHSS (AUC: 0.86 [95% CI: 0.82 to 0.90] and 0.85 [95% CI: 0.81 to 0.89; p = 0.7]). Combined, the accuracy significantly improved (0.92 [95% CI: 0.88 to 0.96; p < 0.01]). The AUC of MR-proANP to predict functional outcome was 0.70 (95% CI: 0.65 to 0.75), similar to the NIHSS (0.75 [95% CI: 0.70 to 0.80]; p = 0.16). The prognostic value of MR-proANP for both outcomes was independent of the NIHSS. Higher MR-proANP concentrations were found in strokes of cardioembolic etiology. The authors concluded that MR-proANP is a prognostic marker in the acute phase of stroke, improving the discriminatory value of the NIHSS and independently predicting post-stroke mortality and functional outcome.

In an editorial that accompanied the paper by Katan et al., Granger and Laskowitz (2010) stated that the current study was performed at a single center with only 44 deaths, and the results need to be validated in an independent study. A number of important questions remain: Does this biomarker change predicted risk enough to alter recommended therapy? Does the use of the biomarker result in improved care and clinical outcomes? And is it cost-effective?

Guidelines from the American College of Cardiology/American Heart Association (2010) and the National Academy of Clinical Biochemistry (2009) do not recommend the measurement of natriuretic peptides for cardiovascular disease (CVD) risk assessment in asymptomatic adults.

Myeloperoxidase (MPO)

Myeloperoxidase (MPO) is an enzyme found in white blood cells that is purportedly linked to inflammation and cardiovascular disease (CVD). Higher levels of the leukocyte enzyme MPO, which is secreted during acute inflammation and promotes the oxidation of lipoproteins, are associated with the presence of coronary disease (Zheng et al., 2001; Zheng et al., 2004) and may be predictive of acute coronary syndrome in patients with chest pain (Brennan et al., 2003). Stefanescu et al. (2008) found that patients with stable coronary artery disease (CAD) had an increased CVD risk if plasma MPO levels were elevated, and a small study demonstrated that MPO deficiency may protect against CVD (Kutter and Devaquet, 2000). Furthermore, among patients with chronic systolic heart failure (HF), elevated plasma MPO levels have been associated with an increased likelihood of more advanced HF and may be predictive of a higher rate of adverse clinical outcomes (Tang et al., 2007).

Although elevated plasma MPO concentration may be associated with a more advanced CVD risk profile, plasma MPO does not predict mortality independent of other CVD risk factors in patients with stable CAD. There is a lack of scientific evidence regarding how measurements of MPO would affect the management of individuals at risk for or patients with coronary heart disease (CHD). Large randomized controlled studies are needed to ascertain the clinical value of MPO in the management of CHD.

OmegaCheck Panel

According to Private MD Labs, omega-3 and omega-6 fatty acids are 2 of the most important types of polyunsaturated fats. The most important omega-3s are commonly called eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Omega-3s help brain function, including memory and normal growth and development. They also can reduce inflammation. Omega-3 is obtained by eating foods such as oily fish and plant oils. The most common form of omega-6 is called arachidonic acid (AA). Omega-6 is obtained by eating animal foods, such as meat and eggs. Docosapentaenoic acid (DPA) has always been a part of healthy nutrition, since infants obtain almost as much DPA as DHA from human milk. Fish oil supplements and ingredients, oily fish, and grass-fed beef can serve as the primary DPA sources for the general population. The OmegaCheck Panel tests whole blood for EPA, DHA, and DPA. OmegaCheck™ Panel

According to Quest Diagnostics, the OmegaCheck Panel may be performed on individuals with hypercholesterolemia, hypertriglyceridemia, hypertension, and/or those with high metabolic or cardiovascular risk. OmegaCheck®.

There is a lack of evidence regarding the clinical value of the OmegaCheck Panel in the management of individuals at risk for coronary heart disease.

Osteoprotegerin

Venuraju and colleagues (2010) stated that osteoprotegerin (OPG) is a glycoprotein that acts as a decoy receptor for receptor activator of nuclear factor kappaB ligand (RANKL) and tumor necrosis factor (TNF)-related apoptosis-inducing ligand. The OPG/RANKL/receptor activator of nuclear factor kappaB axis plays an important regulatory role in the skeletal, immune, and vascular systems. The protective role of OPG, in animal models, against vascular calcification has not been replicated in human trials; moreover, increased OPG levels have been consistently associated with the incidence and prevalence of CAD. There seems to be some dichotomy in the role of OPG, RANKL, and TNF-related apoptosis-inducing ligand in atherosclerosis and plaque stability. These researchers integrated the findings from some of the important studies and try to draw conclusions with a view to gaining some insight into the complex interactions of the OPG/RANKL/receptor activator of nuclear factor kappaB axis and TNF-related apoptosis-inducing ligand in the pathophysiology of atherosclerosis. The authors concluded that while the clinical prognostic utility of OPG appears to be awhile away yet, it does hold a great deal of promise in helping clinicians risk stratify patients with CVD more accurately.

Oxidized Low-Density Lipoprotein as a Biomarker for Cardiovascular Disease Stratification

Hong et al. (2023) noted that low-density lipoprotein cholesterol (LDL-C) is an established marker for cardiovascular disease (CVD) and a therapeutic target. Oxidized LDL (oxLDL) is known to be associated with excessive inflammation and abnormal lipoprotein metabolism. Chronic inflammatory diseases confer an elevated risk of premature atherosclerosis and adverse cardiovascular events. Whether oxLDL may serve as a potential biomarker for CVD stratification in populations with chronic inflammatory conditions remains understudied. In a systematic review and meta-analysis, these investigators examined the relationship between oxLDL and CVD (defined by incident CVD events, carotid intima-media thickness, and presence of coronary plaque) in patients with chronic inflammatory diseases. They carried out a systematic literature search using studies published between 2000 and 2022 from PubMed, Cochrane Library, Embase (Elsevier), CINAHL (EBSCOhost), Scopus (Elsevier), and Web of Science: Core Collection (Clarivate Analytics) databases on the relationship between oxLDL and cardiovascular risk in inflamed populations. The pooled effect size was combined using the random effects model, and publication bias was assessed if p < 0.05 for the Egger or Begg test, along with the funnel plot test. The authors concluded that a total of three observational studies with 1,060 subjects were included in the final meta-analysis. The results showed that oxLDL was significantly elevated in subjects with CVD in the setting of chronic inflammatory conditions. The findings of this meta-analysis suggested that oxLDL may be a useful biomarker in risk stratifying cardiovascular disease in chronically inflamed patients. Moreover, these researchers stated that larger meta-analyses and future mechanistic studies are needed to further examine the relationship between oxidized lipoproteins and cardiovascular disease in patients with long-standing inflammatory conditions.

The authors stated that this meta-analysis had several drawbacks. First, the causal association between oxLDL and CVD outcomes in these populations of interest could not be defined because of the cohort or cross-sectional nature of the included studies. Second, studies using other techniques to estimate CVD outcomes were not included in this meta-analysis. Third, as observational studies demonstrated more heterogeneity than randomized controlled trials (RCTs) and several of the included studies were observational studies, this factor must also be considered, given that heterogeneity interfered with the detection of publication bias. Fourth, the sources of heterogeneity may correlate with study design, participant ages, and whether patients have atherosclerotic risk factors. These researchers stated that while oxLDL is a promising biomarker for CVD risk stratification, it is not yet used in the clinic as a diagnostic tool for CVD. The authors were unable to determine the effects of populational characteristics or pharmacologic therapy on the progression of CVD outcomes in relation to oxLDL in patients with chronic inflammatory diseases.

Peripheral Arterial Tonometry

Endothelium plays an important role in the maintenance of vascular homeostasis. Nitric oxide (NO) is the key mediator of endothelial function; it is a potent vasodilator, inhibits platelet aggregation, and reduces vascular smooth muscle cell migration and proliferation, as well as monocyte adhesion. Cardiovascular risk factors promote the development of endothelial dysfunction, characterized by impairment of endothelium-dependent vasodilation (EDV) and by pro-coagulant/pro-inflammatory endothelial activities. The assessment of EDV is a common parameter for testing endothelial function. Endothelium-dependent vasodilation in the coronary arteries is angiographically evaluated by measurement of the vessel response to endothelial agonists, such as acetylcholine (the gold standard). A non-invasive technique for the detection of EDV employs the ultrasound evaluation of flow-mediated dilation (FMD) of the brachial artery following reactive hyperemia. A close relationship between FMD and coronary vasomotor response to acetylcholine has been reported. Endothelial dysfunction in the coronary circulation may precede the development of angiographically evident coronary atherosclerosis; it has also been associated with a higher prevalence of coronary artery disease (CAD) and is predictive of future cardiovascular events. Recently, it has been associated with a higher risk of re-stenosis after coronary stent implantation. Endothelial dysfunction is considered a reversible phenomenon; drug therapies with angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers, statins, and antioxidant agents have shown a beneficial effect on endothelial function (Patti et al., 2005).

Peripheral arterial tonometry (PAT) has been proposed as a non-invasive method to measure endothelial dysfunction and potentially identify patients with early-stage CAD. Endothelial dysfunction is measured by the PAT signal obtained using the Endo-PAT2000 device (Itamar Medical) and proprietary software. The test involves the measurement of blood flow in the fingertips following compression of the upper arm with an inflatable cuff. The Endo-PAT2000 was cleared by the FDA through the 510(k) process in November 2003. It is indicated for use as a diagnostic aid in the detection of coronary artery endothelial dysfunction (positive or negative) using a reactive hyperemia procedure. The device is not intended for use as a screening test in the general patient population. However, there is currently insufficient evidence to support the use of PAT in assessing CAD risk.

Kuvin et al. (2007) assessed endothelial function in two peripheral vascular beds before and during reactive hyperemia in an outpatient clinic setting. The brachial artery was imaged with a portable ultrasound device, and changes in vessel diameter were expressed as "% FMD." Pulse wave amplitude of the finger was detected by PAT, and PAT hyperemia was defined as the maximal plethysmographic recording compared to baseline. A total of 60 individuals (43 men) were enrolled, with an average age of 53 ± 2 years (mean ± SE). The 31 individuals with more than two cardiac risk factors (CRF) had lower FMD (7.0 ± 1.1%) and PAT hyperemia (2.1 ± 0.9) compared to the 29 persons with 0 to 2 CRF (FMD 11.3 ± 0.8%, PAT hyperemia 2.4 ± 0.1; p < 0.05 for both). The 32 individuals with CAD had lower FMD (6.8 ± 1.1%) and PAT hyperemia (2.0 ± 0.1) compared to the 28 individuals without CAD (FMD 11.5 ± 0.8%, PAT hyperemia 2.4 ± 0.1; p < 0.05 for both). Thus, peripheral vascular endothelial function testing in the ambulatory setting correlates with the extent of CAD risk and the presence or absence of CAD. The authors concluded that these data suggested that peripheral vascular endothelial function testing is feasible in ambulatory patients, and this is an important next step in bringing this technology to clinical applicability.

Ghiadoni et al. (2008) stated that the endothelium plays a key role in the maintenance of vascular homeostasis. A dysfunctional endothelium is an early marker of the development of atherosclerotic changes and can also contribute to cardiovascular events. Vascular reactivity tests represent the most widely used methods in the clinical assessment of endothelial function, and in the last two decades, several methodologies have been developed to study it non-invasively in the peripheral macro-circulation (conduit arteries) and micro-circulation (resistance arteries and arterioles). These investigators reviewed the most relevant available non-invasive techniques in the research on endothelial function, along with their advantages and limitations. Flow-mediated dilation of the brachial artery by ultrasound is the most widely used vascular test to ascertain endothelium-dependent vasodilation. Other approaches include measurement of micro-circulatory reactive hyperemia by forearm venous plethysmography or digital pulse amplitude tonometry, response to beta-2 agonists by applanation tonometry or digital photoplethysmography, and several tests using skin laser Doppler. It appears that FMD is the most reproducible test when an appropriate and accurate methodology is applied. Systemic markers proposed as measures of NO biology, inflammatory cytokines, adhesion molecules, or markers of endothelial damage and repair have only a very limited role due to biological and assay availability and variability; these factors currently have a limited role in the assessment of individual patients. The optimal methodology for investigating the multifaceted aspects of endothelial dysfunction is still under debate. Thus, no available test to assess endothelial function has sufficient sensitivity and specificity to be used yet in clinical practice. Only the growing concordant results from different reproducible and reliable non-invasive methods examining endothelial function with different stimuli will support and strengthen experimental findings, thus providing conclusive answers in this area of research.

Chemla and associates (2008) reviewed recent advances in the non-invasive assessment of arterial pressure (indirect methods) in the field of critical care. Automated oscillometric measurements underestimate intra-arterial systolic blood pressure. Digital photoplethysmography has led to conflicting results, although the obtained respiratory pulse pressure variation correlates with the fluid-challenge-induced changes in stroke volume. The pulse oximetry photoplethysmographic signal recorded at the digital or ear level may be useful in monitoring respiratory arterial pressure variations, although technical improvements and clarifications are needed. Arterial tonometry is increasingly used in the cardiovascular field to reconstruct central aortic pressure. A recent study has shown that radial artery tonometry is feasible in hemodynamically stable patients and that peripheral pulse pressure reflects the combined influences of arterial stiffness and stroke volume, especially in elderly patients. The limitations of this technique include the potential bias related to the use of a generalized transfer function and the difficulty in obtaining reliable recordings in hemodynamically unstable patients. The authors concluded that intra-arterial blood pressure must be preferred over non-invasive blood pressure recordings when critical decisions are required. In hemodynamically stable patients, valuable information may be obtained by using non-invasive techniques, among which arterial tonometry seems promising.

Burg et al. (2009) stated that myocardial ischemia provoked by emotional stress (MSI) in patients with stable CAD predicts major adverse cardiac events. These researchers tested an easily administered, non-invasive technology to identify vulnerability to mental stress ischemia. Patients with documented CAD (n = 68) underwent single photon emission CT myocardial perfusion imaging concurrent with pulse wave amplitude assessment by PAT during a mental stress protocol of sequential rest and anger stress periods. Heart rate and blood pressure were assessed, and blood was drawn for catecholamine assay during rest and stress. Myocardial ischemia provoked by emotional stress was defined by the presence of a new perfusion defect during anger stress (n = 26), and the ratio of stress to rest PAT response was calculated. Patients with MSI had a significantly lower PAT ratio than those without MSI (0.76 ± 0.04 versus 0.91 ± 0.05, p = 0.03). An ROC curve for optimum sensitivity/specificity of the PAT ratio as an index of MSI produced a sensitivity of 0.62 and a specificity of 0.63. Among patients taking ACE inhibitors, the sensitivity and specificity of the test increased to 0.86 and 0.73, respectively; 90% of patients without MSI were correctly identified. The authors concluded that PAT in concert with ACE inhibition may provide a useful approach to assess risk for MSI. They stated that future studies should help determine how best to utilize this approach for risk assessment in the clinical setting.

Receptor for Advanced Glycosylation End Products (RAGE) Gene Gly82Ser Polymorphism Testing

Ma and associates (2016) stated that the receptor for advanced glycosylation end-products (RAGE) has been linked to diabetic atherosclerosis, but its effects on CAD and ischemic stroke (IS) remain controversial. The Gly82Ser polymorphism is located in the ligand-binding V domain of RAGE, suggesting a possible influence of this variant on RAGE function. These researchers examined the association between the RAGE Gly82Ser polymorphism and susceptibility to CAD and IS. Eligible studies were identified through a comprehensive literature search. Odds ratios and 95% CIs were used to evaluate the association of Gly82Ser polymorphism with CAD and IS risk. Fixed- or random-effects model was used depending on the heterogeneity between studies. A funnel plot and Egger linear regression test were applied to assess publication bias. These investigators also performed subgroup analyses to investigate potential sources of heterogeneity. A total of 16 eligible articles containing 18 studies were analyzed. The pooled analysis indicated that the Gly82Ser polymorphism significantly increased CAD risk in recessive and homozygous genetic models (SS versus GS + GG: OR = 1.34, 95% CI: 1.09 to 1.64; SS versus GG: OR = 1.38, 95% CI: 1.12 to 1.71). A significant association between the Gly82Ser polymorphism and IS risk was observed in all tested models except the heterozygous genetic model (GS + SS versus GG: OR = 1.20, 95% CI: 1.04 to 1.38; SS versus GS + GG: OR = 2.20, 95% CI: 1.74 to 2.78; SS versus GG: OR = 2.23, 95% CI: 1.72 to 2.91; S versus G: OR = 1.32, 95% CI: 1.05 to 1.65). Subgroup analysis suggested an association between CAD and IS risk and the Gly82Ser polymorphism in the Chinese population, but not in the non-Chinese population. The authors concluded that the current meta-analysis suggested that the RAGE Gly82Ser polymorphism is associated with an increased risk of CAD and IS, especially in the Chinese population. However, they stated that better-designed studies with larger sample sizes are needed to validate the results.

The main drawbacks of this study were:

heterogeneity in this meta-analysis may influence the reliability of the findings,
the data extracted from each record were based on unadjusted estimates, which may lead to misleading results, and
the language of eligible studies was limited to English and Chinese, and despite no evidence of publication bias from our statistical tests, some may remain.

SLCO1B1 (Statin Induced Myopathy Genetic Testing)

Talameh and, Kitzmiller (2014) noted that statins are the most commonly prescribed drugs in the United States and are extremely effective in reducing major cardiovascular events in the millions of Americans with hyperlipidemia. However, many patients (up to 25%) cannot tolerate or discontinue statin therapy due to statin-induced myopathy (SIM). Patients will continue to experience SIM at unacceptably high rates or experience unnecessary cardiovascular events (as a result of discontinuing or decreasing their statin therapy) until strategies for predicting or mitigating SIM are identified. A promising strategy for predicting or mitigating SIM is pharmacogenetic testing, particularly of pharmacokinetic genetic variants as SIM is related to statin exposure. Data are emerging on the association between pharmacokinetic genetic variants and SIM. A current, critical evaluation of the literature on pharmacokinetic genetic variants and SIM for potential translation to clinical practice is lacking. This review focused specifically on pharmacokinetic genetic variants and their association with SIM clinical outcomes. These investigators also discussed future directions, specific to the research on pharmacokinetic genetic variants, which could speed the translation into clinical practice. For simvastatin, these researchers did not find sufficient evidence to support the clinical translation of pharmacokinetic genetic variants other than SLCO1B1. However, SLCO1B1 may also be clinically relevant for pravastatin- and pitavastatin-induced myopathy, but additional studies assessing SIM clinical outcome are needed. CYP2D6*4 may be clinically relevant for atorvastatin-induced myopathy, but mechanistic studies are needed. The authors concluded that future research efforts need to incorporate statin-specific analyses, multi-variant analyses, and a standard definition of SIM. As the use of statins is extremely common and SIM continues to occur in a significant number of patients, future research investments in pharmacokinetic genetic variants have the potential to make a profound impact on public health.

SmartVascular Dx (SmartHealth Vascular Dx) Test

SmartVascular Dx is designed to diagnoses clinically significant vascular inflammation associated with autoimmune diseases, cancer, COVID-19, infections, and metabolic abnormalities, all of which have an increased risk of heart attack and stroke. This test measures 7 protein biomarkers to identify arterial injury (CTACK, Eotaxin, Fas Ligand, HGF, IL-16, MCP-3, and sFas) for identification of endothelial Inflammation. However, there is a lack of evidence regarding the clinical value of SmartVascular Dx (SmartHealth Vascular Dx) Test.

An UpToDate reviews on “Blood biomarkers for stroke” (Ishida and Cucchiara, 2024) states that “Candidate biomarkers for stroke diagnosis include the following, though none have shown sufficient sensitivity or specificity to be clinically useful”. Moreover, none of the 7 protein biomarkers to identify arterial injury (CTACK, Eotaxin, Fas Ligand, HGF, IL-16, MCP-3, and sFas) in the SmartVascular Dx (SmartHealth Vascular Dx) Test is mentioned.

Furthermore, an UpToDate review on “COVID-19: Cardiac manifestations in adults” (Caforio, 2024) states that “Cardiac troponin and natriuretic peptide (B-type natriuretic peptide [BNP] and N-terminal pro-BNP [NT-proBNP]) biomarkers are commonly elevated among hospitalized patients with COVID-19 and are associated with increased risk of mortality”. Again, none of the 7 protein biomarkers to identify arterial injury (CTACK, Eotaxin, Fas Ligand, HGF, IL-16, MCP-3, and sFas) in the SmartVascular Dx (SmartHealth Vascular Dx) Test is mentioned. Furthermore, the use of biomarkers is not mentioned in the “Summary and Recommendations” section of of this review.

Soluble Cell Adhesion Molecules

Malik and colleagues (2001) noted that previous studies have suggested that circulating concentrations of soluble adhesion molecules are useful predictors of the risk of coronary heart disease (CHD). Larger studies are needed, however, to test this hypothesis. These researchers measured serum concentrations of four soluble cell adhesion molecules (intercellular adhesion molecule-1 [ICAM-1], vascular cell adhesion molecule-1 [VCAM-1], E-selectin, and P-selectin) in the stored baseline serum samples of 643 men with CHD and 1,278 controls nested in a prospective study of 5,661 men who were monitored for 16 years. They also performed a meta-analysis of previous relevant studies to place their findings in context. Concentrations of soluble cell adhesion molecules were significantly associated with one another, with other markers of inflammation, and with some classic coronary risk factors. For ICAM-1, the odds ratio (OR) for CHD was 1.68 (95% CI: 1.32 to 2.14) in a comparison of men in the top third with those in the bottom third of baseline measurements after adjustments for age and town. This decreased to 1.11 (0.75 to 1.64) after adjustment for some classic coronary risk factors and indicators of socioeconomic status. For the three other cell adhesion molecules, the ORs for CHD, first adjusted for age and town only, and then additionally adjusted for other risk factors, were: VCAM-1: 1.26 (0.99 to 1.61) and 0.96 (0.66 to 1.40); E-selectin: 1.27 (1.00 to 1.61) and 1.13 (0.78 to 1.62); and P-selectin: 1.23 (0.96 to 1.56) and 1.20 (0.81 to 1.76). The authors concluded that the measurement of these adhesion molecules is unlikely to add much predictive information to that provided by more established risk factors.

Kilic and co-workers (2015) noted that VCAM-1 and ICAM-1 are two important members of the immunoglobulin gene superfamily of adhesion molecules, and their potential role as biomarkers of diagnosis, severity, and prognosis of cardiovascular disease has been investigated in a number of clinical studies. These researchers determined the relationship between circulating ICAM-1 and VCAM-1 levels and aortic stiffness in patients referred for echocardiographic examination. Aortic distensibility was determined by echocardiography using systolic and diastolic aortic diameters in 63 consecutive patients referred for echocardiography. Venous samples were collected in the morning after a 12-hour overnight fast, and serum concentrations of ICAM-1 and VCAM-1 were measured using commercial enzyme immunoassay kits. Data from a total of 63 participants (mean age of 55.6 ± 10.5 years, 31 males) were included in the study. Circulating levels of adhesion molecules were VCAM-1: 12.604 ± 3.904 ng/ml and ICAM-1: 45.417 ± 31.429 ng/ml. They were unable to demonstrate any correlation between indices of aortic stiffness and VCAM-1 and ICAM-1 levels. The authors concluded that the role of soluble adhesion molecules in cardiovascular disease has not been fully established, and clinical studies have shown inconsistent results. They stated that these findings indicated that levels of circulating adhesion molecules cannot be used as markers of aortic stiffness in patients.

ST2 (Growth Stimulation Expressed Gene 2)

According to the manufacturer, ST2 (for growth stimulation expressed gene 2) (Presage ST2 Assay) quantitatively measures the concentration of soluble ST2, and has been used to assess prognosis in patients with cardiovascular disease. ST2 is a member of the interleukin-1 (IL1) receptor family of cytokines. The manufacturer states that, in the heart, ST2 has a biological role in immunological processes and is involved in a cardiac signaling pathway, which, under healthy conditions, serves to protect the heart during pressure overload or stretch. The manufacturer states that ST2 is an emerging biomarker to predict adverse outcomes and death in individuals with established heart failure and is also a prognostic marker for future cardiovascular disease in the general population.

Published studies of ST2 have focused on its relationship to prognosis in three key areas:

risk of hypertension, heart failure, and cardiovascular mortality in the general population (AbouEzziddine et al., 2012; Wang et al., 2012);
its relationship with adverse outcomes in patients with heart failure (Ky et al., 2011; Felker et al., 2013); and
its prognostic value in acute coronary syndromes (Shrimpo et al., 2002; Kohli et al., 2012).

However, there is a lack of evidence of clinical utility of ST2 and ST2 has not been incorporated in current clinical guidelines.

The Acarix CADScor System for Evaluation of Suspected Coronary Artery Disease

The CE-approved and FDA DeNovo-cleared Acarix CADScor System is intended for patients experiencing chest pain with suspected coronary artery disease (CAD) and is designed to help reduce millions of unnecessary, invasive, and costly diagnostic procedures. The CADScor System calculates a patient-specific CAD score non-invasively in less than 10 minutes and can aid in ruling out more than one-third of patients with at least 96% certainty (in a population with approximately 10% CAD prevalence). However, there is currently insufficient evidence to support the effectiveness of the Acarix CADScor System.

Winther et al. (2018) noted that diagnosing coronary artery disease (CAD) continues to require substantial healthcare resources. Acoustic analysis of transcutaneous heart sounds of cardiac movement and intra-coronary turbulence due to obstructive coronary disease could potentially change this. These researchers tested the diagnostic accuracy of a new portable acoustic device for the detection of CAD. This trial included 1,675 patients consecutively with low-to-intermediate likelihood of CAD who had been referred for cardiac CT angiography (CTA). If significant obstruction was suspected in any coronary segment, patients were referred to invasive angiography and fractional flow reserve (FFR) assessment. Heart sound analysis was carried out in all patients. A pre-defined acoustic CAD score algorithm was evaluated; subsequently, these researchers developed and validated an updated CAD score algorithm that included both acoustic features and clinical risk factors. Low risk is indicated by a CAD score value of 20 or less. Hemodynamically significant CAD assessed from FFR was present in 145 (10.0%) patients. In the entire cohort, the pre-defined CAD score had a sensitivity of 63% and a specificity of 44%. In total, 50% had an updated CAD score value of 20 or less. At this cut-off, sensitivity was 81% (95% CI: 73% to 87%), specificity was 53% (95% CI: 50% to 56%), positive predictive value (PPV) was 16% (95% CI: 13% to 18%), and negative predictive value (NPV) was 96% (95% CI: 95% to 98%) for diagnosing hemodynamically significant CAD. The authors concluded that sound-based detection of CAD enabled risk stratification superior to clinical risk scores. With an NPV of 96%, this new acoustic rule-out system could potentially supplement clinical assessment to guide decisions on the need for further diagnostic investigation.

The authors stated that this study had several drawbacks. First, this study cohort was almost exclusively Caucasian (99%) and had a documented low-to-intermediate prevalence of CAD; therefore, generalization to other populations with different ethnic backgrounds, higher prevalence, and other healthcare environments may not be possible. Second, higher CAD scores were observed for a low number of patients with heart valve disease; however, this was only when adding the clinical risk factors, indicating that patients with heart valve disease were diagnosed accurately. Third, the development of CAD score V3 required splitting the cohort into a training and a validation cohort, which could have over-fitted the results in the total cohort analysis.

Schmidt et al. (2019) examined the potential of a non-invasive acoustic device (CADScor System) to re-classify patients with intermediate pre-test probability (PTP) and clinically suspected stable CAD into a low probability group, thereby ruling out significant CAD. Audio recordings and clinical data from three studies were collected in a single database. In all studies, patients with a coronary CTA indicating CAD were referred to coronary angiography. Audio recordings of heart sounds were processed to construct a CAD score. PTP was calculated using the updated Diamond-Forrester score, and patients were classified according to the current ESC guidelines for stable CAD: low (less than 15%), intermediate (15% to 85%), and high (greater than 85%) PTP. Intermediate PTP patients were re-classified to low probability if the CAD score was less than or equal to 20. Of 2,245 patients, 212 (9.4%) had significant CAD confirmed by coronary angiography (greater than or equal to 50% diameter stenosis). The average CAD score was higher in patients with significant CAD (38.4 ± 13.9) compared to the remaining patients (25.1 ± 13.8; p < 0.001). The re-classification increased the proportion of low PTP patients from 13.6% to 41.8%, reducing the proportion of intermediate PTP patients from 83.4% to 55.2%. Before re-classification, 7 (3.1%) low PTP patients had CAD, whereas post-re-classification this number increased to 28 (4.0%) (p = 0.52). The net re-classification index was 0.209. The authors concluded that they simulated the use of the CAD score to rule out CAD in patients with intermediate PTP and suggested that the method could potentially reduce the number of patients who should be referred for non-invasive testing, without a significant increase in the false-negative rate. Moreover, these researchers stated that if these findings can be replicated in prospective studies, the use of the CAD score could significantly alter the current practice of early rule-out of stable CAD, providing important clinical and economic advantages.

The authors stated that this study had several drawbacks. This trial was a retrospective analysis of pooled data from existing cohorts and might therefore not capture all aspects of the clinical workflow. The database included a group of asymptomatic subjects from a screening study and a group of patients referred for coronary angiography. Neither of these groups was typical representatives for patients referred for non-invasive testing. However, the baseline characteristics such as age, gender, and PTP of the pooled data corresponded well to the characteristics of the Dan-NICAD study, which included only patients referred for non-invasive testing. The conclusion of the current study was limited to low- to intermediate-risk patients since the number of high-risk patients (updated Diamond-Forrester score greater than 85%) was very low in the current study. The CAD score algorithm described in the current paper was fine-tuned in the complete database before implementation in the CAD score device. This induced the risk of overfitting the algorithm to the data; however, the cross-validation of the algorithm showed only a small decrease in AUC of 0.009; thus, the degree of overfitting can be considered unimportant for the overall results. As recommended in the current ESC guidelines, the updated Diamond-Forrester score was used for PTP estimation. Other risk assessment models like the CAD-consortium scores or PROMISE Minimal-Risk Tool estimated lower risk levels that might alter the interaction between PTP and the CAD score. To further understand the interaction between long-term risk and CAD scores, future studies should include long-term follow-up data.

Rasmussen et al. (2019) noted that coronary computed tomography angiography (CTA) is the preferred primary diagnostic modality when examining patients with low-to-intermediate PTP of CAD. Only 20% to 30% of these patients have potentially obstructive CAD. Because of the relatively poor positive predictive value (PPV) of coronary CTA, unnecessary invasive coronary angiographies (ICAs) are conducted, along with the costs and risks associated with the procedure. Hence, an optimized diagnostic CAD algorithm may reduce the number of ICAs not followed by re-vascularization. The Dan-NICAD 2 Trial has three equivalent main aims: First, to examine the diagnostic precision of an acoustic-based diagnostic algorithm, the CADScor System (Acarix A/S, Denmark), in patients with a low-to-intermediate pre-test risk of CAD referred to a primary examination by coronary CTA. These researchers hypothesized that the CADScor System provides better stratification prior to coronary CTA than clinical risk stratification scores alone. Second, to compare the diagnostic accuracy of 3T cardiac magnetic resonance imaging (3T CMRI), 82-rubidium positron emission tomography (82Rb-PET), and CT-derived fractional flow reserve (FFRCT) in patients where obstructive CAD could not be ruled out by coronary CTA, using ICA fractional flow reserve (FFR) as the reference standard. Third, to compare the diagnostic performance of quantitative flow ratio (QFR) and ICA-FFR in patients with low-to-intermediate pre-test probability of CAD using 82Rb-PET as the reference standard. Dan-NICAD 2 is a prospective, cross-sectional, multi-center study including approximately 2,000 patients with low-to-intermediate pre-test probability of CAD and without a previous history of CAD. Patients are referred to coronary CTA because of symptoms suggestive of CAD, as evaluated by a cardiologist. Patient interviews, sound recordings, and blood samples are obtained in connection with the coronary CTA. If coronary CTA does not rule out obstructive CAD, patients will be examined by 3T CMRI, 82Rb-PET, FFRCT, ICA, and FFR. The reference standard is ICA-FFR. Obstructive CAD is defined as an FFR of less than or equal to 0.80 or as high-grade stenosis (greater than 90% diameter stenosis) by visual assessment. Diagnostic performance will be evaluated as sensitivity, specificity, predictive values, likelihood ratios, calibration, and discrimination. The authors concluded that the results of the Dan-NICAD 2 study are expected to contribute to the improvement of diagnostic strategies for patients suspected of CAD in three different steps: risk stratification prior to coronary CTA, diagnostic strategy after coronary CTA, and invasive wireless QFR analysis as an alternative to ICA-FFR. The authors stated that the study is ongoing; the first patient was enrolled on January 24, 2018; enrollment completion was expected in June 2020. Patients are followed for 10 years after inclusion.

Winther et al. (2021) stated that recent technological advances enable the diagnosis of obstructive CAD from heart sound analysis with a high NPV; however, the prognostic impact of this approach remains unknown. These investigators examined the prognostic value of heart sound analysis as two scores, the Acoustic score and the CAD score, in patients with suspected CAD who were treated according to standard of care (SOC). Consecutive patients with angina symptoms referred for coronary CTA were enrolled. The Acoustic score was developed from eight acoustic CAD-related features. This score was combined with risk factors to generate the CAD score. A cut-off score of greater than 20 was pre-specified for both scores to indicate disease. If coronary CTA raised suspicion of obstructive CAD, patients were referred to invasive angiography and re-vascularized when indicated. Of 1,675 enrolled patients, 1,464 (87.4%) were included in this sub-study. The combined primary endpoint was all-cause mortality and myocardial infarction (MI) (n = 26). Follow-up was 3.1 years (2.7 to 3.4). Of patients with primary endpoints, the Acoustic score was greater than 20 in 25 (96%); the CAD score was greater than 20 in 22 (85%). In an unadjusted Cox analysis of the primary endpoints, the hazard ratio (HR) for scores greater than 20 under current standard clinical care was 12.6 (1.7 to 93.2) for the Acoustic score and 5.4 (1.9 to 15.7) for the CAD score. The CAD score contained prognostic information even after adjusting for lipid-lowering therapy initiation, stenosis at CTA, and early re-vascularization. The authors concluded that heart sound analysis appeared to carry prognostic information and may improve initial risk stratification of patients with suspected CAD.

The authors stated that this study had several drawbacks. First, this cohort comprised almost exclusively Caucasians with low-to-intermediate pre-test probability of CAD, and none had previously documented CAD. Patients were referred to coronary CTA as the first-line diagnostic test according to Danish SOC. This approach might have introduced referral bias based on pre-test probability and due to the limitations of coronary CTA (e.g., irregular heart rate and severe obesity). Second, the development of CAD score V3 required splitting the cohort into a consecutive training (n = 593) and a blinded validation (n = 1,082) cohort, which might have over-fitted the results in the total cohort analysis. However, diagnostic accuracy in the validation versus training cohort indicated no overfitting. Third, the CAD score did not distinguish between diastolic murmurs' origin; thus, diastolic valve disease may decrease CAD score specificity as it increased the false positive rate, but sensitivity remains high. Fourth, this study was limited by the low event rates reflecting an overall favorable prognosis of patients with stable CAD; therefore, the results of this study were promising but should be interpreted with caution, and final conclusions regarding the safety of acoustic rule-out strategies should await results of randomized controlled strategy trials.

Javanbakht et al. (2022) stated that CAD is the most common type of heart and circulatory disease and is the leading cause of death worldwide. The current diagnostic pathway can lead to patient complications and is also extremely costly. A new medical device, the CADScor System (Acarix AB), was developed for the acoustic detection of CAD before patients undergo invasive diagnostic procedures. These investigators examined the cost utility of the CADScor System for the diagnosis of CAD at an early stage in the diagnostic testing pathway in England. A two-part economic model, consisting of a decision tree and Markov model, was developed to consider the cost utility (cost per quality-adjusted life-year [QALY] gained) of introducing the CADScor System for the diagnosis of CAD. The decision tree component examined the short-term costs and diagnostic outcomes associated with the introduction of the test compared with the existing testing pathway. A Markov model was developed to examine the longer-term health and economic implications of the condition and original diagnosis, with costs and health effects estimated over different time horizons. Parameter uncertainty was examined in deterministic and probabilistic sensitivity analyses. Base-case results indicated that the CADScor System would result in cost savings (-£131 per patient) and a marginal increase in QALYs (0.00001) over a 1-year time horizon. Probabilistic results indicated that the intervention had a greater than 99% probability of being cost-effective at a willingness-to-pay threshold of £20,000 per QALY gained and a 100% probability of being cost-saving. Results from the deterministic sensitivity analyses indicated that variations in parameters related to the accuracy and cost of the CADScor System and the prevalence of CAD had the greatest impact on the overall results. The overall cost saving was estimated to be over £12.3 million per 100,000 eligible patient population. The authors concluded that the CADScor System is a potentially cost-saving test for the diagnosis of CAD. When initiated before the use of non-invasive cardiac imaging tests such as CT coronary angiography, the test reduced costs to the healthcare service over various time horizons.

The authors stated that this study had several drawbacks. First, the quality-of-life (QOL) impact of undergoing minimally invasive and invasive diagnostic testing was not examined in the decision tree component of the analysis because of a lack of robust data. Instead, these researchers focused on the main complication (i.e., death), diagnostic outcomes, and costs associated with the two strategies being compared; thus, the benefits of the CADScor System are likely to be underestimated in this analysis. If other complications and utility decrements associated with undergoing CTCA, MPS, and ICA were included in the model, the relative impact of the CADScor System on QALYs gained would likely be improved. Second, the decision tree analysis only considered the costs associated with testing without considering further costs that may need to be included in such a diagnostic model, such as additional health service utilization and patient treatment; therefore, these investigators carried out several scenario analyses to address the structural and parameter-related uncertainties. Results indicated that changing the percentage of patients who would need ICA post-CTCA/MPS had a significant impact on the estimated cost savings. The authors used a conservative estimate of performing ICA post-CTCA/MPS in the base-case analysis compared with that reported in a previous study; thus, the estimated cost saving was likely a conservative estimate of potential cost savings that could be achieved via the introduction of the CADScor System into the England NHS. Third, the patient population in this study was assumed to be homogeneous, in that all were assumed to follow the same diagnostic pathway and receive a limited range of treatment options following diagnosis. This did not account for the varying levels of disease and symptom severity that were likely to be present in such a population or the impact of this on diagnostic tests received or on treatment effectiveness. A micro-simulation model, which may be designed to consider individual patient characteristics and complex patient pathways, would be best suited to address this issue. Fourth, only a small amount of data on the diagnostic accuracy of the CADScor System were available, and the available data were based on a Danish cohort rather than an English population. Ideally, given the focus of the analysis, this evidence would be confirmed and validated among an English population to include in the model analysis presented here. However, the clinical guidelines regarding testing for CAD in England were clear, and a robust decision tree model was developed to reflect these pathways, employing published evidence on diagnostic accuracy and costs from peer-reviewed literature and routine U.K. sources. Similarly, economic modeling of the longer-term costs and outcomes associated with CAD is a well-researched area, and these studies were used to inform the analysis presented here. Therefore, the long-term model that was developed can be considered a robust analysis that considers the diagnostic outcomes produced by the decision tree model and extrapolates costs and outcomes over a 1-year time horizon based on initial diagnosis. These models can be used further as additional evidence associated with the CADScor System and related technologies become available.

Schnaubelt et al. (2022) noted that thoracic pain is one of the most frequent chief complaints at emergency departments (EDs); however, a respective work-up in cases without clear electrocardiographic signs is complex. Furthermore, after having ruled out acute coronary syndrome (ACS), patients are often left with an unclear etiology of their symptoms. Ultra-sensitive phonocardiography is already used to rule out stable CAD; however, its feasibility in an ED setting remains unknown. These researchers prospectively used ultra-sensitive phonocardiography via the CADScor System to measure hemodynamically stable patients with the chief complaint of chest pain during routine waiting times at a high-volume tertiary ED. A total of 101 patients (49% men; 94% Caucasian; age of 61 [51 to 71] years; body mass index [BMI] of 28.3 [24.2 to 31.6]) were enrolled. Patient workflow was not hindered, and no adverse events (AEs) were recorded. In 80% of cases, a score was successfully calculated, with 74% at the first attempt, 5% at the second attempt, and 1% at the third attempt. Feasibility was judged as 9.0 (± 1.8) by the patients and 8.9 (± 2.6) by the investigators on a 10-point Likert scale. The authors concluded that ultra-sensitive phonocardiography was found to be feasible in acute chest pain patients presenting to a tertiary ED; thus, the CAD score measured during routine waiting times could potentially serve as an additional tool in a diagnostic pathway for thoracic pain.

These investigators stated that further research appeared warranted in two major domains. First, a CAD score should be evaluated as a potential additional triage tool in chest pain patients – not only towards discrimination of ACS versus non-ACS, but rather concerning its potential power to provide additional information on patients’ CAD status right from the start of their evaluation. This especially applies to those patients who are unable to provide an estimation of their coronary artery status or those in whom anamnesis is not sufficiently possible (e.g., language barriers, dementia, etc.). Hints towards a CAD score potentially identifying sicker patients (e.g., lower SpO2, higher cfPWV, or higher NT-proBNP values) should be examined in future research as well. Second, and of higher priority, the CAD score performed very well in ruling out stable CAD. Applying this information, alternative diagnoses for the chief complaint of chest pain could become more probable after having already ruled out ACS via the known standard algorithm. Presently, many patients are discharged with provisional diagnoses of their complaints (e.g., musculoskeletal thoracic pain or radiating gastric pain). However, with CAD forming a major international health burden with growing incidences, ruling out ACS may in the future not be enough. In fact, after having ruled out ACS, rather than discharging patients with an unknown etiology of their symptoms, a further sub-classification into two groups appears thinkable: (i) stable CAD ruled out by CAD score, making a non-cardiac symptom etiology more probable; (ii) stable CAD not ruled out by CAD score, making further work-up at a cardiologist’s office or a cardiologic outpatient department a logical next step to be advised. With an increasing emphasis on shared decision-making, and expensive resources often being consumed with little effect on clinical outcome, this cost- and time-effective approach could aid in prioritizing care for individuals who are at particular risk for major adverse cardiac events (MACE). A recently shown prognostic potential of the CAD score towards all-cause mortality and future myocardial infarction emphasizes these thoughts. Moreover, these researchers stated that a study setting with a larger sample size and specially designed to examine the aforementioned points should be aimed for in future research endeavors.

Rasmussen et al. (2023) noted that validation studies of the 2019 European Society of Cardiology pre-test probability model (ESC-PTP) for CAD reported that 35% to 40% of patients have low pre-test probability (ESC-PTP 5% to less than 15%). Acoustic detection of coronary stenoses could potentially improve clinical likelihood stratification. These investigators examined the diagnostic performance of an acoustic-based CAD score and studied the re-classification potential of a dual likelihood strategy by the ESC-PTP and a CAD score. Consecutive patients (n = 1,683) with stable angina symptoms referred for coronary CTA underwent heart sound analyses by an acoustic CAD score device. All patients with 50% or greater luminal stenosis in any coronary segment at coronary CTA were referred for investigation with invasive coronary angiography (ICA) with FFR. A pre-defined CAD score cut-off of 20 or less was used to rule out obstructive CAD. A total of 439 patients (26%) had 50% or greater luminal stenosis on coronary CTA. The subsequent ICA with FFR showed obstructive CAD in 199 patients (11.8%). Using the 20 or less CAD score cut-off for obstructive CAD rule-out, sensitivity was 85.4% (95% CI: 79.7% to 90.0%), specificity was 40.4% (95% CI: 37.9% to 42.9%), PPV was 16.1% (95% CI: 13.9% to 18.5%), and NPV was 95.4% (95% CI: 93.4% to 96.9%) in all patients. Applying the cut-off in ESC-PTP of 5% to less than 15%, 316 patients (48%) were down-classified to very-low likelihood. The obstructive CAD prevalence was 3.5% in this group. The authors concluded that in a large contemporary cohort of patients with low CAD likelihood, the additional use of an acoustic rule-out device showed a clear potential to downgrade likelihood and could supplement current strategies for likelihood assessment to avoid unnecessary testing.

Baron et al. (2024) stated that studies have shown that 13% of low-risk patients presenting to the emergency department with chest pain receive further cardiac testing beyond a normal electrocardiogram and cardiac enzymes. The CADScor System is an FDA-cleared device that uses ultra-sensitive phonocardiography to assess the risk of significant CAD. These researchers examined the cost utility of the CADScor System for the diagnosis of CAD in a low-risk patient population (Baron et al., 2024; Baron et al., 2025). A two-part economic model was developed to compare 1-year costs and outcomes associated with the CADScor System versus other non-invasive cardiac tests (TreeAge Pro). Model inputs were derived from the literature, and costs for treatment strategies were calculated from average U.S. reimbursements associated with Current Procedural Terminology (CPT) and Medicare Severity Diagnosis Related Group codes. Values were reported in 2023 U.S. dollars (USD). Base case results showed that the CADScor System was economically dominant over other non-invasive cardiac tests without a substantial difference in adverse events (AEs). Economic results were consistent when CAD prevalence rates varied from 2% to 30%. The overall cost savings was estimated to be $104.7 to $185.4 million USD per 100,000 patients. The authors concluded that the use of the CADScor System in the evaluation of low-risk patients with chest pain may result in substantial cost savings. Moreover, these researchers stated that further investigations are needed to compare the long-term costs and outcomes of this strategy.

The study had a number of drawbacks, many of which are noted by the authors. First, the study used a decision analytic model and a short-term Markov model based on literature-derived inputs and average U.S. reimbursement data, rather than prospective, real-world clinical data. Second, the analysis focused on a 1-year time horizon, which does not address long-term clinical outcomes or costs associated with the CADScor System strategy. Third, the study population was restricted to low-risk patients presenting to the emergency department with chest pain, which limits the applicability of findings to higher-risk groups or to other clinical settings. Fourth, the model did not account for potential variations in provider adherence to protocols, patient preferences, or institutional differences in resource utilization, all of which can significantly influence real-world cost-effectiveness. Fifth, the study compares CADScor to four standard non-invasive tests (exercise SPECT, pharmacologic SPECT, coronary CTA, and exercise stress echocardiography) but does not include newer or more advanced modalities such as CT-derived fractional flow reserve (CT-FFR). Additionally, the study was funded by the manufacturer of the CADScor System. The American College of Cardiology and American Heart Association guidelines emphasize that most low-risk patients can be safely discharged after negative serial high-sensitivity troponins and a non-ischemic ECG, with selective use of additional testing (Gulati et al., 2021; Kontos et al., 2022). Therefore, the incremental value of the CADScor System in this population may be modest, and the study does not address how its use would integrate with or improve upon existing guideline-endorsed pathways.

Bjerking et al. (2025) stated that over-testing of low-risk patients with suspected chronic coronary syndrome (CCS) is widespread. The acoustic-based CAD score has superior rule-out capabilities when added to pre-test probability (PTP). FILTER-SCAD tested whether providing a CAD score and PTP to cardiologists was superior to PTP alone in limiting testing. At six Danish and Swedish outpatient clinics, patients with suspected new-onset CCS were randomized to either standard diagnostic examination (SDE) with PTP or SDE plus CAD score, with cardiologists provided with corresponding recommended diagnostic flowcharts. The primary endpoint was the cumulative number of diagnostic tests at one year, and the key safety endpoint was major adverse cardiac events (MACE). A total of 2,008 patients (46% men, median age of 63 years) were randomized from October 2019 to September 2022. When randomized to CAD score (n = 1,002), it was successfully measured in 94.5%. Overall, 13.5% had a PTP of 5% or less, and 39.5% had a CAD score of 20 or less. Testing was deferred in 22% of patients, with no differences in diagnostic tests between groups (p for superiority = 0.56). In the PTP of 5% or less subgroup, the proportion with deferred testing increased from 28% to 52% (p < 0.001). Overall MACE was 2.4 per 100 person-years. Non-inferiority regarding safety was established, with an absolute risk difference of 0.49% (95% CI: -1.96 to 0.97) (p for non-inferiority = 0.003). No differences were observed in angina-related health status or quality of life (QOL). The authors concluded that the implementation strategy of providing cardiologists with a CAD score alongside SDE did not reduce testing overall but indicated a possible role in patients with low CCS likelihood. These researchers stated that further strategies are needed to address resistance to modifying diagnostic pathways in this patient population. Furthermore, these investigators noted that the rule-out properties of the CAD score have been well documented previously, and the score has the potential to increase test deferral with a different implementation strategy. Further implementation studies may benefit from a more selective use of the CAD score as an add-on to PTP, potentially within a nurse-led framework. Additionally, a more rigorous implementation approach, possibly with institutional financial incentives, might improve test deferral rates. Further detailed analysis of the data could also identify specific subgroups where a selective PTP + CAD score strategy would be particularly relevant.

The authors stated that this study had several drawbacks. First, a CAD score of greater than 20 may inappropriately result in a referral to a diagnostic test. The CAD score displays favorable properties in ruling out but should not be used to rule in. However, this study, which added the CAD score to SDE, did not increase but rather reduced the number of diagnostic tests in the PTP of 5% or less group. Second, cross-overs among participants with PTP of greater than 5% who did not undergo diagnostic tests were not accounted for in the sample size calculation. This reduced the observed difference in the cumulative number of diagnostic tests and thus reduced the power of this study, increasing the likelihood of a type-II error. However, this trial was highly over-powered for the primary endpoint to reach power for the key secondary safety endpoint. Third, these researchers did not have data on redeemed prescriptions for guideline-directed medication. Fourth, the CAD score has not been tested in relation to micro-vascular angina/ischemia with non-obstructive CAD.

The CADence System

The CADence System is a non-invasive, radiation-free, handheld device used to aid clinicians in the assessment of sounds associated with clinically significant coronary artery obstruction, congestive heart failure, and heart valve abnormalities. It comprises a digital stethoscope used to record cardiac sounds, with integrated sensors used to record ECG. The device must be used in a clinical setting by trained personnel. The automated analysis of cardiac sounds by the CADence System is only significant when used in conjunction with physician oversight, as well as consideration of all other relevant patient information.

Thomas et al. (2018) noted that the non-invasive detection of turbulent coronary flow may enable the diagnosis of significant coronary artery disease (CAD) using novel sensor and analytic technology. Eligible patients (n = 1,013) with chest pain and CAD risk factors undergoing nuclear stress testing were studied using the CADence (AUM Cardiovascular Inc., Northfield, MN) acoustic detection (AD) system. The trial was designed to demonstrate non-inferiority of AD for diagnostic accuracy in detecting significant CAD as compared to an objective performance criterion (sensitivity 83% and specificity 80%, with 15% non-inferiority margins) for nuclear stress testing. AD analysis was blinded to clinical, core lab-adjudicated angiographic, and nuclear data. The presence of significant CAD was determined by computed tomographic (CCTA) or invasive angiography. A total of 1,013 subjects without prior coronary re-vascularization or Q-wave myocardial infarction (MI) were enrolled. Primary analysis was performed on subjects with complete angiographic and AD data (n = 763), including 111 subjects (15%) with severe CAD based on CCTA (n = 34) and invasive angiography (n = 77). The sensitivity and specificity of AD were 78% (p = 0.012 for non-inferiority) and 35% (p < 0.001 for failure to demonstrate non-inferiority), respectively. AD results had a high 91% negative predictive value (NPV) for the presence of significant CAD. The authors concluded that AD testing failed to demonstrate non-inferior diagnostic accuracy as compared to the historical performance of a nuclear stress OPC due to low specificity; however, AD sensitivity was non-inferior in detecting significant CAD, with a high NPV supporting a potential value in excluding CAD.

The CardioVision MS-2000

The CardioVision MS-2000 is an electronic BP device that calculates an "arterial stiffness index" ("ASI") related to how fast the BP falls as the air pressure is released from the BP cuff. Sharma et al. (2005) noted that several methods may be used to determine arterial stiffness. One method obtains an ASI from the vascular dynamics of oscillometric-derived brachial artery pressure. These researchers determined the test-retest repeatability of the CardioVision MS-2000. A total of 47 healthy hospital employees had 5 consecutive measurements of ASI measured after a 5- to 10-min period of rest and then repeated after an average of 146.8 days. Their mean age was 37 years and 71% were women. The meanASI was 39.6 +/- 9.7 and 37.2 +/- 10.5 mm Hg x10 (p = 0.22) for the 1st and 2nd time period, respectively. These researchers computed an intra-class correlation coefficient of 0.31 and 0.33 for the 1st and 2nd time periods, which is the measure of consistency or agreement of ASI values within cases. The intra-class correlation coefficient for systolic BP, diastolic BP, heart rate and ASI were 0.68 (p = 0.0001), 0.70 (p = 0.0001), 0.35 (p = 0.02) and 0.25 (p = 0.08), respectively. The authors concluded that the results of this study suggested poor test-retest repeatability if consecutive measurements are used. The intra-class correlation coefficient, however, could be improved by eliminating the highest and lowest value from a set of measurements.

According to Barrett (2010), "[a]rterial stiffness measurements can identify some people who are at risk for cardiovascular disease. However, they are not as reliable or cost-effective as standard blood pressure and cholesterol screenings."

The QuantaFlo System for Evaluation of Peripheral Arterial Disease

The QuantaFlo System (Semler Scientific Inc., San Jose, CA) is designed for a quick (less than 5 minutes) assessment of peripheral artery disease (PAD). It uses a digital sensor placed on the fingers and toes to record blood flow measurements. There is a clinical trial on “Digital ankle-brachial index (ABI) as a screening tool in detecting peripheral arterial disease” (last updated December 13, 2017). The description of this trial notes that “Peripheral artery disease (PAD) affects 8 to 18 million in the U.S. and is an economic burden, currently estimated to be greater than cancer and heart disease. Older age (greater than 65 years), smoking, diabetes, and kidney diseases are some risk factors associated with PAD and are known to have increased morbidity and mortality. Early detection is critical for mitigating PAD progression. Ankle-brachial index (ABI) testing is recommended by the U.S. Preventive Services Task Force as an affordable and effective screening tool for evaluating PAD risk. QuantaFlo (Semler Scientific, Inc., San Jose, CA) is a novel, non-invasive, 510K FDA-approved digital device that is used as a screening tool to measure ABI of patients at risk of PAD. This single-center prospective clinical trial will evaluate the sensitivity and specificity of digital ABI in detecting PAD using color Doppler ultrasound and “gold standard” angiography as reference. Specifically, in patients undergoing dialysis who cannot undergo ABI using pressure cuffs, dialysis grafts, or fistulae in the arms, we will evaluate the value of digital ABI in detecting PAD.”

Schaefer and colleagues (2015) stated that PAD affects 8 to 18 million Americans. Under-diagnosis of the disease remains a clinical dilemma. Doppler ABI with pressure cuffs is the most common initial test performed when suspecting PAD. Since 2011, vascular specialists and primary care physicians have used PAD testing devices such as the FloChec System and, more recently, the QuantaFlo System, a blood volume waveform visualization and evaluation tool, in their evaluation of lower extremity PAD. This study compared the accuracy of the QuantaFlo System to ABI, using primarily duplex ultrasound (Duplex) to confirm the presence or absence of PAD. The PAD testing device, Doppler ABI, and Duplex/Angiogram test data were prospectively collected under an Institutional Review Board (IRB)-approved, multi-center, single-arm, post-market study. Test results for each limb and each technology were analyzed and compared by an imaging core lab. The core lab assigned a severity score to each limb upon interpretation. These data were used to design the QuantaFlo algorithm to optimize accuracy using a cross-validation trial methodology. QuantaFlo was then prospectively validated in a second subject cohort. A total of 360 limbs from 180 patients were evaluable with PAD testing results, ABI, and definitive imaging in the first cohort. Cross-validation trial methodology used test data from 80% of these limbs selected by a random process applied 100 times to create 100 different algorithms. Each algorithm was, in turn, evaluated on the entire 360-limb database. Mean values from the 100 trials achieved an accuracy of 83.6%, sensitivity of 81.3%, and specificity of 90.0% to detect flow obstruction. Corresponding Doppler ABI results on 360 limbs were 75.6% accuracy, 60.6% sensitivity, and 92.8% specificity. Then, the best-performing algorithm was incorporated into QuantaFlo, and a prospective clinical validation on 30 additional limbs from 15 patients demonstrated an accuracy of 89.7%, sensitivity of 89.5%, and specificity of 90.0%. The authors concluded that the QuantaFlo method can detect PAD with greater accuracy and sensitivity than Doppler ABI and can provide a disease severity interpretation. The authors concluded that these results suggested the clinical utility of QuantaFlo in the diagnosis of PAD in the primary care setting.

This study had several drawbacks. It enrolled a relatively small patient cohort without strict inclusion and exclusion screening criteria, and data regarding patient recruitment with respect to the number of consecutive patients not enrolled were not formally tracked. A larger cohort size may uncover clinically significant differences in accuracy, sensitivity, or specificity, and allow for different population subgroup analyses to further assess clinical utility. The study was acute in nature without serial follow-up to assess any changes to the test findings over time. Additionally, the participating clinical sites were mixed physician practices enrolling a more diseased population that may not be representative of the general population, as in terms of the percentage of limbs with flow obstruction (OBS). Having all patients undergo contrast angiography would be more definitive than the current study, which relied predominantly on Duplex results as detection for PAD. Duplex scans have been reported to be unreliable in visualizing arteries adequately in 20% of cases, predominantly below the knee. However, it was not deemed ethically appropriate unless clinically indicated and medically necessary for patients to have an invasive diagnostic procedure such as contrast angiography.

In a cross-sectional study, Lewis et al. (2016) aimed to individually and cumulatively compare the sensitivity and specificity of the ABI and pulse volume waveform (PVW) analysis recorded by the same automated device, with the presence or absence of PAD being verified by ultrasound Duplex scan. Patients (n = 205) referred for lower limb arterial assessment underwent ABI measurement and PVW recording using volume plethysmography, followed by ultrasound Duplex scan. The presence of PAD was recorded if ABI was less than 0.9; PVW was graded as 2, 3, or 4; or if hemodynamically significant stenosis of greater than 50% was evident with ultrasound Duplex scan. The outcome measure was agreement between the measured ABI and interpretation of PVW for PAD diagnosis, using ultrasound Duplex scan as the reference standard. Sensitivity of ABI was 79%, specificity 91%, and overall accuracy 88%; PVW sensitivity was 97%, specificity 81%, and overall accuracy 85%. The combined sensitivity of ABI and PVW was 100%, specificity 76%, and overall accuracy 85%. The authors concluded that these findings suggested that this device could be utilized within the primary care environment to reduce the number of unnecessary referrals to secondary care, with concomitant cost savings, reduced patient inconvenience, and prioritization of urgent PAD cases. Moreover, they stated that future research should investigate the ease of use of PVW analysis, along with the cost and training required to achieve reliable results. The main drawback of this study was that it evaluated the utility of subjective PVW analysis when undertaken by a single clinician who had experience and a personal interest in the procedure; thus, findings were not representative of how less-experienced, non-specialist clinicians would perform.

Toll-Like Receptor 4 (TLR4) Asp299Gly (rs4986790) Polymorphism

Chen and colleagues (2015) stated that previous studies have shown conflicting results on the association between toll-like receptor 4 (TLR4) Asp299Gly (rs4986790) polymorphism and CAD. In a meta-analysis, these investigators evaluated the influence of TLR4 Asp299Gly polymorphism on CAD risk, CRP level and the number of stenotic coronary arteries, as well as to examine if G allele carriers would benefit more from statin treatment. PubMed, EMBASE, and CNKI databases were searched until May 2015. All the statistical tests were performed using R version 3.1.2. Odds ratio and 95% CI were used to assess the association between TLR4 Asp299Gly polymorphism and CAD risk, the number of stenotic vessels, and the incidence of cardiovascular events according to statin-treated patients. Weighted mean difference (WMD) was calculated for the association between Asp299Gly and CRP level. Overall, 12 case-control studies with 10,258 cases and 5,891 controls were included, and no association of TLR4Asp299Gly polymorphism with CAD was found (G allele versus A allele: OR = 0.97, 95% CI: 0.81 to 1.17, p = 0.75; AA versus GG + AG: OR = 0.97, 95% CI: 0.80 to 1.18], p = 0.76; GG versus AG + AA: OR = 1.08, 95% CI: 0.57 to 2.02, p = 0.82; AG versus AA + GG: OR = 1.03, 95% CI: 0.85 to 1.25, p = 0.74). Also, no association was noted between Asp299Gly and CRP level (WMD = -0.10, 95% CI: -0.62 to 0.41, p = 0.69). Furthermore, no synergistic effect of statin and 299Gly was reported (Statin_AA versus Statin_AG/GG: OR = 1.12, 95% CI: 0.41 to 3.09, p = 0.82). The authors concluded that the findings of this meta-analysis suggested no association of TLR4 Asp299Gly polymorphism with CAD and CRP level. It is further indicated that the G allele carriers may not benefit more from statin treatment. Moreover, they stated that further studies should include large sample size and high-quality literature to understand this issue in depth.

Total Cholesterol Content in Erythrocyte Membranes

Plaque rupture in acute coronary syndrome (ACS) depends at least partly on the volume of the necrotic lipid core. Histopathological studies have suggested that cholesterol transported by erythrocytes and deposited into the necrotic core of atheromatous plaques contributes to lipid core growth. Tziakas and colleagues (2007) hypothesized that cholesterol content is increased in the circulating erythrocytes of patients with ACS and may be a marker of clinical instability. Thus, these researchers investigated whether cholesterol content differs in erythrocyte membranes of patients presenting with ACS compared to patients with chronic stable angina (CSA). Consecutive angina patients were prospectively assessed; 120 had CSA (83 men, age of 64 ± 11 years) and 92 had ACS (67 men, age of 66 ± 11 years). Total cholesterol content in erythrocyte membranes (CEM) was measured using an enzymatic assay, and protein content was assessed by the Bradford method. The CEM (median and inter-quartile range) was higher (p < 0.001) in ACS patients (184 µg/mg; range of 130.4 to 260.4 µg/mg) compared with CSA patients (81.1 µg/mg; range of 53.9 to 109.1 µg/mg) (analysis of covariance). Total plasma cholesterol concentrations did not correlate with CEM levels (r = -0.046, p = 0.628). The authors concluded that these findings showed for the first time that CEM is significantly higher in patients with ACS compared with CSA patients. They suggested a potential role of CEM as a marker of atheromatous plaque growth and vulnerability. The authors stated that further studies are needed to elucidate the role of CEM as both a marker of plaque instability and a pathogenic mechanism of rapid CAD progression. In an editorial that accompanied the aforementioned article, Arbustini (2009) noted that “[a]lthough widely investigated either as total cholesterol content or phospholipid/cholesterol ratio, CEM did not find relevant clinical applications.”

Tziakas and associates (2009) evaluated the effect of statin therapy on CEM levels (a novel marker of CAD instability) during a 1-year follow-up in CAD patients. A total of 212 consecutive eligible patients (158 men, mean age of 62 ± 10 years) undergoing diagnostic coronary angiography for the assessment of angina pectoris were assessed. The study population comprised 84 CSA patients and 128 ACS patients. All study participants were commenced on statin treatment in equipotent doses and were followed for up to 1 year (at 1, 3, 6, and 12 months). Repeated measurements analysis of variance after appropriate adjustment showed a significant decrease (p < 0.001) in CEM content during follow-up. Levels of CEM were decreasing at each time point (1 month: 100 µg/mg; 95% CI: 94.3 to 105.6, 3 months: 78.1 µg/mg; 95% CI: 73.2 to 83, 6 months: 67.2 µg/mg; 95% CI: 63.1 to 71.2; 1 year: 45.3 µg/mg; 95% CI: 42.2 to 48.3) compared to admission (112.1 µg/mg; 95% CI: 105.9 to 118.3) and to all previous measurements. The authors concluded that these findings showed that the use of statins is associated with a reduction in CEM, an emerging marker of clinical instability and plaque vulnerability in CAD patients. The pleiotropic effects of statins at the cell membrane level represent a promising novel direction for research in CAD.

Trimethylamine-N-Oxide (TMAO)

Schiattarella et al. (2017) noted that the gut microbiota-derived metabolite trimethylamine-N-oxide (TMAO) is emerging as a new potentially important cause of increased cardiovascular risk. In a meta-analysis, these researchers estimated and quantified the association between TMAO plasma levels, mortality, and major adverse cardio and cerebrovascular events (MACCE). Medline, ISI Web of Science, and SCOPUS databases were searched for ad-hoc studies published up to April 2017. Associations between TMAO plasma levels, all-cause mortality (primary outcome), and MACCE (secondary outcome) were systematically addressed. A total of 17 clinical studies were included in the analytic synthesis, enrolling 26,167 subjects. The mean follow-up in this study population was 4.3 ± 1.5 years. High TMAO plasma levels were associated with increased incidence of all-cause mortality [14 studies for 16 cohorts enrolling 15,662 subjects, hazard ratio (HR): 1.91; 95% confidence interval (CI): 1.40 to 2.61, p < 0.0001, I² = 94%] and MACCE (5 studies for 6 cohorts enrolling 13,944 subjects, HR: 1.67, 95% CI: 1.33 to 2.11, p < 0.00001, I² = 46%). Dose-response meta-analysis revealed that the relative risk (RR) for all-cause mortality increased by 7.6% per each 10 μmol/L increment of TMAO [summary RR: 1.07, 95% CI: 1.04 to 1.11, p < 0.0001; based on 7 studies]. The association of TMAO and mortality persisted in all examined subgroups and across all subject populations. The authors concluded that this was the first systematic review and meta-analysis demonstrating the positive dose-dependent association between TMAO plasma levels and increased cardiovascular risk and mortality.

Haghikia et al. (2018) noted that gut microbiota-dependent metabolites, in particular TMAO, have recently been reported to promote atherosclerosis and thrombosis. These investigators examined for the first time the relation of TMAO and the risk of incident cardiovascular events in patients with recent first-ever ischemic stroke in two independent prospective cohorts. Moreover, the link between TMAO and pro-inflammatory monocytes as a potential contributing factor for cardiovascular risk in stroke patients was studied. In the first study (n = 78), higher TMAO plasma levels were linked with an increased risk of incident cardiovascular events including myocardial infarction (MI), recurrent stroke, and cardiovascular death (4th quartile versus 1st quartile; HR, 2.31; 95% CI: 1.25 to 4.23; p < 0.01). In the second independent validation cohort (n = 593), high TMAO levels again heralded a marked increased risk of adverse cardiovascular events (4th quartile versus 1st quartile; HR, 5.0; 95% CI: 1.7 to 14.8; p < 0.01), and also after adjustments for cardiovascular risk factors including hypertension, diabetes mellitus, LDL (low-density lipoprotein) cholesterol, and estimated glomerular filtration rate (GFR; HR, 3.3; 95% CI: 1.2 to 10.9; p = 0.04). A significant correlation was also found between TMAO levels and the percentage of pro-inflammatory intermediate CD14++CD16+ monocytes (r = 0.70; p < 0.01). Moreover, in mice fed a diet enriched with choline to increase TMAO synthesis, levels of pro-inflammatory murine Ly6Chigh monocytes were higher than in the chow-fed control group (choline: 9.2 ± 0.5 × 10³ per ml versus control: 6.5 ± 0.5 × 10³ per ml; p < 0.01). This increase was abolished in mice with depleted gut microbiota (choline + antibiotics: 5.4 ± 0.7 × 10³ per ml; p < 0.001 versus choline). The authors concluded that the findings of this study suggested a novel link between TMAO and pro-inflammatory monocytes, as well as with an adverse cardiovascular prognosis in patients after stroke. These findings contribute to the understanding of a potential role of gut microbiota–related mechanisms in patients with cerebrovascular disease progression and encourage further investigation of therapeutic strategies aimed at fostering a reduction in systemic levels of the gut microbial metabolite TMAO.

Tan et al. (2019) noted that TMAO is reported to promote the pathogenesis of atherosclerosis and be associated with cardiovascular events risk. It is unknown whether plasma TMAO is associated with plaque morphology in patients with acute MI. These investigators examined the relationship between the culprit plaque morphology and plasma TMAO concentration in patients with ST-segment-elevation MI (STEMI). A prospective series of 211 patients with ST-segment-elevation MI who underwent pre-intervention optical coherence tomography (OCT) examination for the culprit lesion were enrolled; 77 and 69 patients were categorized as plaque rupture and plaque erosion, respectively. Plasma TMAO levels, detected using stable isotope dilution liquid chromatography tandem mass spectrometry, were significantly higher in patients with plaque rupture than in those with plaque erosion (3.33 μM; interquartile range [IQR]: 2.48 to 4.57 versus 1.21 μM; IQR: 0.86 to 1.91; p < 0.001). After adjustments for traditional risk factors, elevated TMAO levels remained independently correlated with plaque rupture (adjusted OR: 4.06, 95% CI: 2.38 to 6.91; p < 0.001). The area under the receiver operating characteristic (ROC) curve for plaque rupture versus plaque erosion was 0.89. At a cut-off level of 1.95 μM, TMAO had a sensitivity of 88.3% and specificity of 76.8% in discriminating plaque rupture from plaque erosion. The authors concluded that high levels of plasma TMAO independently correlated with plaque rupture in patients with ST-segment-elevation MI. Moreover, these researchers stated that TMAO might be a useful biomarker for plaque rupture to improve risk stratification and management in patients with ST-segment-elevation MI; TMAO has the potential to serve as a novel rule-out biomarker for plaque rupture to help improve risk stratification and management in patients with STEMI.

The authors stated that this study had several potential limitations. First, these investigators examined patients with STEMI who underwent OCT examination before the percutaneous coronary intervention (PCI) procedure. Patients with cardiac shock, congestive heart failure (CHF), a history of coronary artery bypass graft (CABG), left main diseases, extremely tortuous or heavily calcified vessels, or chronic total occlusion were not enrolled in this study. Thus, selection bias cannot be excluded. Second, the underlying plaque morphology of the culprit lesion could have been obscured by a residual thrombus in some cases. All subjects enrolled in this study presented with STEMI resulting from thrombotic occlusion of coronary arteries. Although these researchers excluded cases with a massive thrombus after sufficient thrombus aspiration, some ruptured plaques might have been misclassified as eroded plaques because of the invisibility of the ruptured site covered by the thrombus. Third, a second independent cohort to validate the predictive value of TMAO in discriminating between plaque morphologies would significantly strengthen these findings, which the authors aim to do in future studies. Finally, the causal relationship between plasma TMAO levels and plaque rupture remains unclear in this study; a future study should make efforts to clarify this causal association.

Zhong et al. (2019) stated that coronary heart disease (CHD), one of the leading causes of death in the world, is a complex metabolic disorder due to genetic and environmental interactions. The potential mechanisms and diagnostic biomarkers for different types of CHD remain unclear. Metabolomics is increasingly considered to be a promising technology with the potential to identify metabolomic features in an attempt to distinguish the different stages of CHD. These investigators examined serum metabolite profiling between CHD patients and normal coronary artery (NCA) subjects and identified metabolic biomarkers associated with CHD progression in an ethnic Hakka population in southern China. Using a novel targeted metabolomics approach, these researchers explored the metabolic characteristics of CHD patients. Blood samples from 302 patients with CHD and 59 NCA subjects were collected and analyzed using targeted liquid chromatography coupled with tandem mass spectrometry (LC-MS). A total of 361 blood samples were determined using targeted LC-MS. Plasma concentrations for TMAO, choline, creatinine, and carnitine were significantly higher in patients with CHD compared to the NCA cohort. Further, these researchers observed that the concentration of the four metabolites was higher than that of the NCA group in any group of CHD, which included acute MI (AMI), unstable angina (UA), and stable angina (SA). In addition, the diagnostic model was constructed based on the metabolites identified, and the ROC curve of the NCA subjects and CHD patients was performed. For choline and creatinine, the AUCs ranged from 0.720 to 0.733. For TMAO and carnitine, the AUCs ranged from 0.568 to 0.600. The authors concluded that the current study showed the distribution of four metabolites between CHD patients and NCA subjects. These researchers stated that metabolomics analysis may yield novel predictive biomarkers that will potentially provide value for clinical diagnosis of CHD. Moreover, they stated that these preliminary findings need further confirmation studies to clarify their specific mechanisms in CHD.

The authors stated that this study had several drawbacks. First, these researchers enrolled patients based on clinical diagnosis; however, the number of patients enrolled in the study was relatively small due to limited funding, and a follow-up study is ongoing. Second, these metabolites were only tested at one time point, and these researchers were unable to assess the prognostic role of metabolites in CHD patients over time. Finally, these results were based on the Hakka population, which may not apply to individuals of different demographics.

Ge et al. (2020) noted that the gut microbial metabolite TMAO is increasingly regarded as a novel risk factor for cardiovascular events and mortality; however, little is known regarding the association between TMAO and hypertension. In a systematic review and meta-analysis, these investigators examined the relation between the circulating TMAO concentration and hypertension prevalence. The PubMed, Cochrane Library, and Embase databases were systematically searched up to June 17, 2018. Studies recording the hypertension prevalence in members of a given population and their circulating TMAO concentrations were included. A total of 8 studies with 11,750 individuals and 6,176 hypertensive cases were included in the analytic synthesis. Compared with low circulating TMAO concentrations, high TMAO concentrations were correlated with a higher prevalence of hypertension (RR: 1.12; 95% CI: 1.06 to 1.17; p < 0.0001; I² = 64%; p-heterogeneity = 0.007; random-effects model). Consistent results were obtained in all examined subgroups as well as in the sensitivity analysis. The RR for hypertension prevalence increased by 9% per 5-μmol/L increment (RR: 1.09; 95% CI: 1.05 to 1.14; p < 0.0001) and 20% per 10-μmol/L increment of circulating TMAO concentration (RR: 1.20; 95% CI: 1.11 to 1.30; p < 0.0001) according to the dose-response meta-analysis. The authors stated that to their knowledge, this was the first systematic review and meta-analysis demonstrating a significant positive dose-dependent association between circulating TMAO concentrations and hypertension risk. They concluded that the findings of this meta-analysis suggested a significant positive dose-dependent association between the circulating TMAO concentration and hypertension prevalence regardless of different stratifications; further studies are needed to examine the causality of the association and determine the value of modulation of TMAO concentrations in hypertension prognosis.

The authors stated that this study had several drawbacks. Due to limited reports, all studies included in the present meta-analysis enrolled subjects with a high cardiovascular risk, and most of the subjects were from the U.S. These facts indicated that the current meta-analysis could have potential bias. Further examinations are needed to reveal the relation between TMAO and hypertension risk in more comprehensive populations with long-term follow-ups. Moreover, several important values, such as dietary intake, which might influence the production of TMAO, and the long-term concentrations of TMAO, which are more appropriate to confirm this relation than just a single measurement, were not available in the included studies. While hypertension history was validated in the included studies, the BP values of the subjects at the time of sample collection were unavailable in the included studies; therefore, these researchers could not examine the relation between TMAO concentrations and the severity of hypertension. However, the consistent results derived from multiple stratification analyses (including potential confounders) further authenticated the close correlation between TMAO and hypertension. These researchers stated that further studies are needed to confirm such an association given these limitations and examine the association with other clinically significant endpoints such as incidence and severity of hypertension in the general population.

Guasti et al. (2021) noted that unmasking the residual cardiovascular risk is a major research challenge in the attempt to reduce CVD morbidity and mortality. Mounting evidence suggests that a high circulating level of TMAO is a new potential CVD risk factor. These researchers carried out a systematic review of the published studies to clarify the association between circulating high levels of TMAO and cardiovascular events. Studies evaluating the association between TMAO and CVD events were searched by electronic databases up to December 2018. Pooled results were expressed as RR with 95% CI. A total of 3 studies with 923 patients at high/very high CVD risk were included in this analysis. Overall, a high TMAO level was associated with both major adverse cardiovascular events (RR = 2.05; 95% CI: 1.61 to 2.61) and all-cause mortality (RR = 3.42; 95% CI: 2.27 to 5.15). The authors concluded that these findings support a role of high TMAO levels in predicting CVD events. These investigators stated that high levels of TMAO may be a new CVD risk factor, potentially useful to better plan personalized CVD prevention strategies.

Trimethylaminuria (Fish Odor Syndrome)

Zhao et al. (2015) stated that there is a growing interest in analyzing choline, betaine, and their gut microbial metabolites, including TMA and TMAO, in body fluids due to the high relevance of these compounds for human health and diseases. A stable isotope dilution (SID)-LC-MRM-MS assay was developed for the simultaneous determination of choline, betaine, TMA, TMAO, and creatinine in human plasma and urine. The assay was validated using quality control (QC) plasma samples, spiked at low, medium, and high levels. Freeze-thaw stability was also evaluated. The use of this assay for urine was demonstrated using a nutritional clinical study on the effect of various egg doses on TMAO production in humans. This assay has a wide dynamic range (R² > 0.994) for all the analytes (choline: 0.122 to 250 μM; betaine: 0.488 to 1,000 μM; TMA: 0.244 to 250 μM; TMAO: 0.061 to 62.5 μM; and creatinine: 0.977 to 2,000 μM). High intra- and inter-day precision (CV less than 6%) and high accuracy (less than 15% error) were observed from the QC plasma samples. The assay was reliable for samples undergoing multiple freeze-thaw cycles (tested up to 8 cycles). The assay also worked for urine samples, as demonstrated by a clinical study in which these researchers observed a significant positive linear response to various egg doses for urinary concentrations of all the analytes except creatinine. The authors concluded that a rapid SID-LC-MRM-MS assay for simultaneous quantification of choline, betaine, TMA, TMAO, and creatinine has been developed and validated, and was expected to find wide application in nutrition and cardiovascular studies, as well as in the diagnosis and management of trimethylaminuria (TMAU).

Bouchemal et al. (2019) noted that TMAU is a metabolic disorder characterized by the excessive excretion of the malodorous compound TMA. The diagnosis of TMAU is challenging because this disorder is situated at the boundary between biochemistry and psychiatry. These investigators used nuclear magnetic resonance (NMR) spectroscopy to examine TMAU in 13 patients. They also sequenced the FMO3 gene in 11 of these patients, and treatment with vitamin B2 was prescribed. Two patients (aged 3 and 9 years at the initial consultation) had especially unpleasant body odor, as assessed by their parents and the attending physicians. The presence of high urine TMA levels confirmed a metabolic disorder. The two (unrelated) children carried compound heterozygous variants in the FMO3 gene. In both cases, vitamin B2 administration decreased TMA excretion and reduced body odor. The 11 adults complained of an unpleasant body odor; however, the physicians did not confirm this. In all adult patients, the urine TMA level was within the normal range reported for control (non-affected) subjects, although two of the patients displayed an abnormally high proportion of oxidized TMA, and seven of the nine tested adult patients had a hypomorphic variant of the FMO3 gene; the variant was found in the homozygous state, in the heterozygous state, or combined with another hypomorphic variant. All 11 adults presented a particular psychological or psychiatric phenotype, with a subjective perception of unpleasant odor. The authors presented the clinical and biochemical data of patients complaining of unpleasant body odor. Contrary to adult patients, the two children exhibited all criteria of recessively inherited TMAU, suspected by parents in infancy. Vitamin B2 treatment markedly improved the unpleasant body odor and the ratio of TMA/Cr versus TMAO/Cr in the urine in the children. Other patients presented a particular psychological or psychiatric phenotype.

A GeneReviews on “Primary trimethylaminuria” (Phillips and Shephard, 2020) noted that the diagnosis of primary TMAU is established in a proband who excretes (under normal dietary conditions) in the urine more than 10% of total TMA as the free amine and has bi-allelic (homozygous or compound heterozygous) known loss-of-function pathogenic variants in FMO3 on molecular genetic testing.

Veyrat-Durebex et al. (2021) stated that TMAU, also known as fish odor syndrome (FOS), is a condition characterized by the presence of high concentrations of TMA in urine, sweat, and expired air of affected patients. Diagnosis of this benign but unpleasant disease is mainly based on clinical presentation and assessment of TMA and its metabolite, TMAO, concentrations in the urine of patients. These investigators described the validation of an analytical method for measurement of TMA and TMAO in urine using NMR according to the specifications of the ISO 15189 norm. They employed a fast validation protocol based on the exactitude profile method, enabling the determination of accuracy, intra- and inter-day precision from a limited number of samples. The linearity was established from 2.5 to 100 mg/L for TMA measurement and from 10 to 1,000 mg/L for TMAO measurement, with good analytical performances, namely, accuracy and intra- and inter-day precision. These researchers also reported a case that was diagnosed for FOS using this method. The authors concluded that this method validation ensured the robustness of NMR in routine use for the diagnosis of trimethylaminuria, as part of the reference center for inherited metabolic diseases at the Tours hospital.

Nicholas et al. (2022) noted that FOS is a rare metabolic disorder that manifests as "rotten fish" body odor and is caused by the excretion of TMA in body fluids. This disease can have a negative impact on the social life of affected patients. These investigators reported on the cases of two female patients complaining about unpleasant body odor. The diagnosis of FOS was confirmed by the demonstration of TMAU by NMR spectroscopy and by molecular analysis of the FMO3 gene. A restrictive choline diet combined with digestive decontamination reduced odor symptoms and improved the social life of these two patients. The authors concluded that FOS is a rare and unrecognized disease that can affect the quality of life (QOL) of affected persons; moreover, following laboratory diagnosis, treatment is often effective.

TruD MDS ASCVD Test

The TruD MDS ASCVD, by TruDiagnostic, is a blood-based test that uses DNA methylation analysis and an algorithmic model to assess atherosclerotic cardiovascular disease (ASCVD) risk. It analyzes more than 20,000 DNA methylation sites from a whole‑blood sample and reports a binary result (positive or negative) for ASCVD risk, rather than a traditional lipid or numeric risk score.

There is insufficient evidence in peer-reviewed published literature to support the efficacy of this test.

Other Risk Factors

Non-traditional risk markers have been shown to have statistically significant independent associations with incident coronary heart disease (CHD), but Folsom et al. (2006) found that they do very little to improve the predictive value of traditional risk-factor assessment. Using a series of case-cohort studies, the prospective Atherosclerosis Risk in Communities (ARIC) Study assessed the association of 19 novel risk markers with incident CHD in 15,792 adults followed up since 1987 to 1989 (Folsom et al., 2006). Novel markers included measures of inflammation (C-reactive protein [CRP], lipoprotein-associated phospholipase A2 [LpPLA2], interleukin 6, D-dimer), endothelial function (intra-cellular adhesion molecule-1), fibrin formation (plasminogen activator inhibitor-1, tissue inhibitor of metalloproteinase-1, soluble thrombomodulin, E-selectin), fibrinolysis (matrix metalloproteinase-1, plasminogen, tissue plasminogen activator), B vitamins (leptin, homocysteine, folate, vitamin B6), and antibodies to infectious agents (Chlamydia IgG positivity, cytomegalovirus antibody, herpes simplex virus-1 antibody). Change in the area under the curve (AUC) was used to assess the additional contribution of novel risk markers to CHD prediction beyond that of traditional risk factors. The investigators found that the basic risk factor model, which included traditional risk factors (age, race, sex, total and high-density lipoprotein cholesterol levels, systolic blood pressure, anti-hypertensive medication use, smoking status, and diabetes), predicted coronary heart disease well, as evidenced by an AUC of approximately 0.8. The other risk factors did not add significantly to the AUC. Among the novel risk factors, the greatest contribution to AUC was CRP, with an increase in AUC of 0.003. The authors concluded that routine measurement of these novel markers is not warranted for risk assessment. These findings also reinforced the utility of major, modifiable risk factor assessment to identify individuals at risk for CHD for preventive action. The accompanying editorialists explained that these novel markers should not be used for basic risk factor assessment because they do not meaningfully reduce misclassification by traditional risk scoring (Lloyd-Jones and Tian, 2006).

Lloyd-Jones and Tian (2006) explained that the statistical association of a novel marker with cardiovascular disease (CVD) that is "independent" of traditional risk factors is necessary but far from sufficient to demonstrate utility in the prediction of CVD. Rather, predictive utility requires demonstration of improvement in test characteristics, predictive values, AUCs (or C statistics), or likelihood ratios at given cutoff values when a novel marker is added to the existing risk score. They explained that, from a decision-making point of view, the "ultimate" measure of a novel screening test is its ability to reclassify individuals. In other words, a new marker is useful only when it corrects a substantial portion of misclassification by the old test (the existing risk score).

The American Heart Association's (AHA) scientific statement on "Nontraditional risk factors and biomarkers for cardiovascular disease: Mechanistic, research, and clinical considerations for youth" (Balagopal et al., 2011) listed novel biomarkers for CVD in children, including adiponectin, leptin, peroxisome proliferator-activated receptor, retinol binding protein 4, and resistin. Balagopal et al. (2011) also stated that "Numerous other products are secreted by adipocytes... Other discoveries include visfatin, touted to play an important role in the regulation of glycemic homeostasis, and apelin, the function of which appears to be related to the regulation of nutritional intake. The role of these and other adipokines in CVD and type 2 diabetes mellitus (T2DM) remains unclear." Regarding cytokines, the AHA stated: "Further research verifying these findings [cytokines such as IL-6 and TNF-α] and better evaluating non-CRP inflammatory processes as they relate to CVD and CVD risk factors in childhood will be valuable."

In a prospective population-based study, Kavousi et al. (2012) evaluated whether newer risk markers for CHD risk prediction and stratification improve Framingham risk score (FRS) predictions. A total of 5,933 asymptomatic, community-dwelling participants (mean age of 69.1 years [SD, 8.5]) were included in this analysis. Traditional CHD risk factors used in the FRS (age, sex, systolic blood pressure, treatment of hypertension, total and high-density lipoprotein cholesterol levels, smoking, and diabetes) and newer CHD risk factors (N-terminal fragment of prohormone B-type natriuretic peptide levels, von Willebrand factor antigen levels, fibrinogen levels, chronic kidney disease, leukocyte count, CRP levels, homocysteine [Hcy] levels, uric acid levels, coronary artery calcium [CAC] scores, carotid intima-media thickness, peripheral arterial disease, and pulse wave velocity) were assessed. Adding CAC scores to the FRS improved the accuracy of risk predictions (c-statistic increase, 0.05 [95% CI: 0.02 to 0.06]; net reclassification index, 19.3% overall [39.3% in those at intermediate risk, by FRS]). Levels of N-terminal fragment of prohormone B-type natriuretic peptide also improved risk predictions but to a lesser extent (c-statistic increase, 0.02 [CI: 0.01 to 0.04]; net reclassification index, 7.6% overall [33.0% in those at intermediate risk, by FRS]). Improvements in predictions with other newer markers were marginal. The authors concluded that among 12 CHD risk markers, improvements in FRS predictions were most statistically and clinically significant with the addition of CAC scores. Moreover, they stated that further investigation is needed to assess whether risk refinements using CAC scores lead to a meaningful change in clinical outcomes.

Guidelines from the American Association of Clinical Endocrinology (2012) do not recommend the routine measurement of plasminogen activator inhibitor 1 and uric acid because the benefit in doing so is unclear.

Zampetaki et al. (2012) noted that circulating microRNAs (miRNAs) are emerging as potential biomarkers. These researchers examined the association between baseline levels of miRNAs and incident myocardial infarction (MI) in the Bruneck cohort and determined their cellular origin. A total of 19 candidate miRNAs were quantified by real-time polymerase chain reactions in 820 participants. In multivariable Cox regression analysis, 3 miRNAs were consistently and significantly related to incident MI: miR-126 showed a positive association (multivariable HR: 2.69 [95% CI: 1.45 to 5.01], p = 0.002), whereas miR-223 and miR-197 were inversely associated with disease risk (multivariable HR: 0.47 [95% CI: 0.29 to 0.75], p = 0.002, and 0.56 [95% CI: 0.32 to 0.96], p = 0.036). To determine their cellular origin, healthy volunteers underwent limb ischemia-reperfusion generated by thigh-cuff inflation, and plasma miRNA changes were analyzed at baseline, 10 minutes, 1 hour, 5 hours, 2 days, and 7 days. Computational analysis using the temporal clustering by affinity propagation algorithm identified 6 distinct miRNA clusters. One cluster included all miRNAs associated with the risk of future MI. It was characterized by early (1 hour) and sustained activation (7 days) post-ischemia-reperfusion injury and consisted of miRNAs predominantly expressed in platelets. The authors concluded that in subjects with subsequent MI, differential co-expression patterns of circulating miRNAs occur around endothelium-enriched miR-126, with platelets being a major contributor to this miRNA signature. The authors stated that these findings await confirmation in independent cohorts. Also, causality cannot be inferred from associations of biomarkers in population studies. Furthermore, they stated that future studies will need to address whether endothelial and platelet miRNAs can serve as novel biomarkers for clinical decision-making.

In an editorial that accompanied the afore-mention study, Engelhardt (2012) stated that the limitations of the study included:

despite a considerable size of the study cohort, the total number of incident cases of MI is relatively low,
only a pre-defined panel of miRNAs was analyzed in all participants. This panel was initially chosen based on pattern analysis of miRNA expression in a smaller sub-population and then applied to the entire cohort. Important candidates (e.g., miRNAs) that only show deregulation in cases of incident MI may have been over-looked, and
the present findings await confirmation in an independent cohort before one may proceed to make use of these miRNAs as useful tools to enhance stratification for MI.

Jaguszewski et al. (2014) noted that Takotsubo cardiomyopathy (TTC) remains a potentially life-threatening disease, which is clinically indistinguishable from acute myocardial infarction (MI). Currently, no established biomarkers are available for the early diagnosis of TTC and differentiation from MI. MicroRNAs (miRNAs/miRs) emerge as promising sensitive and specific biomarkers for cardiovascular disease. Thus, these investigators sought to identify circulating miRNAs suitable for the diagnosis of acute TTC and for distinguishing TTC from acute MI. After miRNA profiling, 8 miRNAs were selected for verification by real-time quantitative reverse transcription polymerase chain reaction (PCR) in patients with TTC (n = 36), ST-segment elevation acute MI (STEMI, n = 27), and healthy controls (n = 28). They quantitatively confirmed up-regulation of miR-16 and miR-26a in patients with TTC compared with healthy subjects (both, p < 0.001), and up-regulation of miR-16, miR-26a, and let-7f compared with STEMI patients (p < 0.0001, p < 0.05, and p < 0.05, respectively). Consistent with previous publications, cardiac-specific miR-1 and miR-133a were up-regulated in STEMI patients compared with healthy controls (both, p < 0.0001). Moreover, miR-133a was substantially increased in patients with STEMI compared with TTC (p < 0.05). A unique signature comprising miR-1, miR-16, miR-26a, and miR-133a differentiated TTC from healthy subjects [area under the curve (AUC) 0.835, 95% CI: 0.733 to 0.937, p < 0.0001] and from STEMI patients (AUC 0.881, 95% CI: 0.793 to 0.968, p < 0.0001). This signature yielded a sensitivity of 74.19% and a specificity of 78.57% for TTC versus healthy subjects, and a sensitivity of 96.77% and a specificity of 70.37% for TTC versus STEMI patients. Additionally, these researchers noticed a decrease of the endothelin-1 (ET-1)-regulating miRNA-125a-5p in parallel with a robust increase of ET-1 plasma levels in TTC compared with healthy subjects (p < 0.05). The authors concluded that the present study for the first time described a signature of four circulating miRNAs as a robust biomarker to distinguish TTC from STEMI patients. They stated that the significant up-regulation of these stress- and depression-related miRNAs suggested a close connection of TTC with neuropsychiatric disorders. Moreover, decreased levels of miRNA-125a-5p, as well as increased plasma levels of its target ET-1, are in line with the microvascular spasm hypothesis of the TTC pathomechanism.

Roncarati et al. (2014) stated that myocardial miRNAs modulate processes such as cardiomyocyte (CM) hypertrophy, excitation-contraction coupling, and apoptosis; non-CM-specific miRNAs regulate myocardial vascularization and fibrosis. Recently, the possibility that circulating miRNAs may be biomarkers of cardiovascular disease has been raised. These researchers examined if microRNAs (miRNAs) involved in myocardial remodeling were differentially expressed in the blood of hypertrophic cardiomyopathy (HCM) patients, and whether circulating miRNAs correlated with the degree of left ventricular hypertrophy and fibrosis. A total of 41 HCM patients were characterized with conventional transthoracic echocardiography and cardiac magnetic resonance. Peripheral plasma levels of 21 miRNAs were assessed by quantitative real-time PCR and were compared with levels in a control group of 41 age- and sex-matched blood donors. Twelve miRNAs (miR-27a, -199a-5p, -26a, -145, -133a, -143, -199a-3p, -126-3p, -29a, -155, -30a, and -21) were significantly increased in HCM plasma. However, only 3 miRNAs (miR-199a-5p, -27a, and -29a) correlated with hypertrophy; more importantly, only miR-29a correlated also with fibrosis. The authors concluded that these findings suggested that cardiac remodeling associated with HCM determined a significant release of miRNAs into the bloodstream: the circulating levels of both cardiac- and non-cardiac-specific miRNAs were significantly increased in the plasma of HCM patients. However, correlation with left ventricular hypertrophy parameters held true for only a few miRNAs (i.e., miR-199a-5p, miR-27a, and miR-29a), whereas only miR-29a was significantly associated with both hypertrophy and fibrosis, identifying it as a potential biomarker for myocardial remodeling assessment in HCM.

Itabe et al. (2011) stated that accumulating evidence indicates that oxidized low-density lipoprotein (OxLDL) is a useful marker for cardiovascular disease. The uptake of OxLDL by scavenger receptors leads to the accumulation of cholesterol within the foam cells of atherosclerotic lesions. OxLDL has many stimulatory effects on vascular cells, and the presence of OxLDL in circulating blood has been established. According to the classical hypothesis, OxLDL accumulates in the atherosclerotic lesions over a long duration, leading to advanced lesions. However, recent studies on time-course changes of OxLDL in vivo raised the possibility that OxLDL can be transferred between the lesions and the circulation. These investigators discussed the in vivo dynamics of OxLDL. The authors concluded that recent studies have suggested the plasma OxLDL concentrations may change under pre-pathological and post-pathological conditions. OxLDL may be transferred between tissues and plasma and does not merely accumulate in the lesions but is equilibrated between the tissues and circulation. OxLDL can be formed in various sites in addition to the tissue of the vessel wall. The liver is the major organ for the clearance of OxLDL from circulation. However, many unknowns remain to be elucidated regarding the metabolic fate of OxLDL in the liver. A recent study pointed out that stabilins may have an important role in the recognition and clearance of OxLDL and minimally modified LDL (MM-LDL) from circulation. The receptors working in the liver may be different from those of cells in vessel wall tissues. They stated that further studies are needed to understand the in vivo behavior of OxLDL and elucidate the contributions of OxLDL and oxidative stress to the mechanism of atherogenesis.

Berge et al. (2007) examined if common prothrombotic mutations are more prevalent in patients with atrial fibrillation who have had a stroke than in healthy controls. These researchers also wanted to assess whether early recurrent ischemic cerebrovascular events were more frequent among carriers of the factor V Leiden or the prothrombin gene mutations than among others. These investigators used a case-control design with 367 patients with acute ischemic stroke and atrial fibrillation (cases) and 482 healthy blood donors (controls). All mutations were detected with conventional polymerase chain reaction protocols. The odds ratios for carriers of the factor V Leiden, prothrombin gene 20210GA, methylenetetrahydrofolate reductase 677CT, or platelet glycoprotein IIIa 1565TC (Pl(A2)) mutation were 0.91 (95% CI: 0.51 to 1.59), 2.25 (95% CI: 0.61 to 8.90), 0.83 (95% CI: 0.61 to 1.13), and 0.79 (95% CI: 0.57 to 1.10), respectively. Early recurrent ischemic stroke and total recurrent ischemic cerebrovascular events were slightly more frequent among carriers of the factor V Leiden mutation than among non-carriers: odds ratio 1.45 (95% CI: 0.41 to 5.1) and 1.59 (95% CI: 0.61 to 4.1), respectively. None of the patients with recurrent ischemic cerebrovascular events had the prothrombin gene mutation. The authors concluded that these mutations are not important risk factors for thromboembolic stroke associated with atrial fibrillation. Carriers of the factor V Leiden mutation had a small, non-significantly higher risk of early recurrent ischemic cerebrovascular events.

Guidelines from the American Stroke Association and the American Heart Association (Goldstein et al., 2011) state that "[t]he usefulness of genetic screening to detect inherited hypercoagulable states for prevention of first stroke is not well established" and "[t]he usefulness of specific treatments for primary stroke prevention in asymptomatic patients with hereditary or acquired thrombophilia is not well established."

Bonaca et al. (2012) examined if pregnancy-associated plasma protein-A (PAPP-A) is useful for risk assessment in non-ST-segment elevation acute coronary syndrome (NSTE-ACS). These investigators measured PAPP-A at baseline in 3,782 patients with non-NSTE-ACS randomized to ranolazine or placebo in the MERLIN-TIMI 36 (Metabolic Efficiency With Ranolazine for Less Ischemia in Non-ST Elevation Acute Coronary Syndromes) trial and followed for an average of 1 year. A cut-point of 6.0 μIU/ml was chosen from pilot work in this cohort. Pregnancy-associated plasma protein-A greater than 6.0 μIU/ml at presentation was associated with higher rates of cardiovascular death or MI at 30 days (7.4% versus 3.7%, HR: 2.01; 95% CI: 1.43 to 2.82; p < 0.001) and at 1 year (14.9% versus 9.7%, HR: 1.63; 95% CI: 1.29 to 2.05; p < 0.001). Pregnancy-associated plasma protein-A was also associated with higher rates of cardiovascular death (HR: 1.94; 95% CI: 1.07 to 3.52, p = 0.027) and MI (HR: 1.82; 95% CI: 1.22 to 2.71, p = 0.003) individually at 30 days. There was no difference in the risk associated with PAPP-A stratified by baseline cardiac troponin I [Accu-TnI greater than 0.04 μg/l] (p interaction = 0.87). After adjustment for cardiac troponin I, ST-segment deviation, age, sex, diabetes, smoking, hypertension, and coronary artery disease (CAD), PAPP-A was independently associated with cardiovascular death/MI at 30 days (adjusted HR: 1.62, 95% CI: 1.15 to 2.29; p = 0.006) and 1 year (adjusted HR: 1.35, 95% CI: 1.07 to 1.71; p = 0.012). Pregnancy-associated plasma protein-A also improved the net reclassification for cardiovascular death/MI (p = 0.003). There was no significant interaction with ranolazine. The authors concluded that PAPP-A was independently associated with recurrent cardiovascular events in patients with NSTE-ACS. They stated that this finding supported PAPP-A as a candidate prognostic marker in patients with acute coronary syndrome (ACS) and supported continued investigation of its potential therapeutic implications.

The Digital Pulse Analyzer (DPA) provides information on arterial wall stiffness and determines the biological age of arteries in less than 3 minutes. This FDA-approved, user-friendly, non-invasive device uses a finger probe to observe the changes in pressure, blood flow, velocity, and profile throughout the whole pulse wave.

According to AVIIR Corp. (Irvine, CA), the MIRISK VP is a novel, protein-based assay that measures 7 specific, highly predictive biomarkers, which are associated with the formation of vulnerable plaque. Vulnerable plaque is responsible for an estimated 75% of all heart attacks, so detecting vulnerable plaque is key to determining a patient’s cardiac risk. The test relies on a proprietary algorithm applied to 4 clinical risk factors and 7 protein biomarker measurements to determine who is most at risk for a heart attack or unstable angina within a 5-year period. The MIRISK VP test measures serum levels of CTACK, Eotaxin, Fas Ligand, HGF, IL-16, MCP-3, and sFas. These proteins are associated with the biology of vulnerable plaque development. Vulnerable plaque rupture in a coronary artery can cause a heart attack. The MIRISK VP has been shown to identify up to 17% of low- and 25.7% of high-risk patients in a multi-ethnic study who were initially identified in the intermediate risk group for coronary heart disease (CHD) by the Framingham Risk Assessment. The test offers a significant advancement in CHD risk assessment methods. MIRISK VP can further stratify individuals in the intermediate-risk group who may actually be high- or low-risk, enabling high-risk individuals to implement therapeutic lifestyle changes and enabling low-risk individuals to avoid additional testing.

Beggs et al. (2013) stated that “Aviir, Inc. is a venture-funded biotechnology company developing and commercializing laboratory tests to provide personalized information to physicians and patients, with the goal of preventing cardiovascular disease and metabolic syndromes. Leveraging advanced research, Aviir developed and launched MIRISK VP™, a risk assessment test to better identify individuals at risk of a heart attack. Aviir also offers an extensive menu of other cardiovascular and metabolic tests through its Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. Efforts are likewise focused on expanding genomics testing capability to address sudden cardiac death attributed to inherited cardiovascular diseases. This completes their integrated precision diagnostics approach that combines biomarker immunoassays with genomic and transcription analysis, along with core clinical chemistry to deliver a comprehensive personal health solution.”

Galectin-3 is a protein that is associated with the development and progression of heart failure, including progressive fibrosis (stiffening) of the heart muscle. Testing purportedly assists in assessing the prognosis of chronic heart failure.

Cystatin C is a small protein produced by cells of the body; serum testing is proposed to diagnose impaired kidney function.

Serum sterol testing (e.g., Boston Heart Cholesterol Balance Test) measures the levels of plant sterols, such as beta-sitosterol and campesterol, which supposedly indicate cholesterol absorption and potential cardiac disease risk.

Skin cholesterol test (e.g., PREVU) is an in-vitro diagnostic test and the only non-invasive method to assess skin cholesterol. It is suggested to assess the risk of coronary artery disease (CAD) in individuals with a history of heart attack or with clinical symptoms or signs of CAD.

Thromboxane metabolite(s) testing is a urine test to measure the level of thromboxane production, which is purportedly useful in identifying individuals who remain at risk of a cardiovascular event despite being on aspirin therapy.

Troponin I testing (PATHFAST cTnI-II) determines the quantity of cardiac troponin I, a protein that is integral to cardiac muscle contraction, which is purportedly elevated in the bloodstream after damage to the myocardium (heart muscle).

Singulex SMC testing for risk of cardiac dysfunction and vascular inflammation (e.g., SMC Endothelin, SMC IL-6, SMC IL-17A, SMC cTnI, and SMC TNF-α) – These tests are purported to measure levels of cardiac disease biomarkers that may have been previously undetectable.

Atherosclerotic cardiovascular disease (ASCVD) risk testing (individual or panel) – These laboratory tests or panels include, but may not be limited to, c-peptide, islet cell antibodies, nonesterified fatty acids (free fatty acids), proinsulin, and total insulin.

Vicorder Device

Muller et al. (2013) noted that carotid to femoral pulse wave velocity (PWV) is associated with an increase in cardiovascular morbidity and all-cause mortality. The non-invasive approach has made this method applicable for the examination of larger populations. These researchers obtained reference values of PWV measured with the Vicorder device. PWV was obtained using the oscillometric Vicorder in 318 healthy, normotensive patients (165 women, age of 28.7 ± 17.6 years, range of 6 to 83 years). A plethysmographic sensor was placed over the right carotid region to pick up the carotid pulse wave, and a blood pressure (BP) cuff was placed around the upper thigh to trace the femoral pulse wave. Path length was defined as the distance from the suprasternal notch to the top of the thigh cuff. Mean PWV was 6.1 ± 1.4 m/s and significantly increased with age (r = 0.842; p < 0.0001). PWV was associated with mean arterial pressure (MAP; r = 0.546; p < 0.0001) and body mass index (BMI; r = 0.396; p < 0.0001). The authors concluded that in a multiple linear regression model, age, MAP, and body height emerged as independent markers for PWV. These investigators stated that this study established reference values for carotid to femoral PWV derived by oscillometric measures that could be used for risk stratification. Moreover, these researchers noted that the age group of geriatric patients was underpowered; thus, further research in this age group is recommended because Hickson et al. (2009) found significantly higher PWV values measured with the SphygmoCor in comparison with the Vicorder in this age group. They stated that longitudinal studies should be carried out to establish cut-off values for patients at higher risk for cardiovascular events.

Keehn et al. (2014) stated that PWV, a measure of arterial stiffness strongly predictive of cardiovascular risk in adults, is usually measured by sequential ECG-referenced carotid and femoral tonometry. A simplified technique, more suitable for use in children, employs simultaneous volumetric recording from a sensor applied over the carotid artery and a cuff applied over the femoral artery or arm and thigh BP cuffs applied over the brachial and femoral arteries. These researchers compared PWV computed over the carotid-femoral path (PWVcf) with that over the brachial-femoral path (PWVbf) using a volumetric system (Vicorder) and compared values of PWVcf obtained by the volumetric and a tonometric method (SphygmoCor) in children. Vicorder PWVcf and PWVbf were compared in 156 children (age of 3 to 18 years, 110 with chronic kidney disease), and PWVcf by Vicorder was compared to PWVcf by SphygmoCor in a subset of 122 patients. PWVcf by Vicorder was moderately correlated with PWVcf by SphygmoCor (r = 0.50, p < 0.000). PWVbf and PWVcf Vicorder were more closely correlated (r = 0.75, p < 0.0001), but with a significant systematic difference. Applying a correction factor to PWVbf measurements gave results similar to those obtained over the carotid-femoral path. Within-patient coefficients of variation for repeated measures were 5.9%, 7.8%, and 8.5% for PWVbf (Vicorder), PWVcf (Vicorder), and PWVcf (SphygmoCor), respectively. All PWV values showed a similar relation to age. The authors concluded that volumetric methods appeared reproducible and were easy to use in children; however, values obtained by Vicorder and SphygmoCor were not interchangeable even when measured over the same pathway. Moreover, these researchers stated that these findings should be tested in larger cohorts to ascertain their clinical utility.

Parikh et al. (2016) noted that PWV is an important measure of cardiovascular risk and can be measured by several different techniques. These investigators compared age-related changes in PWV derived from carotid and femoral artery waveforms using the Vicorder device and descending thoracic aorta time velocity curves using phase contrast magnetic resonance imaging (MRI) in a group of normal healthy volunteers without cardiovascular disease, aged between 20 and 79 years. A total of 80 subjects underwent same-day measurements of Vicorder and MRI PWV measurements. Both Vicorder and MRI-based PWV measurements were significantly increased with age (r = 0.59 and 0.57, respectively, both p < 0.0001). Vicorder and MRI PWVs were also significantly related to each other (r = 0.27, p < 0.05), and Bland-Altman plots showed that on average, Vicorder measurements were 1.6 m/s greater than MRI. In 5% of cases, agreement between the values of the two techniques was above and below 2 standard deviations, and these were at higher levels of PWVs. Multiple linear stepwise regression analysis confirmed highly significant relationships of both techniques to age (both p < 0.0001), and MRI was also significantly related to heart rate (p = 0.006); however, Vicorder was not. The authors concluded that both Vicorder and MRI performed similarly in detecting age-related changes in PWV; therefore, the choice of using one or the other will depend on other aspects of the investigation, such as the need for portability favoring Vicorder or the need for additional cardiovascular imaging favoring MRI.

The authors stated that these findings were from healthy subjects; thus, in subjects with cardiovascular disease with increased vascular stiffness, the relationships described may be altered, and there may also be additional issues with acquiring high-quality data. Gender differences have been described with measures of vascular stiffness; however, this study was not statistically powered to detect gender differences. There are other methods to measure PWV with MRI. For instance, other areas of the aorta can be evaluated using phase contrast MRI. Hickson et al. (2010) have shown that the abdominal aorta has slightly higher rates of age-related increases in PWV relative to the descending thoracic aorta. Other phase contrast techniques include determination of flow-area and cross-correlation methods where flow is determined at several points along the descending aorta. Ibrahim et al. (2010) have reported that the transit time (as used in this study) and cross-correlation methods resulted in more reproducible measurements compared to the flow-area method. These researchers’ MRI reproducibility studies reported more variation than the data published by Ibrahim et al. (2010), explained by their much larger study number. A dependency of heart rate on augmentation index has been reported for the SphygmoCor device. Moreover, these investigators stated that as there was no published data on this issue with the Vicorder device, they had not normalized data to heart rate.

Loßner et al. (2023) stated that the evaluation of endothelial function is gaining interest and importance during pregnancy, since the impaired adaptation in early pregnancy has been associated with an increased risk of pre-eclampsia and fetal growth restriction. To standardize the risk assessment and to implement the evaluation of vascular function in routine pregnancy care, a suitable, accurate, and easy-to-use method is needed. Flow-mediated dilatation (FMD) of the brachial artery assessed by ultrasound (US) is considered to be the gold standard for measuring vascular endothelial function. The challenges of the FMD measurement have so far prevented its introduction into clinical routine. The Vicorder device allows an automated determination of the flow-mediated slowing (FMS). The equivalence of FMD and FMS has not yet been proven in pregnant women. These researchers collected data from 20 pregnant women randomly and consecutively while they presented for a vascular function assessment in the authors’ hospital. The gestational age at investigation was between 22 and 32 weeks of gestation – 3 had pre-existing hypertensive pregnancy disease and 3 were twin pregnancies. The results for FMD or FMS below 11.3% were considered to be abnormal. Comparing FMD to FMS results in this cohort showed a convergence in 9/9 cases, indicating normal endothelial function (specificity of 100%), and a sensitivity of 72.7%. The authors verified that the FMS measurement is a convenient, automated, and operator-independent test method of endothelial function in pregnant women. Moreover, these researchers stated that prospective, large-scale studies with a longitudinal observation of women throughout and after pregnancy are needed to compare clinical outcomes using the investigator-independent automated method of the FMS measurement via the Vicorder device.

The authors stated that the main drawback of this trial was the small number of pregnant women included. These researchers aimed to include women consecutively in a non-selective approach. As a result of the non-selective approach, individual risk profiles showed large variations (BMI, maternal age, gestational age, parity, etc.). Fortunately, the high consistency of our results comparing the two methods was observed in all pregnant women regardless of their individual risk profile.

Appendix

Framingham Risk Scoring

Framingham risk scoring for men and women below is adapted from Appendix A of the Executive Summary of the ATPIII Report, available at the following web site: National Cholesterol Education Program (NCEP).

Risk assessment for determining the 10-year risk for developing CHD is carried out using Framingham risk scoring (Table 1 for men and Table 2 for women). The 10-year risk for MI and coronary death is estimated from total points, and the person is categorized according to absolute 10-year risk as indicated in the tables.

Table: Estimated 10-Year Risk for Men (Framingham Point Scores)
Age	Points
20-34	-9
35-39	-4
40-44	0
45-49	3
50-54	6
55-59	8
60-64	10
65-69	11
70-74	12
75-79	13

Table: Framingham Point Scores for Total Cholesterol Level with respect to Age for Men
Total Cholesterol	Age 20-39	Age 40-49	Age 50-59	Age 60-69	Age 70-79
< 160	0	0	0	0	0
160-199	4	3	2	1	0
200-239	7	5	3	1	0
240-279	9	6	4	2	1
≥ 280	11	8	5	3	1

Table: Framingham Point Scores for Smoker Type with respect to Age for Men
Smoker Type	Age 20-39	Age 40-49	Age 50-59	Age 60-69	Age 70-79
Non-smoker	0	0	0	0	0
Smoker	8	5	3	1	1

Table: Framingham Point Scores for HDL for Men
HDL (mg/dL)	Points
≥ 60	-1
50-59	0
40-49	1
< 40	2

Table: Framingham Point Scores for Systolic BP level for Men
Systolic BP (mm Hg)	If Untreated	If Treated
≥120	0	0
120-129	0	1
130-139	1	2
140-159	1	2
≥160	2	3

Table: Framingham Total points based on the average of risk for 10 years for Men
Point Total	10-Year Risk%
<0	<1
0	1
1	1
2	1
3	1
4	1
5	2
6	2
7	3
8	4
9	5
10	6
11	8
12	10
13	12
14	16
15	20
16	25
≥17	≥30

Table: Estimated 10-Year Risk for Women (Framingham Point Scores)
Age	Points
20-34	-7
35-39	-3
40-44	0
45-49	3
50-54	6
55-59	8
60-64	10
65-69	12
70-74	14
75-79	16

Table: Framingham Point Scores for Total Cholesterol Level with respect to Age for Women
Total Cholesterol	Age 20-39	Age 40-49	Age 50-59	Age 60-69	Age 70-79
< 160	0	0	0	0	0
160-199	4	3	2	1	1
200-239	8	6	4	2	1
240-279	11	8	5	3	2
≥ 280	13	10	7	4	2

Table: Framingham Point Scores for Smoker Type with respect to Age for Women
Smoker Type	Age 20-39	Age 40-49	Age 50-59	Age 60-69	Age 70-79
Non-smoker	0	0	0	0	0
Smoker	9	7	4	2	1

Table: Framingham Point Scores for HDL for Women
HDL (mg/dL)	Points
≥60	-1
50-59	0
40-49	1
< 40	2

Table: Framingham Point Scores for Systolic BP level for Women
Systolic BP (mm Hg)	If Untreated	If Treated
< 120	0	0
120-129	1	3
130-139	2	4
140-159	3	5
≥ 160	4	6

Table: Framingham Total Points based on the average of risk for 10 years for Women
Point Total	10-Year Risk%
< 9	<1
9	1
10	1
11	1
12	1
13	2
14	2
15	3
16	4
17	5
18	6
19	8
20	11
21	14
22	17
23	22
24	27
≥ 25	≥30

PREVENT-ACVD Equations

The American Heart Association PREVENT™ Online Calculator

In 2023, the American Heart Association (AHA) developed the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations tool to estimate 10-year and 30-year risk for total cardiovascular disease (CVD), including atherosclerotic CVD (ASCVD) and heart failure (HF). The equations were derived and validated using data from over 6.5 million U.S. adults across multiple datasets, and are validated for adults ages 30–79 years without known CVD.

The PREVENT calculator, based on the PREVENT equations, uses required clinical information to estimate CVD risk. Three optional predictors, urine albumin-creatinine ratio (UACR), hemoglobin A1c (HbA1c), and social deprivation index (SDI), can further personalize risk estimates.

The PREVENT calculator provide separate 10-year and 30-year estimates for total CVD (PREVENT-CVD), ASCVD (PREVENT-ASCVD), and HF (PREVENT-HF). The default display is PREVENT-CVD, but each outcome can be selected individually. Because ASCVD and HF are modeled independently, their combined risk may exceed the total CVD estimate.

The PREVENT equations are not to be used for adults with known CVD, evidence of severe subclinical CVD (e.g., left ventricular ejection fraction <40%, coronary artery calcium ≥300), positive genetic testing for a variant known to be pathogenic or for an inherited cardiovascular condition, end-stage kidney disease, or limited life expectancy (<1 year).

The 2026 ACC/AHA dyslipidemia guideline provides a COR 1 (strong recommendation) for the PREVENT-ASCVD Equations in adults aged 30 to 79 years without ASCVD or subclinical atherosclerosis and with an LDL-C level between 70 and 189 mg/dL (1.8-4.9 mmol/L). The PREVENT-ASCVD equations should be used to estimate 10-y ASCVD risk, with categorization as having low (<3%), borderline (3% to <5%), intermediate (5% to <10%), or high (≥10%) risk.

Source: AHA, 2026

References

The above policy is based on the following references:

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Last Review 06/29/2026

Effective: 02/01/2000

Next Review: 04/08/2027
Review History
Definitions

Cardiovascular Disease Risk Tests

Table Of Contents

Policy

Scope of Policy

Medical Necessity

Apolipoprotein B (ApoB)

High-Sensitivity C-Reactive Protein (hsCRP)

Homocysteine Testing

Lipoprotein(a)

Experimental, Investigational, or Unproven

Apolipoprotein B (Apo B)

Cardiac Stress Testing and Stress Echocardiography

High-Sensitivity C-Reactive Protein (hsCRP)

Homocysteine Testing

Measurement of Carotid Intima-Media Thickness

Noninvasive Measurement of Arterial Elasticity

Peripheral Arterial Tonometry

Ultrasound of the Upper and Lower Extremity Arteries

Venous Ultrasound

Experimental, Investigational, or Unproven CHD Risk Tests

Related Policies

CPT Codes / HCPCS Codes / ICD-10 Codes

High-sensitivity C-reactive protein (hs-CRP):

CPT codes covered if selection criteria are met:

Other CPT codes related to the CPB:

Other HCPCS codes related to the CPB:

ICD-10 codes covered if selection criteria are met:

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):

Major risk factors [need at least 2]:

Apolipoprotein B (apo B):

CPT codes covered if selection criteria are met:

Other CPT codes related to the CPB:

Other HCPCS codes related to the CPB:

ICD-10 codes covered if selection criteria are met:

Trimethylamine/trimethylamine N-oxide (TMA/TMAO) profile:

CPT codes covered if selection criteria are met:

ICD-10 codes covered if selection criteria are met:

Carotid Ultrasound Screening:

CPT codes covered for indications listed in the CPB:

Other HCPCS codes related to the CPB:

ICD-10 codes covered if selection criteria are met:

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):

Lipoprotein(a) [Lp(a)]:

CPT codes covered if selection criteria are met:

ICD-10 codes covered if selection criteria are met:

Tests considered experimental and investigational for assessing CHD risk:

CPT codes not covered for indications listed in the CPB:

Other CPT codes related to the CPB:

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):

Cardiac Stress Testing:

CPT codes covered if selection criteria are met:

Other CPT codes related to the CPB:

ICD-10 codes covered if selection criteria are met:

Background

Apo [Apolipoprotein] B Testing

High Sensitivity C-Reactive Protein (hsCRP)

Lipoprotein (a) Enzyme Immunoassay

9p21 and Other Genetic Tests

Algorithmically Scored Multi-Protein Biomarker Tests

Allelica Multi-Ancestry CAD PRS Test

Angiotensin Gene

Apolipoprotein A-1

Apolipoprotein E (apo E) Testing

B-Type Natriuretic Peptides

Cardiac Stress Testing and Stress Echocardiography for Screening of Asymptomatic Patients

CARDIO inCode-Score

CardioRisk+ for Evaluation of Cardiovascular Risk

Carotid Intima-Media Thickness

Carotid Ultrasound Screening

Cholesterol Balance Test

Coenzyme Q Testing

Corus CAD

Endothelin Testing

Epi+Gen CHD / PrecisionCHD for Prediction of Coronary Heart Disease

Fibrinogen

GlycA (Glycosylated Acute Phase Proteins) Testing

HART CADhs

HART CVE Panel

HART KD Panel

HDL Reverse Cholesterol Transport Panel with pCAD Score for Evaluation of Risk of Cardiovascular Diseases