Selected Kidney Function Tests

Number: 0775

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

Policy

Scope of Policy

This Clinical Policy Bulletin addresses selected kidney function tests.

Experimental and Investigational

The following tests are considered experimental and investigational because the effectiveness of these approaches has not been established:

Apolipoprotein L1 (APOL1) renal risk variant genotyping (blood or buccal mucosa)
GFR_NMR test (a combination of multiple metabolites including myo-inositol, dimethyl sulfone, valine, and creatinine and analyzed by nuclear magnetic resonance spectroscopy) for assessment of glomerular filtration rate/kidney function
KidneyIntelX test for the prediction of renal decline in individuals with type 2 diabetes mellitus
NaviDKD test for the prediction of kidney disease in individuals with diabetes
PromarkerD test for the prediction of kidney disease in individuals with diabetes
RenalVysion test (Nephrocor, Glen Allen, VA) for diagnosing and monitoring kidney disease
Use of a transdermal system with pyrazine‐based fluorescent agents for measurement of glomerular filtration rate.

Table:
CPT Codes / HCPCS Codes / ICD-10 Codes
Code	Code Description
*There are no specific codes for RenalVysion*:
ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):
I10 - I16.2	Hypertensive diseases
N18.1 - N18.9	Chronic kidney disease (CKD)
N19	Unspecified kidney failure
N26.2	Page kidney
R10.0 - R10.13 R10.30 - R10.33 R10.84	Abdominal pain
R31.0	Gross hematuria
R60.0 - R60.9	Edema, not elsewhere classified
*KidneyIntelX*:
CPT codes not covered for indications listed in the CPB:
0105U	Nephrology (chronic kidney disease), multiplex electrochemiluminescent immunoassay (ECLIA) of tumor necrosis factor receptor 1A, receptor superfamily 2 (TNFR1, TNFR2), and kidney injury molecule-1 (KIM-1) combined with longitudinal clinical data, including APOL1 genotype if available, and plasma (isolated fresh or frozen), algorithm reported as probability score for rapid kidney function decline (RKFD)
ICD-10 codes not covered for indications listed in the CPB:
E11.00 - E11.9	Type II diabetes
*Transdermal Fluorescent Pyrazine*:
CPT codes not covered for indications listed in the CPB:
0602T	Glomerular filtration rate (GFR) measurement(s), transdermal, including sensor placement and administration of a single dose of fluorescent pyrazine agent
0603T	Glomerular filtration rate (GFR) monitoring, transdermal, including sensor placement and administration of more than one dose of fluorescent pyrazine agent, each 24 hours
ICD-10 codes not covered for indications listed in the CPB:
N17.0	Acute kidney failure with tubular necrosis
N18.1 - N18.9	Chronic kidney disease (CKD)
N19	Unspecified kidney failure
R94.4	Abnormal results of kidney function studies
*GFRNMR Test*:
CPT codes not covered for indications listed in the CPB:
0259U	Nephrology (chronic kidney disease), nuclear magnetic resonance spectroscopy measurement of myo-inositol, valine, and creatinine, algorithmically combined with cystatin C (by immunoassay) and demographic data to determine estimated glomerular filtration rate (GFR), serum, quantitative
ICD-10 codes not covered for indications listed in the CPB:
E11.21-E11.29	Type 2 diabetes mellitus with kidney complications
N18.1- N18.9	Chronic kidney disease (CKD)
*Apolipoprotein L1 (APOL1) renal risk variant genotyping (blood or buccal mucosa)*:
CPT codes not covered for indications listed in the CPB:
0355U	APOL1 (apolipoprotein L1) (eg, chronic kidney disease), risk variants (G1, G2)
*NaviDKD and PromarkerD test*:
CPT codes not covered for indications listed in the CPB:
0384U	Nephrology (chronic kidney disease), carboxymethyllysine, methylglyoxal hydroimidazolone, and carboxyethyl lysine by liquid chromatography with tandem mass spectrometry (LC- MS/MS) and HbA1c and estimated glomerular filtration rate (GFR), with risk score reported for predictive progression to high-stage kidney disease
0385U	Nephrology (chronic kidney disease), apolipoprotein A4 (ApoA4), CD5 antigen-like (CD5L), and insulin-like growth factor binding protein 3 (IGFBP3) by enzyme-linked immunoassay (ELISA), plasma, algorithm combining results with HDL, estimated glomerular filtration rate (GFR) and clinical data reported as a risk score for developing diabetic kidney disease
ICD-10 codes not covered for indications listed in the CPB:
E08.00 - E13.9	Diabetes mellitus [Prediction of kidney disease]

Background

An estimated 7 % of adults aged 20 or older (15.5 million adults) have physiological evidence of chronic kidney disease (CKD) as defined by a moderately or severely reduced glomerular filtration rate (GFR) (Coresh et al, 2007). Patients with kidney disease have a variety of different clinical presentations. Some have symptoms that are directly referable to the kidney (e.g., gross hematuria, flank pain) or to extra-renal sites of involvement (e.g., edema, hypertensive, signs of uremia). Many patients, however, lack specific symptoms and are noted on routine examination to have an elevated plasma creatinine concentration or an abnormal urinalysis.

According to the National Institute for Clinical Excellence (NICE, 2008), 30 % of people with advanced kidney disease are referred late to nephrology services causing increased mortality and morbidity. Strategies aimed at earlier identification and, where possible, prevention of progression to established renal failure are needed.

The indications for performing a renal biopsy varies among nephrologists, being determined in part by the presenting signs and symptoms. A percutaneous renal biopsy is sometimes performed to obtain a diagnosis, help guide therapy, and ascertain the degree of active and chronic changes. The routine evaluation of a percutaneous renal biopsy involves examination of the tissue under light, immunofluorescence, and electron microscopy.

RenalVysion (Nephrocor, Glen Allen, VA), a urine-based test that integrates urine cytopathlogy with urine chemistries (i.e., creatinine, protein, beta-2 microglobulin, microalbumin), is being marketed as a 'liquid biopsy' for the early diagnosis and monitoring of kidney disease. However, there are no published studies on the use of RenalVysion for kidney disease and no medical professional society recommends RenalVysion testing for diagnosing and monitoring kidney disease. Chronic kidney disease can be readily detected with tests for proteinuria, hematuria, and estimated GFR. Randomized controlled studies comparing RenalVysion to these standard methods for diagnosing and monitoring kidney disease are needed.

Apolipoprotein L1 (APOL1) Renal Risk Variant Genotyping

Two variants in the apolipoprotein L1 (APOL1) gene (namely, G1 and G2) have been reported to increase the risk for various types of chronic kidney disease (CKD) including focal segmental glomerulo-sclerosis (FSGS), HIV-associated nephropathy, and hypertension-attributed end-stage kidney disease (ESKD). These APOL1 risk variants are mainly limited to individuals with recent West sub-Saharan African ancestry. Approximately 10 % to 15 % of African American individuals have a high-risk APOL1 genotype (i.e., carry 2 risk variants). The estimated risk of CKD among these individuals is 15 % to 20 %,1 approximately 41 % greater than that of individuals with a low-risk genotype (i.e., carrying 0 or 1 risk variant).

Horowitz et al (2016) stated that individuals of African ancestry (Blacks) have increased risk of kidney failure due to numerous socioeconomic, environmental, and clinical factors. Two variants in the APOL1 gene are now thought to account for much of the racial disparity associated with hypertensive kidney failure in Blacks. However, this knowledge has not been translated into clinical care to help improve patient outcomes and address disparities. GUARDD is a randomized trial examining the effects and challenges of incorporating genetic risk information into primary care. Hypertensive, non-diabetic, adults with self-reported African ancestry, without kidney dysfunction, are recruited from diverse clinical settings and randomized to undergo APOL1 genetic testing at baseline (intervention) or at 1 year (wait-list control). Providers were educated regarding genomics and APOL1. Guided by a genetic counselor, trained staff return APOL1 results to patients and provide low-literacy educational materials. Real-time clinical decision support tools alert clinicians of their patients' APOL1 results and associated risk status at the point of care (POC). The authors’ academic-community-clinical partnership designed a study to generate information regarding the impact of genetic risk information on patient care (blood pressure [BP] and renal surveillance) and on patient and provider knowledge, attitudes, beliefs, and behaviors. The GUARDD Trial will aid in establishing the effective implementation of APOL1 risk-informed management of hypertensive patients at high risk of CKD; and will provide a robust framework for future endeavors investigation to implement genomic medicine in diverse clinical practices. It will also add to the important dialog regarding factors that contribute to and may help eliminate racial disparities in kidney disease.

Bajaj et al (2017) noted that observational studies have long established that African Americans have a higher risk for developing CKD with faster progression to ESRD compared to Americans not of African descent, independent of socioeconomic and traditional clinical risk factors. However, it was not until 2010 that the genetic basis behind this association became more apparent, when 2 separate genome wide association studies identified DNA variants in the APOL1 gene that were strongly associated with kidney disease in African Americans. The story of the APOL1 gene that emerged is a fascinating example of natural selection against infectious disease, and discovery of its link to renal, and possibly cardiovascular (CV), diseases has opened a new field of questions. These researchers stated that the discovery of the association of APOL1 variants with CKD was an exciting beginning to a story that is still unfolding. The question remains as to if there is a link between APOL1 risk genotypes and CVD and if so, if it is distinct from a pathway to CVD driven by impairment in renal function. Given the wealth of data available, a meta-analysis of the existing literature offers a path forward to clarifying the true association between APOL1 and CVD. One of the challenges of evaluating the present collection of studies, and in contemplating a meta-analysis, however, was the heterogeneity of outcomes used to define “cardiovascular disease” and the range of underlying pathophysiological processes these represented; almost every published study has used a different composite CVD definition. Accordingly, future studies that examine APOL1 risk genotypes will need to carefully define outcomes. Electronic health records (EHR) based cohorts that allow for detailed phenotyping at a population scale represent a 2nd pathway to elucidating the relationship between APOL1 and CVD. Such cohorts will have the sample sizes large enough to provide the necessary statistical power to detect, or not, associations between APOL1 genotypes and CVD outcomes, which are likely smaller than those observed with renal pathologies; both the current VA Million Veteran Program, and the developing National Institute of Health (NIH)’s All of Us Research Program will be key resources in this area. Discovering the pathways in which APOL1 connects to renal disease and CVD may guide the development of future therapies, preventive measures, and precision medicine in the high-risk African American population.

Chen et al (2017) stated that among African Americans, the APOL1 risk variants have been associated with various types of renal disease and CKD progression. These investigators examined if these same risk variants also confer an increased risk for CVD. In a cohort of African Americans with hypertension-attributed CKD followed-for up to 12 years, these researchers used Cox proportional hazards models to estimate the relative hazard of a composite CVD outcome (cardiovascular death or hospitalization for myocardial infarction [MI], cardiac re-vascularization procedure, heart failure [HF], or stroke) for the APOL1 high- (2 risk variants) versus low-risk (0 to 1 risk variant) genotypes. They adjusted for age, sex, ancestry, smoking, heart disease history, body mass index (BMI), cholesterol, randomized treatment groups, and baseline and longitudinal eGFR, systolic BP (SBP), and proteinuria. Among 693 subjects with APOL1 genotyping available (23 % high risk), the high-risk group had lower mean eGFR (44.7 versus 50.1 ml/min per 1.73 m2) and greater proteinuria (median 0.19 versus 0.06) compared with the low-risk group at baseline. There was no significant association between APOL1 genotypes and the composite CVD outcome in both unadjusted (hazard ratio [HR] = 1.23; 95 % CI: 0.83 to 1.81) and fully adjusted (HR = 1.16; 95 % CI: 0.77 to1.76) models; however, in using an additive model, APOL1 high-risk variants were associated with increased CV mortality. The authors concluded that among African Americans with hypertension-attributed CKD, APOL1 risk variants were not associated with an overall risk for CVD although some signals for CV mortality were noted. Moreover, these researchers stated that additional research is needed to better understand if the APOL1 risk variants are associated with CVD outcomes, and if so, the pathophysiological basis for an association.

The authors stated that drawbacks of this study included a relatively small number of outcome events, which may have limited the power to detect associations between the APOL1 risk variants and CVD. Indeed, with only 144 cases of the composite CVD outcome, these investigators had 80 % power to detect a minimum HR of 1.85 for the APOL1 high-risk compared to the low-risk genotypes (recessive genetic model). Still, this HR is within the range of what was reported in the Jackson Heart Study (JHS) and Women's Health Initiative (WHI). The sensitivity analyses examining secondary endpoints were likely under-powered, especially for CV death, which had the fewest number of events (minimum detectable HR of 3.74 for 80 % power). In addition, subjects were not followed for CVD events once they developed ESRD. There may have been additional cases of CVD that were not captured, however, the pathophysiology of CVD events post-ESRD (i.e., more sudden cardiac death) likely differed from that of pre-ESRD CVD events.

Freedman et al (2021) noted that variants in the APOL1 gene are thought to be important contributors to a disparity in the incidence of ESKD among Black people, which is approximately 3-fold higher than among White people. No specific treatment or management protocol for APOL1-associated nephropathy currently exists. Using a Delphi consensus process supported by a systematic literature review, a multi-disciplinary group agreed on practical measures for care of patients who may have APOL1-associated nephropathy. The recommendations address 3 areas:

Counseling, Genotyping, and Diagnosis

APOL1 testing in the current clinical setting has potential advantages and disadvantages both for clinicians and for patients. However, it is not common at present in routine clinical practice. To help to address some of the barriers to testing, the Consensus Group defined key aspects of appropriate counseling for patients who are considering APOL1 testing and discussion of results after testing. These investigators recognized that there is currently no established best practice; and that additional professional education may be needed to enable nephrologists to counsel patients regarding APOL1. Given that no specific treatment is currently available for APOL1-associated nephropathy, it is important that patients be made aware of this fact during counseling, testing, and diagnosis.

Disease Awareness and Education

There was consensus that nephrologists, primary care providers, nurses, community leaders, and patient networks all have important roles to play in educating patients regarding APOL1. Individuals responsible for education should have appropriate knowledge, skills, and training, and they should be prepared to share educational materials, refer patients to trusted sources for further information, and provide education in a setting that is familiar and comfortable for patients.

Future Vision

There is a pressing, unmet patient’s need for a specific, effective treatment for APOL1-associated nephropathy. The Consensus Group agreed that if a safe and effective treatment becomes available, all patients suspected of having APOL1-associated nephropathy, including all Black patients with CKD, should be offered genetic testing and that treatment should begin as early as possible following a confirmatory kidney biopsy. Although an effective treatment has the potential to transform care for patients with APOL1-associated nephropathy and reduce health disparities, uncertainties regarding insurance coverage, access to treatment, and inconsistency in clinical practice remain. To enable clinicians to identify which patients could benefit from a specific and effective treatment, the Consensus Group recommended that APOL1-associated nephropathy should be recognized as an adjunct to the clinical diagnosis (e.g., “FSGS, APOL1 variant”).

The authors stated that these recommendations may help clinicians improve awareness and diagnosis of APOL1-associated nephropathy and by doing so, may provide opportunities to reduce health disparities related to kidney disease. The authors concluded that advances in the understanding of APOL1-associated nephropathy provided an opportunity to reduce health disparities related to kidney disease. These investigators have high-lighted key steps needed to improve awareness and diagnosis of APOL1-associated nephropathy and outlined the opportunities for clinical care in the present era and a vision for the future.

Yusuf et al (2021) stated that globally, CKD represents an important non-communicable disease with significant morbidity and mortality. An estimated 10 % of the world’s population had CKD in 2015 with approximately 1.2 million deaths in 2017, and the burden is expected to rise at the rate of 6 % per annum. By 2030, more than 70 % of patients suffering from ESKD worldwide will be in low and lower middle- income countries of the world including African countries. Significant disparities in the burden of CKD exist worldwide, where economically disadvantaged communities, notably those on the African continent and those of the African diaspora, continue to bear a disproportionate burden of the disease. This disparity is fueled by a convergence of genetic and environmental risk factors. A recent meta-analysis showed an overall prevalence of CKD of 15.8 % in the general population in Africa, with up to 4.6 % of adults having moderate-to-severe kidney dysfunction. In Africa, more than 80 % of the continental burden of CKD is in sub-Saharan Africa (SSA), with the highest prevalence in West Africa. The burden of CKD among African Americans, who share substantial genetic ancestry with West Africans, is similarly high; African Americans represent 13 % of the U.S. population, but account for 35 % of the patients on dialysis.

These investigators stated that APOL1 renal risk alleles have profound influence on a spectrum of kidney disease in individuals of recent African descent over the life course. APOL1 risk variants require “second hits” (e.g., viral infection, auto-immune diseases, sickle cell anemia, glomerular hyperfiltration) for renal disease to manifest; up-regulation of APOL1 by interferons (IFNs) is a potent 2d hit. However, beyond exposure to therapeutic IFN and certain viral infections, it is still unclear why most individuals with APOL1 high-risk status never develop kidney disease. Africa comprises 1,000s of ethnolinguistic groups with extensive genetic diversity living on a continent undergoing epidemiological transition. The study of APOL1 associations in case-control studies and longitudinal studies in SSA may shed new light on genetic and environmental exposures that initiate CKD and/or modify CKD progression. Furthermore, knowledge of APOL1 prevalence and disease associations may inform public health policies and resource allocation. Recent advances in understanding the pathophysiological mechanisms of APOL1-associated CKD may lead to new therapeutic options. An APOL1 antisense drug targeting APOL1 has been shown to ameliorate proteinuria in animal models. If particular drug therapies (e.g., BP-lowering drugs that block the renin angiotensin aldosterone [RAA] system) are proven effective in clinical trials, knowledge of at-risk populations susceptible to APOL1-related kidney disease may justify screening for APOL1 or for biomarkers of APOL1-mediated renal injury. APOL1 risk variants provide fertile soil for the development of severe glomerulopathies and progressive kidney disease and warrant further study in sub-Saharan Africa and in the African diaspora.

Jagannathan et al (2021) noted that 2 coding risk variants in the APOL1 gene underlie most of the excess risk for kidney diseases in recent African ancestry patients. Strength and consistency of the relationship between APOL1 high-risk genotypes and the risk of CKD and ESRD are not uniform. In a systematic review and meta-analysis , these investigators examined prospective studies evaluating the association of APOL1 genotypes and the risk of developing CKD, ESRD, and CKD to ESRD in adults. They carried out systematic searches of Medline, Embase, and Google Scholar was performed for prospective studies examining the associations between APOL1 genotypes and CKD, ESRD, and progression from CKD to ESRD. Secondary analyses were to assess the annual kidney function change by APOL1 gene status. Random effects models were used to estimate pooled risk ratios (RRs) and weighted mean differences (WMDs) for outcomes of interest. The search yielded 10 prospective studies during a follow-up period ranging from 4.4 to 25 years. The high-risk APOL1 genotype was associated with the incidence of CKD (RR:1.41 [95 % CI: 1.14 to 1.75]), the progression from CKD to ESRD (RR: 1.70 [95 % CI:1.44 to 2.01]) compared with the low-risk APOL1 genotype. There was no appreciable association between high-risk APOL1 genotype with the incidence of ESRD. In addition, high-risk APOL1 genotype was associated with a marginal decrement in the annual eGFR decline (-0.55 [95 % CI: -0.94 to -0.16]) ml/min/1.73m2) compared with low-risk APOL1 genotype status. The authors concluded that this study showed that there was a modest association between high-risk APOL1 genotype with progression to CKD and from CKD to ESRD progression. To-date, there is insufficient evidence for guidance, and it may be prudent to conduct APOL1 genotype testing only in high-risk individuals (e.g., in patients with the presence of CKD or HIV) who wish to pursue genetic testing after being informed of all advantages and trade-off that are associated with it.

This systematic review and meta-analysis had several drawbacks. First, the ascertainment of CKD and ESRD outcomes by non-uniform methodologies may provide heterogeneous findings, as different methods have various sensitivities and specificities to CKD/ESRD diagnosis. Second, these researchers did not identify any prospective studies available on other forms of APOL1-associated nephropathies, such as HIV-associated nephropathy, due to the stringent inclusion criteria. Third, the available future data were exclusively from the North American African American population and did not include other African regions, as the available studies were of case-control or cross-sectional study design; thus, these findings were not generalizable globally.

Nadkarni et al (2022) stated that risk variants in the APOL1 gene on chromosome 22 are common in individuals of West African ancestry and confer increased risk of kidney failure for individuals with African ancestry and hypertension. Whether disclosing APOL1 genetic testing results to patients of African ancestry and their clinicians would affect BP, kidney disease screening, or patient behaviors is unknown. In a randomized, pilot study, these researchers examined the effects of testing and disclosing APOL1 genetic results to patients of African ancestry with hypertension and their clinicians. They randomly assigned 2,050 adults of African ancestry with hypertension and without existing CKD in 2 U.S. healthcare systems from November 1, 2014, through November 28, 2016; the final date of follow-up was January 16, 2018. Patients were randomly assigned to undergo immediate (intervention) or delayed (wait-list control group) APOL1 testing in a 7:1 ratio. Statistical analysis was carried out from May 1, 2018, to July 31, 2020. Patients randomly assigned to the intervention group received APOL1 genetic testing results from trained staff; their clinicians received results via clinical decision support in EHRs. Wait-list control patients received the results following their 12-month follow-up visit. Co-primary outcomes were the change in 3-month SBP and 12-month urine kidney disease screening comparing intervention patients with high-risk APOL1 genotypes and those with low-risk APOL1 genotypes. Secondary outcomes compared these outcomes between intervention group patients with high-risk APOL1 genotypes and controls. Exploratory analyses included psycho-behavioral factors. Among 2,050 randomly assigned patients (1,360 women [66 %]; mean [SD] age of 53 [10] years), the baseline mean (SD) SBP was significantly higher in patients with high-risk APOL1 genotypes versus those with low-risk APOL1 genotypes and controls (137 [21] versus 134 [19] versus 133 [19] mm Hg; p = 0.003 for high-risk versus low-risk APOL1 genotypes; Pp = 0.001 for high-risk APOL1 genotypes versus controls). At 3 months, the mean (SD) change in SBP was significantly greater in patients with high-risk APOL1 genotypes versus those with low-risk APOL1 genotypes (6 [18] versus 3 [18] mm Hg; p = 0.004) and controls (6 [18] versus 3 [19] mm Hg; p = 0.01). At 12 months, there was a 12 % increase in urine kidney disease testing among patients with high-risk APOL1 genotypes (from 39 of 234 [17 %] to 68 of 234 [29 %]) versus a 6 % increase among those with low-risk APOL1 genotypes (from 278 of 1,561 [18 %] to 377 of 1,561 [24 %]; p = 0.10) and a 7 % increase among controls (from 33 of 255 [13 %] to 50 of 255 [20 %]; p = 0.01). In response to testing, patients with high-risk APOL1 genotypes reported more changes in lifestyle (a subjective measure that included better dietary and exercise habits; 129 of 218 [59 %] versus 547 of 1,468 [37 %]; p < 0.001) and increased BP medication use (21 of 218 [10 %] versus 68 of 1,468 [5 %]; p = 0.005) versus those with low-risk APOL1 genotypes; 1,631 of 1,686 (97 %) declared they would get tested again. The authors concluded that return of APOL1 genetic testing results combined with EHR-based clinical decision support and disclosure of results to patients using laypersons improved SBP control and increased guideline-appropriate kidney function testing. These findings may support an approach of broad implementation of genetic medicine in primary care. This broad implementation will benefit racial and ethnic minority groups that have been traditionally under-represented in both clinical trials and genetic studies. Because it is imperative not to overlook the importance of social determinants of health in affecting chronic disease, it will also be important to understand and address the intersection of social and biological determinants in patient health.

The authors stated that this study had several drawbacks. These investigators excluded patients with CKD, and it is important to study the effects of genetic testing and disclosure of results on patients with abnormal kidney function. The intervention had a modest effect size, possibly owing to increase in medication use and/or lifestyle change, which may have substantial benefits at the population level. However, interventions with more robust components and repeated reminders may demonstrate a more significant effect. The primary outcome was within the intervention group, although these researchers did see effects when comparing patients with high-risk APOL1 genotypes versus controls. Furthermore, these investigators did not have comprehensive data on lifestyle and dietary intake or medication refill data. In this pilot trial, the period of outcome assessment was short (1 year). However, this trial has informed a national multi-center trial entitled GUARDD-US, which is ongoing. These researchers employed change in SBP as a primary outcome because of prior strong associations with CVD. However, diastolic BP (DBP) is an important risk factor and will be examined in future work. These investigators did not have information on treatment fidelity collected as part of the study protocol, and they did not have significant information on the type and dosage of medication and could only address the changes in broad groups. The upcoming GUARDD-US Trial will address these issues in more detail. Confounding (including lifestyle factors, kidney function, and severity and treatment of hypertension) could affect these results. Finally, although these investigators carried out this trial in academic, clinical, and primary care settings, they did so in 1 urban area, and it will be important to validate the findings in other settings.

Chaudhary et al (2022) noted that some but not all African Americans (AA) who carry APOL1 nephropathy risk variants develop ESKD. To identify genetic modifiers, these researchers examined gene-gene interactions in a large, prospective, cohort of the REasons for Geographic and Racial Differences in Stroke (REGARDS) Trial, a population-based study. Genotypes from 8,074 AA subjects were obtained from Illumina Infinium Multi-Ethnic AMR/AFR Extended BeadChip. These investigators compared 388 incident ESKD cases with 7,686 non-ESKD controls, using a 2-locus interaction approach. Logistic regression was employed to examine the effect of APOL1 risk status (using recessive and additive models), single nucleotide polymorphism (SNP), and APOL1*SNP interaction on incident ESKD, adjusting for age, sex, and ancestry. APOL1 *SNP interactions that met the threshold of 1.0 × 10-5 were replicated in the Genetics of Hypertension Associated Treatment (GenHAT) study (626 ESKD cases and 6,165 controls). In a sensitivity analysis, models were additionally adjusted for diabetes status. These researchers carried out additional replication in the BioVU study. Two APOL1 risk alleles prevalence (recessive model) was similar in the REGARDS and GenHAT studies. Only 1 APOL1-SNP interaction, for rs7067944 on chromosome 10, ~10 KB from the PCAT5 gene met the genome-wide statistical threshold (p interaction = 3.4 × 10-8), but this interaction was not replicated in the GenHAT study. Among other relevant top findings (with p interaction < 1.0 × 10-5), a variant (rs2181251) near SMOC2 on chromosome 6 interacted with APOL1 risk status (additive) on ESKD outcomes (REGARDS study, p interaction = 5.3 × 10-6) but the association was not replicated (GenHAT study, p interaction = 0.07, BioVU study, p interaction = 0.53). The association with the locus near SMOC2 persisted further in stratified analyses. Among those who inherited 1 or more alternate allele of rs2181251, APOL1 was associated with an increased risk of incident ESKD (OR [95 % CI: 2.27 [1.53 to 3.37]) but APOL1 was not associated with ESKD in the absence of the alternate allele (OR [95 % CI: 1.34 [0.96 to 1.85]) in the REGARDS study. The associations were consistent after adjusting for diabetes. The authors concluded that using a large genome-wide association study (GWAS) effort in an AA population, these researchers found that SNPs near the SMOC2 gene had a significant interaction with APOL1 in determining the risk of ESKD. In particular, APOL1 was associated with a higher risk of ESKD in the presence of alternate alleles at those SNPs. These investigators stated that these findings could aid in improving the understanding of the potential modifiers of APOL1 risk status that contribute to the observed incomplete penetrance of that locus.

The authors stated that being a population-based study, there were always chances of measurement bias. Specifically, these investigators could not discern the specific type of nephropathy or underlying cause associated with ESKD diagnosis in their cohorts. Better characterizing of phenotypes in this trial as well as harmonizing of the phenotypes with prior studies could have improved the consistency with other published studies. The BioVU Trial was an EHR-based study, and the population was younger compared to REGARDS and GenHAT studies. These investigators did not examine if the interaction effects were age-dependent; however, there was a chance that biological mechanisms driven by these variants could be age-related. These researchers could not rule out the possibility that a higher rate of incident ESKD could also be a proxy for improved survival of CKD. APOL1 high-risk genotypes were associated with better survival after accounting for kidney-related co-morbidities and genetic ancestry. However, high-risk APOL1 status has been associated with the progression of kidney function to ESKD among those with CKD. Due to lack of multiple time-point measurements in their cohorts, the authors could not discern these differences between improved survival and progression of CKD. While the authors found biologically relevant associations, they could not rule out the possibility of type II error due to the under-estimation of interaction effects in the standard regression-based interaction tests or lack of replication. However, the direction of the associations was consistent in the replication study that asserted the significance of these findings to a certain extent. These researchers stated that further translational work and well-powered studies could aid in determining if these associations are valid. APOL1 is located at the chromosome 22 locus that is enriched for intra-chromosomal duplications and duplicated APOL1 genotype segments with apparent risk genotypes have been observed in a few samples of 1,000 genome population. Identifying such duplicated segments in this study population was beyond the scope of this trial.

Chen et al (2022) stated that the APOL1 risk variants (G1 and G2) are associated with kidney disease among Black adults; however, the clinical presentation is heterogeneous. In mouse models and cell systems, increased gene expression of G1 and G2 confers cytotoxicity. How APOL1 risk variants relate to the circulating proteome warrants further investigation. Among 461 African American Study of Kidney Disease and Hypertension (AASK) subjects (mean age of 54 years; 41 % women; mean GFR of 46 ml/min/1.73 m2), these investigators examined associations of APOL1 risk variants with 6,790 serum proteins (measured via SOMAscan) using linear regression models. Co-variates included age, sex, percentage of European ancestry, and protein principal components 1-5. Associated proteins were then evaluated as mediators of APOL1-associated risk for kidney failure. Findings were replicated among 875 Atherosclerosis Risk in Communities (ARIC) study Black subjects (mean age of 75 years; 66 % women; mean eGFR: 67 ml/min/1.73 m2). In the AASK study, having 2 (versus 0 or 1) APOL1 risk alleles was associated with lower serum levels of APOL1 (p = 3.11E-13; p = 3.12E-06 [2 aptamers]), APOL2 (p = 1.45E-10), CLSTN2 (p = 2.66E-06), MMP-2 (p = 2.96E-06), SPOCK2 (p = 2.57E-05), and TIMP-2 (p = 2.98E-05) proteins. In the ARIC study, APOL1 risk alleles were associated with APOL1 (p = 1.28E-11); MMP-2 (p = 0.004) and TIMP-2 (p = 0.007) were associated only in an additive model, and APOL2 was not available. APOL1 high-risk status was associated with a 1.6-fold greater risk of kidney failure in the AASK study; none of the identified proteins mediated this association. APOL1 protein levels were not associated with kidney failure in either cohort. The authors concluded that the APOL1 risk variants were strongly associated with lower circulating levels of APOL1 and other proteins; however, none mediated the APOL1-associated risk for kidney failure. These researchers stated that APOL1 protein level was also not associated with kidney failure.

The authors stated that this study had several drawbacks. First, the sample size was relatively small. Second, the SOMAscan platform may not distinguish between protein products of the G0, G1, and G2 variants. The G1 variant consists of 2 non-synonymous SNPs leading to amino acid (AA) substitutions, whereas the G2 variant consists of a 6–base pair deletion that results in the loss of 2 AAs. These changes in AAs might reduce the ability of the aptamer to bind to APOL1 protein. Third, despite adjusting for multiple baseline co-variates, the possibility of residual confounding remains. Fourth, the study populations of AASK and ARIC were notably different, perhaps making it more difficult to replicate associations present only in specific populations. However, these researchers also viewed this as a strength, as it provided support for the generalizability of these results. Fifth, given that APOL1 high-risk status was not associated with CKD progression in ARIC, perhaps due to limited power from reduced sample size or the study population being older, these investigators were unable to evaluate for mediation in the replication cohort.

Muiru et al (2022) noted that few studies have examined racial disparities in acute kidney injury (AKI), in contrast to the extensive literature on racial differences in the risk of kidney failure. In a prospective, cohort study, these researchers examined potential differences in risk in the setting of CKD. They studied 2,720 self-identified Black or White subjects with CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study from July 1, 2013, to December 31, 2017. Main outcome measure was hospitalized AKI (50 % or higher increase from nadir to peak serum creatinine). Cox regression models adjusting for demographics (age and sex), pre-hospitalization clinical risk factors (diabetes, BP, CVD, eGFR, proteinuria, receipt of angiotensin-converting enzyme inhibitors [ACEi] or angiotensin-receptor blockers [ARBs]), and socioeconomic status (insurance status and education level). In a subset of subjects with genotype data, these investigators adjusted for APOL1 high-risk status and sickle cell trait. Black subjects (n = 1,266) were younger but had a higher burden of pre-hospitalization clinical risk factors. The incidence rate of 1st AKI hospitalization among Black subjects was 6.3 (95 % CI: 5.5 to 7.2) per 100 person-years versus 5.3 (95 % CI: 4.6 to 6.1) per 100 person-years among White subjects. In an unadjusted Cox regression model, Black subjects were at a modestly increased risk of incident AKI (HR, 1.22 [95 % CI: 1.01 to 1.48]) compared with White subjects. However, this risk was attenuated and no longer significant after adjusting for pre-hospitalization clinical risk factors (adjusted HR, 1.02 [95 % CI: 0.83 to 1.25]) . There were only 11 AKI hospitalizations among individuals with high-risk APOL1 risk status and 14 AKI hospitalizations among individuals with sickle cell trait. The authors concluded that in this prospective, multi-center study of CKD patients, racial disparities in AKI incidence were modest and were explained by differences in pre-hospitalization clinical risk factors. The authors stated that the main drawback of this trial included subjects were limited to research volunteers and potentially not fully representative of all CKD patients.

Lentine et al (2022) noted that the incidence of kidney failure in African Americans is 2 to 3 times higher than that in White Americans. A portion of the increased kidney risk appears to be because of polymorphisms in the APOL1 gene. The precise mechanism of how APOL1 renal-risk variants accelerate kidney disease progression is a matter of intense research, and currently there are no proven treatments for APOL1-related kidney disease. Currently, there are large NIH-funded national studies (APOL1 Long-term Kidney Transplantation Outcomes Network [“APOLLO”] and Living Donor Extended Time Study [“LETO”]) underway to help define the role of APOL1 genotyping in the context of kidney donation and transplantation. However, the role of APOL1 genotyping in CKD care remains unclear. In a pilot study, these researchers offered APOL1 genetic testing and counseling and examined the attitudes and concerns related to APOL1 testing and kidney risk management among self-identified African Americans seen in the Hypertension and Nephrology Clinics at a Midwestern academic medical center. In the 1st phase of this ongoing project, these investigators recruited 128 subjects who self-identified as African American. Baseline surveys to evaluate patient attitudes and concerns regarding APOL1 genetic testing and kidney risk management were completed before blood samples were drawn and sent to a Clinical Laboratory Improvement Amendments-approved laboratory for APOL1 genotyping. Among the cohort, 71 (55 %) were women, and the mean age was 57 years. Nearly 40 % of subjects (n = 51; 39 %) reported an annual family income of less than $15,000. Obesity was present in 93 (73 %) subjects. The median CKD Epidemiology Collaboration 2021 equation eGFR was 42 ml/min/1.73 m2 and median urinary albumin-creatinine ratio was 93 mg/g. A mean of 3 anti-hypertensive medications were used to achieve a mean SBP of 146 mm Hg, and 81 (63 %) subjects were receiving ACEi or ARB therapy. Overall, nearly all subjects (120 [94 %]) reported being concerned about kidney disease. When stratified by CKD stages, 36 (94 %), 50 (91 %), and 34 (97 %) subjects with eGFR rates of 60 or higher, 30 to 59, and less than 30 ml/min/1.73 m2, respectively, reported being concerned about kidney disease. Most of the subjects thought it was a good idea to be tested for genes that may impact kidney disease (120 [94 %]) and would want APOL1 testing for their children (104 [81 %]). Only a small portion (21 [16 %]) reported that they would be very upset if genetic results showed that they had a high-risk APOL1 genotype. Survey responses did not differ appreciably when stratified by CKD stages. Subjects reported that knowledge of a high-risk APOL1 genotype would lead to positive changes in health-related behaviors, including seeking medical advice and dietary and lifestyle modification. Few individuals (n = 2; less than 2 %) reported that they would take no action and only 1 (less than 1 %) would stop taking BP medications if they were found to have a high-risk APOL1 genotype. Among the subjects genotyped to-date, 50 (39 %) had 0, 56 (44 %) had 1, and 22 (17 %) had 2 APOL1 renal-risk variants (high-risk genotypes). The authors reported that African American patients at an urban Midwestern medical center were receptive toward APOL1 genetic testing and believed that testing would motivate changes in health-related behavior. Moreover, these researchers stated that further research is needed to determine the optimal patient-centered use of this emerging risk-assessment tool.

The authors stated that this study had several drawbacks. First, results from 1 center may not generalize to other populations and practice settings. Second, subjects who agreed to participate may differ from the broader population (e.g., they may be more interested in their own health), which may contribute to selection bias. Third, the proportion of high-risk genotypes (17 %) was slightly higher than prior general population estimates of approximately 13 %, which likely reflected sampling from nephrology clinics. Fourth, although these investigators found that subjects may be motivated to engage in health-promoting behaviors following genotyping, the current design did not measure the impact of APOL1 testing on patient outcomes. However, a recent trial demonstrated improved BP control, and these researchers intend to create a prospective cohort to provide insights on this topic over time.

Biggers and Pietrangelo (2022) noted that everyone carries the APOL1 gene; however, only individuals with African ancestry inherit certain genetic variants. The authors stated that the high-risk variants do not necessarily increase the risk of kidney disease in African American people with diabetes, but they do speed up disease progression. Researchers do not know why some carriers of the variants develop kidney disease while others do not. Various environmental, lifestyle, and societal factors may play a role. Currently, there are no targeted therapies for APOL1-associated kidney disease; however, ongoing research suggests that change is on the horizon.

Khan et al (2022) stated that CKD is a common complex condition associated with high morbidity and mortality. Polygenic prediction could enhance CKD screening and prevention; however, this approach has not been optimized for ancestrally diverse populations. By combining APOL1 risk genotypes with GWAS of kidney function, these researchers designed, optimized and validated a genome-wide polygenic score (GPS) for CKD. The new GPS was tested in 15 independent cohorts, including 3 cohorts of European ancestry (n = 97,050), 6 cohorts of African ancestry (n = 14,544), 4 cohorts of Asian ancestry (n = 8,625) and 2 admixed Latinx cohorts (n = 3,625). These investigators demonstrated score transferability with reproducible performance across all tested cohorts. The top 2 % of the GPS was associated with nearly 3-fold increased risk of CKD across ancestries. In African ancestry cohorts, the APOL1 risk genotype and polygenic component of the GPS had additive effects on the risk of CKD. These researchers stated that for individuals of African ancestry, APOL1 is an important part of the picture, but not the only part. This information may be significant when new drugs being developed specifically for individuals with APOL1 become available. Individuals with APOL1 but low polygenic risk may not need specific interventions, since their risk could be comparable to that of the general population. In contrast, individuals with the highest genetic risk -- those with APOL1 and a high polygenic risk --may benefit the most from lifestyle changes or drug treatment. These investigators stated that more testing of the new prediction method is needed before it can be used in clinical settings. The method is being tested in a large national study, entitled eMERGE-IV, that will screen subjects and offer additional follow-up and laboratory testing for individuals at high genetic risk . The study will determine if genetic testing for the new risk score affects clinical outcomes, including lifestyle changes and rates of new kidney disease diagnoses.

It should be noted that the Mayo Clinic Laboratories (2022) states that the “APOL1 Genotype, Varies” Test is not useful for clinical management of individuals with APOL1 risk genotypes.

Furthermore, an UpToDate review on “Gene test interpretation: APOL1 (chronic kidney disease gene)” (Bleyer, 2022) states that “While disclosure of actionable genetic test results is well-accepted, there is greater uncertainty about disclosure of results such as APOL1 genotype, since kidney disease may not develop; and management interventions are limited. Learning the results of APOL1 genotype may improve outcomes; in a randomized trial involving >2000 participants with hypertension, those assigned to disclosure had greater improvements in blood pressure control and biomarkers of CKD”.

KidneyIntelX

RenalytixAI is a developer of artificial intelligence (AI) enabled clinical diagnostic solutions for kidney disease. RenalytixAI’s solutions are being designed to make significant improvements in kidney disease risk assessment, clinical care and patient stratification for drug clinical trials. RenalytixAI’s technology platform will draw from distinct sources of patient data, including large electronic health records, predictive blood-based biomarkers and other genomic information for analysis by learning computer algorithms. RenalytixAI intends to build a deep, unique pool of kidney disease-related data for different AI-enabled applications designed to improve predictive capability and clinical utility over time. In 2019, RenalytixAI expects to launch KidneyIntelX, an artificial intelligence in-vitro diagnostic product intended to support physician decision-making by improving identification, prediction, and risk stratification of patients with progressive kidney disease.

On May 2, 2019, the Food and Drug Administration (FDA) granted “Breakthrough Device Designation” to KidneyIntelX. KidneyIntelX is designed to diagnose and improve clinical management of patients with type II diabetes with fast-progressing kidney disease. The diagnostic will use machine learning algorithms to evaluate the combination of predictive blood-based biomarkers, including sTNFR1, sTNFR2 and KIM1, in combination with electronic health record information, to identify progressive kidney disease.

Pena and co-workers (2015) identified a novel panel of biomarkers predicting renal function decline in type 2 diabetes mellitus (T2DM), using biomarkers representing different disease pathways speculated to contribute to the progression of diabetic nephropathy. These researchers carried out a systematic data integration to select biomarkers representing different disease pathways. A total of 28 biomarkers were measured in 82 patients seen at an out-patient diabetes center in the Netherlands. Median follow-up was 4.0 years. These investigators compared the cross-validated explained variation (R2) of 2 models to predict estimated glomerular filtration rate (eGFR) decline, one including only established risk markers, the other adding a novel panel of biomarkers. Least absolute shrinkage and selection operator (LASSO) was used for model estimation. The C-index was calculated to assess improvement in prediction of accelerated eGFR decline defined as less than -3.0 ml/min/1.73 m2/year. Patients' average age was 63.5 years and baseline eGFR was 77.9 ml/min/1.73 m2. The average rate of eGFR decline was -2.0 ± 4.7 ml/min/1.73 m2/year. When modeled on top of established risk markers, the biomarker panel including matrix metallopeptidases, tyrosine kinase, podocin, CTGF, TNF-receptor-1, sclerostin, CCL2, YKL-40, and NT-proCNP improved the explained variability of eGFR decline (R2 increase from 37.7 % to 54.6 %; p = 0.018) and improved prediction of accelerated eGFR decline (C-index increase from 0.835 to 0.896; p = 0.008). The authors concluded that a novel panel of biomarkers representing different pathways of renal disease progression including inflammation, fibrosis, angiogenesis, and endothelial function improved prediction of eGFR decline on top of established risk markers in patients with T2DM. However, these researchers stated that these findings need to be confirmed in a large prospective cohort to validate and assess its applicability in a broad T2DM population.

The authors stated that the main drawback of this study was the measurement of multiple biomarkers in a small sample size. However, as advancing laboratory techniques generate larger amounts of data, methods of data analysis to accommodate “big data” with smaller sample sizes are needed. The rigorous statistical method of the LASSO regression allowed for modeling many biomarkers in the small sample size, and multiple imputation was used to avoid truncating observations due to missing data. The true predictive capacity of the model could have been over-estimated due to the prediction model being developed and tested in the same sample, and these researchers agreed that external validation is necessary. In the absence of external validation, these investigators carried out internal boot-strap validation in an attempt to minimize this limitation; GFR was estimated using a serum creatinine-based equation instead of by direct measurement, which may have contributed to mis-classification bias. However, this could have only resulted in an under-estimation of the strength of the reported associations. These researchers chose to omit 5 biomarkers from their analysis due to many missing or below level of detection (LOD) values. While the exclusion of these biomarkers from their analysis may have resulted in an under-representation of pathways, the omission of biomarkers could have only under-estimated the predictive ability of the biomarker panel. The authors stated that additional drawbacks included the lack of information concerning insulin use, diet, and renin-angiotensin-aldosterone system medication type and dose, which clearly represent unmeasured confounders in this study.

Heinzel and colleagues (2018) stated that the decline of eGFR in patients with T2DM is variable, and early interventions would likely be cost-effective. These researchers examined the contribution of 17 plasma biomarkers to the prediction of eGFR loss on top of clinical risk factors. They studied participants in PROVALID (PROspective cohort study in patients with T2DM for VALIDation of biomarkers), a prospective multi-national cohort study of patients with T2DM and a follow-up of more than 24 months (n = 2,560; baseline median eGFR, 84 ml/min/1.73 m2; urine albumin-to-creatinine ratio, 8.1 mg/g). The 17 biomarkers were measured at baseline in 481 samples using Luminex and ELISA. The prediction of eGFR decline was evaluated by linear mixed modeling. In univariable analyses, 9 of the 17 markers showed significant differences in median concentration between stable and fast-progressing patients. A linear mixed model for eGFR obtained by variable selection exhibited an adjusted R2 of 62 %. A panel of 12 biomarkers was selected by the procedure and accounted for 34 % of the total explained variability, of which 32 % was due to 5 markers (KIM1, FGF23, NTproBNP, HGF, and MMP1). The individual contribution of each biomarker to the prediction of eGFR decline on top of clinical predictors was generally low. When included into the model, baseline eGFR exhibited the largest explained variability of eGFR decline (R2 of 79 %), and the contribution of each biomarker dropped below 1 %. The authors concluded that in this longitudinal study of patients with T2DM and maintained eGFR at baseline, 12 of the 17 candidate biomarkers were associated with eGFR decline, but their predictive power was low. These researchers stated that given the inferior performance of this highly selected set of biomarkers in early-stage chronic kidney disease patients to predict future eGFR loss, these markers are not likely to be useful for clinical decision-making.

Norris and co-workers (2018) stated that albuminuria, elevated serum creatinine and low eGFR are pivotal indicators of kidney decline. Yet, it is uncertain if these and emerging biomarkers such as uric acid represent independent predictors of kidney disease progression or subsequent outcomes among individuals with T2DM. These researchers examined the available literature documenting the role of albuminuria, serum creatinine, eGFR, and uric acid in predicting kidney disease progression and cardio-renal outcomes in persons with T2DM. Embase, Medline, and Cochrane Central Trials Register and Database of Systematic Reviews were searched for relevant studies from January 2000 through May 2016. PubMed was searched from 2013 until May 2016 to retrieve studies not yet indexed in the other databases. Observational cohort or non-randomized longitudinal studies relevant to albuminuria, serum creatinine, eGFR, uric acid and their association with kidney disease progression, non-fatal cardiovascular events, and all-cause mortality as outcomes in persons with T2DM, were eligible for inclusion. Two reviewers screened citations to ensure studies met inclusion criteria. From 2,249 citations screened, 81 studies were retained, of which 39 were omitted during the extraction phase (cross-sectional [n = 16]; no outcome/measure of interest [n = 13]; not T2DM specific [n = 7]; review article [n = 1]; editorial [n = 1]; not in English language [n = 1]). Of the remaining 42 longitudinal study publications, biomarker measurements were diverse, with 7 different measures for eGFR and 5 different measures for albuminuria documented. Kidney disease progression differed substantially across 31 publications, with GFR loss (n = 9 [29.0 %]) and doubling of serum creatinine (n = 5 [16.1 %]) the most frequently reported outcome measures. Numerous publications presented risk estimates for albuminuria (n = 18), serum creatinine/eGFR (n = 13), or both combined (n = 6), with only 1 study reporting for uric acid. Most often, these biomarkers were associated with a greater risk of experiencing clinical outcomes. The authors concluded that despite the utility of albuminuria, serum creatinine, and eGFR as predictors of kidney disease progression, further efforts to harmonize biomarker measurements are needed given the disparate methodologies observed in this review. Such efforts would help better establish the clinical significance of these and other biomarkers of renal function and cardio-renal outcomes in persons with T2DM.

Colombo and associates (2019) noted that as part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) program, these researchers previously reported that large panels of biomarkers derived from 3 analytical platforms maximized prediction of progression of renal decline in T2DM. These investigators hypothesized that smaller (n less than or equal to 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in 3 additional T2DM cohorts. These investigators used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case-control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). They analyzed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. They used the event definition of loss of greater than 20 % of baseline eGFR during follow-up from a baseline eGFR of 30 to 75 ml/min/1.73 m2. A total of 403 individuals experienced an event during a median follow-up of 7 years. These researchers used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function. A total of 12 biomarkers showed significant association with eGFR decline adjusted for co-variates in 1 or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardized odds ratios (ORs) for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical co-variates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the receiver operating characteristic curve (AUROC) in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multi-platform panels previously identified, consistently improved prediction further across all validation sets. The authors concluded that the combination of B2M and KIM-1, measured in serum, in addition to clinical co-variates, significantly improved prediction of renal function decline in T2DM on top of clinical data; use of a larger multi-platform biomarker panel did not consistently improve prediction further.

The authors stated that this study had several limitations. Since there were differences in entry criteria and definition of caseness between their discovery cohort and the cohort sets studied here, they could not consider this strictly as a replication study. The original biomarker panels were identified based on their power to predict a greater than or equal to 40 % decline in eGFR over a maximum follow-up of 3.5 years whereas in the current study these researchers looked at a decline of greater than or equal to 20 % over a longer follow-up period. Thus, they were applying their biomarkers to a much less severe phenotype than previously. Part of the rationale for this study was to examine the use of biomarkers for less extreme phenotypes. These investigators expected that this might diminish associations between biomarkers and outcome. However, they had confirmed that the biomarkers that predict more severe decline in renal function can also predict less severe decline and may be useful at earlier stages of kidney disease. Since a 20 % drop in eGFR will be a noisier outcome measure than a 40 % drop, this meant that these researchers would have had less power to detect biomarker associations. Nevertheless, it would not increase the level of false associations and their strict cross-validation techniques further protect against over-fitting. In the GoDARTS and CARDS sample sets in this study the clinical co-variates were poor predictors compared with the original discovery case-control study and SDR cohort. However, despite this, addition of the biomarkers increased the AUROC to a similar degree in the SDR and GoDARTS cohorts. These investigators did not have the mass spectrometry biomarkers available in the CARDS samples.

Furthermore, an UpToDate review on “Diagnostic approach to the patient with newly identified chronic kidney disease” (Fatehi and Hsu, 2019) does not mention the measurements of biomarkers, KIM1, and sTNFR1, and sTNFR2 as a management tool.

In an observational, cohort study, Chan and colleagues (2021) developed/validated a machine-learned, prognostic risk score (KidneyIntelX) combining electronic health records (EHR) and biomarkers. This study included patients with prevalent diabetic kidney disease (DKD)/banked plasma from 2 EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net re-classification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of greater than or equal to 5 ml/min per year, greater than or equal to 40 % sustained decline, or kidney failure within 5 years. In 1,146 patients, the median age was 63 years, 51 % were women, the baseline eGFR was 54 ml/min/1.73 m2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol, follow-up was 4.3 years and 21 % had the composite endpoint. On cross-validation in derivation (n = 686), KidneyIntelX had an AUC of 0.77 (95 % confidence interval [CI]: 0.74 to 0.79). In validation (n = 460), the AUC was 0.77 (95 % CI: 0.76 to 0.79). By comparison, the AUC for the clinical model was 0.62 (95 % CI: 0.61 to 0.63) in derivation and 0.61 (95 % CI: 0.60 to 0.63) in validation. Using derivation cut-offs, KidneyIntelX stratified 46 %, 37 % and 17 % of the validation cohort into low-, intermediate- and high-risk groups for the composite kidney endpoint, respectively. The PPV for progressive decline in kidney function in the high-risk group was 61 % for KidneyIntelX versus 40 % for the highest risk strata by KDIGO categorization (p < 0.001). Only 10 % of those scored as low risk by KidneyIntelX experienced progression (i.e., NPV of 90 %). The NRIevent for the high-risk group was 41 % (p < 0.05). The authors concluded that KidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD.

The authors stated that this study had several drawbacks. First, uACR was missing in 38 % of the cohort; however, this was representative of current state of care. Moreover, the objective was to develop a risk score using real world data from EHR to predict where uACR is missing in a significant number of patients. More widespread availability of uACR values would enhance the performance of KidneyIntelX, as it was a contributing feature in this model. Moreover, even with this drawback, KidneyIntelX had a more robust performance than the KDIGO very high-risk stratum in the sub-population with uACR measurements. Second, there was no protocolized follow-up resulting in missing data and lack of kidney biopsies. Missing data could lead to biased machine learning models and the data were prone to ascertainment bias. However, the median number of eGFR values per subject was 16, and the median time of follow-up was 4.3 years. Although the primary biobanked cohorts used in the study were broadly representative of individuals with DKD in type 2 diabetes in terms of race/ethnicity and gender, these researchers could not rule out an inherent bias since the recruitment was opt-in recruitment from out-patient clinics and individuals who chose to participate in the cohorts from which the study population was selected may be different from those who did not participate in the primary cohorts. Third, these investigators did not have information on subjects’ socioeconomic status or the duration of the diabetes diagnosis. In the absence of biopsy, these researchers could not exclude the possibility that CKD may be due to other causes. The test performance of KidneyIntelX (random forest algorithm) was higher than a logistic regression model that used the final top biomarker and clinical features that were selected by the random forest approach. However, these investigators chose to employ the machine learning approach because random forests could integrate feature selection and modelling as well as efficiently model potential non-linear interactions between features. Fourth, both cohorts were from Northeast U.S. and an independent validation cohort is needed to ensure generalizability; however, only 1/3 of the subjects were white, so there was adequate representation of racial groups that experienced disparities for kidney disease.

Tokita et al (2022) noted that the lack of precision to identify patients with early-stage DKD at near-term risk for progressive decline in kidney function results in poor disease management often leading to kidney failure requiring unplanned dialysis. The KidneyIntelX is a multiplex, bio-prognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that employs machine learning (ML) to generate a risk score for progressive decline in kidney function over 5-year in adults with early-stage DKD. In an interim analysis, these researchers examined the impact of KidneyIntelX on the management and outcomes in a Health System in the real-world evidence (RWE) study. KidneyIntelX was introduced into a large metropolitan Health System via a population health-defined approved care pathway for patients with stages 1 to 3 DKD between November 2020 to March 2022. Decision impact on visit frequency, medication management, specialist referral, and selected laboratory values was evaluated. These investigators carried out an interim analysis in patients through 6-month post-test date to examine the impact of risk level with clinical decision-making and outcomes. A total of 1,686 patients were enrolled in the RWE study and underwent KidneyIntelX testing and subsequent care pathway management. The median age was 68 years, 52 % were women, 26 % self-identified as Black, and 94 % had hypertension. The median baseline eGFR was 59 ml/min/1.73 m2, uACR was 69 mg/g, and hemoglobin A1c (HbA1c) was 7.7 %. After testing, a clinical encounter in the 1st month occurred in 13 %, 43 %, and 53 % of low-risk, intermediate-risk, and high-risk patients, respectively; and 46 %, 61 %, and 71 % had at least 1 action taken within the first 6 months. High-risk patients were more likely to be placed on sodium-glucose cotransporter-2 (SGLT2) inhibitors (OR = 4.56; 95 % CI: 3.00 to 6.91 versus low-risk), and more likely to be referred to a specialist such as a nephrologist, endocrinologist, or dietician (OR = 2.49; 95 % CI: 1.53 to 4.01) compared to low-risk patients. The authors concluded that the combination of KidneyIntelX, clinical guidelines and educational support resulted in changes in clinical management by clinicians. After testing, there was an increase in visit frequency, referrals for disease management, and introduction to guideline-recommended medications. These differed by risk category, indicating an impact of KidneyIntelX risk stratification on clinical care. Moreover, these researchers stated that it is anticipated that the continued enrollment and follow-up will further support and add to these interim observations.

The authors stated that this study had 2 main drawbacks. First, the patient characterization in this study did not include information such as compliance with filling prescriptions, co-morbidities, or additional clinical laboratory values beyond what was measured in the clinic. Additional information such as health insurance coverage, which would potentially impact the ability for some patients to obtain recommended medications was also not recorded. It is worth noting that these attributes will be included as these researchers reach important study milestones at year 1 and 2. Second, these investigators were unable to capture frequency of emergency room (ER) visits and/or hospitalizations at this interim time-point; however, this also will be captured in the 2 to 5 years outcome and is included as an objective for that future study report. These investigators will also continue to build upon their interrogation of patient and population level controls to re-affirm current observations.

Nadkarni et al (2022) stated that KidneyIntelX risk stratified individuals for kidney, heart failure (HF), and death outcomes in the Canagliflozin Cardiovascular Assessment Study. Individuals scored as high-risk appeared to derive more of benefit from treatment with canagliflozin versus placebo. The authors concluded that KidneyIntelX provided risk stratification for a triple composite endpoint that included not only the kidney-specific outcome of progression, but also clinically relevant outcomes of hospitalizations for HF and all-cause mortality, even after adjusting for several other risk factors for these outcomes. They stated that these findings suggested that KidneyIntelX may have utility as a clinical trial enrichment tool for therapies to ameliorate cardiorenal risk and provides further impetus to increase adoption of under-utilized guideline-recommended therapies to reduce risk of kidney disease progression, hospitalizations for HF (HHF), and death in clinical practice. The authors stated that limitations of this post-hoc analysis included the lack of an independent external validation cohort, the use of an algorithm not specifically trained for the broad clinical composite assessed, and, although these researchers adjusted for 11 clinical co-variates, potential for residual confounding.

Lam et al (2022) noted that KidneyIntelX is a composite risk score, incorporating biomarkers and clinical variables for predicting progression of DKD; however, the use of this score in the context of SGLT2 inhibitors and how changes in the risk score are associated with future kidney outcomes are unknown. In a post-hoc, observational analysis, these researchers measured soluble tumor necrosis factor receptor (TNFR)-1, soluble TNFR-2, and kidney injury molecule 1 on banked samples from CANagliflozin cardioVascular Assessment Study (CANVAS) trial subjects with baseline DKD (eGFR of 30 to 59 ml/min/1.73 m2 or uACR of 30 mg/g or higher) and generated KidneyIntelX risk scores at baseline and years 1, 3, and 6. They examined the association of baseline and changes in KidneyIntelX with subsequent DKD progression (composite outcome of an eGFR decline of 5 ml/min/year or higher [using the 6-week eGFR as the baseline in the canagliflozin group], 40 % or higher sustained decline in the eGFR, or kidney failure). This study included 1,325 CANVAS subjects with concurrent DKD and available baseline plasma samples (mean eGFR 65 ml/min/1.73 m2 and median uACR 56 mg/g). During a mean follow-up of 5.6 years, 131 subjects (9.9 %) experienced the composite kidney outcome. Using risk cut-offs from prior validation studies, KidneyIntelX stratified patients to low- (42 %), intermediate- (44 %), and high-risk (15 %) strata with cumulative incidence for the outcome of 3 %, 11 %, and 26 % (RR 8.4; 95 % CI: 5.0 to 14.2) for the high-risk versus low-risk groups. The differences in eGFR slopes for canagliflozin versus placebo were 0.66, 1.52, and 2.16 ml/min/1.73 m2 in low, intermediate, and high KidneyIntelX risk strata, respectively. KidneyIntelX risk scores declined by 5.4 % (95 % CI: -6.9 to -3.9) in the canagliflozin arm at year 1 versus an increase of 6.3 % (95 % CI: 3.8 to 8.7) in the placebo arm (p < 0.001). Changes in the KidneyIntelX score at year 1 were associated with future risk of the composite outcome (OR per 10 unit decrease 0.80; 95 % CI: 0.77 to 0.83; p < 0.001) after accounting for the treatment arm, without evidence of effect modification by the baseline KidneyIntelX risk stratum or by the treatment arm. The authors concluded that KidneyIntelX successfully risk-stratified a large multi-national external cohort for progression of DKD, and greater numerical differences in the eGFR slope for canagliflozin versus placebo were observed in those with higher baseline KidneyIntelX scores. Canagliflozin treatment reduced KidneyIntelX risk scores over time and changes in the KidneyIntelX score from baseline to 1 year were associated with future risk of DKD progression, independent of the baseline risk score and treatment arm.

The authors stated that this study had several drawbacks. First, the findings presented were post-hoc, observational analyses of a randomized controlled trial (RCT) that could not be used to infer causality. Second, while the prognostic scoring by KidneyIntelX represented external validation of previous findings, the findings of the dynamic changes in KidneyIntelX have not been shown previously; therefore, further validation outside of the CANVAS cohort population is needed. Third, although CANVAS was a very large RCT of more than 4,000 subjects, the proportion with DKD at baseline with available banked samples totaled only 1,325, and the analytic dataset for the dynamic changes was approximately 1,000; therefore, some of the findings were limited by a lack of more robust statistical power. Studies in larger populations would aid in improving the precision of the point estimates and CIs by each KidneyIntelX risk stratum and their associated changes over time. Fourth, while other analyses have demonstrated that KidneyIntelX deployment could result in significant cost savings, the degree of cost savings in a population managed with high baseline use of the SGLT2i is yet to be determined.

Datar et al (2022) stated that eGFR and albuminuria, the current standard-of-care (SOC) tests that predict risk of kidney function decline in early-stage DKD, are only modestly useful. In a prospective, web-based survey administered among primary care physicians (PCPs) in the US, these researchers examined the decision-making impact of an AI-enabled prognostic test, KidneyIntelX, in the management of DKD by PCPs. These researchers employed conjoint analysis with multi-variable logit models to estimate PCP preferences. The survey included hypothetical patient profiles with 6 attributes: albuminuria, eGFR, age, blood pressure (BP), HbA1c, and KidneyIntelX result. Each PCP viewed 8 patient profiles randomly selected from 42 unique profiles having 1 level from each attribute. For each patient, PCPs were asked to indicate whether they would prescribe a SGLT2 inhibitor, increase angiotensin receptor blocker (ARB) dose, and/or refer to a nephrologist. A total of 401 PCPs completed the survey (response rate = 8.8 %). The relative importance of the top 2 attributes for each decision were HbA1c (52 %) and KidneyIntelX result (23 %) for prescribing SGLT2 inhibitors, BP (62 %) and KidneyIntelX result (13 %) for increasing ARB dose, and eGFR (42 %) and KidneyIntelX result (27 %) for nephrologist referral. A high-risk KidneyIntelX result was associated with significantly higher odds of PCPs prescribing SGLT2 inhibitors (OR, 1.64; 95 % CI: 1.29 to 2.08), increasing ARB dose (OR, 1.49; 95 % CI: 1.17 to 1.89), and referring to a nephrologist (OR, 2.47; 95 % CI: 1.99 to 3.08) compared with no test. The authors concluded that the findings of this study showed that if tests such as KidneyIntelX were made readily accessible, PCPs would incorporate the test results into their treatment plan, ultimately making more informed decisions in the management of their patients with DKD, potentially resulting in improved outcomes.

The authors stated that this study had several drawbacks. First, these findings were limited to the range of levels of key attributes included in this analysis and may not completely capture the scope of influence of other attributes on decision-making. However, the probability of this was very low, given that the attributes and levels included in this analysis were based on extensive secondary research and input from practicing clinical experts in the field of DKD. Second, to reduce burden, respondents were shown only a subset of the full factorial design of profiles that represented all the possible attribute and level combinations. However, the potential bias introduced from using a subset of profiles was minimal, because the 42 profiles shown to respondents (fractional factorial design) had a high design efficiency (D-efficiency) of 0.98, which is a measure of the D-efficiency of the conjoint analysis (maximum possible D-efficiency = 1). Third, this was not a real-world study measuring actual physician behavior in practice. However, whereas traditional surveys may suffer from social desirability bias, wherein survey respondents tend to answer questions in a manner that will be viewed favorably by others, this study instead employed a novel preference elicitation technique with an experimental design that examined implicit preferences; thus, minimizing this bias to a significant degree. This survey also had test-retest reliability, demonstrated by the fact that both pre-test participants chose the same response for the 2 hold-out cases. Fourth, this study compared clinical utility of KidneyIntelX with the SOC risk stratification currently being employed in practice (KDIGO risk stratification), as the objective was to derive practice-based utility. These investigators did not test the utility of KidneyIntelX against alternative theoretical risk scores (e.g., Kidney Failure Risk Equations) that predict kidney disease progression. They stated that the findings of this study should be interpreted with this limitation in mind. These researchers stated that future studies should compare the performance and clinical utility of KidneyIntelX with other algorithms.

Measurement of Serum Uric Acid

Goncalves et al (2022) noted that the function of uric acid (UA) in the genesis and evolution of CKD has motivated many studies; however, the results remain unclear. In a systematic review and meta-analysis of cohort studies, these researchers examined the association of UA levels with the incidence and progression of CKD. PubMed/Medline, Lilacs/Bireme and Web of Science were searched to identify eligible studies, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Data were presented for CKD incidence and progression separately. For the meta-analysis, studies with data stratified by subgroups according to serum UA levels were selected. The inverse variance-weighted random effects model was employed to generate a combined effect estimate. Meta-regressions were carried out to identify the causes of heterogeneity. The Newcastle-Ottawa Scale was used to examine the risk of bias. The publication bias was tested by funnel plot and Egger's test. A total of 18 CKD incidence studies (n = 398,663) and 6 CKD progression studies (n = 13,575) were included. An inverse relationship was observed between UA levels and protection from CKD incidence and progression. Lower UA levels were protective for the risk of CKD incidence (RR 0.65 [95 % CI: 0.56 to 0.75]) and progression (RR 0.55 [95 % CI: 0.44 to 0.68]). The authors concluded that UA appeared to be implicated both in the genesis of CKD and its evolution.

The authors stated that this study had several drawbacks that need to be taken into consideration. First, the studies presented heterogeneous sample sizes, which was minimized with the use of appropriate statistical tests in the meta-analysis. Second, different definitions were observed for the final outcome (GFR less than 60, decline in GFR, rapid decline of GFR) and for the independent variable (UA levels). Subgroup analysis based on the UA levels (quartiles or quintiles) was performed to examine if the presentation of exposure contributed to differences in study results. This analysis showed similar results regardless of the method of presenting the exposure. Third, different forms of GFR estimation (modification of diet in renal disease [MDRD], chronic kidney disease epidemiology [CKD-EPI], Cockcroft-Gault, Japanese equation, equation based on insulin clearance, iohexol technique) were adopted. Finally, there was some heterogeneity among risk estimates from the included studies, possibly due to some bias at the study level. However, the variables included in the meta-regression were insufficient to explain the heterogeneity between studies. Other important variables that interfered in the UA metabolism could not be included, as they were not present in all studies, such as gout, basal GFR, diet components, use of UA-lowering agents.

An UpToDate review on "Uric acid kidney diseases" state that there are three different types of kidney disease induced by uric acid or urate crystal deposition: acute uric acid nephropathy, chronic urate nephropathy, and uric acid nephrolithiasis. The review stated that the diagnosis of acute uric acid nephropathy should be suspected when acute kidney injury develops in rapid malignant cell turnover in association with marked hyperuricemia (plasma or serum urate concentration generally above 15 mg/dL or 893 µmol/L). The review said that some feel that chronic urate nephropathy can be considered in patients who have chronic kidney disease, the above nonspecific clinical features, and hyperuricemia out of proportion to the degree of kidney function impairment. The review notes, however, that there is no universally accepted definition for "out of proportion," and previous studies used serum creatinine alone. The review stated that the role of hyperuricemia in the progression of chronic kidney disease, and the role of urate-lowering therapy in preventing chronic kidney disease progression, is controversial. An UpToDate review on uric acid nephrolithiasis stated that persons with uric acid stones do not typically manifest hyperuricosuria.

An UpToDate review on “Chronic kidney disease in children: Overview of management” (Srivastava and Warady, 2022) states that “Hyperuricemia, which is due to decreases in urinary excretion, has been proposed to contribute to CKD progression, in part by decreasing kidney perfusion via stimulation of afferent arteriolar vascular smooth muscle cell proliferation . In addition to adult data that suggest an association between hyperuricemia and progressive CKD, an observational study reported that a serum uric acid level greater than 7.5 mg/dL was an independent risk factor for accelerated progression of CKD in children and adolescents. However, there are no recommendations for intervention or monitoring of serum uric acid in children or adults with CKD. Whereas the Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guideline for Evaluation and Management of Chronic Kidney Disease acknowledges the growing body of evidence regarding the association of hyperuricemia and CKD, they also acknowledge the lack of reliable evidence to warrant intervention to lower serum uric acid in order to slow the rate of GFR decline among both adult and pediatric patients with CKD”.

Furthermore, UpToDate reviews on “Overview of the management of chronic kidney disease in adults” (Rosenberg, 2022), “Chronic kidney disease (newly identified): Clinical presentation and diagnostic approach in adults” (Fatehi and Hsu, 2022) and “Definition and staging of chronic kidney disease in adults” (Levey and Inker, 2022) do not mention measurement of uric acid as a management option.

NaviDKD Test for Prediction of Kidney Disease in Diabetics

The NaviDKD biomarker-based blood test (Journey Biosciences, Inc) was developed to proactively assess the long-term risk of kidney disease in asymptomatic adults with type 1, type 2, or latent autoimmune diabetes (LADA) diabetes. The blood sample is analyzed for the levels of specific Advanced Glycation End Products (AGEs), along with the patient's A1C results and selected risk factors, which is used to calculate individual risk level for developing kidney complications. The results, delivered as a Compass Report, are reported as low, medium, or high risk for developing diabetic kidney disease (DKD) 8 to 12 years in the future, and includes suggested actions that can be considered to better manage kidney health and reduce the risk of DKD.

NaviDKD has not been cleared by the U.S. Food and Drug Administration. In addition, there are no available published peer-reviewed literature on the effectiveness of this approach.

PromarkerD Test

The PromarkerD test (Proteomics International, Ltd.) is a blood test used to assess the risk of diabetic kidney disease (DKD) in patients with type 2 diabetes. The test measures 3 biomarker concentrations (apolipoprotein A-IV [apoA4], CD5 antigen-like [CD5L/AIM] and insulin-like growth factor-binding protein 3 [IGFBP3]) via mass spectrometry immunoassay and incorporates 3 clinical factors (age, serum high-density lipoprotein [HDL]-cholesterol, estimated glomerular filtration rate [eGFR]) to predict future risk of renal decline. PromarkerD uses a software tool (PromarkerD Hub) which uses an algorithm to calculate a prognostic risk score for developing DKD up to 4 years in advance. The risk scores are classified as low, moderate, or high risk of developing DKD.

Peters et al (2017) present their study findings that aimed to assess the ability of plasma apolipoprotein (apo) A-IV (apoA4), apo C-III, CD5 antigen-like (CD5L), complement C1q subcomponent subunit B (C1QB), complement factor H–related protein 2, and insulin-like growth factor binding protein 3 (IBP3) to predict rapid declining eGFR in a representative community-based cohort of individuals with type 2 diabetes. The incremental benefit of biomarkers added to clinical prediction models was determined across four definitions of diabetic kidney disease (DKD) progression: i) rapidly declining eGFR trajectory, ii) incident CKD, iii) an eGFR decline of greater than or equal to 30% over 4 years (or 7.5% per year), and iv) an annual eGFR decline of greater than or equal to 5 mL/min/1.73 m2. The model was then applied to a cohort of 345 community-based patients from the longitudinal observational Fremantle Diabetes Study Phase II (FDS2) in which mass spectrometry was used to measure the baseline biomarkers. “Multiple logistic regression was used to determine clinical predictors of rapid eGFR decline trajectory defined by semiparametric group-based modeling over a 4-year follow-up period. The incremental benefit of each biomarker was then assessed. Similar analyses were performed for a greater than or equal to 30% eGFR fall, incident chronic kidney disease (eGFR <60 mL/min/1.73 m2), and eGFR decline of greater than or equal to 5 mL/min/1.73 m2/year. Based on eGFR trajectory analysis, 35 participants (10.1%) were defined as “rapid decliners” (mean decrease 2.9 mL/min/1.73 m2 /year). After adjustment for clinical predictors, apoA4, CD5L, and C1QB independently predicted rapid decline (odds ratio 2.40 [95% CI 1.24–4.61], 0.52 [0.29–0.93], and 2.41 [1.14–5.11], respectively) and improved model performance and fit (p < 0.001), discrimination (area under the curve 0.75–0.82, p = 0.039), and reclassification (net reclassification index 0.76 [0.63–0.89]; integrated discrimination improvement 6.3% [2.1–10.4%])”. The authors state that these biomarkers, along with IBP3, contributed to improve model performance in predicting other indices of rapid eGFR decline. The authors acknowledge limitations of their study which included a relatively small sample size and that the findings require external validation. In addition, the cohort subjects in this study were composed mostly of Caucasian origin (approximately 80%) and will need to be assessed to determine if findings can be generalized to other ethnicities and subjects with prediabetes or type 1 diabetes. Nonetheless, the authors conclude that their study has identified 4 plasma protein biomarkers (apoA4, CD5L, C1QB, and IBP3) that predict a rapid decline in eGFR in patients with type 2 diabetes independent of other clinical predictors including eGFR and albumin-to-creatinine ratio (ACR). The authors state that this panel “may be useful for risk stratification in future clinical trials, would enable earlier intervention of at-risk individuals and monitoring of disease progression, and would allow improvement in patient outcomes” However, further analysis of these biomarkers via the PromarkerD test in diabetes and more generally in CKD is warranted.

Bringans et al (2017) present their early discovery study on comprehensive mass spectrometry based biomarker discovery and validation platform applied to diabetic kidney disease. The authors state that proteomics-based biomarker development usually proceeds through several phases which include discovery (list of proteins that may play a role in disease progression and require validation), verification and analytical validation. Analytical validation requires testing of biomarker panels across large cohorts which can result in a barrier for biomarker development due to time, cost and reproducibility of such studies. Thus, a protein biomarker discovery workflow was applied to plasma samples from a cohort of patients from the Fremantle Diabetes Study (FDS) who were at different stages of diabetic kidney disease. The authors state that the proteomics platform produced a panel of significant plasma biomarkers that were statistically scrutinized against the current gold standard tests urinary (albumin-creatinine ratio [ACR] and estimated glomerular filtration rate [eGFR]) on an analysis of 572 patients. The authors state that of the 8 candidate biomarkers, 5 were found to be significantly correlated with ACR (APOA4, CFHR2, HBB, IBP3 and AMBP, all p < 0.05), and 5 with eGFR (APOA4, APOC3, CFHR2, IBP3 and AMBP, all p < 0.05). Thus, concluding that these 5 proteins were significantly associated with diabetic kidney disease defined by albuminuria, renal impairment (eGFR) and chronic kidney disease staging. The authors state that their results prove the suitability and efficacy of the process used, and introduce a biomarker panel with the potential to improve diagnosis of diabetic kidney disease.

Peters and colleagues (2019) present their validation study findings that aimed to validate the prognostic utility of PromarkerD for predicting rapid renal decline over a 4-year follow-up period in individuals with type 2 diabetes. The models for predicting rapid eGFR decline were applied to a cohort of 447 participants from the Fremantle Diabetes Study Phase 2 (FDS2) and were defined as i) incident diabetic kidney disease (DKD), ii) eGFR decline ≥30% over four years, and iii) annual eGFR decline ≥5 mL/min/1.73 m2. Model performance was assessed using discrimination and calibration. The authors found that “during 4.2 ± 0.3 years of follow-up, 5–10% of participants experienced a rapid decline in eGFR. A consensus model comprising apolipoprotein A-IV (apoA4), CD5 antigen-like (CD5L), insulin-like growth factor–binding protein 3 (IGFBP3), age, serum HDL-cholesterol and eGFR showed the best performance for predicting incident DKD (AUC = 0.88 (95% CI 0.84–0.93)); calibration Chi-squared = 5.6, P = 0.78). At the optimal score cut-off, this model provided 86% sensitivity, 78% specificity, 30% positive predictive value and 98% negative predictive value for four-year risk of developing DKD”. The authors concluded that the PromarkerD biomarkers (apoA4, CD5L, IGFBP3) proved an accurate prognostic test for future renal decline in an independent validation cohort of people with type 2 diabetes. The authors do acknowledge study limitations which included small sample size, the majority of FDS2 participants were of Caucasian origin (79%), limiting the generalizability of the models to other racial and ethnic groups, and only baseline clinical and biomarker data were used to predict risk, and that subsequent changes in biomarker concentrations or diabetes management were not considered. Thus, “to fully realize the generalizability of the models, additional external validation across different clinical settings and populations with a larger numbers of events, is warranted”.

Peters et al (2020) investigated the prognostic utility of PromarkerD for predicting future renal function decline in individuals with type 2 diabetes. A post-hoc analysis of data was obtained from the CANagliflozin CardioVascular Assessment Study (CANVAS), a randomized control trial of canagliflozin versus placebo which included men and women with type 2 diabetes (glycosylated hemoglobin ≥ 7.0% and ≤10.5%). The CANVAS trial required all participants to have an eGFR >30 mL/min/1.73 m2 at screening. PromarkerD scores were calculated at baseline in 3568 participants (n=1195 placebo; 2373 canagliflozin arm) to give prognostic (to predict incident chronic kidney disease (CKD) and eGFR decline) and diagnostic (baseline CKD) test scores. Clinical data (age, serum HDL-cholesterol and eGFR) from the baseline CANVAS trial visit was combined with protein biomarker concentrations (ApoA4, CD5L, and IGFBP3) measured in this sub-study, to provide PromarkerD prognostic test scores. The diagnostic test score used the same criteria but did not require baseline eGFR. Protein biomarkers were measured by immunoaffinity targeted mass spectrometry (MS). The PromarkerD scores are predicted probabilities of renal outcomes (incident CKD, eGFR decline ≥30% and baseline CKD, separately) ranging from 0% to 100% and categorized as low-, moderate- or high-risk. For the prognostic scores, the cut-offs were set at 10% and 20%, while the diagnostic score cut-offs were set at 30% and 60%. Participants with prognostic scores <10% were categorized as ‘low’ risk, 10% to <20% as ‘moderate’ risk, and ≥20% as ‘high’ risk. The diagnostic scores were categorized in a similar manner. The investigators found that the participants had a median PromarkerD score of 2.9%, with 70.5% categorized as low-risk, 13.6% as moderate-risk and 15.9% as high-risk for developing incident CKD. After adjusting for treatment, baseline PromarkerD moderate-risk and high-risk scores were increasingly prognostic for incident CKD (both p < 0.001). Analysis of the PromarkerD test system in CANVAS shows the test can predict clinically significant incident CKD in this multi-center clinical study but had limited utility for predicting eGFR decline ≥30%.The investigators acknowledged limitations in their study. The majority of participants were Caucasian (81%), limiting the generalizability of the PromarkerD to other racial and ethnic groups; however, they note that a separate analysis in minority groups is underway. Another limitation is that only baseline clinical and biomarker data were used to predict outcomes, and that subsequent changes in biomarker concentrations or the effect of canagliflozin on longitudinal PromarkerD scores were not considered. Nonetheless, the investigators concluded that their data provide external validation of the PromarkerD test for predicting renal decline in type 2 diabetes, but with the caveat that the overall performance observed was less robust than previous studies in community-based people with type 2 diabetes. Thus, PromarkerD has the potential to facilitate preventive management strategies which may lead to improved patient care and patient outcomes.

Bringans et al (2020a) compared the testing methods of the PromarkerD assay. For research and validation purposes, the PromarkerD assay initially used immunodepletion method of plasma samples followed by targeted mass spectrometry. However, this method was found to not be cost-effective or able to provide rapid enough results for a potential clinical application. Thus, a new higher-throughput and more robust test was developed using an immunoaffinity bead-based approach with mass spectrometry detection, a 96 well format with multiple reaction monitoring (MRM) measurements were made using a low-flow liquid chromatography mass spectrometry (LCMS) detection method. The authors conducted a direct comparison of the data between the optimized immunoaffinity method and the original immunodepletion method, both which used a cohort of 100 patient samples. In addition, an inter-lab study was performed of the optimized immunoaffinity method in two independent laboratories. The authors found that by switching to the immunoaffinity method, the processing of plasma samples was greatly simplified, faster and was a more robust microflow LCMS system. Furthermore, processing time was reduced from 7 to 2 days and the chromatography reduced from 90 to 8 min. The authors stated that the “biomarker stability by temperature and time difference treatments passed acceptance criteria. Intra/Inter-day test reproducibility and precision were within 11% CV for all biomarkers”. Their study found that the PromarkerD test results from the new immunoaffinity method demonstrated “excellent” correlation (R=0.96) to the original immunodepletion method, and that the immunoaffinity assay was “successfully transferred to a second laboratory (R=0.98) demonstrating the robustness of the methodology and ease of method transfer”. The authors concluded that the immunoaffinity bead-based approach showed statistically comparable results to those obtained from the original immunodepletion method and also included comparable results when deployed to an independent laboratory. The authors do note that mass spectrometry based protein biomarker assays have not yet become mainstream alternatives to the standard ELISA based approach. However, with their research, the PromarkerD assay has demonstrated clinical validity and capability of immunoaffinity selectivity coupled to targeted mass spectrometry detection, which has the potential to support clinical decision-making.

Bringans et al (2020b) compared PromarkerD testing platforms to evaluate performance characteristics using multiplexed immunoaffinity mass spectrometry assay (IAMS) versus the standard enzyme-linked immunosorbent assay (ELISA). Antibodies against the three plasma protein biomarkers (APOA4, CD5L and IBP3) were developed and applied to a IAMS. In addition, a standard ELISA was developed to measure each protein. The IAMS and ELISA platforms were tested against each other using a cohort of 100 patient samples from the Fremantle Diabetes Study (FDS). “Comparability of the individual PromarkerD biomarker concentrations were examined and a Bland Altman statistical analysis used to show the biomarker concentration data displayed no statistically significant difference between the two methods. However, differences in the relative performance of the two technologies between proteins were observed, specifically assays for CD5L and IBP3 gave higher correlations in sample concentration measurements than APOA4. These differences in performance merit investigation in future studies”. The investigators noted that the ELISA exhibited greater dynamic range than the IAMS assays, which also merits further investigation. “Individual protein concentration measurements were followed by a comparison of the PromarkerD risk score for progression of diabetic kidney disease for each of the 100 samples”. The investigators found a “high correlation between the ‘new’ IAMS and ‘old’ ELISA platforms with >90% of the cohort having risk scores within 5% of each platform’s score. This demonstrates the ability of either of these technologies to be used as clinical level assays for the measurement of PromarkerD biomarkers. The investigators concluded that these technologies illustrate the potential for large scale, high throughput clinical applications of proteomics now and into the future.

The National Institute for Health and Care Excellence (NICE, 2022) conducted a comprehensive literature review on the PromarkerD for predicting the risk of diabetic kidney disease in people with type 2 diabetes. The NICE concluded that there are "key uncertainties around the evidence or technology" for the PromarkerD test. Per NICE, evidence is limited and that further studies that assess how the test leads to subsequent changes in clinical management and patient outcomes would be useful.

The GFR_NMR Test for Assessment of Glomerular Filtration Rate/Kidney Function

The GFR_NMR is a serum-based test that uses multiple metabolites including myo-inositol, dimethyl sulfone, valine, and creatinine and analyzed by nuclear magnetic resonance spectroscopy; it is used to for evaluation of glomerular filtration rate (GFR)/kidney function.

Coresh and associates (2019) noted that estimation of GFR using estimated GFR creatinine (eGFRcr) is central to clinical practice but has limitations. These researchers tested the hypothesis that serum metabolomic profiling can identify novel markers that in combination can provide more accurate GFR estimates. They carried out a cross-sectional study of 200 African American Study of Kidney Disease and Hypertension (AASK) and 265 Multi-Ethnic Study of Atherosclerosis (MESA) participants with measured GFR (mGFR). Untargeted gas chromatography/dual mass spectrometry- and liquid chromatography/dual mass spectrometry-based quantification was followed by the development of targeted assays for 15 metabolites. On the log scale, GFR was estimated from single- and multiple-metabolite panels and compared with eGFR using the Chronic Kidney Disease Epidemiology equations with creatinine and/or cystatin C using established metrics, including the proportion of errors of greater than 30 % of mGFR (1-P30), before and after bias correction. Of untargeted metabolites in the AASK and MESA, 283 of 780 (36% ) and 387 of 1,447 (27 %), respectively, were significantly correlated (p ≤ 0.001) with mGFR. A targeted metabolite panel eGFR developed in the AASK and validated in the MESA was more accurate (1-P30 3.7 and 1.9 %, respectively) than eGFRcr [11.2 % and 18.5 %, respectively (p < 0.001 for both)] and estimating GFR using cystatin C (eGFRcys) [10.6 % (p = 0.02) and 9.1 % (p < 0.05), respectively] but was not consistently better than eGFR using both creatinine and cystatin C [3.7 % (p > 0.05) and 9.1 % (p < 0.05), respectively]. A panel excluding creatinine and demographics still performed well [1-P30 6.4 % (p = 0.11) and 3.4 % (p < 0.001) in the AASK and MESA] versus eGFRcr. The authors concluded that the algorithms presented in this study provided a proof-of-concept (POC) in realizing the potential of translating untargeted metabolomic screening to algorithms. Given the known limitations of serum creatinine and widespread use of GFR estimation, the clinical implications that a panel of metabolites could provide an accurate estimate of GFR with or without serum creatinine or demographics could be substantial if these initial results can be taken through the full diagnostic test development process. These researchers stated that testing in multiple populations should be carried out to confirm the external generalizability of a metabolite panel. More importantly, the final robust algorithm that could be used to estimate GFR would ideally be developed in a more diverse dataset.

The authors stated that this study had several drawbacks. The AASK was a study of African Americans with hypertensive kidney disease and as such is a rather homogeneous population with respect to race, geographical location and diet (U.S. based) and cause of kidney disease. These investigators were able to replicate the findings in U.S. whites and blacks with and without kidney disease and thus knew that these findings were not due to black ethnicity; however, the relative homogeneity of the samples did not allow these researchers to test the generalizability of the findings. Sample handling in the AASK did not follow a standardized protocol and the storage period was many years. As a result, some metabolites may have been missed, but those that were identified were likely to be robust to a range of handling techniques and long-term storage. The identity of some of the most strongly correlated metabolites with mGFR was unknown, limiting the current panel but providing an opportunity for further improvement on this POC. GFR measurement is known to be imprecise, but this inflated the reported GFR estimation errors. Furthermore, iothalamate and iohexol GFR measurement methods differ systematically and standardization of GFR for body surface area may not optimally deal with variation in body composition. More importantly, the performance and practicality of combining metabolites with low molecular weight proteins such as cystatin C was not tested. However, the focus on metabolites measured in a multiplex panel has the potential for economies of scale. These investigators noted that future steps should include evaluation of panels including cystatin C and potentially other low molecular weight proteins. A better understanding of the metabolism of all components of the panel used to estimate GFR will be useful to better predict when they are influenced by non-GFR determinants. The magnitude of such influences on the overall GFR estimate across a range of clinical settings needs to be quantified, especially in clinical settings where creatinine and cystatin are known to be unreliable.

Ehrich and colleagues (2021) stated that evaluation of renal dysfunction includes eGFR as the initial step and subsequent laboratory testing. In a POC study, these researchers hypothesized that combined analysis of serum creatinine, myo-inositol, dimethyl sulfone, and valine would allow both assessment of renal dysfunction and precise GFR estimation. Bio-banked sera were analyzed using nuclear magnetic resonance spectroscopy (NMR). The metabolites were combined into a metabolite constellation (GFRNMR) using n = 95 training samples and tested in n = 189 independent samples. Tracer-mGFR served as a reference. GFRNMR was compared to eGFR based on serum creatinine (eGFRCrea and eGFREKFC), cystatin C (eGFRCys-C), and their combination (eGFRCrea-Cys-C) when available. The renal biomarkers provided insights into individual renal and metabolic dysfunction profiles in selected mGFR-matched patients with otherwise homogenous clinical etiology. GFRNMR correlated better with mGFR (Pearson correlation coefficient r = 0.84 versus 0.79 and 0.80). Overall percentages of eGFR values within 30 % of mGFR for GFRNMR matched or exceeded those for eGFRCrea and eGFREKFC (81 % versus 64 % and 74 %), eGFRCys-C (81 % versus 72 %), and eGFRCrea-Cys-C (81 % versus 81 %). The authors developed and tested a metabolite-based serum test for accurate estimation of GFR in pediatric, adult, and geriatric patients, obviating the need for invasive tracer application and bearing the potential of metabolic phenotyping of patients with chronic kidney disease (CKD).

These researchers stated that their concept is associated with several weaknesses. First, the total number of patient samples of both the training and test cohort would certainly benefit from additional samples. Besides increasing statistical power, validation of the concept in further cohorts, including African-American and Asian ethnic groups, as well as patients with, e.g., type 2 diabetes mellitus under metformin treatment, nephrotic syndrome, or various tubulopathies, would allow a comprehensive evaluation of the potential clinical utility of the method. Second, the training cohort consisted of a sample set with a heterogeneous reference standard with a mixture of inulin, 51Cr-EDTA, or iohexol renal clearances. As even inulin clearance is associated with a coefficient variation of 7 % for repeated measurements, imprecision might increase even more when renal clearances of 51Cr-EDTA or iothalamate and plasma clearances of 51Cr-EDTA or iohexolare applied for measuring GFR. Thus, the errors of inulin and other exogenous clearance markers are often under-estimated when they are used as referenced standards for establishing new eGFR equations. Although these investigators could not determine any dependency of the GFRNMR results from the applied reference method in post-hoc analysis, they could not exclude the possibility of a reference or selection bias. Third, the results obtained for eGFR equations considering cystatin C might have been influenced by both the prolonged storage times of the bio-banked samples and the use of different ELISA assays for cystatin C quantification. Although sample storage was at −80 °C and the applied assays were calibrated to standard reference material, future work should consider an optimized design. Finally, these researchers established the method on serum samples of at least a 630-µl volume, and its transferability to lower volumes or blood plasma could not be considered as simply given. However, this may be less a limitation on its ability to perform in clinical routine than its application in clinical research with bio-banked serum samples.

Schultheiss and co-workers (2021) noted that kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high co-morbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge regarding the key work-flow steps: study planning, sample collection, metabolomics data acquisition and pre-processing, statistical/bioinformatics data analysis, as well as results interpretation within a biomedical context. The authors concluded that the field of metabolomics already has been of unmeasurable value for nephrology research; however, many questions remain and need to be addressed in the future. A first issue will be to understand the differing metabolite patterns across the diverse spectrum of kidney diseases, such as metabolic syndrome/diabetes mellitus, glomerular diseases, and many others; however, within similar phenotypic CKD etiologies, metabolomics also will aid in unraveling the mechanisms that differentiate, e.g., slow from fast CKD progressors. Translation of metabolomics research into routine CKD patient care will pave the way for novel metabolic biomarkers to examine and monitor the safety and efficacy of treatments. Therefore, metabolomics studies will support clinical decision-making. Finally, metabolomics will become an integrated part of CKD diagnostics and will be able to inform the treating physicians on the rate of CKD progression, adverse risk evaluation, and other CKD-related co-morbidities, such as the stage of metabolic syndrome versus diabetes mellitus or others; thus, metabolomics will be a pioneering field for individualized patient treatment.

An UpToDate review on “IgA vasculitis (Henoch-Schonlein purpura): Kidney manifestations” (Niaudet et al, 2021) states that “Metabolomic profiling has identified putative biomarkers that may predict the development of kidney disease among patients with IgAV who do not present with kidney involvement; however, additional studies validating these findings in larger populations are needed”.

Furthermore, an UpToDate review on “Investigational biomarkers and the evaluation of acute kidney injury” (Erdbruegger and Okusa, 2021) states that “Metabolomics is the study of small-molecule metabolites that are produced by the body and provide insight into physiological and pathophysiological conditions. Metabolomic analysis can be readily performed in biofluids such as blood and urine, and, because there are fewer metabolites than there are genes, mRNA, and proteins, analyses are simpler. This method may allow for identification of new markers in AKI”.

Transdermal System with the Use of Pyrazine‐Based Fluorescent Agents for Measurement of Glomerular Filtration Rate

Transdermal glomerular filtration rate (GFR) measurement system entails placement of sensors and administration of a 1 or more dose(s) of a pyrazine-based fluorescent agent. Sensors are usually placed at 2 locations on subjects’ skin, which will remain for 48 hours. Subjects may undergo activities of daily living while measurements are being continuously collected. However, there is currently insufficient evidence regarding the effectiveness of transdermal systems with the use of pyrazine‐based fluorescent agents for measurement of GFR.

Rajagopalan and colleagues (2011) examined various hydrophilic pyrazine-bis(carboxamides) derived from 3,5-diamino-pyrazine-2,5-dicarboxylic acid bearing neutral and anionic groups for use as fluorescent glomerular filtration rate (GFR) tracer agents. Among these, the di-anionic d-serine pyrazine derivatives 2d and 2j, and the neutral dihydroxypropyl 2h, exhibited favorable physicochemical and clearance properties. In-vitro studies show that 2d, 2h, and 2j have low plasma protein binding, a necessary condition for renal excretion. In-vivo animal model results showed that these 3 compounds exhibited a plasma clearance equivalent to iothalamate (a commonly considered gold standard GFR agent). In addition, these compounds had a higher urine recovery compared to iothalamate. Finally, the plasma clearance of 2d, 2h, and 2j remained unchanged upon blockage of the tubular secretion pathway with probenecid, a necessary condition for establishment of clearance via glomerular filtration only. Hence, 2d, 2h, and 2j are promising candidates for translation to the clinic as exogenous fluorescent tracer agents in real-time point-of-care (POC) monitoring of GFR.

Huang and Gretz (2017) noted that the non-invasive assessment of kidney function and diagnosis of kidney disease have long been challenges. Traditional methods are not routinely available, because the existing protocols are cumbersome, time consuming, and invasive. In the past several years, significant progress in the field of diagnosing kidney function and disease on the basis of light‐emitting agents has been made. These researchers reviewed light‐emitting agents, including organic fluorescent agents and inorganic renal clearable luminescent nanoparticles for the non-invasive and real‐time monitoring of kidney function and disease. They developed a non-invasive transcutaneous technique to measure glomerular filtration rate (GFR) on the basis of a miniaturized electronic device attached to the skin. The smart transcutaneous device comprises light‐emitting diodes that excite a fluorescent agent and a photodiode that detects the emission signal of the injected fluorescent agent. The device is attached to the skin of an animal before the fluorescent agent is injected so that a baseline can be recorded for a short time-period. The device enables the fluorescent agent to be excited repeatedly within the interstitial space by blinking each second at the appropriate wavelength. After each flash, the fluorescence emission of the fluorescent agent is detected and converted into a digital signal. These digital data are stored in an internal memory within the device. The authors stated that although GFR is considered the best indicator for overall renal function, further efforts should be made towards the development of novel light‐emitting agents for the detection of region‐specific injury in kidneys (e.g., tubular necrosis and function) so that kidney diseases can be differentiated and that kidney injury can be diagnosed at an early stage.

Debreczeny and Dorshow (2018) developed a prototype medical device for monitoring renal function by transdermal measurement of the clearance rate of the exogenous fluorescent tracer agent MB-102 (administered intravenously). Verification of the device with an in-vitro protocol was described. The expected renal clearance of the agent was mimicked by preparing a dilution series of MB-102 in the presence of a scattering agent. The slope of a linear fit to the logarithm of fluorescence intensity as a function of dilution step agreed with predictions within 5 %, a level of accuracy that would be adequate in assessment of GFR to prevent mis-diagnosis of renal disease. Transdermal measurement was validated using a rat model. A 2-compartment pharmacokinetic dependence was observed, with equilibration of the fluorescent agent between the vascular space into which it was injected and the extracellular space into which it subsequently diffused. The best observed signal-to-noise ratios were about 150, allowing determination of the renal clearance time with 5 % precision using a 10-min fitting window. Based on the verification and validation methods for transdermal fluorescence detection described, the device performance was sufficient to proceed to human trials. This device has subsequently been used in a FDA-approved pilot human clinical study on 16 healthy subjects. The aim of these studies is to establish whether the measurement accuracy and precision is sufficient to warrant proceeding with further studies on patients with renal disease, in which transdermal measurements of GFR will be compared with GFR determined by standard plasma pharmacokinetic analysis. The end goal of the commercial instrument is to provide bedside assessment in near real-time assessment of patient kidney function..

Shieh et al (2020) noted that MB-102 is a fluorescent tracer agent designed for measurement of POC GFR and is currently in clinical studies. MB-102 possesses a strong UV absorbance at 266-nm and 435-nm, and broad fluorescent emission at approximately 560 nm when excited at approximately 440 nm. The MB-102 formulation is stable at 2°C to 8°C for more than 3 years. The pKa's of the 2 acid groups are 2.71 and 3.40. Both X-ray crystallography and HPLC confirmed the D, D chirality of MB-102 in solid, in solution, and in the drug formulation. Initial safety and toxicity was published previously, which enabled the commencement of clinical studies. In-vitro studies showed that 4.1 % of MB-102 is bound to human plasma proteins, compared to 6.0 % for the accepted standard GFR agent iohexol. The blood-to-plasma ratio for MB-102 was 0.590, illustrating minimal distribution of MB-102 into red blood cells. The manufacture of MB-102 under good manufacturing practice yields the designed molecular structure at high purity (greater than 95 % wt/wt). The authors concluded that from the analytical results reported, MB-102 has the necessary properties for use as a tracer agent for GFR determination. Its fluorescence property coupled with transdermal detection following bolus intravenous administration yielded the first true measurement of GFR in real-time and at the POC.

A clinical trial entitled “Tolerability and Background Fluorescence of the MediBeacon Transdermal GFR Measurement System” has been completed (last updated January 10, 2020). However, the findings have not been published. The MediBeacon Transdermal GFR Measurement System is intended to measure the GFR in patients with normal or impaired renal function by non-invasively monitoring fluorescent light emission from an exogenous tracer agent (MB-102) over time. The device utilized in this study is the Brilliance device.

Furthermore, an UpToDate review on “Assessment of kidney function” (Inker and Perrone, 2020) does not mention transdermal system or fluorescent agent as management tools.

References

The above policy is based on the following references:

Bajaj A, Susztak K, Damrauer SM. APOL1 and cardiovascular disease: A story in evolution. Arterioscler Thromb Vasc Biol. 2017; 37(9):1587-1589.
Biggers A, Pietrangelo A. Does the APOL1 gene increase the risk of kidney disease in African Americans with diabetes? MedicalNewsToday. May 23, 2022.
Bleyer A. Gene test interpretation: APOL1 (chronic kidney disease gene). UpToDate., Waltham, MA: UpToDate; reviewed November 2022.
Bringans SD, Ito J, Stoll T, et al. Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease. EuPA Open Proteom. 2017;14:1-10.
Bringans S, Ito J, Casey T, et al. A robust multiplex immunoaffinity mass spectrometry assay (PromarkerD) for clinical prediction of diabetic kidney disease. Clin Proteomics. 2020a;17:37.
Bringans S, Peters K, Casey T, et al. The new and the old: Platform cross-validation of immunoaffinity MASS spectrometry versus ELISA for PromarkerD, a predictive test for diabetic kidney disease. Proteomes. 2020b;8(4):31.
Canadian Agency for Drugs and Technologies in Health (CADTH). Liquid biopsy for the detection of renal disease. Emerging Issues in Diagnostic Technology. Health Technology Update. Issue 12. Ottawa, ON: CADTH; November 2009.
Chan L, Nadkarni GN, Fleming F, et al. Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease. Diabetologia. 2021;64(7):1504-1515.
Chaudhary NS, Armstrong ND, Hidalgo BA, et al. SMOC2 gene interacts with APOL1 in the development of end-stage kidney disease: A genome-wide association study. Front Med (Lausanne). 2022;9:971297.
Chen TK, Appel LJ, Grams ME, et al. APOL1 risk variants and cardiovascular disease: Results from the AASK (African American Study of Kidney Disease and Hypertension). Arterioscler Thromb Vasc Biol. 2017;37(9):1765-1769.
Chen TK, Surapaneni AL, Arking DE, et al. APOL1 kidney risk variants and proteomics. Clin J Am Soc Nephrol. 2022;17(5):684-692.
Colombo M, Looker HC, Farran B, et al; SUMMIT Investigators. Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes. Diabetologia. 2019;62(1):156-168.
Coresh J, Inker LA, Sang Y, et al. Metabolomic profiling to improve glomerular filtration rate estimation: A proof-of-concept study. Nephrol Dial Transplant. 2019;34(5):825-833.
Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038-2047.
Curhan GC. Kidney stones in adults: Uric acid nephrolithiasis. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed August 2022.
Curhan GC, Stamp LK. Uric acid kidney diseases. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed August 2022.
Datar M, Ramakrishnan S, Chong J, et al. A kidney diagnostic's impact on physician decision-making in diabetic kidney disease. Am J Manag Care. 2022;28(12):654-661.
Debreczeny MP, Dorshow RB. Transdermal optical renal function monitoring in humans: Development, verification, and validation of a prototype device. J Biomedical Optics. 2018;23(5):057003.
Ehrich J, Dubourg L, Hansson S, et al. Serum myo-inositol, dimethyl sulfone, and valine in combination with creatinine allow accurate assessment of renal insufficiency -- A proof of concept. Diagnostics (Basel). 2021;11(2):234.
Eknoyan G, Hostetter T, Bakris GL, et al. Proteinuria and other markers of chronic kidney disease: A position statement of the National Kidney Foundation (NKF) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Am J Kidney Dis. 2003;42(4):617-622.
Erdbruegger U, Okusa MD. Investigational biomarkers and the evaluation of acute kidney injury. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed August 2021.
Fatehi P, Hsu C-Y. Chronic kidney disease (newly identified): Clinical presentation and diagnostic approach in adults. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2022.
Fatehi P, Hsu C-Y. Diagnostic approach to the patient with newly identified chronic kidney disease. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed August 2019.
Freedman BI, Burke W, Divers J. Diagnosis, education, and care of patients with APOL1-associated nephropathy: A Delphi consensus and systematic review. J Am Soc Nephrol. 2021;32(7):1765-1778.
Goncalves DLN, Moreira TR, da Silva LS, et al. A systematic review and meta-analysis of the association between uric acid levels and chronic kidney disease. Sci Rep. 2022;12(1):6251.
Heinzel A, Kammer M, Mayer G, et al. Validation of plasma biomarker candidates for the prediction of eGFR decline in patients with type 2 diabetes. Diabetes Care. 2018;41(9):1947-1954.
Horowitz CR, Abul-Husn NS, Ellis S, et al. Determining the effects and challenges of incorporating genetic testing into primary care management of hypertensive patients with African ancestry. Contemp Clin Trials. 2016;47:101-108.
Huang J, Gretz N. Light‐emitting agents for noninvasive assessment of kidney function. ChemistryOpen. 2017; 6(4):456-471.
Inker LA, Perrone RD. Assessment of kidney function. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed June 2020.
Jagannathan R, Rajagopalan K, Hogan J, et al. Association between APOL1 genotype and kidney diseases and annual kidney function change: A systematic review and meta-analysis of the prospective studies. Int J Nephrol Renovasc Dis. 2021;14:97-104.
Joint Specialty Committee on Renal Medicine of the Royal College of Physicians and The Renal Association, and the Royal College of General Practitioners. Identification,management and referral of adults with chronic kidney disease. Guidelines for general physicians and general practitioners. Concise guidance to good practice, No 5. London, UK: RCP; 2006.
Journey BioSciences, Inc. Journey Biosciences launches NaviDKD for people with diabetes to predict long-term kidney complications. Press Release. Lebanon, NH: Journey BioSciences; December 1, 2022.
Journey BioSciences, Inc. NaviDKD. Available at: https://www.journeybio.life/providers. Accessed March 21, 2023.
Khan A, Turchin MC, Patki A, et al. Genome-wide polygenic score to predict chronic kidney disease across ancestries. Nat Med. 2022;28(7):1412-1420.
Kidney Health Australia. Chronic kidney disease (CKD) management in general practice. Melbourne, VIC: Kidney Health Australia; 2007.
Lam D, Nadkarni GN, Mosoyan G, et al. Clinical utility of KidneyIntelX in early stages of diabetic kidney disease in the CANVAS Trial. Am J Nephrol. 2022;53(1):21-31.
Lentine KL, Muiru AN, Lindsay KK, et al. APOL1 genetic testing in patients with recent African ancestry and hypertension: A pilot study of attitudes and perceptions. Kidney Med. 2022;4(12):100549.
Levey AS, Coresh J, Balk E, et al; National Kidney Foundation. National Kidney Foundation practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Ann Intern Med. 2003;139(2):137-147.
Levey AS, Inker LA. Definition and staging of chronic kidney disease in adults. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2022.
MediBeacon. Tolerability and background fluorescence of the MediBeacon transdermal GFR measurement system. ClinicalTrials.gov. Identifier: NCT-3810833. Bethesda, MD: National Library of Medicine; last updated January 10, 2020.
Muiru AN, Yang J, Derebail VK, et al; CRIC Study Investigators. Black and white adults with CKD hospitalized with acute kidney injury: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis. 2022;80(5):610-618.
Nadkarni GN, Fei K, Ramos MA, et al. Effects of testing and disclosing ancestry-specific genetic risk for kidney failure on patients and health care professionals: A randomized clinical trial. JAMA Netw Open. 2022;5(3):e221048.
Nadkarni GN, Takale D, Neal B, et al. A post hoc analysis of KidneyIntelX and cardiorenal outcomes in diabetic kidney disease. Kidney360. 2022;3(9):1599-1602.
National Institute for Clinical Excellence (NICE). Chronic kidney disease: Early identification and management of chronic kidney disease in adults in primary and secondary care. NICE clinical guideline 73. London, UK: NICE; September 2008.
National Institute for Health and Care Excellence (NICE). PromarkerD for predicting the risk of diabetic kidney disease in people with type 2 diabetes. December 2022. Available at: https://www.nice.org.uk. Accessed March 22, 2023.
National Institutes of Health (NIH), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Kidney Disease Education Program. Chronic kidney disease (CKD). Information for health professionals. Bethesda, MD: NIH; December 28, 2005.
Nephrocor. RenalVysion [website]. Glen Allen, VA; Nephrocor; 2008. Available at: http://www.nephrocor.com/Global/services/laboratory-services/renalvysion.aspx. Accessed October 31, 2008.
Niaudet P, Appel GB, Hunder GG. IgA vasculitis (Henoch-Schonlein purpura): Kidney manifestations. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed August 2021.
Norris KC, Smoyer KE, Rolland C, et al. Albuminuria, serum creatinine, and estimated glomerular filtration rate as predictors of cardio-renal outcomes in patients with type 2 diabetes mellitus and kidney disease: A systematic literature review. BMC Nephrol. 2018;19(1):36.
Pena MJ, Heinzel A, Heinze G, et al. A panel of novel biomarkers representing different disease pathways improves prediction of renal function decline in type 2 diabetes. PLoS One. 2015;10(5):e0120995.
Peters KE, Davis WA, Ito J, et al. Identification of novel circulating biomarkers predicting rapid decline in renal function in type 2 diabetes: The Fremantle Diabetes Study Phase II. Diabetes Care. 2017;40(11):1548-1555.
Peters KE, Davis WA, Ito J, et al. Validation of a protein biomarker test for predicting renal decline in type 2 diabetes: The Fremantle Diabetes Study Phase II. J Diabetes Complications. 2019;33(12):107406.
Peters KE, Xu J, Bringans SD, et al. PromarkerD predicts renal function decline in type 2 diabetes in the canagliflozin cardiovascular assessment study (CANVAS). J Clin Med. 2020;9(10):3212.
Post TW, Rose BD. Urinalysis in the diagnosis of renal disease. UpToDate [online serial]. Waltham, MA: UpToDate; 2008.
Proteomics International Pty Ltd. PromarkerD. Available at: https://promarkerd.com/the-test/. Accessed March 21, 2023.
Rajagopalan R, Neumann WL, Poreddy AR, et al. Hydrophilic pyrazine dyes as exogenous fluorescent tracer agents for real-time point-of-care measurement of glomerular filtration rate. J Med Chem. 2011;54(14):5048-5058.
RenalytixAI. FDA grants Breakthrough Device Designation to KidneyIntelX. Press Release. New York, NY: RenalytixAI; May 2, 2019.
Rosenberg M. Overview of the management of chronic kidney disease in adults. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2022.
Schultheiss UT , Kosch R, Kotsis F, et al. Chronic kidney disease cohort studies: A guide to metabolome analyses. Metabolites. 2021;11(7):460.
Scottish Intercollegiate Guidelines Network (SIGN). Diagnosis and management of chronic kidney disease. A national clinical guideline. Edinburgh, Scotland: Scottish Intercollegiate Guidelines Network (SIGN); 2008.
Shieh J-J, Riley IR, Rogers TE, et al. Characterization of MB-102, a new fluorescent tracer agent for point-of-care renal function monitoring. J Pharm Sci. 2020;109(2):1191-1198.
Srivastava T, Warady BA. Chronic kidney disease in children: Overview of management. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2022.
Stevens L, Perrone RD. Assessment of kidney function: Serum creatinine; BUN; and GFR. UpToDate [online serial]. Waltham, MA: UpToDate; 2008.
Tokita J, Vega A, Sinfield C, et al. Real world evidence and clinical utility of KidneyIntelX on patients with early-stage diabetic kidney disease: Interim results on decision impact and outcomes. J Prim Care Community Health. 2022;13:21501319221138196.
Whittier WL, Korbet SM. Indications for and complications of renal biopsy. UpToDate [online serial]. Waltham, MA: UpToDate; 2008.
Yusuf AA, Govender MA, Brandenburg J-T, Winkler CA, Kidney disease and APOL1. Hum Mol Genetics. 2021;30(1):R1291R137.

Last Review 10/11/2023

Effective: 02/06/2009

Next Review: 08/22/2024
Review History
Definitions

Selected Kidney Function Tests

Table Of Contents

Policy

Scope of Policy

Experimental and Investigational

CPT Codes / HCPCS Codes / ICD-10 Codes

There are no specific codes for RenalVysion:

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

KidneyIntelX:

CPT codes not covered for indications listed in the CPB:

ICD-10 codes not covered for indications listed in the CPB:

Transdermal Fluorescent Pyrazine:

CPT codes not covered for indications listed in the CPB:

ICD-10 codes not covered for indications listed in the CPB:

GFRNMR Test:

CPT codes not covered for indications listed in the CPB:

ICD-10 codes not covered for indications listed in the CPB:

Apolipoprotein L1 (APOL1) renal risk variant genotyping (blood or buccal mucosa):

CPT codes not covered for indications listed in the CPB:

NaviDKD and PromarkerD test:

CPT codes not covered for indications listed in the CPB:

ICD-10 codes not covered for indications listed in the CPB:

Background

Apolipoprotein L1 (APOL1) Renal Risk Variant Genotyping

Counseling, Genotyping, and Diagnosis

Disease Awareness and Education

Future Vision

KidneyIntelX

Measurement of Serum Uric Acid

NaviDKD Test for Prediction of Kidney Disease in Diabetics

PromarkerD Test

The GFR_NMR Test for Assessment of Glomerular Filtration Rate/Kidney Function

Transdermal System with the Use of Pyrazine‐Based Fluorescent Agents for Measurement of Glomerular Filtration Rate

References

Policy History

Additional Information

You are now leaving the Aetna website

Selected Kidney Function Tests

Table Of Contents

Policy

Scope of Policy

Experimental and Investigational

CPT Codes / HCPCS Codes / ICD-10 Codes

There are no specific codes for RenalVysion:

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

KidneyIntelX:

CPT codes not covered for indications listed in the CPB:

ICD-10 codes not covered for indications listed in the CPB:

Transdermal Fluorescent Pyrazine:

CPT codes not covered for indications listed in the CPB:

ICD-10 codes not covered for indications listed in the CPB:

GFRNMR Test:

CPT codes not covered for indications listed in the CPB:

ICD-10 codes not covered for indications listed in the CPB:

Apolipoprotein L1 (APOL1) renal risk variant genotyping (blood or buccal mucosa):

CPT codes not covered for indications listed in the CPB:

NaviDKD and PromarkerD test:

CPT codes not covered for indications listed in the CPB:

ICD-10 codes not covered for indications listed in the CPB:

Background

Apolipoprotein L1 (APOL1) Renal Risk Variant Genotyping

Counseling, Genotyping, and Diagnosis

Disease Awareness and Education

Future Vision

KidneyIntelX

Measurement of Serum Uric Acid

NaviDKD Test for Prediction of Kidney Disease in Diabetics

PromarkerD Test

The GFRNMR Test for Assessment of Glomerular Filtration Rate/Kidney Function

Transdermal System with the Use of Pyrazine‐Based Fluorescent Agents for Measurement of Glomerular Filtration Rate

References

Policy History

Additional Information

The GFR_NMR Test for Assessment of Glomerular Filtration Rate/Kidney Function