Metabolite Markers of Incident CKD Risk

The systematic analysis of metabolites (e.g., sugars, amino acids, organic acids, lipids, etc.) in biologic specimens is referred to as metabolomics or metabolite profiling. Downstream of transcriptional and translational processes, metabolite profiles can provide proximal reports of the body’s metabolic state and are also influenced by environmental factors such as diet, medications, and the gut microbiome (1). Increasing interest has been directed toward the metabolomic characterization of kidney disease because of its association with various metabolic disorders, because of the broad effect that renal dysfunction has on circulating metabolites, and because circulating metabolites may themselves participate in pathways relevant to disease pathogenesis and progression.

The value of metabolite profiling in clinical research can be enhanced when applied to large, richly phenotyped cohorts. In addition to increasing statistical power, this approach permits assessment of the association of baseline metabolite levels with longitudinal renal outcomes. For example, Goek et al. examined longitudinal associations of baseline levels of 140 metabolites with change in eGFR and incident CKD over 7 years in 1017 participants of the Cooperative Health Research in the Region of Augsburg S4/F4 (KORA) study (2). Similarly, Rhee et al. examined the longitudinal association of baseline levels of 217 metabolites with incident CKD over 8 years in 1434 participants of the Framingham Heart Study (FHS) (3). In both studies, participants were predominantly of European ancestry and all had an eGFR≥60 ml/min per 1.73 m² at baseline. Incident CKD was defined as an eGFR<60 ml/min per 1.73 m² at follow-up, using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease equations, respectively. A total of 106 individuals in the KORA study and 123 individuals in the FHS developed new-onset CKD. Interestingly, both studies identified markers of tryptophan metabolism as associated with the occurrence of CKD. In the KORA study, the strongest association was with the kynurenine/tryptophan ratio, with an odds ratio (OR) of 1.36 per SD (P=0.003; 95% confidence interval [95% CI], 1.11 to 1.66; [2]) after adjusting for eGFR and other CKD risk factors. The FHS, which did not examine metabolite ratios, also highlighted that kynurenine (OR per +1 SD, 1.49; P<0.001; 95% CI, 1.22 to 1.83; [3]) as well as its downstream metabolite kynurenic acid (OR per +1 SD, 1.53; P<0.001; 95% CI, 1.25 to 1.88; [3]) were related to the risk of future CKD. These findings are of particular interest given animal and cellular studies that implicate tryptophan metabolism through the kynurenine pathway as a mediator of renal injury (4,5), and demonstrate functional roles to kynurenine and kynurenic acid in vascular tone and inflammation (6,7). Other risk markers identified in the FHS included citrulline (OR per +1.48 SD, 1.38; P<0.001; 95% CI, 1.19 to 1.83; [3]) and choline (OR per +1 SD, 1.46; P<0.001; 95% CI, 1.17 to 1.82; [3]). Extending these observations, profiling of urine and plasma obtained from the aorta and renal vein of individuals undergoing invasive catheterization has shown that these metabolites are taken up by and metabolized within the human kidney (3). Together, these observations suggest that metabolite markers of incident CKD risk may report on functional pathway activation and/or may report on alternative (non-GFR) axes of renal metabolite handling.

In this issue of CJASN, Yu et al. (8) extend the existing literature on metabolomics and incident CKD, examining 204 metabolites measured in 1921 African-American participants in the Atherosclerosis Risk in Communities (ARIC) study. In this study, the authors first examined the cross-sectional association of serum metabolite levels with eGFR calculated using the CKD-EPI study equation (eGFR_CKD-EPI). Consistent with several prior reports (9–12), Yu et al. found that the levels of many metabolites are higher among individuals with lower eGFR (8). Extending these cross-sectional observations, Yu et al. linked metabolite profiles at the beginning of follow-up to the future occurrence of CKD, defined as an eGFR_CKD-EPI<60 ml/min per 1.73 m² and <75% of baseline, or a CKD-related hospitalization or death during a median follow-up of 19.6 years. Using Cox proportional hazards models to analyze the 1921 study participants (204 of whom developed CKD), the authors highlight 5-oxoproline (hazard ratio , 0.70 per +1 SD; P<0.001; 95% CI, 0.60 to 0.82; [8]) and 1,5-anhydroglucitol (hazard ratio, 0.68 per +1 SD; P<0.001; 95% CI, 0.58 to 0.80; [8]) as significant markers whose higher levels were associated with a decreased risk of future CKD, after adjusting for baseline eGFR, age, sex, systolic BP, antihypertensive medication use, diabetes status, smoking status, coronary heart disease, as well as HDL and LDL cholesterol.

The explanation for why higher levels of these markers were associated with lower disease risk is unclear. The authors speculate that elevated 5-oxoproline levels may be salutary by contributing to the biosynthesis of glutathione, an antioxidant that could attenuate the oxidative injury thought to contribute to CKD pathogenesis. However, plasma 5-oxoproline levels are known to rise secondary to acquired glutathione depletion (13), and are markedly elevated with inherited disorders of glutathione synthesis (Online Mendelian Inheritance in Man database ID MIM266130). Furthermore, in cross-sectional analysis, the authors found that 5-oxoproline had a significant, inverse association with eGFR. It is unclear why higher levels of this metabolite would be associated with lower eGFR at baseline but a lower rate of incident CKD during longitudinal follow-up. The association between low 1,5-anhydroglucitol levels and new-onset CKD is interesting because this metabolite is primarily derived from dietary constituents. As noted, one of the attractive features of metabolite profiling is that it integrates both endogenous and environmental inputs into an individual’s metabolic state. As the authors note, prior studies have outlined the potential value of 1,5-anhydroglucitol as a marker of short-term glycemic control (14), but this is the first study to highlight its potential value as a marker of CKD risk.

Although the study of ARIC participants by Yu et al. reveals a number of interesting observations, several limitations warrant mention (8). First, adjusting for diabetes status in the Cox proportional hazards models may not adequately isolate the effects of plasma metabolites from the effects of diabetes, insulin resistance, and impaired glucose tolerance on new-onset CKD. Notably, the authors reported that the association between 1,5-anhydroglucitol and incident CKD persisted even after adding baseline glucose levels to the model. Second, adjusting for baseline eGFR may not adequately address potential confounding by baseline renal function. The mean eGFR among individuals who developed CKD was 97.5 ml/min per 1.73 m² compared with 106.2 ml/min per 1.73 m² among individuals who did not develop CKD. Because 37.8% of the individuals who developed CKD had diabetes at baseline compared with 13% of the individuals who did not develop CKD, the mean eGFR in the former group presumably subsumed some individuals in the hyperfiltration phase of their disease. Thus, eGFR measurements likely failed to fully capture the degree of renal dysfunction, thereby impeding the ability to fully adjust for kidney disease. This limitation was compounded by the fact that proteinuria was not measured at the baseline visit in the ARIC study, and thus could not be adjusted for in the Cox proportional hazards analysis. Third, there was no evidence that measurement of 5-oxoproline or 1,5-anhydroglucitrol would be clinically useful disease predictors, because the combination of these metabolites did not improve the area under the curve over traditional risk factors alone (P=0.29). Although not examined, given these findings, it is unlikely that either of these markers or their combination would improve classification accuracy. Fourth, as in the KORA study and the FHS, the number of metabolites surveyed was relatively low compared with the estimated >4500 constituents of the plasma metabolome (15).

Although the findings in this study will require replication, heterogeneity across metabolomics platforms makes such replication efforts more challenging. For example, neither 5-oxoproline nor 1,5-anhydroglucitrol was measured in the KORA study or the FHS. Conversely, Yu et al. did not report on kynurenine and kynurenic acid (kynurenine is measured by the commercial platform utilized in the ARIC study, but presumably did not meet the quality control criteria for inclusion in this analysis). Citrulline and choline, however, were measured by Yu et al. but were not associated with incident CKD. To what extent the difference in results across the epidemiologic cohorts studied reflects differences in metabolomics platform coverage, sample storage duration, quality of data produced, or racial composition is uncertain. Ideally, a study designed to identify metabolite signatures underlying racial differences in CKD risk would apply the same metabolomics platform to a single, racially diverse study cohort.

In summary, Yu et al. have made a valuable contribution to the growing literature on renal metabolomics (8). However, many challenges and opportunities remain. As metabolomics technologies continue to improve, in both breadth and throughput, future studies will survey wider and increasingly overlapping portions of the metabolome. This will provide both new opportunities for marker discovery as well as improved ability for meta-analyses and replication across studies. Furthermore, repeated metabolite measures over time will provide deeper insights into how changes in metabolite markers over time add further risk information beyond a single metabolic snapshot in time. In parallel, characterization of renal metabolite handling at the organ level will be important to disentangle whether metabolite alterations are directly attributable to loss of kidney function, or are due to systemic changes in metabolism that accompany kidney disease. Furthermore, functional interrogation of metabolites and metabolic pathways highlighted by these studies in model systems will be critical to provide insights on underlying disease mechanisms. Finally, whereas the studies of incident CKD reviewed here compared individuals who did or did not cross a calculated GFR threshold, identifying markers of a hard endpoint like progression to ESRD is arguably of greater clinical and scientific interest. One small study to date has examined this question (16), and future studies will no doubt build on this foundation to help expand our understanding of the metabolomic underpinnings of CKD onset and progression.

Disclosures

H.I.F. has received honoraria from Boston University, Kyowa Hakko Kirin, and GlaxoSmithKline, and is a scientific advisor to the American College of Epidemiology Board of Directors Steering Committee. He is also chair of both the National Institutes of Health (NIH) National Institute of Diabetes and Digestive and Kidney Disease (NIDDK) Hemodialysis Fistula Maturation Consortium Steering Committee and the NIH NIDDK Chronic Renal Insufficiency Cohort Study Steering Committee.