Rationale & objective

Determining whether a change in estimated glomerular filtration rate (eGFR) or albuminuria is clinically significant requires knowledge of short-term within-person variability of the measurements, which few studies have addressed in the setting of chronic kidney disease.

Study design

Cross-sectional study with multiple collections over less than 4 weeks.

Setting & participants

Clinically stable outpatients with chronic kidney disease (N=50; mean age, 56.8 years; median eGFR, 40mL/min/1.73m²; median urinary albumin-creatinine ratio (UACR), 173mg/g).

Exposure

Repeat measurements from serially collected samples across 3 study visits.

Outcomes

Measurements of urine albumin concentration (UAC), UACR, and plasma creatinine, cystatin C, β₂-microglobulin (B2M), and beta trace protein (BTP).

Analytical approach

We calculated within-person coefficients of variation (CV_w) values and corresponding reference change positive and negative (RCV_pos and RCV_neg) values using log-transformed measurements.

Results

Median CV_w (RCV_pos; RCV_neg) values of filtration markers were 5.4% (+16%; -14%) for serum creatinine, 4.1% (+12%; -11%) for cystatin C, 7.4% (+23%; -18%) for BTP, and 5.6% (+17%; -14%) for B2M. Results for albuminuria were 33.2% (+145%; -59%) for first-morning UAC, 50.6% (+276%; -73%) for random spot UAC, 32.5% (+141%; -58%) for first-morning UACR, and 29.7% (124%; -55%) for random spot UACR. CV_w values for filtration markers were comparable across the range of baseline eGFRs. CV_w values for UAC and UACR were comparable across the range of baseline albuminuria values.

Limitations

Small sample size limits the ability to detect differences in variability across markers. Participants were recruited and followed up in a clinical and not research setting, so some preanalytical factors could not be controlled.

Conclusions

eGFR markers appear to have relatively low short-term within-person variability, whereas variability in albuminuria appears to be high, making it difficult to distinguish random variability from meaningful biologic changes.

Metabolomics genome wide association study (GWAS) help outline the genetic contribution to human metabolism. However, studies to date have focused on relatively healthy, population-based samples of White individuals. Here, we conducted a GWAS of 537 blood metabolites measured in the Chronic Renal Insufficiency Cohort (CRIC) Study, with separate analyses in 822 White and 687 Black study participants. Trans-ethnic meta-analysis was then applied to improve fine-mapping of potential causal variants. Mean estimated glomerular filtration rate was 44.4 and 41.5 mL/min/1.73m² in the White and Black participants, respectively. There were 45 significant metabolite associations at 19 loci, including novel associations at PYROXD2, PHYHD1, FADS1-3, ACOT2, MYRF, FAAH, and LIPC. The strength of associations was unchanged in models additionally adjusted for estimated glomerular filtration rate and proteinuria, consistent with a direct biochemical effect of gene products on associated metabolites. At several loci, trans-ethnic meta-analysis, which leverages differences in linkage disequilibrium across populations, reduced the number and/or genomic interval spanned by potentially causal single nucleotide polymorphisms compared to fine-mapping in the White participant cohort alone. Across all validated associations, we found strong concordance in effect sizes of the potentially causal single nucleotide polymorphisms between White and Black study participants. Thus, our study identifies novel genetic determinants of blood metabolites in chronic kidney disease, demonstrates the value of diverse cohorts to improve causal inference in metabolomics GWAS, and underscores the shared genetic basis of metabolism across race.