High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study

Abstract

<b><i>Introduction:</i></b> Metabolomics could offer novel prognostic biomarkers and elucidate mechanisms of diabetic kidney disease (DKD) progression. Via metabolomic analysis of urine samples from 995 CRIC participants with diabetes and state-of-the-art statistical modeling, we aimed to identify metabolites prognostic to DKD progression. <b><i>Methods:</i></b> Urine samples (<i>N</i> = 995) were assayed for relative metabolite abundance by untargeted flow-injection mass spectrometry, and stringent statistical criteria were used to eliminate noisy compounds, resulting in 698 annotated metabolite ions. Utilizing the 698 metabolites’ ion abundance along with clinical data (demographics, blood pressure, HbA1c, eGFR, and albuminuria), we developed univariate and multivariate models for the eGFR slope using penalized (lasso) and random forest models. Final models were tested on time-to-ESKD (end-stage kidney disease) via cross-validated C-statistics. We also conducted pathway enrichment analysis and a targeted analysis of a subset of metabolites. <b><i>Results:</i></b> Six eGFR slope models selected 9–30 variables. In the adjusted ESKD model with highest C-statistic, valine (or betaine) and 3-(4-methyl-3-pentenyl)thiophene were associated (<i>p</i> < 0.05) with 44% and 65% higher hazard of ESKD per doubling of metabolite abundance, respectively. Also, 13 (of 15) prognostic amino acids, including valine and betaine, were confirmed in the targeted analysis. Enrichment analysis revealed pathways implicated in kidney and cardiometabolic disease. <b><i>Conclusions:</i></b> Using the diverse CRIC sample, a high-throughput untargeted assay, followed by targeted analysis, and rigorous statistical analysis to reduce false discovery, we identified several novel metabolites implicated in DKD progression. If replicated in independent cohorts, our findings could inform risk stratification and treatment strategies for patients with DKD.

Point-of-care applicable metabotyping using biofluid-specific electrospun MetaSAMPs directly amenable to ambient LA-REIMS

“…2) metabolite recovery was evinced. With respect to metabolome coverage (the number of detected metabolic features), our biofluid-specific MetaSAMPs outperformed what has been previously reported in the literature for AIMS-based metabolomics analysis of human biofluids, including feces ( 1 , 16 , 18 ), saliva ( 1 , 13 , 69 ), and urine ( 1 , 70 , 71 ).…”

Branched‐chain amino acid levels are inversely associated with incident and prevalent chronic kidney disease in people with type 2 diabetes

“…Over time, incident cases exhibit higher plasma BCAA levels, while prevalent cases experience reduced levels because of impaired kidney function, resulting in elevated urine BCAA levels. 13 This effect is primarily seen in the lower BMI group. BCAAs, branched-chain amino acids; BMI, body mass index; CKD, chronic kidney disease.…”

“…Consequently, there is an increased excretion of total BCAAs and other solutes in the urine, as supported by previous studies that observed elevated levels of urinary valine associated with a higher risk of end-stage renal disease. 13 The proximal tubule is responsible for amino acids reabsorption. 14 Mounting evidence strongly suggests the involvement of proximal tubular injury in the been consistently observed in obesity and type 2 diabetes.…”

Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol

“…A model with a shrinkage factor of 0.9, an anticipated R 2 of 0.15, and 20 parameters required a minimum sample size of N =984; for 50 parameters and an R 2 of 0.35 the sample size was N =996. For the eGFR slope, we anticipated an R 2 of 0.15 and a mean (SD) from two published studies of 1.28 (2.5) and 1.8 (1.9), respectively [ 22 , 35 ]. In both scenarios, a model with 40 parameters resulted in a minimum sample size of N =2090.…”