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

Jing Zhang, Tobias Fuhrer, Hongping Ye, Brian Kwan, Daniel Montemayor, Jana Tumova, Manjula Darshi, Farsad Afshinnia, Julia J. Scialla, Amanda Anderson, Anna C. Porter, Jonathan J. Taliercio, Hernan Rincon-Choles, Panduranga Rao, Dawei Xie, Harold Feldman, Uwe Sauer, Kumar Sharma, Loki Natarajan

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: 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. Methods: Urine samples (N = 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. Results: 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 (p < 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. Conclusions: 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.

Original languageEnglish (US)
Pages (from-to)215-225
Number of pages11
JournalAmerican journal of nephrology
Volume53
Issue number2-3
DOIs
StatePublished - Apr 1 2022

Keywords

  • Diabetes
  • Kidney disease
  • Lasso
  • Metabolomics
  • Pathways
  • Prognostic modeling
  • Random forest

ASJC Scopus subject areas

  • Nephrology

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