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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 penaliz
SUBMITTER: Zhang J
PROVIDER: S-EPMC9116599 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature