Diabetic kidney disease (DKD) affects up to 40% of individuals with diabetes and remains the leading cause of end-stage renal disease worldwide. Current biomarkers, such as albuminuria and estimated glomerular filtration rate, detect disease only after substantial kidney injury, limiting early intervention. Metabolomics offers unique potential to identify early biochemical changes preceding the clinical onset of DKD. This review synthesizes evidence from animal and human studies in diabetes without overt kidney disease, highlighting early perturbations in energy metabolism (TCA cycle, beta-oxidation, glycolysis) as well as alterations in amino acid, nucleotide and urea cycle pathways associated with future DKD risk. We discuss methodological considerations, translational relevance, and current research gaps and outline strategies for integrating metabolomics into predictive diagnostics. Early, non-invasive metabolic biomarkers may enable more precise risk stratification and timely intervention to improve patient outcomes.
Garoufis et al. (2026) studied this question.