Integrative metabolomic and proteomic analysis of diabetic kidney disease progression with younger‐onset type 2 diabetes

Abstract Aim Younger‐onset type 2 diabetes (YT2D) confers a disproportionately high risk of diabetic kidney disease (DKD), yet early biomarkers and underlying mechanisms remain poorly defined. We aimed to identify metabolites associated with DKD progression and integrate metabolomic and proteomic data to elucidate pathways involved in a multi‐ethnic Asian cohort. Materials and Methods In this prospective study, 787 YT2D patients (diagnosed at ≤ age 40) were followed for a median of 5.7 years. DKD progression was defined as an annual decline in estimated glomerular filtration rate (eGFR) of ≥3 mL/min/1.73 m 2 or ≥ 40% reduction in eGFR from baseline. Plasma metabolites were measured by nuclear magnetic resonance spectroscopy. Multivariable regression analysis was performed in a discovery ( N = 550) and internal validation cohort ( N = 237). Integrative metabolomic‐proteomic analysis ( N = 428) was performed using sparse partial least squares discriminant analysis (sPLS‐DA). Results Ninety‐eight metabolites were differentially expressed between DKD progressors and non‐progressors, of which total branched‐chain amino acids (BCAAs) (OR = 0.60, 95% CI 0.46–0.79), valine (OR = 0.62, 95% CI 0.48–0.81), and leucine (OR = 0.56, 95% CI 0.43–0.74) associated with DKD progression, independent of metabolic risk factors. Integrative analysis identified three components comprising 23 proteins and 30 metabolites, involved in the citrate cycle and apoptosis, which improved prediction of DKD progression beyond clinical risk factors (AUC 0.69–0.83). Conclusion Lower plasma BCAA levels are independently associated with DKD progression in YT2D. Integrative multi‐omics analysis highlights disruptions in metabolic and apoptotic pathways, providing insights into DKD pathophysiology and potential biomarkers for early risk stratification.