FluxPro-DKD fusion model integration shows higher AUC for early-stage DKD detection in type 2 diabetesNew fusion model spots early kidney disease in diabetes better than standard care

BackgroundCurrent screening for diabetic kidney disease (DKD) relies on the estimated glomerular filtration rate (eGFR) and albuminuria, which often fail to detect early tubular dysfunction and non-albuminuric phenotypes. The integration of macroscopic urine physical characteristics with metabolic signatures may offer a novel approach to precision stratification.MethodsWe conducted a multicenter, prospective-retrospective cohort study involving 364 participants with type 2 diabetes. We developed “FluxPro-DKD fusion model,” that integrates “Digital Physicalomics” (computer-vision quantification of urine foam stability and chromaticity) and “Dual-Fluid Metabolomics” (serum-to-urine flux ratios). The model was trained in a discovery cohort (n=282) and tested in an independent external validation cohort (n=82). The primary outcome was the detection of early-stage DKD. We also assessed the model’s prognostic utility for major adverse renal events over a simulated 3-year period.ResultsMetabolic profiling identified a distinct “serum-to-urine flux mismatch” of protein-bound uremic toxins (e.g., indoxyl sulfate), suggesting tubular secretory failure prior to glomerular damage. Digital physicalomics revealed that urine foam half-life was correlated with albuminuria (r=0.78). In the discovery cohort, the FluxPro-DKD fusion model achieved an area under the receiver operating characteristic curve (AUC) of 0.90 (95% confidence interval [CI], 0.87 to 0.93), significantly outperforming the standard clinical model (AUC, 0.78; P