Integration of digital physicalomics and dual-fluid metabolomics flux ratios reveals tubular secretory dysfunction in early diabetic kidney disease

Background: Current 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. Methods: =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. Results: <0.001). The model maintained robust discrimination in the external validation cohort (AUC, 0.85; 95% CI, 0.79 to 0.91). Among patients with normoalbuminuria, those classified as high-risk by the model had a significantly higher projected 3-year event rate than those classified as low-risk (35.3% vs. 2.3%). Conclusions: The integration of digital urine physical phenotypes and metabolic flux ratios effectively reveals early tubular secretory dysfunction and improved risk stratification for diabetic kidney disease compared with standard clinical metrics.