Frontiers in endocrinology · 2026
Integration of digital physicalomics and dual-fluid metabolomics flux ratios reveals tubular secretory dysfunction in early diabetic kidney disease.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Zhang Haili, Cai Qian, Gao Jing, Wang Fen, Zhang Liping, Zhao Huihui, Ren Xiaoqiao, Zhang Mianzhi
Аннотация
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: We 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.
RESULTS: Metabolic 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<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.
METHODS: We 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.
RESULTS: Metabolic 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<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.