Development and validation of a model that predicts the risk of diabetic kidney disease in type 2 diabetes mellitus patients: a retrospective study - Deng, Zhiling; Yang, Jian; Zhou, Hairong (Frontiers in Endocrinology, 2026)
IntroductionThis study aimed to identify independent risk factors for DKD in T2DM patients and develop a risk prediction model with internal validation.MethodsWe retrospectively collected data from 1,049 T2DM patients undergoing community health checks in Longhua District (2024). Patients were divided into DKD and non-DKD groups, then randomly divided into training (n=735) and validation (n=314) sets in 7:3 ratio. ResultsThe results of the binary logistic regression analysis showed that the duration of diabetes (OR 1.037, 95% CI: 1.005-1.07, P = 0.024), BMI (OR 0.869, 95% CI: 0.762-0.992, P = 0.037), Scr (OR 1.019, 95% CI: 1.010-1.028, P = 0.000), WBC (OR 1.141, 95% CI: 1.019-1.279, P = 0.023), and TyG-BMI (OR 1.019, 95% CI: 1.1007-1.030, P = 0.002) were independent risk factors for the occurrence of DKD in T2DM. Seven predictors including duration of diabetes, BMI, Scr, WBC, TyG-BMI, hypertension, and HDL-C, which were identified via binary logistic analysis. We visualized the predictive model in the form of a nomogram and evaluated its predictive performance. The model demonstrated good discrimination (AUC: training 0.725, validation 0.698) and calibration (H-L test P>0.05 for both groups). Decision curve analysis confirmed its clinical utility by showing higher net benefit than extreme scenarios. ConclusionAll seven indicators in this model are readily obtainable in primary healthcare settings, providing a practical tool for primary care physicians to conduct DKD risk prediction in general practice.