Abstract
INTRODUCTION: This study aimed to identify independent risk factors for DKD in T2DM patients and develop a risk prediction model with internal validation. METHODS: We 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. RESULTS: The 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. CONCLUSION: All 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.