AUTHOR=Fang Yuan , Rao Siyi , Zhuo Yongjie , Lin Jiaqun , Zhang Xiaohong , Wan Jianxin 

TITLE=Risk prediction model for progression of type 2 diabetic nephropathy with and without metabolic syndrome: a retrospective cohort study

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1592180

DOI=10.3389/fendo.2025.1592180

ISSN=1664-2392

ABSTRACT=ObjectivesTo construct a risk prediction model for type 2 diabetic nephropathy (T2DN) progression in patients with and without metabolic syndrome (MetS).MethodsIn this retrospective study, we enrolled 130 T2DN patients diagnosed by renal biopsy. The clinicopathological characteristics of participants were analyzed. Survival analysis was performed using the Kaplan-Meier method. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were conducted to identify risk factors for T2DN progression, and a risk prediction model was constructed for T2DN progression. ROC curves, C-index and calibration curves were used to evaluate the discrimination and calibration of the model. Sensitivity analysis was conducted by redefining MetS using the 2004 Chinese Diabetes Society (CDS) criteria.ResultsThe Kaplan-Meier survival curve shows that the cumulative incidence rate of T2DN progression in patients with MetS is significantly higher than in those without MetS (Log-rank test: χ2 = 11.76, P<0.001). The number of MetS components was an independent risk factor for T2DN progression (HR=2.567, P=0.039; HR=3.392, P<0.001; HR=4.225, P=0.001 for 3,4,5 components respectively). A T2DN progression prediction model by nomogram was constructed, the AUC of ROC curves was 0.794 (95% CI: 0.685-0.908) at 1 year, 0.826 (95% CI: 0.739-0.913) at 2 years, 0.794 (95% CI: 0.694-0.893) at 3 years, and 0.833 (95% CI: 0.735-0.931) at 4 years. the C-index remained above 0.70 for the entire 5-year period. The calibration curves showed a good fit with the reference curves.ConclusionMetS is significantly relevant with T2DN progression. Our prediction model helps clinicians to make individualized medical decisions for T2DN patients.