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

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Objectives: To construct a risk prediction model for type 2 diabetic nephropathy (T2DN) progression in patients with and without metabolic syndrome (MetS). Methods: In 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. Results: <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. Conclusion: MetS is significantly relevant with T2DN progression. Our prediction model helps clinicians to make individualized medical decisions for T2DN patients.

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