ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Clinical Diabetes
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1592180
This article is part of the Research TopicHighlights in Diabetes NephropathyView all 18 articles
Risk prediction model for progression of type 2 diabetic nephropathy with and without metabolic syndrome: a retrospective cohort study
Provisionally accepted
- 1First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- 2Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
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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, Cindex 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:The 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: χ 2 =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. Conclusion:MetS is significantly relevant with T2DN progression. Our prediction model helps clinicians to make individualized medical decisions for T2DN patients.
Keywords: type 2 diabetic nephropathy, metabolic syndrome, Clinicopathologic features, prognosis, Prediction model
Received: 12 Mar 2025; Accepted: 15 Jul 2025.
Copyright: © 2025 Fang, Rao, Zhuo, Lin, Zhang and Wan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jianxin Wan, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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