New Models Predict Diabetic Kidney Disease Progression to End-Stage Renal Disease in T2D Patients: Study

"Key predictors in the model included BMI, eGFR, urinary total protein, and the systemic immune-inflammatory index. The combined model, which incorporated tubular atrophy, interstitial fibrosis, and other factors, demonstrated improved accuracy and strong predictive power over 5 years, enabling more personalized treatment strategies," the researchers wrote in Diabetes, Metabolic Syndrome and Obesity.

Diabetic kidney disease is a leading cause of chronic kidney disease (CKD) and ESRD, conditions that significantly impact patients' health and quality of life. Despite advancements in diabetes care, early identification of patients at risk for DKD progression remains challenging. Considering this, Huiyue Hu, Department of Nephrology, The Third Affiliated Hospital of Soochow University, Changzhou, People’s Republic of China, and colleagues aimed to develop a predictive model for the progression of diabetic kidney disease to end-stage renal disease and assess the effectiveness of renal pathology alongside the kidney failure risk equation (KFRE) in this process.

For this purpose, the researchers conducted the study in two phases. The first phase included 555 patients with clinically diagnosed diabetic kidney disease, while the second phase focused on 85 patients with biopsy-proven DKD. Cox regression analysis and competing risk regression were used to identify independent predictors.

The prediction performance was evaluated using time-dependent receiver operating characteristic (ROC) analysis, and the area under the curve (AUC) was calculated to assess the accuracy of the model.

Key Findings:

• The Cox regression model for 555 patients with clinically diagnosed DKD identified five key predictors: body mass index (BMI), estimated glomerular filtration rate (eGFR), 24-hour urinary total protein (UTP), systemic immune-inflammatory index (SII), and controlling nutritional status (CONUT).
• The Competing risks model, which focused on four predictors, included BMI, eGFR, UTP, and CONUT.
• In the group of 85 patients with biopsy-proven diabetic DKD, a combined prognostic model incorporating the kidney failure risk equation (KFRE), interstitial fibrosis and tubular atrophy (IFTA), SII, and BMI showed enhanced predictive ability over 5 years.
• The newly developed models offered improved accuracy compared to existing methods by integrating renal pathology and novel inflammatory indices, making them more applicable for clinical use.

The researchers identified BMI, eGFR, UTP, SII, and CONUT have emerged as critical predictors of diabetic kidney disease (DKD) progression to end-stage renal disease. The study emphasized the pivotal roles of SII and CONUT in the progression of DKD. The authors also developed a combined predictive model that integrates the kidney failure risk equation (KFRE), interstitial fibrosis and tubular atrophy (IFTA), SII, and BMI, demonstrating high predictive accuracy for pathologically diagnosed DKD cases.

Looking ahead, the researchers stress the importance of large-scale, multicenter, and multiethnic cohort studies to validate these findings and enhance the generalizability of these predictors across diverse populations. Specific research gaps identified by the authors include investigating regional variations in DKD progression, exploring the inclusion of additional biomarkers—such as genetic markers or novel inflammatory markers—and evaluating the role of individualized risk factors in different demographic groups.

The authors also suggest that the practical application of KFRE and renal pathology in DKD management should be further examined in real-world clinical settings while optimizing patient care.

Reference:

Hu H, Mu X, Zhao S, Yang M, Zhou H. Development of Predictive Models for Progression from Diabetic Kidney Disease to End-Stage Renal Disease in Type 2 Diabetes Mellitus: A Retrospective Cohort Study. Diabetes Metab Syndr Obes. 2025;18:383-398. https://doi.org/10.2147/DMSO.S500992