Front Endocrinol (Lausanne). 2026 Mar 23;17:1746570. doi: 10.3389/fendo.2026.1746570. eCollection 2026.
ABSTRACT
INTRODUCTION: This study externally validated a machine learning-based model for type 2 diabetes progression (ML-PR) and evaluated its clinical utility in individuals with prediabetes.
METHODS: We included 3,081 participants from the Diabetes Prevention Program (DPP) and the DPP Outcome Study (DPPOS). The ML-PR model was assessed using dicrimination, calibration curves, and decision curve analysis, and its performance was compared with existing diabetes prediction models. Based on ML-PR scores, patients were stratified into high- or low-risk categories. Cox proportional hazards and logistic regression models were used to evaluate the incidence of type 2 diabetes, microvascular complications, and cardiovascular events across risk and intervention groups.
RESULTS: The ML-PR model achieved an area under the ROC curve of 0.74 (95% confidence interval: 0.71-0.78) for predicting 3-year progression to type 2 diabetes. Calibration and decision curve analyses indicated good agreement and net clinical benefit. High-risk individuals exhibited a significantly higher risk of developing type 2 diabetes in both the DPP and DPPOS cohorts (P < 0.001), as well as a 67% increased risk of microvascular complications in DPPOS (P < 0.001), though no significant difference in cardiovascular risk was observed. Significant interactions between treatment and risk group were identified, indicating that high-risk participants benefited more from lifestyle modification and metformin interventions (P for interaction = 0.03 in DPP; P = 0.014 in DPPOS).
DISCUSSION: Externally validated in U.S. cohorts, the ML-PR model effectively identifies individuals with prediabetes at elevated risk of diabetes progressing and microvascular complications. These findings suggest that intensive lifestyle interventions and metformin therapy may be particularly beneficial for individuals at higher risk, highlighting the potential for more precise treatment strategies in type 2 diabetes.
PMID:41948552 | PMC:PMC13051266 | DOI:10.3389/fendo.2026.1746570