Prediction models for progression from prediabetes to diabetes: a systematic review and meta-analysis
By
Yanxian Wang
Cuili Wang
Jianxiu Wang
July 8, 2026
Clinical Report: Systematic Review and Meta-Analysis of Prediction Models for Transitioning from Prediabetes to Diabetes
Overview This systematic review evaluates the accuracy of prediction models for the progression from prediabetes to type 2 diabetes mellitus (T2DM). The findings indicate pooled C-indices of 0.76 for training sets and 0.84 for validation sets, although external validation remains limited.
Background Prediabetes is a critical risk factor for the development of T2DM, with an estimated 10% annual progression risk. Effective prediction models are essential for early intervention and personalized risk stratification.
Data Highlights Metric Pooled Value (95% CI) Incidence of progression to T2DM 42.3‰ (27.2‰–60.4‰) C-index (Training Set) 0.76 (0.71–0.80) C-index (Validation Set) 0.84 (0.82–0.86) C-index (Logistic Regression) 0.81 C-index (Random Forest) 0.86
Key Findings Sixteen studies included, covering 1,368,130 prediabetic individuals. 187,225 individuals progressed to T2DM during the study period. Pooled incidence of progression to T2DM was 42.3‰. Pooled C-indices for training and validation sets were 0.76 and 0.84, respectively. Logistic regression and random forest models yielded C-indices of 0.81 and 0.86. Evidence remains limited due to a lack of external validation of the models.
Clinical Implications The findings highlight the need for external validation and improved reporting quality to enhance the applicability of prediction models.
Conclusion Further research is necessary to validate these models externally and improve their robustness.
Related Resources & Content
Frontiers in Endocrinology, 2026 -- Prediction models for progression from diabetic kidney disease to end-stage renal disease: a systematic review and meta-analysis
The Journal of Clinical Endocrinology & Metabolism, 2026 -- Creation and Assessment of a Personalized Diabetes Risk Prediction Model Incorporating Tailored Preventive Intervention Outcomes
European Journal of Preventive Cardiology, 2026 -- External Assessment of Cardiovascular Risk Assessment Models in Type 2 Diabetes Patients Utilizing the CARDIANA Cohort from Spain
Frontiers in Endocrinology, 2026 -- External validation and application of a machine learning–based model for diabetes progression in prediabetes
ADA Standards of Care 2026 -- Standards of Care in Diabetes
Effects of Long-term Metformin and Lifestyle Interventions on Cardiovascular Events in the Diabetes Prevention Program and its Outcome Study - PMC
https://ada.silverchair-cdn.com/ada/content_public/journal/care/issue/49/supplement_1/6/standards-of-care-2026.pdf
Effects of Long-term Metformin and Lifestyle Interventions on Cardiovascular Events in the Diabetes Prevention Program and its Outcome Study - PMC