Prediction models for progression from prediabetes to diabetes: a systematic review and meta-analysis

Prediabetes increases the risk of type 2 diabetes mellitus (T2DM). Accurate prediction is crucial for early prevention, but evidence on prediction models has not been comprehensively synthesized. This study systematically evaluated the accuracy of such models in predicting prediabetes-to-T2DM progression.

Databases including Cochrane Library, Embase, PubMed, and Web of Science were searched up to June 2, 2025. PROBAST was applied to evaluate the risk of bias. STATA 15.0 was employed to analyze the pooled concordance index (C-index) with 95% CI, to conduct subgroup and sensitivity analyses, and to assess publication bias.

Sixteen studies were included, covering 1,368,130 prediabetic individuals with 187,225 progressing to T2DM. Pooled incidence was 42.3‰ (95% CI: 27.2‰-60.4‰). Pooled C-indices of the training and validation sets were 0.76 (0.71-0.80) and 0.84 (0.82-0.86), respectively. Logistic regression and random forest yielded C-indices of 0.81 and 0.86, respectively.

Prediction models show promising accuracy for predicting progression from prediabetes to T2DM, although the evidence remains limited, particularly due to the lack of external validation. Future research should strengthen model development, external validation, and reporting quality to improve the robustness and clinical applicability of prediction models for the progression of prediabetes.

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251104222.