BACKGROUND Type 2 diabetes mellitus (T2DM) prediction models are increasingly used to identify undiagnosed disease and estimate future T2DM risk, but the clinical relevance of potentially modifiable predictors remains unclear. AIM To systematically review diagnostic and prognostic T2DM prediction models, focusing on potentially modifiable predictors, model performance, validation, and methodological quality. METHODS PubMed, Embase, and Web of Science were searched for studies published from January 2016 to February 2026 that developed or substantially updated multivariable models for prevalent undiagnosed or incident T2DM and included at least 1 potentially modifiable predictor. Study characteristics, modeling methods, predictors, validation, and performance were extracted. Risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool. Findings were synthesized descriptively because of substantial heterogeneity. RESULTS Eighty-two studies were included. Most were conducted in China or Japan, and 47.6% used prospective cohort designs. Only 28 studies performed external validation. Overall risk of bias was high in 67 studies (81.7%), mainly because of analytical limitations, including data-driven predictor selection and insufficient calibration assessment. Common predictors included age, body mass index, glucose measures, sex, family history, and triglycerides. Potentially modifiable domains included adiposity, smoking, physical inactivity, diet, blood pressure, and glycemic and lipid biomarkers. CONCLUSION T2DM prediction models increasingly incorporate potentially modifiable predictors, but clinical translation is limited by high risk of bias, inadequate calibration, and insufficient external validation. More rigorous development and prospective evaluation are needed before routine implementation.
