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Background The prevalence of type 2 diabetes mellitus (T2DM) is increasing throughout the world. Six out of the top 10 countries with the highest number of adults with diabetes in 2021 were in Asia. Reliable type 2 diabetes risk prediction models can identify individuals at risk of developing T2DM, which may provide a basis for decision-making in the prevention and intervention of T2DM. Objective To perform a systematic review of risk prediction models for T2DM, providing a reference for the prevention and treatment of T2DM. Methods In April 2021, we searched for studies on risk prediction models for T2DM in Asian adults in databases of PubMed, EmBase, and the Cochrane Library from inception to April 1, 2021. Two reviewers independently screened the literature, extracted data, and evaluated the risk of bias and applicability of included studies using the Prediction model Risk Of Bias Assessment Tool (PROBAST) . A descriptive analysis was used to summarise the basic characteristics of the models and the risk of bias and applicability of included studies. Results A total of 31 studies were included, among which 17 are prospective cohort studies and other 14 are retrospective cohort studies. Logistic regression and Cox regression were widely used to construct the models. The models were externally validated in 5 studies, internally validated in 22 studies, and externally and internally validated in 4 studies. The number of predictors included in the models ranged from 3 to 24, with performance measured by the area under the curve of receiver operating characteristic curve lying between 0.62 and 0.92. There was a high risk of bias in the included studies, which may mainly due to inappropriate treatment of continuous variables and missing data, and ignoring the overfitting of the model. Conclusion The included prediction models may have proven to have good predictive performance, which could support medical workers in early identification of the population at high risk of T2DM. Recommendations for future studies developing risk prediction models for T2DM with good performance and low risk of bias are as follows: improving methods for data modeling and statistical analysis, and attaching great importance to external verification and recalibration of the models. |
