DOI: 10.1111/dom.15457 ISSN: 1462-8902

A meta‐analysis of diabetes risk prediction models applied to prediabetes screening

Yujin Liu, Sunrui Yu, Wenming Feng, Hangfeng Mo, Yuting Hua, Mei Zhang, Zhichao Zhu, Xiaoping Zhang, Zhen Wu, Lanzhen Zheng, Xiaoqiu Wu, Jiantong Shen, Wei Qiu, Jianlin Lou
  • Endocrinology
  • Endocrinology, Diabetes and Metabolism
  • Internal Medicine



To provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes.


The Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS‐2) tool was used for risk of bias assessment and

and R software were used to pool model effect sizes.


A total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information.


Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.

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