Development and validation of risk prediction models for new-onset type 2 diabetes in adults with impaired fasting glucose

Abstract

Aims: To develop and validate sex-specific risk prediction models based on easily obtainable clinical data for predicting 5-year risk of type 2 diabetes (T2D) among individuals with impaired fasting glucose (IFG), and generate practical tools for public use.

Methods: The data used for model training and internal validation came from a large prospective cohort (N = 18,384). Two independent cohorts were used for external validation. A two-step approach was applied to screen variables. Coefficient-based models were constructed by multivariate Cox regression analyses, and score-based models were subsequently generated. The predictive power was evaluated by the area under the curve (AUC).

Results: During a median follow-up of 7.55 years, 5697 new-onset T2D cases were identified. Predictor variables included age, body mass index, waist circumference, diastolic blood pressure, triglycerides, fasting plasma glucose, and fatty liver. The proposed models outperformed five existing models. In internal validation, the AUCs of the coefficient-based models were 0.741 (95% CI 0.723-0.760) for men and 0.762 (95% CI 0.720-0.802) for women. External validation yielded comparable prediction performance. We finally constructed a risk scoring system and a web calculator.

Conclusions: The risk prediction models and derived tools had well-validated performance to predict the 5-year risk of T2D in IFG adults.

Keywords: Impaired fasting glucose; LASSO; Risk prediction model; Type 2 diabetes; Web calculator.