Glycosylated Hemoglobin A1c Improves the Performance of the Nomogram for Predicting the 5-Year Incidence of Type 2 Diabetes

AIM: To develop and validate a model, which combines traditional risk factors and glycosylated hemoglobin A1c (HbA1c) for predicting the risk of type 2 diabetes (T2DM). MATERIALS AND METHODS: This is a historical cohort study from a collected database, which included 8419 males and 7034 females without diabetes at baseline with a median follow-up of 5.8-years and 5.1-years, respectively. Multivariate cox regression analysis was used to select significant prognostic factors of T2DM. Two nomograms were constructed to predict the 5-year incidence of T2DM based on traditional risk factors (Model 1) and traditional risk factors plus HbA1c (Model 2). C-index, calibration curve, and time-dependent receiver-operating characteristic (ROC) curve were conducted in the training sets and validation sets. RESULTS: In males, the C-index was 0.824 (95% CI: 0.795–0.853) in Model 1 and 0.867 (95% CI: 0.840–0.894) in Model 2; in females, the C-index was 0.830 (95% CI: 0.770–0.890) in Model 1 and 0.856 (95% CI: 0.795–0.917) in Model 2. The areas under curve (AUC) in Model 2 for prediction of T2DM development were higher than in Model 1 at each time point. The calibration curves showed excellent agreement between the predicted possibility and the actual observation in both models. The results of validation sets were similar to the results of training sets. CONCLUSION: The proposed nomogram can be used to accurately predict the risk of T2DM. Compared with the traditional nomogram, HbA1c can improve the performance of nomograms for predicting the 5-year incidence of T2DM.