Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China

Abstract

To derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China. Methods A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort. Results In the derivation cohort, 257 patients were identified from a total of 4498 cases. In the validation cohort, 92 patients were identified from a total of 1525 cases. Four models performed nicely for both calibration and discrimination. The AUC in the derivation cohort for models A, B, C and D were 0.788 (0.761-0.816), 0.807 (0.780-0.834), 0.905 (0.879-0.932) and 0.882 (0.853-0.912), respectively. The Youden index for models A, B, C and D were 1.46, 1.48, 1.67 and 1.65, respectively. Model C showed the highest sensitivity and model D showed the highest specificity. Conclusion Models A and B were non-invasive and can be used to identify high-risk patients for broad screening. Models C and D may be used to provide more accurate assessments of diabetes risk. Furthermore, model C showed the best performance for predicting T2DM risk and identifying individuals who are in need of interventions, current approach improvement and additional follow-up.

New risk score model for identifying individuals at risk for diabetes in southwest China

“…In a retrospective cohort study, Lin constructed a nomogram for predicting 5-year incidence of type 2 diabetes, which integrated age, sex, BMI, hypertension, dyslipidemia, smoking status and family history ( Lin, Guo, Chen, & Zheng, 2020 ). In a recently published article, Shao et al incorporated not only demographic and anthropometric parameters but also dietary and biochemical data to develop four different large population-based type 2 diabetes predictive models ( Shao et al, 2020 ). However, none of those models have been routinely used in clinical practice so far.…”