A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study - Wang, Ying; Zhang, Wei Sen; Hao, Yuan Tao; Jiang, Chao Qiang; Jin, Ya Li; Cheng, Kar Keung; Lam, Tai Hing; Xu, Lin (Frontiers in Endocrinology, 2022)
Background Existing diabetes risk prediction models based on regression were limited in dealing with collinearity and complex interactions. Bayesian network (BN) model that considers interactions may provide additional information to predict risk and infer causation. Methods BN model was constructed for new-onset diabetes using prospective data of 15,934 participants without diabetes at baseline (73% women; mean (standard deviation) age =61.0 (6.9) years). Participants were randomly assigned to a training (n=12,748) and a validation (n=3,186) set. Model performances were assessed using area under receiver operating characteristic curve (AUC). Results During an average follow-up of 4.1 (interquartile range=3.3-4.5) years, 1,302 (8.17%) participants developed diabetes. The constructed BN model showed the associations (direct, indirect or no) among twenty-four risk factors, and only hypertension, impaired fasting glucose (IFG; fasting glucose of 5.6-6.9 mmol/L) and greater waist circumference (WC) were directly associated with new-onset diabetes. The risk prediction model showed that the post-test probability of developing diabetes in participants with hypertension, IFG and greater WC was 27.5%, with AUC of 0.746 (95% CI=0.732-0.760), sensitivity of 0.727 (95% CI=0.703-0.752), specificity of 0.660 (95% CI=0.652-0.667). This prediction model appeared to perform better than a logistic regression model using the same three predictors (AUC=0.734, 95% CI=0.703-0.764, sensitivity 0.604, specificity 0.745). Conclusions We have first reported a BN model in predicting new-onset diabetes with the smallest number of factors among existing models in the literature. BN yielded a more comprehensive figure showing graphically the inter-relations for multiple factors with diabetes than existing regression models.