Non‐laboratory‐based risk assessment model for case detection of diabetes mellitus and pre‐diabetes in primary care

Introduction More than half of diabetes mellitus (DM) and pre‐diabetes (pre‐DM) cases remain undiagnosed, while existing risk assessment models are limited by focusing on diabetes mellitus only (omitting pre‐DM) and often lack lifestyle factors such as sleep. This study aimed to develop a non‐laboratory risk assessment model to detect undiagnosed diabetes mellitus and pre‐diabetes mellitus in Chinese adults. Methods Based on a population‐representative dataset, 1,857 participants aged 18–84 years without self‐reported diabetes mellitus, pre‐diabetes mellitus, and other major chronic diseases were included. The outcome was defined as a newly detected diabetes mellitus or pre‐diabetes by a blood test. The risk models were developed using logistic regression (LR) and interpretable machine learning (ML) methods. Models were validated using area under the receiver‐operating characteristic curve (AUC‐ROC), precision‐recall curve (AUC‐PR), and calibration plots. Two existing diabetes mellitus risk models were included for comparison. Results The prevalence of newly diagnosed diabetes mellitus and pre‐diabetes mellitus was 15.08%. In addition to known risk factors (age, BMI, WHR, SBP, waist circumference, and smoking status), we found that sleep duration, and vigorous recreational activity time were also significant risk factors of diabetes mellitus and pre‐diabetes mellitus. Both LR (AUC‐ROC = 0.812, AUC‐PR = 0.448) and ML models (AUC‐ROC = 0.822, AUC‐PR = 0.496) performed well in the validation sample with the ML model showing better discrimination and calibration. The performance of the models was better than the two existing models. Conclusions Sleep duration and vigorous recreational activity time are modifiable risk factors of diabetes mellitus and pre‐diabetes in Chinese adults. Non‐laboratory‐based risk assessment models that incorporate these lifestyle factors can enhance case detection of diabetes mellitus and pre‐diabetes. An interpretable machine learning model could clearly present the nonlinear effect of the non‐laboratory‐based risk factors on diabetes mellitus and pre‐diabetes mellitus, which is the reason why it is able to provide more accurate risk assessment, also allowing clinical experts to review and modify the model. Interpretability, reliability, and usability decide the future of the ML application in healthcare. This article might present a good example.