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

Abstract

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. BACKGROUNDDiabetes mellitus (DM) is a major public health burden as it is common and chronic, and its complications including cardiovascular diseases, renal disease, and retinopathy can lead to disabilities and premature mortality 1 . Diabetes mellitus develops slowly and the progression from normal blood glucose to diabetes mellitus may take up to a decade 2 . Prediabetes mellitus (pre-DM) refers to the condition where blood glucose is between normal and diabetic levels. Globally, the prevalence of diabetes mellitus was estimated to be 9.3%

Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire

“…However, prediabetes presents overlapping pathophysiology of impaired insulin sensitivity and secretion [25,26]. Although screening tools for prediabetes have been developed [14,[16][17][18][19][20], this is the first study to develop a model to identify the glucose metabolism status of individuals without diabetes. This model encourages individuals to understand their glucose metabolism status and learn how they should change their lifestyle to prevent diabetes.…”

“…Unlike diabetics who need to take their medications, nondiabetic people have no strong motivation or coercion to take screening tests. Clinical measurement values, such as fasting plasma glucose and abdominal circumference, are valid predictors of glucose metabolism status [14,[17][18][19][20]. However, the need to link a tool to clinical laboratory data may limit their scope of use.…”

“…The algorithms utilized included RF, GBM, XGBoost (XGB), LR, and deep learning (DL). Moreover, Dong et al [18] developed a risk assessment model to detect individuals with high FPG or high HbA1c. Eight noninvasively measured risk factors, including age, BMI, waist-to-hip ratio, systolic blood pressure, waist circumference, sleep duration, smoking, and recreational activity time were selected.…”

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

“…Moreover, the Boruta algorithm was utilized to choose substantial statistical risk factors by introducing shadow (randomized) variables. The results showed that BMI, waist‐to‐hip ratio, waist circumference, age, systolic blood pressure, smoking status, sleep duration, and vigorous recreational activity time were the remarkable risk factors of pre‐diabetes mellitus and diabetes mellitus [ 193 ].…”

Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review

“…As a result, there has been increasing attention on using modifiable predictors to develop risk prediction tools. For instance, despite being developed by different methods, both of the non-laboratory-based risk prediction tools developed by Dong et al in 2022 included sleeping hours as one of the predictors [ 7 ], which could indicate the clinical and statistical significance of such predictors in predicting pre-DM risks. Having said that, the effects of such lifestyle predictors on the prediction accuracy and performance of non-laboratory-based pre-DM risk prediction tools has not been reviewed.…”