Determinants and prediction of hypertension among Chinese middle-aged and elderly adults with diabetes: A machine learning approach

Objective: Multimorbidity, particularly diabetes combined with hypertension (DCH), is a significant public health concern. Currently, there is a gap in research utilizing machine learning (ML) algorithms to predict hypertension risk in Chinese middle-aged and elderly diabetic patients, and gender differences in DCH comorbidity patterns remain unclear. We aimed to use ML algorithms to predict DCH and identify its determinants among middle-aged and elderly diabetic patients in China. Study design: Cross-sectional study. Methods: Data were collected on 2775 adults with diabetes aged ?45 years from the 2015 China Health and Retirement Longitudinal Study. We employed nine ML algorithms to develop prediction models for DCH. The performance of these models was evaluated using the area under the curve (AUC). Additionally, we conducted variable importance analysis to identify key determinants. Results: Our results showed that the best prediction models for the overall population, men, and women were extreme gradient boosting (AUC = 0.728), light gradient boosting machine (AUC = 0.734), and random forest (AUC = 0.737), respectively. Age, waist circumference, body mass index, creatinine level, triglycerides, taking Western medicine, high-density lipoprotein cholesterol, blood urea nitrogen, total cholesterol, low-density lipoprotein cholesterol, and sleep disorders were identified as common important predictors by all three populations. Conclusions: ML algorithms showed accurate predictive capabilities for DCH. Overall, non-linear ML models outperformed traditional logistic regression for predicting DCH. DCH predictions exhibited variations in predictors and model accuracy by gender. These findings could help identify DCH early and inform the development of personalized intervention strategies.

This work was supported by the Traditional Chinese Medicine Research Project of the Shanghai Municipal Health Commission, grant number 2024QN108; Shanghai University of Traditional Chinese Medicine, grant number 2021LK008; and Shanghai University of Traditional Chinese Medicine, grant number KECJ2024019. Our study aimed to develop predictive models for DCH using nine ML algorithms: LR, adaptive boosting (AdaBoost), gradient boosting machine (GBM), gaussian naive bayes (GNB), light gradient boosting machine (LGBM), RF, support vector machine (SVM), k-nearest neighbor classification (KNN), and extreme gradient boosting (XGBoost). Additionally, we aimed to identify key factors influencing DCH and explore variations in model performance and significant predictors among different gender groups. These findings have the potential to deepen our insights into DCH pathogenesis, while also informing gender-specific personalized medical recommendations and hypertension management strategies for diabetics, ultimately enhancing the quality of life for diabetics.This work was supported by the Traditional Chinese Medicine Research Project of the Shanghai Municipal Health Commission, grant number 2024QN108; Shanghai University of Traditional Chinese Medicine, grant number 2021LK008; and Shanghai University of Traditional Chinese Medicine, grant number KECJ2024019.The study was conducted in accordance with the Declaration of Helsinki. The Peking University Institutional Review Board granted ethical approval for all CHARLS waves. The Institutional Review Board (IRB) approval number for the self-reported questionnaire (incorporating physical examination) was IRB00001052-11015, while the approval number for biomarkers was IRB00001052-11014.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Xianglong Xu reports financial support was provided by Shanghai Municipal Health Commission. Hualing Song reports financial support was provided by Shanghai University of Traditional Chinese Medicine. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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