ORIGINAL RESEARCH article
Front. Physiol.
Sec. Clinical and Translational Physiology
Multiple machine-learning-driven metabolic frameworks for long-term prognostic risk assessment in patients with coexisting hypertension and obstructive sleep apnea:Insights from a Multicenter Cohort Study
Provisionally accepted
- 1The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
- 2West China Hospital of Sichuan University, Chengdu, China
- 3Anhui Medical University, Hefei, China
- 4Xinjiang Medical University, Urumqi, China
- 5Wuhan University Renmin Hospital, Wuhan, China
- 6Department of Orthopedics, the First Affiliated Hospital of Bengbu Medical University, Bengbu, China
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Background: Predictive obesity indices are often based on the body mass index (BMI). Although BMI is widely used, it does not provide a direct measure of obesity. We aimed to utilize multiple machine learning-driven metabolic frameworks to investigate the long-term risk of major adverse cardiovascular and cerebrovascular events (MACCEs) in individuals with hypertension and obstructive sleep apnea (OSA). Methods: This study included 708 patients with hypertension and OSA between January 2017 and December 2021. The measurements of height, weight, neck circumference (NC), waist circumference (WC), neck-circumference-to-height ratio (NHtR), and waist-to-height ratio (WHtR) were collected to calculate the triglyceride-glucose (TyG)-BMI, as well as TyG-NC, TyG-WC, TyG-NHtR, and TyG-WHtR indices. Results: All patients were allocated to the training cohort (n=446) and independent validation cohort (n=262). The Boruta plot presented for identifying key predictors is as follows: male sex, age, TyG, TyG-BMI, HbA1c, FPG, triglyceride, creatinine, fibrinogen and AHI. We constructed nine machine learning models-XGBoost, Light Gradient Boosting Machine, Random Forest, Decision Tree, Gradient Boosting, Multi-Layer Perceptron, Support Vector Machine, K-Nearest Neighbors, and Gaussian Naive Bayes-to predict MACCEs. The XGBoost model was selected due to its superior performance evidenced by an AUC of 0.898 (95% CI: 0.822 - 0.973) and net clinical benefit. SHAP analysis further clarified variable contributions to MACCE risk. Conclusion: This study employed various machine-learning techniques and multidimensional data assessment, allowing for enhanced prediction of metabolic results and supporting the timely detection of high-risk patients with OSA and hypertension in need of focused preventive measures.
Keywords: Hypertension, machine-learning, metabolic framework, Obesity, obstructive sleep apnea
Received: 04 Nov 2025; Accepted: 29 Jan 2026.
Copyright: © 2026 Xu, Xu, Wang, Liu, Yang, Zhao, Duan and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jun Wang
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