Martínez-García Mireya, Gutiérrez-Esparza Guadalupe O, Márquez Manlio F, Amezcua-Guerra Luis M, Hernández-Lemus Enrique
Department of Immunology, Instituto Nacional de Cardiología Ignacio Chávez, México City, México.
Investigadora por México CONAHCYT Consejo Nacional de Humanidades, Ciencias y Tecnologías, México City, México.
Front Cardiovasc Med. 2025 Jan 17;11:1434418. doi: 10.3389/fcvm.2024.1434418. eCollection 2024.
Hypertension is a significant public health concern. Several relevant risk factors have been identified. However, since it is a complex condition with broad variability and strong dependence on environmental and lifestyle factors, current risk factors only account for a fraction of the observed prevalence. This study aims to investigate the emerging early-onset hypertension risk factors using a data-driven approach by implementing machine learning models within a well-established cohort in Mexico City, comprising initially 2,500 healthy adults aged 18 to 50 years.
Hypertensive individuals were newly diagnosed during 6,000 person-years, and normotensive individuals were those who, during the same time, remained without exceeding 140 mm Hg in systolic blood pressure and/or diastolic blood pressure of 90 mm Hg. Data on sociodemographic, lifestyle, anthropometric, clinical, and biochemical variables were collected through standardized questionnaires as well as clinical and laboratory assessments. Extreme Gradient Boosting (XGBoost), Logistic Regression (LG) and Support Vector Machines (SVM) were employed to evaluate the relationship between these factors and hypertension risk.
The Random Forest (RF) Importance Percent was calculated to assess the structural relevance of each variable in the model, while Shapley Additive Explanations (SHAP) analysis quantified both the average impact and direction of each feature on individual predictions. Additionally, odds ratios were calculated to express the size and direction of influence for each variable, and a sex-stratified analysis was conducted to identify any gender-specific risk factors.
This nested study provides evidence that sleep disorders, a sedentary lifestyle, consumption of high-fat foods, and energy drinks are potentially modifiable risk factors for hypertension in a Mexico City cohort of young and relatively healthy adults. These findings underscore the importance of addressing these factors in hypertension prevention and management strategies.
高血压是一个重大的公共卫生问题。已经确定了几个相关风险因素。然而,由于它是一种复杂的病症,具有广泛的变异性且强烈依赖环境和生活方式因素,目前的风险因素仅占观察到的患病率的一部分。本研究旨在通过在墨西哥城一个成熟的队列中实施机器学习模型,采用数据驱动的方法来调查新出现的早发性高血压风险因素,该队列最初包括2500名年龄在18至50岁的健康成年人。
在6000人年期间新诊断出高血压患者,而血压正常的个体是指在同一时期收缩压未超过140毫米汞柱和/或舒张压未超过90毫米汞柱的人。通过标准化问卷以及临床和实验室评估收集社会人口统计学、生活方式、人体测量学、临床和生化变量的数据。采用极端梯度提升(XGBoost)、逻辑回归(LG)和支持向量机(SVM)来评估这些因素与高血压风险之间的关系。
计算随机森林(RF)重要性百分比以评估模型中每个变量的结构相关性,而Shapley加法解释(SHAP)分析量化了每个特征对个体预测的平均影响和方向。此外,计算比值比以表达每个变量的影响大小和方向,并进行了性别分层分析以识别任何特定性别的风险因素。
这项嵌套研究提供了证据,表明睡眠障碍、久坐不动的生活方式、高脂肪食物的消费和能量饮料是墨西哥城一组年轻且相对健康的成年人中高血压的潜在可改变风险因素。这些发现强调了在高血压预防和管理策略中解决这些因素的重要性。