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ORIGINAL RESEARCH article

Front. Cardiovasc. Med.
Sec. Hypertension
Volume 11 - 2024 | doi: 10.3389/fcvm.2024.1434418
This article is part of the Research Topic Influence of Lifestyle on Cardiometabolic Diseases View all 6 articles

Machine Learning Analysis of Emerging Risk Factors for Early-onset Hypertension in the Tlalpan 2020 Cohort

Provisionally accepted
Mireya Martínez-García Mireya Martínez-García 1*Guadalupe O Gutierrez Esparza Guadalupe O Gutierrez Esparza 1Manlio F. Marquez, MD, FACC Manlio F. Marquez, MD, FACC 1Luis M Amezcua-Guerra Luis M Amezcua-Guerra 1Enrique Hernandez-Lemus Enrique Hernandez-Lemus 2*
  • 1 National Institute of Cardiology Ignacio Chavez, Mexico, México, Mexico
  • 2 Computational Genomics, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico

The final, formatted version of the article will be published soon.

    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 2500 healthy adults aged 18 to 50 years. Hypertensive individuals were newly diagnosed during 6000 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.

    Keywords: machine learning models, Hypertension, Sleep Disorders, Sedentary Lifestyle, high-fat foods consumption, Energy drink consumption, Anxiety, family history

    Received: 21 May 2024; Accepted: 30 Dec 2024.

    Copyright: © 2024 Martínez-García, Gutierrez Esparza, Marquez, MD, FACC, Amezcua-Guerra and Hernandez-Lemus. 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:
    Mireya Martínez-García, National Institute of Cardiology Ignacio Chavez, Mexico, 14080, México, Mexico
    Enrique Hernandez-Lemus, Computational Genomics, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.