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ORIGINAL RESEARCH article

Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 15 - 2024 | doi: 10.3389/fphys.2024.1339866
This article is part of the Research Topic Machine Learning-based Disease Diagnosis in Physiology and Pathophysiology View all 5 articles

Sex-specific cardiovascular risk factors in the UK Biobank

Provisionally accepted
  • 1 Stanford University, Stanford, United States
  • 2 Delft University of Technology, Delft, Netherlands

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

    The lack of sex-specific cardiovascular disease criteria contributes to the under-diagnosis of women compared to men. For more than half a century, the Framingham Risk Score has been the gold standard to estimate an individual's risk of developing cardiovascular disease based on age, sex, cholesterol levels, blood pressure, diabetes, and smoking. Now, machine learning can offer a much more nuanced insight into predicting the risk of cardiovascular disease. The UK Biobank is a large database that includes traditional risk factors as well as tests related to the cardiovascular system: magnetic resonance imaging, pulse wave analysis, electrocardiograms, and carotid ultrasounds. Here we leverage 20,542 datasets from the UK Biobank to build more accurate cardiovascular risk models than the Framingham Risk Score, and quantify the under-diagnosis of women compared to men. Strikingly, for first-degree atrioventricular block and dilated cardiomyopathy, two conditions with non-sex-specific diagnostic criteria, our study shows that women are under-diagnosed 2x and 1.4x more than men. Similarly, our results demonstrate the need for sex-specific criteria in essential primary hypertension and hypertrophic cardiomyopathy. Our feature importance analysis reveals that, out of the top 10 features across three sex and four disease categories, traditional Framingham factors made up between 40-50%, electrocardiogram 30-33%, pulse wave analysis 13-23%, and magnetic resonance imaging and carotid ultrasound 0-10%. Improving the Framingham Risk Score by leveraging big data and machine learning allows us to incorporate a wider range of biomedical data and prediction features, enhance personalization and accuracy, and continuously integrate new data and knowledge, with the ultimate goal to improve accurate prediction, early detection, and early intervention in cardiovascular disease management.Our analysis pipeline and trained classifiers are freely available at https://github.com/ LivingMatterLab/CardiovascularDiseaseClassification

    Keywords: cardiovascular, sex differences, Risk factors, Heart disease, UK Biobank

    Received: 16 Nov 2023; Accepted: 26 Feb 2024.

    Copyright: © 2024 St. Pierre, Kaczmarski, Peirlinck and Kuhl. 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: Skyler R. St. Pierre, Stanford University, Stanford, United States

    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.