Sex-specific cardiovascular risk factors in the UK Biobank - St. Pierre, Skyler R.; Kaczmarski, Bartosz; Peirlinck, Mathias; Kuhl, Ellen (Frontiers in Physiology, 2024)
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