Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

作者：Gabin Drouard, Juha Mykkänen, Jarkko S. Heiskanen, Joona Pohjonen, Saku Ruohonen, Katja Pahkala, Terho Lehtimäki, Xiao-Ling Wang, Miina Ollikainen, Samuli Ripatti, Matti Pirinen, Olli T. Raitakari, Jaakko Kaprio · 发表于：BMC Medical Informatics and Decision Making · 年份：2024 · DOI：10.1186/s12911-024-02521-3 · 被引用次数：30 · 研究领域：Genetic Associations and Epidemiology、Epigenetics and DNA Methylation、Nutrition, Genetics, and Disease

BACKGROUND: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios. METHODS: We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised...