Exposomics and Cardiovascular Diseases: A Scoping Review of Machine Learning Approaches

Abstract

Cardiovascular disease has been established as the world's number one killer, causing over 20 million deaths per year. This fact, along with the growing awareness of the impact of exposomic risk factors on cardiovascular diseases, has led the scientific community to leverage machine learning strategies as a complementary approach to traditional statistical epidemiological studies that are challenged by the highly heterogeneous and dynamic nature of exposomics data. The principal objective served by this work is to identify key pertinent literature and provide an overview of the breadth of research in the field of machine learning applications on exposomics data with a focus on cardiovascular diseases. Secondarily, we aimed at identifying common limitations and meaningful directives to be addressed in the future. Overall, this work shows that, despite the fact that machine learning on exposomics data is under-researched compared to its application on other members of the -omics family, it is increasingly adopted to investigate different aspects of cardiovascular diseases.