Review of various Integrative Approaches to Cardiovascular Disease and COVID-19: Biomarkers, AI, and Natural Treatments

This article reviews the advanced integrative approaches for estimating cardiovascular disease (CVD) risk by means of machine learning and genetic-epigenetic analyses. A methodical review of 35 studies indicating the prognostic value of several biomarkers-including cardiac troponins, and DNA methylation surrogates-is available in various literatures. These biomarkers were found to improve forecasts of adversative cardiovascular (CV) events and mortality and other CV outcomes. Especially in short-term prediction windows, the integrated model composing of genetics-epigenetics proved better than usual risk assessment models. The results from different literature show how well-advanced biomarker studies and machine learning methods might reduce CV risk assessment, opening the path for tailored preventive programs and improved clinical results. This review article aggregates the outcomes of the various literature for the prediction of CVD, so that the validation of these research models in various populations and merging multi-omics data for more general use will become the future research direction.