The role of multi-omics technologies and genetic analysis in the diagnosis and prediction of cardiovascular diseases

Abstract

   Risk factor identification and their subsequent reduction is one of the fundamental strategies in cardiovascular disease prevention and treatment (CVD). Any biological mechanism comprises many crucial elements which ensure its function. Thorough cross-level molecular assessment is required in order to obtain relevant information, therefore gaining insight into disease pathogenesis. Numerous advances in the identification of CVD associated biomarkers have undoubtedly expanded our understanding. However, lifestyle, environmental factors and genetic predisposition are ought to be taken into account. Given the presence of numerous factors affecting the course of CVD, there is a demand for new sensitive diagnostic methods. One of those new approaches is the usage of omics technologies, which make it possible to obtaina large array of biological data at the molecular level. Integration of various methods helps to accumulate a colossal amount of data. High-tech tools for data analysis, such as artificial intelligence and machine learning ensure the identification of interrelated significant data between variables. Multi-omics technologies in combination with genetic analysis are attracting more attention worldwide. It can be perceived as a new stage in CVD prediction and recurrent cardiovascular events risk assessment. These approaches can help to improve our understanding of the molecular genetic pathology of CVD and provide an objective evaluation of pathophysiological processes.