Evidence has shown that lipoprotein(a) (Lpa) is an independent, causal, genetic risk factor for cardiovascular disease (CVD) that promotes the progression of high-risk, vulnerable atherosclerotic plaque phenotypes. Systems biology integrates multiomics datasets to study linear and nonlinear relationships to enhance understanding of the molecular patterns of disease. One such example is the Genetic Loci and the Burden of Atherosclerotic Lesions (GLOBAL) study, which utilizes multiomics profiling to unravel the molecular signatures of Lp(a)-driven CVD. Using deep phenotyping of coronary atherosclerosis by coronary computed tomography angiography, whole-genome sequencing for genetic analysis, and evaluation of thousands of omics measurements and circulating biomarkers, it is possible to describe the atherogenic milieu associated with Lp(a)-driven CVD. By leveraging the multiomic evaluation of Lp(a)-driven coronary phenotypes, we can begin to translate these findings into real-world strategies for earlier recognition of distinct Lp(a)-driven CVD, which may contribute to improved risk mitigation strategies in clinical practice.
Vörös et al. (Thu,) studied this question.