Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of morbidity and mortality worldwide. Traditional concepts of a single “vulnerable plaque” phenotype are increasingly recognized as oversimplified. To better capture this complex heterogeneity, there is a growing need for novel biomarkers and integrative approaches that reflect the complexity of atherogenesis, arterial wall inflammation, and patient-specific risk factors. Thus, the main objective of this PhD project is the stratification of atherosclerosis and ASCVD using genetics and multi-omics in large cohorts.
Omics approaches and biomarkers have gained relevance in cardiovascular disease research, as they can provide new insights into disease mechanisms, diagnostics and the progression of ASCVD. Recent technological advances make it possible to generate and analyze large-scale multi-omics data, facilitating the identification of new biomarkers and the validation of those previously associated with ASCVD.
Our first aim is to identified new or confirm existing omics biomarkers of ASCVD (metabolomics, proteomics, (epi)genomics, incl. CHIP) by using statistical methods and machine learning (ML) algorithms and to compare their predictive value with traditional clinical and risk biomarkers (e.g., blood pressure, cholesterol) for ASCVD incidence and progression. Our second aim is to determine if the significant biomarkers identified in our first aim are causally associated with ASCVD by performing Mendelian Randomization analyses and causal ML/AI techniques. The third aim is to integrate multiple levels of omics data and map these to immune, lipid, demographic, and clinical imaging data, to achieve a better understanding of ASCVD.
