Metabolic risk and cardiovascular disease: insights from large biobanks with genetic epidemiological approaches

Abstract

The overall aim of this thesis was to gain additional in-depth insights into the metabolic risk assessment and management of cardiovascular disease (CVD), based on multiple omics data sources in large population studies and biobanks. The studies described in this thesis contributed to improved insight into the CVD in three main research areas: 1) the pathophysiological mechanisms of atherosclerosis (Part I), chapters 2 and 3 captured distinct metabolomic profiles associated with CVD and to characterize a potential novel therapeutic target; 2) the underlying genetics of CVD risk factors (Part II), chapter 4 examined the age-dependent genetic effects on metabolic risk factors of CVD, and chapter 5 investigated the effects of structural genetic variants on sleep disturbances, which is being increasingly considered an important cardiovascular risk factor; 3) the heterogeneity of CVD risk across populations with different metabolic characteristics (Part III), chapters 6, 7 and 8 addressed the role of metabolic syndrome, Lp(a) and subclinical hypothyroidism in CVD risk.

This thesis highlights the potential of integrating genomics, metabolomics, and personalized approaches to enable more precise and individualized management of metabolic risk associated with CVD over the life course.