Multi-omics data integration and sex-specific analyses to improve the understanding of cardiovascular disease

Abstract

Cardiovascular disease (CVD) remains a leading global cause of death. Observational studies suggest that some risk factors, such as smoking and type 2 diabetes, are more strongly associated with CVD in women than in men. However, it is unclear whether these differences also reflect sex differences in causal effects. This thesis uses Mendelian randomisation (MR), a method that uses genetic variants to estimate causal effects, to address this question. Chapter 2 elaborates on MR and its relevance for sex-specific applications. In chapters 3 and 4, we perform sex-specific MR analyses on smoking and diabetes. We find that smoking has similar causal effects on CVD in both sexes, with potentially stronger effects in females for subarachnoid haemorrhage. For diabetes, causal effects on CVD are similar between the sexes. Chapter 5 examines how methodological choices in MR, particularly in the use of sex-specific versus sex-combined genome-wide association data, can affect results. We demonstrate that such choices could influence MR outcomes, especially when there are considerable sex differences in genetic instruments. Chapters 6 and 7 integrate multi-omics data, including plasma proteins, urinary metabolites, and atherosclerotic plaque tissue, to uncover causal pathways underlying atrial fibrillation, heart failure, dilated cardiomyopathy, hypertrophic cardiomyopathy, and coronary heart disease. We identify proteins related to metabolism pathways as potential therapeutic targets, some of which are already targeted by approved or in-development drugs. We also find that existing drugs might be repurposed for treating CVD. By combining MR results with tissue-level expression data from carotid plaque samples in chapter 7, we further validate the therapeutic relevance of the identified proteins. We conclude in chapter 8 with a call for MR applications where both subgroup analyses and the integration of multiple omics layers are incorporated. This will advance our understanding of the complex biology of CVD and support more effective, personalised therapeutic strategies for both prevention and treatment.