Computational strategies in cardiometabolic diseases: a portal to deeper mechanistic understanding

Worldwide, cardiometabolic diseases (e.g., cardiovascular disease (CVD), diabetes, and non-alcoholic fatty liver disease (NAFLD)) have acquired almost epidemic proportions in the past few decades due to the widespread adoption of a western lifestyle. They compromise heart and liver functions and underlie the two main causes of death worldwide, ischemic heart disease and stroke. The development of these diseases is characterized by lipid accumulation, inflammatory responses, and metabolic dysfunction in the arterial wall (a process referred to as atherosclerosis) and liver (“fatty liver disease” or NAFLD). For both, macrophages and their precursors, monocytes, are important contributors. The rapid development in high-throughput and imaging technologies have enabled researchers to detect profiles of genes, proteins, and metabolites within individuals and cells. These techniques are increasingly applied to study cardiometabolic diseases. However, their potential to explore the pathogenesis has not been fully exploited. This thesis analysed high-dimensional omics and imaging data of plaque and liver through a combination of computational strategies including statistical inference, machine learning and image processing, to dissect the pathogenesis of atherosclerosis and NAFLD at the molecular and spatial level.