Identifying Observational and Causal Factors for Cardiovascular Diseases through Large-scale Cohorts
Background Starting in the 1940s, prospective studies like the Framingham Heart Study helped scientists generate new knowledge on risk factors for cardiovascular diseases. While these data sources set in motion the decline in age-adjusted cardiovascular disease seen over the last five decades, they were limited by their size in the kinds of research questions they could answer. Since then, larger data sources have emerged, some with genotyping on the order of hundreds of thousands of people, that have afforded immense power. Here, I use multiple data sources that contain more than 500,000 individuals, some with and some without genetic data, to identify novel insights into observational and causal risk factors for cardiovascular diseases. Objectives To use observational (non-genetic) epidemiological approaches to examine and compare risk factors for subtypes of stroke. To elucidate the observational and causal associations of kidney function with stroke and coronary heart disease (CHD). To perform Mendelian randomization (MR) using transcriptomic and proteomic data on CHD. To develop race- and sex- specific risk prediction models for subtypes of heart failure. Results Observational risk factor profiles for subarachnoid hemorrhage and intracerebral hemorrhage, two subtypes of hemorrhagic stroke, differed quantitatively, suggesting distinct pathogenesis. I found U-shaped observational associations between estimated glomerular filtration rate (eGFR) and CHD. Associations between genetically-predicted eGFR and CHD displayed a threshold effect starting roughly around 75 mL/min/1.73 m2, highlighting the potential for reno-protective therapeutics, like sodium-glucose cotransporter 2 inhibitors, to prevent primary CHD events. I prioritized 582 genes/proteins within 275 genomic regions with possible causal relevance to CHD. I identified cilostazol, an inhibitor of Phosphodiesterase 3A (PDE3A), as a repurposing opportunity for the primary prevention of CHD and a safer alternative to existing antiplatelets. Lastly, I developed race- and sex- risk prediction models for two subtypes of heart failure, defined by preserved (HFpEF) or reduced (HFrEF) ejection fraction, Conclusions The emergence of large-scale data sources, with and without genetics, has allowed for analyses that require immense power, leading to novel insights into cardiovascular diseases.