Biomarkers are used in clinical and research settings to enable prevention, subtype disease, quantify severity, guide treatment, and prioritize therapeutic targets. Large scale high throughput platforms have allowed for measurement of high quantities of proteins in large epidemiologic samples. By combining proteomic assays with results from genomic platforms, biological insights can be gleaned to facilitate clinical relevance of emerging markers. In Study 1, we used the Prospective Urban Rural Epidemiology (PURE) study analyzed in a case-cohort fashion to evaluate the importance of angiotensin-converting enzyme 2 (ACE2) as a cardiometabolic risk marker. Using long-run prospective data, we showed ACE2 predicted incident diabetes, cardiovascular disease, and death beyond traditional cardiac risk factors. In Study 2, we evaluate M-CSF as a causal driver of vascular disease using a similar case-cohort design. We find M-CSF is a strong predictor of stroke, myocardial infarction, heart failure, and death. Using Mendelian randomization, an approach that leverages genetic variants as instrumental variables, we find M-CSF is not only associated with cardiovascular disease and death, but is also a causal driver of the development of vascular disease. In a subsequent Mendelian randomization analysis, we find that body-mass index correlates with increased plasma M-CSF, indicating that M-CSF may play a mediating role between BMI and cardiovascular disease. Finally, in Study 3, we use polygenic risk scores to agnostically prioritize among a set of 539 plasma proteins which ones are dysregulated early in the heart failure disease course. We identify 7 proteins representing a diverse set of pathways including IL6 (Interleukin 6), HGF (Hepatocyte growth factor), and CPM (Carboxypeptidase M) as markers associated with both incident heart failure as well as genetic predisposition to heart failure. Of the 7 identified proteins, 3 maintained prognostic significance for death and hospitalization in those with heart failure (IL6, KIM1, HGF). / Thesis / Doctor of Philosophy (PhD) / Finding risk factors for heart disease can assist in efforts to prevent disease and prioritize targets for treatment. Advances in the ability to measure entities like proteins and genetic markers at scale allow researchers to evaluate these new risk factors in large databases. We take advantage of these advances in technology to uncover new insights in global studies with participants representing 5 continents. In Study 1, we performed a comprehensive evaluation of the emerging biomarker angiotensin-converting enzyme 2 (ACE2) wherein we evaluated the association of ACE2 with future risk of cardiometabolic events, but also gained insights into possible drivers of plasma protein concentration. In Study 2, we employ a similar analytic profiling of the biomarker macrophage-colony stimulating factor (M-CSF). Available evidence is conflicting as it relates to whether M-CSF serves a harmful or beneficial role in the development of cardiovascular disease. We perform an analysis examining the upstream determinants and downstream consequences of M-CSF levels. In Study 3, we adopt an approach to identify early markers of heart failure by using genetic predisposition to heart failure as an additional filter for biomarker relevance. Overall, this work relies on complementary approaches to better understand risk factors evaluated in large global databases including the Prospective Urban Rural Epidemiology study and Global Congestive Heart Failure study.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29972 |
| Date | January 2024 |
| Creators | Narula, Sukrit |
| Contributors | Paré, Guillaume, Health Research Methodology |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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