Genetic approaches to identifying causal pathways to cardiometabolic diseases
Background: In the era of large-scale biobanks and detailed multi-omic and clinical phenotyping, it is now possible to simultaneously measure several thousands of biological traits and test their associations with health outcomes. The unprecedented scale of the available epidemiological data has led to a surge in the number of proposed risk factors for cardiometabolic and other diseases identified through observational research. However, prominent failures of randomised controlled trials to replicate observational findings highlight that new approaches are urgently required to prioritise risk factors for interventional studies based on their likelihood of causality.
Aim: Genetic evidence provides the opportunity to obtain unconfounded associations in an observational setting and can help to efficiently identify causal, aetiological pathways to cardiometabolic diseases. This work aims to identify and prioritise causal pathways to cardiometabolic diseases by integrating genetic data with detailed metabolic phenotypes, including blood metabolites and objective measures of body size and composition.
Methods: The first chapter summarises and critically evaluates existing genetic strategies for assessing causality in an observational setting and reviews the literature on reported associations between blood metabolites and incident type 2 diabetes (T2D). As a proof-of-concept, I then adopt a genetic approach to a) generate genetic predictors and b) assess evidence of causality for T2D and coronary heart disease (CHD) risk for a selected metabolite (glycine), for which consistent observational evidence suggests protective associations with T2D and CHD development. The approach is then extended to move beyond studying simple indices of obesity and enable causal assessment of refined anthropometric traits. For this, I have led collaborative efforts to develop genetic instruments for overall and regional fat and lean mass.
Findings: Genetic approaches to studying causality in an observational setting rely on important assumptions that are sensitive to violations, particularly in the context of highly correlated ‘omic’ measures. Existing methods generally consider overall risk factor “levels”, rather than assessing the causality of the distinct mechanisms contributing to levels. I provide genetic evidence for a causal cardio-protective effect of the glycine pathway in men and women, with blood pressure as a potential mediating factor. In contrast, no strong evidence for a causal link between glycine and T2D was found, with evidence suggesting that the inverse glycine-to-T2D association is the consequence of a glycine-lowering effect of insulin resistance. Despite total body fat percentage (BF%) and fat-free mass index (FFMI) being strongly observationally and genetically correlated with BMI, I identify 16 loci associated with higher BF% and lower FFMI but not BMI. Based on observational and genetic studies of regional fat and lean mass, I identify patterns of fat distribution which may not be captured by traditional anthropometric phenotypes, such as fat mass in the arms and in the subcutaneous android region. Fat mass in the arms and subcutaneous android region were observationally only weakly correlated with BMI, WHR and other fat compartments, and genetic loci specific to arm and subcutaneous android fat mass were identified. Conversely, no evidence was found that genetic loci associated with lean mass have heterogeneous effects on lean mass in different regions of the body.
Conclusion: This thesis demonstrates how the integration of large-scale genetic, metabolomic and clinical data can not only prioritise novel aetiological pathways to cardiometabolic conditions, but also formulate hypotheses regarding the underlying physiological mechanisms. The genetic factors identified for refined anthropometric traits, such as a high relative body fat mass in the absence of overweight, allow for a causal assessment of novel and specific body size and composition traits. In summary, this thesis demonstrates and utilises the opportunities that arise from integrating genetic data with refined phenotypes at scale to identify novel targets for the treatment and prevention of cardiometabolic diseases.