Genetic approaches to identifying causal pathways to cardiometabolic diseases
type: CreativeWork
description: 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 …
abstract: 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 …
author: Wittemans, Laura Brigitte Leen
language: en
keywords: Genetics, Metabolomics, Epidemiology, Type 2 Diabetes, Cardio-metabolic diseases, Obesity, Body composition, Genome-wide association studies, Mendelian randomization
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/be2d9ba1-c098-4f12-9cf1-f9c6fc230f89/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2019-06-15
date modified: 2026-07-22T22:24:03Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.39067