Interactions between Sugar Consumption and Genetic Variation on Cardiometabolic Traits
Principal Investigator: Dr Nicola McKeown
Approved Research ID: 35835
Approval date: January 8th 2019
Accumulating evidence indicates that excess added sugar intake increases multiple cardiovascular risk factors and this risk may be further increased in genetically susceptible individuals. Two major dietary sources of sugar are sugar sweetened beverages (SSBs) and fruit juice. We aim to examine the relationship between varying intakes of these beverages with dyslipidemia and glycemia-related phenotypes. We propose to test whether genetic variation in pathways related to carbohydrate metabolism interacts with beverage consumption patterns (SSB and fruit juice) to influence lipid and glycemia-related phenotypes. Our research is aimed at understanding how excessive sugar consumption contributes to risk of cardiovascular disease. This project will test hypotheses regarding the underlying mechanisms linking sugars and common genetic variants to the increasing prevalence of cardiometabolic disease. Data from the UK Biobank?s large population-based study will provide additional insight into whether certain genetic subgroups of people may be more susceptible to the increases in cardiometabolic risk associated with increasing sugar intake, derived from excess consumption from sugar-sweetened beverages and/or fruit juice, for example. This study will use observational datasets and genetic epidemiological approaches. Statistical models will be employed to analyze associations between genetic variants, beverage consumption patterns, and lipid and glycemia-related phenotypic data. Linear regression models that incorporate lifestyle factors will be used for association and interaction studies to investigate whether genetic variation modifies an individual?s metabolic responses to sugar intake, especially in the form of sweet drinks. We would like to request access to full cohort including everyone with information on sugar sweetened beverages, phenotypic outcomes of interest as well as genetic data.