Incorporating Biomarkers in Social Science Research on Healthy Ageing: An Intersectionalities Approach
Principal Investigator: Dr Daniel Holman
Approved Research ID: 22295
Approval date: January 8th 2019
This project aims to incorporate biomarker data with a social science approach to healthy ageing research. Existing research has identified a number of biomarkers/measures indicative of healthy ageing. At the same time, an emerging perspective in social science health inequalities research is intersectionalities, the idea that a combination of social factors together lead to health outcomes. This research seeks to unravel the intersections between biomarkers of healthy ageing and socio-demographic intersections. The overarching research question is: ?Is it possible to identify particular socio-demographic intersections where biomarkers of healthy ageing, alone and in combinations, are most prevalent?? This project is highly relevant to the Biobank?s purpose as it is specifically focussed on mapping out the distribution of health in the population and the role that socio-demographic factors play in this distribution. It will fill a gap in understanding since it will use a specifically sociologically-informed analysis of Biobank data. Furthermore, identifying particular socio-demographic intersections lends itself to targeting and tailoring of health interventions. Key biomarkers of ageing will be analysed, including HbA1c (diabetes); forced expiratory volume (lung function); bone density (bone health/frailty) and; systolic blood pressure (CVD), as well as other healthy ageing outcomes including cognitive and physical function and wellbeing. Regression models will be specified with these biomarkers as outcomes and socio-demographics as predictors. Interactions effects will be used to test for intersectional effects. The UK Biobank sample size will allow for fine-grained analyses, for example to test whether intersectional effects apply across age groups. Only the baseline data will be requested as the analysis will be cross-sectional. All available data relating to the variables of interest for the full cohort will be analysed.