Metabolic rate and risk of diabetes and cardiovascular disease

BAckground

Cardiometabolic diseases, including type 2 diabetes and cardiovascular diseases, are increasingly prominent global health challenges. Adiposity is an important and modifiable risk factor for these, influenced by various lifestyle considerations such as diet and physical activity, which are themselves potentially independently associated with these same diseases. The mechanisms and pathways underlying these relationships are incompletely understood, but basic metabolic rate has been proposed to play a role. The interplay between basic metabolic rate, physical exercise and the composition of body mass is complex, but is importantly potentially amenable to intervention. This project aims to assess the relationships between these particular determinants of vascular-metabolic health, and their joint and separate associations with risk of type 2 diabetes and major cardiovascular risk factors and disease in the UK Biobank. Mediation analyses, effect modification and potentially Mendelian Randomisation techniques could help understand the associations of interest. 

The UK Biobank is a prospective cohort study of 0.5 million adults recruited before 2010, with extensive information on lifestyle, physical characteristics, including bio-impedance measures of adiposity and body composition, biochemistry and metabolomics assays, medical histories, including hospitalization, and cause-specific mortality. This project will apply a range of analytical techniques to examine the association of metabolic rate with various measures of adiposity and body composition, additionally investigating how physical exercise and different circulating biomarkers mediate the relationships with risks of vascular-metabolic diseases and their risk factors.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will gain experience in non-communicable diseases epidemiological research and analysis of large-scale prospective data. They will develop skills in conducting systematic literature reviews, study design and planning, statistical programming, data analysis, including different types of mediation analyses, and presentation skills. The student will be supported to publish peer-reviewed papers emerging from their DPhil.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

Training in advanced statistics, epidemiological methods, programming, and scientific writing will be provided. Attendance at seminars, workshops and courses provided by the department and University will also be encouraged. There will be opportunity to present research work at relevant international/national conferences. 

PROSPECTIVE STUDENT

The ideal candidate will have a Masters degree in a relevant area (e.g. statistics/epidemiology/biomedical or  life sciences) and proficiency with programing analyses in STATA, R or SAS packages.