Obesity and long-term health risks in diverse populations
Worldwide, obesity affects about 700 million adults and the prevalence is still rising steadily in most countries. Although the effects of obesity, and of adiposity more generally, on cardio-metabolic diseases and many cancers are well established, uncertainty remains about their role in the aetiology of many other diseases. Moreover, there are still large knowledge gaps regarding the biological mechanisms linking adiposity with different diseases. Large prospective studies, such as China Kadoorie Biobank (CKB) and UK Biobank (UKB) are well positioned to address these evidence gaps.
The project will utilise existing and emerging data in CKB and UKB, each involving 0.5 million adults. CKB has already recorded >70,000 deaths and >1.5 million episodes of hospitalisation for >5000 different disease types. Exposure and long-term health data within the study are being complemented by genetics, metabolomics (e.g. ~250 metabolites) and proteomics (e.g. ~3000 circulating proteins) data in a subset of participants. These, together with similar data in UKB, will enable comprehensive investigation of the long-term health effects of adiposity in diverse populations.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The specific DPhil project will be developed according to the candidate’s interests and aptitude, and may cover some of the following objectives:
- To examine the associations of adiposity with risks of selected specific diseases (e.g. neurological diseases, musculoskeletal diseases);
- To investigate the relative importance of different measures of adiposity in predicting risks of specific diseases;
- To determine the causal relevance of adiposity for risks of specific diseases, using Mendelian randomisation approaches;
- To assess, using the emerging omics and genomic data, mechanisms linking adiposity with specific diseases and traits;
- To compare the associations of adiposity with specific diseases and traits in CKB versus UKB and to explore factors contributing to observed differences.
The student will work within a multi-disciplinary team. There will be in-house training in systematic literature review, statistical programming, data analysis and scientific writing, and attendance at relevant courses if required. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large datasets, and to report research findings, including publications in peer-reviewed journals as the lead author and presentation at conferences.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
The project will be based within the CKB group in the Big Data Institute building. There are excellent facilities and a world-class community of population health, data science, and genomic medicine researchers.
Candidates should have a good first degree (2.1) and MSc in epidemiology, statistics, genetics, biomedical science, or a related discipline, with a strong interest in population health.