Adiposity, mechanisms of action and long-term health risks

BACKGROUND

Worldwide obesity affects >700 million adults and the prevalence is still rising steadily. Although the health effects of adiposity, or obesity, on cardio-metabolic diseases and many cancers are well established, uncertainty remains about its associations with many other diseases and about the biological mechanisms linking adiposity with different diseases. The project will utilise existing and emerging data in the prospective China Kadoorie Biobank (CKB), which includes >0.5 million adults and has recorded a large number of fatal and non-fatal disease events (~100,000 deaths and ~3 million ICD-10 coded episodes of hospitalisation). The baseline exposure and long-term health data are being complemented by sample assays in subsets of participants, involving genetics, metabolomics (e.g. >5000 metabolites) and proteomics (e.g. ~10,000 proteins). These, together with similar data in the UK Biobank, will enable comprehensive investigation of the long-term health effects of adiposity in diverse populations and of the biological mechanisms linking adiposity with disease risks.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The specific project will be developed according to candidate’s interests and aptitude, and may include part of the following objectives:

1. to examine and compare the associations of adiposity with risks of specific diseases, disease subtypes and multi-morbidity in Chinese and UK populations
2. to determine the causal relevance of adiposity for disease risks, using Mendelian randomisation (MR) approaches
3. to explore the mechanisms linking adiposity with specific diseases and traits
4. to identify protein biomarkers that may causally affect levels of adiposity using pQTLs identified in GWAS, and to explore potential druggability of known (e.g. GDF15, RET, GLP1, Leptin, FABP4) and new proteins using various downstream analyses (e.g. enrichment, KO mouse models, PheWAS).

Advanced in-house training will be provided in statistics, statistical programing (e.g. SAS, R), genetics (e.g. MR), and scientific writing. 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 a few peer-reviewed publications as the lead author.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

There may be opportunities to collaborate with industry partners and laboratory research groups. Attendance at seminars, workshops and courses provided by the department and University will be encouraged. There will be opportunity to present research work at relevant international/national conferences.

PROSPECTIVE  STUDENT

The ideal candidate should have a good first degree (2.1) and MSc in epidemiology, statistics, genetics, biomedical science, or a related discipline, with good statistical software and programming skills and a strong interest in population health and molecular epidemiology.