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Obesity and diabetes are increasing globally, while premature mortality decreased in most Western populations. As life expectancy among European populations increases, the burden of the comorbidities associated with excess adiposity and diabetes also increases. Some studies suggest important changes in the specific comorbid diseases associated with adiposity and diabetes in recent decades, but they relied largely on electronic records. The advent of novel biomarkers (eg, metabolomics and proteomics) now available in the UK Biobank, enable to characterise potentially novel omics-profiles associated with disease-specific risk. The UK Biobank is a prospective cohort of 0.5 million adults recruited before 2010, with a wealth of information on lifestyle, medical history, measures of adiposity, biochemistry, 150 individual metabolomic assays, 3000 individual proteomic assays, genetic information, hospitalization and cause-specific mortality.

The project could focus on a particular area of interest for the candidate, and may include examining the observed and genetic association of excess adiposity and diabetes with a broader range of specific diseases and events (i.e, micro versus macrovascular, organ failure, neurological, infective, etc). Mendelian Randomization and mediation analysis techniques could assess the associations of interest, and map-up differences in the potential pathways underlying these associations, by leveraging the novelty of the NMR-metabolomic and proteomic assays. Approaches could also include comparative assessment of the additive predictive value of novel bioassays to conventional biochemistry, or of polygenic versus conventional risk factors for disease-specific risk prediction.


The student will gain experience in non-communicable diseases epidemiological research, genetic epidemiology and analysis of large-scale prospective data. They will develop skills in conducting systematic literature reviews, study design for causal inference in a general population context, statistical programming and 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.


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 Master's degree in a relevant area (e.g. genetic epidemiology/biomedical or life sciences/medical statistics) and proficiency with programing analyses in STATA, R or SAS packages.