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Observational studies suggest that adiposity is associated with increased risk of chronic or infective respiratory diseases. However, most studies have been limited to diagnosed chronic obstructive pulmonary disease, lung cancer, or pneumonia. Only a few studies have investigated objective measures of respiratory functions, and the causal relationships between adiposity traits and such outcomes are largely unknown. Furthermore, different components of body composition or body fat distribution might have very different relevance to risk from different respiratory conditions, particularly in the older ages. These questions could be addressed in detail in diverse populations from the UK Biobank (UKB).


The UKB is a cohort of 0.5M participants with extensive phenotypic information from questionnaire and spirometry, biochemistry, 150 individual NMR-metabolites, 3,000 proteomics, body imaging, large-scale whole genome genotyping, polygenic risk scores, hospitalisation and mortality registers. This project aims to systematically assess the observational and genetic relevance of various markers of adiposity to multiple respiratory outcomes within the UKB study population, and to elucidate the causal relationships between genetic signatures of different markers of adiposity and specific respiratory diseases. In-depth analyses will include both diagnosed and un-diagnosed lung impairment, their severity, and complications. 

The aims for this project may include the following:

  1. Assess the shape and strength of associations of multiple markers of adiposity with lung function-measures, diagnosed respiratory diseases, fatal and non-fatal events.
  2. Employ Mendelian randomisation approaches (one- and two-sample methods) to assess causality of associations between adiposity traits and respiratory diseases.
  3. Assess the relevance of potential mediators from biomarker assays.

Comparable data are available in Chinese and Mexican populations from the China Kadoorie Biobank (CKB) and the Mexico City Prospective Study (MCPS), which could allow between-population comparisons across cohorts.


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. 


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