Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.


Basal metabolic rate (BMR) is defined as the energy required for maintaining body function at rest, and represents the largest component (~60-75%) of the total daily energy expenditure. It reflects the steady-state level of energy homeostasis and a higher level has been associated with higher systemic inflammation. By contrast, lower metabolic rate has been associated with excess adiposity, insulin resistance and type 2 diabetes, while an association of opposite direction has been found between BMR and cancer risk. In addition, the interplay between BMR, physical exercise, diet and body composition is complex, and might differ across Caucasians, Hispanic and Asian populations. 

This project aims to assess the relationships of BMR , including associated single nucleotide polymorphism, and other correlates (patterns of diet, physical activity, adiposity traits), with circulating biomarkers (biochemistry, metabolites and proteomics) and with specific disease and mortality risks, using the data from the UK Biobank, the Mexico City Prospective Study and the China Kadoorie Biobank. Mediation and effect modification analyses, together with Mendelian Randomisation techniques will be conducted to understand the associations of interest. Differences in vascular-metabolic risk associated with BMR might also be explored by ancestry admixture. 

The UK Biobank is a prospective cohort study of 0.5 million adults recruited before 2010, with extensive information on lifestyle and physical characteristics, including bio-impedance measures of adiposity and body composition, biochemistry, metabolomics and proteomics assays, genetic information,  medical histories, including hospitalization and cause-specific mortality. Similar data are available from 0.5 million participants in the China Kadoorie Biobank and 0.1 million participants from the Mexico City Prospective Study.


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.


Training in advanced statistics, epidemiological methods, programming, and scientific writing will be available as needed. 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. 


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