Metabolic rate and risk of specific diseases and mortality in different populations
- 8 September 2025 to 2 December 2025
- Project No: D26008
- DPhil Project 2026
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU)
Background
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. BMR levels have been associated with systemic inflammation, excess adiposity, insulin resistance, type 2 diabetes, and cancer risk. 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 polymorphisms, 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 Randomization 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.
research experience, research methods and skills training
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.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
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 opportunities to present research work at relevant international/national conferences.
PROSPECTIVE STUDENT
The ideal candidate will have a Bachelor’s or Masters degree in a relevant area (e.g. genetics/epidemiology/biomedical or life sciences/statistics) with strong computational skills and advanced programming of analyses in STATA, R, Python or ML packages.