Alcohol consumption, mechanisms of action, and long-term health effects in diverse populations
- 8 September 2025 to 2 December 2025
- Project No: D26033
- DPhil Project 2026
- China Kadoorie Biobank (CKB) Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU)
Background
Alcohol consumption is widespread and increasing worldwide, especially in lower- and middle-income countries. Alcohol consumption is a major cause of many conditions, such as cardiovascular disease, certain cancers, liver cirrhosis and injuries. However, uncertainty remains about alcohol’s causal relevance for many other less-studied, including non-fatal, diseases, about the health effects of moderate drinking (e.g. the controversial beneficial effects of moderate drinking in many observational studies), and about the underlying biological mechanisms through which alcohol influences different diseases, which may differ in diverse populations with different lifestyle, disease rates and genetic profiles.
To address the evidence gap, the project will involve in-depth analysis of data from two large prospective studies, the China Kadoorie Biobank (CKB) and the UK Biobank (UKB), each of 0.5 million participants with a wide range of health outcome data recorded along with genomic and multi-omic data. The information generated will provide novel insights into the alcohol-related disease burden, and aetiology and mechanisms of action, informing prevention and treatment strategies in diverse populations.
research experience, research methods and skills training
The specific project will be developed according to the candidate’s interests and aptitude but may involve:
- Assessing the prospective associations of alcohol drinking with health outcomes and intermediate traits;
- Using genetic approaches (Mendelian randomisation) to assess causality of alcohol-disease associations;
- Analysis of novel multi-omics data (e.g. proteomics, metabolomics, gut microbiome) to identify alcohol-associated biomarkers and explore potential mechanisms underlying disease risks;
- Using machine learning methods to build and validate a novel alcohol biomarker score, and assess their associations with health outcomes;
- Estimates of the health burden (e.g. alcohol-attributable deaths, healthcare costs) of alcohol drinking in diverse populations.
The candidate will work closely with a strong interdisciplinary team of researchers with expertise in epidemiology, genetic and molecular epidemiology, statistics and population health.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
In-house training will be provided in statistics, programming, genetics, and scientific writing, along with attendance of specific courses where necessary. There will be opportunity to present research work at relevant international/national conferences. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large-scale high-dimensional data using state-of-art techniques, and to report research findings in peer-reviewed journals.
PROSPECTIVE STUDENT
The ideal candidates should have a good first degree (2.1) and postgraduate training (e.g. MSc) in epidemiology, data science, medical statistics, biomedical science, or a related discipline, with proficiency with statistical software and strong programming skills. Candidates should have an interest in population health.