Alcohol consumption, mechanisms of action and long-term health effects in diverse populations

BACKGROUND

Alcohol consumption is widespread and increasing worldwide. Alcohol consumption is a major cause of many conditions, such as cardiovascular disease, certain cancers, liver cirrhosis and injuries. However, uncertainty remains about its 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 mechanisms through which alcohol influences different diseases. These may vary greatly 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 TRAINING

The specific project will be developed according to the candidate’s interests and aptitude but may involve:

• Assessing the associations of alcohol drinking with health outcomes and intermediate traits; 
• Using Mendelian randomisation (MR) to assess causality of alcohol-disease associations; 
• Analysis of novel multi-omics data (e.g. proteomics and metabolomics) to identify alcohol-associated biomarkers and explore potential mechanisms underlying disease risks; 
• Using machine learning methods to build and validate an alcohol biomarkers score based on novel multi-omics biomarkers, and assess their associations with health outcomes; 
• Estimating the health burden 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 good statistical software and programming skills and a strong interest in population health.