Assessing the roles of inflammatory markers in cardiometabolic diseases
2025/28
BACKGROUND
Cardiometabolic diseases (CMD), including IHD, stroke, and type 2 diabetes, are the leading causes of morbidity and mortality worldwide. Although many important causes of CMD are well established (e.g. hypertension, obesity, dyslipidaemia), substantial uncertainty remains about the roles of many emerging biomarkers, particularly those related to chronic inflammation, in CMD aetiology. This project will investigate the relationships of inflammatory protein markers with CMD, utilising available and emerging proteomics data in subsets of China Kadoorie Biobank (CKB: ~10,000 proteins) and the UK Biobank (UKB: ~3000 proteins) participants. The integrated analyses of lifestyle, clinical, proteomics, and genetics data in these biobanks will greatly improve understanding of the inflammatory processes underlying development of CMD, informing disease prevention and treatment strategies.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The specific DPhil project will be developed according to the candidate’s interests and aptitude, and may cover some of the following objectives:
- to examine and compare the associations of inflammatory markers with traits (e.g. adiposity, smoking) and CMD risks in CKB versus UKB and to explore factors contributing to any observed differences;
- to develop inflammation-based protein scores, using conventional and machine learning approaches, and to assess their predictive utilities for disease risks in diverse populations;
- to clarify the causal associations of inflammatory markers with CMD risks, using Mendelian randomisation (MR) and colocalisation test;
- to assess potential druggability of certain inflammatory markers using various downstream analyses (e.g. enrichment, KO mouse models, tissue expression, PheWAS);
- to explore, using genetic instruments, the up- and down-regulating roles of specific proteins (e.g. IL6) on signalling network and their likely effects on disease risks.
Advanced in-house training will be provided in statistics, statistical programing (e.g. SAS, R), genetics (e.g. MR, colocalisation analyses), and scientific writing. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large datasets, and to report research findings, including a few peer-reviewed publications as the lead author.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
There may be opportunities to collaborate with industry partners and laboratory scientists. 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.
PROSPECTIVE STUDENT
The ideal candidate should have a good first degree (2.1) and MSc in epidemiology, statistics, genetics, biomedical science, or a related discipline, with good statistical software and programming skills and a strong interest in molecular epidemiology.