Characterising the metabolomic profiles of IHD and associated traits
2025/29
BACKGROUND
Metabolomics offers great promise for obtaining new insights into human biology and disease aetiology, as well as for identifying potential pathways and targets for effective intervention. It is particularly informative when integrated with genomics and proteomics data in prospective studies. In the China Kadoorie Biobank (CKB), More than 5400 metabolites are being quantified using Metabolon’s Global DiscoveryTM Panel in a nested IHD case-cohort study of 4,000 participants, which covers eight super biological pathways (e.g. amino acid metabolism, nucleotide metabolism, and microbiome metabolism) and 70 major pathways. The project will utilise the emerging metabolomics data and other available exposures, intermediate traits (e.g. adiposity, bone density, cIMT, liver steatosis, and retinal images), genetics, and proteomics data to characterise the metabolomic profiles of IHD and its associated traits. The information generated will improve understanding of disease mechanisms and inform risk prediction and development of new prevention and treatment strategies for IHD.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The specific project will be developed according to the student’s interest and aptitude, and may cover some of the following objectives:
- to examine the cross-sectional associations of individual metabolites with CVD risk factors (e.g. diet, smoking, physical activity) and physical measurements (e.g. adiposity, blood pressure);
- to explore the prospective associations of individual metabolites with risks of IHD and other major diseases captured among sub-cohort participants;
- to develop and validate metabolomics-based risk prediction models and to assess, in combination with conventional, proteomics and genetic factors, their utilities for predicting risks of IHD, intermediate CVD traits and other diseases;
- to assess the causal relevance of plasma metabolites for risk of IHD and other major diseases (e.g. T2D, stroke) using genetic (two-sample Mendelian randomisation, colocalization) approaches, and explore the potential of specific metabolites as drug targets.
The student will work within a multi-disciplinary team, with in-house training provided in epidemiology, statistical programming, computational genetics, and attendance of relevant courses. 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 publications as the lead author in peer-reviewed journal and presentations at conference.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
The project will be based within the CKB group in the Big Data Institute, with excellent facilities and a world-class research community. There will be opportunities to work with external research institutions.
PROSPECTIVE STUDENT
The ideal candidate should have a good first degree (2.1) and an MSc in a relevant area (e.g. epidemiology, statistics, bio-medicine), with a strong interest in molecular epidemiology.