Metabolomic signatures of diet and healthy lifestyle in relation to cardiometabolic diseases

Background

Metabolomics offers great promise for obtaining new insights into human biology and aetiology of common diseases, e.g. cardiometabolic diseases. Assessing the metabolomic signatures of diet and healthy lifestyle will improve the understanding of underlying pathways linking them with diseases, and may contribute to personalized medicine and precision nutrition. 

In the China Kadoorie Biobank, multi-omics, lifestyle, physical measurements and disease incidence data are available, including both NMR metabolomics (~220 metabolites) and untargeted MS metabolomics (>5400 metabolites covering 70 major biological pathways such as amino acid metabolism, nucleotide metabolism, and microbiome metabolism), proteomics (~10,000 proteins from Onlink & somascan), metagenomics (both gut and oral) and whole genome sequencing data.

The project will utilise this greatly enriched data resource to identify metabolomic signatures and pathways linking various dietary exposure and healthy lifestyle with cardiometabolic traits (e.g. adiposity, blood pressure, cIMT, liver steatosis) and diseases.

research experience, research methods and skills training

The specific project will be developed according to student’s interest and aptitude, and may cover some of the following objectives:

1. To perform literature study to identify metabolomic markers of specific dietary exposure;
2. To explore the relationship between metabolites with diet and healthy lifestyle;
3. To examine the associations of diet- & lifestyle-related metabolites conventional cardiometabolic risk factors (e.g. adiposity, blood pressure) & incidence of cardiometabolic diseases;
4. To explore the inter-relationship between diet- & lifestyle-related metabolites with other omics markers and to assess the causal relevance of identified metabolites for cardiometabolic diseases using MR approach.

The student will work within a multi-disciplinary team. There will be in-house training in epidemiology, statistics, 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 a MSc in relevant area (e.g. medicine, epidemiology and statistics), with strong interest in molecular epidemiology.