Ageing clocks: mechanisms and heath relevance in diverse populations

BACKGROUND

Ageing affects all organ systems and is strongly associated with increased risks of adult diseases (e.g. heart disease, stroke, neurodegeneration and cancer). Life expectancy can vary considerably among individuals with equal or similar chronological ages (CAs) due to difference in genotypes, lifestyles and living environments. Biological age (BA) quantifies the ageing of our body's functions rather than simply measure the passage of time, and can be assessed using molecular biomarkers that reflect biological functions of a cell type, tissue, organ (e.g. heart or brain) or whole organism.

The project will utilise available and emerging high-dimensional data, including lifestyle, physical characteristics, blood biochemistry, genomics, proteomics and metabolomics in the prospective China Kadoorie Biobank (CKB), UK Biobank (UKB) and Mexico City Prospective Study (MCPS) to identify relevant markers that capture different ageing processes and consequences of ageing. The information generated will inform development of preventive measures and new drugs to slow down the ageing process.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The precise details of the project will be developed according to candidate’s interests and aptitude. Specifically the DPhil may include, but is not limited to, the following aims:

1. utilise machine learning methods to build and validate a clinical “ageing clock (ClinAge)” based on conventional markers (e.g. lipids, inflammation, adiposity, blood pressure) and an omics “ageing clock (OmicsAge)” based on novel biomarkers (e.g. proteomics and metabolomics); 
2. identify the “age gap” or rate of aging as assessed by ClinAge and OmicsAge and its utility in predicting frailty, all-cause and cause-specific mortality and morbidity within and between the diverse populations; 
3. utilise intermediate traits (e.g. renal function) to understand the potential mechanisms of ClinAge and OmicsAge ageing clocks across the diverse populations.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

Advanced in-house training will be provided in epidemiology, statistics, data science plus external training courses. 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 candidate should have postgraduate training in medical statistics, epidemiology or a closely related subject. Proficiency with statistical software and strong programming skills are essential. Candidates should have an interest in ageing and research related to chronic disease in general.