Aging clocks: mechanisms and heath relevance in diverse populations
- 8 September 2025 to 2 December 2025
- Project No: D26038
- DPhil Project 2026
- China Kadoorie Biobank (CKB) Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) Mexico City Prospective Study
Background
Ageing contributes to many diseases that affect all organ systems and is the greatest risk factor for heart disease, neurodegeneration and cancer. Chronological age (CA) is the amount of time an organism has been alive for, and is typically measured in years for humans and tracked by birthdays. However, life expectancy varies considerably among individuals with equal or similar CAs due to diversity in genotypes, living habits, and environments. Biological age (BA) quantifies the ageing of our body's functions rather than simply measure the passage of time. As such, molecular biomarkers that reflect the biological age of a cell type, tissue, organ (such as the heart or brain) or whole organism are needed to develop drugs that target ageing.
Recent advances in high-throughput genomic, proteomic and metabolomic methods are enabling the characterization and quantification of thousands of novel markers that have the potential to provide new insights into the aging process. These complex high-throughput data require the use of sophisticated statistical approaches in order to identify relevant markers of biological aging. Aging clocks are composite measures of biological age that capture different aging processes and consequences of aging.
research experience, research methods and skills training
This project will involve participants from three contemporary prospective studies in the UK, China and Mexico with available or soon to be collected detailed genetic, metabolomic and proteomic data linked to a wide range of disease outcomes. Specifically the DPhil will investigate:
Utilise machine learning methods to build and validate an “ageing clock” based on conventional markers (e.g. lipids, and novel biomarkers (e.g. proteomics and metabolomics)
Assess the robustness of the analysis by investigating different methodological approaches to assess organismal and organ-specific ageing.
Evaluate the rate of organismal and organ-specific aging and its observational and causal relationships with major chronic diseases within and between the three populations
Assess the translational potential of biological age metrics as surrogate endpoints for clinical trials.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
There will be in-house training 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 candidates will have postgraduate training in medical statistics, epidemiology, computer science, engineering or a closely related subject. Proficiency with statistical software and strong programming skills are essential. Candidates should have an strong interest in Ageing research.