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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 measuring 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 characterisation and quantification of thousands of novel markers that have the potential to provide new insights into the ageing process. These complex high-throughput data require the use of sophisticated statistical approaches in order to identify relevant markers of biological aging. Ageing clocks are composite measures of biological age that capture different ageing processes and consequences of ageing.


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

  • Utilise machine learning methods to build and validate an  “ageing clock”  based on  conventional markers (e.g. lipids, inflammation, adiposity, blood pressure) commonly measured in a clinical setting [ClinAge]
  • Utilise machine learning methods to build and validate an  “ageing clock”  based on novel biomarkers (e.g. proteomics and metabolomics) [OmicsAge]
  • Identify the “age gap” or rate of aging as assessed by ClinAge and OmicsAge and its association with predicting all-cause and cause-specific mortality within and between the three populations.
  • Utilise intermediate traits to understand the potential mechanisms of ClinAge and OmicsAge ageing clocks across these diverse populations.


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, including 2-3 publications as the lead author in peer-reviewed journals.

prospective student

Candidates should have postgraduate training in medical statistics, epidemiology, computer science, data science, engineering 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.