Aging and chronic disease: Statistical analysis of physical, biochemical and imaging biomarkers in UK Biobank
Project Reference: NDPH/MT16/022
Age is the strongest risk factor for most chronic diseases but the extent to which aging effects can be attributed to modifiable factors remains unclear. Presence of one disease can increase the risk of another disease (e.g. diabetes and arthritis each carry an increased risk of cardiovascular disease) which could in part be attributable to a biological aging effect. The large prospective UK Biobank  study provides an unrivalled opportunity to investigate aging, as the study includes an exceptional range of biomarkers (such as blood pressure, lung function, measures of cardio-respiratory fitness, blood biomarkers of inflammation and kidney function and key biomarkers from body imaging).[2, 3]
This project will involve systematic investigation to identify biomarkers strongly associated with both age and major chronic diseases of age (e.g. cardiovascular disease, arthritis, osteoporosis, respiratory disease and cognitive impairment/dementia) in older UK Biobank participants and will explore relationships between these biomarkers. The aim of the project will be to assess the extent to which the biomarkers account for the relationships between the diseases and age and to consider the value of forming vascular, cognitive and overall biological aging scores. When key markers from imaging (including coronary, eye and, in particular, brain imaging) become available their relationships with the biomarkers and scores will be explored. Expressing the effects of modifiable risk factors for diseases in terms of biological aging and brain deterioration may provide a powerful means of conveying health care messages.
1. UK Biobank http://www.ukbiobank.ac.uk/
2. Wang Assessing the Role of Circulating, Genetic, and Imaging Biomarkers in Cardiovascular Risk Prediction. Circulation 2011;123:551-565.
3. MacGillivray TJ et al. Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions. Br J Radiol; 87:20130832
Research Experience, Research Methods and Training
Learning from working within a multi-disciplinary team including statisticians, statistical programmers and bioinformaticians with experience of large-scale data analysis. Developing planning and design skills for future research.
Field Work, Secondments, Industry Placements and Training
Further training in statistical programming through a range of courses run by NDPH, Oxford University and the SAS Institute. Opportunities to present research findings at relevant meetings.
In addition to the supervision team listed above, this project will be supervised by Professor Cathie Sudlow of UK Biobank.
The project involves statistical analysis of big data to improve population health and, therefore, requires previous statistical programming training/experience (e.g. in R, SAS) and interest in developing these skills further and acquiring basic knowledge of a wide range of biomarkers. Examples of desirable prior qualifications are an MMath or MSc in Medical/Applied Statistics.