Adiposity, osteoporosis and fractures: statistical analysis of biomarkers and outcomes in one million participants in the UK and China Kadoorie Biobanks
As lifespans extend, diseases and reduced capabilities associated with older age may affect several decades of a person’s life and so are increasing social and healthcare burdens. Fractures due to osteoporosis – a deterioration of bones that occurs with aging – are a serious problem associated with substantial co-morbidity and increased rates of mortality. Obesity – and adiposity in general – has a complex relationship to bone condition and its rate of deterioration.1 Greater weight has a strengthening effect from mechanical loading but recent studies have suggested that fat around bones may have an adverse effect. It is important to understand how the modifiable factors, obesity and adiposity throughout life, can affect disease in old age.
The UK Biobank (UKB) and China Kadoorie Biobank (CKB) prospective studies of 0.5M participants each with long term follow-up for incident events through electronic record linkage to hospital episodes offer excellent opportunity for this investigation.1,2 The large range of adiposity measures, standing and sitting height, measured in both studies at baseline and in large subsets at resurveys, together with MRI bone and fat imaging biomarkers in about 50K UKB participants (by mid-term of the project) provide an unrivalled resource. There are substantial differences in patterns of adiposity in the UK and China which should provide added leverage to distinguish between causal and confounding associations. Both studies have extensive genotyping allowing further investigation of causality by the Mendelian randomisation principle using genetic risk scores.
1. Palermo, A et al. BMI and BMD: The Potential Interplay between Obesity and Bone Fragility,” Int. J. Environ. Res. Public Health, 2016.
2. UK Biobank http://www.ukbiobank.ac.uk/
3. China Kadoorie Biobank http://www.ckbiobank.org/site/
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.
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 range of biomarkers. Examples of suitable prior qualifications are an MMath or MSc in Medical/Applied Statistics.