Brain signatures of diabetes in UK Biobank
As lifespans increase dementia poses an increasing social and healthcare burden. Diabetes is a major risk factor for cognitive impairment and dementia, of particular concern because the prevalence of diabetes is increasing with the global epidemic of obesity.
Diabetes is associated with a greatly increased risk of vascular and microvascular disease, including both large and small artery strokes, which can lead to dementia. In addition, alterations in glucose, insulin, and amyloid metabolism may underlie brain pathophysiology associated with diabetes.1 Evidence on whether diabetes drugs could prevent dementia has been inconclusive.
The UK Biobank prospective population study in 0.5M participants is acquiring multi-modal MRI brain imaging on 100K of its participants (with data from 20-50K expected during the timeframe of this project).2 This includes modalities with potential to provide measures of structure and volumes, white matter lesions and integrity, functional connectivity and iron distribution (indicative of bleeding) in different regions of the brain. Blood plasma glucose and HbA1c measurements will become available in 2018, making this an excellent resource for investigation of the brain signature of diabetes-related features.
The project will begin with a review of the literature on the association of diabetes-related traits with brain imaging biomarkers and attempt corroboration/refutation by analyses using UK Biobank. The project will proceed to assess the associations of diabetes-related traits with ~2000 imaging-derived phenotypes and cognitive test data available in UK Biobank. The third stage of the project will delve deeper by using native MR-image based analysis to identify regions of the brain showing structural and functional changes associated with diabetes.3
Identifying brain imaging phenotypes strongly associated with diabetes-related pathology would be valuable in defining surrogate endpoints for early evaluation of the effects of therapy to prevent dementia.
1. Biessels GJ, et al. Lancet Neurol 2006;5:64–74.
2. Miller K, et al. Nature Neuroscience 2016;19:1523–36.
3. Biessels GJ, et al. 2014;63:2244–52
Research experience, research methods and training
Learning from working within a multi-disciplinary team including statisticians, statistical programmers, neuroscientists and neurologists with experience of analysis of large-scale data, including MR imaging data. Developing planning and design skills for future research.
fieldwork, secondments, industry placements and training
Further training in statistical programming through a range of courses run by NDPH, Oxford University and the SAS Institute. Training in the theory and practice of functional and structural brain image analysis. 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) with an interest in developing these skills further and acquiring knowledge of brain imaging analysis. Examples of suitable prior qualifications are an MMath or MSc in Medical/Applied Statistics.