4-year PhD Studentship: Non-invasive imaging of the eye to predict Alzheimer’s disease

Alzheimer’s Disease (AD) is an increasing global health burden. Several genetic, health and lifestyle factors are associated with increased AD risk and it is estimated that ~1/3 AD cases are attributable to modifiable lifestyle factors. Given the long prodrome of AD of up to 20 years, identifying high risk individuals for drug trials and lifestyle changes to prevent the onset of disease may be the best strategy to combat the disease; however, screening programmes, e.g., based on MRI scans, are currently prohibited by their cost, impracticality, and limited availability.

AD is associated with neurodegenerative changes of the retina at the back of the eyes, and there is now exciting evidence that retinal imaging using optical coherence tomography (OCT) scans may be a rapid low-cost and non-invasive alternative to screening for AD, particularly since OCT imaging is now available at most optician practices.

Aims and objectives

Our hypothesis is that OCT is a sensitive, objective, and quantitative method to assess AD risk. Our aim is to determine the genetic, environmental, and lifestyle factors which increase the risk of OCT changes typically associated with AD and neurodegeneration of the brain.

Methodology

UK Biobank is a UK population cohort study of 502,656 people aged 40-69 years at recruitment, of whom 133668 had an eye exam and 87633 had OCT scans. Given the long prodrome of AD and the 6.5% prevalence of AD among people >65 years, we anticipate that ~31,000 Biobank participants are currently in the prodrome of AD (~1700 have already been diagnosed with AD).

We have obtained UK Biobank data on participant genotypes, physical and cognitive assessments, demographic factors, education, job, lifestyle, environmental exposures, past medical history, image-derived phenotypes (IDPs) taken from OCT and MRI scans as well as the raw MRI and OCT imaging data. Using this data, we will test the statistical associations and causal effects of genetic (e.g., APOE), health (e.g., diabetes, hypertension, BMI), lifestyle and environmental exposures (e.g., smoking) on the IDPs using standard univariate and multivariate statistical analysis methods and Mendelian Randomization (MR). Additional hypothesis-free analyses using MR-base and machine learning algorithms applied to the raw MRI and OCT imaging data will be used to identify other exposures in UK Biobank linked to neurodegeneration of the retina and brain.

How to apply for this project

This project will be based in Bristol Medical School - Translational Health Sciences in the Faculty of Health Sciences at the University of Bristol.

Please visit the Faculty of Health Sciences website for details of how to apply