Project ID BE-MI2024_04


Co Supervisor 1A Faculty of Life Sciences & Medicine, School of Life Course & Population Sciences, Section of OphthalmologyWebsite

Co Supervisor 1B Faculty of Life Sciences & Medicine, School of Life Course & Population Sciences, Section of OphthalmologyWebsite

Using artificial intelligence to extract retinal and neural information and to use Big Data multi-omics to discover biomarkers of disease.

(i) The retina is the only part of the body where blood vessels and nerves can be directly visualised. Measures of retinal arterioles predict cardiovascular disease, retinal venules are associated metabolic diseases such as diabetes, and the retinal nerve fibre layer thickness is associated with cognitive function. With the explosion of artificial intelligence in image analysis, this project aims to extract data from longitudinal retinal photographs and scans in over 6,000 participants from the TwinsUK cohort, taken over 20 years.
(ii) The purpose is to use novel deep learning techniques to create retina and neural datasets that would match existing omics information currently available in this unique and fascinating twin cohort, to enable “Big Data” modelling of common diseases in our populations.
The aims of the project are to extract longitudinal retinal measures and to examine available genomics, metabolomics, proteomics and microbiome datasets to look for biomarkers and predictors of diseases including diabetes, cognitive loss and cardiovascular disease. Associations will be validated using available datasets such as the UK Biobank study.
(iii) The student will develop a wide range of bioinformatics skills, including deep learning image analysis techniques (such as Automorph, already publicly available for retinal photographs), machine learning techniques such as random forest decision trees, and programming skills to use the many bioinformatics techniques needed for analysis of large, complex omics datasets.
(iv) For the first year the objective will be to complete a systematic review of retinal image analysis techniques, and to extract cross-sectional and longitudinal data on retinal blood vessels (including arteriolar and venous diameter, fractals and branching patterns) and neural measures (such as retinal nerve fibre layer thickness) and to perform simple linear regression studies on outcomes of interest such as cardiovascular disease and diabetes. The second year objectives will be to perform machine and statistical learning analyses of longitudinal metabolomic arrays matched to longitudinal retinal vessel measurements, to examine predictors of change and identify novel biomarkers across a wide range of phenotypes. The third year of the project will develop these analyses further, incorporating genomic and microbiome measures in the analyses to perform truly multi-omics studies with unique longitudinal datasets available within TwinsUK.

Representative Publications

1. Association between dietary niacin and retinal nerve fibre layer thickness in healthy eyes of different ages. Charng J, Ansari AS, Bondonno NP, Hunter ML, O’Sullivan TA, Louca P, Hammond CJ, Mackey DA. Clin Exp Ophthalmol. 2022. doi: 10.1111/ceo.14120.
2. The correlation between cognitive performance and retinal nerve fibre layer thickness is largely explained by genetic factors. Jones-Odeh E, Yonova-Doing E, Bloch E, Williams KM, Steves CJ, Hammond CJ. Scientific Reports. 2016. doi: 10.1038/srep34116.
3. Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing to refractive error and myopia. Hysi PG, Choquet H, Khawaja AP, Wojciechowski R, Tedja MS, Yin J, Simcoe MJ, Patasova K, Mahroo OA, Thai KK, Cumberland PM, Melles RB, Verhoeven VJM, Vitart V, Segre A, Stone RA, Wareham N, Hewitt AW, Mackey DA, Klaver CCW, MacGregor S; Consortium for Refractive Error and Myopia, Khaw PT, Foster PJ; UK Eye and Vision Consortium, Guggenheim JA; 23andMe Inc., Rahi JS, Jorgenson E, Hammond CJ. Nature Genetics. 2020. doi: 10.1038/s41588-020-0599-0
1. Hysi PG, Khawaja AP, Menni C, Tamraz B, Wareham N, Khaw KT, Foster PJ, Benet LZ, Spector TD, Hammond CJ. Ascorbic acid metabolites are involved in intraocular pressure control in the general population. Redox Biol. 2019. doi: 10.1016/j.redox.2018.10.004. 2. Machine learning identifying peripheral circulating metabolites associated with intraocular pressure alterations. Qian C, Nusinovici S, Thakur S, Soh ZD, Majithia S, Chee ML, Zhong H, Tham YC, Sabanayagam C, Hysi PG, Cheng CY. Br J Ophthalmol. 2022. doi: 10.1136/bjophthalmol-2021-320584
3. Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data. Ferizi U, Besser H, Hysi P, Jacobs J, Rajapakse CS, Chen C, Saha PK, Honig S, Chang G. J Magn Reson Imaging. 2019. doi: 10.1002/jmri.26280