A Big Data investigation of the influence of life-long metabolic factors that contribute to age-related eye diseases

There is a body of evidence linking age-related eye diseases (AREDs), and in particular age-related macular degeneration (AMD), glaucoma and age-related cataract to metabolic dysfunction. Previous metabolomic studies of AREDs have performed metabolome-wide analyses in different cohorts with a range of different metabolite evaluation techniques. However, many cohorts were clinic-based, underpowered and used a variety of platforms and there has been little convergence in the literature.



For example, a meta-review of 13 AMD studies(1) revealed that a broad range of potential biomarkers, including phenylalanine, adenosine, hypoxanthine, tyrosine, creatine, citrate, carnitine, proline and maltose, and that lipid, carbohydrate, amino acid and nucleotide metabolism may be dysregulated in affected individuals. The lack of convergence of results from the studies in the meta-analysis supports the need for further studies to select and validate metabolite biomarkers of AMD with higher confidence.



In glaucoma, a recent review of 18 metabolomics studies(2) suggested that hydroxyproline and methionine were consistently increased in plasma, while results from aqueous humour revealed alanine, creatine, glycine and lysine increased in glaucoma. The authors argued that plasma-based findings were less consistent than those using aqueous humour. Since plasma is the only practical and relatively non-invasive source of data for large population studies, there is therefore need for further improvements in plasma metabolomics studies of AREDs.



The supervisors of this project published a 2019 study(3) from TwinsUK of 313 metabolites and found a vitamin C metabolite O-methylascorbate is associated with and causally lowers IOP, the main risk factor for glaucoma, highlighting the power of antioxidants in combating photooxidative stress. This particular study also sets an example for the methodological approach in this project by using random forest machine learning to identify the most influential metabolites, and Mendelian randomization to investigate a causal effect of metabolites on IOP.



Age-related cataract has to date received less attention, with few high-throughput metabolomic studies: a search for plasma-based metabolomic profiling returned no published data except for one article on glaucoma where sex-matched controls with cataract were used. While there is an effective surgical remedy, revealing metabolic signatures of cataract patients would improve understanding of molecular mechanisms and if an intervention could delay cataract, then significant surgical volume reduction and cost savings could follow.



The supervisors of this project have access to data from four large cohorts of over 500,000 people, including the UK Biobank, meaning that this study has the power to identify novel associations to further understand the pathophysiology of age-related eye diseases, to potentially examine for causality raising the prospect of novel therapies, and to use machine learning models to predict those at risk of disease using metabolomics platforms.

Hou XW et al. (2020) Metabolomics in Age-Related Macular Degeneration: A Systematic Review. Invest Ophthalmol Vis Sci. 61, 13-13

Tang Y et al. (2022) Metabolomics in Primary Open Angle Glaucoma: A Systematic Review and Meta-Analysis. Front Neurosci. 16, 835736

Hysi PG et al. (2019) Ascorbic acid metabolites are involved in intraocular pressure control in the general population. Redox Biol. 20, 349-353