Primary open-angle glaucoma is a major source of disability and blindness. Genetic studies have been hugely successful in improving our understanding of this disease, but the mechanisms through which, in interaction with non-genetic factors, they can lead to glaucoma is not well understood. The relative lack knowledge about the intermediate processes linking genes and glaucoma impedes the translation of genetic knowledge into improved glaucoma care.
Metabolites are small molecules that are intermediate or end products of molecular processes taking place within the cell that reflect their functional state and ability to fulfill their physiological roles. Metabolites have a role in the pathophysiology of systemic diseases, and existing evidence suggests that several metabolites, particularly those involved in anti-oxidative stress response, also modulate the risk of developing glaucoma as well as other age-related eye diseases.
Despite the strong suggestions that metabolites are important to glaucoma and other ocular diseases, achieving enough power to fully characterize the correlations that exist between systemic changes in metabolite levels with impaired eye metabolism and disease requires obtaining accurate measurements of metabolites in a large number of samples. A sufficient power to obtain a sufficiently complete understanding of metabolic impairments in glaucoma may have been achieved only recently through the availability of metabolomic and genomic information from the UK Biobank with up to 500,000 participants. Here we propose to use advanced statistical methodologies to characterize associations between glaucoma (and intraocular pressure, its main known risk factor) and circulating metabolites, including machine learning and artificial-intelligence “Big Data” techniques. both directly measured and imputed, in the large UK Biobank cohort.
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