A Data Mining Metabolomics Exploration of Glaucoma.

Glaucoma is an age related disease characterized by the progressive loss of retinal ganglion cells, which are the neurons that transduce the visual information from the retina to the brain. It is the leading cause of irreversible blindness worldwide. To gain further insights into primary open-angle glaucoma (POAG) pathophysiology, we performed a non-targeted metabolomics analysis on the plasma from POAG patients (n = 34) and age- and sex-matched controls (n = 30). We investigated the differential signature of POAG plasma compared to controls, using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). A data mining strategy, combining a filtering method with threshold criterion, a wrapper method with iterative selection, and an embedded method with penalization constraint, was used. These strategies are most often used separately in metabolomics studies, with each of them having their own limitations. We opted for a synergistic approach as a mean to unravel the most relevant metabolomics signature. We identified a set of nine metabolites, namely: nicotinamide, hypoxanthine, xanthine, and 1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline with decreased concentrations and N-acetyl-L-Leucine, arginine, RAC-glycerol 1-myristate, 1-oleoyl-RAC-glycerol, cystathionine with increased concentrations in POAG; the modification of nicotinamide, N-acetyl-L-Leucine, and arginine concentrations being the most discriminant. Our findings open up therapeutic perspectives for the diagnosis and treatment of POAG.