Next Article in Journal
Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy
Previous Article in Journal
Metabolically Healthy Obesity—Heterogeneity in Definitions and Unconventional Factors
Previous Article in Special Issue
Salivary Metabolomics: From Diagnostic Biomarker Discovery to Investigating Biological Function
Open AccessArticle

A Data Mining Metabolomics Exploration of Glaucoma

Faculté de santé, Institut MITOVASC, UMR CNRS 6015, INSERM U1083, Université d'Angers, 49933 Angers, France
Faculté de Pharmacie de Paris, CiTCoM UMR 8038 CNRS, Université Paris Descartes, 75270 Paris, France
Département d’Ophtalmologie, Centre Hospitalier Universitaire, 49933 Angers, France
Centre de Ressources Biologiques, BB-0033-00038, Centre Hospitalier Universitaire, 49933 Angers, France
Département de Biochimie et Génétique, Centre Hospitalier Universitaire, 49933 Angers, France
Singapore Eye Research Institute, Singapore National Eye Centre, Duke-NUS, Singapore 168751, Singapore
Authors to whom correspondence should be addressed.
Metabolites 2020, 10(2), 49;
Received: 2 December 2019 / Revised: 10 January 2020 / Accepted: 24 January 2020 / Published: 28 January 2020
(This article belongs to the Special Issue Metabolomics in Human Tissues and Materials)
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.
Keywords: data mining; metabolomics; mitochondrial dysfunction; optic neuropathy; primary open-angle glaucoma data mining; metabolomics; mitochondrial dysfunction; optic neuropathy; primary open-angle glaucoma
MDPI and ACS Style

Kouassi Nzoughet, J.; Guehlouz, K.; Leruez, S.; Gohier, P.; Bocca, C.; Muller, J.; Blanchet, O.; Bonneau, D.; Simard, G.; Milea, D.; Procaccio, V.; Lenaers, G.; Chao de la Barca, J.M.; Reynier, P. A Data Mining Metabolomics Exploration of Glaucoma. Metabolites 2020, 10, 49.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop