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L. Goodwin Burgess, Karan Uppal, Rachel M. Roberson, ViLinh Tran, John Kuchtey, Rachel W Kuchtey, Dean P Jones, Milam A Brantley; Metabolic divergence among patients with primary open angle glaucoma. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4521.
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To determine if metabolic phenotyping reveals differences among patients with primary open angle glaucoma (POAG).
We performed metabolomic analysis using C18 liquid chromatography-Fourier-transform mass spectrometry on frozen plasma samples from 72 POAG patients and 72 controls. Data were collected from mass/charge ratio (m/z) 85-2000 on a Thermo LTQ-Velos Orbitrap mass spectrometer, and metabolic features were extracted using an adaptive processing software package with xMSanalyzer. Log2 fold change filtering based on k-fold cross-validation accuracy criteria was used to maximize true positives and minimize false positives. The Limma package in R was used to identify differentially expressed metabolites. P-values were corrected using Benjamini and Hochberg False Discovery Rate (FDR) to account for multiple testing. Hierarchical Clustering Analysis (HCA) was used to depict the relationship between participants and the metabolites that differentiated POAG patients and controls.
Of the 2440 m/z features recovered after filtering, 41 were significantly different between POAG cases and controls using FDR at q=0.05. HCA comparing these 41 features to the 144 patients generated 8 m/z clusters and 17 patient clusters. Based on metabolic phenotype, patients were separated into two major groups, A and B. Group A (n=51) consisted of 98% controls. Group B (n=93) was further divided into two metabolic subgroups, Groups B1 (n=63) and B2 (n=30). B1 contained 72.2% of all POAG patients while B2 contained only 26.4%. Despite this large disparity, the m/z features primarily differentiating the B Groups were contained in a single metabolite cluster.
Metabolomic analysis can identify metabolites that differentiate POAG patients from controls and from each other. These data suggest that the metabolic phenotypes of POAG patients can possess distinct associations and dissociations from one another, perhaps offering insight into variable disease progression and treatment outcome.
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