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Megan Parks, Youngja Park, L. Goodwin Burgess, Kichun Lee, Paul Sternberg, Dean Jones, Milam Brantley; Metabolites that discriminate between neovascular AMD and control patients associate with different ARMS2 genotypes. Invest. Ophthalmol. Vis. Sci. 2013;54(15):4115.
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© ARVO (1962-2015); The Authors (2016-present)
To determine if metabolites that distinguish between neovascular age-related macular degeneration (NVAMD) and controls are associated with the AMD-associated ARMS2 rs10490924 polymorphism.
We performed metabolomic analysis using anion-exchange liquid chromatography with Fourier-transform mass spectrometry (LC-FTMS) on plasma samples from 26 NVAMD patients and 19 controls. Data were collected by a Thermo LTQ-FT mass spectrometer from mass/charge ratio (m/z) 85 to 850 over 10 minutes, and individual m/z features were matched to the Metlin metabolomics database. False Discovery Rate (FDR) analysis (q=0.05) was employed to identify the metabolic features discriminating between NVAMD patients and controls. All participants were genotyped for the rs10490924 single nucleotide polymorphism in the ARMS2 gene. Orthogonal partial least squares discriminatory analysis (OPLS-DA) was performed to identify the top 5% of metabolic features that account for 95% discrimination between ARMS2 GG genotype and ARMS2 GT+TT genotypes.
A total of 94 metabolic features were found to discriminate between NVAMD and controls by FDR. OPLS-DA identified a total of 113 m/z features that contribute to the separation of the ARMS2 GG genotype (no risk alleles) from the ARMS2 GT+TT genotypes (1-2 risk alleles). We overlaid the 94 FDR significant features onto the OPLS-DA loading plot and found a common subset of 26 features that discriminated NVAMD from controls and ARMS2 GG from ARMS2 GT+TT genotypes. These features were searched against the Metlin metabolomics database and found to include matches to cholesterol sulfate, modified amino acids, and di-/ tripeptides.
The results suggest that approximately one-third of the metabolites that discriminate between NVAMD and control are influenced by the AMD-associated ARMS2 genotype. These data suggest that the combination of metabolomic and genetic data will be useful in developing clinically-relevant biomarkers for AMD.
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