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Milam A. Brantley, Jr., Melissa P. Osborn, Youngja Park, Dean P. Jones, Paul Sternberg, Jr.; Metabolic Profiling Can Distinguish AMD Patients from Controls. Invest. Ophthalmol. Vis. Sci. 2012;53(14):2270.
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To determine if metabolomic profiles can distinguish patients with neovascular age-related macular degeneration (NVAMD) from similarly-aged controls.
Metabolic profiling, a technique in which thousands of metabolites are simultaneously quantified and grouped by biochemical pathway, provides a comprehensive analysis of an individual’s environmental exposures. We performed metabolomic analysis using liquid chromatography with Fourier-transform mass spectrometry 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 on an anion-exchange column, and peak extraction and quantification of ion intensities were performed by adaptive processing software. Individual m/z features were matched to two metabolomics databases. A False Discovery Rate of 0.05 was employed to account for multiple testing. Principal component analysis and orthogonal partial least squares discriminatory analysis (OPLS-DA) were performed to identify metabolic features that distinguish AMD patients from controls. All participants were genotyped for rs1061170 (Y402H) in complement factor H(CFH), a known genetic risk factor for AMD. These genotypes were incorporated into the metabolomic analysis using OPLS-DA.
Ninety-four distinct m/z features were significantly different between NVAMD patients and controls. Linear discriminant analysis showed the ability to separate metabolites related to NVAMD from those related to controls with 99.1% accuracy. Additionally, 34 of the NVAMD-related metabolites were associated with either CFH CC (n=18) or CFH TC+TT (n=16) genotypes.
These data suggest that a panel of individual metabolites may be differentially regulated in AMD patients and controls. This type of comprehensive, quantitative analysis strengthens our ability to evaluate environmental contributions to AMD risk, and the combination of metabolomic and genotype data holds significant promise for the identification of clinically-relevant biomarkers for AMD.
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