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Plasma metabolic profiles associated with neovascular age-related macular degeneration

Session Details

Session Title: AMD III

Session Date/Time: Sunday 29/09/2013 | 11:00-13:00

Paper Time: 11:00

Venue: Hall G1 (Level 2)

First Author: M.Brantley USA

Co Author(s):    Y. Park   M. Parks   G. Burgess   K. Uppal     

Abstract Details

Purpose:

To determine if plasma metabolic profiles can detect differences between patients with neovascular age-related macular degeneration (NVAMD) and similarly-aged controls.

Setting:

Vanderbilt Eye Institute, Vanderbilt University, Nashville, Tennessee.

Methods:

Metabolomic analysis using liquid chromatography with Fourier-transform mass spectrometry (LC-FTMS) was performed on plasma samples from 26 NVAMD patients and 19 controls. Data were collected from mass/charge ratio (m/z) 85 to 850 on a Thermo LTQ-FT mass spectrometer, and metabolic features were extracted using an adaptive processing software package. Both non-transformed and log2 transformed data were used for testing with Benjamini and Hochberg False Discovery Rate (FDR) to account for multiple testing. Orthogonal Partial Least Squares-Discriminant Analysis was also performed to determine metabolic features that distinguished NVAMD patients from controls. Individual m/z features were matched to the Kyoto Encyclopedia of Genes and Genomes database and the Metlin metabolomics database, while metabolic pathways associated with NVAMD were identified using MetScape.

Results:

Of the 1680 total m/z features detected by LC-FTMS, 94 unique m/z features were significantly different between NVAMD patients and controls using FDR (q= 0.05). A comparison of these features to those found with log2 transformed data (n= 132, q= 0.2) revealed 40 features in common, reaffirming the involvement of certain metabolites. These features included metabolites which were elevated in NVAMD patients such as di- and tripeptides and covalently modified amino acids, as well as molecules with lower levels in NVAMD patients such as bile acids and vitamin D-related metabolites. Correlation analysis revealed associations among certain significant metabolic features, and pathway analysis demonstrated changes in tyrosine metabolism, sulfur amino acid metabolism, and amino acids related to urea metabolism.

Conclusions:

These data suggest that metabolomic analysis can identify both individual metabolites and metabolic profiles that differ between NVAMD patients and controls. Pathway analysis can assess the broader involvement of these metabolites in AMD pathogenesis. Plasma metabolic phenotyping could thus improve current diagnostic methods for AMD by identifying disease or disease risk prior to clinical manifestation as well as provide further insight into the pathophysiology of AMD.

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