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Subclassification of clinically indistinguishable AMD patients based on metabolic phenotype

Session Details

Session Title: AMD III

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

Paper Time: 11:08

Venue: Hall G1 (Level 2)

First Author: M.Brantley USA

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

Abstract Details

Purpose:

To determine if clinically indistinguishable age-related macular degeneration (AMD) patients can be subclassified based on metabolic phenotype.

Setting:

Vanderbilt Eye Institute, Vanderbilt University, Nashville, Tennessee.

Methods:

Metabolomic analysis using liquid chromatography with C18 Fourier-transform mass spectrometry (LC-FTMS) was performed on plasma samples from 44 AMD patients and 29 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. Data were corrected using Benjamini and Hochberg False Discovery Rate (FDR) to account for multiple testing. Principal component analysis (PCA) was performed to identify metabolic features that distinguish AMD patients from controls. Hierarchical Clustering Analysis (HCA) was used to depict the relationship between participants and the metabolites that differentiated AMD patients and controls. Individual m/z features were matched to the Kyoto Encyclopedia of Genes and Genomes database and the Metlin metabolomics database.

Results:

Metabolomic analysis yielded 2708 m/z features after quality control. A total of 16 unique m/z features were significantly different between AMD patients and controls using FDR (q= 0.1). HCA applied to the 16 m/z features and the 73 participants generated 14 clusters of individuals distributed into two major groupings: Group A, made up of 58% controls, and Group B, consisting of 78% AMD patients. Cluster 13 from Group B and Cluster 14 from Group A both consisted entirely of AMD patients but showed complete separation by PCA. FDR analysis of the m/z features for the individuals in these two clusters showed that 335 features were significantly different (q=0.01).

Conclusions:

High-resolution plasma metabolomic analysis can be used to subcategorize AMD patients with clinically-indistinguishable phenotypes. Metabolic phenotyping may reveal previously unknown pathophysiologic mechanisms of AMD and may help reveal the differences among AMD patients that account for variable disease progression or treatment response.

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