June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Subclassification of clinically-indistinguishable AMD patients based on metabolic characteristics
Author Affiliations & Notes
  • Milam Brantley
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, TN
  • Youngja Park
    Department of Medicine, Emory University, Atlanta, GA
  • Megan Parks
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, TN
  • L. Goodwin Burgess
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, TN
  • Karan Uppal
    Department of Medicine, Emory University, Atlanta, GA
  • Paul Sternberg
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, TN
  • Dean Jones
    Department of Medicine, Emory University, Atlanta, GA
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3664. doi:
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      Milam Brantley, Youngja Park, Megan Parks, L. Goodwin Burgess, Karan Uppal, Paul Sternberg, Dean Jones; Subclassification of clinically-indistinguishable AMD patients based on metabolic characteristics. Invest. Ophthalmol. Vis. Sci. 2013;54(15):3664.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: To determine if clinically-indistinguishable AMD patients can be subclassified based on metabolic characteristics.

Methods: We performed metabolomic analysis using C18 liquid chromatography-Fourier-transform mass spectrometry on frozen plasma samples from 44 AMD patients and 29 controls. Data were collected by a Thermo LTQ-FT mass spectrometer from mass/charge ratio (m/z) 85 to 850 over 20 minutes, and peak extraction and quantification of ion intensities were performed by adaptive processing software. Benjamini and Hochberg False Discovery Rate (FDR) correction was employed 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.

Results: Metabolomic analysis yielded a total of 2708 m/z features after quality control. Following quantile normalization, replicate averaging, and log2 transformation, m/z features exhibiting greater or less than 0.4-fold change were selected for analysis. With FDR q=0.1, 16 m/z features were significantly different in AMD and controls. HCA using Pearson correlations was applied to the 16 m/z features and the participants; certain m/z features clustered and subsets of individuals clustered as well. The analysis 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 2708 m/z features for the individuals in these clusters showed that 715 features significantly differed at q = 0.05 and 335 differed at q = 0.01.

Conclusions: These results show that high-resolution metabolomics can be useful for subclassification of AMD. 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.

Keywords: 412 age-related macular degeneration • 592 metabolism • 464 clinical (human) or epidemiologic studies: risk factor assessment  
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