March 2012
Volume 53, Issue 14
Free
ARVO Annual Meeting Abstract  |   March 2012
Serum Metabolic Profiling in Patients with Diabetic Retinopathy
Author Affiliations & Notes
  • Megan B. Parks
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, Tennessee
  • Allison J. Ferreira
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, Tennessee
  • Melissa P. Osborn
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, Tennessee
  • Youngja Park
    Department of Medicine, Emory University, Atlanta, Georgia
  • Dean P. Jones
    Department of Medicine, Emory University, Atlanta, Georgia
  • Milam A. Brantley, Jr.
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, Tennessee
  • Footnotes
    Commercial Relationships  Megan B. Parks, None; Allison J. Ferreira, None; Melissa P. Osborn, None; Youngja Park, None; Dean P. Jones, None; Milam A. Brantley, Jr., None
  • Footnotes
    Support  NIH Grant P30 EY008126 and an unrestricted departmental grant from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science March 2012, Vol.53, 2875. doi:
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    • Get Citation

      Megan B. Parks, Allison J. Ferreira, Melissa P. Osborn, Youngja Park, Dean P. Jones, Milam A. Brantley, Jr.; Serum Metabolic Profiling in Patients with Diabetic Retinopathy. Invest. Ophthalmol. Vis. Sci. 2012;53(14):2875.

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

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Abstract

Purpose: : To determine if metabolic profiling of serum samples can be used to differentiate between patients with proliferative diabetic retinopathy (PDR) and non-diabetic controls.

Methods: : Metabolomic analysis with liquid chromatography-mass spectrometry was performed on serum samples collected from 21 PDR patients and 78 non-diabetic controls at the Vanderbilt Eye Institute. Data were collected by liquid chromatography with C18 reverse column coupled with a Thermo LTQ-Orbitrap Velos mass spectrometer from mass/charge ratio (m/z) 85 to 2000 over 10 minutes. Peak extraction and quantification of ion intensities were performed by an adaptive processing software package (apLCMS), which provided m/z values, retention times, and integrated ion intensities for each m/z feature. Individual features were matched to the Madison Metabolomics Consortium and Metlin metabolomics databases. Principle component analysis (PCA) using Pirouette version 4.0 (InfoMetrix) was performed to identify metabolic features that distinguish PDR patients from controls.

Results: : A total of 5578 distinct m/z features were recovered from the unsupervised analysis. A PCA score plot separated the two phenotypes, and the first two principle components revealed 279 metabolites most closely related to both PDR and control status. The 588 distinguishing m/z features matched to multiple KEGG metabolic pathways, most commonly the steroid and primary acid biosynthesis pathways and the arginine-and-proline metabolism pathway.

Conclusions: : These results suggest that comprehensive metabolomic analysis of frozen serum samples with LC-FTMS can identify a set of metabolites that effectively discriminate PDR patients from non-diabetic controls. Metabolomic surveillance of diabetic patients via standard blood draws could permit adjustment of an individual’s treatment regimen, resulting in earlier intervention for diabetics who would otherwise progress to sight-threatening retinopathy.

Keywords: diabetic retinopathy • metabolism • clinical (human) or epidemiologic studies: risk factor assessment 
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