June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Metabolomic analysis of urine in patients with age related macular degeneration
Author Affiliations & Notes
  • Sreekumari Pushpoth
    Ophthalmology, The James Cook University Hospital, Billingham, United Kingdom
    University of Birmingham, Birmingham, United Kingdom
  • Martin Fitzpatrick
    University of Birmingham, Birmingham, United Kingdom
  • James S Talks
    Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
  • Stephen Young
    University of Birmingham, Birmingham, United Kingdom
  • Yit C Yang
    The Wolverhampton Hospitals NHS Trust, Wolverhampton, United Kingdom
  • Graham R Wallace
    University of Birmingham, Birmingham, United Kingdom
  • Footnotes
    Commercial Relationships Sreekumari Pushpoth, None; Martin Fitzpatrick, None; James Talks, Novartis, Bayer (S); Stephen Young, None; Yit Yang, Novartis, Bayer (S); Graham Wallace, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 368. doi:
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    • Get Citation

      Sreekumari Pushpoth, Martin Fitzpatrick, James S Talks, Stephen Young, Yit C Yang, Graham R Wallace; Metabolomic analysis of urine in patients with age related macular degeneration. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):368.

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

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Abstract

Purpose: Age related macular degeneration is a complex disease where multiple factors show associations but do not explain the full nature of the disease. We hypothesized that a systems approach based on metabolomic analysis would be able to segregate diseases types and provide insights into the pathology. Metabolomics assesses the broad range of low molecular weight metabolites in biofluids and, since these are influenced by important AMD-related factors including diet, age and smoking, this approach may provide a useful novel window into the AMD-disease process.

Methods: Serum and urine samples were collected from 104 patients with dry and wet macular degeneration. Serum samples were centrifuged to remove cells, and 0.5ml aliquots stored at -80 degree C. After thawing, serum was filtered through 3kD MW cutoff filter to remove proteins. The filtrate was made with 10% in D2O, 100mM phosphate 0.5mM TMSP and pH 7.00.One-dimensional 1H spectra were acquired using a standard spin-echo pulse sequence on a Bruker DRX 600MHz NMR spectrometer equipped with a 1.7mm cryoprobe. 2D JRes spectra were also acquired to aid metabolite identification. Spectra were be segmented into 0.005-ppm (2.5 Hz) chemical shift ‘bins’ between 0.2 and 10.0 ppm, and the spectral area within each bin integrated. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of the processed data was conducted using PLS Toolbox (Eigenvector Research) within MATLAB.

Results: The serum samples from dry AMD patients cluster together reasonably well based on their metabolomics profile. With regards to serum samples from patients with wet AMD the clustering is far more complex. Samples from patients with dry AMD show an increase in arginine, and decreased glucose, lactate, glutamine and reduced glutathione. Urine sample cluster also showed similar interestig pattern

Conclusions: Metabolomic analysis showed clear separation between body fluid samples from patients with wet and dry AMD. Several samples from patients with wet AMD cluster with samples from dry AMD strongly indicates that common pathways are involved in both types of disease and that dry AMD can develop into the wet form.

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