April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Metabolomic Analysis of Serum in Graves’ Disease and Thyroid-Associated Ophthalmopathy: A Diagnostic Tool?
Author Affiliations & Notes
  • Matthew R Edmunds
    Academic Unit of Ophthalmology, University of Birmingham, Birmingham, United Kingdom
    Birmingham & Midland Eye Centre, Birmingham, United Kingdom
  • Kristien Boelaert
    School of Clinical & Experimental Medicine, University of Birmingham, Birmingham, United Kingdom
  • Jayne A Franklyn
    School of Clinical & Experimental Medicine, University of Birmingham, Birmingham, United Kingdom
  • Omar M Durrani
    Birmingham & Midland Eye Centre, Birmingham, United Kingdom
  • John Curnow
    Academic Unit of Ophthalmology, University of Birmingham, Birmingham, United Kingdom
    Centre for Translational Inflammation Research, University of Birmingham, Birmingham, United Kingdom
  • Stephen P Young
    Centre for Translational Inflammation Research, University of Birmingham, Birmingham, United Kingdom
  • Footnotes
    Commercial Relationships Matthew Edmunds, None; Kristien Boelaert, None; Jayne Franklyn, None; Omar Durrani, None; John Curnow, None; Stephen Young, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4096. doi:
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      Matthew R Edmunds, Kristien Boelaert, Jayne A Franklyn, Omar M Durrani, John Curnow, Stephen P Young; Metabolomic Analysis of Serum in Graves’ Disease and Thyroid-Associated Ophthalmopathy: A Diagnostic Tool?. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4096.

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

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Abstract

Purpose: Although 30-50% of Graves’ Disease (GD) patients develop Thyroid-Associated Ophthalmopathy (TAO), it is not yet possible to predict which will be affected, or which will be in the 3-5% who develop sight-threatening manifestations. Distinct pathologies may result in specific changes in metabolism, and the systems biology methodology used in metabolomics can derive a “metabolic fingerprint” from analysis of metabolites in body fluids or tissues. This approach has provided useful insights in, for example, diabetes mellitus, rheumatoid arthritis and multiple sclerosis. We assessed metabolomic profiling as a novel means of differentiating patient groups and investigating underlying pathological processes in a large cohort of GD, TAO and healthy controls (HC).

Methods: Nuclear magnetic resonance (NMR) spectra of the serum of 110 TAO (20 clinically active, 90 quiescent), 67 GD and 78 HC (matched for age, sex and cigarette smoking status) were acquired by 1H-NMR spectroscopy. Thyroid autoantibody status, thyroid function and PTPN22 GD susceptibility genotype (R620W) were established for each subject. Data were subjected to principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Correlation between metabolite profiles and GD and TAO diagnostic criteria, as well as TAO activity and severity, were assessed by partial least squares regression (PLS-R).

Results: PCA and PLS-DA analyses of serum NMR spectra were able to segregate TAO from GD and HC patients, although there was no such differentiation of GD and HC subjects. Metabolomic analysis was also able to discriminate those with inactive and active TAO, as well as severe and mild TAO, with sensitivities and specificities of 70-80%. In addition, there was separation of subjects on the basis of thyroid autoantibody status and PTPN22 genotype. In particular, metabolite markers of oxidative stress, including lactate (increased) and ascorbate (decreased), were altered in TAO subjects.

Conclusions: Metabolomic analysis of serum differentiates GD and TAO patients, as well as TAO patients of different clinical activity and severity. This demonstrates the potential of metabolomics to become a useful diagnostic, prognostic and monitoring tool in these conditions. Our findings highlight the importance of oxidative stress in TAO, as well as the role of thyroid autoimmunity and PTPN22 genotype in TAO pathogenesis.

Keywords: 592 metabolism • 432 autoimmune disease • 631 orbit  
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