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Metabolite profiling in posttraumatic stress disorder

BACKGROUND: Traumatic stress does not only increase the risk for posttraumatic stress disorder (PTSD), but is also associated with adverse secondary physical health outcomes. Despite increasing efforts, we only begin to understand the underlying biomolecular processes. The hypothesis-free assessment...

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Autores principales: Karabatsiakis, Alexander, Hamuni, Gilava, Wilker, Sarah, Kolassa, Stephan, Renu, Durairaj, Kadereit, Suzanne, Schauer, Maggie, Hennessy, Thomas, Kolassa, Iris-Tatjana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367823/
https://www.ncbi.nlm.nih.gov/pubmed/25848535
http://dx.doi.org/10.1186/s40303-015-0007-3
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author Karabatsiakis, Alexander
Hamuni, Gilava
Wilker, Sarah
Kolassa, Stephan
Renu, Durairaj
Kadereit, Suzanne
Schauer, Maggie
Hennessy, Thomas
Kolassa, Iris-Tatjana
author_facet Karabatsiakis, Alexander
Hamuni, Gilava
Wilker, Sarah
Kolassa, Stephan
Renu, Durairaj
Kadereit, Suzanne
Schauer, Maggie
Hennessy, Thomas
Kolassa, Iris-Tatjana
author_sort Karabatsiakis, Alexander
collection PubMed
description BACKGROUND: Traumatic stress does not only increase the risk for posttraumatic stress disorder (PTSD), but is also associated with adverse secondary physical health outcomes. Despite increasing efforts, we only begin to understand the underlying biomolecular processes. The hypothesis-free assessment of a wide range of metabolites (termed metabolite profiling) might contribute to the discovery of biological pathways underlying PTSD. METHODS: Here, we present the results of the first metabolite profiling study in PTSD, which investigated peripheral blood serum samples of 20 PTSD patients and 18 controls. We performed liquid chromatography (LC) coupled to Quadrupole/Time-Of-Flight (QTOF) mass spectrometry. Two complementary statistical approaches were used to identify metabolites associated with PTSD status including univariate analyses and Partial Least Squares Discriminant Analysis (PLS-DA). RESULTS: Thirteen metabolites displayed significant changes in PTSD, including four glycerophospholipids, and one metabolite involved in endocannabinoid signaling. A biomarker panel of 19 metabolites classifies PTSD with 85% accuracy, while classification accuracy from the glycerophospholipid with the highest differentiating ability already reached 82%. CONCLUSIONS: This study illustrates the feasibility and utility of metabolite profiling for PTSD and suggests lipid-derived and endocannabinoid signaling as potential biological pathways involved in trauma-associated pathophysiology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40303-015-0007-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-43678232015-04-06 Metabolite profiling in posttraumatic stress disorder Karabatsiakis, Alexander Hamuni, Gilava Wilker, Sarah Kolassa, Stephan Renu, Durairaj Kadereit, Suzanne Schauer, Maggie Hennessy, Thomas Kolassa, Iris-Tatjana J Mol Psychiatry Research Article BACKGROUND: Traumatic stress does not only increase the risk for posttraumatic stress disorder (PTSD), but is also associated with adverse secondary physical health outcomes. Despite increasing efforts, we only begin to understand the underlying biomolecular processes. The hypothesis-free assessment of a wide range of metabolites (termed metabolite profiling) might contribute to the discovery of biological pathways underlying PTSD. METHODS: Here, we present the results of the first metabolite profiling study in PTSD, which investigated peripheral blood serum samples of 20 PTSD patients and 18 controls. We performed liquid chromatography (LC) coupled to Quadrupole/Time-Of-Flight (QTOF) mass spectrometry. Two complementary statistical approaches were used to identify metabolites associated with PTSD status including univariate analyses and Partial Least Squares Discriminant Analysis (PLS-DA). RESULTS: Thirteen metabolites displayed significant changes in PTSD, including four glycerophospholipids, and one metabolite involved in endocannabinoid signaling. A biomarker panel of 19 metabolites classifies PTSD with 85% accuracy, while classification accuracy from the glycerophospholipid with the highest differentiating ability already reached 82%. CONCLUSIONS: This study illustrates the feasibility and utility of metabolite profiling for PTSD and suggests lipid-derived and endocannabinoid signaling as potential biological pathways involved in trauma-associated pathophysiology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40303-015-0007-3) contains supplementary material, which is available to authorized users. BioMed Central 2015-02-08 /pmc/articles/PMC4367823/ /pubmed/25848535 http://dx.doi.org/10.1186/s40303-015-0007-3 Text en © Karabatsiakis et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Karabatsiakis, Alexander
Hamuni, Gilava
Wilker, Sarah
Kolassa, Stephan
Renu, Durairaj
Kadereit, Suzanne
Schauer, Maggie
Hennessy, Thomas
Kolassa, Iris-Tatjana
Metabolite profiling in posttraumatic stress disorder
title Metabolite profiling in posttraumatic stress disorder
title_full Metabolite profiling in posttraumatic stress disorder
title_fullStr Metabolite profiling in posttraumatic stress disorder
title_full_unstemmed Metabolite profiling in posttraumatic stress disorder
title_short Metabolite profiling in posttraumatic stress disorder
title_sort metabolite profiling in posttraumatic stress disorder
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367823/
https://www.ncbi.nlm.nih.gov/pubmed/25848535
http://dx.doi.org/10.1186/s40303-015-0007-3
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