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Untargeted metabolomics analysis in drug-naïve patients with severe obsessive–compulsive disorder

INTRODUCTION: Obsessive–compulsive disorder (OCD), characterized by the presence of obsessions and/or compulsions, is often difficult to diagnose and treat in routine clinical practice. The candidate circulating biomarkers and primary metabolic pathway alteration of plasma in OCD remain poorly under...

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Autores principales: Li, Zheqin, Gao, Jian, Lin, Liangjun, Zheng, Zifeng, Yan, Susu, Wang, Weidi, Shi, Dongdong, Wang, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272357/
https://www.ncbi.nlm.nih.gov/pubmed/37332872
http://dx.doi.org/10.3389/fnins.2023.1148971
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author Li, Zheqin
Gao, Jian
Lin, Liangjun
Zheng, Zifeng
Yan, Susu
Wang, Weidi
Shi, Dongdong
Wang, Zhen
author_facet Li, Zheqin
Gao, Jian
Lin, Liangjun
Zheng, Zifeng
Yan, Susu
Wang, Weidi
Shi, Dongdong
Wang, Zhen
author_sort Li, Zheqin
collection PubMed
description INTRODUCTION: Obsessive–compulsive disorder (OCD), characterized by the presence of obsessions and/or compulsions, is often difficult to diagnose and treat in routine clinical practice. The candidate circulating biomarkers and primary metabolic pathway alteration of plasma in OCD remain poorly understood. METHODS: We recruited 32 drug-naïve patients with severe OCD and 32 compared healthy controls and applied the untargeted metabolomics approach by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) to assess their circulating metabolic profiles. Both univariate and multivariate analyses were then utilized to filtrate differential metabolites between patients and healthy controls, and weighted Correlation Network Analysis (WGCNA) was utilized to screen out hub metabolites. RESULTS: A total of 929 metabolites were identified, including 34 differential metabolites and 51 hub metabolites, with an overlap of 13 metabolites. Notably, the following enrichment analyses underlined the importance of unsaturated fatty acids and tryptophan metabolism alterations in OCD. Metabolites of these pathways in plasma appeared to be promising biomarkers, such as Docosapentaenoic acid and 5-Hydroxytryptophan, which may be biomarkers for OCD identification and prediction of sertraline treatment outcome, respectively. CONCLUSION: Our findings revealed alterations in the circulating metabolome and the potential utility of plasma metabolites as promising biomarkers in OCD.
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spelling pubmed-102723572023-06-17 Untargeted metabolomics analysis in drug-naïve patients with severe obsessive–compulsive disorder Li, Zheqin Gao, Jian Lin, Liangjun Zheng, Zifeng Yan, Susu Wang, Weidi Shi, Dongdong Wang, Zhen Front Neurosci Neuroscience INTRODUCTION: Obsessive–compulsive disorder (OCD), characterized by the presence of obsessions and/or compulsions, is often difficult to diagnose and treat in routine clinical practice. The candidate circulating biomarkers and primary metabolic pathway alteration of plasma in OCD remain poorly understood. METHODS: We recruited 32 drug-naïve patients with severe OCD and 32 compared healthy controls and applied the untargeted metabolomics approach by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) to assess their circulating metabolic profiles. Both univariate and multivariate analyses were then utilized to filtrate differential metabolites between patients and healthy controls, and weighted Correlation Network Analysis (WGCNA) was utilized to screen out hub metabolites. RESULTS: A total of 929 metabolites were identified, including 34 differential metabolites and 51 hub metabolites, with an overlap of 13 metabolites. Notably, the following enrichment analyses underlined the importance of unsaturated fatty acids and tryptophan metabolism alterations in OCD. Metabolites of these pathways in plasma appeared to be promising biomarkers, such as Docosapentaenoic acid and 5-Hydroxytryptophan, which may be biomarkers for OCD identification and prediction of sertraline treatment outcome, respectively. CONCLUSION: Our findings revealed alterations in the circulating metabolome and the potential utility of plasma metabolites as promising biomarkers in OCD. Frontiers Media S.A. 2023-06-02 /pmc/articles/PMC10272357/ /pubmed/37332872 http://dx.doi.org/10.3389/fnins.2023.1148971 Text en Copyright © 2023 Li, Gao, Lin, Zheng, Yan, Wang, Shi and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Zheqin
Gao, Jian
Lin, Liangjun
Zheng, Zifeng
Yan, Susu
Wang, Weidi
Shi, Dongdong
Wang, Zhen
Untargeted metabolomics analysis in drug-naïve patients with severe obsessive–compulsive disorder
title Untargeted metabolomics analysis in drug-naïve patients with severe obsessive–compulsive disorder
title_full Untargeted metabolomics analysis in drug-naïve patients with severe obsessive–compulsive disorder
title_fullStr Untargeted metabolomics analysis in drug-naïve patients with severe obsessive–compulsive disorder
title_full_unstemmed Untargeted metabolomics analysis in drug-naïve patients with severe obsessive–compulsive disorder
title_short Untargeted metabolomics analysis in drug-naïve patients with severe obsessive–compulsive disorder
title_sort untargeted metabolomics analysis in drug-naïve patients with severe obsessive–compulsive disorder
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272357/
https://www.ncbi.nlm.nih.gov/pubmed/37332872
http://dx.doi.org/10.3389/fnins.2023.1148971
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