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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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. |
format | Online Article Text |
id | pubmed-10272357 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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