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The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders
Background: Gut microbiota have been associated with many psychiatric disorders. However, the changes in the composition of gut microbiota in patients with post-stroke sleep disorders (PSSDs) remain unclear. Here, we determined the gut microbial signature of PSSD patients. Methods: Fecal samples of...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530055/ https://www.ncbi.nlm.nih.gov/pubmed/37761337 http://dx.doi.org/10.3390/diagnostics13182970 |
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author | Xie, Huijia Chen, Jiaxin Chen, Qionglei Zhao, Yiting Liu, Jiaming Sun, Jing Hu, Xuezhen |
author_facet | Xie, Huijia Chen, Jiaxin Chen, Qionglei Zhao, Yiting Liu, Jiaming Sun, Jing Hu, Xuezhen |
author_sort | Xie, Huijia |
collection | PubMed |
description | Background: Gut microbiota have been associated with many psychiatric disorders. However, the changes in the composition of gut microbiota in patients with post-stroke sleep disorders (PSSDs) remain unclear. Here, we determined the gut microbial signature of PSSD patients. Methods: Fecal samples of 205 patients with ischemic stroke were collected within 24 h of admission and were further analyzed using 16 s RNA gene sequencing followed by bioinformatic analysis. The diversity, community composition, and differential microbes of gut microbiota were assessed. The outcome of sleep disorders was determined by the Pittsburgh Sleep Quality Index (PSQI) at 3 months after admission. The diagnostic performance of microbial characteristics in predicting PSSDs was assessed by receiver operating characteristic (ROC) curves. Results: Our results showed that the composition and structure of microbiota in patients with PSSDs were different from those without sleep disorders (PSNSDs). Moreover, the linear discriminant analysis effect size (LEfSe) showed significant differences in gut-associated bacteria, such as species of Streptococcus, Granulicatella, Dielma, Blautia, Paeniclostridium, and Sutterella. We further managed to identify the optimal microbiota signature and revealed that the predictive model with eight operational-taxonomic-unit-based biomarkers achieved a high accuracy in PSSD prediction (AUC = 0.768). Blautia and Streptococcus were considered to be the key microbiome signatures for patients with PSSD. Conclusions: These findings indicated that a specific gut microbial signature was an important predictor of PSSDs, which highlighted the potential of microbiota as a promising biomarker for detecting PSSD patients. |
format | Online Article Text |
id | pubmed-10530055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105300552023-09-28 The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders Xie, Huijia Chen, Jiaxin Chen, Qionglei Zhao, Yiting Liu, Jiaming Sun, Jing Hu, Xuezhen Diagnostics (Basel) Article Background: Gut microbiota have been associated with many psychiatric disorders. However, the changes in the composition of gut microbiota in patients with post-stroke sleep disorders (PSSDs) remain unclear. Here, we determined the gut microbial signature of PSSD patients. Methods: Fecal samples of 205 patients with ischemic stroke were collected within 24 h of admission and were further analyzed using 16 s RNA gene sequencing followed by bioinformatic analysis. The diversity, community composition, and differential microbes of gut microbiota were assessed. The outcome of sleep disorders was determined by the Pittsburgh Sleep Quality Index (PSQI) at 3 months after admission. The diagnostic performance of microbial characteristics in predicting PSSDs was assessed by receiver operating characteristic (ROC) curves. Results: Our results showed that the composition and structure of microbiota in patients with PSSDs were different from those without sleep disorders (PSNSDs). Moreover, the linear discriminant analysis effect size (LEfSe) showed significant differences in gut-associated bacteria, such as species of Streptococcus, Granulicatella, Dielma, Blautia, Paeniclostridium, and Sutterella. We further managed to identify the optimal microbiota signature and revealed that the predictive model with eight operational-taxonomic-unit-based biomarkers achieved a high accuracy in PSSD prediction (AUC = 0.768). Blautia and Streptococcus were considered to be the key microbiome signatures for patients with PSSD. Conclusions: These findings indicated that a specific gut microbial signature was an important predictor of PSSDs, which highlighted the potential of microbiota as a promising biomarker for detecting PSSD patients. MDPI 2023-09-17 /pmc/articles/PMC10530055/ /pubmed/37761337 http://dx.doi.org/10.3390/diagnostics13182970 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xie, Huijia Chen, Jiaxin Chen, Qionglei Zhao, Yiting Liu, Jiaming Sun, Jing Hu, Xuezhen The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders |
title | The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders |
title_full | The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders |
title_fullStr | The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders |
title_full_unstemmed | The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders |
title_short | The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders |
title_sort | diagnostic value of gut microbiota analysis for post-stroke sleep disorders |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530055/ https://www.ncbi.nlm.nih.gov/pubmed/37761337 http://dx.doi.org/10.3390/diagnostics13182970 |
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