Gut microbiota in adults with moyamoya disease: characteristics and biomarker identification
Background and purposeWhen it comes to the onset of moyamoya disease (MMD), environmental variables are crucial. Furthermore, there is confusion about the relationship between the gut microbiome, an environmental variable, and MMD. Consequently, to identify the particular bacteria that cause MMD, we...
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2023.1252681/full |
| _version_ | 1797658549365506048 |
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| author | Xiaofan Yu Xiaofan Yu Xiaofan Yu Xiaofan Yu Xiaofan Yu Peicong Ge Peicong Ge Peicong Ge Peicong Ge Peicong Ge Yuanren Zhai Yuanren Zhai Yuanren Zhai Yuanren Zhai Yuanren Zhai Wei Liu Wei Liu Wei Liu Wei Liu Wei Liu Qian Zhang Qian Zhang Qian Zhang Qian Zhang Qian Zhang Xun Ye Xun Ye Xun Ye Xun Ye Xun Ye Xingju Liu Xingju Liu Xingju Liu Xingju Liu Xingju Liu Rong Wang Rong Wang Rong Wang Rong Wang Rong Wang Yan Zhang Yan Zhang Yan Zhang Yan Zhang Yan Zhang Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Dong Zhang Dong Zhang Dong Zhang Dong Zhang Dong Zhang Dong Zhang |
| author_facet | Xiaofan Yu Xiaofan Yu Xiaofan Yu Xiaofan Yu Xiaofan Yu Peicong Ge Peicong Ge Peicong Ge Peicong Ge Peicong Ge Yuanren Zhai Yuanren Zhai Yuanren Zhai Yuanren Zhai Yuanren Zhai Wei Liu Wei Liu Wei Liu Wei Liu Wei Liu Qian Zhang Qian Zhang Qian Zhang Qian Zhang Qian Zhang Xun Ye Xun Ye Xun Ye Xun Ye Xun Ye Xingju Liu Xingju Liu Xingju Liu Xingju Liu Xingju Liu Rong Wang Rong Wang Rong Wang Rong Wang Rong Wang Yan Zhang Yan Zhang Yan Zhang Yan Zhang Yan Zhang Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Dong Zhang Dong Zhang Dong Zhang Dong Zhang Dong Zhang Dong Zhang |
| author_sort | Xiaofan Yu |
| collection | DOAJ |
| description | Background and purposeWhen it comes to the onset of moyamoya disease (MMD), environmental variables are crucial. Furthermore, there is confusion about the relationship between the gut microbiome, an environmental variable, and MMD. Consequently, to identify the particular bacteria that cause MMD, we examined the gut microbiome of MMD individuals and healthy controls (HC).MethodsA prospective case-control investigation was performed from June 2021 to May 2022. The fecal samples of patients with MMD and HC were obtained. Typically, 16S rRNA sequencing was employed to examine their gut microbiota. The QIIME and R softwares were used to examine the data. The linear discriminant analysis effect size analysis was used to determine biomarkers. Multivariate analysis by linear models (MaAsLin)2 were used to find associations between microbiome data and clinical variables. Model performance was assessed using the receiver operating characteristic curve and the decision curve analysis.ResultsThis investigation involved a total of 60 MMD patients and 60 HC. The MMD group’s Shannon and Chao 1 indices were substantially lower than those of the HC cohort. β-diversity was significantly different in the weighted UniFrac distances. At the phylum level, the relative abundance of Fusobacteriota/Actinobacteria was significantly higher/lower in the MMD group than that in the HC group. By MaAsLin2 analysis, the relative abundance of the 2 genera, Lachnoclostridium and Fusobacterium, increased in the MMD group, while the relative abundance of the 2 genera, Bifidobacterium and Enterobacter decreased in the MMD group. A predictive model was constructed by using these 4 genera. The area under the receiver operating characteristic curve was 0.921. The decision curve analysis indicated that the model had usefulness in clinical practice.ConclusionsThe gut microbiota was altered in individuals with MMD, and was characterized by increased abundance of Lachnoclostridium and Fusobacterium and decreased abundance of Bifidobacterium and Enterobacter. These 4 genera could be used as biomarkers and predictors in clinical practice. |
| first_indexed | 2024-03-11T18:00:41Z |
| format | Article |
| id | doaj.art-0669661353ea4c6eb14b5255f45925b0 |
| institution | Directory Open Access Journal |
| issn | 2235-2988 |
| language | English |
| last_indexed | 2024-03-11T18:00:41Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj.art-0669661353ea4c6eb14b5255f45925b02023-10-17T08:51:42ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882023-10-011310.3389/fcimb.2023.12526811252681Gut microbiota in adults with moyamoya disease: characteristics and biomarker identificationXiaofan Yu0Xiaofan Yu1Xiaofan Yu2Xiaofan Yu3Xiaofan Yu4Peicong Ge5Peicong Ge6Peicong Ge7Peicong Ge8Peicong Ge9Yuanren Zhai10Yuanren Zhai11Yuanren Zhai12Yuanren Zhai13Yuanren Zhai14Wei Liu15Wei Liu16Wei Liu17Wei Liu18Wei Liu19Qian Zhang20Qian Zhang21Qian Zhang22Qian Zhang23Qian Zhang24Xun Ye25Xun Ye26Xun Ye27Xun Ye28Xun Ye29Xingju Liu30Xingju Liu31Xingju Liu32Xingju Liu33Xingju Liu34Rong Wang35Rong Wang36Rong Wang37Rong Wang38Rong Wang39Yan Zhang40Yan Zhang41Yan Zhang42Yan Zhang43Yan Zhang44Jizong Zhao45Jizong Zhao46Jizong Zhao47Jizong Zhao48Jizong Zhao49Jizong Zhao50Dong Zhang51Dong Zhang52Dong Zhang53Dong Zhang54Dong Zhang55Dong Zhang56Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaSavaid Medical School, University of Chinese Academy of Sciences, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Hospital, Beijing, ChinaBackground and purposeWhen it comes to the onset of moyamoya disease (MMD), environmental variables are crucial. Furthermore, there is confusion about the relationship between the gut microbiome, an environmental variable, and MMD. Consequently, to identify the particular bacteria that cause MMD, we examined the gut microbiome of MMD individuals and healthy controls (HC).MethodsA prospective case-control investigation was performed from June 2021 to May 2022. The fecal samples of patients with MMD and HC were obtained. Typically, 16S rRNA sequencing was employed to examine their gut microbiota. The QIIME and R softwares were used to examine the data. The linear discriminant analysis effect size analysis was used to determine biomarkers. Multivariate analysis by linear models (MaAsLin)2 were used to find associations between microbiome data and clinical variables. Model performance was assessed using the receiver operating characteristic curve and the decision curve analysis.ResultsThis investigation involved a total of 60 MMD patients and 60 HC. The MMD group’s Shannon and Chao 1 indices were substantially lower than those of the HC cohort. β-diversity was significantly different in the weighted UniFrac distances. At the phylum level, the relative abundance of Fusobacteriota/Actinobacteria was significantly higher/lower in the MMD group than that in the HC group. By MaAsLin2 analysis, the relative abundance of the 2 genera, Lachnoclostridium and Fusobacterium, increased in the MMD group, while the relative abundance of the 2 genera, Bifidobacterium and Enterobacter decreased in the MMD group. A predictive model was constructed by using these 4 genera. The area under the receiver operating characteristic curve was 0.921. The decision curve analysis indicated that the model had usefulness in clinical practice.ConclusionsThe gut microbiota was altered in individuals with MMD, and was characterized by increased abundance of Lachnoclostridium and Fusobacterium and decreased abundance of Bifidobacterium and Enterobacter. These 4 genera could be used as biomarkers and predictors in clinical practice.https://www.frontiersin.org/articles/10.3389/fcimb.2023.1252681/fullgut microbiotacharacteristicsbiomarkermoyamoya diseaseadults |
| spellingShingle | Xiaofan Yu Xiaofan Yu Xiaofan Yu Xiaofan Yu Xiaofan Yu Peicong Ge Peicong Ge Peicong Ge Peicong Ge Peicong Ge Yuanren Zhai Yuanren Zhai Yuanren Zhai Yuanren Zhai Yuanren Zhai Wei Liu Wei Liu Wei Liu Wei Liu Wei Liu Qian Zhang Qian Zhang Qian Zhang Qian Zhang Qian Zhang Xun Ye Xun Ye Xun Ye Xun Ye Xun Ye Xingju Liu Xingju Liu Xingju Liu Xingju Liu Xingju Liu Rong Wang Rong Wang Rong Wang Rong Wang Rong Wang Yan Zhang Yan Zhang Yan Zhang Yan Zhang Yan Zhang Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Dong Zhang Dong Zhang Dong Zhang Dong Zhang Dong Zhang Dong Zhang Gut microbiota in adults with moyamoya disease: characteristics and biomarker identification Frontiers in Cellular and Infection Microbiology gut microbiota characteristics biomarker moyamoya disease adults |
| title | Gut microbiota in adults with moyamoya disease: characteristics and biomarker identification |
| title_full | Gut microbiota in adults with moyamoya disease: characteristics and biomarker identification |
| title_fullStr | Gut microbiota in adults with moyamoya disease: characteristics and biomarker identification |
| title_full_unstemmed | Gut microbiota in adults with moyamoya disease: characteristics and biomarker identification |
| title_short | Gut microbiota in adults with moyamoya disease: characteristics and biomarker identification |
| title_sort | gut microbiota in adults with moyamoya disease characteristics and biomarker identification |
| topic | gut microbiota characteristics biomarker moyamoya disease adults |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2023.1252681/full |
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