Altered microbiomes distinguish Alzheimer’s disease from amnestic mild cognitive impairment and health in a Chinese cohort
•An altered fecal microbiome was associated with the Alzheimer’s disease (AD).•The depleted Firmicutes and enriched Proteobacteria linked with severity of AD.•Enterobacteriaceae was associated with the presence and progression of AD.•Models based on fecal microbiota discriminate AD from aMCI and hea...
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| Published in: | Brain, behavior, and immunity Vol. 80; pp. 633 - 643 |
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| Main Authors: | , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Netherlands
Elsevier Inc
01.08.2019
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| Subjects: | |
| ISSN: | 0889-1591, 1090-2139, 1090-2139 |
| Online Access: | Get full text |
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| Abstract | •An altered fecal microbiome was associated with the Alzheimer’s disease (AD).•The depleted Firmicutes and enriched Proteobacteria linked with severity of AD.•Enterobacteriaceae was associated with the presence and progression of AD.•Models based on fecal microbiota discriminate AD from aMCI and health subjects.
(Background): Alzheimer’s disease (AD), clinically characterized by the progressive neurodegenerative condition and cognitive impairment, is one of the main causes of disability in elder people worldwide. Recently, several animal studies indicated that the ‘gut-brain’ axis might contribute to the amyloid deposition of AD. However, data about gut dysbiosis in human AD remains scarce in the literature, especially including the whole process of AD. In this prospective and cross-sectional study, we aimed at identifying differences in microbiome between patients with AD (Pre-onset stage amnestic mild cognitive impairment, aMCI; and AD) and the normal cognition healthy controls (HC). Additionally, the potential association between IM and clinical characteristics of AD was evaluated.
A total of 97 subjects (33 AD, 32 aMCI, and 32 HC) were recruited in the study. The composition of gut bacterial communities was determined by 16S ribosomal RNA Miseq sequencing. In addition, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict function shift of intestinal microbiota. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) or Clinical Dementia Rating (CDR) scores were used to evaluate the severity of cognitive impairment in patients.
The fecal microbial diversity was decreased in AD patients compared with aMCI patients and HC. And the microbial composition was distinct among aMCI, AD and healthy control groups. Among bacterial taxa, the proportion of phylum Firmicutes was significantly reduced (P = 0.008), whereas Proteobacteria (P = 0.024) was highly enriched in the AD compared with HC. In addition, similar alterations were observed at the order, class and family levels of these two phyla. And Gammaproteobacteria, Enterobacteriales and Enterobacteriaceae showed a progressive enriched prevalence from HC to aMCI and AD patients. Further, a significant correlation was observed between the clinical severity scores of AD patients and the abundance of altered microbiomes. Moreover, the KEGG results showed the increased modules related to glycan biosynthesis and metabolism in AD and aMCI patients and decreased pathways related to immune system in AD patients. Importantly, the discriminating models based on predominant microbiota could effectively distinguish aMCI and AD from HC (AUC = 0.890, 0.940, respectively), and also AD from aMCI (AUC = 0.925). Notably, the models based on the abundance of family Enterobacteriaceae could distinguish AD from both aMCI (AUC = 0.688) and HC (AUC = 0.698).
Distinct microbial communities, especially enriched Enterobacteriaceae, were associated with patients with AD when compared with predementia stage aMCI and healthy subjects. These novel findings will give new clues to understand the disease and provide new therapeutic target for intervention or a marker for this disease. |
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| AbstractList | •An altered fecal microbiome was associated with the Alzheimer’s disease (AD).•The depleted Firmicutes and enriched Proteobacteria linked with severity of AD.•Enterobacteriaceae was associated with the presence and progression of AD.•Models based on fecal microbiota discriminate AD from aMCI and health subjects.
(Background): Alzheimer’s disease (AD), clinically characterized by the progressive neurodegenerative condition and cognitive impairment, is one of the main causes of disability in elder people worldwide. Recently, several animal studies indicated that the ‘gut-brain’ axis might contribute to the amyloid deposition of AD. However, data about gut dysbiosis in human AD remains scarce in the literature, especially including the whole process of AD. In this prospective and cross-sectional study, we aimed at identifying differences in microbiome between patients with AD (Pre-onset stage amnestic mild cognitive impairment, aMCI; and AD) and the normal cognition healthy controls (HC). Additionally, the potential association between IM and clinical characteristics of AD was evaluated.
A total of 97 subjects (33 AD, 32 aMCI, and 32 HC) were recruited in the study. The composition of gut bacterial communities was determined by 16S ribosomal RNA Miseq sequencing. In addition, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict function shift of intestinal microbiota. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) or Clinical Dementia Rating (CDR) scores were used to evaluate the severity of cognitive impairment in patients.
The fecal microbial diversity was decreased in AD patients compared with aMCI patients and HC. And the microbial composition was distinct among aMCI, AD and healthy control groups. Among bacterial taxa, the proportion of phylum Firmicutes was significantly reduced (P = 0.008), whereas Proteobacteria (P = 0.024) was highly enriched in the AD compared with HC. In addition, similar alterations were observed at the order, class and family levels of these two phyla. And Gammaproteobacteria, Enterobacteriales and Enterobacteriaceae showed a progressive enriched prevalence from HC to aMCI and AD patients. Further, a significant correlation was observed between the clinical severity scores of AD patients and the abundance of altered microbiomes. Moreover, the KEGG results showed the increased modules related to glycan biosynthesis and metabolism in AD and aMCI patients and decreased pathways related to immune system in AD patients. Importantly, the discriminating models based on predominant microbiota could effectively distinguish aMCI and AD from HC (AUC = 0.890, 0.940, respectively), and also AD from aMCI (AUC = 0.925). Notably, the models based on the abundance of family Enterobacteriaceae could distinguish AD from both aMCI (AUC = 0.688) and HC (AUC = 0.698).
Distinct microbial communities, especially enriched Enterobacteriaceae, were associated with patients with AD when compared with predementia stage aMCI and healthy subjects. These novel findings will give new clues to understand the disease and provide new therapeutic target for intervention or a marker for this disease. (Background): Alzheimer's disease (AD), clinically characterized by the progressive neurodegenerative condition and cognitive impairment, is one of the main causes of disability in elder people worldwide. Recently, several animal studies indicated that the 'gut-brain' axis might contribute to the amyloid deposition of AD. However, data about gut dysbiosis in human AD remains scarce in the literature, especially including the whole process of AD. In this prospective and cross-sectional study, we aimed at identifying differences in microbiome between patients with AD (Pre-onset stage amnestic mild cognitive impairment, aMCI; and AD) and the normal cognition healthy controls (HC). Additionally, the potential association between IM and clinical characteristics of AD was evaluated.OBJECTIVE(Background): Alzheimer's disease (AD), clinically characterized by the progressive neurodegenerative condition and cognitive impairment, is one of the main causes of disability in elder people worldwide. Recently, several animal studies indicated that the 'gut-brain' axis might contribute to the amyloid deposition of AD. However, data about gut dysbiosis in human AD remains scarce in the literature, especially including the whole process of AD. In this prospective and cross-sectional study, we aimed at identifying differences in microbiome between patients with AD (Pre-onset stage amnestic mild cognitive impairment, aMCI; and AD) and the normal cognition healthy controls (HC). Additionally, the potential association between IM and clinical characteristics of AD was evaluated.A total of 97 subjects (33 AD, 32 aMCI, and 32 HC) were recruited in the study. The composition of gut bacterial communities was determined by 16S ribosomal RNA Miseq sequencing. In addition, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict function shift of intestinal microbiota. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) or Clinical Dementia Rating (CDR) scores were used to evaluate the severity of cognitive impairment in patients.METHODSA total of 97 subjects (33 AD, 32 aMCI, and 32 HC) were recruited in the study. The composition of gut bacterial communities was determined by 16S ribosomal RNA Miseq sequencing. In addition, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict function shift of intestinal microbiota. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) or Clinical Dementia Rating (CDR) scores were used to evaluate the severity of cognitive impairment in patients.The fecal microbial diversity was decreased in AD patients compared with aMCI patients and HC. And the microbial composition was distinct among aMCI, AD and healthy control groups. Among bacterial taxa, the proportion of phylum Firmicutes was significantly reduced (P = 0.008), whereas Proteobacteria (P = 0.024) was highly enriched in the AD compared with HC. In addition, similar alterations were observed at the order, class and family levels of these two phyla. And Gammaproteobacteria, Enterobacteriales and Enterobacteriaceae showed a progressive enriched prevalence from HC to aMCI and AD patients. Further, a significant correlation was observed between the clinical severity scores of AD patients and the abundance of altered microbiomes. Moreover, the KEGG results showed the increased modules related to glycan biosynthesis and metabolism in AD and aMCI patients and decreased pathways related to immune system in AD patients. Importantly, the discriminating models based on predominant microbiota could effectively distinguish aMCI and AD from HC (AUC = 0.890, 0.940, respectively), and also AD from aMCI (AUC = 0.925). Notably, the models based on the abundance of family Enterobacteriaceae could distinguish AD from both aMCI (AUC = 0.688) and HC (AUC = 0.698).RESULTSThe fecal microbial diversity was decreased in AD patients compared with aMCI patients and HC. And the microbial composition was distinct among aMCI, AD and healthy control groups. Among bacterial taxa, the proportion of phylum Firmicutes was significantly reduced (P = 0.008), whereas Proteobacteria (P = 0.024) was highly enriched in the AD compared with HC. In addition, similar alterations were observed at the order, class and family levels of these two phyla. And Gammaproteobacteria, Enterobacteriales and Enterobacteriaceae showed a progressive enriched prevalence from HC to aMCI and AD patients. Further, a significant correlation was observed between the clinical severity scores of AD patients and the abundance of altered microbiomes. Moreover, the KEGG results showed the increased modules related to glycan biosynthesis and metabolism in AD and aMCI patients and decreased pathways related to immune system in AD patients. Importantly, the discriminating models based on predominant microbiota could effectively distinguish aMCI and AD from HC (AUC = 0.890, 0.940, respectively), and also AD from aMCI (AUC = 0.925). Notably, the models based on the abundance of family Enterobacteriaceae could distinguish AD from both aMCI (AUC = 0.688) and HC (AUC = 0.698).Distinct microbial communities, especially enriched Enterobacteriaceae, were associated with patients with AD when compared with predementia stage aMCI and healthy subjects. These novel findings will give new clues to understand the disease and provide new therapeutic target for intervention or a marker for this disease.CONCLUSIONSDistinct microbial communities, especially enriched Enterobacteriaceae, were associated with patients with AD when compared with predementia stage aMCI and healthy subjects. These novel findings will give new clues to understand the disease and provide new therapeutic target for intervention or a marker for this disease. (Background): Alzheimer's disease (AD), clinically characterized by the progressive neurodegenerative condition and cognitive impairment, is one of the main causes of disability in elder people worldwide. Recently, several animal studies indicated that the 'gut-brain' axis might contribute to the amyloid deposition of AD. However, data about gut dysbiosis in human AD remains scarce in the literature, especially including the whole process of AD. In this prospective and cross-sectional study, we aimed at identifying differences in microbiome between patients with AD (Pre-onset stage amnestic mild cognitive impairment, aMCI; and AD) and the normal cognition healthy controls (HC). Additionally, the potential association between IM and clinical characteristics of AD was evaluated. A total of 97 subjects (33 AD, 32 aMCI, and 32 HC) were recruited in the study. The composition of gut bacterial communities was determined by 16S ribosomal RNA Miseq sequencing. In addition, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict function shift of intestinal microbiota. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) or Clinical Dementia Rating (CDR) scores were used to evaluate the severity of cognitive impairment in patients. The fecal microbial diversity was decreased in AD patients compared with aMCI patients and HC. And the microbial composition was distinct among aMCI, AD and healthy control groups. Among bacterial taxa, the proportion of phylum Firmicutes was significantly reduced (P = 0.008), whereas Proteobacteria (P = 0.024) was highly enriched in the AD compared with HC. In addition, similar alterations were observed at the order, class and family levels of these two phyla. And Gammaproteobacteria, Enterobacteriales and Enterobacteriaceae showed a progressive enriched prevalence from HC to aMCI and AD patients. Further, a significant correlation was observed between the clinical severity scores of AD patients and the abundance of altered microbiomes. Moreover, the KEGG results showed the increased modules related to glycan biosynthesis and metabolism in AD and aMCI patients and decreased pathways related to immune system in AD patients. Importantly, the discriminating models based on predominant microbiota could effectively distinguish aMCI and AD from HC (AUC = 0.890, 0.940, respectively), and also AD from aMCI (AUC = 0.925). Notably, the models based on the abundance of family Enterobacteriaceae could distinguish AD from both aMCI (AUC = 0.688) and HC (AUC = 0.698). Distinct microbial communities, especially enriched Enterobacteriaceae, were associated with patients with AD when compared with predementia stage aMCI and healthy subjects. These novel findings will give new clues to understand the disease and provide new therapeutic target for intervention or a marker for this disease. |
| Author | Tang, Ruiqi Peng, Guoping Wang, Baohong Yue, Siqing Ge, Jianping Luo, Benyan Li, Lanjuan Zhang, Lijiang Zhou, Kai Wu, Li Han, Yuqiu Liu, Ping Jia, Longfei |
| Author_xml | – sequence: 1 givenname: Ping surname: Liu fullname: Liu, Ping organization: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road No.79, Hangzhou 310003, China – sequence: 2 givenname: Li surname: Wu fullname: Wu, Li organization: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road No.79, Hangzhou 310003, China – sequence: 3 givenname: Guoping surname: Peng fullname: Peng, Guoping organization: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road No.79, Hangzhou 310003, China – sequence: 4 givenname: Yuqiu surname: Han fullname: Han, Yuqiu organization: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road No.79, Hangzhou 310003, China – sequence: 5 givenname: Ruiqi surname: Tang fullname: Tang, Ruiqi organization: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road No.79, Hangzhou 310003, China – sequence: 6 givenname: Jianping surname: Ge fullname: Ge, Jianping organization: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road No.79, Hangzhou 310003, China – sequence: 7 givenname: Lijiang surname: Zhang fullname: Zhang, Lijiang organization: Center of Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou, China – sequence: 8 givenname: Longfei surname: Jia fullname: Jia, Longfei organization: Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, Zhejiang, China – sequence: 9 givenname: Siqing orcidid: 0000-0002-0190-7013 surname: Yue fullname: Yue, Siqing organization: Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Research Center of Environmental Science, Zhejiang University of Technology, Hangzhou 310032, China – sequence: 10 givenname: Kai surname: Zhou fullname: Zhou, Kai organization: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road No.79, Hangzhou 310003, China – sequence: 11 givenname: Lanjuan surname: Li fullname: Li, Lanjuan email: ljli@zju.edu.cn organization: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road No.79, Hangzhou 310003, China – sequence: 12 givenname: Benyan surname: Luo fullname: Luo, Benyan email: luobenyan@zju.edu.cn organization: Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China – sequence: 13 givenname: Baohong surname: Wang fullname: Wang, Baohong email: wangbaohongzju@zju.edu.cn organization: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road No.79, Hangzhou 310003, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31063846$$D View this record in MEDLINE/PubMed |
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| Title | Altered microbiomes distinguish Alzheimer’s disease from amnestic mild cognitive impairment and health in a Chinese cohort |
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