ArticleJournal of translational medicine2025
Identification of enterotype for patients with Alzheimer's disease.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging artificial intelligence for analysis of the gut microbiome for dementia diagnosis: a scoping review and discussion.Frontiers in dementia · 2026Pooled it
- Community Structure and Functional Prediction of Gut Microbiota in Mandarin Fish (Siniperca Chuatsi) Under Different Culture and Feeding Conditions.Life (Basel, Switzerland) · 2026Article
- A novel enterotype enriched with respiratory super-dominant pathobionts is associated with immunosuppression in pulmonary tuberculosis patients.Journal of translational medicine · 2026Article
- Indigenous gut microbes modulate neural cell state and neurodegenerative disease susceptibility.Cell systems · 2026Article
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- Intestinal epithelial Dicer1 regulates gut microbiome and Alzheimer's pathology in App-knock-in mice.Alzheimer's research & therapy · 2025Article
- The role of probiotics, prebiotics, and postbiotics: cellular and molecular pathways activated on glial cells in Alzheimer's disease.Frontiers in neuroscience · 2025Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlzheimer's disease (AD) is a progressive and chronic neurodegenerative disorder of the central nervous system, characterized by behavioral and dysexecutive deficits. Its pathogenesis is closely associated with the intestinal flora. This study aimed to explore the enterotypes in AD by identifying key bacteria through machine learning and species co-occurrence network analysis.
methodsThe collection of fecal samples from AD patients was followed by 16 S rRNA analysis using QIIME2. Enterotype clustering was conducted at the genus level, and deep neural network (DNN) classification models were developed for AD and healthy controls within each enterotype.
resultsAnalysis of three 16 S rRNA gut microbiome datasets identified three distinct enterotypes: Escherichia_Shigella (ET-E), Faecalibacterium (ET-F), and Bacteroides (ET-B). The ET-E is mainly characterized by the absence of Akkermansia in AD group. The Akkermansia was significantly positively correlated with Eubacterium_coprostanoligenes_group and negatively correlated with biosynthesis and amino acid metabolism. The ET-F highly expressed Agathobacter, un_f__Lachnospiraceae, Lachnoclostridium, and low expressed Dorea in AD group. Among them, Agathobacter was significantly positively correlated with un_f__Lachnospiraceae, and un_f__Lachnospiraceae was significantly positively correlated with Lachnoclostridium. The Dorea was significantly negatively correlated with Lachnoclostridium. The AD from ET-B group had high expression of two beneficial bacteria, Butyricicoccus and Parabacteroides. The findings suggest that the ET-E enterotype may predispose individuals to AD, with Akkermansia identified as a potential risk factor. Conversely, the ET-B enterotype appears to be associated with milder symptoms, with Butyricicoccus and Parabacteroides potentially serving as protective factors. Therefore, a comprehensive understanding of the species characteristics and interactions within different enterotypes is essential for modulating the gut-brain axis and mitigating AD symptoms.
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