Altered microbiomes distinguish Alzheimer’s disease from amnestic mild cognitive impairment and health in a Chinese cohort
Bibliographic record
Abstract
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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".