Gut microbiota dysbiosis in patients with Alzheimer’s disease and correlation with multiple cognitive domains
Bibliographic record
Abstract
Background Accumulating evidence suggested that Alzheimer’s disease (AD) was associated with altered gut microbiota. However, the relationships between gut microbiota and specific cognitive domains of AD patients have yet been fully elucidated. The aim of this study was to explore microbial signatures associated with global cognition and specific cognitive domains in AD patients and to determine their predictive value as biomarkers. Methods A total of 64 subjects (18 mild AD, 23 severe AD and 23 healthy control) were recruited in the study. 16 s rDNA sequencing was performed for the gut bacteria composition, followed by liquid chromatography electrospray ionization tandem mass spectrometry (LC/MS/MS) analysis of short-chain fatty acids (SCFAs). The global cognition, specific cognitive domains (abstraction, orientation, attention, language, etc.) and severity of cognitive impairment, were evaluated by Montreal Cognitive Assessment (MoCA) scores. We further identified characteristic bacteria and SCFAs, and receiver operating characteristic (ROC) curve was used to determine the predictive value. Results Our results showed that the microbiota dysbiosis index was significantly higher in the severe and mild AD patients compared to the healthy control (HC). Linear discriminant analysis (LDA) showed that 12 families and 17 genera were identified as key microbiota among three groups. The abundance of Butyricicoccus was positively associated with abstraction, and the abundance of Lachnospiraceae_UCG-004 was positively associated with attention, language, orientation in AD patients. Moreover, the levels of isobutyric acid and isovaleric acid were both significantly negatively correlated with abstraction, and level of propanoic acid was significantly positively associated with the attention. In addition, ROC models based on the characteristic bacteria Lactobacillus , Butyricicoccus and Lachnospiraceae_UCG-004 could effectively distinguished between low and high orientation in AD patients (area under curve is 0.891), and Butyricicoccus and Agathobacter or the combination of SCFAs could distinguish abstraction in AD patients (area under curve is 0.797 and 0.839 respectively). Conclusion These findings revealed the signatures gut bacteria and metabolite SCFAs of AD patients and demonstrated the correlations between theses characteristic bacteria and SCFAs and specific cognitive domains, highlighting their potential value in early detection, monitoring, and intervention strategies for AD patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".