Association of human gut microbiota with Alzheimer’s disease pathogenesis: An exploratory clinical study
Bibliographic record
Abstract
Abstract The relationship between Alzheimer’s disease (AD) onset and the brain–gut axis has garnered increasing attention. This study aimed to investigate the potential role of the brain–gut axis in AD pathogenesis, with a specific focus on microbiota composition. This exploratory study enrolled 10 patients with AD and 13 healthy adults, grouped by age (≤30 years, 31–40 years, and ≥41 years). Fecal samples were collected, and 16S rRNA gene sequencing was employed to analyze differences in fecal microbiota composition at the bacterial species level. Certain bacterial species appeared more abundant in the AD group (e.g., Ruminococcus inulinivorans and Ruminococcus torques ), while others were relatively more abundant in healthy adults (e.g., Prevotella vulgatus 1 , Bacteroides wexlerae , Clostridium butyricum , and Alistipes rectalis ). However, these differences were not statistically significant, likely because of the limited sample size. These findings suggest that fecal microbiota composition may differ between patients with AD and healthy individuals, with a potential intermediate group at risk of AD development. Larger-scale clinical studies are necessary to further elucidate the bacterial species associated with AD pathogenesis, potentially enabling the use of microbiota composition as a screening tool to distinguish between healthy individuals, patients with AD, and those with preclinical AD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".