The Oral and Fecal Microbiota in a Canadian Cohort of Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Despite decades of research, our understanding of Alzheimer's disease (AD) etiology remains incomplete. In recent years, appreciation has grown for potential roles for the microbiota in shaping neurological health. OBJECTIVE: This study aimed to examine associations between the microbiota and AD in a human cross-sectional cohort. METHODS: Forty-five AD patients and 54 matched controls were recruited in Vancouver, Canada. Fecal and oral samples underwent 16S microbiota sequencing. A wide array of demographic and clinical data were collected. Differences between participant groups were assessed, and associations between microbes and clinical variables were examined within the AD population. RESULTS: The gut microbiota of AD patients displayed lower diversity relative to controls, although taxonomic differences were sparse. In contrast, the AD oral microbiota displayed higher diversity, with several taxonomic differences relative to controls, including a lower abundance of the families Streptococcaceae and Actinomycetaceae, and a higher abundance of Weeksellaceae, among others. The periodontitis-associated oral microbe Porphyromonas gingivalis was 5 times more prevalent among patients. No significant associations between gut or oral microbes and cognition were detected, but several correlations existed between microbes and mood disorders and BMI among patients, including a strong positive correlation between Alphaproteobacteria and depression score. CONCLUSION: The gut microbiota of AD patients was not overtly different from controls, although it displayed lower diversity, an overall marker of microbiota health. The oral microbiota did display marked differences. Cognition was not associated with a microbial signature, but other relevant AD factors including mood and BMI did demonstrate an association.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".