Distinctive subgingival microbial signatures in older adults with different levels of cognitive function
Bibliographic record
Abstract
AIM: To examine association between subgingival microbial signatures and levels of cognitive impairment in older adults. MATERIALS AND METHODS: We analysed subgingival plaque samples and 16S ribosomal RNA sequences for microbiota among 165 participants (normal controls [NCs]: 40, subjective cognitive decline [SCD]: 40, mild cognitive impairment [MCI]: 49 and dementia: 36). RESULTS: The bacterial richness was lower among individuals with worse cognitive function, and subgingival microbial communities differed significantly among the four groups. Declining cognitive function was associated with decreasing relative abundance of genera Capnocytophaga, Saccharibacteria_genera_incertae_sedis, Lautropia and Granulicatella, and increasing abundance of genus Porphyromonas. Moreover, there were differentially abundant genera among the groups. Random forest model based on subgingival microbiota could distinguish between cognitive impairment and NC (AUC = 0.933, 95% confidence interval 0.873-0.992). Significant correlations were observed between oral microbiota and sex, Montreal Cognitive Assessment (MoCA) score and Mini-Mental State Examination score. Partial correlation analysis showed that Leptotrichia and Burkholderia were closely negatively associated with the MoCA score after adjusting for multiple covariates. Gene function was not significantly different between SCD and NC groups, whereas three homozygous genes were altered in MCI patients and two in dementia patients. CONCLUSIONS: This is the first study to demonstrate an association between the composition, function and metabolic pathways of subgingival microbiota and different levels of cognitive function among older individuals. Future cohort studies should assess its diagnostic usefulness for cognitive impairment.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".