Characterization of the salivary microbiome of adults with inflammatory bowel disease
Bibliographic record
Abstract
Background Perturbations of the gut microbiota in patients with inflammatory bowel disease (IBD) have been extensively characterised, but changes to the oral microbiome remain understudied. This study aimed to evaluate the oral microbiome of adults with IBD and of matched controls.Methods Saliva samples and data were obtained from a Canadian population cohort (n = 320). The salivary microbiome was characterised using 16S rRNA gene sequencing and examined for differences between control participants and those with IBD, as well as disease subcategories (Crohn’s Disease and Ulcerative Colitis).Results Alpha diversity was significantly lower in participants with IBD than controls in unadjusted models and many remained significant after adjusting for covariates. Significant differences in some beta diversity metrics between participants with IBD and controls were found, although these did not remain significant when adjusted for covariates. Ten genera were significantly differentially abundant between cases and controls. Veillonella and Streptococcus were both increased in abundance in IBD cases vs controls (25% vs 22% and 14% vs 12%, respectively).Conclusion These results showcase changes in oral microbial diversity and composition in those living with IBD and highlight the potential of using the salivary microbiome as a biomarker for screening or monitoring IBD.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".