Gut microbiome composition is related to anxiety and aggression score in companion dogs
Bibliographic record
Abstract
Abstract Background There is mounting evidence for a link between behaviour and gut microbiome composition in several animal models and human health. However, the role of the gut microbiota in the development and severity of behavioural issues in companion dogs is not yet fully understood. In this work, we investigated the relationship between gut microbiome composition and aggression or anxiety in pet dogs. Pet dogs (n = 48) were assigned to higher or lower anxiety and aggression groups based on their owner’s responses to the Canine Behavioral Assessment & Research Questionnaire (C-BARQ). Then the gut microbiome of each animal, sequenced from microbial DNA extracted from fecal samples, was assessed for association with the dog’s assigned behavioural group using multiple approaches. Results While minimal differences in relative abundance were seen between behavioural groups, we were successful in predicting behavioural group based on gut microbiome composition using machine-learning based approaches and compositional balances. The generated models were particularly successful when distinguishing higher and lower anxiety dogs. The genus Blautia was identified across all our analyses, suggesting a strong link between this genus and anxiety in pet dogs. Conclusions This study builds on a growing area of research of great interest to dog owners, trainers, and behaviour professionals, and provides insight into specific bacteria that are linked to increased anxiety and aggression in pet dogs. Further research is required to identify bacteria to the species level, and to better understand the specific role of Blautia in the canine gut-brain axis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".