Distinct healthy and atopic canine gut microbiota is influenced by diet and antibiotics
Bibliographic record
Abstract
The rising trend in non-communicable chronic inflammatory diseases coincides with changes in Western lifestyle. While changes in the human microbiota may play a central role in the development of chronic diseases, estimating the contribution of associated lifestyle factors remains challenging. We studied the influence of lifestyle-diet, antibiotic use, and residential environment with housing and family-on the gut microbiota of healthy and owner-reported atopic pet dogs, searching for associations between the lifestyle factors, atopy and microbiota. The results showed that atopic and healthy dogs had contrasting gut microbial composition. The gut microbiota also differed between two breeds, Labrador Retriever and Finnish Lapphund, selected for our study. Among all lifestyle factors studied, diet was most significantly associated with gut microbiota but only weakly with atopic symptoms. Thus, diet- and atopy-associated changes in the microbiota were not interrelated. Instead, the severity of symptoms was positively associated with the usage of antibiotics, which in turn was associated with the microbiota composition. Urban lifestyle was significantly associated with the increased prevalence of allergies but not with the gut microbiota. Our results from pet dogs supported previous evidence from humans, demonstrating that antibiotics, gut microbiota and atopic manifestation are interrelated. This congruence suggests that canine atopy might be a promising model for understanding the aetiology of human allergy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".