Characterization of intestinal microbiota in normal weight and overweight Border Collie and Labrador Retriever dogs
Bibliographic record
Abstract
Obesity in dogs is an emerging issue that affects canine health and well-being. Its development is ascribed to several factors, including genetic predisposition and dietary management, and recent evidence suggests that intestinal microbiota may be involved as well. Previous works have shown obesity to be linked to significant changes in gut microbiota composition in humans and mice, but only limited information is available on the role played by canine gut microbiota. The aim of this exploratory study was to investigate whether composition of canine faecal microbiota may be influenced by overweight condition and breed. All the enrolled companion dogs were young adults, intact, healthy, and fed commercial extruded pet food; none had received antibiotics, probiotics or immunosuppressant drugs in the previous six months. Labrador Retriever (LR) and Border Collie (BC) were chosen as reference breeds and Body Condition Score (BCS) on a 9-point scale as reference method for evaluating body fat. The faecal microbial communities of 15 lean (BCS 4-5/9; 7 LRs and 8 BCs) and 14 overweight (BCS > 5/9; 8 LRs and 6 BCs) family dogs were analysed using 16S rRNA gene sequencing. Moreover, for each dog, the daily intake of energy (kcal/d) and dietary macronutrients (g/d) were calculated according to an accurate feeding history collection. Firmicutes and Bacteroidetes resulted the predominant phyla (51.5 ± 10.0% and 33.4 ± 8.5%, respectively) in all dogs. Bioinformatic and statistical analysis revealed that no bacterial taxon differed significantly based on body condition, except for genus Allisonella (p < 0.05); BC gut microbiota was richer (p < 0.05) in bacteria belonging to phyla Actinobacteria (family Coriobacteriaceae in particular) and Firmicutes (Allobaculum and Roseburia genera). No remarkable differences were recorded either for diversity indices (i.e., alpha diversity, p > 0.10) or for divergence within the sample set (i.e., beta diversity, p > 0.05). PERMANOVA tests performed on single factors demonstrated the tendency of dietary protein to influence the recruited dogs' microbiota beta-diversity at amplicon sequence variant level (p = 0.08). In conclusion, the faecal microbiota of dogs involved in this exploratory study showed no major variations based on body condition. However, our findings suggested that certain bacterial taxa previously acknowledged in obesity-related studies may be detected in dissimilar amounts depending on canine breed.
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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 | 0.001 | 0.001 |
| 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".