Analysis of the gut microbiome in sled dogs reveals glucosamine- and activity-related effects on gut microbial composition
Bibliographic record
Abstract
The composition of the microbiome influences many aspects of physiology and health, and can be altered by environmental factors, including diet and activity. Glucosamine is a dietary supplement often administered to address arthritic symptoms in humans, dogs, and other mammals. To investigate how gut microbial composition varies with glucosamine supplementation, we performed 16S rRNA sequence analysis of fecal samples from 24 Alaskan and Inuit huskies and used mixed effects models to investigate associations with activity, age, and additional factors. Glucosamine ingestion, age, activity, sex, and diet were correlated with differences in alpha-diversity, with diversity decreasing in dogs consuming glucosamine. Beta-diversity analysis revealed clustering of dogs based on glucosamine supplementation status. Glucosamine supplementation and exercise-related activity were associated with greater inter-individual pairwise distances. At the family level, Lactobacillaceae and Anaerovoracaceae relative abundances were lower in supplemented dogs when activity was accounted for. At the genus level, Eubacterium [brachy], Sellimonus, Parvibacter, and an unclassified genus belonging to the same family as Parvibacter (Eggerthellaceae) all were lower in supplemented dogs, but only significantly so post-activity. Our findings suggest that glucosamine supplementation alters microbiome composition in sled dogs, particularly in the context of exercise-related activity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".