The fecal metabolomic signature of a plant-based (vegan) diet compared to an animal-based diet in healthy adult client-owned dogs
Bibliographic record
Abstract
Despite the rising popularity of plant-based (vegan) diets for dogs, the metabolic effects of plant-based diets in dogs have not been thoroughly investigated. Evaluating the impact of a vegan diet on the fecal metabolome in dogs could offer valuable insight into the effects on gastrointestinal and overall health. This study evaluated the fecal metabolic signature of an experimental extruded vegan diet (PLANT) compared to a commercial extruded animal-based diet (MEAT) in healthy adult dogs. Sixty-one client-owned healthy adult dogs completed a randomized, double-blinded longitudinal study consisting of a 4-wk acclimation period, where all dogs received the MEAT diet, followed by a 12-wk experimental period where they either continued with the MEAT diet or switched to the PLANT diet. Fecal collections occurred at baseline (after 4-wk acclimation) and exit (after the experimental period). Fecal metabolites were quantified using 1H nuclear magnetic resonance spectroscopy. Multiple mixed model gamma linear regression was used to evaluate the association of metabolite concentration against age, sex, and body weight, along with an interaction between diet and time. Sixty-six metabolites were quantified. Only 2/66 metabolites differed between groups at baseline and within the MEAT diet group over time. In contrast, 46/66 metabolites differed in concentrations over time in response to feeding the PLANT diet. At the exit time-point, dogs fed the PLANT diet had increased metabolites related to carbohydrate fermentation, such as acetic (P < 0.01) and propanoic (P < 0.01) acid and increases in sugar metabolites when compared to the MEAT group. These findings indicate that the fecal metabolic signature of dogs fed a plant-based diet is distinct from dogs fed an animal-based diet, even if both diets have a similar nutrient profile and are processed similarly.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".