Effects of feeding Candida utilis-fermented pea starch on overall, metabolic and intestinal health of dogs and cats
Bibliographic record
Abstract
Pulse-based pet foods often contain peas or pea starch, which tend to impart a bitter taste. Fermentation increases feed palatability, but also has the potential to improve overall health. Therefore, the current study used the yeast, Candida utilis, to ferment pea starch for use in pet food and assessed health effects, focusing on metabolic and intestinal health in dogs and cats. Whole diets had ~30% starch inclusion of either C. utilis-fermented pea starch, unfermented pea starch, or a control corn diet fed over a 20-day period to beagle dogs and domestic cats. Complete blood count, biochemistry, adipokines, and triglyceride levels were assessed, along with fecal short chain fatty acids, microbial diversity and abundance to measure intestinal health. It was found that pea-based diets (regardless of fermentation) generally resulted in improved metabolic health by both species, indicated by lower plasma triglycerides, cholesterol, and leptin levels compared to the control corn diet. Additionally, the C. utilis-fermented pea starch diet improved dog fecal microbial diversity, while both pea diets increased richness and evenness in the microbial population and a larger Faecalibacterium population compared to dogs fed the corn-based diet. In contrast, cat microbiome could not be quantitatively evaluated due to poor fecal quality. Taken together, inclusion of pea starch improves metabolic and intestinal health after 20 days consumption in dogs, while fermentation of pea starch with C. utilis may provide additional benefit.
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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 | 0.001 |
| 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 | 0.001 |
| 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".