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Record W4414831170 · doi:10.1093/jas/skaf300.467

PSV-3 Impacts of rotational ingredient supplementation on canine gastrointestinal microbiota upon exposure to rapid dietary change.

2025· article· en· W4414831170 on OpenAlexaboutno aff
Cierra N Crowell, Erin Perry, Daisy Kaplan, Eileen Jenkins, Debra L. Zoran, Tracy A. Darling, George E. Moore, Mike Davis, Jan S. Suchodolski

Bibliographic record

VenueJournal of Animal Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
Fundersnot available
KeywordsIngredientButyric acidButyrateFecesBasal (medicine)Gut floraIsobutyric acid

Abstract

fetched live from OpenAlex

Abstract Rapid dietary changes have been reported to disrupt gastrointestinal health, but there is limited data on effective mitigation strategies. The objective of this study was to assess the impact of increased dietary diversity in response to an abrupt dietary transition as measured by short-chain fatty acid (SCFA) concentrations and microbial composition. Labrador retrievers (n = 16) aged 1-3 yo, BCS (5-6), enrolled in detection dog training and receiving daily conditioning and exercise, participated in a 120-day study. Dogs were enrolled into one of two treatment groups. Control dogs (CON) received a single basal diet, while rotational ingredient dogs (ROT) received the same basal diet with rotating supplemental ingredients (2.5% of total daily DM intake) over 90 days, including oatmeal, blueberries, pumpkin, beef liver, salmon, and rabbit. Following a 30-day acclimation period, both groups underwent a rapid, untransitioned dietary change (dietary challenge) for two meals (am/pm feedings) on days 0, 45, and 90. Fecal samples were collected across three consecutive days for each testing period beginning at d0, 45, and 90 and analyzed for SCFA concentrations and microbial composition. Statistical analysis was conducted using SAS (v 9.4) with significance set at 5%. Concentrations of SCFA were impacted across both treatment groups following the dietary challenge. All dogs experienced increased concentrations of butyric acid (P = 0.0015) and propionic acid (P = 0.0209) following the dietary challenge. Conversely, isovaleric (P = 0.0038) and isobutyric acid (P = 0.0027) were decreased following dietary challenge. Interestingly, concentrations of propionic acid increased throughout the study for all dogs (P = 0.0038). Analysis of the microbial composition revealed beneficial impacts of rotational ingredient supplementation including an increase in Faecalibacterium (P = 0.01) and Streptococcus (P = 0.04) as well as a reduction in E. coli (P = 0.023). Inclusion of dietary challenge increased levels of Turicibacter (P = 0.01), Streptococcus (P = 0.003), Blautia (P = 0.002), and C. hiranonis (P = 0.01). The recovery meal resulted in increased concentrations of Fusobacterium (P < 0.001) and Faecalibacterium (P < 0.0001). These findings suggest that while rotational ingredient supplementation did not significantly affect SCFA concentrations, there were significant benefits within the microbial profile including an increase in beneficial bacteria such as Faecalibacterium, along with reduced numbers of pathogens such as E. coli. Interestingly, the dietary challenge prompted changes including increased concentrations of butyric acid and propionic acid which may indicate a potential anti-inflammatory response of the gastrointestinal tract following abrupt dietary transitions. These findings provide further evidence to support dietary diversity as a method for improving gastrointestinal health and diversity. Future research is needed to determine guidelines and practices for rotational diets and other ingredients that may be used for improving gut health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.321
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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