Does canine inflammatory bowel disease influence gut microbial profile and host metabolism?
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel disease (IBD) refers to a diverse group of chronic gastrointestinal diseases, and gut microbial dysbiosis has been proposed as a modulating factor in its pathogenesis. Several studies have investigated the gut microbial ecology of dogs with IBD but it is yet unclear if this microbial profile can alter the nutrient metabolism of the host. The aim of the present study was to characterize the faecal bacterial profile and functionality as well as to determine host metabolic changes in IBD dogs. Twenty-three dogs diagnosed with IBD and ten healthy control dogs were included. Dogs with IBD were given a clinical score using the canine chronic enteropathy clinical activity index (CCECAI). Faecal short-chain fatty acids (SCFA) and ammonia concentrations were measured and quantitative PCR was performed. The concentration of plasma amino acids, acylcarnitines, serum folate, cobalamin, and indoxyl sulfate was determined. RESULTS: No significant differences in the abundance of a selection of bacterial groups and fermentation metabolites were observed between the IBD and control groups. However, significant negative correlations were found between CCECAI and the faecal proportion of Lactobacillus as well as between CCECAI and total SCFA concentration. Serum folate and plasma citrulline were decreased and plasma valine was increased in IBD compared to control dogs. Increased plasma free carnitine and total acylcarnitines were observed in IBD compared with control dogs, whereas short-chain acylcarnitines (butyrylcarnitine + isobutyrylcarnitine and, methylmalonylcarnitine) to free carnitine ratios decreased. Dogs with IBD had a higher 3-hydroxyisovalerylcarnitine + isovalerylcarnitine to leucine ratio compared to control dogs. CONCLUSIONS: Canine IBD induced a wide range of changes in metabolic profile, especially for the plasma concentrations of short-chain acylcarnitines and amino acids, which could have evolved from tissue damage and alteration in host metabolism. In addition, dogs with more severe IBD were characterised by a decrease in faecal proportion of Lactobacillus.
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 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 | 0.001 | 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".