The impact of dietary fermentable carbohydrates on a postinflammatory model of irritable bowel syndrome
Bibliographic record
Abstract
BACKGROUND: A low fermentable carbohydrate (FODMAP) diet is used in quiescent inflammatory bowel disease when irritable bowel syndrome-like symptoms occur. There is concern that the diet could exacerbate inflammation by modifying microbiota and short-chain fatty acid (SCFA) production. We examined the effect of altering dietary FODMAP content on inflammation in preclinical inflammatory models. METHODS: C57BL/6 mice were given 3% dextran sodium sulfate (DSS) in drinking water for 5 days and recovered for 3 weeks (postinflammatory, n = 12), or 5 days (positive-control, n = 12). Following recovery, DSS-treated or control mice (negative-control, n = 12) were randomized to 2-week low- (0.51 g/100 g total FODMAP) or high-FODMAP (4.10 g) diets. Diets mimicked human consumption containing fructose, sorbitol, galacto-oligosaccharide, and fructan. Colons were assessed for myeloperoxidase (MPO) activity and histological damage. Supernatants were generated for perforated patch-clamp recordings and cytokine measurement. Cecum contents were analyzed for microbiota, SCFA, and branched-chain fatty acids (BCFA). Data were analyzed by two-way ANOVA with Bonferroni. KEY RESULTS: Inflammatory markers were higher in the positive-control compared with negative-control and postinflammatory groups, but no differences occurred between the two diets within each treatment (MPO P > .99, histological scores P > .99, cytokines P > .05), or the perforated patch-clamp recordings (P > .05). Microbiota clustered mainly based on DSS exposure. No difference in SCFA content occurred. Higher total BCFA occurred with the low-FODMAP diet in positive-control (P < .01) and postinflammatory groups (P < .01). CONCLUSIONS AND INFERENCES: In this preclinical study, reducing dietary FODMAPs did not exacerbate nor mitigate inflammation. Microbiota profile changes were largely driven by inflammation rather than diet. Low FODMAP intake caused a shift toward proteolytic fermentation following inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".