Prebiotic selection influencing inflammatory bowel disease treatment outcomes: a review of the preclinical and clinical evidence
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is characterised by chronic inflammation in the gastrointestinal tract, with unclear aetiology but with known factors contributing to the disease, including genetics, immune responses, environmental factors and dysbiosis of the gut microbiota. Existing pharmacotherapies mainly target the inflammatory symptoms of disease, but recent research has highlighted the capacity for microbial-accessible carbohydrates that confer health benefits (ie, prebiotics) to selectively stimulate the growth of beneficial gut bacteria for improved IBD management. However, since prebiotics vary in source, chemical composition and microbiota effects, there is a clear need to understand the impact of prebiotic selection on IBD treatment outcomes. This review subsequently explores and contrasts the efficacy of prebiotics from various sources (β-fructans, galacto-oligosaccharides, xylo-oligosaccharides, resistant starch, pectin, β-glucans, glucomannans and arabinoxylans) in mitigating IBD symptomatology, when used as either standalone or adjuvant therapies. In preclinical animal colitis models, prebiotics have revealed type-dependent effects in positively modulating gut microbiota composition and subsequent attenuation of disease indicators and proinflammatory responses. While prebiotics have demonstrated therapeutic potential in animal models, clinical evidence for their precise efficacy remains limited, stressing the need for further investigation in human patients with IBD to facilitate their widespread clinical translation as microbiota-targeting IBD therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 teacher head, 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".