Butyrate’s role in human health and the current progress towards its clinical application to treat gastrointestinal disease
Bibliographic record
Abstract
Butyrate is a key energy source for colonocytes and is produced by the gut microbiota through fermentation of dietary fiber. Butyrate is a histone deacetylase inhibitor and also signals through three G-protein coupled receptors. It is clear that butyrate has an important role in gastrointestinal health and that butyrate levels can impact both host and microbial functions that are intimately coupled with each other. Maintaining optimal butyrate levels improves gastrointestinal health in animal models by supporting colonocyte function, decreasing inflammation, maintaining the gut barrier, and promoting a healthy microbiome. Butyrate has also shown protective actions in the context of intestinal diseases such as inflammatory bowel disease, graft-versus-host disease of the gastrointestinal tract, and colon cancer, whereas lower levels of butyrate and/or the microbes which are responsible for producing this metabolite are associated with disease and poorer health outcomes. However, clinical efforts to increase butyrate levels in humans and reverse these negative outcomes have generated mixed results. This article discusses our current understanding of the molecular mechanisms of butyrate action with a focus on the gastrointestinal system, the links between host and microbial factors, and the efforts that are currently underway to apply the knowledge gained from the bench to bedside.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".