Metabolomics: The Key to Unraveling the Role of the Microbiome in Visceral Pain Neurotransmission
Bibliographic record
Abstract
Inflammatory bowel disease (IBD), comprising Crohn's disease and Ulcerative colitis, is a relapsing and remitting disease of the gastrointestinal tract, presenting with chronic inflammation, ulceration, gastrointestinal bleeding, and abdominal pain. Up to 80% of patients suffering from IBD experience acute pain, which dissipates when the underlying inflammation and tissue damage resolves. However, despite achieving endoscopic remission with no signs of ongoing intestinal inflammation or damage, 30-50% of IBD patients in remission experience chronic abdominal pain, suggesting altered sensory neuronal processing in this disorder. Furthermore, effective treatment for chronic pain is limited such that 5-25% of IBD outpatients are treated with narcotics, with associated morbidity and mortality. IBD patients commonly present with substantial alterations to the microbial community structure within the gastrointestinal tract, known as dysbiosis. The same is also true in irritable bowel syndrome (IBS), a chronic disorder characterized by altered bowel habits and abdominal pain, in the absence of inflammation. An emerging body of literature suggests that the gut microbiome plays an important role in visceral hypersensitivity. Specific microbial metabolites have an intimate relationship with host receptors that are highly expressed on host cell and neurons, suggesting that microbial metabolites play a key role in visceral hypersensitivity. In this review, we will discuss the techniques used to analysis the metabolome, current potential metabolite targets for visceral hypersensitivity, and discuss the current literature that evaluates the role of the post-inflammatory microbiota and metabolites in visceral hypersensitivity.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".