Interrogating the Gut-Brain Axis in the Context of Inflammatory Bowel Disease: A Translational Approach
Bibliographic record
Abstract
This review examines preclinical and clinical studies relevant to our understanding of how the bidirectional gut-brain axis influences the natural history of inflammatory bowel disease. Preclinical studies provide proof of concept that preexisting behavioral illness, such as depression, results in increased susceptibility to inflammatory stimuli and that commonly used classes of antidepressants protect against this vulnerability. However, clinical studies suggesting behavioral illness as a risk factor for IBD and a protective role for antidepressants have relied primarily on symptom-reporting rather than objective measurements of inflammation. In terms of gut-to-brain signaling, there is emerging evidence from preclinical and clinical observation that intestinal inflammation alters brain functions, including the induction of mood disorders, alteration of circadian rhythm both centrally and peripherally, and changes in appetitive behaviors. Furthermore, preclinical studies suggest that effective treatment of intestinal inflammation improves associated behavioral impairment. Taken together, the findings of this review encourage a holistic approach to the management of patients with IBD, accommodating lifestyle issues that include the avoidance of sleep deprivation, optimized nutrition, and the monitoring and appropriate management of behavioral disorders. The review also acknowledges the need for better-designed clinical studies evaluating the impact of behavioral disorders and their treatments on the natural history of IBD, utilizing hard end points to assess changes in the inflammatory process as opposed to reliance on symptom-based assessments. The findings of the review also encourage a better understanding of changes in brain function and circadian rhythm induced by intestinal 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 0.001 |
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".