Understanding Role of Nutrition in Inflammatory Bowel Disease: A Comprehensive Review With Incorporation of AGA Update
Bibliographic record
Abstract
This comprehensive review delves into the evolving landscape of nutrition's role in Inflammatory Bowel Disease (IBD), while incorporating transformative insights from the recently published American Gastroenterology Association (AGA) clinical practice update (CPU) on diet in IBD. Recent years have witnessed a paradigm shift, recognizing diet not only as an influencer in disease development but also as a disease-modifying factor and treatment avenue for IBD. The AGA CPU endorses a balanced Mediterranean-style diet, emphasizing the need for personalized recommendations tailored to individual patient characteristics, including disease course, surgical history, and pharmacotherapy. The implementation of complex nutritional strategies necessitates the involvement of both gastroenterologists and registered dietitians. Guidance for patients and caregivers is available through organizations such as Crohn's and Colitis Canada and Crohn's and Colitis Foundation. While animal models offer valuable insights, the review underscores their limitations in replicating the complexity of IBD biology observed in humans. It advocates for an exploration of nutrigenomics, utilizing molecular tools to analyze how diet-derived bioactive compounds influence gene expression for precision nutrition insights. Practical challenges in dietary intervention studies, including regional variations and complexities in food handling, require attention for enhanced comparability. Limited data exist on combined dietary and medical therapies, but promising outcomes suggest potential synergies. Precision treatments could develop through an understanding of the function of particular food metabolites in influencing inflammation. The objective of this review is to offer a summary of the present comprehension of diet, serving as both a potential factor contributing to and a therapeutic avenue for IBD. Clinical Trial Registration: This study is not a part of a clinical trial.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".