Impact of diet on inflammatory bowel disease risk: systematic review, meta-analyses and implications for prevention
Bibliographic record
Abstract
Background: Data on dietary risk factors for inflammatory bowel disease (IBD), while extensive, are inconsistent. Our aim was to systematically review and meta-analyze available data unraveling the relationship between diet and IBD subtypes, Crohn's disease (CD) and ulcerative colitis (UC). Methods: We conducted a systematic literature review following PRISMA guidelines, from inception to May 8 2025, using OVID Medline, Embase, and Scopus databases, to identify prospective cohorts of healthy participants, on the association between diet and the risk of CD or UC. Meta-analyses were performed using random-effects model, pooling hazard ratios (HRs) for each exposure category, relative to the lowest. Findings: Of 7916 studies identified by the search, 72 studies (65 in adults, 7 in children) met the inclusion criteria. The 65 adult cohort studies included 2.043.601 participants; 62.3% were women, the mean age at recruitment was 53.1 years and mean follow up was 12.8 years. Overall, 1902 participants developed CD and 4617 developed UC. Inflammatory diet (pooled aHR 1.63, 95% CI: 1.26, 2.11) and ultra-processed foods (pooled aHR 1.71, 95% CI: 1.36-2.14) were associated with an increased risk of CD. High fiber intake (pooled aHR 0.53, 95% CI: 0.41-0.70), Mediterranean diet (pooled aHR 0.59, 95% CI: 0.43-0.81), healthy diet (pooled aHR 0.70, 95% CI: 0.54-0.91), and unprocessed or minimally processed foods (pooled aHR 0.71, 95% CI: 0.53-0.94) were associated with a lower risk of CD. No consistent associations were found between individual foods or food patterns and the risk of UC. Interpretation: This study summarizes evidence on the link between specific dietary items or patterns and the risk of IBD. These data will help inform the design of prevention trials that include a dietary component as well as prevention strategies overall. Funding: This study received no funding.
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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 | 0.031 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".