Meat Intake Is Associated with a Higher Risk of Ulcerative Colitis in a Large European Prospective Cohort Studyø
Bibliographic record
Abstract
BACKGROUND AND AIMS: We aimed to investigate the association between protein intake and risk of inflammatory bowel disease [IBD] in the European Prospective Investigation into Cancer and Nutrition. METHODS: A total of 413 593 participants from eight European countries were included. Dietary data were collected at baseline from validated food frequency questionnaires. Dietary data were calibrated to correct errors in measures related to each country-specific questionnaire. Associations between proteins [total, animal, and vegetable] or food sources of animal proteins, and IBD risk were estimated by Cox proportional hazard models. RESULTS: After a mean follow-up of 16 years, 177 patients with Crohn's disease [CD] and 418 with ulcerative colitis [UC], were identified. There was no association between total protein, animal protein, or vegetable protein intakes and CD or UC risks. Total meat and red meat intakes were associated with UC risk (hazard ratio [HR] for the 4th vs 1st quartile = 1.40, 95% confidence interval [CI] = 0.99-1.98, p-trend = 0.01; and 1.61, 95% CI = 1.10-2.36, p-trend = 0.007, respectively]. There was no association between other food sources of animal protein [processed meat, fish, shellfish, eggs, poultry] and UC. We found no association between food sources of animal proteins and CD risk. CONCLUSIONS: Meat and red meat consumptions are associated with higher risks of UC. These results support dietary counselling of low meat intake in people at high-risk of IBD.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".