Dietary Patterns and Risk of Inflammatory Bowel Disease in Europe
Bibliographic record
Abstract
Specific nutrients or foods have been inconsistently associated with ulcerative colitis (UC) or Crohn's disease (CD) risks. Thus, we investigated associations between diet as a whole, as dietary patterns, and UC and CD risks. Within the prospective EPIC (European Prospective Investigation into Cancer) study, we set up a nested matched case–control study among 366,351 participants with inflammatory bowel disease data, including 256 incident cases of UC and 117 of CD, and 4 matched controls per case. Dietary intake was recorded at baseline from validated food frequency questionnaires. Incidence rate ratios of developing UC and CD were calculated for quintiles of the Mediterranean diet score and a posteriori dietary patterns produced by factor analysis. No dietary pattern was associated with either UC or CD risks. However, when excluding cases occurring within the first 2 years after dietary assessment, there was a positive association between a “high sugar and soft drinks” pattern and UC risk (incidence rate ratios for the fifth versus first quintile, 1.68 [1.00–2.82]; Ptrend = 0.02). When considering the foods most associated with the pattern, high consumers of sugar and soft drinks were at higher UC risk only if they had low vegetables intakes. A diet imbalance with high consumption of sugar and soft drinks and low consumption of vegetables was associated with UC risk. Further studies are needed to investigate whether microbiota alterations or other mechanisms mediate this association.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".