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Record W2999588267 · doi:10.1093/ecco-jcc/jjz203.016

OP17 Protein intakes and risk of inflammatory bowel disease in the European Prospective Investigation into Cancer and Nutrition cohort (EPIC-IBD)

2020· article· en· W2999588267 on OpenAlexaff
Catherine Dong, Yahya Mahamat‐Saleh, Antoine Racine, Prévost Jantchou, Sau-Wai Mandy Chan, Ailsa Hart, Franck Carbonnel, M.-C. Boutron-Ruault

Bibliographic record

VenueJournal of Crohn s and Colitis · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEuropean Prospective Investigation into Cancer and NutritionMedicineInflammatory bowel diseaseQuartileProspective cohort studyUlcerative colitisHazard ratioCohortInternal medicineProportional hazards modelCohort studyRelative riskColorectal cancerCancerLower riskDiseaseConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background Diet may contribute to inflammatory bowel disease (IBD) pathogenesis. In a previous French cohort, we found an association between high protein intake and increased risk of IBD. We aimed to investigate this relationship in the EPIC-IBD (European Prospective Investigation into Cancer and Nutrition – Inflammatory Bowel Diseases) cohort. Methods 413 593 participants from 8 European countries were included. Dietary data were collected at baseline from validated food frequency questionnaires. Mean daily intake of nutrients was assessed using the EPIC nutrient database. To reduce bias in the estimation of relative risks, calibrated dietary data were obtained from the country and sex-specific calibration models for all participants. 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, 595 incident cases of IBD were identified, including 177 Crohn’s disease (CD) and 418 ulcerative colitis (UC) cases. No association was observed between total protein intake and IBD risk (adjusted HR for the fourth vs. the first quartile = 1.25; CI 95% = 0.89–1.77, P-trend = 0.33). There was a significant association between the calibrated continuous variable of animal protein intake and IBD risk (adjusted HR per 10 g/day: 1.10; 95% CI = 1.004–1.21) although no association was found for extreme quartiles (HR: 0.99; 95% CI = 0.73–1.34; P-trend = 0.91). There was no association between vegetable protein intake and IBD risk. There was an association between meat consumption and IBD risk (adjusted HR for the fourth vs.. the first quartile = 1.37; CI95% = 1.02–1.82, P-trend = 0.003) and between red meat consumption and IBD risk (adjusted HR for the fourth vs. the first quartile = 1.41; CI95% = 1.03–1.92, P-trend = 0.006). In separate analyses for CD and UC, there was an association between total meat and UC risk, and between red meat and UC risk. No association was found between food sources of animal proteins and CD risk. Conclusion Animal protein intake is associated with IBD risk in the EPIC-IBD cohort. Observed associations between meat consumption and IBD or UC risk, and between red meat consumption and IBD or UC risk deserve further investigation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.005
GPT teacher head0.216
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2020
Admission routes1
Has abstractyes

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