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Record W3133786139 · doi:10.1093/jcag/gwab002.017

A18 DIETARY PREDICTORS OF BIOLOGICAL ACTIVITY IN CROHN’S DISEASE: A RETROSPECTIVE COHORT STUDY

2021· article· en· W3133786139 on OpenAlexaffabout
Elaine Chiu, Zhengxiao Zhang, Lloyd M. Taylor, Sandeep Kaur, Subrata Ghosh, Remo Panaccione, Raylene A. Reimer, M Raman

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsCalprotectinMedicineRetrospective cohort studyCrohn's diseaseInternal medicineFaecal calprotectinDiseaseCohortInflammatory bowel diseaseGastroenterology

Abstract

fetched live from OpenAlex

Abstract Background Patients with Crohn’s disease (CD) often seek advice on optimizing their diet to reduce gut inflammation. The relationship between dietary patterns, major food groups and individual nutrients, with disease activity in Crohn’s disease (CD) is incompletely understood and warrants further investigation. Aims 1.To determine whether a diversified (DD) or nondiversified (NDD) dietary pattern is related to biological activity in CD (BACD) in long-term follow up. 2.To determine if specific foods or nutrients are associated with increased BACD. Methods In this retrospective cohort study, forty-six CD patients (52% male) in remission completed 3-day food records between 2015–2017 for a 3-month intervention study and were classified as DD or NDD. Remission was defined by a Harvey Bradshaw Index <5 and no endoscopic ulcerations within 6 months of baseline data collection. Patients were classified as NDD if dietary fibre was ≤15 g/day or total fruit/vegetable servings ≤3/week, and if they consumed ≥3 servings/week of red and processed meat. Patients were otherwise defined as DD. A retrospective chart review captured BACD data. BACD was defined as one of either fecal calprotectin (FCP) ≥250 ug/g, hospitalization for CD flare, bowel resection for active CD, biologic dose escalation/switch due to non-response (not therapeutic drug monitoring), corticosteroid use, endoscopic evidence of apthous or large ulcers, or active disease on contrast enhanced ultrasound or magnetic resonance enterography. Machine learning methods with random forest prediction models assessed if diet composition was associated with BACD followed by univariate Mann-Whitney tests to compare differences between high and low disease activity. Results Sixteen patients (35%) had BACD during the mean 42 month follow up (31–54 months,SD ± 6.6). See Table 1 for additional demographics. Based on the random forest prediction model, both vitamins and minerals, food groups and Mediterranean diet cut-points could predict disease activity responses (ROC-AUC = 0.68 and 0.75, respectively). For these models, baseline intake of vitamins E, D, B1, and C and leafy greens, and fruit intake were the most important predictors of BACD. For the univariate analysis, the high disease group had lower intakes of fiber, vitamin E, and C (p = 0.047, 0.066, and 0.09, respectively). A higher proportion of patients consumed a NDD with BACD compared to those without BACD (50% vs. 23.3%, p=0.07). Conclusions To our knowledge, this is the first study to assess if dietary patterns, foods and nutrients are able to predict disease activity over a mean 42 month follow up. Further research into the dietary determinants of BACD in CD is warranted. With higher baseline FCP observed in the BACD, multivariate analyses to assess the independent effect of diet to predict BACD is required. Funding Agencies Litwin IBD Pioneers Foundation, Alberta’s Collaboration of Excellence for Nutrition in Digestive Diseases (Ascend)

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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