A85 PERCEIVED FOOD INTOLERANCES IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE COMPARED TO HEALTHY CONTROLS
Bibliographic record
Abstract
Abstract Background Inflammatory bowel disease (IBD) patients commonly exhibit food intolerance. However, the precise dietary triggers and mechanistic pathways responsible for food intolerances are unknown. The characterization of the main offending foods in intestinal inflammation could guide targeted dietary interventions in IBD management. Aims To examine self-reported food intolerances in IBD participants compared to healthy controls. Methods IBD participants (18-75 years old with a confirmed ulcerative colitis (UC) or Crohn’s disease (CD) diagnosis) were recruited from the Gastroenterology clinic at McMaster University from August 2021 to September 2023. Healthy controls (18-75 years old with medical condition) were recruited through recruitment posters at McMaster University. Participants completed questionnaires pertaining to self-reported food intolerances, gastrointestinal rating symptoms, demographic information, and past medical conditions. Results A total of 80 IBD participants (48 CD and 32 UC) and 26 controls completed the questionnaires. We found that IBD participants (88% CD and 90% UC) reported at least one food intolerance compared to controls (30%). The mean number of food intolerances IBD participants reported was 3 (SD=1.78). The most common adverse reactions were reported to dairy (60% CD and 63% UC), wheat (37% CD and 40% UC), peanuts/tree nuts (27% CD and 25% UC), caffeine (27% CD and 25% UC) and fiber (22% CD and 28% UC). The rates of intolerances to all of the food groups were higher than reported by controls. The most common symptoms include discomfort (75% CD and 50% UC), bloating (66% CD and 62% UC), diarrhea (62% CD and 56% UC) and pain (56% CD and 37% UC) when consuming offending foods. About half of IBD participants had these symptoms before their diagnosis and 30% reported worsening of intolerances after their IBD diagnosis. Conclusions Our current findings reveal that IBD participants commonly report food intolerances that are associated with symptomatology. Common offending foods include dairy, wheat, peanuts/tree nuts and caffeine. IBD participants commonly experience discomfort, bloating and diarrhea when eating offending foods, which can persist after diagnosis. Ongoing studies will provide insights into the relationship between stool bacterial composition and food intolerance symptoms. Funding Agencies CCC
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".