A193 MICROBIOTA CHARACTERIZATION OF PATIENTS WITH INFLAMMATORY BOWEL DISEASE BASED ON SELF-REPORTED FOOD INTOLERANCES
Bibliographic record
Abstract
Abstract Background Approximately, 20% of the world’s population experiences adverse reactions to different food items. Patients with inflammatory bowel disease (IBD) including Crohn’s disease (CD) and ulcerative colitis (UC) commonly exhibit more food intolerances. The dietary triggers and mechanistic pathways involved in food intolerances are unknown. As IBD patients present an altered intestinal microbiota, we hypothesized that IBD-related adverse reactions are associated with defective microbial metabolism. Aims We aim to 1) understand dietary triggers and patterns of adverse reactions to foods in IBD and 2) characterize the intestinal microbiota of IBD patients based on food intolerance. Methods 127 participants (85 IBD and 42 healthy controls) were recruited from the Gastroenterology clinic and recruitment posters at McMaster University between August 2021 to September 2023. Inclusion criteria for IBD included a confirmed diagnosis of CD or UC and between 18 to 75 years old. All participants completed questionnaires related to food intolerances, symptoms, demographics, and provided a stool sample for 16S rRNA Illumina sequencing to determine microbial composition. Results 86% of IBD patients reported at least 1 food intolerance compared to 33% of controls. The mean number of food intolerances reported in patients with CD was 3.5 (SD=1.90), UC was 3.2 (SD=1.80), and controls was 1.3 (SD=0.83). Among IBD participants who reported a food intolerance, the common reactions were reported to dairy (72% CD and 73% UC), wheat (44% CD and 37% UC), and peanuts/tree nuts (28% CD and 30% UC). Regarding microbiota analysis, IBD patients had an altered fecal microbiota with lower alpha diversity (Shannon index) compared to healthy controls (p<0.0001). Alpha diversity was lower in IBD patients who reported 2 or more food intolerances compared to those who had none or 1. Also, specific adverse reactions such as dairy intolerance is associated with low alpha diversity in UC patients. IBD patients who reported a food intolerance had a higher abundance of Bacteroidota (p<0.05) compared to IBD with no food intolerance. Conclusions Overall, IBD patients reported a high number of intolerances to foods including dairy, wheat, peanuts/tree nuts, and caffeine. IBD patients have a lower alpha diversity measure compared to controls, which was more pronounced in patients experiencing several food intolerances. Further research to understand these offending foods in intestinal inflammation could help guide dietary interventions for IBD patients. 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".