A238 BILE ACID COMPOSITION AND DIETARY FAT: IMPLICATIONS FOR CROHN’S DISEASE IN A COHORT OF HEALTHY FIRST-DEGREE RELATIVES
Bibliographic record
Abstract
Abstract Background Crohn’s disease (CD) is a chronic relapsing inflammatory disease of the gastrointestinal tract. The etiology of CD may arise from complex interactions including host genetics, diet, and the intestinal microbiome. Increased consumption of saturated fats, characteristic of the Western diet, is a known risk factor for CD. Dietary fat (DF) is absorbed by the host through the release of primary bile acids (PBAs) and bio-transformed by the microbiome into secondary bile acids (SBAs). Altogether, bile acids (BAs) can act as signaling molecules involved in host immune regulation and potentially in CD onset. Purpose To investigate the relationship between CD risk, BAs, and DF, and evaluate the predictive performance of CD onset of these factors by developing machine learning models. Method We used samples healthy first-degree relatives (FDRs) recruited as part of the Crohn’s Colitis Canada- Genes, Environment, Microbial (GEM) project. Those who developed CD (n=87) were matched 1:4 by age, sex, follow-up time, and geographic location with control FDRs remaining healthy (n=347). Serum, urine, and stool BA were measured using ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy. DF types were derived from food frequency questionnaire data. We used conditional logistic regressions to identify associations between CD onset, BAs (n=93), and DFs (n=9). We further explored the relationships of significant CD-related BAs and DF via Generalized Estimation Equations. Finally, we used a tree-based machine-learning algorithm (XGBoost) with 5-fold cross-validation to assess the prediction performance of CD onset using BA from all sources as well as DF. Two-sided p<0.05 was considered significant. Result(s) In total, 10 of 93 BAs, and two of nine DFs were significantly associated with increased odds of CD onset (p<0.05). Additionally, five BAs were significantly associated with DF (p<0.05). Serum-derived BAs had the best predictive performance for CD, with a mean AUC of 0.70 [95% CI: 0.63;0.76], followed by stool derived BAs with a mean AUC= 0.65 [0.55;0.75], and followed by urine derived Bas with a mean AUC= 0.57 [0.48;0.66]. Lastly DF was not a predictive marker of CD onset with a mean AUC= 0.50 [0.41;0.60]. Conclusion(s) This study suggests that BAs are associated with the pathogenesis of CD and the effects may be influenced by DF. Serum-derived BAs may be able to better predict the risk of CD than other stool or urine derived BA, while DF is not directly implicated in CD risk. Submitted on behalf of the CCC-GEM consortium. Funding Crohn’s and Colitis Canada Genetics Environment Microbial (CCC-GEM) III The Leona M. and Harry B. Helmsley Charitable Trust Kenneth Croitoru is the recipient of the Canada Research Chair in Inflammatory Bowel Diseases The International Organization for the Study of Inflammatory Bowel Diseases (IOIBD) Jingcheng Shao is the recipient of a Data Science Institute Summer Undergraduate Data Science award Disclosure of Interest None Declared
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".