A238 BILE ACID COMPOSITION AND DIETARY FAT: IMPLICATIONS FOR CROHN’S DISEASE IN A COHORT OF HEALTHY FIRST-DEGREE RELATIVES
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».