A236 ASSOCIATION OF STOOL METABOLOMIC PROFILE AND MICROBIOME COMPOSITION RISK SCORE WITH FUTURE ONSET OF CROHN’S DISEASE
Notice bibliographique
Résumé
Abstract Background Microbial composition-based risk score (MRS) was recently developed and validated to predict future risk of developing Crohn’s disease (CD) among healthy first-degree relatives (FDR) of CD patients. We hypothesized that stool metabolomic profiles, some of which are linked to the gut microbiome, are associated with future risk of CD. Aims To assess the association of stool metabolomic profile with onset of CD and to determine the correlation between stool metabolites and the MRS Methods Healthy FDR of CD patients were recruited as part of the nested case-control cohort of the CCC-GEM Project. Healthy FDRs who later developed CD (n=56) were matched approximately 1:1 by age, sex, follow-up duration, and geographical location with control FDRs remaining healthy (n=66). Stool metabolomics were assessed using the Metabolon’s DiscoveryHD4™ platform, and the stool microbiome characterised by 16s rDNA amplicon sequencing. We fitted a multivariable conditional logistic regression model on the disease status as a function of individual stool metabolites. We additionally performed Spearman correlation between each stool metabolite and the MRS. Results Among 1,029 stool metabolites that were analyzed, 79 were associated with future risk of CD (p<0.05); however, none remained significant after multiple testing correction (FDR correction). Considering the exploratory nature of this study with limited sample size, we focused on the top seven metabolites associated with CD onset (p<0.01). Of these, two stool metabolites (dimethylglycine, methylmyristate) were associated with increased risk of CD onset while five (cytosine, guanine, cytidine, hydroxyglutarate, nervonate) were associated with decreased risk of developing CD. The two metabolites positively associated with CD onset were positively correlated with the MRS, while the five metabolites negatively associated with CD onset, were negatively correlated with the MRS. Meanwhile, 24 stool metabolites had significant correlation with MRS (FDR-corrected p<0.2). Among those, a total of four stool metabolites (cytosine, guanine, methymyristate, cytidine) overlapped with the top seven stool metabolites associated with CD onset. Conclusions Stool metabolite profiles may predict future risk of CD. A subset of these metabolites have significant correlation with the MRS with consistent direction of effect. This may suggest that stool metabolites mediate the putative effect of the gut microbiome on CD risk. Further validation in the full GEM cohort is warranted. Funding Agencies CCC, CIHRThe Leona M. and Harry B. Helmsley Charitable Trust; Kenneth Croitoru is the recipient of the Canada Research Chair in Inflammatory Bowel Diseases; Sun-Ho Lee is a recipient of the Imagine/ CIHR/CAG Fellowship Award; Sun-Ho Lee, Juan Antonio Raygoza Garay, and Williams Turpin are recipients of fellowship awards from the Department of Medicine, Mount Sinai Hospital, Toronto, Canada.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».