A162 OBESITY IS A RISK FACTOR FOR THE DEVELOPMENT OF THE EXTRAINTESTINAL MANIFESTATIONS IN ULCERATIVE COLITIS, BUT NOT IN CROHN’S DISEASE
Notice bibliographique
Résumé
Abstract Background Several inflammatory markers have been associated with both obesity and the risk of adverse outcomes. Studies exploring obesity as a potential risk factor in extraintestinal manifestations (EIMs) development in patients with inflammatory bowel disease (IBD) are limited. Aims To describe the relationship between obesity and EIMs development, taking into consideration various confounding risk factors. Methods We performed a retrospective cohort study using data of 5,023 IBD patients diagnosed between 1954 and 2020. We collected data on demographics, clinical features, biochemistry, medications, smoking, weight status and EIMs (hepatobiliary, musculoskeletal, dermatological, urogenital, ophthalmological, and pulmonary). Obesity was defined as measured BMI≥30.00 kg/m2, prolonged steroid use – as using any corticosteroid formulations for at least 6 months. Rates were compared using Pearson’s chi-squared test with Bonferroni’s p-value adjustment. Univariate and multivariate logistic regression models were used to determine the association between obesity, potential contributing factors and EIMs. Results Data of 2,367 ulcerative colitis (UC) patients (47.8% females) and 2,656 Crohn’s disease (CD) patients (52.2% females), aged 18–97 (median 48.0, IQR 27.0) years, were analysed. Obesity was common among IBD patients (30.1%; 95% CI 28.7–31.6%) and the rate was higher than the Alberta’s population-based one (28.2%; 95% CI 28.17–28.23%); p=0.013. Obesity was less prevalent in the UC (28.5%; 95% CI 26.3–30.6%) vs CD cohort (31.4%; 95% CI 29.4–33.4%); p=0.049. In both cohorts, the EIMs prevalence tended to be slightly higher among IBD patients living with obesity compared to those without it (UC: 19.5% vs 16.1%, p=0.106; CD: 20.2% vs. 19.6%, p=0.767); the prevalence of specific EIMs subtypes and the proportion of IBD patients with over 2 or 3 EIMs also did not differ significantly. Among UC patients, obesity was proven to be a risk factor for EIMs development (OR 1.75, 95% CI 1.15–2.67; p=0.009), along with male sex (OR 1.90, 95% CI 1.25–2.89; p=0.02), and prolonged steroid use (OR 1.88, 95% CI 1.03–3.45; p=0.04). Among CD patients, logistic regression analysis showed that stricturing and penetrating disease behaviour (OR 1.69, 95% CI 1.04–2.75; p=0.033), iron deficiency (OR 1.55, 95% CI 1.16–2.07; p=0.003) and calcium deficiency (OR 2.43, 95% CI 1.36–4.36; p=0.003) were associated with EIMs development; obesity was not an independent or adjusted risk factor (Table). Conclusions In a large IBD cohort, obesity prevalence was found to be higher than in the general population. Interestingly, obesity was established as a risk factor for the EIMs development in UC, but not in CD. Our findings highlight the need for timely assessment and management of obesity in these disorders, which may help in preventing EIMs development. Funding Agencies AbbVie
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 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».