A162 OBESITY IS A RISK FACTOR FOR THE DEVELOPMENT OF THE EXTRAINTESTINAL MANIFESTATIONS IN ULCERATIVE COLITIS, BUT NOT IN CROHN’S DISEASE
Bibliographic record
Abstract
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
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".