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S867 Consequences of Obesity and IBD: Insights From a Large Outpatient Cohort

2023· article· en· W4387750781 on OpenAlexaboutno aff
Surya Khadilkar, Katie Hsia, Jennifer Youn, Tanya Zeina, Puja Rai, Akash Rastogi, Sureya Hussani, Pranay Adavelly, J.M. de Miguel Yanes, Jacob Kotlier, Sonia Friedman, Alexander N. Levy

Bibliographic record

VenueThe American Journal of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBody mass indexInflammatory bowel diseaseUnderweightObesityInternal medicineOverweightUlcerative colitisRetrospective cohort studyCrohn's diseaseCohortWeight lossColectomyDiseaseGastroenterology

Abstract

fetched live from OpenAlex

Introduction: Previous studies have shown that obesity in inflammatory bowel disease (IBD) is associated with an increased risk of morbidity. We aimed to examine the effect of obesity on disease presentation and complications. Methods: We conducted a retrospective chart review of all IBD patients treated at our tertiary care center between 1996 and 2022. Data collected included demographics, clinical characteristics and current medications. We defined underweight as body mass index (BMI) < 18.5, normal weight as 18.5-24.9, overweight 25.0-29.9 and obese as >30. Pearson’s Chi Squared with alpha 0.05 and cross tabulation were calculated using Excel and Minitab software. Results: Of 1493 patients, the average age was 45.6 years and 50.8% were female. There were 775 (52%) patients with Crohn’s disease (CD) and 691 (46.4%) with ulcerative colitis (UC). Mean disease duration was 15.1 years. In total, 314 (22.2%) of the patients were obese. There was no significant correlation between obesity and Montreal classification for CD or UC. Extra-intestinal manifestations (EIMs) were found in 429 patients (28%) and were more common in obese than non-obese IBD patients (P =0.024). Arthritic manifestations (7.3%) were the most common but were not more prevalent in obese patients. In all, 278 (26.1%) patients had a history of IBD abdominal surgery and 114 (7.6%) had a history of perianal surgery. Obesity did not increase the risk of having had previous IBD abdominal surgery ( P=0.64) or perianal surgery (P=0.88). Regarding medical therapy, 605 patients (62%) (CD: 423; UC:182) were currently prescribed biologic medications. Obese patients with CD ( P=0.069) or UC (P=0.11) were not significantly more likely to require biologic therapy. Medications for hyperlipidemia, hypertension and diabetes were more common in obese patients. Medications prescribed more frequently included aspirin ( P=0.008), statins (P=0.00), ACE/ARBs (P=0.002) and metformin (P=0.00) (see Table 1) Opioid prescriptions did not correlate with BMI type. Conclusion: Obese IBD patients were more likely to have EIMs compared to patients in other weight categories. Obese IBD patients used significantly more medications for diabetes, hypertension and hyperlipidemia and there was a trend towards increased biologic use. Obesity did not have an impact on disease phenotype or surgical history. Further research should examine the impact of weight loss on reducing IBD complications and polypharmacy. Table 1. - Medications Prescriptions for Pain, Hypertension, Hyperlipidemia, Diabetes, Biologics within IBD Population BMI Type Number of Patients with IBD Percent BMI Type of Total (%) Chronic Opioids Aspirin Statin ACE/ARB Metformin Biologics Underweight (< 18.5) 83 5.9 2 0 0 0 0 42 Normal (18.5-24.9) 547 41.5 3 10 18 15 5 271 Overweight (25-29.9) 431 30.5 2 20 27 22 7 167 Obese ( >30.0) 314 22.2 6 17 31 24 16 132 Total 1415 100 13 47 76 61 28 612 P value No association 0.008 0.00 0.002 0.00 0.34

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.006
GPT teacher head0.226
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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