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Aim: To compare the incidence of adverse events (AEs) related to antiobesity medications (AOMs; glucagon‐like peptide‐1 receptor agonists [GLP‐1RAs] vs. non‐GLP‐1RAs) after bariatric surgery. Methods: This single‐centre retrospective cohort included patients (aged 16–65 years) who had undergone laparoscopic Roux‐en‐Y gastric bypass or sleeve gastrectomy (cohort entry date) and initiated AOMs. Participants were categorized as users of US Food and Drug Administration (FDA)‐approved, off‐label, or GLP‐1RA AOMs if documented as receiving the medication on or after cohort entry date. Non‐GLP‐1RA AOMs were phentermine, orlistat, topiramate, canagliflozin, dapagliflozin, empagliflozin, naltrexone, bupropion/naltrexone and phentermine/topiramate. GLP‐1RA AOMs included: semaglutide, dulaglutide, exenatide and liraglutide. The primary outcome was AE incidence. Logistic regression was used to determine the association of AOM exposure with AEs. Results: We identified 599 patients meeting our inclusion criteria, 83% of whom were female. Their median (interquartile range [IQR]) age was 47.8 (40.9–55.4) years. The median duration of surgery to AOM exposure was 30 months. GLP‐1RAs use was not associated with higher odds of AEs: adjusted odds ratio (aOR) 1.1 (95% confidence interval [CI] 0.5–2.6) and aOR 1.1 (95% CI 0.6–2.3) for GLP‐1RA versus FDA‐approved and off‐label AOM use, respectively. AOM initiation ≥12 months after surgery was associated with lower risk of AEs compared to <12 months (aOR 0.01 [95% CI 0.0–0.01]; p < 0.001). Conclusion: Our results showed that GLP‐1RA AOMs were not associated with an increased risk of AEs compared to non‐GLP‐1RA AOMs in patients who had previously undergone bariatric surgery. Prospective studies are needed to identify the optimal timeframe for GLP‐1RA initiation. [ABSTRACT FROM AUTHOR]

Introduction: Obesity, defined as a body mass index ≥30 kg/m 2 , is a major public health concern in the United States. Preventative approaches are essential, but they are limited by an inability to accurately predict individuals at highest risk of weight gain. Our objective was to develop accurate weight gain prediction models using the National Institutes of Health All of Us dataset. We hypothesized that machine learning models using both electronic health record and behavioral survey data would outperform models using electronic health record data alone., Methods: The All of Us dataset was used to identify adults between 18 and 70 ys old with weight measurements 2 y apart between 2008 and 2022. Patients with a history of cancer, bariatric surgery, or pregnancy were excluded. Demographics, vital signs, laboratory results, comorbidities, and survey data (Alcohol Use Disorder Identification Test, Patient-Reported Outcomes Measurement Information System physical and mental health scores) were included as model parameters. Elastic net and XGBoost machine learning models were developed with and without survey data to predict ≥10% total body weight gain within 2 y. The data were split into a training sample (60%) and a testing sample (40%), and parameters were tuned using 10-fold cross-validation. Performance was compared using area under the receiver operating characteristic curves (AUCs)., Results: Our cohort consisted of 34,715 patients (mean [SD] age 50.9 [13.4] y; 45.7% White; 55.3% female). Over a 2-y span, 10.4% of the cohort gained ≥10% total body weight. AUCs were 0.677 [95% DeLong confidence interval 0.665-0.688] for elastic net and 0.706 [0.695-0.717] for XGBoost. Incorporation of survey data did not improve predictability, with AUCs of 0.681 [0.669-0.692] and 0.705 [0.694-0.716], respectively., Conclusions: Our machine learning weight gain prediction models had modest performance that was not improved by survey data. The addition of other All of Us variables, including genomic data, may be informative in future studies., (Published by Elsevier Inc.)