Real-world and computational identification of herbal candidates associated with adverse event patterns in glucagon-like peptide-1 therapy for obesity.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are widely prescribed for obesity management; however, adverse events (AEs) remain a clinical concern. This study presents a computational pharmacovigilance framework integrating real-world safety data with graph-based modeling to characterize AE patterns associated with GLP-1 RA therapy and to explore herb-AE associations in an exploratory manner. We conducted a cross-sectional analysis of AE reports from the food and drug administration adverse event reporting system (2015-2025). Clinical characteristics included outcomes, reporting frequency, demographics, time-to-onset, and subgroup distributions. Signal detection employed disproportionality metrics and Bayesian approaches. Herb-compound-target-AE networks were constructed using HERB 2.0 and the Comparative Toxicogenomics Database, incorporating drug-likeness and pharmacokinetic filtering. Graph convolutional networks were applied to model herb-AE associations, followed by literature-based contextual evaluation. Among 142,705 GLP-1 RA AE reports, 4,090 involved obesity indications. Gastrointestinal events predominated, with 76% of reports involving female patients and onset clustering within 0-30 days. Semaglutide demonstrated a distinct onset distribution, including a higher proportion of late-onset cases (≥ 360 days). Strong signals were detected for biliary, pancreatic, renal, and coagulation events, with semaglutide-associated pairs showing reporting odds ratios > 10, whereas tirzepatide exhibited negative log-transformed reporting odds ratios for several gastrointestinal events. Network analysis and graph convolutional network modeling prioritized established medicinal herbs including Liquorice Root, Mulberry Leaf, Dahurian Angelica Root, Danshen Root, and Ginkgo Leaf as top candidates following degree debiasing and exclusion of non-herbal database entries. The graph convolutional network achieved area under the receiver operating characteristic curve/area under the precision-recall curve values of 0.798/0.841 (validation) and 0.666/0.719 (test), indicating moderate predictive performance within a sparse pharmacovigilance context. These findings describe real-world AE reporting patterns associated with GLP-1 receptor agonists and present a hypothesis-generating computational framework for prioritizing herb-AE signal associations. The results are exploratory and should be interpreted in light of the inherent limitations of spontaneous reporting systems and computational modeling frameworks, and require independent experimental and clinical validation prior to any clinical application.