Real-world and computational identification of herbal candidates associated with adverse event patterns in glucagon-like peptide-1 therapy for obesity.
This cross-sectional pharmacovigilance study analyzed 142,705 adverse event (AE) reports for GLP-1 receptor agonists (GLP-1 RAs) from the FDA Adverse Event Reporting System (2015–2025), focusing on 4,090 reports linked to obesity indications. The authors found gastrointestinal events were most common, 76% of reports involved female patients, and most events onset within 0–30 days. Semaglutide showed a distinct distribution including a higher proportion of late-onset events (≥360 days), while tirzepatide showed negative reporting odds ratios for several gastrointestinal events. Strong disproportionality signals were identified for biliary, pancreatic, renal, and coagulation events. Separately, the study constructed herb-compound-target-AE networks using HERB 2.0 and the Comparative Toxicogenomics Database, applying graph convolutional network (GCN) modeling to identify herbal candidates—including Liquorice Root, Mulberry Leaf, Dahurian Angelica Root, Danshen Root, and Ginkgo Leaf—potentially associated with GLP-1 RA AE profiles. GCN performance was moderate (AUC-ROC 0.666–0.798). The authors explicitly note findings are exploratory and hypothesis-generating, with results limited by spontaneous reporting biases and computational modeling constraints. Independent experimental and clinical validation is required before any clinical application.
Why this grade: The human data component is a large cross-sectional analysis of spontaneous AE reports, which is inherently subject to reporting bias, confounding, and lack of causality; the herbal candidate findings are purely computational and hypothesis-generating with no clinical validation.
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.
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