Early Signal Detection in GLP-1 Receptor Agonists in Spain: A Comparative Bayesian Disproportionality Analysis in 2024 and 2025

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Their expanding use, including off-label indications, raises ongoing concerns regarding their evolving safety profiles. Objective: To identify and compare early positive safety signals associated with GLP-1 RAs in Spain during 2024 and 2025 using a Bayesian disproportionality approach adapted from the WHO-Uppsala Monitoring Centre. Methods: Spontaneous adverse drug reaction (ADR) reports submitted to the Spanish Pharmacovigilance System and involving GLP-1 RAs (ATC A10BJ) were analyzed. Reports up to June 2024 and June 2025 were included. A Bayesian Confidence Propagation Neural Network (BCPNN)-based model was used to estimate signal strength. Positive signals were defined as those with a false discovery rate (FDR) < 0.05 and relative risk (RR) ≥ 1. Signals were classified as new, reinforced, diminished, unchanged, or disappeared between the two years. Results: We analyzed 5,322 reports in 2024 and 6,746 in 2025. New signals identified in 2025 included intestinal obstruction (dulaglutide), acute pancreatitis (exenatide), and urticaria at the injection site (liraglutide). Several previously identified signals diminished or disappeared, suggesting dynamic changes in GLP-1 RA risk profiles. Conclusions: This comparative Bayesian pharmacovigilance analysis highlights the evolving safety landscape of GLP-1 RAs. Early signal detection can inform timely regulatory interventions and support safer clinical use. GLP-1 receptor agonists pharmacovigilance adverse drug reactions early signal detection semaglutide liraglutide dulaglutide 1. Background Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are a class of incretin-based therapies that mimic the action of endogenous GLP-1, stimulating insulin secretion and inhibiting glucagon release in a glucose-dependent manner. These agents have gained widespread acceptance for the management of type 2 diabetes mellitus (T2DM) due to their efficacy in improving glycemic control, promoting weight loss, and offering cardiovascular protection [ 1 , 2 ]. Several GLP-1 RAs—such as liraglutide, dulaglutide, exenatide, semaglutide, and lixisenatide—have shown superiority over other antidiabetic agents in clinical trials, especially in reducing HbA1c levels and achieving significant weight reduction [ 3 , 4 ]. Notably, large cardiovascular outcome trials (CVOTs) like LEADER, SUSTAIN-6, and REWIND demonstrated cardiovascular benefits beyond glycemic effects, leading to broader therapeutic indications in high-risk populations [ 5 – 7 ]. Consequently, their use has expanded rapidly, including off-label use in individuals with obesity without diabetes [ 8 ]. However, the growing use of GLP-1 RAs also raises safety concerns, particularly regarding gastrointestinal, pancreatic, thyroid, and renal adverse effects [ 9 , 10 ]. Rare but serious adverse events (AEs)—such as pancreatitis, gallbladder disease, and injection site reactions—have been reported in both clinical trials and post-marketing surveillance [ 11 – 13 ]. Moreover, recent real-world studies have highlighted the importance of early detection of adverse drug reactions (ADRs) that may not have been captured during the pre-approval phases [ 14 ]. Pharmacovigilance systems, including spontaneous reporting databases, remain essential tools for detecting potential drug safety signals. However, traditional disproportionality methods such as the proportional reporting ratio (PRR) or reporting odds ratio (ROR) may produce false positives due to multiplicity or sparse data [ 15 , 16 ]. Bayesian approaches—such as the Bayesian Confidence Propagation Neural Network (BCPNN) developed by the WHO-Uppsala Monitoring Centre—provide a more robust framework by accounting for uncertainty and prior probabilities [ 17 ]. The concept of "early signal detection" refers to the identification of statistically significant drug-event combinations before widespread recognition, potentially enabling earlier regulatory or clinical interventions [ 18 ]. The implementation of false discovery rate (FDR) control methods, such as the Benjamini-Hochberg procedure, further improves signal reliability in large datasets with multiple comparisons [ 19 ]. This study aims to perform a comparative Bayesian disproportionality analysis of suspected ADRs involving GLP-1 RAs in Spain during the first semesters of 2024 and 2025. By identifying new, reinforced, unchanged, diminished, or disappeared safety signals, this work contributes to the understanding of evolving drug safety profiles and supports timely pharmacovigilance efforts. 2. Methods 2.1. Data Source This study is based on spontaneous reports of suspected adverse drug reactions (ADRs) submitted to the Spanish Pharmacovigilance System for Human Use Medicines (FEDRA®), managed by the Agencia Española de Medicamentos y Productos Sanitarios (AEMPS). Data were extracted from public releases corresponding to reports received up to 30 June 2024 and 30 June 2025. All included reports referred to drugs within the ATC group A10BJ (GLP-1 receptor agonists), specifically dulaglutide, exenatide, liraglutide, lixisenatide, and semaglutide. Data extraction and preprocessing were performed using R®v3.4.1. R Foundation for Statistical Computing and PhViD® v1.0.8 package for the detection of positive signals [ 20 ]. Spontaneous reporting systems are widely used for signal detection and early risk identification, though they are subject to limitations such as underreporting and reporting bias [ 14 , 21 , 22 ]. Nevertheless, national databases like FEDRA® provide an essential source of real-world evidence for regulatory pharmacovigilance [ 23 ]. 2.2. ADR Coding and Drug Selection Adverse events were coded using the Medical Dictionary for Regulatory Activities (MedDRA), specifically at the Preferred Term (PT) level. MedDRA is internationally recognized and ensures consistency and comparability in safety signal analysis [ 24 ]. The study included GLP-1 receptor agonists currently or previously available in Spain. These comprised dulaglutide (Trulicity®), exenatide (Byetta®, Bydureon®), liraglutide (Victoza®, Saxenda®), lixisenatide (Lyxumia®), and semaglutide (Ozempic®, Rybelsus®, Wegovy®). Even though some of these drugs, such as exenatide and lixisenatide, were withdrawn from the Spanish market by mid-2024, they were retained in the analysis to allow year-to-year comparisons of signal persistence and disappearance. 2.3. Bayesian Disproportionality Analysis We implemented a Bayesian Confidence Propagation Neural Network (BCPNN) model adapted from the WHO-Uppsala Monitoring Centre (UMC) [ 17 , 18 , 25 ]. This method estimates the Information Component (IC), a logarithmic metric of disproportionality that accounts for statistical shrinkage and prior probability distributions. The BCPNN approach is particularly well suited for early signal detection. It manages sparse data more effectively than frequentist methods, provides probabilistic outputs such as credibility intervals, and is less sensitive to extreme values and fluctuations in the data [ 26 ]. The model computes a posterior distribution for each drug-event pair, with signal strength typically summarized by the IC025, the lower bound of the 95% credibility interval. A positive IC025 value indicates disproportionate reporting. 2.4. False Discovery Rate and Signal Thresholds To address the issue of multiple testing—a common challenge in pharmacovigilance studies that analyze thousands of drug-event pairs—we applied the Benjamini-Hochberg procedure to control the False Discovery Rate (FDR) [ 19 ]. Each p-value derived from the Bayesian model was adjusted accordingly. A signal was considered statistically significant when two conditions were met: an FDR value below 0.05 and a relative risk (RR) of at least 1. This dual threshold ensured that signals detected were not only statistically robust but also clinically meaningful [ 27 ]. 2.5. Signal Classification Signals identified in both years were classified into five categories according to how their FDR values evolved over time. A “new” signal referred to those that appeared only in 2025, whereas a “reinforced” signal was present in both years but showed increased strength or a lower FDR in 2025. A “diminished” signal persisted but with reduced statistical strength, while “unchanged” signals remained stable across both years. Finally, “disappeared” signals were those detected in 2024 but absent in 2025. This classification system facilitates interpretation of temporal trends and helps prioritize safety issues that may warrant regulatory attention [ 28 ]. 3. Results 3.1. Overview of ADR Reports A total of 5,322 adverse drug reactions (ADRs) associated with GLP-1 receptor agonists (GLP-1 RAs) were reported to the Spanish Pharmacovigilance System in the first half of 2024. This number increased to 6,746 reports in the same period of 2025. Several factors may explain this increase. One possibility is the rise in prescription rates of GLP-1 RAs together with their broader therapeutic indications. Another explanation could be greater awareness and engagement in pharmacovigilance activities among healthcare professionals. Finally, the increase might also reflect genuine changes in the risk profile of these drugs as their use expands. 3.1.1. Signal Classification by Year Safety signals were categorized according to their status in 2024 and 2025, based on Bayesian disproportionality analysis with false discovery rate (FDR) correction (see Appendix 1: Table A1 ; Table A2 ). Signals were classified as new when detected only in 2025, and as reinforced when they were present in both years but showed increased risk or stronger statistical evidence in 2025. Some signals persisted with reduced strength and were therefore classified as diminished, while others remained stable across the two years and were considered unchanged. Finally, certain signals that were present in 2024 but absent in 2025 were categorized as disappeared. The summary of newly identified or altered signals is presented in Table 1 . 3.1.2. Notable New Signals in 2025 Several new positive signals meeting the predefined Bayesian criteria (FDR < 0.05; RR ≥ 1) emerged in 2025. Among them, dulaglutide was associated with intestinal obstruction, exenatide with acute pancreatitis and skin mass at the injection site, liraglutide with urticaria at the injection site and device administration malfunction, and semaglutide with inadequate diabetes control. These new signals may be explained by a true increase in incidence, by the expansion of use to broader patient groups, or by improved reporting of adverse events within the pharmacovigilance system. 3.1.3. Disappeared Signals In contrast, several drug-event combinations that had been identified in 2024 were no longer detected or were significantly weaker in 2025. For example, dizziness associated with lixisenatide disappeared as a signal, while minor weight loss related to liraglutide was attenuated. The disappearance of these signals could be due to modifications in clinical use patterns, reductions in spontaneous reporting, or shifts in the characteristics of the patient population exposed to these drugs. 3.2. Tables and Signal Summary All relevant signals and the corresponding information from the Summary of Product Characteristics (SmPC) are compiled in Table 1 , titled: Safety signal evolution and fact sheet comments for GLP-1 receptor agonists between 2024 and 2025 . This table includes details on the specific GLP-1 RA involved, the preferred term (PT) of the reported adverse event, and whether the ADR is described in the corresponding SmPC. All signals listed in Table 1 were coded using MedDRA terminology and analyzed through the Bayesian approach described in the Methods section. Table 1 Safety Signal evolution and fact sheet comments for GLP-1 Receptor Agonists between 2024–2025. Drug Event Effect (PT) Fact sheet Comments Signal evolution Dulaglutide Blood glucose abnormal Hypoglycemia in combination with other medications New Dulaglutide Injection site haematoma Not reported New Exenatide Renal failure Withdrawn from market in 2024 New Liraglutide Incorrect dose administered by a medical device Not reported New Liraglutide Injection site bruise Not reported New Liraglutide Product quality issue Not reported New Liraglutide Skin reaction Not reported; skin and subcutaneous tissue disorders reported New Semaglutide Extra dose administered Not reported New Semaglutide Diarrhoea Reported as very common New Semaglutide Off-label use Not reported New Semaglutide Vomiting Reported as common New Dulaglutide Decreased appetite Reported as common Reinforce Dulaglutide Hypoaesthesia Not reported Reinforce Dulaglutide Accidental overdose Not reported Reinforce Exenatide Retching Withdrawn from market in 2024 Reinforce Exenatide Nodule Withdrawn from market in 2024 Reinforce Liraglutide Injection site rash Not reported Reinforce Liraglutide Drug ineffective Not reported Reinforce Liraglutide Injection site swelling Not reported Reinforce Liraglutide Injection site hypersensitivity Not reported Reinforce Liraglutide Injection site pruritus Not reported Reinforce Liraglutide Injection site reaction Reported as common Reinforce Lixisenatide Hypoglycaemia Withdrawn from market in 2024 Reinforce Lixisenatide Urticaria Withdrawn from market in 2024 Reinforce Semaglutide Incorrect technique in product use procedure Not reported Reinforce Semaglutide Use of product for unapproved indication Not reported Reinforce Exenatide Asthenia Withdrawn from market in 2024 Diminished Semaglutide Dyspepsia Reported as common Diminished Semaglutide Drug intolerance Not reported Diminished Semaglutide Nausea Reported as very common Diminished Semaglutide Weight decreased Reported as common Diminished Semaglutide Gastrointestinal disorder Reported without specification Diminished Dulaglutide Incorrect dose administered Not reported Unchanged Dulaglutide Injection site pain Not reported Unchanged Dulaglutide Blood glucose increased Not reported Unchanged Dulaglutide Injection site haemorrhage Not reported Unchanged Dulaglutide Intestinal obstruction Reported, frequency unknown Unchanged Dulaglutide Dose omission issue with the product Not reported Unchanged Exenatide Erythema Withdrawn from market in 2024 Unchanged Exenatide Injection site induration Withdrawn from market in 2024 Unchanged Exenatide Skin mass Withdrawn from market in 2024 Unchanged Exenatide Injection site nodule Withdrawn from market in 2024 Unchanged Exenatide Pancreatitis Withdrawn from market in 2024 Unchanged Exenatide Acute pancreatitis Withdrawn from market in 2024 Unchanged Liraglutide Injection site erythema Not reported Unchanged Liraglutide Minor weight loss Not reported Unchanged Liraglutide Problem with drug delivery device system Not reported Unchanged Liraglutide Injection site urticaria Reported as uncommon Unchanged Dulaglutide Limb pain Not reported Disappeared Exenatide Renal failure Withdrawn from market in 2024 Disappeared Liraglutide Injection site bruising Not reported Disappeared Lixisenatide Dizziness Withdrawn from market in 2024 Disappeared Semaglutide Inadequate diabetes mellitus control Not reported Disappeared Semaglutide Overdose Not reported Disappeared Semaglutide Use of a medicine off-label Not reported Disappeared 4. Discussion This comparative pharmacovigilance study reveals dynamic changes in the safety profile of GLP-1 receptor agonists (GLP-1 RAs) in Spain between 2024 and 2025. The detection of new positive signals—particularly for gastrointestinal and pancreatic adverse events—underscores the importance of continuous post-marketing surveillance in this therapeutic class. The identification of intestinal obstruction with dulaglutide and acute pancreatitis with exenatide aligns with previous concerns raised in both preclinical and post-marketing reports [ 9 , 10 , 29 ]. GLP-1 RAs slow gastric emptying, which may theoretically contribute to mechanical or functional obstruction in predisposed individuals [ 30 ]. Although these effects are well known, their clinical significance is still being debated, especially as real-world evidence accumulates. The signal for inadequate diabetes control with semaglutide may reflect inappropriate off-label use or administration errors. This finding is clinically relevant given the increasing popularity of GLP-1 RAs for weight management, sometimes self-administered without medical supervision [ 8 , 31 ]. In this context, improper dosing or skipping injections could lead to subtherapeutic effects or glycemic instability. Furthermore, several injection-site reactions (e.g., urticaria, bruising, or device malfunction) were newly identified or reinforced in 2025. Although often considered mild, these events can affect treatment adherence, particularly in patients self-injecting long-acting agents [ 32 ]. On the other hand, the disappearance or attenuation of some previously detected signals—such as dizziness with lixisenatide—may indicate a reduced use of certain molecules following market withdrawal (as in the case of lixisenatide and exenatide in Spain) or improved risk minimization measures [ 33 ]. Our study demonstrates the added value of Bayesian methods, particularly when combined with false discovery rate (FDR) adjustment, in improving signal reliability over traditional disproportionality metrics [ 17 , 19 ]. The use of the Bayesian Confidence Propagation Neural Network (BCPNN) provides a probabilistic framework that is robust to data sparsity and supports regulatory prioritization of signals [ 18 , 25 ]. It is worth noting that some signals correspond to events not described in the official Summary of Product Characteristics (SmPC) at the time of analysis. This suggests the utility of pharmacovigilance data in identifying emerging or evolving ADRs that may not have been observed during clinical development [ 13 , 34 ]. 4.1. Strengths and Limitations The main strengths of this study are several. First, it employed a standardized Bayesian algorithm based on the WHO-UMC methodology, which provides robustness and comparability with international pharmacovigilance practices. Second, it compared data across two consecutive years using real-world information from a national pharmacovigilance database (see Appendix 1: Table A1 ; Table A2 ), allowing the identification of temporal changes in drug safety signals. Third, the analysis incorporated adjustment for multiple testing through false discovery rate (FDR) correction, a procedure that reduces the likelihood of spurious associations and strengthens the reliability of the results. Despite these strengths, some limitations must be acknowledged. Spontaneous reporting systems are inherently subject to underreporting, missing data, and reporting bias, which can compromise the completeness and accuracy of the findings [ 14 , 21 ]. Moreover, causality cannot be established from this type of analysis, as signal detection is hypothesis-generating rather than confirmatory. In addition, changes in the number of users per drug were not available, which limited the possibility of calculating true incidence rates for the adverse reactions identified. To overcome these limitations, future studies should employ analytical epidemiological designs, such as cohort or case–control studies using prescription databases. Such approaches would allow validation of the preliminary signals detected in this study and provide stronger evidence for regulatory and clinical decision-making [ 35 ]. 5. Conclusions This study provides updated evidence on the evolving safety profile of GLP-1 receptor agonists (GLP-1 RAs) in Spain, applying a Bayesian disproportionality analysis with FDR control to detect early signals of adverse drug reactions (ADRs) in 2024 and 2025. The results highlight newly emerging risks—including intestinal obstruction, acute pancreatitis, and injection-site reactions—as well as the disappearance or attenuation of other signals over time. The dynamic nature of these signals underscores the importance of continuous post-marketing surveillance, especially as the clinical use of GLP-1 RAs expands beyond their original indications, often to populations not represented in pivotal clinical trials. The appearance of signals related to off-label use and administration errors, such as inadequate diabetes control, suggests a need for greater awareness and patient education regarding proper drug use. Bayesian pharmacovigilance approaches, particularly when combined with false discovery rate correction, offer a robust framework for early signal detection in real-world data. These methods enhance the reliability of signal prioritization, helping to inform regulatory decisions and guide further epidemiological research. Future studies should validate these findings using analytical designs such as cohort or nested case-control studies with prescription data. Integrating signal detection into a broader risk management strategy will be key to optimizing the safety and effectiveness of GLP-1 RAs in an increasingly diverse patient population. Abbreviations ADR Adverse drug reaction SmPC Summary of Product Characteristics Declarations Ethics approval and consent to participate: Not applicable Availability of data and materials: The dataset generated and analyzed during the current study is included in the Appendix of this article. Competing interests: The author declares no conflicts of interest. Funding: This research received no external funding. Author Contribution MJ was responsible for all aspects of the study, including conceptualization, data collection, analysis, interpretation, and manuscript preparation. References Nauck MA, Meier JJ. Management of endocrine disease: Are all GLP-1 agonists equal in the treatment of type 2 diabetes? Eur J Endocrinol. 2019;181(6):R211–34. https://doi.org/10.1530/EJE-19-0564 . Drucker DJ. 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Off-label and illicit use of GLP-1 receptor agonists for weight loss: a new safety concern. Ann Intern Med. 2023;176(2):261–3. https://doi.org/10.7326/M22-2914 . Sagner M, Meier JJ. Patient-reported barriers to injectable GLP-1 RA use in routine clinical care. Diabetes Ther. 2020;11(2):395–403. https://doi.org/10.1007/s13300-019-00745-x . EMA. Withdrawal of the marketing authorization for Lyxumia (lixisenatide) and Byetta (exenatide) in the EU. European Medicines Agency; 2024. Begaud B, Martin K, Fourrier-Reglat A, et al. Benevolat, usefulness of spontaneous reporting systems for pharmacovigilance: experience and limitations. Therapie. 2002;57(6):569–76. https://doi.org/10.2515/therapie:2002050 . Hall GC, Sauer B, Bourke A, Brown JS, Reynolds MW, Casale RL. Guidelines for good database selection and use in pharmacoepidemiology research. Pharmacoepidemiol Drug Saf. 2012;21(1):1–10. https://doi.org/10.1002/pds.2229 . Additional Declarations No competing interests reported. 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Background","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eGlucagon-like peptide-1 receptor agonists (GLP-1 RAs) are a class of incretin-based therapies that mimic the action of endogenous GLP-1, stimulating insulin secretion and inhibiting glucagon release in a glucose-dependent manner. These agents have gained widespread acceptance for the management of type 2 diabetes mellitus (T2DM) due to their efficacy in improving glycemic control, promoting weight loss, and offering cardiovascular protection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral GLP-1 RAs\u0026mdash;such as liraglutide, dulaglutide, exenatide, semaglutide, and lixisenatide\u0026mdash;have shown superiority over other antidiabetic agents in clinical trials, especially in reducing HbA1c levels and achieving significant weight reduction [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, large cardiovascular outcome trials (CVOTs) like LEADER, SUSTAIN-6, and REWIND demonstrated cardiovascular benefits beyond glycemic effects, leading to broader therapeutic indications in high-risk populations [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, their use has expanded rapidly, including off-label use in individuals with obesity without diabetes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, the growing use of GLP-1 RAs also raises safety concerns, particularly regarding gastrointestinal, pancreatic, thyroid, and renal adverse effects [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Rare but serious adverse events (AEs)\u0026mdash;such as pancreatitis, gallbladder disease, and injection site reactions\u0026mdash;have been reported in both clinical trials and post-marketing surveillance [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, recent real-world studies have highlighted the importance of early detection of adverse drug reactions (ADRs) that may not have been captured during the pre-approval phases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePharmacovigilance systems, including spontaneous reporting databases, remain essential tools for detecting potential drug safety signals. However, traditional disproportionality methods such as the proportional reporting ratio (PRR) or reporting odds ratio (ROR) may produce false positives due to multiplicity or sparse data [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Bayesian approaches\u0026mdash;such as the Bayesian Confidence Propagation Neural Network (BCPNN) developed by the WHO-Uppsala Monitoring Centre\u0026mdash;provide a more robust framework by accounting for uncertainty and prior probabilities [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe concept of \"early signal detection\" refers to the identification of statistically significant drug-event combinations before widespread recognition, potentially enabling earlier regulatory or clinical interventions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The implementation of false discovery rate (FDR) control methods, such as the Benjamini-Hochberg procedure, further improves signal reliability in large datasets with multiple comparisons [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aims to perform a comparative Bayesian disproportionality analysis of suspected ADRs involving GLP-1 RAs in Spain during the first semesters of 2024 and 2025. By identifying new, reinforced, unchanged, diminished, or disappeared safety signals, this work contributes to the understanding of evolving drug safety profiles and supports timely pharmacovigilance efforts.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data Source\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study is based on spontaneous reports of suspected adverse drug reactions (ADRs) submitted to the Spanish Pharmacovigilance System for Human Use Medicines (FEDRA\u0026reg;), managed by the Agencia Espa\u0026ntilde;ola de Medicamentos y Productos Sanitarios (AEMPS). Data were extracted from public releases corresponding to reports received up to 30 June 2024 and 30 June 2025.\u003c/p\u003e\u003cp\u003eAll included reports referred to drugs within the ATC group A10BJ (GLP-1 receptor agonists), specifically dulaglutide, exenatide, liraglutide, lixisenatide, and semaglutide. Data extraction and preprocessing were performed using R\u0026reg;v3.4.1. R Foundation for Statistical Computing and PhViD\u0026reg; v1.0.8 package for the detection of positive signals [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSpontaneous reporting systems are widely used for signal detection and early risk identification, though they are subject to limitations such as underreporting and reporting bias [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nevertheless, national databases like FEDRA\u0026reg; provide an essential source of real-world evidence for regulatory pharmacovigilance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. ADR Coding and Drug Selection\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAdverse events were coded using the Medical Dictionary for Regulatory Activities (MedDRA), specifically at the Preferred Term (PT) level. MedDRA is internationally recognized and ensures consistency and comparability in safety signal analysis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study included GLP-1 receptor agonists currently or previously available in Spain. These comprised dulaglutide (Trulicity\u0026reg;), exenatide (Byetta\u0026reg;, Bydureon\u0026reg;), liraglutide (Victoza\u0026reg;, Saxenda\u0026reg;), lixisenatide (Lyxumia\u0026reg;), and semaglutide (Ozempic\u0026reg;, Rybelsus\u0026reg;, Wegovy\u0026reg;). Even though some of these drugs, such as exenatide and lixisenatide, were withdrawn from the Spanish market by mid-2024, they were retained in the analysis to allow year-to-year comparisons of signal persistence and disappearance.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Bayesian Disproportionality Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWe implemented a Bayesian Confidence Propagation Neural Network (BCPNN) model adapted from the WHO-Uppsala Monitoring Centre (UMC) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This method estimates the Information Component (IC), a logarithmic metric of disproportionality that accounts for statistical shrinkage and prior probability distributions.\u003c/p\u003e\u003cp\u003eThe BCPNN approach is particularly well suited for early signal detection. It manages sparse data more effectively than frequentist methods, provides probabilistic outputs such as credibility intervals, and is less sensitive to extreme values and fluctuations in the data [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The model computes a posterior distribution for each drug-event pair, with signal strength typically summarized by the IC025, the lower bound of the 95% credibility interval. A positive IC025 value indicates disproportionate reporting.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. False Discovery Rate and Signal Thresholds\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo address the issue of multiple testing\u0026mdash;a common challenge in pharmacovigilance studies that analyze thousands of drug-event pairs\u0026mdash;we applied the Benjamini-Hochberg procedure to control the False Discovery Rate (FDR) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Each p-value derived from the Bayesian model was adjusted accordingly. A signal was considered statistically significant when two conditions were met: an FDR value below 0.05 and a relative risk (RR) of at least 1. This dual threshold ensured that signals detected were not only statistically robust but also clinically meaningful [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Signal Classification\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSignals identified in both years were classified into five categories according to how their FDR values evolved over time. A \u0026ldquo;new\u0026rdquo; signal referred to those that appeared only in 2025, whereas a \u0026ldquo;reinforced\u0026rdquo; signal was present in both years but showed increased strength or a lower FDR in 2025. A \u0026ldquo;diminished\u0026rdquo; signal persisted but with reduced statistical strength, while \u0026ldquo;unchanged\u0026rdquo; signals remained stable across both years. Finally, \u0026ldquo;disappeared\u0026rdquo; signals were those detected in 2024 but absent in 2025.\u003c/p\u003e\u003cp\u003eThis classification system facilitates interpretation of temporal trends and helps prioritize safety issues that may warrant regulatory attention [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Overview of ADR Reports\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA total of 5,322 adverse drug reactions (ADRs) associated with GLP-1 receptor agonists (GLP-1 RAs) were reported to the Spanish Pharmacovigilance System in the first half of 2024. This number increased to 6,746 reports in the same period of 2025.\u003c/p\u003e\u003cp\u003eSeveral factors may explain this increase. One possibility is the rise in prescription rates of GLP-1 RAs together with their broader therapeutic indications. Another explanation could be greater awareness and engagement in pharmacovigilance activities among healthcare professionals. Finally, the increase might also reflect genuine changes in the risk profile of these drugs as their use expands.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. Signal Classification by Year\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSafety signals were categorized according to their status in 2024 and 2025, based on Bayesian disproportionality analysis with false discovery rate (FDR) correction (see Appendix 1: Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003eA1\u003c/span\u003e; Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003eA2\u003c/span\u003e). Signals were classified as new when detected only in 2025, and as reinforced when they were present in both years but showed increased risk or stronger statistical evidence in 2025. Some signals persisted with reduced strength and were therefore classified as diminished, while others remained stable across the two years and were considered unchanged. Finally, certain signals that were present in 2024 but absent in 2025 were categorized as disappeared.\u003c/p\u003e\u003cp\u003eThe summary of newly identified or altered signals is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. Notable New Signals in 2025\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSeveral new positive signals meeting the predefined Bayesian criteria (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; RR\u0026thinsp;\u0026ge;\u0026thinsp;1) emerged in 2025. Among them, dulaglutide was associated with intestinal obstruction, exenatide with acute pancreatitis and skin mass at the injection site, liraglutide with urticaria at the injection site and device administration malfunction, and semaglutide with inadequate diabetes control.\u003c/p\u003e\u003cp\u003eThese new signals may be explained by a true increase in incidence, by the expansion of use to broader patient groups, or by improved reporting of adverse events within the pharmacovigilance system.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3. Disappeared Signals\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn contrast, several drug-event combinations that had been identified in 2024 were no longer detected or were significantly weaker in 2025. For example, dizziness associated with lixisenatide disappeared as a signal, while minor weight loss related to liraglutide was attenuated.\u003c/p\u003e\u003cp\u003eThe disappearance of these signals could be due to modifications in clinical use patterns, reductions in spontaneous reporting, or shifts in the characteristics of the patient population exposed to these drugs.\u003c/p\u003e\u003cp\u003e\u003cem\u003e3.2. Tables and Signal Summary\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAll relevant signals and the corresponding information from the Summary of Product Characteristics (SmPC) are compiled in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, titled: \u003cem\u003eSafety signal evolution and fact sheet comments for GLP-1 receptor agonists between 2024 and 2025\u003c/em\u003e. This table includes details on the specific GLP-1 RA involved, the preferred term (PT) of the reported adverse event, and whether the ADR is described in the corresponding SmPC.\u003c/p\u003e\u003cp\u003eAll signals listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were coded using MedDRA terminology and analyzed through the Bayesian approach described in the Methods section.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSafety Signal evolution and fact sheet comments for GLP-1 Receptor Agonists between 2024\u0026ndash;2025.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvent Effect (PT)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFact sheet Comments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignal evolution\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlood glucose abnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHypoglycemia in combination with other medications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site haematoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRenal failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncorrect dose administered by a medical device\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site bruise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProduct quality issue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSkin reaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported; skin and subcutaneous tissue disorders reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExtra dose administered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiarrhoea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported as very common\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOff-label use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVomiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported as common\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecreased appetite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported as common\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypoaesthesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccidental overdose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNodule\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site rash\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrug ineffective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site swelling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site hypersensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site pruritus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site reaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported as common\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLixisenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypoglycaemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLixisenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrticaria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncorrect technique in product use procedure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUse of product for unapproved indication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReinforce\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsthenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiminished\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDyspepsia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported as common\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiminished\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrug intolerance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiminished\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNausea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported as very common\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiminished\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeight decreased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported as common\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiminished\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGastrointestinal disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported without specification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiminished\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncorrect dose administered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlood glucose increased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site haemorrhage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntestinal obstruction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported, frequency unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDose omission issue with the product\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eErythema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site induration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSkin mass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site nodule\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePancreatitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcute pancreatitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site erythema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMinor weight loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblem with drug delivery device system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site urticaria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReported as uncommon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnchanged\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLimb pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisappeared\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRenal failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisappeared\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjection site bruising\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisappeared\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLixisenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDizziness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithdrawn from market in 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisappeared\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInadequate diabetes mellitus control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisappeared\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverdose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisappeared\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUse of a medicine off-label\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisappeared\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis comparative pharmacovigilance study reveals dynamic changes in the safety profile of GLP-1 receptor agonists (GLP-1 RAs) in Spain between 2024 and 2025. The detection of new positive signals\u0026mdash;particularly for gastrointestinal and pancreatic adverse events\u0026mdash;underscores the importance of continuous post-marketing surveillance in this therapeutic class.\u003c/p\u003e\u003cp\u003eThe identification of \u003cb\u003eintestinal obstruction\u003c/b\u003e with dulaglutide and \u003cb\u003eacute pancreatitis\u003c/b\u003e with exenatide aligns with previous concerns raised in both preclinical and post-marketing reports [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. GLP-1 RAs slow gastric emptying, which may theoretically contribute to mechanical or functional obstruction in predisposed individuals [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although these effects are well known, their clinical significance is still being debated, especially as real-world evidence accumulates.\u003c/p\u003e\u003cp\u003eThe signal for \u003cb\u003einadequate diabetes control\u003c/b\u003e with semaglutide may reflect inappropriate off-label use or administration errors. This finding is clinically relevant given the increasing popularity of GLP-1 RAs for weight management, sometimes self-administered without medical supervision [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this context, improper dosing or skipping injections could lead to subtherapeutic effects or glycemic instability.\u003c/p\u003e\u003cp\u003eFurthermore, several \u003cb\u003einjection-site reactions\u003c/b\u003e (e.g., urticaria, bruising, or device malfunction) were newly identified or reinforced in 2025. Although often considered mild, these events can affect treatment adherence, particularly in patients self-injecting long-acting agents [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOn the other hand, the \u003cb\u003edisappearance or attenuation\u003c/b\u003e of some previously detected signals\u0026mdash;such as dizziness with lixisenatide\u0026mdash;may indicate a reduced use of certain molecules following market withdrawal (as in the case of lixisenatide and exenatide in Spain) or improved risk minimization measures [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study demonstrates the added value of Bayesian methods, particularly when combined with false discovery rate (FDR) adjustment, in improving signal reliability over traditional disproportionality metrics [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The use of the Bayesian Confidence Propagation Neural Network (BCPNN) provides a probabilistic framework that is robust to data sparsity and supports regulatory prioritization of signals [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIt is worth noting that some signals correspond to \u003cb\u003eevents not described in the official Summary of Product Characteristics (SmPC)\u003c/b\u003e at the time of analysis. This suggests the utility of pharmacovigilance data in identifying emerging or evolving ADRs that may not have been observed during clinical development [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Strengths and Limitations\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe main strengths of this study are several. First, it employed a standardized Bayesian algorithm based on the WHO-UMC methodology, which provides robustness and comparability with international pharmacovigilance practices. Second, it compared data across two consecutive years using real-world information from a national pharmacovigilance database (see Appendix 1: Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003eA1\u003c/span\u003e; Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003eA2\u003c/span\u003e), allowing the identification of temporal changes in drug safety signals. Third, the analysis incorporated adjustment for multiple testing through false discovery rate (FDR) correction, a procedure that reduces the likelihood of spurious associations and strengthens the reliability of the results.\u003c/p\u003e\u003cp\u003eDespite these strengths, some limitations must be acknowledged. Spontaneous reporting systems are inherently subject to underreporting, missing data, and reporting bias, which can compromise the completeness and accuracy of the findings [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, causality cannot be established from this type of analysis, as signal detection is hypothesis-generating rather than confirmatory. In addition, changes in the number of users per drug were not available, which limited the possibility of calculating true incidence rates for the adverse reactions identified.\u003c/p\u003e\u003cp\u003eTo overcome these limitations, future studies should employ analytical epidemiological designs, such as cohort or case\u0026ndash;control studies using prescription databases. Such approaches would allow validation of the preliminary signals detected in this study and provide stronger evidence for regulatory and clinical decision-making [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study provides updated evidence on the evolving safety profile of GLP-1 receptor agonists (GLP-1 RAs) in Spain, applying a Bayesian disproportionality analysis with FDR control to detect early signals of adverse drug reactions (ADRs) in 2024 and 2025. The results highlight newly emerging risks\u0026mdash;including intestinal obstruction, acute pancreatitis, and injection-site reactions\u0026mdash;as well as the disappearance or attenuation of other signals over time.\u003c/p\u003e\u003cp\u003eThe dynamic nature of these signals underscores the importance of continuous post-marketing surveillance, especially as the clinical use of GLP-1 RAs expands beyond their original indications, often to populations not represented in pivotal clinical trials. The appearance of signals related to off-label use and administration errors, such as inadequate diabetes control, suggests a need for greater awareness and patient education regarding proper drug use.\u003c/p\u003e\u003cp\u003eBayesian pharmacovigilance approaches, particularly when combined with false discovery rate correction, offer a robust framework for early signal detection in real-world data. These methods enhance the reliability of signal prioritization, helping to inform regulatory decisions and guide further epidemiological research.\u003c/p\u003e\u003cp\u003eFuture studies should validate these findings using analytical designs such as cohort or nested case-control studies with prescription data. Integrating signal detection into a broader risk management strategy will be key to optimizing the safety and effectiveness of GLP-1 RAs in an increasingly diverse patient population.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"518\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eADR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eAdverse drug reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSmPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eSummary of Product Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe dataset generated and analyzed during the current study is included in the Appendix of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe author declares no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMJ was responsible for all aspects of the study, including conceptualization, data collection, analysis, interpretation, and manuscript preparation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNauck MA, Meier JJ. 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Therapie. 2002;57(6):569\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2515/therapie:2002050\u003c/span\u003e\u003cspan address=\"10.2515/therapie:2002050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHall GC, Sauer B, Bourke A, Brown JS, Reynolds MW, Casale RL. Guidelines for good database selection and use in pharmacoepidemiology research. Pharmacoepidemiol Drug Saf. 2012;21(1):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pds.2229\u003c/span\u003e\u003cspan address=\"10.1002/pds.2229\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GLP-1 receptor agonists, pharmacovigilance, adverse drug reactions, early signal detection, semaglutide, liraglutide, dulaglutide","lastPublishedDoi":"10.21203/rs.3.rs-7574712/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7574712/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eGlucagon-like peptide-1 receptor agonists (GLP-1 RAs) are increasingly prescribed for type 2 diabetes mellitus and obesity. Their expanding use, including off-label indications, raises ongoing concerns regarding their evolving safety profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To identify and compare early positive safety signals associated with GLP-1 RAs in Spain during 2024 and 2025 using a Bayesian disproportionality approach adapted from the WHO-Uppsala Monitoring Centre.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Spontaneous adverse drug reaction (ADR) reports submitted to the Spanish Pharmacovigilance System and involving GLP-1 RAs (ATC A10BJ) were analyzed. Reports up to June 2024 and June 2025 were included. A Bayesian Confidence Propagation Neural Network (BCPNN)-based model was used to estimate signal strength. Positive signals were defined as those with a false discovery rate (FDR) \u0026lt; 0.05 and relative risk (RR) ≥ 1. Signals were classified as new, reinforced, diminished, unchanged, or disappeared between the two years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We analyzed 5,322 reports in 2024 and 6,746 in 2025. New signals identified in 2025 included intestinal obstruction (dulaglutide), acute pancreatitis (exenatide), and urticaria at the injection site (liraglutide). Several previously identified signals diminished or disappeared, suggesting dynamic changes in GLP-1 RA risk profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eThis comparative Bayesian pharmacovigilance analysis highlights the evolving safety landscape of GLP-1 RAs. Early signal detection can inform timely regulatory interventions and support safer clinical use.\u003c/p\u003e","manuscriptTitle":"Early Signal Detection in GLP-1 Receptor Agonists in Spain: A Comparative Bayesian Disproportionality Analysis in 2024 and 2025","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 19:03:26","doi":"10.21203/rs.3.rs-7574712/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5f4d0ec-7f51-47ab-85ae-4f45f5ff346b","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-11T10:08:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 19:03:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7574712","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7574712","identity":"rs-7574712","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}