Dongsheng Zheng, Jinsuai Ren, Duo Lv, Qingwei Zhao, Dongsheng Hong
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引用次数: 0
Abstract
Introduction: Statins are widely prescribed for cardiovascular disease prevention, but their potential to increase diabetes risk has prompted regulatory warnings. Different statin drugs have varying physicochemical properties, yet comprehensive comparative assessments of their individual diabetes-related safety profiles remain limited in post-marketing surveillance data. Therefore, this study aimed to evaluate and compare the risk of diabetes-related adverse events among different statin drugs using pharmacovigilance data.
Methods: We analyzed adverse event reports from the FDA Adverse Event Reporting System (FAERS) database from 2004 to 2022. Diabetes-related adverse events were identified using relevant MedDRA Preferred Terms. Four pharmacovigilance algorithms-Reporting Odds Ratio (ROR), Medicines and Healthcare products Regulatory Agency (MHRA) standard method, Bayesian Confidence Propagation Neural Network, and Multi-Item Gamma Poisson Shrinkage-were employed to detect signals. Positive signals were defined when all four methods showed significance. Outcome severity and time-to-event were also analyzed.
Results: Among 13,438,409 ADE reports, 63,583 identified statins as primary suspect drugs, with 11,562 reporting diabetes-related events. Positive signals were detected for atorvastatin, rosuvastatin, simvastatin, pravastatin, and pitavastatin. Signal strength ranking showed atorvastatin had the strongest association (ROR 36.70; 95% CI 35.92-37.51), followed by rosuvastatin (ROR 9.63; 95% CI 9.10-10.19), pitavastatin (ROR 5.46; 95% CI 4.03-7.41), simvastatin (ROR 2.96; 95% CI 2.54-3.45), and pravastatin (ROR 2.82; 95% CI 2.14-3.71). In patients under 45, only atorvastatin showed a positive signal. Atorvastatin was associated with a higher risk of serious adverse events (PRR=1.37; 95% CI: 1.09-1.71) with a median time to event of 1,012 days.
Discussion: Our findings revealed differences in diabetes-related risk profiles among statins, with atorvastatin demonstrating the strongest signals across different age groups. The observed risk hierarchy may be attributed to differences in lipophilicity, potency, and metabolic effects. The age-dependent patterns and extended timeto- event for diabetic events underscore the importance of long-term monitoring, complementing clinical trial data with post-marketing surveillance evidence for improved statin selection.
Conclusion: Different statins demonstrate varying associations with diabetes-related adverse events, with atorvastatin showing the strongest signal across age groups. These findings may inform clinical decisionmaking when prescribing statins, particularly for patients with pre-existing diabetes risk factors.
他汀类药物被广泛用于预防心血管疾病,但其增加糖尿病风险的潜力已引起监管部门的警告。不同的他汀类药物具有不同的物理化学性质,但在上市后监测数据中,对其个体糖尿病相关安全性的全面比较评估仍然有限。因此,本研究旨在利用药物警戒数据评估和比较不同他汀类药物发生糖尿病相关不良事件的风险。方法:我们分析2004年至2022年FDA不良事件报告系统(FAERS)数据库中的不良事件报告。使用相关的MedDRA首选术语确定糖尿病相关不良事件。采用报告优势比(ROR)、药品和保健产品监管局(MHRA)标准方法、贝叶斯置信传播神经网络和多项目伽玛泊松收缩法4种药物警戒算法检测信号。当四种方法均显示显著性时,定义为阳性信号。结果严重程度和事件发生时间也进行了分析。结果:在13438409例ADE报告中,63583例将他汀类药物确定为主要可疑药物,11562例报告了糖尿病相关事件。检测到阿托伐他汀、瑞舒伐他汀、辛伐他汀、普伐他汀和匹伐他汀阳性信号。信号强度排序显示,阿托伐他汀的相关性最强(ROR 36.70; 95% CI 35.92-37.51),其次是瑞舒伐他汀(ROR 9.63; 95% CI 9.10-10.19)、匹伐他汀(ROR 5.46; 95% CI 4.03-7.41)、辛伐他汀(ROR 2.96; 95% CI 2.54-3.45)和普伐他汀(ROR 2.82; 95% CI 2.14-3.71)。在45岁以下的患者中,只有阿托伐他汀显示阳性信号。阿托伐他汀与严重不良事件的高风险相关(PRR=1.37; 95% CI: 1.09-1.71),发生严重不良事件的中位时间为1012天。讨论:我们的研究结果揭示了他汀类药物在糖尿病相关风险方面的差异,其中阿托伐他汀在不同年龄组中表现出最强的信号。观察到的风险等级可能归因于亲脂性、效力和代谢影响的差异。年龄依赖性模式和糖尿病事件发生时间的延长强调了长期监测的重要性,用上市后监测证据补充临床试验数据,以改善他汀类药物的选择。结论:不同的他汀类药物与糖尿病相关不良事件的相关性不同,其中阿托伐他汀在各年龄组中表现出最强的相关性。这些发现可以为临床决策提供参考,特别是对于已经存在糖尿病风险因素的患者。
期刊介绍:
Current Pharmaceutical Design publishes timely in-depth reviews and research articles from leading pharmaceutical researchers in the field, covering all aspects of current research in rational drug design. Each issue is devoted to a single major therapeutic area guest edited by an acknowledged authority in the field.
Each thematic issue of Current Pharmaceutical Design covers all subject areas of major importance to modern drug design including: medicinal chemistry, pharmacology, drug targets and disease mechanism.