探索药物与自杀之间关联的风险信号：2004年至2024年的回顾性调查

摘要(中文译文)

自杀是一个严重的公共卫生问题，与生物、心理和社会因素的相互作用有关。尽管相关研究已相对成熟，但药物与自杀之间关联的研究仍然有限，且缺乏系统分析。本研究旨在利用美国食品药品监督管理局不良事件报告系统（FAERS）数据库，探索药物与自杀相关不良事件（SAEs）之间潜在的关联信号。本研究收集了FAERS数据库中2004年第一季度至2024年第四季度的数据。我们将10个自杀相关首选术语与主要可疑药物进行关联，采用比例失衡法进行风险信号检测，使用累积分布曲线评估用药后发病时间特征，并按年龄和性别进行亚组分析。本研究收集了247，657份涉及193种药物的SAEs报告。与SAEs关联最密切的药物类别为中枢神经系统药物；这些药物大多表现为早期失败型，即SAEs更易在用药初期发生。在<18岁人群中，报告病例数排名前三的药物为孟鲁司特、异维A酸和舍曲林；氢可酮/对乙酰氨基酚在≥65岁人群中显示出显著更高的ROR。喹硫平和对乙酰氨基酚在所有年龄组中均显示阳性信号，且ROR强度随年龄增长而增加。不同年龄组和性别之间在SAEs发生时间及相关药物类别方面存在显著差异。这些发现支持对高危人群进行针对性药物安全监测。未来研究中结合多种数据集可加深机制理解，改进风险评估体系，并进一步保障公众用药安全。

Abstract

Suicide is a serious public health issue associated with the interaction of biological, psychological and social factors. Although relevant studies are relatively mature, research on the association between drugs and suicide remains limited and lacks systematic analysis. This study aimed to explore the potential association signals between drugs and suicide-related adverse events (SAEs) using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. This study collected data from the FAERS database spanning the first quarter of 2004 to the fourth quarter of 2024. We associated 10 suicide-related preferred terms with the primary suspected drug, employed a disproportionality method for risk signal detection, used cumulative distribution curves to assess time to onset characteristics after drug use, and conducted subgroup analyses by age and gender. This study collected 247,657 reports of SAEs involving 193 drugs. The drug class most closely associated with SAEs were central nervous system medications; the majority of these drugs exhibited an early failure type, meaning that SAEs were more likely to occur during the initial stages of medication use. Among individuals <18 years old, the top three drugs by reported cases were montelukast, isotretinoin, and sertraline; hydrocodone/acetaminophen showed significantly higher ROR in individuals ≥65 years old. Quetiapine and paracetamol showed positive signals across all age groups, with ROR strength increasing with age. Significant differences exist in SAEs timing and associated drug classes across age groups and genders. These findings support targeted drug safety monitoring for high-risk populations. Combining multiple datasets in future research could deepen mechanistic understanding, improve risk assessment systems, and further ensure public drug use safety.

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