Real-world analysis of depression-related adverse events associated with the adsorbed anthrax vaccine: integrating pharmacovigilance signals, machine learning risk prediction, and transcriptomic mechanisms.

IF 5.5 1区 医学
Guangwei Qing, Yimei Zhao, Jian Yang, Gang Wang
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引用次数: 0

Abstract

Background: Anthrax remains a life-threatening zoonotic disease in resource-limited settings. Adsorbed anthrax vaccine (AVA, BioThrax) is the only United States-authorized preexposure prophylaxis for high-risk Bacillus anthracis exposure. Although AVA safety has been described, depression-related adverse events (AEs) remain insufficiently characterized. This study evaluated real-world depression-related safety signals associated with AVA and anthrax-related antibacterial agents.

Methods: AVA-related AE reports were extracted from the Vaccine Adverse Event Reporting System (VAERS), and depression-related AE reports for seven anthrax-related antibacterial agents were retrieved from the FDA Adverse Event Reporting System (FAERS). AEs were coded using Medical Dictionary for Regulatory Activities (MedDRA). Disproportionality analyses were performed using four standard signal-detection methods, with sensitivity analysis excluding concurrent vaccination. Seven complementary machine learning algorithms, covering regression, tree-based, boosting, kernel, instance-based, and probabilistic methods, explored predictors, with Shapley Additive Explanation (SHAP) for interpretation. Time-to-onset, clinical priority, and exploratory transcriptomic signatures were also assessed.

Results: In VAERS, 9446 AVA-related AE reports were identified, including 180 depression-related cases [total depression population n = 180, ROR 5.02 (95% CI: 4.33-5.81)]. Males (78.2%) and individuals aged 31-45 years (49.1%) were most represented, with a median time-to-onset of 10 days and 44.1% classified as serious. Stronger signals were observed in individuals aged 46-65 years [n = 35, ROR 10.31 (95% CI: 7.37-14.42)] and males [n = 142, ROR 6.11 (95% CI: 5.16-7.24)]. Machine learning highlighted serious AEs and age as key predictors. Transcriptomic analysis suggested immune-inflammatory pathway overlap, including neutrophil activation and Yersinia infection. In FAERS, levofloxacin [n = 634, ROR 1.37 (95% CI: 1.27-1.49)] and doxycycline [n = 332, ROR 1.53 (95% CI: 1.38-1.71)] showed positive depression-related reporting signals; "Depression suicidal" had moderate clinical priority.

Conclusions: AVA was associated with a potential depression-related pharmacovigilance signal, particularly among males and adults, with early post-vaccination onset. These findings support targeted mental health monitoring after AVA, clearer safety communication in anthrax prevention programs, and vigilance when levofloxacin or doxycycline is used for anthrax therapy. As spontaneous reporting cannot establish causality or incidence, controlled epidemiological studies are needed.

与吸附炭疽疫苗相关的抑郁症相关不良事件的现实世界分析:整合药物预警信号、机器学习风险预测和转录组学机制。
背景:在资源有限的环境中,炭疽仍然是一种危及生命的人畜共患疾病。吸附炭疽疫苗(AVA, biothrx)是美国唯一批准的高危炭疽杆菌暴露前预防措施。尽管AVA安全性已被描述,但抑郁症相关不良事件(ae)仍未充分表征。本研究评估了与AVA和炭疽相关抗菌药物相关的真实世界抑郁相关安全信号。方法:从疫苗不良事件报告系统(VAERS)中提取与ava相关的AE报告,从FDA不良事件报告系统(FAERS)中检索7种炭疽相关抗菌药物的抑郁症相关AE报告。使用医学规范活动词典(MedDRA)对ae进行编码。使用四种标准信号检测方法进行歧化分析,并进行敏感性分析,排除同时接种疫苗。七种互补的机器学习算法,包括回归、基于树的、增强、核、基于实例的和概率方法,探索了预测器,并使用Shapley加性解释(SHAP)进行解释。发病时间、临床优先级和探索性转录组特征也被评估。结果:在VAERS中,共发现9446例与ava相关的AE报告,其中包括180例与抑郁症相关的病例[抑郁症总人群n = 180, ROR 5.02 (95% CI: 4.33-5.81)]。男性(78.2%)和31-45岁的个体(49.1%)最为常见,中位发病时间为10天,44.1%为严重。在46-65岁的个体中观察到较强的信号[n = 35, ROR 10.31 (95% CI: 7.37-14.42)]和男性[n = 142, ROR 6.11 (95% CI: 5.16-7.24)]。机器学习强调严重ae和年龄是关键的预测因素。转录组学分析表明免疫炎症途径重叠,包括中性粒细胞激活和耶尔森菌感染。在FAERS中,左氧氟沙星[n = 634, ROR 1.37 (95% CI: 1.27 ~ 1.49)]和多西环素[n = 332, ROR 1.53 (95% CI: 1.38 ~ 1.71)]显示抑郁相关的阳性报告信号;“抑郁症自杀”具有中等临床优先级。结论:AVA与潜在的抑郁相关药物警戒信号有关,特别是在男性和成人中,接种疫苗后发病早期。这些发现支持AVA后有针对性的心理健康监测,在炭疽预防计划中更明确的安全沟通,以及在使用左氧氟沙星或多西环素进行炭疽治疗时保持警惕。由于自发报告不能确定因果关系或发病率,因此需要进行流行病学对照研究。
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来源期刊
Infectious Diseases of Poverty
Infectious Diseases of Poverty INFECTIOUS DISEASES-
自引率
1.20%
发文量
368
期刊介绍: Infectious Diseases of Poverty is an open access, peer-reviewed journal that focuses on addressing essential public health questions related to infectious diseases of poverty. The journal covers a wide range of topics including the biology of pathogens and vectors, diagnosis and detection, treatment and case management, epidemiology and modeling, zoonotic hosts and animal reservoirs, control strategies and implementation, new technologies and application. It also considers the transdisciplinary or multisectoral effects on health systems, ecohealth, environmental management, and innovative technology. The journal aims to identify and assess research and information gaps that hinder progress towards new interventions for public health problems in the developing world. Additionally, it provides a platform for discussing these issues to advance research and evidence building for improved public health interventions in poor settings.
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