Suicide risk modification by statin prescriptions in US Veterans with common inflammation-mediated clinical conditions- a controlled, quasi-randomized epidemiological approach

除了其代谢和心血管保护作用外，他汀类药物还可以重复地参与多种作用 与自杀行为有关的病理生理因素——神经炎症、氧化增加 应激、兴奋性毒性和内皮功能障碍。据报道，添加他汀类药物也可以改善 身体和心理健康的治疗控制。退伍军人自杀率持续较高 挑战仍然有增无减，因此需要新的方式来理解和参与 预防工作。我们小组的长期目标是发现新的可修改目标、新颖的 并重新调整自杀预防治疗的用途，并确定最有可能自杀的高危人群 从具体的干预措施中受益。使用电子病历的宏观流行病学方法 在自杀研究中具有不可替代的能力，因为他们有能力解释多种相互作用的风险因素， 调节因素和混杂因素，以及产生直接影响的潜力。拟议的主要目标 研究项目旨在： 1) 估计创伤性脑损伤 (TBI) 之间的潜在相互作用，这是一种非常常见的疾病。 美国退伍军人的常见病症，以及炎症介导的医疗病症（IMC：过敏、 感染和自身免疫性疾病），预测美国退伍军人的自杀。我们的初步数据 支持协同相互作用的假设。 2) 估计持续与长期自杀的保护作用 不持续的他汀类药物治疗 3) 确定退伍军人的人口统计和临床特征以及 他汀类药物的药理学特征（剂量、亲脂性、效力、持续时间）有利于更强的减毒作用 他汀类药物对自杀行为的影响。我们将在退伍军人健康管理局测试这些假设 (VHA) 回顾性队列（在全国 VA 医疗中心有过临床经历的个人 从 2004 年开始，持续了 13 年），其中包括 5,446,318 名退伍军人，其中 28,749 人自杀。的 Cox比例风险模型将用于评估TBI免疫之间的相互作用 中介条件，由于相互作用而产生的相对超额风险（RERI），可归因比例 （AP）归因于相互作用，协同指数（SI）用于测试加性规模上的协同作用（目标 1）。考克斯 比例风险模型也将应用于测试他汀类药物的风险衰减，并有倾向 对与时间无关的混杂因素和边际结构性 Cox 比例风险进行评分（目标 2）。 最后，我们将确定人口统计学、临床（诊断代码、药物、实验室标志物） 预计受益的退伍军人的炎症（例如白细胞计数）和药理学特征 使用聚合机器学习方法（the SuperLearner 综合方法）。考虑到 TBI 病史的高患病率及其持续存在的情况 后遗症（“无声的流行病”），特别是在退伍军人管理局，并证实了它们与 IMC 可能有助于专门针对那些人制定降低自杀风险的干预措施 亚人群。 PI 的初步数据嵌套在丹麦登记册中，我们团队的试点证实了这一点 初步降低了精神病住院率（被视为自杀的替代指标） 被诊断患有精神分裂症或双相情感障碍并接受治疗的美国退伍军人使用他汀类药物的风险） 精神药物（附录 4C），以及我们对潜在异质效应的成功评估 使用本文中提出的特定机器学习算法的替代可修改自杀风险 项目（附录 4B）支持我们的假设、整合和目的，以及总体项目完成情况 能力。使用定制的重新用途药物，例如他汀类药物，专门针对分子， 与自杀行为直接相关的细胞和组织学机制，对于有自杀风险的个体 通过机器学习识别出可能从治疗中获得最大益处，可能会提供 自杀风险管理和预防方面急需的突破。

In addition to their metabolic and cardiovascular protective effects, statins reproducibly engage multiple pathophysiological factors implicated in suicidal behavior - neuroinflammation, increased oxidative stress, excitotoxicity, and endothelial dysfunction. Add-on statins have been also reported to improve therapeutic control in physical and mental health. The Veterans’ persistent higher rates of suicide have remained unabated challenges and, and thus, demanding new ways of understanding and engaging in preventative efforts. The long-term objective of our group is to uncovering new modifiable targets, novel and repurposed treatments in suicide prevention, and identifying individuals at risk who are likely to most benefit from specific interventions. Macro-epidemiological approaches using electronic medical records in suicide research are irreplaceable for their capability to account for multiple interactive risk factors, moderators and confounders, and potential for immediate impact. The primary aims of the proposed research project are to: 1) Estimate potentiating interactions between traumatic brain injury (TBI), a very common condition in US Veterans, and inflammation-mediated medical conditions (IMCs: allergies, infection, and autoimmune conditions), in predicting suicide in US Veterans. Our preliminary data support hypothesizing synergistic interactions. 2) Estimate the suicide protective effect of sustained vs. unsustained statin treatment 3) Identify demographic and clinical Veteran characteristics and pharmacological statin features (dose, lipophilia, potency, duration) conducive to stronger attenuating effects of statins on suicidal behavior. We will test these hypotheses on a Veterans Health Administration (VHA) retrospective cohort (individuals with clinical encounters in VA Medical Centers nationwide beginning in 2004 and followed for 13 years) including 5,446,318 Veterans with 28,749 suicides. The Cox proportional hazard model will be applied to evaluate the interactions between TBI immune mediated conditions , with Relative Excess Risk due to Interaction (RERI), the Attributable Proportion (AP) due to interaction, and the Synergy Index (SI) to test synergism on an additive scale (Aim 1). A Cox proportional hazard model will also be applied to testing risk attenuation with statins, with propensity scoring for time-independent confounding and marginal structural Cox proportional hazards (Aim 2). Finally, we will identify the demographic, clinical (diagnostic codes, medications, laboratory markers of inflammation (e.g., white blood count) and pharmacological characteristic of Veterans expected to benefit the most from sustained statin treatment using an aggregate machine learning approach (the SuperLearner integrative methodology). Considering the high prevalence of TBI history and its ongoing sequelae,( “a silent epidemic”) , especially in the VA, and confirming their synergistic interaction with IMCs may contribute to developing suicide risk-attenuating interventions specifically for those subpopulations. The PI’s preliminary data nested in Danish registers, our team’s piloting confirming preliminarily a reduction in rates of psychiatric hospitalization (considered a proxy measure of suicide risk) with statins in US Veterans diagnosed with schizophrenia or bipolar disorder and treated with psychotropic medication (Appendix 4C), and our successful evaluation of potential heterogenous effects of an alternative modifiable suicide risk using the specific machine learning algorithms proposed in this project (Appendix 4B) support our hypotheses, integration, and purpose, and overall, project completion capability. Using tailored repurposed medications, such as statins, targeting specifically molecular, cellular and histological mechanisms directly implicated in suicidal behavior, to individuals at risk who are identified by machine learning to potentially derive the greatest benefit from treatment , may provide a much-needed breakthrough in suicide risk management and prevention.