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

除了代谢和心血管保护作用外，他汀类药物还可重复地参与多种代谢和心血管保护作用。 与自杀行为有关的病理生理因素-神经炎症，氧化性 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改善 在身心健康方面的治疗控制。退伍军人的持续高自杀率 仍然是有增无减的挑战，因此，需要以新的方式理解和参与 预防的努力。我们小组的长期目标是发现新的可改变的靶点， 以及在自杀预防中重新利用治疗方法，并确定那些最有可能自杀的高危人群。 受益于具体的干预措施。使用电子病历的宏观流行病学方法 在自杀研究中是不可替代的，因为它们能够解释多种相互作用的危险因素， 调节因素和混杂因素，以及潜在的直接影响。建议的主要目的 研究项目是：1）估计创伤性脑损伤（TBI）之间的相互作用， 美国退伍军人的常见病症和炎症介导的医学病症（IMC：过敏， 感染和自身免疫性疾病），预测美国退伍军人自杀。我们的初步数据 支持假设协同作用。2)估计持续与持续的自杀保护效果。 3）确定人口统计学和临床退伍军人特征， 他汀类药物的药理学特征（剂量、亲脂性、效力、持续时间）有助于更强的衰减 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医疗中心进行临床治疗的个体 从2004年开始，跟踪了13年），包括5，446，318名退伍军人，其中28，749人自杀。的 将采用考克斯比例风险模型评价TBI免疫与 介导的条件，由于相互作用（RERI），归因比例 (AP)由于相互作用，和协同指数（SI），以测试协同作用的加和规模（目标1）。考克斯 比例风险模型也将用于检验他汀类药物的风险衰减， 时间无关混杂和边际结构考克斯比例风险评分（目标2）。 最后，我们将确定人口统计学，临床（诊断代码，药物，实验室标记物）， 炎症（例如，白色血细胞计数）和预期受益退伍军人的药理学特征 使用聚合机器学习方法（The SuperLearner综合方法论）。考虑到TBI病史的高患病率及其正在进行的 后遗症，（“沉默的流行病”），特别是在退伍军人事务部，并确认他们的协同作用， IMCs可能有助于开发自杀风险降低干预措施，特别是针对那些 亚群PI的初步数据嵌套在丹麦注册，我们的团队的试点确认 初步降低精神病住院率（被认为是自杀的替代指标 风险）在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中使用他汀类药物 精神药物（附录4C），以及我们对潜在异质性效应的成功评估 使用本文提出的特定机器学习算法， 项目（附录4 B）支持我们的假设，整合和目的，以及整体，项目完成 能力。使用量身定制的药物，如他汀类药物，针对特定的分子， 直接涉及自杀行为的细胞和组织学机制， 被机器学习识别为可能从治疗中获得最大益处的患者， 这是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.