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，他汀类药物还可重复地参与多个 与自杀行为有关的病理生理因素--神经炎症、氧化增加 应激、兴奋毒性和内皮功能障碍。据报道，附加的他汀类药物也有改善 身心健康方面的治疗控制。退伍军人持续较高的自杀率 仍然是有增无减的挑战，因此需要以新的方式理解和参与 预防性的努力。我们团队的长期目标是发现新的可修改的目标，新的 并在预防自杀和识别最有可能发生自杀的高危人群方面进行了重新调整的治疗 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 在自杀研究中是不可替代的，因为它们能够解释多种交互风险因素， 主持人和混杂因素，以及产生立竿见影效果的可能性。建议的主要目标是 研究项目是：1)评估创伤性脑损伤(TBI)之间的增强相互作用，一个非常 美国退伍军人中的常见疾病，以及炎症介导的医疗条件(IMCs：过敏， 感染和自身免疫状况)，以预测美国退伍军人的自杀。我们的初步数据 支持假设协同作用。2)评估持续与持续的自杀保护效果。 非持续的他汀类药物治疗3)确定人口统计学和临床退伍军人的特征 他汀类药物的药理特性(剂量、亲脂性、效力、持续时间)有助于更强的减毒作用 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上测试这些假设 (VHA)回顾队列(在全国退伍军人医疗中心有临床接触的个人 从2004年开始，随后13年)，包括5,446,318名退伍军人，28,749人自杀。这个 COX比例风险模型将用于评估脑损伤免疫之间的相互作用 中介条件，相互作用造成的相对超额风险(RERI)，归因比例 (Ap)由于相互作用而产生的协同效应，以及在相加尺度上测试协同效应的协同指数(目标1)。考克斯 比例风险模型也将被用于测试他汀类药物的风险降低，具有倾向性 时间无关混杂和边缘结构性COX比例风险的评分(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 预期受益的退伍军人的炎症(如白血球计数)和药理学特征 使用聚合机器学习方法的持续他汀类药物治疗的最大收益( 超级学习者综合方法论)。考虑到颅脑损伤病史及其持续的高患病率 后遗症(“一种无声的流行病”)，特别是在退伍军人事务部，并证实它们与 IMC可能有助于开发专门针对这些人的降低自杀风险的干预措施 亚群。私家侦探的初步数据嵌套在丹麦的登记簿上，我们团队的飞行员证实 初步降低了精神病住院率(被认为是自杀的替代指标 美国退伍军人中使用他汀类药物的风险)，诊断为精神分裂症或双相情感障碍并接受 精神药物(附录4C)，以及我们对潜在异质性效应的成功评估 使用在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、集成和目的，以及总体项目完成 能力。使用量身定制的重新调整用途的药物，如他汀类药物，针对特定的分子， 直接牵涉到自杀行为的细胞和组织机制，对处于危险中的人来说 被机器学习识别为潜在地从治疗中获得最大好处，可以提供 在自杀风险管理和预防方面亟需的突破。

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.