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

通过他汀类药物处方降低患有常见炎症介导临床病症的美国退伍军人的自杀风险——一种受控、准随机的流行病学方法

• 批准号: 10487844
• 负责人: TEODOR T POSTOLACHE
• 金额: --
• 依托单位: BALTIMORE VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10487844
• 关键词: Age; Autoimmune; Autoimmune Diseases; Bipolar Disorder; Blood; Brain; Cardiovascular system; Categories; Cells; Characteristics; Clinical; Code; Cognition Disorders; Computerized Medical Record; Cox Proportional Hazards Models; Data; Development; Diagnosis; Diagnostic; Disease susceptibility; Dose; Encephalitis; Epidemic; Epidemiology; Equation; Evaluation; Future; Gender; General Population; Hematology; High Prevalence; Histologic; Hospitalization; Hypersensitivity; Immune; Impairment; Individual; Infection; Inflammation; Intervention; LDL Cholesterol Lipoproteins; Laboratory Markers; Link; Machine Learning; Measures; Mediating; Mediation; Medical; Medical center; Mental Depression; Mental Health; Mental disorders; Metabolic; Methodology; Microglia; Modeling; Modification; Molecular; Outcome; Oxidative Stress; Pharmaceutical Preparations; Pharmacodynamics; Post-Traumatic Stress Disorders; Prevention; Process; Proxy; Randomized; Recording of previous events; Regimen; Relative Risks; Reporting; Reproducibility; Research; Research Project Grants; Retrospective cohort; Risk; Risk Factors; Risk Management; Schizophrenia; Self Direction; Severities; Suicide; Suicide attempt; Suicide prevention; Testing; Therapeutic; Time; Traumatic Brain Injury; United States; Veterans; Veterans Health Administration; Violence; Vitamin D Deficiency; attenuation; clinical encounter; comorbidity; design; disability; drug repurposing; endothelial dysfunction; evidence base; excitotoxicity; hazard; immune activation; immunoregulation; improved; indexing; inflammatory marker; machine learning algorithm; member; neuroinflammation; neuroprotection; novel; novel strategies; pharmacologic; physical conditioning; protective effect; psychologic; randomized, clinical trials; resilience; suicidal; suicidal behavior; suicidal morbidity; suicidal risk; suicide rate; synergism; trait; treatment duration

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.

除了其代谢和心血管保护作用外，他汀类药物还可重复参与多个 在自杀行为中实施的病理生理因素 - 神经炎症，氧化增加增加 压力，兴奋性和内皮功能障碍。据报道，附加他汀类药物可以改善 身心健康中的治疗控制。退伍军人持续的自杀率持续 仍然是不利的挑战，因此，要求新的理解和参与的新方法 预防性努力。我们小组的长期目标是揭示新的可修改目标，新颖的目标 并在预防自杀中重新使用治疗，并确定可能大多数人可能有风险的人 受益于特定干预措施。使用电子病历的宏观流行学方法 在自杀研究中，他们的能力是多种交互式风险因素的能力，无法替代 主持人和混杂因素，并有可能立即影响。提议的主要目的 研究项目是：1）估计创伤性脑损伤（TBI）之间的增强相互作用，非常 美国退伍军人的常见状况和炎症介导的医疗状况（IMC：过敏， 感染和自身免疫性条件），预测美国退伍军人的自杀。我们的初步数据 支持假设的协同相互作用。 2）估计持续VS的自杀保护作用。 未经固定的他汀类药物3）确定人口统计和临床退伍军人特征以及 药理学他汀类药物特征（剂量，亲脂，效力，持续时间）导电到更强的衰减 他汀类药物对自杀行为的影响。我们将在退伍军人卫生管理局上检验这些假设 （VHA）回顾性队列（在全国范围内在VA医疗中心进行临床相遇的人 从2004年开始，随后13年），其中包括5,446,318名自杀自杀的退伍军人。这 COX比例危害模型将用于评估TBI免疫之间的相互作用 介导条件，由于相互作用（RERI）而具有相对多余的风险，可归因于比例 （AP）由于相互作用和协同指数（SI）以额外的比例测试协同作用（AIM 1）。 Cox 比例危害模型也将用于用他汀类药物测试风险衰减，并承诺 与时间无关的混杂和边缘结构COX比例危害的评分（AIM 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 预计将受益的退伍军人的炎症（例如白血计数）和药物特征 使用总体机器学习方法得出的二他汀类药物治疗最多（ 超级综合方法​​）。考虑到TBI历史的高流行及其持续的率 后遗症（“沉默的流行病”），尤其是在VA中，并证实了他们与 IMC可能有助于为那些专门开发自杀风险增加干预措施 亚群。 PI的初步数据嵌套在丹麦注册表中，我们的团队的驾驶确认 初步降低了精神病住院率（被认为是自杀的替代措施 风险）患有诊断患有精神分裂症或躁郁症的美国退伍军人的他汀类药物，并接受 精神药物（附录4C），以及我们对潜在异质效应的成功评估 使用特定的机器学习算法提出的替代修改自杀风险 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。使用定制的重新利用药物，例如他汀类药物，针对分子， 直接与自杀行为有关的细胞和组织学机制，与有风险的人 通过机器学习来确定可能从治疗中获得最大的好处，可能会提供 自杀风险管理和预防方面急需的突破。