Identifying Cardiotoxic Manifestations of Posttraumatic Psychopathology: A Population-based Longitudinal Investigation

项目总结 创伤影响了绝大多数人的一生(50%-)，它可以对 不仅是精神疾病，还包括心血管健康。创伤暴露和创伤后应激障碍 创伤后应激障碍(PTSD)--一种典型的创伤相关精神障碍--被预期与 发生一系列心血管后果的风险。然而，正如最近NHLBI工作中所描述的那样 小组报告，关键的知识差距必须在创伤之前解决，否则其精神后遗症可能会 降低心血管疾病(CVD)风险的新目标。事实上，我们目前的理解受到一个 在检查创伤暴露个人的后续心血管风险时，不成比例地关注创伤后应激障碍。 创伤后精神病理学以不同的方式表现(例如，抑郁、焦虑、药物滥用) 这与非创伤暴露样本中心血管疾病风险增加有关。此外，尽管在 创伤后精神病理学和心血管疾病在男性和女性中的患病率和表现，很少有研究 直接研究了这些关联中的性别差异。实地调查这些问题的能力 由于缺乏严格的创伤暴露措施和创伤后精神病理学 大多数现有的电子病历数据库，其中有大量关于心血管疾病结果的数据。本研究 将通过利用独特的、基于人群的创伤队列来解决这些知识差距 以确定男性和女性创伤后精神病理学的“心脏毒性”表现。这 创伤队列使用丹麦电子健康登记(EHR)数据作为R01MH110453的一部分(PI： Gradus)，并确定了1994至2016年间有超过140万人受到创伤。这 已建立的数据源包括丰富、高度有效和完整的基于注册表的数据，具有长达25年的 对创伤后的精神和心血管疾病的诊断进行随访。我们将利用现有的资源， 仅限于18岁及以上无心血管疾病史(n=1,068,100)的人， 检查创伤后精神病理学作为心血管事件的预测因子。在目标1中，我们将利用尖端技术， 新的数据科学技术(机器学习)，以识别特别“心脏毒性”的表现 创伤后的精神病理学。考虑到精神障碍和心血管疾病的性别差异， 与心血管事件风险相关的精神病理学特征将在分层分析中进行检查。在目标2中， 我们将使用传统分析来量化已发现的精神病学预测因素和未预料到的/新颖的 机器学习分析中与心血管疾病风险相关的精神疾病共病概况，以及先验 基于文献的增加心血管疾病风险的精神障碍的组合。这一基于EHR的创伤队列 提供了一个全面考虑创伤后精神病理学星座的独特机会 这可能会预测心血管疾病，这项研究的结果将被用来最终告知 在创伤暴露人群中开展有针对性的心血管疾病预防工作。

PROJECT SUMMARY Trauma affects the vast majority of people (50-89%) during their lifetime, and it can have lasting impacts on not only psychiatric but also cardiovascular health. Both trauma exposure and posttraumatic stress disorder (PTSD)—the quintessential trauma-related psychiatric disorder—have been linked prospectively to increased risk of developing a range of cardiovascular outcomes. However, as described in a recent NHLBI Working Group report, critical knowledge gaps must be addressed before trauma or its psychiatric sequelae might be novel targets for reducing cardiovascular disease (CVD) risk. Indeed, our current understanding is limited by a disproportionate focus on PTSD when examining subsequent CVD risk in trauma-exposed individuals. Posttraumatic psychopathology manifests in heterogeneous ways (e.g., depression, anxiety, substance abuse) that have been linked to elevated CVD risk in non-trauma-exposed samples. Further, despite differences in the prevalence and manifestations of posttraumatic psychopathology and CVD in men and women, few studies have directly examined sex differences in these associations. The field's ability to investigate these questions has been hampered by a lack of rigorous measures of trauma exposure and posttraumatic psychopathology in most existing electronic medical record databases, which have extensive data on CVD outcomes. This study will address these knowledge gaps by harnessing a unique prospective, population-based trauma cohort in order to characterize “cardiotoxic” manifestations of posttraumatic psychopathology in men and women. This trauma cohort was created using Danish electronic health registry (EHR) data as part of R01MH110453 (PI: Gradus), and it identified over 1.4 million individuals exposed to a trauma between 1994 and 2016. This established data source includes rich, highly valid, and complete registry-based data with up to 25 years of follow-up on psychiatric and cardiovascular diagnoses following trauma. We will use this existing resource, restricted to persons age 18 years and older with no prior CVD events (n = 1,068,100), to comprehensively examine posttraumatic psychopathology as a predictor of incident CVD. In Aim 1, we will harness cutting edge, novel data science techniques (machine learning) to identify particularly “cardiotoxic” manifestations of psychopathology after trauma. Given sex differences in psychiatric disorders and CVD, sex-specific psychopathology profiles associated with incident CVD risk will be examined in stratified analyses. In Aim 2, we will use traditional analyses to quantify discovered psychiatric predictors and unanticipated/novel psychiatric comorbidity profiles associated with CVD risk in the machine learning analyses, as well as a priori literature-based combinations of psychiatric disorders that increase CVD risk. This EHR-based trauma cohort provides a unique opportunity to consider comprehensively the constellation of posttraumatic psychopathology that may predict CVD, and the results of this study will be used to ultimately inform the development of targeted CVD prevention efforts in trauma-exposed populations.