PTSD and Autoimmune Disease: Towards Causal Effects, Risk Factors, and Mitigators

创伤后应激障碍(PTSD)是退伍军人中一种常见的、慢性的和衰弱的精神疾病。 除了精神特征外，由于健康状况较差，创伤后应激障碍还与多种身体健康状况有关 行为和生物过程的失调，如免疫失调和慢性炎症。 先前的证据表明创伤后应激障碍和自身免疫(AI)疾病的风险之间存在关联，一组 80多种涉及自身反应性免疫反应的复杂疾病。然而，将创伤后应激障碍和人工智能联系起来的研究 疾病风险主要集中在少数几种流行的人工智能疾病上，尚未估计潜在的原因 在欧洲主要是白人样本中，没有检查风险或缓解因素。 因果方法，如边际构造模型，可以解释观测中的时变因素 数据，以更好地估计因素之间的因果联系，提供比先前关联更精确的推断 学习。此外，还需要进行研究，以确定创伤后应激障碍与所有人工智能疾病之间的联系， 在很大程度上是不同的，但有共同的潜在病因。事实上，确定创伤后应激障碍和某些 AI失调的形式可能指向构成疾病风险的免疫过程模式。给出更高的 少数族裔群体中创伤后应激障碍和一些人工智能疾病的发病率，有必要挖掘潜力 创伤后应激障碍和人工智能疾病之间的健康差异。此外，其他重要的风险或保护 影响创伤后应激障碍人工智能疾病风险的因素可以通过利用大量临床样本和 在机器学习环境中测试多个预测器。与此相关的是，目前还没有研究确定 创伤后应激障碍的治疗，如抗抑郁药物或循证心理治疗，可能会降低人工智能疾病的风险 在患有创伤后应激障碍的个体中。这项研究旨在通过估计来回应文献中的这些空白。 在大量不同的美国退伍军人样本中，创伤后应激障碍和人工智能疾病之间的因果关系。第一个目标是 评估创伤后应激障碍对人工智能疾病风险的因果影响(例如，任何人工智能疾病、个别人工智能状况)以及 检查精神疾病共病(例如，多个精神病诊断)对人工智能疾病的影响。第二 目的是确定种族和民族是否改变了创伤后应激障碍和人工智能疾病之间的联系，并 使用数据驱动的方法来探索增加或降低患有人工智能疾病的风险的临床因素 创伤后应激障碍。第三个目标是调查是否接受治疗(例如，抗抑郁药物， 与那些没有接受治疗的PTSD患者相比，PTSD患者的心理治疗)降低了AI疾病的风险。 对于所有目标，来自国家退伍军人电子健康记录(EHR)的大约900万退伍军人的数据将是 访问和分析以确定创伤后应激障碍、人工智能疾病和相关协变量的诊断。我们 将应用边际结构模型、用于特征选择的机器学习算法和Logistic回归 与倾向分数相匹配，以解决目标。与研究目标相一致，培训目标将 支持我作为一名独立研究人员的发展，包括：1)临床创伤后应激障碍知识 病理和治疗；2)创伤后应激障碍的心理神经免疫过程的专业知识；3)了解 人工智能障碍及其病因；以及4)熟练使用大数据方法，包括实施因果推理 以及大规模电子病历数据中的机器学习。我的研究和培训目标将得到一个优秀的 由跨学科研究人员组成的导师团队，将在旧金山退伍军人事务部进行 医疗保健系统。这个职业发展奖是我迈向全面科学和 职业目标是将数据科学和流行病学应用于退伍军人管理局的数据，以了解关系 将创伤、创伤后应激障碍和身体疾病联系起来，以改善患有创伤后应激障碍的退伍军人的健康状况。

Posttraumatic stress disorder (PTSD) is a common, chronic, and debilitating psychiatric condition in Veterans. Beyond psychiatric features, PTSD has been linked multiple physical health conditions due to poorer health behaviors and dysregulation of biological processes such as immune dysregulation and chronic inflammation. Prior evidence has indicated an association between PTSD and risk for autoimmune (AI) conditions, a group of over 80 complex diseases involving self-reactive immune responses. However, research linking PTSD and AI disease risk has largely focused on only a few prevalent AI conditions, has not estimated potential causal relationships, has been in European mostly White samples, and has not examined risk or mitigating factors. Causal methods, such as marginal structural modeling, can account for time-varying factors in observational data to better estimate causal links between factors, providing more precise inferences than prior associational studies. Additionally, research is needed to determine associations between PTSD and all AI diseases, which are largely heterogeneous but share underlying etiology. Indeed, determining links between PTSD and certain forms of AI dysregulation may point to patterns of immune processes that underlie disease risk. Given higher rates of PTSD and some AI diseases in racial or ethnic minority groups, it is necessary to explore potential health disparities in associations between PTSD and AI disease. Moreover, other important risk or protective factors influencing AI disease risk in PTSD can be examined empirically by utilizing a large clinical sample and testing multiple predictors in a machine learning context. Relatedly, no studies have determined whether treatment for PTSD, such as antidepressants or evidence-based psychotherapy, may mitigate AI disease risk among individuals with PTSD. This study is designed to respond to these gaps in the literature by estimating causal associations between PTSD and AI disease in a large, diverse sample of US Veterans. The first aim is to estimate the causal impact of PTSD on AI disease risk (e.g., any AI disease, individual AI conditions) and examining the effect of psychiatric comorbidity (e.g., multiple psychiatric diagnoses) on AI disease. The second aim is to determine whether race and ethnicity modify the association between PTSD and AI disease and to use data-driven methods to explore clinical factors that increase or mitigate risk for AI disease in those with PTSD. The third aim is to investigate whether receiving treatment (e.g., antidepressant medications, psychotherapy) for PTSD attenuates risk for AI disease compared to those with PTSD not receiving treatment. For all aims, data from national VA electronic health records (EHR) of approximately 9 million Veterans will be accessed and analyzed to identify diagnoses of PTSD, AI disease, and relevant covariates across time. We will apply marginal structural models, machine learning algorithms for feature selection, and logistic regression with propensity score matching to address the aims. Aligned with the research aims, the training aims will support my development as an independent researcher, including to develop: 1) knowledge of clinical PTSD pathology and treatment; 2) expertise in psychoneuroimmunological processes in PTSD; 3) understanding of AI disorders and their etiology; and 4) proficiency in big data methods including implementing causal inference and machine learning in large-scale EHR data. My research and training aims will be supported by an excellent mentorship team of interdisciplinary researchers and will be conducted at the San Francisco Veterans Affairs Health Care System. This Career Development Award is the critical next step towards my overall scientific and career goals, which are to apply data science and epidemiology to VA data to understand relationships between trauma, PTSD, and physical disease in order to improve the health of Veterans with PTSD.