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

创伤后应激障碍 (PTSD) 和自身免疫性疾病：因果效应、危险因素和缓解措施

• 批准号: 10696671
• 负责人: Kristen Marie Nishimi
• 金额: --
• 依托单位: VETERANS AFFAIRS MED CTR SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10696671
• 关键词: Accounting; Address; Antibodies; Antidepressive Agents; Antiinflammatory Effect; Attenuated; Autoimmune; Autoimmune Diseases; Big Data Methods; Biological Process; Black race; Chronic; Chronic Disease; Clinical; Complex; Data; Data Science; Data Scientist; Databases; Demographic Factors; Development; Diagnosis; Disease; Electronic Health Record; Epidemiologic Methods; Epidemiology; Ethnic Origin; Etiology; European; Funding; Goals; Health; Health behavior; Healthcare Systems; Immune; Immune response; Incidence; Individual; Inflammation; Inflammatory; Intervention; K-Series Research Career Programs; Knowledge; Lasso; Latinx; Link; Literature; Logistic Regressions; Machine Learning; Measures; Mediating; Mental Health; Mental disorders; Mentors; Mentorship; Methods; Modification; Nature; Pathology; Patients; Pattern; Pharmaceutical Preparations; Post-Traumatic Stress Disorders; Prevention; Process; Psychiatric Diagnosis; Psychoneuroimmunology; Psychotherapy; Race; Records; Research; Research Personnel; Risk; Risk Factors; Sampling; San Francisco; Selective Serotonin Reuptake Inhibitor; Severities; Structural Models; Testing; Time; Training; Trauma; Veterans; Veterans Health Administration; Work; career; design; disorder risk; disorder subtype; ethnic disparity; ethnic minority population; evidence base; experience; feature selection; health disparity; health record; high risk; improved; machine learning algorithm; machine learning model; military veteran; novel; physical conditioning; prospective; protective factors; psychiatric comorbidity; psychologic; racial disparity; racial minority; random forest; risk mitigation; sociodemographics

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.

创伤后应激障碍（PTSD）是退伍军人中常见，慢性和使人衰弱的精神病。 除了精神科功能之外，PTSD由于健康状况较差而与多种身体健康状况联系起来 生物学过程（例如免疫失调和慢性炎症）的行为和失调。 先前的证据表明，PTSD与自身免疫性（AI）条件的风险之间存在关联，一组 超过80种涉及自反应反应的复杂疾病。但是，与PTSD和AI联系的研究 疾病风险主要集中在一些普遍的AI条件上，尚未估计潜在因果 关系，主要是欧洲主要是白色样本，并且没有检查风险或减轻因素。 因果方法，例如边际结构建模，可以解释观察性的时变因素 数据以更好地估计因素之间的因果关系，提供比先前的关联的更精确的推论 研究。此外，还需要研究以确定PTSD与所有AI疾病之间的关联，这 在很大程度上是异质的，但具有基本的病因。确实，确定PTSD与某些人之间的联系 AI失调的形式可能指出疾病风险的免疫过程模式。给出更高的 种族或少数民族群体中PTSD和一些AI疾病的率，有必要探索潜力 PTSD与AI疾病之间的关联中的健康差异。此外，其他重要的风险或保护性 可以通过使用大型临床样本和 在机器学习环境中测试多个预测指标。相关的是，没有研究确定是否 PTSD的治疗（例如抗抑郁药或循证心理治疗）可能会减轻AI病风险 在有PTSD的人中。这项研究旨在通过估计文献中的这些差距来应对这些差距 PTSD与AI疾病之间的因果关系，在美国退伍军人的大量样本中。第一个目的是 估计PTSD对AI疾病风险的因果影响（例如，任何AI疾病，单个AI疾病）和 检查精神病合并症（例如多次精神诊断）对AI病的影响。第二个 目的是确定种族和种族是否会改变PTSD与AI疾病之间的关联以及 使用数据驱动方法来探索临床因素，以增加或减轻患有AI病风险的临床因素 PTSD。第三个目的是调查是否接受治疗（例如，抗抑郁药， 与未接受治疗的PTSD相比，PTSD的心理治疗）减弱了AI疾病的风险。 对于所有目标，来自国家VA电子健康记录（EHR）的数据将大约900万退伍军人 访问和分析以识别PTSD，AI疾病和相关协变量的诊断。我们 将应用边缘结构模型，机器学习算法进行特征选择以及逻辑回归 倾向得分匹配以解决目标。与研究目标保持一致，培训目标将 支持我作为独立研究人员的发展，包括发展：1）临床PTSD知识 病理和治疗； 2）PTSD的心理肌免疫过程专业知识； 3）理解 AI疾病及其病因； 4）精通大数据方法，包括实施因果推理 和大规模EHR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支持 跨学科研究人员的指导团队将在旧金山退伍军人事务 医疗保健系统。该职业发展奖是迈向我整体科学和的关键下一步 职业目标，将数据科学和流行病学应用于VA数据以了解关系 在创伤，PTSD和身体疾病之间，以改善PTSD退伍军人的健康。