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

基本信息

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
项目类别：
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

项目摘要

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）是退伍军人中一种常见的、慢性的、使人衰弱的精神疾病。除了精神特征之外，创伤后应激障碍还与由于健康状况不佳而导致的多种身体健康状况有关生物过程的行为和失调，例如免疫失调和慢性炎症。先前的证据表明 PTSD 与自身免疫 (AI) 疾病的风险之间存在关联，一组超过 80 种涉及自身反应性免疫反应的复杂疾病。然而，将 PTSD 和人工智能联系起来的研究疾病风险主要集中在少数流行的人工智能状况上，尚未估计潜在的因果关系关系，已在欧洲大多数白人样本中进行，并且尚未检查风险或缓解因素。因果方法，例如边际结构模型，可以解释观察中的时变因素数据可以更好地估计因素之间的因果关系，提供比先前关联更精确的推论研究。此外，还需要进行研究来确定 PTSD 与所有 AI 疾病之间的关联，这很大程度上是异质的，但具有共同的潜在病因。事实上，确定 PTSD 和某些特定疾病之间的联系人工智能失调的形式可能表明疾病风险背后的免疫过程模式。给予更高少数族裔群体中 PTSD 和某些 AI 疾病的发病率，有必要探索潜在的创伤后应激障碍（PTSD）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 PTSD 中 AI 疾病风险的因素可以通过利用大量临床样本进行实证检验，在机器学习环境中测试多个预测变量。相关地，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，可能会降低 AI 疾病的风险患有创伤后应激障碍 (PTSD) 的个体。本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，PTSD 与 AI 疾病之间存在因果关系。第一个目标是评估 PTSD 对 AI 疾病风险（例如任何 AI 疾病、个体 AI 状况）的因果影响，以及检查精神共病（例如多种精神疾病诊断）对 AI 疾病的影响。第二个目的是确定种族和民族是否会改变 PTSD 与 AI 疾病之间的关联，并使用数据驱动的方法来探索增加或减轻患有 AI 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、与未接受治疗的 PTSD 患者相比，针对 PTSD 的心理治疗可降低 AI 疾病的风险。为了实现所有目标，大约 900 万退伍军人的国家 VA 电子健康记录 (EHR) 数据将被访问和分析以识别 PTSD、AI 疾病和随时间变化的相关协变量的诊断。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逻辑回归通过倾向得分匹配来实现目标。与研究目标相一致，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 的知识病理学和治疗； 2) 创伤后应激障碍（PTSD）心理神经免疫过程的专业知识； 3）了解人工智能疾病及其病因； 4）熟练掌握大数据方法，包括实施因果推理以及大规模 EHR 数据中的机器学习。我的研究和培训目标将得到优秀的支持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整体科学和技术的关键的下一步职业目标，即将数据科学和流行病学应用于 VA 数据以理解关系创伤、创伤后应激障碍 (PTSD) 和身体疾病之间的关系，以改善患有创伤后应激障碍 (PTSD) 退伍军人的健康。