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）是退伍军人中一种常见的、慢性的、使人衰弱的精神疾病。