The Impact of Environmental Exposures on Sarcoidosis Incidence and Mortality

项目摘要 结节病是一种病因不明、临床结果多变的多器官疾病，具有潜在的 受环境暴露的影响，如有机燃料燃烧或无机粉尘。更高的发病率 退伍军人中结节病的患病率高于平民，这为研究退伍军人结节病的作用提供了理论基础。 在疾病的自然病史中接触与服务相关的有毒物质。这样的研究与目标是一致的 最近批准的PACT法案，该法案承认结节病是一种与服务相关的疾病，并要求进行研究 与服务相关的暴露是肺部疾病的风险因素。退伍军人健康管理局(VHA)， 美国最大的综合医疗系统，获得数百万退伍军人的全面纵向数据， 包括他们与服务相关的有毒物质暴露的独特历史。这些丰富的数据使我们能够探索 这些关系的精确度很高。我的中心假设是，与服务相关的有毒物质暴露会影响 结节病发病率和全因死亡率。我提出两个具体目标：(1)在嵌套情况下确定- 对照研究与服务相关的暴露是否会增加退伍军人中结节病的发病风险 用时间依赖的COX确定退伍军人结节病全因死亡率的预测因素 比例风险模型和另外两个机器学习模型；在两个子目标中，我将确定 个人临床特征可预测全因死亡率以及是否包括与服务相关的暴露 进入模型将改善这一预测。这项研究将揭示接触有毒物质对结节病的影响。 发病率，开发全原因死亡率的预测模型，并将退伍军人分为风险亚组-这将 改进纵向护理并指导努力识别新的结节病表型。这些发现将为 职业发展奖提案的基础，侧重于现实世界的循证管理 结节病的治疗策略。

PROJECT ABSTRACT Sarcoidosis, a multiorgan disease with an uncertain etiology and variable clinical outcomes, is potentially influenced by environmental exposures, such as organic fuel burning or inorganic dust. The higher incidence and prevalence of sarcoidosis among veterans than in civilians provides a rationale to examine the role of service-related toxic exposures in the natural history of the disease. Such studies are consistent with the goals of the recently approved PACT Act, which recognizes sarcoidosis as a service-related illness and calls for studies of service-related exposures as risk factors for lung disease. The Veteran's Health Administration (VHA), the largest integrated healthcare system in the U.S., obtains comprehensive longitudinal data on millions of veterans, including their unique history of service-related toxic exposures. This wealth of data enables the exploration of these relationships with high precision. My central hypothesis is that service-related toxic exposures affect the incidence of sarcoidosis and all-cause mortality. I propose two specific aims: (1) to determine in a nested case- control study whether service-related exposures increase sarcoidosis incidence risk among veterans and (2) to identify predictors of all-cause mortality among veterans with sarcoidosis using a time-dependent Cox proportional hazards model and two other machine learning models; in two subaims, I will determine whether individual clinical features predict all-cause mortality and whether incorporating the service-related exposures into the models will improve this prediction. This study will reveal the effects of toxic exposures on sarcoidosis incidence, develop predictive models for all-cause mortality, and stratify veterans into risk subgroups—which will improve longitudinal care and guide efforts to identify new sarcoidosis phenotypes. The findings will lay the foundation for a Career Development Award proposal focusing on real-world, evidence-based management strategies for sarcoidosis.