Methods for modeling air pollution effects on exhaled biomarkers

描述（由申请人提供） 未来的环境健康研究将包括更加重视暴露或潜在疾病过程的生物机制和生物标志物。环境健康研究中生物标志物的统计方法在这一研究中可以发挥重要作用，但迄今为止尚未得到足够的重视。此应用程序是一个K22过渡到独立的环境健康研究职业发展奖。候选人提出了一个K22项目的统计方法，用于模拟空气污染对呼出气体生物标志物的影响。这将是环境生物统计学独立研究计划的基础研究项目，重点是开发和应用统计方法，以全面了解环境暴露效应的机制。 大多数呼出生物标志物的高级分析仍然相对较新，但迄今为止研究得最好的呼出生物标志物之一是呼出一氧化氮[FeNO]，这是一种指示气道炎症的生物标志物。该项目的重点是在空气污染健康影响研究的背景下模拟FeNO数据。 然而，类似的建模问题已经被确定为呼出气体冷凝物和呼出挥发性有机化合物的组分，并且随着该领域的发展，可能会发现更多。流行病学数据将空气污染暴露与FeNO升高联系起来，导致儿童哮喘发病风险增加。一种更新的方法，在多个流速的收集允许FeNO被划分为气道和肺泡源，通过估计参数在下呼吸系统中的NO交换的两室确定性模型。 现有的多流分析方法是针对来自控制良好的实验环境的数据开发的，参与者数量较少，但这些方法对大规模流行病学研究的直接适用性是值得怀疑的。多个流动 在几项流行病学研究中收集了FeNO数据，其中最大的一项是南加州儿童健康研究（n=1640）。现有的统计方法在流行病学研究与多个流FeNO使用数据效率低下，忽略了参数估计的不确定性。为了解决这些问题，候选人提出了K22项目的3个目标，使用模拟数据和儿童健康研究的数据来完成。目的1是开发、评估和应用非线性混合模型，以同时估计两室模型参数，并将这些参数与潜在决定因素（例如，细颗粒物和粗颗粒物空气污染）， 考虑到参数估计的不确定性。 目的2是开发，评估和应用贝叶斯马尔可夫链蒙特卡罗方法直接估计参数，从简单和强大的两室模型和一个更复杂的喇叭形模型与轴向扩散，然后比较适合这两个模型的儿童健康研究数据。 目的3是确定多个流量测量研究的最佳设计。 候选人是博士。她将利用K22奖提供的桥梁期，从生物医学工程的角度获得生理系统数学建模的专业知识，这将是必要的：a）完成本研究项目和B）将拟议的研究项目扩展和推广到她的独立研究计划的下一阶段。该奖项的主要研究者的赞助商是博士弗兰克D。Gilliland是一位在环境健康研究方面有着良好记录的物理学家兼科学家，为该奖项聚集的多学科顾问/合作者网络包括生物统计学专家（大卫孔蒂博士）、环境生物统计学专家（邓肯C.托马斯），生物医学工程（大卫德米利奥博士）和计算生物学（保罗马约兰博士）。

DESCRIPTION (provided by applicant) The future of environmental health research will include increased emphasis on biological mechanisms and biomarkers of exposure or latent disease processes. Statistical methods for biomarkers in environmental health research can play an important role in this research, but they have received insufficient attention to date. This application is for a K22 Transition to Independent Environmental Health Research Career Development Award. The candidate proposes a K22 project on statistical methods for modeling the effects of air pollution on exhaled breath biomarkers. This will be the foundational research project in an independent research program in environmental biostatistics, focused on developing and applying statistical methods to gain an integrated understanding of the mechanisms of environmental exposure effects. Advanced analytics of most exhaled biomarkers is still relatively new, but one of the best studied exhaled biomarkers to date is exhaled nitric oxide [FeNO], a biomarker indicative of airway inflammation. This project focuses on modeling FeNO data in the context of air pollution health effects research. However, similar modeling issues have already been identified for components of exhaled breath condensate and exhaled volatile organic compounds, and more will likely be discovered as the field advances. Epidemiologic data have linked air pollution exposure with elevated FeNO, leading to increased risk of incident asthma in children. A newer method of collection at multiple flow rates allows FeNO to be partitioned into airway and alveolar sources by estimating parameters in a two-compartment deterministic model of NO exchange in the lower respiratory system. Existing multiple flow analysis methods were developed for data from well-controlled experimental settings with a small number of participants, but direct applicability of these methods to large- scale epidemiologic studies is questionable. Multiple flow FeNO data collection has been collected in several epidemiologic studies, one of the largest of which is the Southern California Children's Health Study (n=1640). Existing statistical methods employed in epidemiologic studies with multiple flows FeNO use data inefficiently and ignore the uncertainty in the estimation of the parameters. To address these issues, the candidate proposes 3 aims for the K22 project to be completed using simulated data and data from the Children's Health Study. Aim 1 is to develop, evaluate, and apply non-linear mixed models to simultaneously estimate two- compartment model parameters and to relate these parameters to potential determinants (e.g., fine and coarse particulate matter air pollution) while appropriately accounting for the uncertainty in parameter estimation. Aim 2 is to develop, evaluate, and apply Bayesian Markov Chain Monte Carlo methods to directly estimate parameters from the simple and robust two-compartment model and a more complex trumpet-shaped model with axial diffusion, and then to compare the fit of both models to Children's Health Study data. Aim 3 is to determine optimal designs for multiple flow measurement studies. The candidate is a Ph.D. biostatistician, and she will use the bridge period afforded by the K22 award to gain expertise in mathematical modeling of physiologic systems, from a biomedical engineering perspective, which will be necessary: a) to complete this research project and b) to extend and generalize the proposed research project into the next phase of her independent research program. The principal investigator's sponsor for this award is Dr. Frank D. Gilliland, a physician-scientist with a strong track-record in environmental health research, and the multidisciplinary network of advisor/collaborators assembled for this award include experts in biostatistics (Dr. David Conti), environmental biostatistics (Dr. Duncan C. Thomas), biomedical engineering (Dr. David D'Argenio), and computational biology (Dr. Paul Marjoram).