Methods for modeling air pollution effects on exhaled biomarkers

描述(由申请人提供) 环境健康研究的未来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记物。环境健康研究中生物标志物的统计方法可以在这项研究中发挥重要作用，但到目前为止还没有得到足够的重视。这份申请是为K22过渡到独立环境健康研究职业发展奖。候选人提出了一个K22项目，该项目旨在对空气污染对呼气生物标记物的影响进行建模的统计方法。这将是环境生物统计学独立研究计划中的基础研究项目，重点是开发和应用统计方法，以全面了解环境暴露影响的机制。 大多数呼气生物标记物的高级分析仍然是相对较新的，但迄今为止研究最好的呼气生物标记物之一是呼出一氧化氮[FeNO]，这是一种指示呼吸道炎症的生物标记物。本项目侧重于在空气污染健康影响研究的背景下对FeNO数据进行建模。然而，呼气冷凝液和呼气挥发性有机化合物的成分已经确定了类似的建模问题，随着该领域的进展，可能会发现更多的问题。流行病学数据已将空气污染暴露与FeNO升高联系在一起，导致儿童哮喘发病风险增加。一种在多种流速下收集FeNO的新方法允许通过估计下呼吸系统中NO交换的两室确定性模型中的参数来将FeNO划分为呼吸道和肺泡源。现有的多重流动分析方法是针对受试者人数较少的受控实验环境中的数据而开发的，但这些方法直接适用于大规模流行病学研究是值得怀疑的。多流 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)。现有的用于多流FeNO流行病学研究的统计方法利用数据效率低，并且忽略了参数估计中的不确定性。为了解决这些问题，候选人提出了使用模拟数据和儿童健康研究数据完成K22项目的3个目标。目标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，并适当地将这些参数与潜在的决定因素(例如，细颗粒物和粗颗粒物空气污染)联系起来 说明了参数估计中的不确定性。目的2是发展、评估和应用贝叶斯马尔可夫链蒙特卡罗方法直接估计简单稳健的两室模型和具有轴向扩散的喇叭形模型的参数，并比较这两种模型对儿童健康研究数据的拟合。目标3是确定多个流量测量研究的最佳设计。 候选人是一名生物统计学家博士，她将利用K22奖项提供的过渡期，从生物医学工程的角度获得生理系统数学建模方面的专业知识，这将是必要的：a)完成这项研究项目，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。该奖项的主要研究人员是弗兰克·D·吉利兰博士，他是一位在环境健康研究方面有着良好记录的内科科学家，为该奖项聚集的顾问/合作者的多学科网络包括生物统计学(David Conti博士)、环境生物统计学(Duncan C.Thomas博士)、生物医学工程(David D‘argenio博士)和计算生物学(Paul Marjoram博士)的专家。

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).