Exhaled nitric oxide (FeNO) is a non-invasive biomarker of airway inflammation, with applications in the clinical assessment of asthma and environmental epidemiology. Conventional FeNO?assessed at a flow rate of 50 ml/s (FeNO50)?is well-established, with international society guidelines for assessment and clinical interpretations. A promising but less well-established method of assessing FeNO is ?multiple flow NO analysis?, which uses FeNO measured at multiple flow rates to estimate ?NO parameters?, quantifying airway and alveolar sources of nitric oxide (NO), from deterministic physiological models of the lower respiratory tract. While these physiological models are quite well-developed in adults, the statistical methods for estimating their parameters are not, especially for children. Most researchers use estimation methods based on overly simplistic assumptions (e.g., a fixed airway size under a steady state) and linearizations of the resultant nonlinear models. These methods are easy to implement, but have poor statistical performance and do not account for the smaller airway size of children. These are major barriers to progress in this field. For this project, we will develop a hierarchal Bayesian modeling approach implemented using Markov chain Monte Carlo based-methods. A key advantage of a Bayesian approach is the ability to incorporate outside data into the model, producing refined parameter estimates and, potentially, refining our understanding of airway inflammation. In particular, we plan to incorporate two types of outside information: measurements related to airway size, and data on potential determinants of inflammation, such as environmental exposures. By incorporating measured phenotype data explicitly into the estimation process, the resulting parameter estimates will better adjust for the impact of each individual?s unique physiology. Given the major changes that occur throughout adolescence, our expectation is that this adjustment will prove particularly useful when applying these models to children. This project has three specific aims:
Aim 1. To develop methods to estimate NO parameters in a modified deterministic 2CM that personalizes the airway length for each participant (Aim 1a) and/or more realistic airway shapes (Aim 1b) within a hierarchical Bayesian framework for NO parameter estimation.
Aim 2. To develop methods to estimate associations of potential determinants (e.g., environmental exposures) with NO parameters from the deterministic 2CM using a hierarchical Bayesian framework for cross-sectional (Aim 2a) and longitudinal multiple flow NO data (Aim 2b).
Aim 3. To disseminate resultant software in an R package (Aim 3a) and a web application, running directly in a browser using a newly converted JavaScript numerical library (Aim 3b). The outcome of this work will be refined statistical methods for studying airway and alveolar NO in both environmental epidemiology and clinical settings for children and adults. The development of web-based software will increase the likelihood of widespread adoption of these methods.
In this project, we propose to develop methods to ?meld? deterministic physiological models of exhaled nitric oxide?a biomarker of airway inflammation used in clinical asthma and environmental epidemiology applications?with statistical methods for estimating parameters that quantify airway vs. alveolar sources of nitric oxide. We propose to use a hierarchical Bayesian framework to incorporate two key types of outside information that have the potential to dramatically refine nitric oxide parameter estimation and our understanding of determinants of airway inflammation: measurements related to airway size, and data on potential determinants of inflammation, such as inhaled environmental exposures. By incorporating data related to airway size explicitly into the estimation process, we can personalize these models to each individual?s unique physiology. Given the major changes in physiology that occur throughout adolescence, our work will be particularly useful for children with asthma.
| Habre, Rima; Zhou, Hui; Eckel, Sandrah P et al. (2018) Short-term effects of airport-associated ultrafine particle exposure on lung function and inflammation in adults with asthma. Environ Int 118:48-59 |
