Nitric oxide (NO) was first detected in the exhaled breath more than a decade ago and has since been investigated as a noninvasive means of assessing lung inflammation. Exhaled NO arises from the airway and alveolar compartments, and new analytical methods have been developed to characterize these sources. A simple two-compartment model can adequately represent many of the observed experimental observations of exhaled concentration, including the marked dependence on exhalation flow rate. The model characterizes NO exchange by using three flow-independent exchange parameters. Two of the parameters describe the airway compartment (airway NO diffusing capacity and either the maximum airway wall NO flux or the airway wall NO concentration), and the third parameter describes the alveolar region (steady-state alveolar NO concentration). A potential advantage of the two-compartment model is the ability to partition exhaled NO into an airway and alveolar source and thus improve the specificity of detecting altered NO exchange dynamics that differentially impact these regions of the lungs. Several analytical techniques have been developed to estimate the flow-independent parameters in both health and disease. Future studies will focus on improving our fundamental understanding of NO exchange dynamics, the analytical techniques used to characterize NO exchange dynamics, as well as the physiological interpretation and the clinical relevance of the flow-independent parameters.

The most common technique employed to describe pulmonary gas exchange of nitric oxide (NO) combines multiple constant flow exhalations with a two-compartment model (2CM) that neglects 1) the trumpet shape (increasing surface area per unit volume) of the airway tree and 2) gas phase axial diffusion of NO. However, recent evidence suggests that these features of the lungs are important determinants of NO exchange. The goal of this study is to present an algorithm that characterizes NO exchange using multiple constant flow exhalations and a model that considers the trumpet shape of the airway tree and axial diffusion (model TMAD). Solution of the diffusion equation for the TMAD for exhalation flows >100 ml/s can be reduced to the same linear relationship between the NO elimination rate and the flow; however, the interpretation of the slope and the intercept depend on the model. We tested the TMAD in healthy subjects (n = 8) using commonly used and easily performed exhalation flows (100, 150, 200, and 250 ml/s). Compared with the 2CM, estimates (mean +/- SD) from the TMAD for the maximum airway flux are statistically higher (J'aw(NO) = 770 +/- 470 compared with 440 +/- 270 pl/s), whereas estimates for the steady-state alveolar concentration are statistically lower (CA(NO) = 0.66 +/- 0.98 compared with 1.2 +/- 0.80 parts/billion). Furthermore, CA(NO) from the TMAD is not different from zero. We conclude that proximal (airways) NO production is larger than previously predicted with the 2CM and that peripheral (respiratory bronchioles and alveoli) NO is near zero in healthy subjects.

BackgroundAsthma is a disease of varying severity and differing disease mechanisms. To date, studies aimed at stratifying asthma into clinically useful phenotypes have produced a number of phenotypes that have yet to be assessed for stability and to be validated in independent cohorts. The aim of this study was to define and validate, for the first time ever, clinically driven asthma phenotypes using two independent, severe asthma cohorts: ADEPT and U-BIOPRED.MethodsFuzzy partition-around-medoid clustering was performed on pre-specified data from the ADEPT participants (n = 156) and independently on data from a subset of U-BIOPRED asthma participants (n = 82) for whom the same variables were available. Models for cluster classification probabilities were derived and applied to the 12-month longitudinal ADEPT data and to a larger subset of the U-BIOPRED asthma dataset (n = 397). High and low type-2 inflammation phenotypes were defined as high or low Th2 activity, indicated by endobronchial biopsies gene expression changes downstream of IL-4 or IL-13.ResultsFour phenotypes were identified in the ADEPT (training) cohort, with distinct clinical and biomarker profiles. Phenotype 1 was “mild, good lung function, early onset”, with a low-inflammatory, predominantly Type-2, phenotype. Phenotype 2 had a “moderate, hyper-responsive, eosinophilic” phenotype, with moderate asthma control, mild airflow obstruction and predominant Type-2 inflammation. Phenotype 3 had a “mixed severity, predominantly fixed obstructive, non-eosinophilic and neutrophilic” phenotype, with moderate asthma control and low Type-2 inflammation. Phenotype 4 had a “severe uncontrolled, severe reversible obstruction, mixed granulocytic” phenotype, with moderate Type-2 inflammation. These phenotypes had good longitudinal stability in the ADEPT cohort. They were reproduced and demonstrated high classification probability in two subsets of the U-BIOPRED asthma cohort.ConclusionsFocusing on the biology of the four clinical independently-validated easy-to-assess ADEPT asthma phenotypes will help understanding the unmet need and will aid in developing tailored therapies.Trial registration NCT01274507 (ADEPT), registered October 28, 2010 and NCT01982162 (U-BIOPRED), registered October 30, 2013.Electronic supplementary materialThe online version of this article (doi:10.1186/s12931-016-0482-9) contains supplementary material, which is available to authorized users.

The reproducibility of nasal patency measurements was assessed by acoustic rhinometry and active rhinomanometry using previously described Toronto methodologies. Six subjects with normal upper airways were tested with both procedures on six separate occasions within a 2-month period. Topical decongestant was applied to minimize the effects of mucosal variation on the nasal airway. The mean coefficients of variation (mean +/- s.d; %) over time of the measurements were 8.1 +/- 4.1 and 9.7 +/- 5.2 for minimal unilateral cross-sectional area and 4.8 +/- 1.8 and 5.5 +/- 3.5 for nasal volume (0-5 cm) of the right and left sides, respectively. For active rhinomanometry, the mean coefficients of variation (mean +/- s.d.; %) over time of the measurements were 15.9 +/- 7.3, 12.9 +/- 4.6, and 8.5 +/- 2.8 for right, left and combined nasal airflow resistance. The intraclass correlation coefficient was 0.76, 0.70, and 0.96 for right, left, and combined nasal resistance, 0.91 and 0.87 for right and left minimal cross sectional area, and 0.86 and 0.69 for right and left nasal volumes, respectively, also confirming a high level of reproducibility for both methods. In conclusion, performed by an experienced operator under controlled circumstances, the reproducibility of both methods of nasal patency assessment compared favorably with many widely accepted clinical tests.