Abstract
Currently accepted techniques utilize the plateau concentration of nitric oxide (NO) at a constant exhalation flow rate to characterize NO exchange, which cannot sufficiently distinguish airway and alveolar sources. Using nonlinear least squares regression and a twocompartment model, we recently described a new technique (Tsoukias et al. J Appl Physiol 91: 477–487, 2001), which utilizes a preexpiratory breath hold followed by a decreasing flow rate maneuver, to estimate three flowindependent NO parameters: maximum flux of NO from the airways (J _{NO,max}, pl/s), diffusing capacity of NO in the airways (D _{NO,air}, pl · s^{−1} · ppb^{−1}), and steadystate alveolar concentration (C_{alv,ss}, ppb). In healthy adults (n = 10), the optimal breathhold time was 20 s, and the mean (95% intramaneuver, intrasubject, and intrapopulation confidence interval) J _{NO,max},D _{NO,air}, and C_{alv,ss} are 640 (26, 20, and 15%) pl/s, 4.2 (168, 87, and 37%) pl · s^{−1} · ppb^{−1}, and 2.5 (81, 59, and 21%) ppb, respectively. J _{NO,max}can be estimated with the greatest certainty, and the variability of all the parameters within the population of healthy adults is significant. There is no correlation between the flowindependent NO parameters and forced vital capacity or the ratio of forced expiratory volume in 1 s to forced vital capacity. With the use of these parameters, the twocompartment model can accurately predict experimentally measured plateau NO concentrations at a constant flow rate. We conclude that this new technique is simple to perform and can simultaneously characterize airway and alveolar NO exchange in healthy adults with the use of a single breathing maneuver.
 diffusing capacity
 airways
 alveolar
exhaled nitric oxide(NO) arises from the airways and alveoli in human lungs and continues to hold promise as a noninvasive marker of airway inflammation (4, 7, 22, 26, 28, 32, 35). However, reported exhaled NO concentrations vary widely in healthy and diseased populations (3, 10, 12, 13, 19, 21, 24, 27, 31). The variability can be attributed, in part, to differences in the technique, the origin of the exhaled NO (airway or alveolar source), and the presence of an inflammatory disease (11, 16, 18, 27).
The American Thoracic Society (ATS) and the European Respiratory Society recently recommended a constant exhalation flow rate (∼50 and ∼250 ml/s, respectively) breathing maneuver as the standardized procedure for collection of NO (15, 30). This technique utilizes the plateau concentration (C_{NO,plat}) to characterize NO metabolism or exchange. Although C_{NO,plat}at an exhalation flow rate of 50 and 250 ml/s is predominantly from the airway and alveolar compartments, respectively, C_{NO,plat}alone cannot fully characterize NO exchange in the airway and alveolar regions of the lungs. Therefore, we recently described a new technique (34), which utilizes a preexpiratory breath hold followed by a decreasing flow rate maneuver, to separately characterize airway and alveolar NO exchange dynamics. Characterization of the airways consists of two parameters: maximum flux of NO from the airways (J _{NO,max}, pl/s) and the diffusing capacity of NO in the airways (D _{NO,air}, pl · s^{−1} · ppb^{−1}). Characterization of alveolar exchange is accomplished using the steadystate alveolar concentration (C_{alv,ss}, ppb). The preexpiratory breath hold and decreasing flow rate exhalation sample a large range of gas bolus airway compartment residence times, which are necessary for characterization of all three parameters (34). Thus the technique takes advantage of the flow dependence of exhaled NO concentration to simultaneously estimate three flowindependent parameters. We hypothesize that the flowindependent parameters not only provide greater specificity for NO exchange dynamics but also can be used to accurately predict exhaled NO concentration at a constant flow rate.
Our initial description (34) focused on characterizing the intrinsic variance (intramaneuver) of the technique in estimating the parameters, i.e., the contribution to the variance in the parameter estimates due to limitations in the analytic instrumentation and the twocompartment model. However, variability within a subject (intrasubject) and within a population (intrapopulation) needs further characterization before the technique might be used as a clinical tool. Thus the aims of this study are fivefold: 1) to determine average values for the flowindependent parameters in a healthy population of adults without lung disease, 2) to characterize the intrasubject and intrapopulation variability in the flowindependent parameters, 3) to determine correlation of flowindependent parameters with standard spirometry [e.g., forced expiratory volume in 1 s (FEV_{1})], 4) to determine the optimal preexpiratory breathhold time, and 5) to demonstrate that the flowindependent parameters can be used in a twocompartment model to accurately predict exhaled NO concentration at a constant flow rate.
METHODS
Subjects.
Ten nonsmoking healthy adults, between 20 and 35 yr of age (6 men and 4 women), were recruited to participate in the study. Subjects were categorized as healthy on the basis of their standard spirometry [>80% of the predicted value of forced vital capacity (FVC), FEV_{1}, and FEV_{1}/FVC], the absence of pulmonary disease by history, and the absence of smoking and allergies by history_{.} The Institutional Review Board at the University of California, Irvine, approved the protocol, and informed consent was obtained from all subjects before the experiments.
Experimental protocol.
Standard spirometry (Vmax229, Sensormedics, Yorba Linda, CA) was performed in triplicate in all subjects to measure FVC and FEV_{1} before the exhaled NO measurements (Table1).
Before performing a single exhalation maneuver, each subject was allowed 3–5 min of comfortable tidal breathing. The subject then performed two types of exhalation maneuvers while wearing noseclips:1) vital capacity maneuvers in triplicate at a constant exhalation flow of ∼50 and ∼250 ml/s according to ATS and European Respiratory Society guidelines to determine C_{NO,plat} and2) five repetitions of a maneuver consisting of an inspiration of NOfree air from the Mylar bag to total lung capacity, a preexpiratory breath hold, and a decreasing flow rate exhalation. The breathhold time was 10, 20, 30, or 45 s, and during exhalation the expiratory flow rate progressively decreased from ∼6% to 1% of the vital capacity per second (34). During the breath hold, a positive pressure of >5 cmH_{2}O was maintained by the subject to prevent nasal contamination (30), and NO was sampled from an NOfree reservoir. Just before exhalation, a valve on the NOsampling line was changed to sample from the exhaled breath, and the exhalation valve was opened, allowing the patient to expire. Control of the exhalation flow rate was facilitated via a Starling resistor (Hans Rudolph, Kansas City, MO) with a variable resistance. A schematic of the experimental apparatus is presented in Fig.1, and details have been previously described (34).
Airstream analysis.
A rapidresponse chemiluminesence NO analyzer (model NOA280, Sievers, Boulder, CO) with a 10–90% response time of <200 ms was used to measure exhaled NO concentration. The sampling flow rate was adjusted to 200 ml/min with an operating reaction cell pressure of 7.4 mmHg. The instrument was calibrated on a daily basis using a certified NO gas (45 ppm in N_{2}, Sievers) tank and zero gas. The zero point calibration was performed with an NO filter (Sievers) and performed immediately before the collection of a profile. The flow rate and pressure signals were measured using a pneumotachometer (model RSS100, Hans Rudolph). The pneumotachometer was also calibrated daily and set to provide the flow in units of stpd and pressure in cmH_{2}O. The analog signals of NO, flow, and pressure were digitized using an analogtodigital card at a rate of 50 Hz and stored for further analysis.
Parameter estimation.
A previously described twocompartment model was used to estimate three flowindependent parameters (J
_{NO,max},D
_{NO,air}, and C_{alv,ss}) (32, 34,35). Figure 2 is a simple schematic of the twocompartment model and flowindependent parameters. Mathematical identification of the parameters has been previously described in detail (34), and only the salient features are presented here. Equation 1
is the governing equation for the model, which predicts the exhaled concentration (C_{exh}, ppb) as a function of the residence time (τ_{res}) of each differential bolus of air in the airway compartment, the volume of the airway compartment (V_{air}), and the remaining three parameters (J
_{NO,max},D
_{NO,air}, and C_{alv,ss})
Identification of the unknown parameters (J _{NO,max},D _{NO,air}, and C_{alv,ss}) is accomplished by nonlinear least squares utilizing a conjugated direction algorithm to minimize the sum of square of the residuals (R _{LS}) between the model's prediction and the experimental data. Figure 3 is a representative exhalation profile simulated by the model. The model does not precisely predict phases I and II of the exhalation profile, where the accumulated NO during breath holding in the conducting airways and transition region of the lungs exits the mouth. This discrepancy is attributed primarily to axial diffusion, which our model neglects. Although the precise shape of phase I cannot be accurately simulated with the model, the absolute amount of NO in phases I and II can be predicted. Thus our technique utilizes the information from phases I and II (where τ_{res} is large and, hence, the sensitivity to D _{NO,air} is high) by forcing the model to simulate the total amount of NO exiting in phases I and II of the exhalation in addition to simulating the precise C_{exh}over phase III. Thus the fitting of the experimental data includes a minimization of the sum of two terms: 1) the squared residual in the average concentrations in phases I and II weighted by the number of data points and 2) the sum of the squared residual of C_{exh} in phase III of the exhalation profile (34). To ensure complete emptying of the airway compartment after breath hold, we define the transition from phases II and III as the point in the exhalation for which the slope (dC_{exh}/dV, where V is volume) of the exhalation profile is zero.
An alternative presentation of the three flowindependent parameters includes the use of the mean (over radial position) tissue concentration in the airways (C̄_{tiss,air}), instead ofJ _{NO,max} (28).C̄_{tiss,air} is simply the ratioJ _{NO,max}/D _{NO,air}.
Statistical analysis.
One of the aims of the present study is to characterize the variability or uncertainty in the estimate of the flowindependent parameters. If one estimates the three parameters from a single maneuver from a single subject, the variability in the estimated values of the parameters is due to the intrinsic variability of the technique, which includes the accuracy of the model and the analytic instrumentation (intramaneuver). One can then repeat the same maneuver multiple times, and the variability in the repeated estimates is due to reproducibility of the breathing maneuver (intermaneuver or intrasubject). Finally, one can repeat the same series of breathing maneuvers across a population of individuals, and the variability is due to the intrinsic variation of the population (intersubject or intrapopulation). The intramaneuver variability has been described previously (34) and can be characterized by the 100(1 − α)% normalized confidence interval (ΔĪ
The normalized intrasubject (intermaneuver) confidence interval is defined by using the standard deviation (SD) of the estimate of each of the parameters for the five repeated maneuvers
RESULTS
The population mean for each of the parameters (Ĵ
_{NO,max},D̂
_{NO,air}, and Ĉ_{alv,ss}) is presented at the four different breathhold times in Fig.4. Ĵ
_{NO,max},D̂
_{NO,air}, and Ĉ_{alv,ss} do not depend significantly on the breathhold time and range from 610 to 647 pl/s, from 3.2 to 4.5 pl · s^{−1} · ppb, and from 2.5 to 2.8 ppb, respectively. In addition, mean values for ΔĪ
The effect of breathhold time on the population means of ΔĪ
On the basis of the above results, there is significant improvement in ΔĪ
There is no correlation between estimated flowindependent parameters and standard spirometry measurements (FVC and FEV_{1}/FVC) for healthy adults. In addition, there is no correlation between experimentally measured or modelpredicted C_{NO,plat} at exhalation flow rates (50 and 250 ml/s) and FVC or FEV_{1}/FVC (P ≥ 0.05)_{.}
Figure 6 presents the predicted C_{NO,plat} (using Eq. 1 with a fixed τ_{res} based on a constant exhalation flow rate) using population mean values from Table 2 (i.e., those determined utilizing a 20s breath hold) as a function of exhalation flow rate. Experimentally obtained C_{NO,plat} (mean ± SD) at flow rates of 4.2–1,550 from Silkoff et al. (27) are also shown as well as those obtained in the present study at ∼50 and ∼250 ml/s. The predicted C_{NO,plat} values are in very close agreement with the measured values from the present study but are lower than those of Silkoff et al. However, this difference is not significant (paired ttest with P > 0.05). The stippled region in Fig. 6 demonstrates the range of flow rates used to estimate the flowindependent parameters. Thus predictions of C_{NO,plat} outside this region represent extrapolation.
DISCUSSION
In this study, we further characterized our new technique (34) to determine three flowindependent NO exchange parameters in healthy adults. One or more of these parameters have been estimated in healthy adults by four previous studies (14, 22, 28,35). Each of these previous studies utilized the governing equations from the same twocompartment model, but each used a different breathing technique to estimate the parameters. All the previous studies utilized breathing techniques that require multiple constant exhalation flows. Table 3summarizes the results from healthy subjects by these previous studies. The values for the parameters estimated by our new technique are similar to those previously estimated.
It is remarkable to note that, independent of the technique employed, the intrapopulation variance in these parameters (as demonstrated by the 95% confidence interval) within a healthy population is substantial relative to other endogenously produced gases such as CO_{2}. The mechanisms underlying this variation are not known. One possibility is simply the size of the subject. For example,J
_{NO,max} and D
_{NO,air}depend on the surface area or volume participating in the exchange process. However, there is no correlation between the estimated values for J
_{NO,max} and D
_{NO,air}with V_{air} (r = 0.25, P = 0.48, and r = 0.30, P = 0.40, respectively). If one expresses these parameters per unit volume of the airway compartment by dividing by V_{air}, ΔĪ
The correlation of the flowindependent parameters with standard spirometry is of particular interest to the potential clinical application and interpretation of the flowindependent NO parameters. None of the flowindependent parameters is correlated with FVC or FEV_{1}/FVC, suggesting that these parameters are characterizing information other than lung volume or airway resistance. Recently, Silkoff et al. (28) reported elevatedJ _{NO,max} and D _{NO,air} in patients with bronchial asthma who had reduced FEV_{1}/FVC relative to healthy controls. Of interest is the fact that, within the asthmatic group, Silkoff et al. reported a positive correlation betweenJ _{NO,max} and FEV_{1}/FVC. Thus correlation between flowindependent NO parameters and airway resistance may depend on the presence of disease.
Although this technique has not characterized the flowindependent NO exchange parameters in populations with lung diseases, the large ΔĪ
We previously demonstrated theoretically that the intramaneuver variability of the parameter estimates would depend on the residence time of the air in the airway compartment (34) and, thus, on the breathhold time. Not surprisingly, theory predicted that breathhold time would affect largely the parameters that characterize the airway compartment (J _{NO,max} andD _{NO,air}), inasmuch as a longer breathhold time would increase the residence within the airway compartment.
As depicted schematically in Fig. 2, the net flux of NO from the airway compartment is the sum of two terms: 1)J
_{NO,max} and 2) −D
_{NO,air} ∗ C_{air}. Thus, if C_{air} is small enough (small residence times), the second term is negligible and the flux is entirely characterized byJ
_{NO,max} (i.e., D
_{NO,air}cannot be characterized). Hence, the variability ofD
_{NO,air} should be larger thanJ
_{NO,max}, and the variability in both parameters would be inversely related to breathhold time. Our data in healthy subjects are consistent with our theoretical prediction. ΔĪ
Breathhold time did not significantly affect the intrasubject or intrapopulation variability. This finding suggests that the reproducibility of the breathing maneuver does not depend strongly on the breathhold time, despite the fact that the effort on the part of the subject progressively increases with increasing breathhold time. Among our 10 healthy subjects, one (subject 7) is not able to hold his breath for 45 s. On the basis of these findings, we conclude that breathhold times >20 s do not provide significant improvement in the accuracy of the parameter estimates.
The accuracy in the estimate of J
_{NO,max} is significantly better than that of D
_{NO,air} and C_{alv,ss}, as evidenced by smaller intramaneuver and intrasubject confidence intervals. The improved ability to estimateJ
_{NO,max} is due primarily to the fact that the entire exhalation profile (phases I, II, and III) depends on the value of J
_{NO,max}. In contrast, the estimate ofD
_{NO,air} weakly depends on only phases I and II (breath holding), and the estimate of C_{alv,ss} depends primarily on phase III (decreasing flow rate portion). An additional source of variance for C_{alv,ss} is the fact that the limit of resolution of the instrument is ∼1 ppb, which is similar to the estimated values in healthy subjects (1–4 ppb). The accuracy of the estimate of D
_{NO,air} and C_{alv,ss}may improve in disease states, in which NO production is increased and the signaltonoise ratio improves. For example, NO elimination is dramatically increased in bronchial asthma (1, 17, 28), and thus a much larger NO signal should be attained during all phases of the exhalation profile, but particularly during phases I and II, which reflect production of NO from the airways. In addition, it has recently been demonstrated that alveolar concentration levels may increase two to threefold in alveolitis (20), which would also increase the signaltonoise ratio and thus reduce ΔĪ
The intramaneuver confidence interval is an a priori estimate of the uncertainty in the parameter estimate made from a single breathing maneuver. Thus ΔĪ
There are several possible confounding variables in the technique that may impact the parameter estimates. During inspiration, NO from the nasal cavity may be absorbed through the nasopharynx and the soft palate, which was not closed. Although this additional NO would be absorbed in the alveolar region during the breath hold and thus would not likely impact C_{alv,ss}, it may artificially increase the NO concentration in the airway compartment during the breath hold. If this amount of NO were significant, we would anticipate a larger effect at the shorter breathhold times, where the NO entrained from the nasal cavity would be a larger fraction of the total at the end of the breath hold; thus we would observe an inverse dependence between D _{NO,air} and/orJ _{NO,max} and the breathhold time. This concept can be demonstrated quantitatively by demonstrating that the relative sensitivity (34) of D _{NO,air} andJ _{NO,max} to the initial concentration is an inverse function of the breathhold time. Experimentally, these two parameters do not depend on the breathhold time (Fig. 4); thus it is unlikely that nasal NO is a significant confounding variable.
A second possible source of error is performing the spirometric breathing maneuvers before the NO breathing maneuver. Silkoff et al. (29) and Deykin et al. (8, 9) recently demonstrated that spirometry can depress exhaled NO levels by 10–36% from the baseline in healthy subjects and subjects with asthma. This may impact one or more of the flowindependent parameters and should be considered in any future studies.
A third possible source of error is the estimate in the airway compartment volume with the use of the subjects' ideal body weight (pounds) plus age (years). The estimate ofD _{NO,air} is a positive function of the estimate of V_{air}, whereas J _{NO,max} and C_{alv,ss} are nearly independent of V_{air}(34). The present technique could be combined with a nitrogen washout (Fowler method) to estimate dead space (23,25); however, the accuracy of the Fowler method is compromised by the presence of diseases that impact emptying patterns. The dependence of D _{NO,air} on V_{air} may explain some of the intersubject variability and suggests that intrasubject longitudinal changes in the flowindependent parameters may have the greatest clinical utility.
Finally, we previously demonstrated that, during a vital capacity maneuver at a constant exhalation flow rate that includes a 15s breath hold (35), the slope of phase III for the NO exhalation profile has a statistically negative slope. This could be due to a decreasing alveolar concentration and/or a decreasing flux of NO from the airway compartment. It is not likely due to a decreasing alveolar concentration, inasmuch as our laboratory previously demonstrated that the alveolar diffusing capacity for NO decreases with decreasing lung volume, which would serve to increase the alveolar concentration (33).
In contrast, the airways are somewhat flexible and will distend with inspiration and contract with expiration. During expiration, the airways contract slightly, which may decrease V_{air} as well as the surface area for exchange of NO between the airway wall and gas phase. A decrease in the surface area would decreaseD _{NO,air}. Interestingly, a decrease in V_{air} during expiration has no impact on the model equations, inasmuch as the concentrating effect of the smaller volume is precisely offset by the reduced residence time in the smaller volume (mathematical proof not shown). In addition, the loss of NO to the passing gas stream during expiration may decreaseC̄_{tiss,air}, which, in turn, would decrease the flux of NO from the airway wall (and J _{NO,max}) and create a negative phase III slope. Thus the flowindependent parameters may not be constant during a vital capacity maneuver but may depend on factors such as lung volume. Further investigation is necessary before it is known whether nuances such as lung volume need to be considered. Hence, the simplifying assumptions of the twocompartment model require that the flowindependent parameters be interpreted as global descriptors of NO exchange dynamics.
In summary, we have quantified flowindependent parameters (J _{NO,max}, D _{NO,air},C̄_{tiss,air}, and C_{alv,ss}) in a healthy adult population utilizing a technique that employs a breath hold followed by a decreasing flow rate maneuver. Mean population values compare favorably with previous reports, which utilized techniques requiring multiple breathing maneuvers. There is no correlation between the flowindependent NO parameters and FVC or FEV_{1}/FVC, suggesting that these NO parameters are providing different information regarding lung function. Importantly, we have also quantified the intramaneuver, intrasubject, and intrapopulation confidence intervals in healthy adults for each of the parameters. We conclude that there is significant variation within the population of healthy adults in terms of the magnitude of the parameters as well as the confidence interval. Thus longitudinal tracking within a given subject may provide the most useful information. In addition, J _{NO,max} can be estimated with the highest level of certainty and, therefore, may be the most useful parameter to monitor in disease states. Future studies must quantify these parameters in key inflammatory diseases such as bronchial asthma, cystic fibrosis, and chronic obstructive pulmonary disease before their potential clinical utility is known.
Acknowledgments
We acknowledge the staff of the General Clinical Research Center at the University of California, Irvine.
Footnotes

This work was supported by National Institutes of Health Grants R29HL60636, R0123969, and M01RR00827S1.

Address for reprint requests and other correspondence: S. C. George, Dept. of Chemical and Biochemical Engineering and Materials Science, 916 Engineering Tower, University of California, Irvine, Irvine, CA 926972575 (Email: scgeorge{at}uci.edu).

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 Copyright © 2001 the American Physiological Society