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Exhaled nitric oxide and clinical phenotypes of childhood asthma


Whether exhaled NO helps to identify a specific phenotype of asthmatic patients remains debated. Our aims were to evaluate whether exhaled NO (FENO0.05) is independently associated (1) with underlying pathophysiological characteristics of asthma such as airway tone (bronchodilator response) and airway inflammation (inhaled corticosteroid [ICS]-dependant inflammation), and (2) with clinical phenotypes of asthma.

We performed multivariate (exhaled NO as dependent variable) and k-means cluster analyses in a population of 169 asthmatic children (age ± SD: 10.5 ± 2.6 years) recruited in a monocenter cohort that was characterized in a cross-sectional design using 28 parameters describing potentially different asthma domains: atopy, environment (tobacco), control, exacerbations, treatment (inhaled corticosteroid and long-acting bronchodilator agonist), and lung function (airway architecture and tone).

Two subject-related characteristics (height and atopy) and two disease-related characteristics (bronchodilator response and ICS dose > 200 μg/d) explained 36% of exhaled NO variance. Nine domains were isolated using principal component analysis. Four clusters were further identified: cluster 1 (47%): boys, unexposed to tobacco, with well-controlled asthma; cluster 2 (26%): girls, unexposed to tobacco, with well-controlled asthma; cluster 3 (6%): girls or boys, unexposed to tobacco, with uncontrolled asthma associated with increased airway tone, and cluster 4 (21%): girls or boys, exposed to parental smoking, with small airway to lung size ratio and uncontrolled asthma. FENO0.05 was not different in these four clusters.

In conclusion, FENO0.05 is independently linked to two pathophysiological characteristics of asthma (ICS-dependant inflammation and bronchomotor tone) but does not help to identify a clinically relevant phenotype of asthmatic children.


Numerous studies have evaluated exhaled nitric oxide (NO) correlates in asthma. For instance, exhaled NO fraction (FENO) has been linked to atopy rather than to asthma per se which could be due to the underlying relationship between FENO and eosinophilic inflammation of airways[1]. We and others have emphasized that FENO is also linked to other intrinsic dimensions of asthma such as airway reactivity/tone [2, 3] and remodeling of airways[1, 4]. All these relationships may explain the complex and still debated relationship between exhaled NO and asthma control/severity[46]. Moreover, extrinsic factors also affect FENO such as tobacco exposure and asthma treatment[6, 7]. Finally, the epithelial surface of airways, which is linked to the height of the subject and possibly to sex, also affects exhaled NO[8]. Despite this considerable background, the usefulness of its assessment in clinical practice remains debated because of its multidimensional nature, precisely. Furthermore, all these intrinsic and extrinsic exhaled NO modifiers seem to contribute for a minor part of exhaled NO variance,[6] which constitutes a main limitation.

The recent study of Dweik and colleagues has shown that FENO may define an asthma phenotype. They demonstrated that their high FENO phenotype (FENO 0.05 > 35 ppb) was characterized by greatest airway reactivity, airflow limitation, hyperinflation, sputum eosinophilia and levels of symptoms[9]. Nevertheless, FENO levels were similar among patients with severe and non-severe asthma in this latter study. One may hypothesize that the establishment of one or several relationships between FENO and asthma characteristics (either clinical or physiological) is not sufficient to demonstrate that exhaled NO identifies a specific phenotype with clinical relevance, accordingly to a more focused definition of phenotype[10].

The aims of our study were therefore (1) to evaluate the strength of the relationships between exhaled NO and physiopathological asthma characteristics, and (2) to assess whether a specific clinical phenotype can be isolated using exhaled NO measurement. For that purpose, we used two different statistical approaches. The first approach determined the clinical and physiological correlates of exhaled NO, and the degree of FENO variance explained by the correlates. The second approach used a more complex statistical tool, namely cluster analysis, which describes the dimensions of disease without the need for arbitrary a priori assumptions about classification, and was specifically designed to test the hypothesis that FENO is associated with a phenotype of childhood asthma.


Design of the study

La Berma Cohort

This single centre cohort conducted in a secondary care out-hospital clinic enrolls asthmatic children since 1997. Since 2008, exhaled NO and clinical events were recorded. Levels of asthma control were systematically assessed using only two levels of GINA guidelines during past three months:[11] controlled versus partially/uncontrolled asthma (omitting lung function since PFT were obtained without treatment). Severe exacerbations, according to ATS/ERS definition, [12] and the number of days (1) with symptoms (GINA guidelines) [11] and (2) with systemic steroid were specifically recorded. This cohort has been declared to our regulatory agency for computer data collection (Commission Nationale Informatique et Libertés, n°1408710), and approval from the Ethics Committee of French learned Society of Pulmonology - SPLF was obtained (CEPRO 2009/019). All children and parents were informed of the prospective recording of clinical and physiological data.

Patients and criteria of selection from the cohort

We selected a sample of children, meeting the criteria of clinical (episodic symptoms of airflow obstruction with excluded alternative diagnoses) and functional (documented bronchodilator response based on FEV1 or sRaw)[13] diagnosis of asthma and who satisfied a full description of their asthma: these 28 variables (see Table 1) are categorized as (1) anthropometrics, (2) past history, (3) parental smoking (more than 5 cigarettes per day), level of control, treatment, and (3) pulmonary function. All data were those specifically determined at the time of only one visit, corresponding to routine evaluation in France. These variables allowed the assessment of three domains of asthma severity: level of current prescribed treatment, level of current baseline control of asthma and immediate past burden of asthma exacerbations, accordingly to Bush and Saglani[14]. The population included in the current retrospective, post hoc, database design study overlaps to some extent with the populations of children published previously[2, 6, 1517].

Table 1 Clinical and physiological characteristics of the asthmatic children

Exhaled NO (FENO, 0.05)

Exhaled NO was measured online, using the Nitric Oxide Analyzer (NIOX; Aerocrine AB; Solna, Sweden: measurement at a constant 50 mL/s expiratory flow rate: FENO,0.05). Measurements were performed according to the ERS/ATS guidelines before pulmonary function tests[18].

Pulmonary function tests (PFT)

All PFT were performed without inhaled treatment (bronchodilator or LABA/ICS association) on the day of the measurement, by the same operator (BM). Spirometry and plethysmographic measurement of specific airway resistance and thoracic gas volume were performed according to international guidelines and as previously described[13, 15, 19]. The bronchodilator response to salbutamol 400 μg: (post minus baseline)/baseline FEV1 was systematically assessed. Reference values were based on equations edited by Zapletal,[20] as commonly done in Europe[21].

Statistical analysis

First approach

Potential explanatory variable were: age, gender, height, atopy, tobacco exposure, control, treatment and pulmonary function tests. The association between the different explanatory variables and FENO was examined in a multiple linear regression model using the procedure for general linear models with log-transformed FENO values as the dependent variable. The multivariate analysis was performed with a backward selection method and variables with P values of less than 0.05 were retained in the FENO model.

Second approach

We used the same approach than Haldar and colleagues[22]. Briefly, a cluster analysis methodology was applied to define homogeneous groups of patients. Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. The number of principal components is usually less than the number of original variables (data reduction). To obtain reliable results, the minimal number of subjects providing usable data for the analysis should be five times the number of variables being analyzed (28 × 5 = 140). This transformation is defined in such a way that the first principal component has as high a variance as possible, and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (uncorrelated with) the preceding components. Then we requested a rotation of the resulting factors which follows completion of the analysis of the data[23]. It has been shown that the relaxed solution of k-means clustering, a common method of cluster analysis,[24] specified by the cluster indicators, is given by the PCA principal components, and thus PCA facilitates k-means clustering to find near-optimal solutions[25]. Cluster analysis allows the partitioning of data into meaningful subgroups (phenotypes), when the number of subgroups and other information about their composition may be unknown. We hypothesised that FENO measurement could be associated with one of these subgroups of asthmatic children. First, variables for the cluster analysis were selected using a principal component analysis (PCA). When considering variable selection for the cluster analysis, our aims were (1) to choose variables that were measured in clinical practice and contributed to the clinical evaluation of asthma, and to avoid choosing different variables that were representative of the same aspect of the disease as this would introduce further bias when the cluster analysis was performed. We thus performed PCA of our 28 commonly measured clinical variables. Orthogonal varimax rotation was performed and the results are summarized in table 2. To avoid weighting the analysis, we selected only one parameter that was representative of each factor. Two additional variables were also included (see Table 2 legend). Then, a uniform cluster analysis methodology was applied accordingly to Haldar and colleagues (dendrogram for estimation of the number of likely clusters that was further prespecified in a k-means cluster analysis)[22]. Finally, characteristics of clusters were compared using analysis of variance for continuous variables or Kruskal Wallis Rank test (non normal values) and χ2 test for proportions. Statistical analyses were performed using MedCalc 11.3.8 (Mariakerke, Belgium) and OpenStat (version 5) softwares.

Table 2 Orthogonal varimax rotation results


Between December 1, 2008 and April 14, 2010, 1001 NO measurements were performed in 592 asthmatic children (clinical diagnose) of which 398 had a positive bronchodilator response in their history. Of these 398 subjects, 169 met the criteria for inclusion in the cluster analysis (all the children fulfilling inclusion criteria were included). Non inclusion criteria were the absence of recent skin pricks test (n = 62), the absence of bronchodilator test on the day of visit (absence of treatment withdrawal, n = 130) and miscellaneous (n = 37). Table 1 shows the characteristics of the 169 asthmatic children.

First approach

In univariate analysis, FENO was more elevated in atopic than in non atopic children (35[19-57] pbb versus 12 [9-16] ppb, p < 0.001), less elevated in controlled children (20 [13 - 37] ppb versus 34 [16-57] ppb, p = 0.018) and more elevated in children with ICS dose ≤ 200 μg/d (19 [13-34] versus 31 [16-57], p = 0.031). FENO correlated with height (r = 0.29, p < 0.001), age (r = 0.25, p = 0.001), FEV1 (r = 0.18, p = 0.021) and bronchodilator response (r = 0.26, p < 0.001). FENO was not decreased by tobacco exposure in univariate analysis. The multivariate analysis demonstrated that 4 variables independently correlated with log FENO (model: r = 0.60, p < 0.001): atopy (r = 0.46, p < 0.001), height (r = 0.29 p < 0.001), bronchodilator response (r = 0.26, p = 0.011) and ICS dose > 200 μg/d (r = - 0.17, p = 0.019). Age, control and forced expiratory flows did not independently contribute to FENO variance.

Second approach

Orthogonal varimax rotation was performed and the results are summarized in table 2. Based on the pattern of loading we identified the factors as being representative of:

Factor 1: airway obstruction due to increased airway tonus (and airway to lung size ratio)

Factor 2: anthropometrics

Factor 3: treatment

Factor 4: severe exacerbation

Factor 5: atopy

Factor 6: FEV1 (airway remodeling)

Factor 7: symptoms

Factor 8: gender

Factor 9: tobacco smoke exposure

Communality of all variables was > 60% (excepting sRaw) and 74.8% of the total variance was explained by the factors (all factors had Eigenvalues > 1). The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.643.

From this result we determined the 9 variables selected for cluster analyses (we favored continuous variables): gender, height, parental smoking, ICS dose, number of days with symptoms and requiring oral steroid, FEV1/FVC, FEV1 post BD and exhaled NO. Two additional variables were selected (1) FEF50% /TLC post-BD (index of airway/lung size) and (2) bronchodilator response (index of airway tonus).

A four-cluster model best fitted the population dataset, which were the following (Table 3): Cluster 1 (47%) described a subgroup of asthmatic boys, unexposed to tobacco, with well-controlled asthma, Cluster 2 (26%) described a subgroup of girls, unexposed to tobacco, with well-controlled asthma (similar to Cluster 1, excepting gender), Cluster 3 (6%) described a subgroup of girls or boys, unexposed to tobacco, with uncontrolled asthma associated with increased airway tone, and Cluster 4 (21%) also described a subgroup of girls or boys, exposed to parental smoking (either father, mother or both), with small airway to lung size ratio and uncontrolled asthma. The only difference (related to gender) between Cluster 1 and 2 was their airway to lung size ratio (p = 0.016, Fisher test).

Table 3 Clinical and physiological characteristics according to the four clusters


The main results of our study is the demonstration that single-flow rate exhaled NO (FENO0.05) is independently associated with two main asthma pathophysiological characteristics, namely airway inflammation and airway tone, but that FENO0.05 does not help to distinguish a relevant clinical phenotype of childhood asthma in a cross-sectional assessment.

Design issues

We specifically assessed different components of asthma control definition, such as symptoms and exacerbations because whether severe exacerbation constitutes the ultimate expression of loss of control and/or a more unpredictable even remains controversial[12, 14]. Table 2 further showed that control and exacerbations were two different dimensions of asthma, which could be related to our pediatric population[14]. Only two levels of control were assessed (controlled versus partially/uncontrolled patients) because the achievement of control is the main clinical issue. Our percentage of controlled children (33%) is in accordance with a French cross-sectional study in childhood asthma[26]. Since a dose effect of ICS on exhaled NO can be demonstrated,[2] we used it as an indirect index of airway inflammation, which is an obvious short-cut inasmuch as the explained variance of FENO by ICS is only ~3% in our study. Sputum eosinophilia can be regarded as the gold standard measure of inflammation but is not routinely assessed in most centres. The recent study of Schleich and colleagues demonstrates that FENO is able to identify a sputum eosinophil count ≥ 3% with reasonable accuracy and thresholds which vary according to dose of ICS[27]. Finally, we deliberately chose to add two variables in the cluster analyses, namely post-bronchodilator FEF50% /TLC and bronchodilator response. The former is an index of airway size/lung size[28]. We hypothesized that such an index, which has been linked to the risk of airway responsiveness,[29] may influence symptoms. The latter may also be an important pathophysiological characteristic of asthma associated with poor clinical outcomes in childhood asthma[30].

Four clusters were identified in our pediatric population. The first two clusters can be considered similar when excluding gender and corresponds to the most prevalent group of asthmatic children in an out-hospital specialized clinic (73%, 123/169) with 75/123 children having partially/uncontrolled asthma. The remaining two clusters (27%, 46/169) were constituted of boys and girls with a more severe (or undertreated) disease (38/46 with partially/uncontrolled asthma, 20/46 with a recent exacerbation). Interestingly, these two clusters only differ by their underlying severity factors, namely increased airway tonus (cluster 3, 6% of the population) and parental tobacco exposure while having small airway/lung size ratio (cluster 4, 21%). The prevalence of cluster 3 is similar to that of difficult-to-treat patients (~5%), and may also constitute an asthma phenotype characterized by increased airway tone and lability[14, 30]. The fourth cluster segregates children exposed to passive smoking that have a more severe disease, which is in line with the results of a French cross-sectional study in 3431 children demonstrating that unacceptable asthma control was associated with passive exposure to parental tobacco smoke[26]. Overall, the phenotypes that have been identified by the cluster analysis can be a posteriori explained, which further validate the statistical approach to assess exhaled NO usefulness. It has to be emphasized that the degree of asthma control did not clearly differentiate the clusters in our study. Several explanations can be discussed. Firstly, our study deals with childhood asthma, this specific population is often partially controlled because some degree of exercise limitation (or symptoms) is often present and exacerbations are often unpredictable events, mostly related to viral infections [31, 32]. Secondly, normal lung function (under treatment) is the rule in asthmatic children [15, 17] and a « phenotype » of children exhibiting a decline in lung function is almost impossible to isolate[17]. Thirdly, therapeutic compliance (and inability to use the inhaler properly) in children may be difficult to obtain [33] that may further explain the presence of mild symptoms.

Usefulness of FENO0.05measurement

We confirm by our first statistical approach that FENO is linked to its classical modifiers such as height and atopy. The link with ICS dose is more controversial, but we previously evidenced such a link with a plateau effect of ICS[6]. We show that exhaled NO and bronchodilator response are linked, a result that was previously obtained by different research groups[2, 3, 9]. More interestingly, we demonstrate for the first time that the relationships between exhaled NO and both ICS dose (an indirect marker of airway inflammation) and bronchodilator response (a marker of airway tonus) are independent. Exhaled NO measurement is claimed to be an allergic inflammometer, but it is also a marker of airway smooth muscle tone[34].

The recent study of Dweik and colleagues has shown that a high FENO "phenotype" (FENO 0.05 > 35 ppb) was characterized by greatest airway reactivity, airflow limitation, hyperinflation, sputum eosinophilia and levels of symptoms[9]. Consequently, our results (first statistical approach) are in agreement with their data, but we further suggest that FENO is not specifically associated with a clinically relevant phenotype in asthmatic children. Haldar and colleagues elegantly demonstrated that eosinophilic inflammation helps to characterize adult asthma phenotypes[22]. Consequently our results may seem at variance, but exhaled NO and airway eosinophilia could be discordant in childhood[14]. In summary, to our best knowledge, this is the first study in childhood asthma, showing that exhaled NO does help to describe a clinically useful phenotype despite its ability to describe underlying pathophysiology (inflammation and modified airway smooth muscle function).

Limitations of the study

Principal among these is the cluster analysis methodology. The use of an algorithm that separates the population into discrete clusters may not be realistic. The limited size of our out-hospital population, the restricted analysis of childhood asthma (that could be a more homogeneous disease per se), our choice of clustering parameters and the loss of clinical material through attrition of the data set may have introduced some bias, but we hypothesized that the clinical relevance of exhaled NO should be "easily" demonstrated. It has to be stated that, in a cross-sectional design mainly assessing asthma control, FENO0.05 was not associated with a specific cluster, which is in accordance with recent trials failing to demonstrate the clinical usefulness of this measure for asthma control[35].


It has to be emphasized that, in a cross-sectional design mainly assessing asthma control, FENO0.05 was not associated with a specific cluster, which is in accordance with recent trials failing to demonstrate the clinical usefulness of this measure for asthma control[35]. Whether peripheral airway/alveolar NO concentration after correction for axial NO back-diffusion, which is elevated in a subset of asthmatic patients (~25%), could help to identify a specific "phenotype" of asthma warrants further studies[4, 36, 37].

In conclusion, FENO0.05 is independently linked to two pathophysiological characteristics of childhood asthma (ICS-dependant inflammation and bronchomotor tone) but does not help to identify a clinically relevant phenotype of asthmatic children in a cross-sectional analysis of routinely recorded parameters.


  1. 1.

    Mahut B, Delclaux C, Tillie-Leblond I, Gosset P, Delacourt C, Zerah-Lancner F, Harf A, de Blic J: Both inflammation and remodeling influence nitric oxide output in children with refractory asthma. J Allergy Clin Immunol. 2004, 113 (2): 252-256. 10.1016/j.jaci.2003.10.038.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Mahut B, Trinquart L, Bokov P, Peiffer C, Delclaux C: The link between exhaled NO and bronchomotor tone depends on the dose of inhaled steroid in asthma. Respir Med. 2010, 104 (7): 945-50. 10.1016/j.rmed.2010.02.003.

    Article  PubMed  Google Scholar 

  3. 3.

    Puckett JL, Taylor RW, Leu SY, Guijon OL, Aledia AS, Galant SP, George SC: An elevated bronchodilator response predicts large airway inflammation in mild asthma. Pediatr Pulmonol. 2010, 45 (2): 174-181. 10.1002/ppul.21172.

    Article  PubMed  Google Scholar 

  4. 4.

    Mahut B, Trinquart L, Le Bourgeois M, Becquemin MH, Beydon N, Aubourg F, Jala M, Bidaud-Chevalier B, Dinh-Xuan AT, Randrianarivelo O, et al: Multicentre trial evaluating alveolar NO fraction as a marker of asthma control and severity. Allergy. 2010, 65 (5): 636-644. 10.1111/j.1398-9995.2009.02221.x.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Mahut B, Delacourt C, Zerah-Lancner F, De Blic J, Harf A, Delclaux C: Increase in alveolar nitric oxide in the presence of symptoms in childhood asthma. Chest. 2004, 125 (3): 1012-1018. 10.1378/chest.125.3.1012.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Mahut B, Peiffer C, Thibaudon M, Chevalier-Bidaud B, Defrance-Hutinet MF, Trinquart L, Delclaux C: What does a single exhaled nitric oxide measurement tell us in asthmatic children?. J Asthma. 2009, 46 (8): 810-814.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Laoudi Y, Nikasinovic L, Sahraoui F, Grimfeld A, Momas I, Just J: Passive smoking is a major determinant of exhaled nitric oxide levels in allergic asthmatic children. Allergy. 2010, 65 (4): 491-497. 10.1111/j.1398-9995.2009.02190.x.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Delclaux C, Mahut B, Zerah-Lancner F, Delacourt C, Laoud S, Cherqui D, Duvoux C, Mallat A, Harf A: Increased nitric oxide output from alveolar origin during liver cirrhosis versus bronchial source during asthma. Am J Respir Crit Care Med. 2002, 165 (3): 332-337.

    Article  PubMed  Google Scholar 

  9. 9.

    Dweik RA, Sorkness RL, Wenzel S, Hammel J, Curran-Everett D, Comhair SA, Bleecker E, Busse W, Calhoun WJ, Castro M, et al: Use of exhaled nitric oxide measurement to identify a reactive, at-risk phenotype among patients with asthma. Am J Respir Crit Care Med. 2010, 181 (10): 1033-1041. 10.1164/rccm.200905-0695OC.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Han MK, Agusti A, Calverley PM, Celli BR, Criner G, Curtis JL, Fabbri LM, Goldin JG, Jones PW, Macnee W, et al: Chronic obstructive pulmonary disease phenotypes: the future of COPD. Am J Respir Crit Care Med. 2010, 182 (5): 598-604. 10.1164/rccm.200912-1843CC.

    Article  PubMed  Google Scholar 

  11. 11.

    The Global Initiative for Asthma (GINA). 2007, (accessed on october, 2nd 2010)., []

  12. 12.

    Reddel HK, Taylor DR, Bateman ED, Boulet LP, Boushey HA, Busse WW, Casale TB, Chanez P, Enright PL, Gibson PG, et al: An official American Thoracic Society/European Respiratory Society statement: asthma control and exacerbations: standardizing endpoints for clinical asthma trials and clinical practice. Am J Respir Crit Care Med. 2009, 180 (1): 59-99. 10.1164/rccm.200801-060ST.

    Article  PubMed  Google Scholar 

  13. 13.

    Mahut B, Peiffer C, Bokov P, Delclaux C, Beydon N: Use of specific airway resistance to assess bronchodilator response in children. Respirology. 2011, 16 (4): 666-671. 10.1111/j.1440-1843.2011.01953.x.

    Article  PubMed  Google Scholar 

  14. 14.

    Bush A, Saglani S: Management of severe asthma in children. Lancet. 2010, 376 (9743): 814-825. 10.1016/S0140-6736(10)61054-9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Mahut B, Bokov P, Delclaux C: Abnormalities of plethysmographic lung volumes in asthmatic children. Respir Med. 2010, 104 (7): 966-971. 10.1016/j.rmed.2010.01.015.

    Article  PubMed  Google Scholar 

  16. 16.

    Mahut B, Peiffer C, Bokov P, Beydon N, Delclaux C: Gas trapping is associated with severe exacerbation in asthmatic children. Respir Med. 2010, 104 (8): 1230-1233. 10.1016/j.rmed.2010.05.005.

    Article  PubMed  Google Scholar 

  17. 17.

    Mahut B, Trinquart L, Bokov P, Peiffer C, Delclaux C: Lung Function Impairment Evidenced by Sequential Specific Airway Resistance in Childhood Persistent Asthma: A Longitudinal Study. J Asthma. 2010, 47 (6): 655-9. 10.3109/02770901003615786.

    Article  PubMed  Google Scholar 

  18. 18.

    ATS/ERS recommendations for standardized procedures for the online and offline measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide, 2005. Am J Respir Crit Care Med. 2005, 171 (8): 912-930.

  19. 19.

    Mahut B, Trinquart L, Bokov P, Peiffer C, Delclaux C: The link between exhaled NO and bronchomotor tone depends on the dose of inhaled steroid in asthma. Respir Med. 2010, 104 (7): 945-950. 10.1016/j.rmed.2010.02.003.

    Article  PubMed  Google Scholar 

  20. 20.

    Zapletal A, Samanek M: [Flow resistance of airways and pulmonary flow resistance in children and juveniles. Normal values and their significance for the evaluation of airway obstruction]. Cesk Pediatr. 1977, 32 (9): 513-522.

    CAS  PubMed  Google Scholar 

  21. 21.

    Quanjer PH, Borsboom GJ, Kivastik J, Merkus PJ, Hankinson JL, Houthuijs D, Brunekreef B, Ihorst G, Kuhr J: Cross-sectional and longitudinal spirometry in children and adolescents: interpretative strategies. Am J Respir Crit Care Med. 2008, 178 (12): 1262-1270. 10.1164/rccm.200805-700OC.

    Article  PubMed  Google Scholar 

  22. 22.

    Haldar P, Pavord ID, Shaw DE, Berry MA, Thomas M, Brightling CE, Wardlaw AJ, Green RH: Cluster analysis and clinical asthma phenotypes. Am J Respir Crit Care Med. 2008, 178 (3): 218-224. 10.1164/rccm.200711-1754OC.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Kaiser H: The varimax criterion for analytic rotation in factor analysis. Psychometrika. 1958, 23: 187-200. 10.1007/BF02289233.

    Article  Google Scholar 

  24. 24.

    Hartigan JA, Wong MA: Algorithm AS 136: a K-means clustering algorithm. Appl Stat. 1978, 28: 100-108.

    Article  Google Scholar 

  25. 25.

    Ding C, He X: K-means Clustering via Principal Component Analysis. Proc of Int'l Conf Machine Learning (ICML 2004): 2004. 2004, 225-232.

    Google Scholar 

  26. 26.

    de Blic J, Boucot I, Pribil C, Robert J, Huas D, Marguet C: Control of asthma in children: still unacceptable? A French cross-sectional study. Respir Med. 2009, 103 (9): 1383-1391. 10.1016/j.rmed.2009.03.006.

    Article  PubMed  Google Scholar 

  27. 27.

    Schleich FN, Seidel L, Sele J, Manise M, Quaedvlieg V, Michils A, Louis R: Exhaled nitric oxide thresholds associated with a sputum eosinophil count >= 3% in a cohort of unselected patients with asthma. Thorax. 2010, 65 (12): 1039-1044. 10.1136/thx.2009.124925.

    Article  PubMed  Google Scholar 

  28. 28.

    Merkus PJ, Borsboom GJ, Van Pelt W, Schrader PC, Van Houwelingen HC, Kerrebijn KF, Quanjer PH: Growth of airways and air spaces in teenagers is related to sex but not to symptoms. J Appl Physiol. 1993, 75 (5): 2045-2053.

    CAS  PubMed  Google Scholar 

  29. 29.

    Litonjua AA, Sparrow D, Weiss ST: The FEF25-75/FVC ratio is associated with methacholine airway responsiveness. The normative aging study. Am J Respir Crit Care Med. 1999, 159 (5 Pt 1): 1574-1579.

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Sharma S, Litonjua AA, Tantisira KG, Fuhlbrigge AL, Szefler SJ, Strunk RC, Zeiger RS, Murphy AJ, Weiss ST: Clinical predictors and outcomes of consistent bronchodilator response in the childhood asthma management program. J Allergy Clin Immunol. 2008, 122 (5): 921-928 e924. 10.1016/j.jaci.2008.09.004.

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Mahut B, Refabert L, Marchac V, Iniguez JL, Aubertin G, Tamalet A, Lebras MN, Troadec C, Chatellier G, Delclaux C: Influenza-like illness responsible for severe exacerbations in asthmatic children during H1N1 pandemic: a survey before vaccination. J Asthma. 2011, 48 (3): 224-227. 10.3109/02770903.2011.555032.

    Article  PubMed  Google Scholar 

  32. 32.

    Mahut B, Trinquart L, Delclaux C: Influence of age on the risk of severe exacerbation and asthma control in childhood. J Asthma. 2011, 48 (1): 65-68. 10.3109/02770903.2010.529225.

    Article  PubMed  Google Scholar 

  33. 33.

    Megas F, Benmedjahed K, Lefrancois G, Mueser M, Dusser D: [The "Compli'Asthme" therapeutic observation survey on good use of inhaled drugs for asthma: perception by general practitioners]. Rev Pneumol Clin. 2004, 60 (3): 158-165.

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Tadie JM, Henno P, Leroy I, Danel C, Naline E, Faisy C, Riquet M, Levy M, Israel-Biet D, Delclaux C: Role of nitric oxide synthase/arginase balance in bronchial reactivity in patients with chronic obstructive pulmonary disease. Am J Physiol Lung Cell Mol Physiol. 2008, 294 (3): 489-497.

    Article  Google Scholar 

  35. 35.

    Petsky HL, Cates CJ, Li A, Kynaston JA, Turner C, Chang AB: Tailored interventions based on exhaled nitric oxide versus clinical symptoms for asthma in children and adults. Cochrane Database Syst Rev. 2009, CD006340-4

  36. 36.

    Mahut B, Delclaux C: Peripheral airway/alveolar nitric oxide concentration in asthma. Thorax. 2010, E-pub Alert

    Google Scholar 

  37. 37.

    Puckett JL, Taylor RW, Leu SY, Guijon OL, Aledia AS, Galant SP, George SC: Clinical patterns in asthma based on proximal and distal airway nitric oxide categories. Respir Res. 2010, 11: 47-10.1186/1465-9921-11-47.

    Article  PubMed  PubMed Central  Google Scholar 

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Correspondence to Christophe Delclaux.

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The authors declare that they have no competing interests.

Authors' contributions

BM carried out the measurements, participated in the design of the study and drafted the manuscript. SP performed the statistical analysis and helped to draft the manuscript. CD2 conceived of the study, participated in its design and helped to draft the manuscript. All authors read and approved the final manuscript.

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Mahut, B., Peyrard, S. & Delclaux, C. Exhaled nitric oxide and clinical phenotypes of childhood asthma. Respir Res 12, 65 (2011).

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  • Nitric Oxide
  • Asthma
  • Asthma Control
  • Asthmatic Child
  • Tobacco Exposure