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COPD: Journal of Chronic Obstructive Pulmonary Disease Volume 17, 2020 - Issue 4
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Research Article

Fractional Exhaled Nitric Oxide as an Inflammatory Biomarker in Chronic Obstructive Pulmonary Disease (COPD) with or without Concurrent Diagnosis of Asthma: The Canadian Cohort Obstructive Lung Disease (CanCOLD)

Seyed-Mohammad-Yousof Mostafavi-Pour-Manshadia Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, McGill University, Montreal, CanadaORCID Iconhttps://orcid.org/0000-0001-8183-4901View further author information
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Nafiseh Naderia Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, McGill University, Montreal, CanadaView further author information
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Palmina Mancinoa Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, McGill University, Montreal, CanadaView further author information
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Pei Zhi Lia Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, McGill University, Montreal, CanadaView further author information
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Wan Tanb UBC Centre for Heart Lung Innovation, St Paul’s Hospital, Vancouver, CanadaORCID Iconhttps://orcid.org/0000-0001-9289-3595View further author information
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Jean Bourbeaua Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, McGill University, Montreal, Canada;c Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, CanadaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-7649-038XView further author information
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Pages 355-365 | Received 06 Jan 2020, Accepted 31 May 2020, Published online: 25 Jun 2020

Abstract

We studied whether fractional exhaled nitric oxide (FENO) can differentiate chronic obstructive pulmonary disease (COPD) with concurrent diagnosis of asthma from COPD-only as well as its ability to predict disease severity and progression.

This study was embedded in the Canadian Cohort Obstructive Lung Disease (CanCOLD). Subjects of ≥40 years old completed FENO measurements were subdivided into four groups, including COPD (N = 86 [COPD-only (N = 35) and COPD with concurrent diagnosis of asthma (N = 51)], healthy (N = 72), and at risk (N = 151). Three of the most common clinical definitions were used for characterizing COPD with concurrent diagnosis of asthma: 1) atopy and self-reported physician diagnosis of asthma, 2) ≥12% and ≥200 ml post-bronchodilator FEV1; 3) self-reported physician diagnosis of asthma. FENO values were classified using quartiles and the American Thoracic Society (ATS) guideline 2011.

Compared to COPD-only, more COPD with concurrent diagnosis of asthma had a significant FENO50 level of ≥ 33.5 ppb (fourth quartile) than COPD-only (p = 0.045, 0.011, and 0.006, for definition 1, 2, and 3, respectively). Considering the ATS guideline 2011, fewer COPD with concurrent diagnosis of asthma had FENO50 < 25 than COPD-only, which was statistically significant with definition 1 and 3 (p = 0.038 and 0.026, respectively).

FENO as a biomarker has the potential to be used as a complementary value for differentiating COPD with concurrent diagnosis of asthma from COPD-only. Further studies should be conducted on validated definitions of COPD with concurrent diagnosis of asthma, which may include a reference to the type of airway inflammation in addition to the clinical definition.

Introduction

Asthma and chronic obstructive pulmonary disease (COPD) are two common chronic inflammatory airway diseases that share some common clinical features. Asthma can present in all age groups; it is characterized by chronic airway inflammation with airway hyperresponsiveness (AHR) and airflow limitation reversibility [Citation1–3]. COPD is present in adults over 40 years old and most commonly in individuals who have been smoking characterized by chronic inflammation of the small airways and the parenchyma, and persistent, progressive, and incomplete reversible airflow limitation. COPD is confirmed by spirometry either with a post-bronchodilator ratio of forced expiratory volume in 1 s (FEV1) over forced vital capacity (FVC) <0.7 [Citation3–5] or as a lower limit of normal (LLN), for instance by using the equation of National Health and Nutrition Examination Survey (NHANES) III data set [6–11]. Although COPD and asthma have distinct clinical and pathophysiological features, there is no definitive test allowing clinicians to confirm when COPD is associated with concomitant asthma [Citation12]. According to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) report 2020 [Citation13], it is no longer referred to asthma and COPD overlap (ACO), in fact, asthma and COPD are two different disorders. However, they may coexist in individual patients (COPD with concurrent diagnosis of asthma), and some common traits and clinical features might be shared by them. Patients with features of both COPD and asthma have been associated with increased respiratory symptoms, more exacerbations, and poorer outcomes than those COPD-only [Citation14–17]. Several diagnostic criteria have been developed by national and international societies for the diagnosis of patients, who share features of both COPD and asthma [Citation18–21], but there are limitations in applying some of these criteria in clinical practice. There is no test or biomarker easy to measure in order to help characterize individual COPD patients with asthma and guide therapeutic decision.

Fractional exhaled nitric oxide (FENO) is an easy, reproducible, sensitive, and noninvasive marker for identifying eosinophilic airway inflammation in asthma and is well established in research [Citation22–24]. The American Thoracic Society clinical practice guideline 2011 [Citation25] suggests using FENO in asthma management as well as recognizing and monitoring eosinophilic airway inflammations and inhaled corticosteroid treatment responses. Considering that, some patients with COPD share features of asthma; it has been suggested that FENO could have a complementary value of indicating the presence of concomitant asthma. There is no recommendation to the general use of FENO for COPD patients in clinical practice. According to a recent scoping review [Citation26], it was demonstrated that FENO level is higher in COPD patients with concurrent diagnosis of asthma than COPD-only; however, it is still unclear if there exists a FENO cutoff that could be used to characterize COPD patients with concurrent diagnosis of asthma to guide the prescription of an inhaled corticosteroid (ICS)/glucocorticoid (GCS) therapy in COPD patients. The exact role of FENO in COPD patients with concurrent diagnosis of asthma to differentiate them from those having COPD-only remains unclear. We hypothesized that FENO as a surrogate marker of eosinophilic and asthmatic airway inflammation [Citation1, Citation27–29] could be a useful noninvasive marker to identify COPD patients with concurrent diagnosis of asthma, and therefore, may be used in future studies to guide treatment, e.g. in prescribing ICS containing regimens. The present study embedded in a prospective cohort study, the Canadian Cohort Obstructive Lung Disease (CanCOLD) aimed: i) to describe the characteristics of subjects with COPD and FENO levels in a sample of the general population; ii) to assess if FENO could be used to differentiate COPD patients with concurrent diagnosis of asthma (applying three commonly used clinical definitions) from COPD patients without concurrent diagnosis of asthma and; iii) to assess if FENO can predict risk of disease severity (lung function, exacerbations, and patient-reported outcomes) and disease progression (FEV1 annual decline) in COPD.

Methods

Study design

This study was embedded in CanCOLD, which is a prospective longitudinal cohort that includes individuals recruited in 9 cities across Canada. Men and women, ≥40 years of age identified by random digit dialing from the general population and invited for questionnaire andspirometry (pre- and post-bronchodilator). Subjects were defined as having COPD based on airflow obstruction in -post-bronchodilator using a lower limit of normal (LLN) [Citation6, Citation7]. Participants were followed up at 18 months and 3 years. The CanCOLD study clinicaltrials.gov number registration is NCT00920348.

Study population

For the purpose of this study embedded in CanCOLD, we included all COPD subjects [including nonsmoker COPD [Citation30] subjects] within the CanCOLD population, defined as LLN of post-bronchodilator FEV1/FVC, using the equation of National Health and Nutrition Examination Survey (NHANES) III data set [6–11]. Individuals were subdivided into subsets of COPD, at risk and healthy: i) COPD defined as having airflow obstruction in post-bronchodilator using LLN, and then subdivided in GOLD 1 and 2+; ii) at risk defined as ever smoker without airflow obstruction; and iii) healthy defined as never smoker and without airflow obstruction. We identified COPD patients with concurrent diagnosis of asthma among COPD subjects and compared characteristics of these two groups (COPD with concurrent diagnosis of asthma versus COPD-only). Currently, there are no globally accepted criteria for identifying COPD patients with concurrent diagnosis of asthma, therefore, subjects were defined by using three commonly used clinical definitions and features based on the literature and expert opinions [Citation15, Citation31–35]: 1) atopy and self-reported physician diagnosis of asthma, 2) ≥12% and ≥200 ml of increment in the FEV1 post-bronchodilator; 3) self-reported physician diagnosis of asthma. According to a recent study [Citation34], definitions 1 and 3 were selected because they were the most stable over time among seven definitions. The protocol of this study was approved by the research ethics board (REB) of the research institute of McGill University Health Center (RI-MUHC, REB approval number 2010-1897), and all participants gave written informed consent.

Study procedures and measurements

Tests performed in CanCOLD in all three visits (at baseline, 18 months, and 3 years) [Citation36] included blood tests, St George’s Respiratory Questionnaire (SGRQ), and COPD assessment test (CAT). For the purpose of this study, individuals in 2 CanCOLD sites had additional tests completed as part of a regular visit in CanCOLD or on a separate visit. These additional tests included FENO50, immunoglobulin E (IgE) and blood eosinophils, repeated pre- and post-bronchodilator spirometry. Socio-demographic and baseline characteristics were collected at the time of the visit. Information of acute exacerbations of COPD (AECOPD) was collected prospectively by scheduled phone calls/online or clinic visits every 3 months. AECOPD was defined as an event in the natural course of the disease characterized by a change in the patient’s baseline symptoms including dyspnea, cough, and/or sputum that is beyond normal day-to-day variations; regular medications; health care utilizations, i.e. unplanned physician visits, emergency visits, and hospitalizations. Disease severity was defined according to the level of FEV1, CAT, and SGRQ as well as exacerbations. Disease progression was defined according to FEV1 annual decline, CAT, and SGRQ score.

Terminology of fractional exhaled nitric oxide

According to the recent recommendation [Citation37], this format, FENO, was used for the fractional concentration of exhaled nitric oxide in the gas phase [parts per billion (ppb)]. The exhalation flow rate was given as a subscript in mLs−1. For instance, a flow rate of 50 mLs−1 was written FENO50.

FENO50 measurement

FENO50 was performed according to the American Thoracic Society (ATS)/European Respiratory Society (ERS) [Citation38] guideline using the Niox Mino [Circassia (formerly Aerocrine), New York, USA]. Patients were advised to avoid eating, drinking, smoking, and doing exercise at least one hour before the test. We performed the test to obtain two quality measures with a difference no more than 10%. If subjects had a lower or upper respiratory infection, the FENO50 measurement was deferred until recovery. Patients who were unable to provide two quality and reproducible FENO50 measures were excluded from the study. Patients’ respiratory medications were recorded. FENO50 measurement was performed before spirometry tests as well as other respiratory maneuvers according to the ATS/ERS guideline [Citation38]. Subjects were instructed how to do the test. First, the subject exhaled completely, then inhaled NO-free air to their total lung capacity through the device and then exhaled fully in a constant speed at a flow rate of 50 mL/s for 10 s [Citation38]. We used a mirror as a visual aid provided by Circassia (formerly Aerocrine) to help subjects keep their speed of exhalation constantly; furthermore, the device had a sound (as an aid) for this purpose as well. We recorded and reported the mean of two quality FENO50 values (within 10%) for each subject. FENO50 values were classified as quartiles and also according to the ATS 2011 guideline [Citation25].

Blood biomarker measurements

Blood samples were obtained on the same day as FENO50 measurement to determine blood eosinophil count and percentage, and to quantify the level of serum IgE. The cutoff value of high blood eosinophil count and percentage, and total serum IgE level were set at >0.45 × 109 cells/L, > 4%, and > 250 IU/mL, respectively, according to the reference range of the clinical laboratory at our hospital.

Statistical analysis

Results are expressed as mean ± standard deviation (SD) or median (interquartile range) for continuous variables, and count (proportion) for category variables. Continuous variables were compared using the T-test for those that were normally distributed and/or Wilcoxon Mann Whitney for those that were not normally distributed. Chi-square or Fisher exact test was used to compare categorical variables. FENO50 values used as continuous variables and also classified as quartiles according to ATS 2011 guideline [Citation20] were compared between COPD-only and definitions of COPD with concurrent diagnosis of asthma. Univariate and multiple logistic regression models were conducted as well to see whether there is any association between FENO50 and COPD with concurrent diagnosis of asthma definitions among all COPD subjects, and models were adjusted for age, sex, and current smoking. We performed constructing receiver operating characteristic (ROC) curve and measured the area under the curve (AUC). Different cutoff values were determined, quantifying sensitivity and specificity of the test, false negative, and positive rates over cut-point values. Statistical significance was defined as p < 0.05. Analyses were performed using SAS version 9.4.

Results

A total of 315 CanCOLD subjects were invited to participate in the study. Of whom, 309 subjects completed the study (). Six subjects were not able to perform quality FENO50 measurements; 86 subjects qualified as COPD defined as LLN of post-bronchodilator FEV1/FVC in the study.

Figure 1. Study participant flow diagram CanCOLD: Canadian Cohort Obstructive Lung Disease; FeNO: Fractional exhaled nitric oxide; COPD: Chronic obstructive pulmonary disease; GOLD: Global Initiative for Chronic Obstructive Lung Disease.

Figure 1. Study participant flow diagram CanCOLD: Canadian Cohort Obstructive Lung Disease; FeNO: Fractional exhaled nitric oxide; COPD: Chronic obstructive pulmonary disease; GOLD: Global Initiative for Chronic Obstructive Lung Disease.

Baseline characteristics of the whole population

shows the baseline characteristics among COPD, at risk, and healthy subjects. Subjects were similar in age and sex; more current smokers in the at risk than those with COPD. COPD subjects, especially those with GOLD 2+ had more dyspnea, lower pulmonary function and health status, and more likely to be prescribed an inhaled respiratory medication.

Table 1. Baseline characteristics among all study subjects (COPD, healthy, and at risk).Table Footnote*

Baseline biomarkers of the whole population

shows blood and exhaled breath biomarkers among study subjects. COPD subjects had a higher level of IgE and blood eosinophil count than those at risk and healthy subjects (not statistically significant). The difference between mean FENO50 levels among the groups [COPD (GOLD 1 and GOLD 2+), healthy, and at risk] was not statistically significant. There were more COPD subjects with FENO50 level 33.5> ppb ≥22.5 ppb (third quartile) and FENO50 level 33.5≥ ppb (fourth quartile) than those at risk or healthy subjects.

Table 2. Baseline biomarkers among all study subjects (COPD, healthy, and at risk).Table Footnote*

COPD with concurrent diagnosis of asthma and COPD-only

shows the baseline characteristics and FENO50 levels of COPD subjects with concurrent diagnosis of asthma according to the three pre-specified definitions (defs) and those with COPD-only. The subjects with the three predefined definitions of COPD with concurrent diagnosis of asthma compared to those with COPD-only were similar in age, sex (more males in both groups), had lower pulmonary function test (PFT) and higher FEV1 post-bronchodilator reversibility, lower emphysema score, higher dyspnea score, lower health status, and more were prescribed inhaled respiratory medications, especially ICS alone or ICS combined with long-acting bronchodilators. More females were observed in def 3 than the other definitions and COPD-only group. Increased level of blood eosinophil was observed in COPD patients with concurrent diagnosis of asthma, def 1 and 3, than COPD-only. FENO50 levels were higher in COPD subjects with concurrent diagnosis of asthma (all definitions) than COPD-only, which was statistically significant (, ). More COPD patients with concurrent diagnosis of asthma had a significant FENO50 level of ≥ 33.5 ppb (fourth quartile) than COPD-only (p = 0.045, 0.011, and 0.006 for definition 1, 2, and 3, respectively) (, ). Fewer COPD subjects with concurrent diagnosis of asthma had FENO50 level of <16 ppb (first quartile) than COPD-only, which was statistically significant for all definitions (p = 0.021, 0.10, 0.028, for def 1, 2 and 3, respectively). Furthermore, and present FENO50 levels classified according to the ATS 2011 guideline [Citation25]. Fewer COPD subjects with concurrent diagnosis of asthma had FENO50 < 25 than those with COPD-only; statistically significant with definition 1 and 3 (p = 0.038, 0.026, respectively). Uni-variate and multi-variate analyses showed an association between FENO50 values classified as quartiles and definitions of COPD with concurrent diagnosis of asthma among all COPD subjects ().

Figure 2. FENO50 levels by definitions of COPD with concurrent diagnosis of asthma.*

FENO: Fractional exhaled nitric oxide; ppb: parts per billion; COPD: Chronic obstructive pulmonary disease.

*Definition1: Atopy and self-reported physician diagnosis of asthma; Definition 2: ≥12% and ≥200 ml of improvement in the FEV1 post bronchodilator; Definition 3: Self-reported physician diagnosis of asthma; Non-COPD with concurrent diagnosis of asthma = COPD-only.

Figure 2. FENO50 levels by definitions of COPD with concurrent diagnosis of asthma.*FENO: Fractional exhaled nitric oxide; ppb: parts per billion; COPD: Chronic obstructive pulmonary disease.*Definition1: Atopy and self-reported physician diagnosis of asthma; Definition 2: ≥12% and ≥200 ml of improvement in the FEV1 post bronchodilator; Definition 3: Self-reported physician diagnosis of asthma; Non-COPD with concurrent diagnosis of asthma = COPD-only.

Figure 3. FENO50 last quartile by definitions of COPD with concurrent diagnosis of asthma.*

FENO: Fractional exhaled nitric oxide; ppb: parts per billion; COPD: Chronic obstructive pulmonary disease.

*Definition1: Atopy and self-reported physician diagnosis of asthma; Definition 2: ≥12% and ≥200 ml of improvement in the FEV1 post bronchodilator; Definition 3: Self-reported physician diagnosis of asthma; Non-COPD with concurrent diagnosis of asthma = COPD-only.

Figure 3. FENO50 last quartile by definitions of COPD with concurrent diagnosis of asthma.*FENO: Fractional exhaled nitric oxide; ppb: parts per billion; COPD: Chronic obstructive pulmonary disease.*Definition1: Atopy and self-reported physician diagnosis of asthma; Definition 2: ≥12% and ≥200 ml of improvement in the FEV1 post bronchodilator; Definition 3: Self-reported physician diagnosis of asthma; Non-COPD with concurrent diagnosis of asthma = COPD-only.

Figure 4. FENO50 classifications (ATS guideline 2011) by definitions of COPD with concurrent diagnosis of asthma.*

FENO: Fractional exhaled nitric oxide; ppb: parts per billion; ATS: American Thoracic Society; COPD: Chronic obstructive pulmonary disease.

*Definition1: Atopy and self-reported physician diagnosis of asthma; Definition 2: ≥12% and ≥200 ml of improvement in the FEV1 post bronchodilator; Definition 3: Self-reported physician diagnosis of asthma; Non-COPD with concurrent diagnosis of asthma = COPD-only.

Figure 4. FENO50 classifications (ATS guideline 2011) by definitions of COPD with concurrent diagnosis of asthma.*FENO: Fractional exhaled nitric oxide; ppb: parts per billion; ATS: American Thoracic Society; COPD: Chronic obstructive pulmonary disease.*Definition1: Atopy and self-reported physician diagnosis of asthma; Definition 2: ≥12% and ≥200 ml of improvement in the FEV1 post bronchodilator; Definition 3: Self-reported physician diagnosis of asthma; Non-COPD with concurrent diagnosis of asthma = COPD-only.

Table 3. Baseline characteristics and FENO50 levels by COPD-only and COPD with concurrent diagnosis of asthma definitions.Table Footnote*

FENO50 levels for definitions of COPD with concurrent diagnosis of asthma

presents the criterion values for FENO50 level to predict COPD with concurrent diagnosis of asthma and coordinates of the ROC curve, and present the ROC curves with different cutoff points for each pre-specified definitions of COPD with concurrent diagnosis of asthma and COPD-only subjects. For each of the 3 predefined definitions of COPD with concurrent diagnosis of asthma, the AUC was 0.73, 0.69, and 0.67, 95% confidence interval (CI) of 0.60-0.80, 0.57-0.76, 0.56-0.73, for definition 1, 2, and 3, respectively. With a low FENO50 cutoff value of ≥18 or ≥18.5, def 2 and 3 had a sensitivity of 0.80 and a specificity of 0.40-0.46. With the addition of atopy to the definition of self-reported physician diagnosis of asthma (def 3), i.e. def 1, the sensitivity went up from 0.80 to 0.94 and the specificity remained at 0.40-0.46; the false-negative rate went down from 20% to 6%. With a high FENO50 cutoff value of ≥30.5, ≥33.5, or ≥36, the 3 definitions performed similarly with a sensitivity of 0.47-0.43 to 0.30 and a specificity of 0.86-0.91; the false positive rate was approximately 10%.

Figure 5. ROC curve analysis of the sensitivity and specificity of FENO for identifying COPD with concurrent diagnosis of asthma.*

ROC: Receiving operative curve; FENO: Fractional exhaled nitric oxide; AUC: Area under the curve; COPD: Chronic obstructive pulmonary disease.

*Definition1: Atopy and self-reported physician diagnosis of asthma; Definition 2: ≥12% and ≥200 ml of improvement in the FEV1 post bronchodilator; Definition 3: Self-reported physician diagnosis of asthma; Non-COPD with concurrent diagnosis of asthma = COPD-only.

Figure 5. ROC curve analysis of the sensitivity and specificity of FENO for identifying COPD with concurrent diagnosis of asthma.*ROC: Receiving operative curve; FENO: Fractional exhaled nitric oxide; AUC: Area under the curve; COPD: Chronic obstructive pulmonary disease.*Definition1: Atopy and self-reported physician diagnosis of asthma; Definition 2: ≥12% and ≥200 ml of improvement in the FEV1 post bronchodilator; Definition 3: Self-reported physician diagnosis of asthma; Non-COPD with concurrent diagnosis of asthma = COPD-only.

Table 4. Criterion values for FENO50 level to predict COPD with concurrent diagnosis of asthma and coordinates of the ROC curve.Table Footnote*

shows the predicted probabilities of COPD patients with concurrent diagnosis of asthma among COPD patients (all definitions) based on some particular levels of FENO50 (FENO50 levels were not used as continuous values but category values, unit = 10 ppb). The FENO50 level of more than 35 ppb had a predicted probability of 81% for identifying Def 3, and 63% and 68% to identify Def 1 and Def 2, respectively.

Table 5. Predicted probabilities of COPD with concurrent diagnosis of asthma based for some particular level of FENO50.Table Footnote*

FENO levels and disease severity and progression

There was no statistically significant difference between FENO50 for disease severity (FEV1, CAT, SGRQ, and exacerbations) and disease progression (annual FEV1 decline). However, COPD patients having exacerbations (symptoms-based and/or event-based) had higher level of FENO50.

Discussion

The study results showed that using three common clinical definitions, COPD subjects with concurrent diagnosis of asthma are more frequently male, have more severe symptoms, worse lung function, and health status compared to COPD-only. Compared to COPD-only, COPD individuals with concurrent diagnosis of asthma have higher mean FENO50 levels and more frequently FENO50 levels ≥ 33.5 ppb (all definitions); rarely FENO50 levels were <25 ppb (def 1 and 3) and <16 ppb (all definitions). The AUC-ROC curve performance measurement of FENO50 appears to have a limited measure of separability; the AUC was 0.67 to 0.73 depending on the predefined definitions of COPD with concurrent diagnosis of asthma. With the selection of a low threshold, e.g. 18 or ≥18.5, it would allow recognizing more COPD subjects with concurrent diagnosis of asthma (up to 94% with def 1 combining physician diagnosis of asthma and atopy) but at a cost of a high false-positive rate (54 to 60%). With the selection of a high threshold, e.g. 30.5 to 36, the specificity was high (0.86 to 0.91) but at a cost of a very high false-negative rate (53 to 57%). Finally, we showed that FENO50 in COPD subjects neither related to disease severity nor predicted disease progression.

There was no statistically significant difference in FENO50 levels between COPD and healthy subjects. This may be due to the neutrophilic predominance nature of COPD patients [Citation22], while FENO is a surrogate of eosinophilic airway inflammation [Citation39]. This result is in line with the result of one retrospective study conducted on 689 patients including 500 asthmatics, 132 COPD, and 57 COPD with concurrent diagnosis of asthma [Citation40]. Patients were divided into asthma alone group, COPD alone group, and COPD with concurrent diagnosis of asthma group according to clinical history, lung function test values, and bronchial hyperresponsiveness or bronchodilator test. In contrast, another study [Citation41] reported a higher level of FENO50 in COPD patients than nonsmoker healthy subjects. They conducted a cross-sectional study on 192 patients including 103 with COPD; 16 healthy nonsmokers; 30 healthy smokers; and 43 asthmatics. Patients’ data were gathered on lung function, FENO50, CAT, and COPD clinical phenotype. No statistically significant difference was observed in FENO50 levels between GOLD 1 and GOLD 2+, which is similar to the results from other studies [Citation22, Citation41].

A recent study [Citation22] reported that 8% of COPD patients had FENO50 levels of 25-50 ppb, and 3% of them had FENO50 levels of >50 ppb. Another study [Citation42] conducted on 331 COPD patients showed that 20.6% of COPD patients had FENO50 levels of 25-50 ppb, and 5.1% of them had FENO50 levels of >50 ppb. In our current study, 36% of COPD individuals had FENO50 levels of 25-50 ppb and 10.5% of them had FENO50 levels of >50 ppb. Many factors could contribute to explain this discrepancy between studies. One factor may be the differences in age distribution among studies. The patients enrolled in one [Citation22] of these studies had mean age ± SD of 63.9 ± 11.34 years. In the present study, the mean age ± SD of enrolled COPD subjects was 67.9 ± 10.2 years. Studies [Citation43, Citation44] have reported that the prevalence of COPD with concurrent diagnosis of asthma tends to increase with age. However, this factor by itself may not be sufficient to explain the difference between the two studies. In the current study, another factor could include that a large proportion of subjects were undiagnosed and not treated with ICS. There were fewer smokers in this study population [Citation42] which is recognized to decrease FENO50 levels. Finally, the difference in the sampling could also be considered, as participants were from a random population sampling in the CanCOLD study and not a clinical convenient sample as in most of the studies.

In accordance with previous studies [Citation45, Citation46], in the current study population, COPD subjects with concurrent diagnosis of asthma had worse outcomes. The present study adds to our knowledge showing that COPD subjects with concurrent diagnosis of asthma have a statistically significant higher mean level of FENO50 than COPD-only. In agreement with this result, one previous study [Citation40] showed that COPD patients with concurrent diagnosis of asthma had a significantly higher level of FENO50 than COPD (p < 0.01). Moreover, another study [Citation47] showed that COPD patients with concurrent diagnosis of asthma had a significantly higher level of FENO50 than COPD-only patients (38.5 ppb vs. 20.3 ppb, p < 0.001).

In our current study, we also tested FENO50 cutoff values classified as the ATS guideline 2011 [Citation25] and quartile values. FENO50 had a limited class separation capacity between COPD with concurrent diagnosis of asthma and COPD-only with an AUC of 0.67 to 0.73 depending on which definition of COPD with concurrent diagnosis of asthma was used. One other study [Citation40] conducted on 132 COPD and 57 COPD with concurrent diagnosis of asthma, showed that FENO50 had a slightly better separation capacity with an AUC of 0.78. This variation in the results creates uncertainty and calls for studies to be done using the same types of measuring tools for FENO, describing the population selection and reference to a common definition of COPD with concurrent diagnosis of asthma.

Finally, the current study showed that FENO50 was not able to predict disease severity/progression in COPD patients. Most studies are in line with these results regarding no association between FENO50 and disease severity (decline in pulmonary function tests, especially FEV1) in COPD patients [Citation48–50]. With respect to exacerbations, one study [Citation51] showed that there was no association between FENO50 at presentation and after discharge for exacerbations while other studies have shown a relationship [Citation52, Citation53]. A recent study [Citation35] conducted on 4677 individuals (1649 were healthy never-smokers, 1581 ever-smokers, 449 asthma, 404 COPD, 138 COPD with concurrent diagnosis of asthma, and 456 nonspecific airflow limitation) showed that a combination of FENO50 and blood eosinophil count values had an additive value in characterizing chronic airway disease in a general population. However, we did not find any association of blood eosinophil count and FENO50 values in differentiating COPD patients with concurrent diagnosis of asthma from COPD-only patients. This discrepancy may be due to fewer number of COPD subjects participated in our study as well as the different type of studied population. Furthermore, the normal reference range for the blood eosinophil count for our study was different from this study. In addition to this, we used LLN for COPD definition while the other study used the fixed ratio definition for this purpose.

The current study had strengths including the random population-based sampling and longitudinal design (CanCOLD) presenting the COPD population in Canada with an equitable distribution of men and women and is comparable to the whole COPD population in Canada as well. FENO was standardized according to the ATS/ERS guideline [Citation38] and factors were avoided such as smoking, exercising, eating, and drinking before testing. Finally, there was an extensive assessment of disease severity and longitudinal follow-up for the annual decline in FEV1. In our study, many potential limitations need to be mentioned. The definitions used for COPD with concurrent diagnosis of asthma as in most studies have not been validated. Although we have tried to standardize the method of FENO measurement by using ATS/ERS guideline [Citation38], the difference between study measurements may not allow inference to those obtained in other centers. The relatively small number of subjects in our study may have limited the ability to detect an association when there is one to be detected. Finally, atopy and physician diagnosis of asthma were self-reported. Self-reported diagnosis of asthma has been frequently used in previous studies [Citation4, Citation22, Citation26, Citation41], but we acknowledge that in some cases the diagnosis could be misleading.

Conclusion

In conclusion, although FENO has the potential to be used as a biomarker for differentiating COPD with concurrent diagnosis of asthma from COPD-only as we have demonstrated the high or low values of FENO50 identified in the current study would need to be externally validated in another population-based COPD cohort. Although it is no longer referred to the term COPD and asthma (ACO), COPD and asthma can coexist in COPD patients (COPD with concurrent diagnosis of asthma) [Citation13]. Studies are needed to explore and validate the use of FENO, not relying only on a clinical definition but along with blood/sputum eosinophil count and chest imaging as part of a comprehensive algorithm for risk stratification before being applied in clinical practice.

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Acknowledgments

The authors thank Circassia (formerly Aerocrine) for its support and providing the measurement instrument, Niox Mino. We also thank the men and women who participated in the study and individuals in the CanCOLD Collaborative research Group: Executive Committee: Jean Bourbeau, (Mcgill University, Montreal, QC, Canada); Wan C Tan, J Mark FitzGerald; D D Sin. (UBC, Vancouver, BC, Canada); D D Marciniuk (University of Saskatoon, Saskatoon, SASK, Canada) D E O’Donnell (Queen’s University, Kingston, ON, Canada); Paul Hernandez (University of Halifax, Halifax, NS, Canada); Kenneth R Chapman (University of Toronto, Toronto, ON, Canada); Robert Cowie (University of Calgary, Calgary, AB, Canada); Shawn Aaron (University of Ottawa, Ottawa, ON, Canada); F Maltais (University of Laval, Quebec City, QC, Canada); International Advisory Board: Jonathon Samet (the Keck School of Medicine of USC, California, USA); Milo Puhan (John Hopkins School of Public Health, Baltimore, USA) ; Qutayba Hamid (McGill University, Montreal, Qc, Canada); James C Hogg (UBC James Hogg Research Center, Vancouver, BC, Canada). Operations Center: Jean Bourbeau (PI), Carole Baglole, Carole Jabet, Palmina Mancino, Yvan Fortier, (University of McGill, Montreal, QC, Canada); Wan C Tan (co-PI), Don Sin, Sheena Tam, Jeremy Road, Joe Comeau, Adrian Png, Harvey Coxson, Miranda Kirby, Jonathon Leipsic, Cameron Hague (University of British Columbia James Hogg Research Center, Vancouver, BC, Canada). Economic Core: Mohsen Sadatsafavi (University of British Columbia, Vancouver, BC). Public Health core: Teresa To, Andrea Gershon (University of Toronto) Data management and Quality Control: Wan C Tan, Harvey Coxson, (UBC, Vancouver, BC, Canada); Jean Bourbeau, Pei-Zhi Li, Jean-Francois Duquette, Yvan Fortier, Andrea Benedetti, Denis Jensen (Mcgill University, Montreal, QC,Canada), Denis O’Donnell (Queen’s University, Kingston, ON, Canada. Field Centers: Wan C Tan (PI), Christine Lo, Sarah Cheng, Cindy Fung, Nancy Ferguson, Nancy Haynes, Junior Chuang, Licong Li, Selva Bayat, Amanda Wong, Zoe Alavi, Catherine Peng, Bin Zhao, Nathalie Scott-Hsiung, Tasha Nadirshaw (UBC James Hogg Research Center, Vancouver, BC); Jean Bourbeau (PI), Palmina Mancino, David Latreille, Jacinthe Baril, Laura Labonte (McGill University, Montreal, QC, Canada); Kenneth Chapman (PI), Patricia McClean, Nadeen Audisho, (University of Toronto, Toronto, ON, Canada); Brandie Walker, Robert Cowie (PI), Ann Cowie, Curtis Dumonceaux, Lisette Machado(University of Calgary,Calgary, AB, Canada); Paul Hernandez (PI), Scott Fulton, Kristen Osterling (University of Halifax, Halifax, NS, Canada); Shawn Aaron (PI), Kathy Vandemheen, Gay Pratt, Amanda Bergeron (University of Ottawa, Ottawa, ON, Canada); Denis O’Donnell (PI), Matthew McNeil, Kate Whelan (Queen’s University, Kingston, ON, Canada); Francois Maltais (PI), Cynthia Brouillard (University of Laval, Quebec City, QC, Canada); Darcy Marciniuk (PI), Ron Clemens, Janet Baran (University of Saskatoon, Saskatoon, SK, Canada).

Declaration of interest

SMY Mostafavi-PourManshadi, Nafiseh Naderi, Palmina Mancino, and Pei Zhi Li have no conflict of interest. Wan Tan reports grants from WCT reports grants from Canadian Institute of Heath Research (CIHR/Rx&D Collaborative Research Program Operating Grants- 93326) with industry partners Astra Zeneca Canada Ltd., Boehringer-Ingelheim Canada Ltd, GlaxoSmithKline Canada Ltd, Merck, Novartis Pharma Canada Inc., Nycomed Canada Inc., Pfizer Canada Ltd., during the conduct of the study., during the conduct of the study. Jean Bourbeau reports grants from CIHR, grants from Canadian Respiratory Research Network (CRRN), personal fees from Canadian Thoracic Society, personal fees from CHEST, grants from Foundation of the MUHC, grants from Aerocrine, grants and personal fees from AstraZeneca, grants and personal fees from Boehringer Ingelheim, grants and personal fees from Grifols, grants and personal fees from GlaxoSmithKline, grants and personal fees from Novartis, grants and personal fees from Trudell, outside the submitted work.

Additional information

Funding

This project was specifically funded Circassia (formerly Aerocrine). The Canadian Cohort Obstructive Lung Disease (CanCOLD) study is currently funded by the Canadian Respiratory Research Network (CRRN); industry partners: Astra Zeneca Canada Ltd; Boehringer Ingelheim Canada Ltd; GlaxoSmithKline Canada Ltd; Novartis. Researchers at RI-MUHC Montreal and Icapture Center Vancouver lead the project. Previous funding partners are the CIHR (CIHR/Rx&D Collaborative Research Program Operating Grants- 93326); the Respiratory Health Network of the FRQS; industry partners: Almirall; Merck Nycomed; Pfizer Canada Ltd; and Theratechnologies. The funders/sponsors had no role in the study design, data collection and analysis, or preparation of the manuscript.

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