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Distribution of sputum cellular phenotype in a large asthma cohort: predicting factors for eosinophilic vs neutrophilic inflammation
© Schleich et al; licensee BioMed Central Ltd. 2013
Received: 13 September 2012
Accepted: 22 February 2013
Published: 26 February 2013
Phenotyping asthma according to airway inflammation allows identification of responders to targeted therapy. Induced sputum is technically demanding. We aimed to identify predictors of sputum inflammatory phenotypes according to easily available clinical characteristics.
This retrospective study was conducted in 508 asthmatics with successful sputum induction recruited from the University Asthma Clinic of Liege. Receiver-operating characteristic (ROC) curve and multiple logistic regression analysis were used to assess the relationship between sputum eosinophil or neutrophil count and a set of covariates. Equations predicting sputum eosinophils and neutrophils were then validated in an independent group of asthmatics.
Eosinophilic (≥3%) and neutrophilic (≥76%) airway inflammation were observed in 46% and 18% of patients respectively. Predictors of sputum eosinophilia ≥3% were high blood eosinophils, FENO and IgE level and low FEV1/FVC. The derived equation was validated with a Cohen’s kappa coefficient of 0.59 (p < 0.0001). ROC curves showed a cut-off value of 220/mm3 (AUC = 0.79, p < 0.0001) or 3% (AUC = 0.81, p < 0.0001) for blood eosinophils to identify sputum eosinophilia ≥3%. Independent predictors of sputum neutrophilia were advanced age and high FRC but not blood neutrophil count.
Eosinophilic and paucigranulocytic asthma are the dominant inflammatory phenotypes. Blood eosinophils provide a practical alternative to predict sputum eosinophilia but sputum neutrophil count is poorly related to blood neutrophils.
Asthma is a heterogeneous disease of the airways. The traditional guidelines for asthma diagnosis include suggestive clinical symptoms and the demonstration of airflow variability. However, symptoms and lung function are insensitive in reflecting the underlying airway inflammation. There is increasing evidence that phenotyping asthma according to airway inflammation can allow the identification of subgroups of patients who are more likely to respond to targeted therapy. In particular, important studies have confirmed that eosinophilic airway inflammation most reliably predicts the response to anti-inflammatory treatment such as inhaled corticosteroid [1, 2] and anti-IL5 [3, 4]. Recent studies have demonstrated the usefulness of induced sputum to guide asthma treatment [5, 6]. These studies showed that normalizing airway eosinophilic inflammation allowed better control of asthma with reduced exacerbations and hospital admissions. There is however no evidence that inhaled corticosteroids may improve short term asthma control in the absence of uncontrolled eosinophilic inflammation as encountered in pauci-granulocytic asthma . On the other hand data suggest that neutrophilic asthma could be best targeted by using clarithromycin . Characterising the inflammatory phenotype in patients with chronic respiratory symptoms can thus be more important than giving an “asthma” label to predict response to anti-inflammatory treatment. The technique of induced sputum that allows non-invasive collection of airway cells is considered as the gold standard to identify inflammatory asthma phenotype. It is however technically demanding and time consuming. Exhaled nitric oxide (FENO) has already been identified [9, 10] as a valid marker of airway eosinophilic inflammation. We currently however lack of marker predicting airway neutrophilic inflammation. It is unclear whether systemic inflammation is able to predict inflammatory phenotypes either eosinophilic or neutrophilic. This alternative test is biologically plausible since the infiltrating granulocytes in the airway are bone marrow-derived cells which access the airway through the circulation. The appeal of the approach comes from the ease of sample collection of peripheral blood from subjects of all ages and clinical characteristics.
The purpose of this study was twofold. In a large asthmatic population encompassing the all disease severity spectrum, we first sought to assess the proportion of asthmatic patients displaying eosinophilic vs neutrophilic vs paucigranulocytic phenotypes based on sputum cell analysis. Secondly we aimed at determining factors associated with eosinophilic and neutrophilic phenotypes.
Subject characteristics and study design
Demographic, functional and inflammatory characteristics for the whole population
167 ± 9
74 ± 16
Current Smoker (n) (pack-yr)
101 (22 (0.5-60) pack-yr)
Ex-smokers (n) (pack-yr)
99 (15 (0.5-90) pack-yr)
84 ± 19
Low dose ICS
Moderate dose ICS
High dose ICS
The results were expressed as mean ± SD for continuous variables; median and interquartile ranges (IQR) were preferred for skewed distributions. For categorical variables, the number of observations and percentages were given in each category. Comparisons between different subgroups were performed with a Kruskal-Wallis test. The Spearman correlation coefficient was used to measure the association between clinical parameters. The receiver-operating characteristic (ROC) curve was constructed to determine the concentration of blood eosinophils or neutrophils which best identified a sputum eosinophil count ≥3% or a sputum neutrophil count ≥76% respectively. Logistic regression analysis was used to assess the relationship between the binary outcome (sputum eosinophil count ≥3% or sputum neutrophil count ≥76%) and a set of covariates, individually or in combination (gender, age, height, weight, atopy, smoking status, IgE, blood eosinophil and neutrophil count, FEV1%, FEV1/FVC, TLC, FRC, KCO, PC20M, Reversibility, Fibrinogen, CRP, FENO, ACQ, AQLQ, ICS therapy). The results were considered to be significant at the 5% critical level (p < 0.05). The ability of the equation to predict sputum eosinophilia or neutrophilia was tested in an independent population of 178 asthmatics recruited between July 2011 and May 2012. The demographic, functional and inflammatory characteristics of the validation population were similar to those of the study population.
As IgE levels were missing in 40 patients, the equation predicting sputum eosinophilia was validated in 138 patients. The agreement between predicted and observed value was tested by Cohen Kappa’s coefficient. Calculations were done using SAS version 9.1 (SAS Institute, Cary, North Carolina, USA).
This study was conducted with the approval of the ethics committee of CHU Liege B70720096732, reference Liege 2009/161.
The demographic and functional characteristic of the patients are given in Table 1.
Demographic characteristics according to the inflammatory phenotype
Mixed granulocytic phenotype
Functional and inflammatory characteristics according to the inflammatory phenotype
Mixed granulocytic phenotype
90 ± 17
80 ± 20***
79 ± 20***
72 ± 14***
77 ± 9
71 ± 10***
72 ± 11***
69 ± 9***
99 ± 16
102 ± 18
102 ± 18
101 ± 14
103 ± 27
104 ± 19
119 ± 32
111 ± 22
90 ± 19
92 ± 21
91 ± 19
100 ± 10
8 ± 9
15 ± 17**
8 ± 10
12 ± 10
87 (77 – 100)
1.82 ± 1.15
2.16 ± 1.36*
2.09 ± 1.88
2.09 ± 1.16
4.6 ± 1.3
4.58 ± 1.34
4.76 ± 1.46
4.45 ± 1.74
4.92 ± 1.35
4.57 ± 1.63
4.9 ± 1.76
4.64 ± 1.83
4.46 ± 1.46
4.42 ± 1.43
4.65 ± 1.54
4.39 ± 1.5
4.71 ± 1.36
4.79 ± 1.44
4.84 ± 1.49
4.55 ± 1.80
4.48 ± 1.50
4.55 ± 1.55
4.81 ± 1.67
4.70 ± 2.15
Proportion of asthma inflammatory phenotypes in steroid naïve and steroid-treated patients
Mixed granulocytic phenotype
Steroid naïve (n = 153)
Steroid-treated (n = 355)
π: probability of sputum eosinophil count ≥ 3%.
Blood eos%: Blood eosinophil count in %.
Independent predictors of sputum eosinophilia
Logit Blood eosinophils,%
P < 0.0001
P = 0.0085
FEV 1 /FVC,%
P = 0.0021
P < 0.0001
As far as the sputum neutrophilic phenotype is concerned, there was a weak correlation between sputum and blood neutrophil count taken in percentage (r = 0.19, p = 0.0015) but not in absolute value (r = 0.19, p = 0.11). Using the ROC curve method, we found a cut-off of 4960/mm3 and 66% respectively giving a sensitivity of 49% and 37%, a specificity of 70% and 90%, p = 0.03 and p = 0.003, AUC = 0.59 and AUC = 0.63 respectively.
π: probability of sputum neutrophil count ≥ 76%.
To test the ability of the equation to predict sputum neutrophilia, we recruited a validation population of 178 asthmatics that underwent spirometry and sputum induction. The agreement between predicted and observed value of sputum neutrophil counts gave a Cohen Kappa’s coefficient of 0.24 (p < 0.0001, lower limit of confidence interval = 0.12). The specificity and sensitivity were 21% and 97% respectively. Patients receiving moderate to high dose ICS had higher sputum neutrophil count (47.3%) than patients receiving low dose ICS (38.8%, p = 0.017). Smokers did not have significantly higher proportion of neutrophils in their sputum (Median 48.9%) than ex-smokers (Median 50.6%, p = 0.68) or never smokers (Median 44%, p = 0.19). However, neither smoking status nor the dose of inhaled corticosteroids was able to predict elevated sputum neutrophil count.
In a large cohort of asthmatics encompassing all disease severity spectrum, eosinophilic and pauci-granulocytic were the most frequent phenotypes while neutrophilic asthma represented less than one fifth of the inflammatory patterns. Independent predictors of sputum eosinophil count ≥3% were the percentage of blood eosinophils, low FEV1/FVC, high FENO and IgE levels. A cut-off value of 220/mm3 or 3% for blood eosinophils performed equally to FENO50 to identify the presence of a sputum eosinophil count ≥3%. Independent predictors of sputum neutrophilia were advanced age and high FRC while blood neutrophil count was not.
The proportion of asthmatics with raised sputum eosinophil counts was 46% in our series. Gibson et al. found that 41% of non-smoking asthmatics had a sputum eosinophil counts >2.5% . The proportion of eosinophilic asthma reported by Louis  and Green  was higher but the thresholds used in those studies were 2 and 1.9% respectively. Our results are similar to the study of Simpson conducted on 93 subjects using thresholds of 1 and 61% for eosinophilic and neutrophilic inflammation respectively. This group found 41% eosinophilic asthma, 20% neutrophilic asthma, 31% paucigranulocytic asthma and 8% mixed granulocytic asthma . The highest thresholds used in our study certainly explain the lower proportion of mixed granulocytic and neutrophilic asthma and the higher proportion of paucigranulocytic asthma in our patients. A very recent American multicentre study from McGrath et al. showed that paucigranulocytic asthma was the dominant phenotype accounting for more than 50% of patients while eosinophilic asthma (sputum eosinophil count ≥2%) represented roughly 25%. As in our study, the neutrophilic phenotype (sputum neutrophil count > 61%) was quite rare representing less than 15% of the patients while the mixed granulocytic phenotype was less than 5% .
Lung function was less altered in those patients with paucigranulocytic phenotype while eosinophilic phenotype exhibited higher FENO levels, higher proportion of males and atopic patients, higher bronchial hyperresponsiveness and lower asthma control. Those results are in accordance with previous studies [11, 21–24]. Like Hastie et al. , we identified patients with mixed granulocytic sputum inflammation exhibiting the lowest lung function. Moreover, the mixed granulocytic phenotype had higher serum fibrinogen values pointing to systemic inflammation in this subgroup. This interesting finding has not been reported so far in asthma but raised fibrinogen levels have been demonstrated to be associated with reduced FEV1 and increased risk of Chronic Obstructive Pulmonary Disease (COPD) in a population study . As for neutrophilic asthma there was no special characteristic that distinguishes this group from the other inflammatory patterns. In particular neutrophilic asthmatics did not display higher serum C-Reactive Protein and fibrinogen levels.
Among the factors shown to contribute to airway eosinophilia, blood eosinophils came first when performing a multiple logistic regression. There are few studies assessing the ability of blood eosinophils to identify airway eosinophilic inflammation. Previous studies showed that peripheral blood eosinophil count was correlated with bronchoalveolar lavage eosinophil count  and sputum eosinophil count [28–30]. These studies have, however, investigated a limited number of asthmatics and did not provide any threshold value of blood eosinophils as marker of airway eosinophilia. The recent American multicentre study found a threshold value of 220/mm3 as the best compromise for predicting sputum eosinophil count ≥2% . Another recent study conducted in COPD showed that a cutoff of 2% peripheral blood eosinophils had a sensitivity of 90% and specificity of 60% for identifying a sputum eosinophilia of greater than 3% at exacerbation. In our study, we confirmed the correlation between blood and sputum eosinophilic inflammation in a large cohort of patients. We found the best threshold being 220/mm3 and 3% and these thresholds being as effective as FENO at predicting uncontrolled airway eosinophilic inflammation. Our findings are in keeping with those recently reported by McGrath et al. . Compared to blood eosinophils, FENO has however the advantage of giving immediate results and its measurement is more comfortable to the patient. In this study the performance (threshold, sensitivity, specificity) of FENO to identify sputum eosinophil count is very similar to the one we reported previously . The fact that FENO and blood eosinophil counts came out as independent predictors of sputum eosinophilia in the multiple logistic regression suggests that these two markers reflect different mechanisms promoting the recruitment of eosinophils into the airways.
Other independent factors to be shown associated with prominent sputum eosinophilia are FEV1/FVC and total serum IgE level. Previous studies have shown that FEV1/FVC, an index of airway narrowing, was correlated to sputum eosinophilia  and eosinophilic asthma has been recognized to be frequently associated with atopic disease [1, 7]. In our study, total serum IgE levels were however best predictor of sputum eosinophilia than atopy per se, in line with a recent study . From the biological properties of IgE it can be speculated that high tissue IgE may prime local mast cells and activate them even without intervention of allergens . In this view it is interesting to note that airway mast cell activation demonstrated by tryptase release is a phenomenon found to be associated with sputum eosinophils in asthma  and COPD .
The same approach as for eosinophils was used to predict the presence of sputum neutrophils. Despite statistically significant correlation between sputum and percentage blood neutrophils, the strength of the relationship was rather poor. As opposed to what we found with eosinophils, multiple logistic regression analysis demonstrated the inability of blood neutrophils to predict uncontrolled sputum neutrophilic inflammation. The accumulation of airway neutrophils has been reported to be directly associated with the activation state of circulating neutrophils in response to the chemokine Interleukin 8 [35, 36]. Baines et al. found a correlation between plasma neutrophil elastase and neutrophilic airway inflammation . Those data suggest that airway neutrophilic accumulation could be due to an enhanced neutrophil activation and migration to the airways independently of the number of circulating cells. Moreover, it has been shown that neutrophils can be retained in the pulmonary microvasculature due to their low deformability, resulting in a higher concentration than in the systemic circulation. It is thought that this high concentration of the cells facilitates their effective recruitment to sites of inflammation . It seems likely that many could leave the circulation by chemoattraction, entering the lung without necessarily having a detectable effect on circulating levels.
From a multiple logistic regression two factors came out as being independently associated with sputum neutrophilia. In keeping to what was found by Thomas et al. in healthy subjects  and Woodruff et al.  in asthmatics, age appeared to be a critical factor in our cohort with sputum neutrophilia rising with age. In addition to age we also found that FRC was an independent factor. This suggests that airway neutrophils may contribute to reduction of inspiratory capacity seen in some asthmatics. Accordingly, two pediatric studies reported that percentage neutrophils in bronchoalveolar lavage directly correlated with air trapping (FRC) in children with cystic fibrosis [39, 40]. However, neither smoking status nor the dose of inhaled corticosteroids was able to predict the presence of sputum neutrophil count.
This study shows that eosinophilic and pauci-granulocytic are the most frequent asthma phenotypes in a large unselected asthmatic population. Like FENO, blood eosinophil counts may provide a practical alternative to predict sputum eosinophilia ≥3%. In contrast, sputum neutrophilia is only poorly related to blood neutrophil count.
We acknowledge Professor Albert (Medical Informatics and Biostatistics) for his contribution to analysis and interpretation of data. This work was supported by Interuniversity Attraction Poles (IAP) Project P6/35 and P7/30 and by unrestricted research grants from GSK, Astra-Zeneca and Novartis.
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