Asthma and lower airway disease
Lung function decline and variable airway inflammatory pattern: Longitudinal analysis of severe asthma

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Background

Eosinophilic airway inflammation measured by using induced sputum is an important treatment stratification tool in patients with severe asthma. In addition, sputum eosinophilia has been shown to be associated with severe exacerbations and airflow limitation.

Objectives

We sought to identify whether eosinophilic inflammation in sputum is associated with FEV1 decrease in patients with severe asthma and whether we could identify subgroups of decrease behavior based on the variation of eosinophilic airway inflammation over time.

Methods

Ninety-seven patients with severe asthma from the Glenfield Asthma Cohort were followed up with scheduled 3-month visits; the median duration of follow-up and number of visits was 6 years (interquartile range, 5.6-7.6 years) and 2.7 visits per year. Induced sputum was analyzed for eosinophilic inflammation at scheduled visits. Linear mixed-effects models were used to identify variables associated with lung function and overall decrease. In addition, using individual patients' mean and SD sputum eosinophil percentages over time, a 2-step cluster analysis was performed to identify patient clusters with different rates of decrease.

Results

FEV1 decrease was −25.7 mL/y in the overall population. Postbronchodilator FEV1 was also dependent on exacerbations, age of onset, height, age, sex, and log10 sputum eosinophil percentages (P < .001). Three decrease patient clusters were identified: (1) noneosinophilic with low variation (mean decrease, −14.0 mL/y), (2) eosinophilic with high variation (mean decrease, −40.9 mL/y), and (3) hypereosinophilic with low variation (mean decrease in lung function, −19.2 mL/y).

Conclusion

The amplitude of sputum eosinophilia was associated with postbronchodilator FEV1 in asthmatic patients. In contrast, high variability rather than the amplitude at baseline or over time of sputum eosinophils was associated with accelerated FEV1 decrease.

Section snippets

Patients

We identified patients from our Leicester Difficult Asthma Database cohort at Glenfield Hospital who met the following screening criteria: (1) a physician's diagnosis of asthma with objective evidence (≥1 peak flow variation of ≥20% over a 2-week period, bronchodilator reversibility of ≥12%/200 mL, or airway hyperresponsiveness [methacholine PC20 ≤8 mg/mL]); (2) a minimum of 5 years of follow-up with scheduled 3-month visits assessing airway inflammation based on induced sputum and

Eosinophilic airway inflammation and postbronchodilator FEV1 in patients with severe asthma

We observed a significant decrease in postbronchodilator FEV1 of 25.7 mL/y in our final lung function decline model (P = .0059; model 30, see Table E1; Fig 2). Eosinophilic airway inflammation significantly influenced postbronchodilator FEV1 in our final lung function model, with a mean postbronchodilator FEV1 reduction of 81.4 mL per log10 unit increase in sputum eosinophil percentages (P < .0001). In addition, age of onset, exacerbations, sex, and height all significantly influenced

Discussion

For the first time, we have shown that the variability rather than amplitude of sputum eosinophils over time is significantly associated with the rate of postbronchodilator FEV1 decrease in patients with severe asthma. These observations might have an important effect on future therapies that use eosinophilic biomarkers to modulate asthma risk. Our findings extend previous cross-sectional observations in asthmatic patients, showing that sputum eosinophil counts are increased in patients with

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  • Cited by (0)

    Supported by the National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit. This report presents independent research funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Additional funding was received from the Airway Disease PRedicting Outcomes through Patient Specific Computational Modelling (AirPROM) project (funded through Seventh Framework Programme FP7/2007-2013 under grant agreement no. 270194) and from a Wellcome Senior Fellowship (to C.B.).

    Disclosure of potential conflict of interest: R. Green has received lecture fees from GlaxoSmithKline, AstraZeneca, and Novartis. I. Pavord has received consultancy fees and lecture fees from GlaxoSmithKline, AstraZeneca, Novartis, Merck, BI, and Aerocrine and has received travel support from BI and GlaxoSmithKline. C. Brightling has been supported by a Wellcome Trust Senior Clinical Fellowship, AirPROM EU FP7, and the NIHR Biomedical Research Unit; has received consultancy fees from GlaxoSmithKline, AstraZeneca, MedImmune, Novartis, Roche/Genentech, Boehringer Ingelheim, Chiesi, and Merck; and has received research support from Novartis, Chiesi, AstraZeneca, MedImmune, GlaxoSmithKline, and Roche/Genentech. S. Siddiqui has a gift in aid from Chiesi for the study of small airways disease. The rest of the authors declare that they have no relevant conflicts of interest.

    These authors contributed equally as co-senior authors.

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