Asthma and lower airway diseaseLung function decline and variable airway inflammatory pattern: Longitudinal analysis of severe asthma
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.
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These authors contributed equally as co-senior authors.