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To the Editor:
Asthma is characterized by strong time-of-day rhythms: symptoms worsen around 04:00 (1) along with increased airway narrowing, as reflected by a reduced peak expiratory flow or FEV1 (2). Clinically useful biomarkers in asthma include sputum and blood eosinophils (3), and results from several small studies looking at circadian variations in airway inflammation in asthma are conflicting (4-9). The sputum eosinophil percentage is used to guide management decisions in severe-asthma clinics (3), and therefore any diurnal variation in sputum eosinophilia could influence the management of patients.
We studied circadian variations in blood and sputum eosinophils in a cohort of patients with mild/moderate, atopic asthma compared with healthy control subjects. We then retrospectively compared sputum eosinophil counts from a severe-asthma clinic cohort in relation to the time of day of collection (morning vs. afternoon).
Methods
Ten healthy volunteers and 10 adults with mild/moderate, atopic asthma (Asthma Control Questionnaire 7, 0.9 [0.8-1.1]) who were on regular inhaled corticosteroids (equivalent to beclomethasone dipropionate 400 mg [400-650 mg]) were recruited for this study. The study protocol was approved by the Health Research Authority, National Research Ethics Service Committee of North West-Greater Manchester Central (REC 14/NW/1352). Written informed consent was obtained from all participants. Spirometry, blood eosinophils, induced sputum, and serum were collected during four visits, each 1 week apart, avoiding any potentiating effect of sputum induction on a subsequent sample. Visit 1 (V1) occurred at 16:00, V3 was at 10:00, and V4 was at 22:00. V2 involved an overnight stay, with sampling occurring at 16:00, 22:00, 04:00, and 10:00 the following morning.
Next, a circadian analysis was performed retrospectively on sputum eosinophil counts obtained from patients with severe asthma attending either a morning or afternoon clinic.
The median (interquartile range) is reported. Wilcoxon's test, the Mann-Whitney U test, and two-way ANOVA were used to analyze the data. A post hoc power analysis performed on the sputum eosinophil data resulted in a power of 81.86% for our observed effect size (1.057), and the minimum detectable effect size was 1.037 given 80% power. Power calculations were based on a Wilcoxon signed-rank test to detect the time-of-day difference in asthma patients. General linear modeling was used to analyze the severe-asthma cohort data and potential confounders. Chi-squared Fisher's exact test and...