Exploring the relevance and extent of small airways dysfunction in asthma: baseline data from the AssessmenT of smalL Airways involvemeNT In aSthma (ATLANTIS) prospective cohort study

Background Small airways dysfunction (SAD) is well-recognized in asthma, yet its role in asthma severity and asthma control is unclear. Our study aimed to assess which (combination of) biomarkers, physiological testing and imaging markers best measures the presence and extent of SAD in asthma. Methods This multinational observational study investigated participants without and with asthma (GINA severity stage 1-5). Asthma inclusion criteria were: 1) age 18-65 years; 2) clinical asthma diagnosis > 6 months, confirmed by a chest physician 2, supported by objective evidence of any of the following at the baseline visit or in the previous 5 years: a) positive airway hyperresponsiveness to methacholine, or b) positive reversibility (ΔFEV1≥ 12% and ≥ 200 mL within 30 minutes after 400 μg of salbutamol pMDI with or without a spacer or c) PEF variability >20%, measured during 7 days or d) documented reversibility after a cycle (e.g. 4 weeks) of maintenance anti-asthma treatment; 3) stable asthma on any previous regular asthma treatment (“rescue” β2-agonists alone included) at a stable dose for > 8 weeks before baseline; 4) lifetime smoking ≤ 10 pack-years. They underwent spirometry, body plethysmography, impulse oscillometry (IOS), Multiple Breath Nitrogen Washout (MBNW), computed tomography (CT) and questionnaires. Structural equation modeling (SEM) was applied in asthma to assess the contribution of all physiological and CT parameters to SAD. With SEM, we defined a clinical-SAD and CT-SAD score. Asthma subjects were classified in SAD groups using model-based clustering. Asthma severity, control and health care utilization in the past year were compared with the SAD scores and SAD groups. Findings We investigated 773 asthma and 99 control participants (median [interquartiles] age 46 [34, 54] and 41 [29, 52] years, 58% and 57% females, respectively). All physiologic measures contributed to the clinical SAD model with SEM analysis. The prevalence of SAD in asthma was dependent on the measure used and lowest with MBNW Sacin that reflects ventilation heterogeneity in the most peripheral, pre-acinar/acinar airways. IOS and spirometry, reflecting dysfunction of small-to-midsized airways, contributed most to the Clinical-SAD score and differentiated the two SAD Groups. Clinical-SAD Group1 (n=452) had “milder“ SAD, i.e. comparable MBNW Sacin with controls. Group2 (n=312) had more abnormal physiologic SAD measures than Group1, particularly IOS and spirometry, and more severe asthma (asthma control, treatments, exacerbations, quality of life). Clinical-SAD scores were higher in Group2 (“more severe” SAD) and related to asthma control, severity, and exacerbations. Clinical-SAD and CT-SAD scores did not significantly correlate. Interpretation SAD is a complex and silent signature of asthma, which is likely to be directly or indirectly captured by combinations of physiologic tests: spirometry, body plethysmography, IOS, and MBNW. SAD is present across all asthma severity and particularly in severe disease. The clinical classification of SAD in two groups, i.e. a “milder” and “more severe” SAD group, by the easy-to conduct measures IOS and spirometry, is meaningful given its association with GINA asthma severity stages, asthma control, quality of life, and exacerbations. The longitudinal part of ATLANTIS will show the relevance of the SAD score for future risks in asthma, and additionally which parameter best associates with future asthma control. Moreover, we will report on development of a Small Airways Dysfunction Tool (SADT), a questionnaire as an easy measure to suggest SAD, and on the measures of inflammation that best discriminate between the large and small airways’ compartments, with bronchial and transbronchial biopsies, in a smaller subset of participants.