Abstract
Rationale.
Severe asthma is a heterogeneous disease with various clinical expressions and diverse pathophysiology. Recent ‘omics’ technologies allow high-throughput characterisation of composite molecular samples in inflammatory airway diseases [Wheelcock ERJ 2013]. This includes breathomics that represents non-invasive metabolomics in exhaled air.
Aim.
To discover severe asthma phenotypes by unbiased cluster analysis based on metabolomic fingerprints from exhaled breath by electronic nose (eNose).
Methods.
This was a cross-sectional analysis of the U-BIOPRED cohort. Severe asthma was defined by IMI-criteria [Bel Thorax 2011]. Exhaled volatile organic compounds (VOCs) trapped on adsorption tubes were analysed by centralized eNose platform (Owlstone Lonestar, Cyranose 320, Comon Invent, Tor Vergata TEN) with 190 sensors in total. Ward clustering followed by one-way ANOVA was done in R.
Results.
Data were available for 57 patients (age 55±13yr, 39% male, 47% (ex-)smokers, >1000μg FP eq). Three clusters of eNose data were delineated, that differed significantly regarding: BMI (p=0.02), postbr FEV1% predicted (p=0.04), postbr FVC% predicted (p=0.001) and sputum eosinophils (p=0.03) (figure 1).
Conclusion.
Unbiased fingerprinting by eNose provides clusters of severe asthma patients that differ in four clinical parameters. This suggests that metabolomics in exhaled air is suitable for phenotyping of severe asthma.
- © 2013 ERS
