European Respiratory Society Annual Congress 2013 Abstract Number: 3909 Publication Number: 3041 Abstract Group: 5.2. Monitoring Airway Disease Keyword 1: Biomarkers Keyword 2: Asthma - mechanism Keyword 3: Breath test Title: Unbiased cluster analysis of severe asthma based on metabolomics by the U-BIOPRED electronic nose platform Mr. Paul 24142 Brinkman
[email protected] 1, Ms. Ariane 28207 Wagener
[email protected] MD 1, Mr. Hugo 28662 Knobel
[email protected] 2, Dr. Anton 28663 Vink
[email protected] 2, Dr. Nicholas 28673 Rattray
[email protected] 3, Prof. Dr Stephen 28731 Fowler
[email protected] MD 4, Dr. Marco 28764 Santonico
[email protected] 5, Dr. Giorgio 28815 Pennazza
[email protected] 5, Prof. Dr Paolo 28848 Montuschi
[email protected] MD 6, Prof. Dr Peter 28856 Sterk
[email protected] MD 1 and 28893 U-BIOPRED Study Group
[email protected] . 1 Dept. Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands ; 2 Philips Research Laboratories, Philips, Eindhoven, Netherlands ; 3 Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom ; 4 Respiratory Medicine, University of Manchester and Lancashire Teaching Hospitals NHS Trust, Manchester, United Kingdom ; 5 Center for Integrated Research - CIR, Unit of Electronics For Sensor Systems, Campus Bio-Medico University, Rome, Italy and 6 Department of Pharmacology, Faculty of Medicine, Catholic University of the Sacred Heart, Rome, Italy . Body: 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.
