Profiling allergic asthma volatile metabolic patterns using a headspace-solid phase microextraction/gas...

Allergic asthma represents an important public health issue with significant growth over the years, especially in the paediatric population. Exhaled breath is a non-invasive, easily performed and rapid method for obtaining samples from the lower respiratory tract. In the present manuscript, the metabolic volatile profiles of allergic asthma and control children were evaluated by headspace solid-phase microextraction combined with gas chromatography-quadrupole mass spectrometry (HS-SPME/GC-qMS). The lack of studies in breath of allergic asthmatic children by HS-SPME led to the development of an experimental design to optimize SPME parameters. To fulfil this objective, three important HS-SPME experimental parameters that influence the extraction efficiency, namely fibre coating, temperature and time extractions were considered. The selected conditions that promoted higher extraction efficiency corresponding to the higher GC peak areas and number of compounds were: DVB/CAR/PDMS coating fibre, 22 °C and 60 min as the extraction temperature and time, respectively. The suitability of two containers, 1L Tedlar® bags and BIOVOC®, for breath collection and intra-individual variability were also investigated. The developed methodology was then applied to the analysis of children exhaled breath with allergic asthma (35), from which 13 had also allergic rhinitis, and healthy control children (15), allowing to identify 44 volatiles distributed over the chemical families of alkanes (linear and ramified) ketones, aromatic hydrocarbons, aldehydes, acids, among others. Multivariate studies were performed by Partial Least Squares-Discriminant Analysis (PLS-DA) using a set of 28 selected metabolites and discrimination between allergic asthma and control children was attained with a classification rate of 88%. The allergic asthma paediatric population was characterized mainly by the compounds linked to oxidative stress, such as alkanes and aldehydes. Furthermore, more detailed information was achieved combining the volatile metabolic data, suggested by PLS-DA model, and clinical data.