Proﬁling allergic asthma volatile metabolic patterns using a headspace-solid phase microextraction/gas chromatography based methodology - Caldeira, M. (2011)
Allergicasthmarepresentsanimportantpublichealthissuewithsigniﬁcantgrowthovertheyears,especially in the paediatric population. Exhaled breath is a non-invasive, easily performed and rapid method forobtainingsamplesfromthelowerrespiratorytract.Inthepresentmanuscript,themetabolicvolatile 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 ofstudiesinbreathofallergicasthmaticchildrenbyHS-SPMEledtothedevelopmentofanexperimental 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 60min 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 allergicasthma(35),fromwhich13hadalsoallergicrhinitis,andhealthycontrolchildren(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 LeastSquares–DiscriminantAnalysis(PLS–DA)usingasetof28selectedmetabolitesanddiscrimination 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.