Abstract

Exhaled breath condensate (EBC) collection is a noninvasive method to investigate lung diseases. EBC is usually collected with commercial/custom-made condensers, but the optimal condensing temperature is often unknown. As such, the physical and chemical properties of exhaled metabolites should be considered when setting the temperature, therefore requiring validation and standardization of the collecting procedure. EBC is frequently used in nuclear magnetic resonance (NMR)-based metabolomics, which unambiguously recognizes different pulmonary pathological states. Here we applied NMR-based metabolomics to asthmatic and healthy EBC samples collected with two commercial condensers operating at −27.3 and −4.8 °C. Thirty-five mild asthmatic patients and 35 healthy subjects were included in the study, while blind validation was obtained from 20 asthmatic and 20 healthy different subjects not included in the primary analysis. We initially analyzed the samples separately and assessed the within-day, between-day, and technical repeatabilities. Next, samples were interchanged, and, finally, all samples were analyzed together, disregarding the condensing temperature. Partial least-squares discriminant analysis of NMR spectra correctly classified samples, without any influence from the temperature. Input variables were either integral bucket areas (spectral bucketing) or metabolite concentrations (targeted profiling). We always obtained strong regression models (95%), with high average-quality parameters for spectral profiling (<i>R</i><sup>2</sup> = 0.84 and <i>Q</i><sup>2</sup> = 0.78) and targeted profiling (<i>R</i><sup>2</sup> = 0.91 and <i>Q</i><sup>2</sup> = 0.87). In particular, although targeted profiling clustering is better than spectral profiling, all models reproduced the relative metabolite variations responsible for class differentiation. This warrants that cross comparisons are reliable and that NMR-based metabolomics could attenuate some specific problems linked to standardization of EBC collection

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