Separating smoking-related diseases using NMR-based metabolomics of exhaled breath condensate.

Abstract

Nuclear magnetic resonance (NMR)-based metabolomics separates exhaled breath condensate (EBC) profiles of patients affected by pulmonary disease from those of healthy subjects. Here we show the discriminatory ability of NMR-based metabolomics in separating patients exposed to the same risk factor, namely, smoking habit in smoking-related diseases. Fifty duplicated EBC samples from a cohort of current smokers without chronic obstructive pulmonary disease (COPD, henceforth HS), COPD smokers, and subjects with established pulmonary Langerhans cell histiocytosis (PLCH) were analyzed by means of NMR spectroscopy followed by principal component analysis (PCA) and projection to latent structures discriminant analysis (PLS-DA). Clusterization of EBC spectra was disease-specific. COPD and PLCH samples present a profile different from that of HS, showing acetate increase and 1-methylimidazole reduction. An inverse behavior of 2-propanol and isobutyrate characterized COPD with respect to PLCH (high/low in COPD, low/high in PLCH). Both the 2-component and the 3-component PLS-DA models showed a 96% cross-validated accuracy, presenting R(2) and Q(2) values in the ranges of 0.97-0.87 and 0.91-0.78, respectively, and R(2) = 0.87 and Q(2) = 0.78, indicating that data variation is well explained by each model (R(2)), with a good predictivity (Q(2)). NMR spectra of EBC discriminate COPD and PLCH patients from HS and between them, with well-defined metabolic profiles for each class. The specificity of EBC profiles suggests that disease itself drives metabolic separation overwhelming the "common background" due to smoking habit. EBC-NMR investigation offers a powerful tool for assessing the evolution of airway diseases even in the presence of a strong common factor.