Untargeted Metabolomic Analysis of Exhaled Breath Condensate Identifies Disease-Specific Signatures in Adults With Asthma.

Abstract

Purpose: An objective test for the auxiliary diagnosis of asthma is still lacking. The aim of this study was to discriminate asthma signatures via an untargeted metabolomic analysis of exhaled breath condensate.

Materials and methods: This study enrolled 19 patients diagnosed with asthma and 23 healthy volunteers. Samples of exhaled breath condensate (EBC) were collected from both groups. Untargeted metabolomic analyses of EBC were used to identify disease-specific signatures for asthma.

Result: There were 30 identifiable differentially expressed metabolites and 7 disordered metabolic pathways between the EBCs of asthmatic patients and healthy control subjects. The main differential pathways included biosynthesis of unsaturated fatty acids, HIF-1 signalling pathway, Glutathione metabolism, Ascorbate and aldarate metabolism, and fatty acid biosynthesis. The integrated machine learning method was used to construct an asthma EBC metabolomic signature model from four differential metabolites; 3,4'-dimethoxy-2'-hydroxychalcone, C17-sphinganine, (z)-6-octadecenoic acid, and 2-butylaniline. The model showed a high level of discrimination efficiency (area under curve (AUC) = 0.98), with robust validation through logistic regression (LR), random forest (RF), and support vector machine (SVM) (LR AUC = 0.98, RF AUC = 0.94, SVM AUC = 1.00). The discriminative ability of the EBC metabolomic signature model in both the training set (AUC = 1.0) and testing data (AUC = 0.817) was superior to that of FeNO (AUC = 0.515 and 0.567, respectively) and FEV1/FVC % predicted (AUC = 0.767 and 0.765, respectively). Among the four biomarkers, (z)-6-octadecenoic acid was significantly correlated with serum IgE.

Conclusion: The EBC metabolomic signature model demonstrated good feasibility for assisting in the diagnosis of asthma in adults.