Background Asthma is a heterogeneous disease and its different phenotypes need to be better characterized from a biochemical-inflammatory standpoint. This study aimed to apply the metabolomic approach to exhaled breath condensate (breathomics) to discriminate different asthma phenotypes, with a particular focus on severe asthma in children.Methods In this cross-sectional study, we recruited 42 asthmatic children (age, 8–17 years): 31 with nonsevere asthma (treated with inhaled steroids or not) and 11 with severe asthma. Fifteen healthy children were enrolled as controls. Children performed exhaled nitric oxide measurement, spirometry, exhaled breath condensate (EBC) collection. Condensate samples were analyzed using a metabolomic approach based on mass spectrometry.Results A robust Bidirectional-Orthogonal Projections to Latent Structures-Discriminant Analysis (O2PLS-DA) model was found for discriminating both between severe asthma cases and healthy controls (R2 = 0.93; Q2 = 0.75) and between severe asthma and nonsevere asthma (R2 = 0.84; Q2 = 0.47). The metabolomic data analysis leads to a robust model also when the 3 groups of children were considered altogether (K = 0.80), indicating that each group is characterized by a specific metabolomic profile. Compounds related to retinoic acid, adenosine and vitamin D (Human Metabolome Database) were relevant for the discrimination between groups.Conclusion The metabolomic profiling of EBC could clearly distinguish different biochemical-metabolic profiles in asthmatic children and enabled the severe asthma phenotype to be fully discriminated. The breathomics approach may therefore be suitable for discriminating between different asthma metabolic phenotypes. Background Asthma is a heterogeneous disease and its different phenotypes need to be better characterized from a biochemical-inflammatory standpoint. This study aimed to apply the metabolomic approach to exhaled breath condensate (breathomics) to discriminate different asthma phenotypes, with a particular focus on severe asthma in children. Methods In this cross-sectional study, we recruited 42 asthmatic children (age, 8-17 years): 31 with nonsevere asthma (treated with inhaled steroids or not) and 11 with severe asthma. Fifteen healthy children were enrolled as controls Condensate samples were analyzed using a metabolomic approach based on mass spectrometry. Results A robust Bidirectional-Orthogonal Projections to Latent Structures-Discriminant Analysis (O2PLS-DA) model. Children performed exhaled nitric oxide measurement, spirometry, exhaled breath condensate was found for discriminating both both severe asthma cases and healthy controls (R2 = 0.93; Q2 = 0.75) an The metabolomic data analysis leads to a robust model also when the 3 groups of children were altogether (K ​​= 0.80), indicating that each group is characterized by a Specific metabolomic profile. Compounds related to retinoic acid, adenosine and vitamin D (Human Metabolome Database) were relevant for the discrimination between groups. Confusion The metabolomic profiling of EBC could clearly distinguish different biochemical-metabolic profiles in asthmatic children and enabled the severe asthma phenotype to be fully discriminated. The breathomics approach may therefore be suitable for discriminating between different asthma metabolic phenotypes.