New perspectives on 'Breathomics': metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS.
Madiha MalikTobias J DemetrowitschKarin SchwarzThomas KunzePublished in: Communications biology (2024)
Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. "Breathomics" as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.
Keyphrases
- mass spectrometry
- ms ms
- gas chromatography
- alcohol consumption
- multiple sclerosis
- fatty acid
- liquid chromatography
- metabolic syndrome
- physical activity
- escherichia coli
- climate change
- type diabetes
- biofilm formation
- high resolution
- machine learning
- single cell
- staphylococcus aureus
- high resolution mass spectrometry
- artificial intelligence
- pseudomonas aeruginosa
- cystic fibrosis
- neural network