PR001815 (Project)

Description:While the burden of nontuberculous mycobacterial pulmonary disease (NTM-PD) continues to increase, knowledge of biomarkers for NTM-PD remains insufficient. Furthermore, metabolic changes in NTM-PD have not yet been investigated. The identification of specific metabolites and associated metabolic pathways unique to NTM-PD will provide a deeper understanding of its pathogenesis. In this study, we aimed to discover specific metabolic biomarkers for NTM-PD using a metabolomics approach. In this study, we underwent untargeted metabolomic profiling using a liquid chromatography system coupled with the quadrupole-orbitrap mass spectrometer to analyze serum samples from patients with NTM-PD (n = 50), patients with non-NTM bronchiectasis (n = 50), and HC subjects (n = 60). To validate the results, an additional 86 serum samples for each group were analyzed using the same analytical methods. We identified several NTM-PD significant metabolites that differentiate patients with NTM-PD from healthy individuals. The machine learning-based classification model demonstrated the proficiency of the selected metabolic features in distinguishing between patients with NTM-PD and healthy individuals. These findings may contribute to a better understanding of the pathogenesis of NTM-PD and provide insights for the development of novel treatment approaches.