Multi-omics and machine learning-based profiling of severity signatures in Mycoplasma pneumoniae infection in children

pneumonia (MPP) is a common respiratory infection in children; however, the mechanisms driving its progression to severe disease remain poorly understood. This study employs a comprehensive proteomic and metabolomic approach to elucidate severity-related pathways and identify potential biomarkers for improved diagnosis and targeted therapy. By analyzing blood proteomes from pediatric patients with varying MPP severities alongside healthy controls, and integrating multi-omics data from bronchoalveolar lavage fluid (BALF), we uncovered key severity-associated proteins and metabolites linked to inflammatory and metabolic dysregulation such as efferocytosis pathway, and Fanconi anemia pathway. Machine learning analysis further identified 8 critical biomarkers that accurately distinguished between mild and severe MPP cases. The identification of severity-specific biomarkers offers a foundation for enhanced diagnostic precision, improved disease stratification, and the development of targeted therapeutic strategies to optimize the management of severe MPP in pediatric patients.