小児におけるマイコプラズマ肺炎感染の重症度シグネチャのマルチオミクスおよび機械学習ベースのプロファイリング

Mycoplasma pneumoniae 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.