Unveiling the systemic impact of airborne microplastics: Integrating breathomics and machine learning with dual-tissue transcriptomics

Airborne microplastics (MPs) pose significant respiratory and systemic health risks upon inhalation; however, current assessment methods remain inadequate. This study integrates breathomics and transcriptomics to establish a non-invasive approach for evaluating MP-induced damage to the lungs and heart. C57BL/6 mice were exposed to polystyrene MPs (0.1 μm, 2 μm, and 10 μm), and their exhaled volatile organic compounds (VOCs) were analyzed using photoinduced associative ionization time-of-flight mass spectrometry. Machine learning algorithms identified hydrogen sulfide, acetone, acrolein, propionitrile, and butyronitrile as key VOC biomarkers, linking MP exposure to oxidative stress and metabolic dysregulation. Transcriptomic analysis further revealed significant gene expression alterations in pulmonary and cardiac tissues, implicating immune dysregulation, metabolic disturbance, and cardiac dysfunction. Pathway enrichment analysis, supported by histological and immunohistochemical validation, confirmed pulmonary inflammation and cardiac injury. By integrating exhaled biomarker profiling with transcriptomic insights, this study advances non-invasive detection strategies for MP-related health effects, offering valuable prospects for public health monitoring and early diagnosis.