지재호 (충남대학교 단백질시스템연구실)

김현수 (충남대학교 생명과학과/생명정보융합학과)

지재호 (충남대학교 단백질시스템연구실)

손아름 (울산대학교 의과학대학원)

홍수종 (국립중앙의료원)

김현수 (충남대학교 생명과학과/생명정보융합학과)

Exposure to humidifier disinfectants has been associated with various pulmonary disorders and impaired lung function, particularly reduced Forced Vital Capacity (FVC). This study aimed to identify diagnostic biomarkers for early detection of children at elevated risk of developing chronic respiratory conditions following such exposure. We conducted a comprehensive multi-omics analysis of 70 pediatric patients and 10 controls, integrating clinical assessments with transcriptomic, methylomic, proteomic, and metabolomic data. Our analytical approach combined Non-negative Matrix Factorization (NMF), Multi-Omics Factor Analysis (MOFA), and machine learning algorithms. NMF clustering revealed distinct protein expression patterns associated with integrin-mediated signaling pathways and immune response mechanisms. MOFA identified latent factors correlating with lung function metrics, highlighting the roles of integrin-mediated signaling and lipid metabolism regulation. Through machine learning analysis, we developed a multi-marker panel comprising IGHV2-70, LysoPC (16:0), and hexadecyl ferulate, which demonstrated 81.46% accuracy in identifying children with pulmonary dysfunction. These findings represents a significant step toward personalized medicine approaches for managing the long-term health consequences of humidifier disinfectant exposure in pediatric populations.

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