Multiomics and Machine Learning Reveal Distinct Immune-Metabolic Signatures and Diagnostic Biomarkers for Asthma...

Background: bronchial asthma is a highly heterogeneous chronic inflammatory disease, with eosinophilic (EA) and neutrophilic (NEA) asthma subtypes exhibiting distinct pathological mechanisms and treatment responses. However, the protein-metabolite interactions underlying the pathogenesis of asthma remain poorly understood, limiting the development of precision diagnostics and therapeutics. Methods: this study integrated proteomics, metabolomics, and bioinformatics analyses to perform multiomics profiling of sputum samples from 53 asthma patients (27 EA, 20 NEA) and 53 healthy controls. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and CIBERSORT immune cell infiltration analysis were systematically applied to characterize the molecular features of EA and NEA. Machine learning approaches (LASSO regression, SVM-RFE, and Random Forest) were further employed to screen biomarkers and construct diagnostic models. Results: the study systematically characterized proteins and metabolites in asthma patients and healthy controls. We constructed comprehensive interaction networks that reveal critical protein-metabolite relationships and dysregulated pathways, elucidate potential molecular mechanisms underlying asthma heterogeneity, including inflammatory signaling cascades, oxidative stress responses, and metabolic reprogramming. The study also revealed that EA is characterized by Th2-type immune responses and dysregulated histidine/purine metabolism, whereas NEA exhibits neutrophil-mediated oxidative stress (HIF-1 signaling activation, arginine metabolism disturbance) and predominant innate immunity. Multiomics network analysis identified reactive oxygen species (ROS) metabolism and cytokine production as key features in EA, while hypoxia response and NF-κB pathway enrichment were prominent in NEA. A diagnostic model based on 7 core proteins (ASS1, C4B, etc.) effectively distinguished asthma from healthy controls (AUC = 0.944), while 2 overlapping biomarkers (CLCA1, NAMPT) accurately stratified EA and NEA (AUC = 0.903). Conclusion: this study comprehensively delineated the distinct immune-metabolic signatures of asthma patient and healthy controls, as well as EA and NEA through multiomics integration and to establish high-accuracy noninvasive diagnostic models. These findings provide novel targets for molecular subtyping and precision treatment of asthma, particularly offering a theoretical foundation for hypoxia-pathway-targeted interventions in NEA.