From disease to syndrome pattern: a study on the differentiation of traditional Chinese medicine syndrome patterns in asthma using multi-omics characterization.

Background: Bronchial asthma is a highly heterogeneous chronic inflammatory airway disorder. Syndrome differentiation-guided Traditional Chinese Medicine (TCM) treatment for asthma delivers distinct advantages, including fewer adverse reactions and superior anti-inflammatory efficacy. Nevertheless, the intrinsic biological mechanisms underlying TCM asthma syndromes remain largely uncharacterized, restricting the objective and standardized differentiation of TCM subtypes. Methods: We integrated transcriptomic, proteomic, metabolomic and oral microbiome data from asthma patients and healthy controls to map asthma molecular and microbiome landscapes. Two representative TCM subtypes-Phlegm-Dampness Obstructing the Lungs (PZLP) and Lung-Qi Deficiency (LQD)-were stratified and compared via differential analysis, WGCNA, LEfSe and random forest machine learning to dissect shared asthma-related molecular features, subtype-specific molecular disparities and potential objective diagnostic biomarkers. Results: Comprehensive multi-omics and oral microbiome comparisons revealed correlational molecular discrepancies between asthma patients and healthy individuals. Our omics data suggest that asthma-related molecular alterations are tightly associated with three core pathological cascades: dysregulated glycerophospholipid metabolism, potential excessive activation of the NF-κB inflammatory signaling pathway, and impaired phagosome function. Beyond the universal disease-associated molecular signatures of asthma, prominent metabolic and inflammatory phenotypic divergences were detected between the two TCM subtypes. The PZLP subtype showed correlational omics signatures suggestive of elevated lipogenic activity, aberrant MAPK inflammatory pathway activation, and massive intracellular lipid accumulation. In contrast, the LQD subtype exhibited correlated downregulation of lipid transporter genes (e.g., ABCA13), which hypothetically implies compromised lipid transport capacity and disrupted cell membrane homeostasis. Oral microbiota profiling demonstrated profound structural remodeling in asthmatic patients. Although PZLP and LQD patients shared analogous overall microbial community structures, their microbial functional pathways diverged substantially: the LQD group was enriched in methane and glycerol metabolic pathways, whereas the PZLP group displayed overrepresentation of serotonergic and dopaminergic synaptic regulatory pathways. A diagnostic classification model built on differential microbial biomarkers achieved an area under the receiver operating characteristic curve (AUC) of 0.889, demonstrating robust discriminative capacity for distinguishing the two TCM syndromes. Conclusion: This study delineates correlational immunometabolic and oral microbiome signatures of asthma and uncovers potential subtype-specific molecular and microbial markers for two classic TCM asthma patterns. These observations provide testable mechanistic hypotheses for interpreting the molecular pathogenesis of asthma and could supply a preliminary theoretical foundation for objective TCM syndrome differentiation, personalized intervention and targeted asthma therapeutic development.