AUTHOR=Lyu Xiaohong , Liu Yi , Li Hongna , Wu Zhoujie , Sun Yi , Jiang Xuehan , Wu Shandong , Wu Shanhong , Tang Rui , Gao Yue , Sun Jinlyu 

TITLE=Metabolomic insights into variable antihistamine responses in allergic rhinitis: unveiling biomarkers for precision treatment

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1565972

DOI=10.3389/fimmu.2025.1565972

ISSN=1664-3224

ABSTRACT=BackgroundThe clinical response to antihistamine therapy exhibits substantial heterogeneity among individuals with allergic rhinitis (AR). While these medications represent a cornerstone in AR management, the molecular basis underlying differential treatment outcomes remains incompletely understood. This investigation sought to delineate specific metabolomic profiles that distinguish between AR patients who demonstrate favorable responses to antihistamine treatment and those who exhibit therapeutic resistance.MethodsThis investigation encompassed a cohort of 57 patients diagnosed with AR, stratified into antihistamine-effective (n=49) and antihistamine-ineffective (n=8) groups. The study protocol integrated multiple analytical approaches, including clinical phenotyping, serum vitamin D quantification, mRNA expression, and untargeted metabolomic analysis. Metabolomic profiling was conducted using a state-of-the-art liquid chromatography-mass spectrometry (LC-MS) platform, enabling comprehensive characterization of the serum metabolome.ResultsWhile demographic characteristics and vitamin D levels showed no significant differences between two groups, blood H1R mRNA expression was significantly higher in antihistamine-ineffective patients (P=0.046), and nasal TPSB mRNA expression was elevated (P=0.006). Nineteen metabolites showed significant differences (p<0.05, fold change>2.0, VIP>1.0) between groups. ROC curve analysis identified nine metabolites with high diagnostic potential (AUC>0.70), with Methotrexate (AUC=0.862), Pro-Val-Ala-Glu-Val (AUC=0.804), and TyrMe-Ile-OH (AUC=0.791) showing the strongest discriminatory power. Pathway analysis highlighted the involvement of caffeine metabolism and tryptophan metabolism pathways.ConclusionsThis study identified distinct metabolomic signatures between antihistamine-effective and antihistamine-ineffective AR patients, providing potential biomarkers for predicting treatment response and new insights into the metabolic mechanisms underlying treatment efficacy in AR.