Prediction of sublingual immunotherapy efficacy in allergic rhinitis by serum metabolomics analysis

Background: Allergen-specific immunotherapy (ASIT) is currently the only therapy for allergic rhinitis (AR) that can induce immune tolerance to allergens. However, the course of ASIT is long and there is no objective biomarker to predict treatment efficacy. The present study aimed to explore potential biomarkers predictive of efficacy of AIT based on serum metabolomics profiles. Methods: This prospective study recruited 72 consecutive eligible patients who were assigned to receive sublingual immunotherapy (SLIT). Serum samples were collected prior to SLIT and utilized to obtain metabolomics profiling by applying ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). Treatment response was determined 3 years after SLIT, and patients were divided into effective group and ineffective group. Orthogonal partial least square-discriminate analysis (OPLS-DA) was performed to evaluate the metabolite differences between two groups. Results: Sixty-eight patients completed the whole SLIT, 39 patients were categorized into effective group and 29 patients were classified into ineffective group. A total of 539 metabolites were obtained, and 197 of which were identified as known substances. Using these 197 known metabolites, the OPLS-DA results showed that effective group and ineffective group exhibited distinctive metabolite signatures and metabolic pathways. Six metabolites including lactic acid, ornithine, linolenic acid, creatinine, arachidonic acid and sphingosine were identified to exhibit good performance in predicting the efficacy of SLIT, and these metabolite changes mainly involved glycolysis and pyruvate metabolism, arginine and proline metabolism and fatty acid metabolism pathways. Conclusion: By metabolomics analysis, we identified several serum biomarkers that can reliably and accurately predict the efficacy of SLIT in AR patients. The discriminative metabolites and related metabolic pathways contributed to better understand the mechanisms of SLIT in AR patients.

Automated summary

This study identified several serum biomarkers through metabolomics analysis that can reliably predict the efficacy of ASIT in AR patients, with these biomarkers mainly involving glycolysis, fatty acid metabolism, and other metabolic pathways.