Abstract
Objective:To investigate the clinical characteristics and major allergens of patients with pollen-food allergy syndrome（PFAS） and their correlation with the severity of symptoms, and to provide a basis for identifying high-risk patients, optimizing the allergen testing process and developing individualized dietary management strategies. Methods:The clinical data of 166 patients with PFAS admitted to our hospital from January 2021 to July 2023 were retrospectively analyzed. The clinical symptoms, pollen types and food allergy of the patients were analyzed by questionnaire survey and serum specific IgE detection. phi coefficient, Apriori algorithm modeling and multivariate logistic regression analysis were used to evaluate the association between allergen and symptom severity. Results:Artemisia pollen was the most common allergen in this area, with a positive rate of 96.39%. Peach and mango were the most common food allergens, which caused allergic reactions in 24.10% and 22.89% of patients, respectively. Oral mucosal symptoms were the main symptoms. Correlation analysis showed that there was a correlation between pollen allergens and allergenic food. Association rule analysis showed that when the patient was allergic to the combination of peanuts and trees, the probability of high severity of symptoms was 82.35%. Multivariate analysis showed that ragweed allergy was significantly positively correlated with the severity of PFAS symptoms. Conclusion:Artemisia pollen and related food allergens play an important role in the pathogenesis of PFAS. Association rule mining and network map analysis revealed direct associations between peanut and tree combination allergy and symptom severity, as well as potential links between other inhaled allergens and specific food allergies. Ragweed and peach allergy are independent risk factors for the aggravation of PFAS symptoms, which can be used as early warning indicators. These results help to improve the screening of high-risk patients and the construction of regional allergen databases.