[Analysis of nasal microbial characteristics in patients with allergic rhinitis and non-allergic rhinitis].

Objective: To investigate the characteristics of nasal flora and the pathogenic role of differential microbiome in patients with allergic rhinitis (AR) and non-allergic rhinitis (nAR). Methods: Thirty-five patients with AR who attended the rhinology outpatient clinic of the Second Hospital of Harbin Medical University from February to July 2022 were selected. A total of 35 nAR patients were selected as the test group, and 20 cases of healthy people with physical examination at the same period were selected as the control group, including 39 males and 51 females, aged 8 to 55 years. 16SrDNA High-throughput sequencing was used to analyze the relative abundance from nasal flora in the three groups of subjects. Alpha diversity index analysis was conducted with R software, and differences between groups were analyzed with LEfSe, Metastats, and t tests. At the same time, the role of microbiome and its relationship with environmental factors were analyzed with R software. Results: There was a significant difference in the bacterial composition of the samples from the three groups, with the relative abundance of Staphylococcus aureus (P=0.032) and Corynebacterium proinquum (P=0.032) within the AR group being significantly higher than that of the nAR group, and that of Lactobacillus murinus, Lactobacillus kunkeei, and Alcaligenes faecalis (P value was 0.016, 0.005, and 0.001, respectively) being significantly lower than that of the nAR group. The relative abundance of Ackermannia muciniphila within the nAR group was higher than that of the control group (P=0.009). Correlation analysis of environmental factors showed a negative correlation between Lactobacillus kunkeei and IgE (P=0.044), and a positive correlation between Lactobacillus murinus and age (P=0.019). AR and nAR random forest prediction models were constructed for the five genera, respectively, and the area under the curve (AUC) of the models of Streptococcus-SP-FF10, Pseudoalteromonas luteoviolacea, Pseudomonas parafulva, Acinetobacter ursingii, and Azotobacter chroococcum in the AR group was 100% (95%CI: 100% to 100%). The AUC for the Pseudomonas parafulva, Azotobacter chroococcum, Closoridium baratii, Turicibacter-SP-H121, and Streptococcus lutetiensis models in the nAR group was 98.4% (95%CI: 94.9% to 100%). Conclusions: The distribution of nasal flora in AR patients, nAR patients and healthy subjects is significantly different, and the changes of bacterial flora abundance are significantly related to the occurrence of AR and nAR. Combined detection of microbiota has the potential to diagnose AR and nAR patients.目的： 探究变应性鼻炎（AR）及非变应性鼻炎（nAR）患者的鼻腔菌群特征及差异菌的致病作用。 方法： 选取2022年2—7月于哈尔滨医科大学附属第二医院鼻科门诊就诊的AR患者35例、nAR患者35例为试验组，选取同期体检的健康人群20名为对照组，合计男性39例，女性51例，年龄8~55岁。利用16SrDNA高通量测序分析3组受试者鼻腔菌群相对丰度特征，使用R软件进行Alpha多样性指数分析，利用LEfSe、Metastats、t检验进行组间差异分析，同时利用R软件分析菌群的作用及与环境因子的关系。 结果： 3组样本的菌群构成存在显著差异，AR组内金黄色葡萄球菌（P=0.032）、丙曲棒状杆菌（P=0.032）的相对丰度显著高于nAR组，鼠李乳杆菌、昆可乳杆菌、产碱菌（P值分别为0.016、0.005、0.001）的相对丰度显著低于nAR组，nAR组内嗜黏蛋白阿克曼菌的相对丰度高于对照组（P=0.009）。环境因子相关性分析显示昆可乳杆菌与IgE呈负相关关系（P=0.044），鼠李乳杆菌与年龄呈正相关关系（P=0.019）。分别构建5个属的AR、nAR随机森林预测模型，AR组肺炎链球菌-FF10、黄体假单胞菌、副氏假单胞菌、熊不动杆菌和固氮杆菌模型的曲线下面积（AUC）为100%（95%CI：100%~100%），nAR组副氏假单胞菌、固氮杆菌、巴拉蒂梭菌、土瑞酸杆菌和黄链球菌模型的AUC为98.4%（95%CI：94.9%~100%）。 结论： AR、nAR患者与健康受试者鼻腔菌群相对丰度显著不同，菌群丰度的改变与AR及nAR的发生具有显著关系。菌群的联合检测具有诊断AR及nAR患者的潜力。.