Chinese General Practice ›› 2022, Vol. 25 ›› Issue (24): 2984-2991.DOI: 10.12114/j.issn.1007-9572.2022.0242

• Article • Previous Articles     Next Articles

Inflammatory Phenotypes and Associated Factors in Bronchial Asthma Patients: a Cross-sectional Survey

  

  1. 1Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-constructed by Henan Province & Education Ministry of P.R.China, Henan University of Chinese Medicine/Henan Key Laboratory of Chinese Medicine for Respiratory Disease, Zhengzhou 450046, China
    2Respiratory Department, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, China
    3Gongyi City People's Hospital, Zhengzhou 451200, China
    4Zhumadian Chinese Medicine Hospital, Zhumadian 463000, China
  • Received:2022-02-26 Revised:2022-05-16 Published:2022-08-20 Online:2022-06-09
  • Contact: Minghang WANG
  • About author:
    HAN W H, ZHAO H H, TU X M, et al. Inflammatory phenotypes and associated factors in bronchial asthma patients: a cross-sectional survey[J]. Chinese General Practice, 2022, 25 (24) : 2984-2991.

基于横断面调查的支气管哮喘炎症表型分布及识别因素分析

  

  1. 1450046 河南省郑州市,河南中医药大学呼吸疾病中医药防治省部共建协同创新中心 河南省中医药防治呼吸病重点实验室
    2450000 河南省郑州市,河南中医药大学第一附属医院呼吸科
    3451200 河南省郑州市,巩义市人民医院
    4463000 河南省驻马店市中医院
  • 通讯作者: 王明航
  • 作者简介:
    韩伟红,赵欢欢,屠新敏,等.基于横断面调查的支气管哮喘炎症表型分布及识别因素分析[J].中国全科医学,2022,25(24):2984-2991.[www.chinagp.net] 作者贡献:王明航负责研究的构思与设计、可行性分析及文章的整体把关、质量控制、监督管理,对文章负责;韩伟红、赵欢欢、屠新敏负责数据收集及标本处理;韩伟红、赵欢欢负责数据整理、统计学处理及论文的撰写;韩伟红、王明航负责对论文进行修订和英文的修订。
  • 基金资助:
    国家自然科学基金资助项目(81873278); 河南省特色骨干学科中医学学科建设项目(STG-ZYXKY-2020001、STG-ZYXKY-2020002); 国家中医药管理局中医药古籍文献和特色技术传承专项(GZY-KJS-2020-075)

Abstract:

Background

The various inflammatory phenotypes of asthma, a common heterogeneous respiratory disease, are closely related to the pathogenesis, treatment, and prognosis of the disease. So screening the associated factors of inflammatory phenotypes will be helpful for the evaluation of patient condition and delivery of individualized diagnosis and treatment services.

Objective

To explore the distribution of inflammatory phenotypes and associated factors in bronchial asthma patients, providing a basis for the implementation of individualized diagnosis and treatment of the disease.

Methods

A cross-sectional study design was used. Clinical data of bronchial asthma outpatients and inpatients (n=184) were collected from the First Affiliated Hospital of Henan University of Chinese Medicine from November 2018 to December 2020. Inflammatory phenotypes in the patients were classified into four categories according to the type of inflammatory cells in the induced sputum: neutrophilic asthma (NA) , eosinophilic asthma (EA) , mixed granulocytic asthma (MA) , and paucigranulocytic asthma (PA) . Factors possibly associated with each of the inflammatory phenotypes were screened by three statistical methods (univariate analysis, multivariate Logistic regression analysis, and Spearman rank correlation analysis) , and were determined as the associated factors if they had significant associations with the phenotype by two of the aforementioned three methods.

Results

The prevalence of NA, EA, MA, and PA was 45.7% (84/184) , 20.7% (38/184) , 20.7% (38/184) , and 13.0% (24/184) , respectively. Univariate analysis showed that the prevalence of allergic rhinitis and fractioned exhaled nitric oxide (FeNO) level differed significantly between NA and non-NA patients (P<0.05) . And they also varied significantly between EA and non-EA patients (P<0.05) . There was significant difference in FeNO level between MA and non-MA patients (P<0.05) . There were significant differences in mean age, prevalence of previous respiratory disease and mean FeNO level between PA and non-PA patients (P<0.05) . Multivariate Logistic regression analysis showed that allergic rhinitis〔OR=0.417, 95%CI (0.205, 0.848) 〕 and FeNO〔OR=0.978, 95%CI (0.968, 0.989) 〕were associated with NA (P<0.05) ; FeNO〔OR=1.017, 95%CI (1.009, 1.025) 〕 was associated with EA (P<0.05) ; FeNO〔OR=1.007, 95%CI (1.000, 1.014) 〕was associated with MA (P<0.05) ; BMI〔OR=1.165, 95%CI (1.015, 1.337) 〕 and FeNO〔OR=0.981, 95%CI (0.965, 0.998) 〕were associated with PA (P<0.05) . Spearman rank correlation analysis indicated that NA prevalence decreased with increased allergic rhinitis prevalence and FeNO level (rs=-0.244, -0.361, P<0.05) ; EA prevalence increased with increased allergic rhinitis prevalence and FeNO level (rs=0.157, 0.341, P<0.05) ; MA prevalence increased with increased FeNO (rs=0.236, P<0.05) ; PA prevalence decreased with older age, prevalence of previous respiratory disease and increased FeNO (rs=-0.156, -0.163, -0.159, all P<0.05) . Based on the above analyses, allergic rhinitis and FeNO were associated factors for both EA and NA; FeNO was associated factors of MA; age, prevalence of previous respiratory disease and FeNO were associated factors of PA.

Conclusion

NA accounted for the largest percentage of the inflammatory phenotypes, while PA accounted for the least. FeNO was the associated factor for each inflammatory phenotype. It has specificity in recognizing EA and MA. FeNO combined with allergic rhinitis was associated with NA and EA. FeNO combined with age was associated with PA.

Key words: Asthma, Inflammation, Phenotype, Inflammatory phenotype, Cross-sectional studies, Root cause analysis

摘要:

背景

支气管哮喘为呼吸系统常见的异质性疾病,具有不同的炎症表型。支气管哮喘炎症表型与患者发病、治疗及预后关系密切。筛选支气管哮喘炎症表型识别因素有助于评估病情和个体化诊疗。

目的

探索支气管哮喘炎症表型分布并筛选其识别因素,为疾病个体化诊疗提供依据。

方法

采用横断面调查方法,收集2018年11月至2020年12月河南中医药大学第一附属医院门诊及住院部收治的184例支气管哮喘患者的临床资料。根据诱导痰中炎性细胞占比进行炎症表型分类,按照是否为中性粒细胞型哮喘(NA)分为NA组和非NA组,按照是否为嗜酸粒细胞型哮喘(EA)分为EA组和非EA组,按照是否为混合粒细胞型哮喘(MA)分为MA组和非MA组,按照是否为寡细胞型哮喘(PA)分为PA组和非PA组。采用多元统计学方法(单因素分析、多因素Logistic回归分析、Spearman秩相关分析)筛选各表型相关影响因素,3种统计学方法中2种有统计学意义时为最终识别因素。

结果

184例支气管哮喘患者中NA 84例(45.7%)、EA 38例(20.7%)、MA 38例(20.7%)、PA 24例(13.0%)。单因素分析:NA组与非NA组过敏性鼻炎发生率、呼出气一氧化氮(FeNO)比较,差异有统计学意义(P<0.05);EA组与非EA组过敏性鼻炎发生率、FeNO比较,差异有统计学意义(P<0.05);MA组与非MA组FeNO比较,差异有统计学意义(P<0.05);PA组与非PA组年龄、呼吸系统疾病史、FeNO比较,差异有统计学意义(P<0.05)。多因素Logistic回归分析结果显示,NA的影响因素为过敏性鼻炎〔OR=0.417,95%CI(0.205,0.848)〕、FeNO〔OR=0.978,95%CI(0.968,0.989)〕,EA的影响因素为FeNO〔OR=1.017,95%CI(1.009,1.025)〕,MA的影响因素为FeNO〔OR=1.007,95%CI(1.000,1.014)〕,PA的影响因素为体质指数(BMI)〔OR=1.165,95%CI(1.015,1.337)〕、FeNO〔OR=0.981,95%CI(0.965,0.998)〕(P<0.05)。Spearman秩相关分析:NA分别与过敏性鼻炎、FeNO呈负相关(rs=-0.244、-0.361,P均<0.05),EA分别与过敏性鼻炎、FeNO呈正相关(rs=0.157、0.341,P均<0.05),MA与FeNO呈正相关(rs=0.236,P<0.05),PA分别与年龄、呼吸系统疾病史、FeNO呈负相关(rs=-0.156、-0.163、-0.159,P均<0.05)。综合3种统计学方法最终筛选出NA、EA的识别因素为过敏性鼻炎、FeNO,MA的识别因素为FeNO,PA的识别因素为年龄、呼吸系统疾病史、FeNO。

结论

在支气管哮喘炎症表型中NA最多,PA最少;FeNO为各哮喘炎症表型的识别因素,对识别EA和MA具有特异性,结合过敏性鼻炎可能识别NA、EA,结合年龄可能识别PA。

关键词: 哮喘, 炎症, 表型, 炎症表型, 横断面研究, 影响因素分析