哮喘联盟

    摘要
    背景：慢性阻塞性肺疾病（COPD）、哮喘、支气管扩张（BE）和囊性纤维化（CF）之间存在显著的特征重叠，每种疾病都以炎症和黏液纤毛功能障碍为特征。
    研究问题：慢性呼吸系统疾病中，炎症与黏液纤毛清除是否存在关联？能否基于生物学特征而非疾病标签对患者进行具有治疗指导意义的亚型分层？
    研究设计与方法：按原发疾病和临床特征对患者进行分类，收集痰样本，检测炎症标志物（中性粒细胞弹性蛋白酶NE及19种细胞因子）、痰液特性（DNA含量、黏蛋白、流变学特性、干重）和微生物组（长读长16S测序）。采用K均值聚类法分析，比较组间和组内参数。对照组为无呼吸系统疾病的既往吸烟者。
    结果：研究纳入哮喘（76例）、COPD（91例）、BE（54例）、CF（24例）患者及对照组（26例）。多项指标在疾病组与对照组间存在显著差异（p<0.05），包括九种细胞因子（IFN-γ, IL-4, IL-5, Eotaxin, Eotaxin-3, TARC, G-CSF, Fractalkine, IL-22）、中性粒细胞弹性蛋白酶（NE）、痰液干重、黏蛋白水平及流变学参数。K均值聚类识别出2个以中性粒细胞性或Th2炎症为特征的簇。Th2簇表现为痰液干重和DNA含量较低，而MUC5B水平较高。嗜酸性粒细胞组的流变学参数G'、G''和G*（分别代表痰液弹性、黏性和坚固性）显著较高，而Tan(delta）在中性粒细胞组较高，表明黏弹性比率更高（所有比较p<0.05）。中性粒细胞簇的微生物组α多样性降低（p=0.04），变形菌门丰度较Th2簇增加（p=0.01）。CF和BE以中性粒细胞炎症为主（COPD和哮喘患者中中性粒细胞型分别占42%和46%，而BE和CF中分别占78%和87%，p<0.0001）。所有疾病组中均存在两种簇类型。
    解读：慢性气道疾病具有异质性黏液特性，患者更应根据炎症内型而非疾病标签聚类。在疾病标签评估基础上，结合炎症和黏液纤毛清除生物标志物的内型分型，可能有助于精准诊疗。
    关键词：慢性呼吸系统疾病；内型分型；炎症生物标志物；痰液特性

（南方医科大学南方医院 汤亦心 赵海金）
（Cant E,et al. Inflammatory and mucociliary dysfunction based endotypes across the spectrum of chronic airway diseases.Chest. 2025 Aug 26:S0012-3692(25)05121-9.）

Abstract
Background: There is substantial overlap between features of COPD, asthma, bronchiectasis(BE) and cystic fibrosis(CF). Each is characterised by inflammation and mucociliary dysfunction.
Research question: Is there a relationship between inflammation and mucociliary clearance in chronic respiratory conditions and can biology rather than disease labels stratify patients into therapeutically relevant subtypes?
Study design and methods: Patients were categorized by primary disease and clinical characteristics, spontaneous sputum was collected, inflammatory markers (neutrophil elastase(NE) and 19 cytokines), sputum properties (DNA content, mucins, rheology, dry weight) and microbiome (long read 16S sequencing) were measured. K-means clustering was performed and parameters compared between and within disease groups. Controls were former smokers without respiratory disease.
Results: The study included patients with asthma(76), COPD(91), BE(54), CF(24) and controls(26). Nine c ytokines (IFN-γ, IL-4, IL-5, Eotaxin, Eotaxin-3, TARC, G-CSF, Fractalkine, IL-22), NE, dry weight, mucins and sputum rheology parameters were significantly different between disease groups and controls (p<0.05). K-means clustering identified 2 clusters defined by neutrophilic or Th2 inflammation. The Th2 cluster was associated with lower sputum dry weight, DNA content and higher MUC5B. Rheological parameters G', G'' and G* were significantly higher in the eosinophilic group while Tan(delta) was higher in the neutrophilic group, indicating a higher viscous to elastic ratio. (p<0.05 all comparisons). The neutrophilic cluster was associated with decreased alpha diversity (p=0.04) and increased presence of proteobacteria in their sputum microbiome compared to the Th2 cluster (p=0.01). More neutrophilic inflammation was present in CF and BE (42% of COPD and 46% of asthma patients were neutrophilic vs 78% of BE and 87% of CF,p<0.0001). Both clusters were present in all disease groups.
Interpretation: Airways diseases have heterogenous mucus properties. Patients cluster according to inflammatory endotype rather than disease label. Assessment based on disease labels may be aided by endotyping using inflammatory and mucociliary clearance biomarkers.
Keywords: Chronic respiratory conditions; endotyping; inflammatory biomarkers; sputum properties.