在重度哮喘患者中通过电子鼻识别炎症表型的可行性与稳定性研究
2020/01/02
摘要
背景：基于人口统计学、临床和炎症特征的独立聚类分析表明，严重哮喘是一种异质性疾病。因此，接下来的任务是使用“组学”技术鉴定分子驱动的表型。呼出气的分子指纹与炎症相关，并且可能成为严重哮喘表型的非侵入性评估指标。
目标：我们的目标是：1）通过从电子鼻复合体（eNoses）获得的呼出代谢组学指纹识别严重的哮喘表型; 2）评估与患者中临床和炎症变化相关的eNose衍生表型的稳定性。
方法：在这项纵向多中心研究中，呼气样本取自U-BIOPRED队列中未经选择的成人重症哮喘受试者，并由eNoses组合集中分析呼出的代谢物。我们应用了通过相似性概况分析（SPA）与K-Means聚类加以增强的无监督Ward聚类，并通过中心周围分区（PAM）和拓扑数据分析（TDA）进行内部验证。处于前瞻性随访第12-18个月的样本被用于评估纵向患者内的稳定性。
结果：数据来源于78名受试者（年龄55岁 [IQR：45-64]年，41％男性）。三个eNose驱动簇（n = 26/33/19）被发现，其中循环嗜酸性粒细胞（p = 0.045）、中性粒细胞百分比（p = 0.017）和使用口服皮质类固醇的患者比例（p = 0.035）存在差异。纵向患者群内稳定性与痰嗜酸性粒细胞的变化相关（p = 0.045）。
结论：我们已经确定并随访了严重哮喘的呼出分子表型。这些表型与炎症特征的改变和口服类固醇药物的使用有关。本研究表明呼吸分析可能有助于严重哮喘的治疗。
Identification and prospective stability of eNose derived inflammatory phenotypes in severe asthma
P. Brinkman, et al. J Allergy Clin Immunol. 2018 Dec.
Abstract
Background ：Severe asthma is a heterogeneous condition as shown by independent cluster analyses based on demographic, clinical and inflammatory characteristics. A next step is to identify molecular driven phenotypes using ‘omics’-technologies. Molecular fingerprints of exhaled breath are associated with inflammation and may qualify as non-invasive assessment of severe asthma phenotypes.
Objectives ：We aimed: 1) to identify severe asthma phenotypes by exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses); 2) to assess stability of eNose derived phenotypes in relation to within-patient clinical and inflammatory changes.
Methods ：In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adult severe asthma subjects from the U-BIOPRED cohort. Exhaled metabolites were centrally analyzed by an assembly of eNoses. Unsupervised Ward clustering enhanced by Similarity Profile Analysis (SPA) together with K-Means clustering was performed. For internal validation Partitioning Around Medoids (PAM) and topological data analysis (TDA) were applied. Samples at 12-18 months of prospective follow-up were used to assess longitudinal within-patient stability.
Results：Data were available for 78 subjects (age 55 [IQR: 45-64] years, 41% male). Three eNose-driven clusters (n=26/33/19) were revealed, showing differences in circulating eosinophil- (p=0.045) and neutrophil percentages (p=0.017) and ratio of patients using oral corticosteroids (p=0.035). Longitudinal within-patient cluster stability was associated to changes in sputum eosinophils (p=0.045).
Conclusions：We have identified and followed-up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid usage. This suggests that breath analysis might contribute to the management of severe asthma.
背景：基于人口统计学、临床和炎症特征的独立聚类分析表明，严重哮喘是一种异质性疾病。因此，接下来的任务是使用“组学”技术鉴定分子驱动的表型。呼出气的分子指纹与炎症相关，并且可能成为严重哮喘表型的非侵入性评估指标。
目标：我们的目标是：1）通过从电子鼻复合体（eNoses）获得的呼出代谢组学指纹识别严重的哮喘表型; 2）评估与患者中临床和炎症变化相关的eNose衍生表型的稳定性。
方法：在这项纵向多中心研究中，呼气样本取自U-BIOPRED队列中未经选择的成人重症哮喘受试者，并由eNoses组合集中分析呼出的代谢物。我们应用了通过相似性概况分析（SPA）与K-Means聚类加以增强的无监督Ward聚类，并通过中心周围分区（PAM）和拓扑数据分析（TDA）进行内部验证。处于前瞻性随访第12-18个月的样本被用于评估纵向患者内的稳定性。
结果：数据来源于78名受试者（年龄55岁 [IQR：45-64]年，41％男性）。三个eNose驱动簇（n = 26/33/19）被发现，其中循环嗜酸性粒细胞（p = 0.045）、中性粒细胞百分比（p = 0.017）和使用口服皮质类固醇的患者比例（p = 0.035）存在差异。纵向患者群内稳定性与痰嗜酸性粒细胞的变化相关（p = 0.045）。
结论：我们已经确定并随访了严重哮喘的呼出分子表型。这些表型与炎症特征的改变和口服类固醇药物的使用有关。本研究表明呼吸分析可能有助于严重哮喘的治疗。
(中国医科大学附属一院呼吸与危重症学科 李文扬 摘译 杨冬 审校)
(P. Brinkman, et al. J Allergy Clin Immunol. 2018 Dec.)
(P. Brinkman, et al. J Allergy Clin Immunol. 2018 Dec.)
Identification and prospective stability of eNose derived inflammatory phenotypes in severe asthma
P. Brinkman, et al. J Allergy Clin Immunol. 2018 Dec.
Abstract
Background ：Severe asthma is a heterogeneous condition as shown by independent cluster analyses based on demographic, clinical and inflammatory characteristics. A next step is to identify molecular driven phenotypes using ‘omics’-technologies. Molecular fingerprints of exhaled breath are associated with inflammation and may qualify as non-invasive assessment of severe asthma phenotypes.
Objectives ：We aimed: 1) to identify severe asthma phenotypes by exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses); 2) to assess stability of eNose derived phenotypes in relation to within-patient clinical and inflammatory changes.
Methods ：In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adult severe asthma subjects from the U-BIOPRED cohort. Exhaled metabolites were centrally analyzed by an assembly of eNoses. Unsupervised Ward clustering enhanced by Similarity Profile Analysis (SPA) together with K-Means clustering was performed. For internal validation Partitioning Around Medoids (PAM) and topological data analysis (TDA) were applied. Samples at 12-18 months of prospective follow-up were used to assess longitudinal within-patient stability.
Results：Data were available for 78 subjects (age 55 [IQR: 45-64] years, 41% male). Three eNose-driven clusters (n=26/33/19) were revealed, showing differences in circulating eosinophil- (p=0.045) and neutrophil percentages (p=0.017) and ratio of patients using oral corticosteroids (p=0.035). Longitudinal within-patient cluster stability was associated to changes in sputum eosinophils (p=0.045).
Conclusions：We have identified and followed-up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid usage. This suggests that breath analysis might contribute to the management of severe asthma.
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应用三维CT断层扫描评价哮喘和哮喘-慢阻肺重叠患者的气道结构变化的差异
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IOS定义的哮喘小气道功能障碍的临床特点和预测因子
