Early Clinical Development, AstraZeneca, Cambridge, United Kingdom.
BioPharmaceuticals Medical, AstraZeneca, Cambridge, United Kingdom.
J Allergy Clin Immunol Pract. 2023 Sep;11(9):2803-2811. doi: 10.1016/j.jaip.2023.05.013. Epub 2023 May 23.
Asthma and chronic obstructive pulmonary disease (COPD) are complex diseases, the definitions of which overlap.
To investigate clustering of clinical/physiological features and readily available biomarkers in patients with physician-assigned diagnoses of asthma and/or COPD in the NOVEL observational longiTudinal studY (NOVELTY; NCT02760329).
Two approaches were taken to variable selection using baseline data: approach A was data-driven, hypothesis-free and used the Pearson dissimilarity matrix; approach B used an unsupervised Random Forest guided by clinical input. Cluster analyses were conducted across 100 random resamples using partitioning around medoids, followed by consensus clustering.
Approach A included 3796 individuals (mean age, 59.5 years; 54% female); approach B included 2934 patients (mean age, 60.7 years; 53% female). Each identified 6 mathematically stable clusters, which had overlapping characteristics. Overall, 67% to 75% of patients with asthma were in 3 clusters, and approximately 90% of patients with COPD were in 3 clusters. Although traditional features such as allergies and current/ex-smoking (respectively) were higher in these clusters, there were differences between clusters and approaches in features such as sex, ethnicity, breathlessness, frequent productive cough, and blood cell counts. The strongest predictors of the approach A cluster membership were age, weight, childhood onset, prebronchodilator FEV, duration of dust/fume exposure, and number of daily medications.
Cluster analyses in patients from NOVELTY with asthma and/or COPD yielded identifiable clusters, with several discriminatory features that differed from conventional diagnostic characteristics. The overlap between clusters suggests that they do not reflect discrete underlying mechanisms and points to the need for identification of molecular endotypes and potential treatment targets across asthma and/or COPD.
哮喘和慢性阻塞性肺疾病（COPD）是复杂的疾病，其定义存在重叠。
在 NOVEL 观察性纵向研究（NOVELTY；NCT02760329）中，调查医生诊断为哮喘和/或 COPD 的患者的临床/生理特征和易于获得的生物标志物聚类情况。
使用基线数据采用两种方法进行变量选择：方法 A 是数据驱动的、无假设的，使用 Pearson 不相似矩阵；方法 B 使用无监督随机森林，由临床输入指导。使用中值分区进行了 100 次随机重采样的聚类分析，然后进行共识聚类。
方法 A 纳入 3796 名个体（平均年龄 59.5 岁；54%为女性）；方法 B 纳入 2934 名患者（平均年龄 60.7 岁；53%为女性）。每种方法均确定了 6 个数学稳定的聚类，这些聚类具有重叠的特征。总体而言，3 个聚类中有 67%至 75%的哮喘患者，大约 90%的 COPD 患者在 3 个聚类中。尽管传统特征（如过敏和当前/吸烟（分别））在这些聚类中更高，但聚类之间以及聚类与方法之间在特征（如性别、种族、呼吸困难、频繁有痰咳嗽和血细胞计数）方面存在差异。方法 A 聚类成员的最强预测因子是年龄、体重、儿童期发病、支气管扩张剂前 FEV1、灰尘/烟雾暴露持续时间和每日用药次数。
NOVELTY 中哮喘和/或 COPD 患者的聚类分析产生了可识别的聚类，具有与传统诊断特征不同的几个鉴别特征。聚类之间的重叠表明它们不反映离散的潜在机制，并指出需要在哮喘和/或 COPD 中识别分子内型和潜在的治疗靶点。
