Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung, Taiwan.
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Chang Gung Memorial Hospital-Kaohsiung Medical Center, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Allergol Int. 2024 Apr;73(2):214-223. doi: 10.1016/j.alit.2024.01.001. Epub 2024 Jan 29.
Asthma has been identified as different phenotypes due to various risk factors. Age differences may have potential effects on asthma phenotypes. Our study aimed to identify potential asthma phenotypes among adults divided by age as either younger or older than 65 years. We also compared differences in blood granulocyte patterns, occupational asthmagens, and asthma control-related outcomes among patient phenotype clusters.
We recruited nonelderly (<65 years old) (n = 726) and elderly adults (≥65 years old) (n = 201) with mild-to-severe asthma. We conducted a factor analysis to select 17 variables. A two-step cluster analysis was used to classify subjects with asthma phenotypes, and a discriminant analysis was used to verify the classification of cluster results.
There were three clusters with different characteristics identified in both the nonelderly and elderly asthmatic adults. In the nonelderly patient group, cluster 2 (obese, neutrophilic phenotypes) had a 1.85-fold significantly increased risk of asthma exacerbations. Cluster 3 (early-onset, atopy, and smoker with an eosinophil-predominant pattern) had a 2.37-fold risk of asthma exacerbations and higher oral corticosteroid (OCS) use than cluster 1 (late-onset and LMW exposure with paucigranulocytic blood pattern). Among elderly patients, cluster 2 had poor lung function and more ex-smokers. Cluster 3 (early-onset, long asthma duration) had the lowest paucigranulocytic blood pattern percentages in the elderly group.
The novelty of the clusters was found in age-dependent clusters. We identified three distinct phenotypes with heterogeneous characteristics, asthma exacerbations and medicine use in nonelderly and elderly asthmatic patients, respectively. Classification of age-stratified asthma phenotypes may lead to precise identification of patients, which provides personalized disease management.
由于各种风险因素，哮喘已被确定为不同的表型。年龄差异可能对哮喘表型有潜在影响。我们的研究旨在确定年龄分为<65 岁和≥65 岁的成年患者中潜在的哮喘表型。我们还比较了不同年龄组患者表型簇之间的血液粒细胞模式、职业性哮喘原和哮喘控制相关结局的差异。
我们招募了非老年（<65 岁）（n=726）和老年（≥65 岁）（n=201）的轻至重度哮喘患者。我们进行了因子分析以选择 17 个变量。两步聚类分析用于对哮喘表型进行分类，判别分析用于验证聚类结果的分类。
在非老年和老年哮喘患者中均确定了具有不同特征的三个聚类。在非老年患者组中，聚类 2（肥胖、中性粒细胞表型）哮喘加重的风险增加了 1.85 倍。聚类 3（早发、过敏、吸烟且嗜酸性粒细胞占优势）的哮喘加重风险增加了 2.37 倍，比聚类 1（晚发和低分子量暴露、粒细胞减少性血液模式）使用更多的口服皮质类固醇（OCS）。在老年患者中，聚类 2 的肺功能较差，更多的是前吸烟者。聚类 3（早发、哮喘持续时间长）在老年组中粒细胞减少性血液模式的比例最低。
聚类的新颖性在于年龄依赖性的聚类。我们分别在非老年和老年哮喘患者中发现了三个具有不同特征、哮喘加重和药物使用的不同表型。年龄分层哮喘表型的分类可能导致对患者的精确识别，从而提供个性化的疾病管理。
