基于无监督方法识别哮喘症状亚型以支持可治疗特征策略
2025/12/23
背景：哮喘具有高度异质性，需采取个体化治疗策略。然而，在低收入和中等收入国家及非专科医疗环境中，用于评估可治疗特征的客观检测手段（如肺功能测定、呼出气一氧化氮[FeNO]等）往往难以普及。为实现资源有限条件下的精准医疗，本研究采用无监督机器学习技术，基于哮喘患者报告结局（PRO）构建算法，以识别关键可治疗特征（包括气流受限、2型炎症及频繁急性加重）。
方法：研究纳入两个哮喘队列（发现队列1697例，验证队列157例），采用分层聚类与均匀流形近似和投影（UMAP）方法，对哮喘控制问卷（ACQ-5）中五项症状评分进行分析。
结果：共识别出五个症状亚组，其特征分别对应不同的可治疗特征：亚型1：症状轻微；亚型2：症状较轻，伴轻度气流受限；亚型3：以气短与喘息为主，伴气流受限；亚型4：以晨间症状与夜间觉醒为主，伴2型炎症；亚型5：所有症状均严重，同时存在气流受限、2型炎症及频繁急性加重。通过UMAP将五维ACQ-5数据降维至二维可视化，可清晰显示控制不佳的哮喘患者分布于亚型3、4、5中。该结果在独立队列中得到外部验证。
结论：本研究基于哮喘PRO数据开发的无监督算法，可将患者划分为五种症状亚型，并为关键可治疗特征的识别提供依据。这一数据驱动的数字健康策略有望推动精准医疗在哮喘管理中的应用，即使在资源受限的环境中也能实施。
关键词：哮喘症状亚型；医疗资源差异；患者报告结局；可治疗特征；无监督机器学习
Unsupervised identification of asthma symptom subtypes supports treatable traits approach
Abstract
Background: Heterogeneity of asthma requires a personalized therapeutic approach. However, objective measurements, such as spirometry and fraction of exhaled nitric oxide (FeNO) for implementing treatable traits approach, are limited in low- and middle-income countries and non-specialist settings. To implement precision medicine even with minimal resources, we developed an algorithm using unsupervised machine learning techniques that estimates key treatable traits (airflow limitation, type 2 [T2] inflammation, and frequent exacerbations) based on an asthma patient-reported outcome (PRO).
Methods: We applied hierarchical clustering and Uniform Manifold Approximation and Projection (UMAP) to Asthma Control Questionnaire (ACQ)-5 including five residual symptoms from two asthma cohorts (the discovery cohort with 1697 patients and validation cohort with 157 patients).
Results：We identified five symptom clusters, characterized by key treatable traits: Cluster 1, minimal asthma symptoms; Cluster 2, a little symptom, mild airflow limitation; Cluster 3, predominant shortness of breath and wheezes, airflow limitation; Cluster 4, predominant morning symptoms and nocturnal awakening, T2 inflammation; and Cluster 5, all symptoms severe, airflow limitation, T2 inflammation and frequent exacerbations. The UMAP projections of ACQ-5 (five-dimensional) to two-dimensions allowed to visualize datapoints and clusters, which visually revealed that patients with poorly-controlled asthma were divided into Clusters 3, 4 and 5. These results were externally validated in an independent cohort.
Conclusions：Based on asthma PRO data, the developed algorithm categorized asthma patients into five symptom-based subtypes that provide insights into key treatable traits. Our data-driven digital health approach will extend precision medicine of asthma to medical facilities even in resource-constrained settings.
Keywords：Asthma symptom subtypes；Healthcare disparities；Patient-reported outcome；Treatable traits；Unsupervised machine learning
方法：研究纳入两个哮喘队列（发现队列1697例，验证队列157例），采用分层聚类与均匀流形近似和投影（UMAP）方法，对哮喘控制问卷（ACQ-5）中五项症状评分进行分析。
结果：共识别出五个症状亚组，其特征分别对应不同的可治疗特征：亚型1：症状轻微；亚型2：症状较轻，伴轻度气流受限；亚型3：以气短与喘息为主，伴气流受限；亚型4：以晨间症状与夜间觉醒为主，伴2型炎症；亚型5：所有症状均严重，同时存在气流受限、2型炎症及频繁急性加重。通过UMAP将五维ACQ-5数据降维至二维可视化，可清晰显示控制不佳的哮喘患者分布于亚型3、4、5中。该结果在独立队列中得到外部验证。
结论：本研究基于哮喘PRO数据开发的无监督算法，可将患者划分为五种症状亚型，并为关键可治疗特征的识别提供依据。这一数据驱动的数字健康策略有望推动精准医疗在哮喘管理中的应用，即使在资源受限的环境中也能实施。
关键词：哮喘症状亚型；医疗资源差异；患者报告结局；可治疗特征；无监督机器学习
（南方医科大学南方医院 胡玉玲 龚钊乾 赵海金）
(Hamada K, Abe T, Oishi K, et al. Unsupervised identification of asthma symptom subtypes supports treatable traits approach. Allergol Int. Published online July 28, 2025. doi:10.1016/j.alit.2025.06.004)
Unsupervised identification of asthma symptom subtypes supports treatable traits approach
Abstract
Background: Heterogeneity of asthma requires a personalized therapeutic approach. However, objective measurements, such as spirometry and fraction of exhaled nitric oxide (FeNO) for implementing treatable traits approach, are limited in low- and middle-income countries and non-specialist settings. To implement precision medicine even with minimal resources, we developed an algorithm using unsupervised machine learning techniques that estimates key treatable traits (airflow limitation, type 2 [T2] inflammation, and frequent exacerbations) based on an asthma patient-reported outcome (PRO).
Methods: We applied hierarchical clustering and Uniform Manifold Approximation and Projection (UMAP) to Asthma Control Questionnaire (ACQ)-5 including five residual symptoms from two asthma cohorts (the discovery cohort with 1697 patients and validation cohort with 157 patients).
Results：We identified five symptom clusters, characterized by key treatable traits: Cluster 1, minimal asthma symptoms; Cluster 2, a little symptom, mild airflow limitation; Cluster 3, predominant shortness of breath and wheezes, airflow limitation; Cluster 4, predominant morning symptoms and nocturnal awakening, T2 inflammation; and Cluster 5, all symptoms severe, airflow limitation, T2 inflammation and frequent exacerbations. The UMAP projections of ACQ-5 (five-dimensional) to two-dimensions allowed to visualize datapoints and clusters, which visually revealed that patients with poorly-controlled asthma were divided into Clusters 3, 4 and 5. These results were externally validated in an independent cohort.
Conclusions：Based on asthma PRO data, the developed algorithm categorized asthma patients into five symptom-based subtypes that provide insights into key treatable traits. Our data-driven digital health approach will extend precision medicine of asthma to medical facilities even in resource-constrained settings.
Keywords：Asthma symptom subtypes；Healthcare disparities；Patient-reported outcome；Treatable traits；Unsupervised machine learning
