Identification of Asthma Subtypes Using Clustering Methodologies

Abstract

Asthma is a heterogeneous disease comprising a number of subtypes which may be caused by different pathophysiologic mechanisms (sometimes referred to as endotypes) but may share similar observed characteristics (phenotypes). The use of unsupervised clustering in adult and paediatric populations has identified subtypes of asthma based on observable characteristics such as symptoms, lung function, atopy, eosinophilia, obesity, and age of onset. Here we describe different clustering methods and demonstrate their contributions to our understanding of the spectrum of asthma syndrome. Precise identification of asthma subtypes and their pathophysiological mechanisms may lead to stratification of patients, thus enabling more precise therapeutic and prevention approaches.

Keywords: Adult asthma; Asthma; Clustering; Endotypes; Paediatric asthma; Phenotypes.

Fig. 1

Overview of the difference between agglomerative and divisive hierarchical clustering

Fig. 2

A silhouette plot used for non-hierarchical clustering (k-means) (from [20], with permission). A silhouette plot shows how close observations from neighbouring clusters are to each other using a measure of −1 to +1. A value of +1 indicates that observations are far away, 0 indicates that the observations are very close to the boundary of deciding exactly which cluster they belong to, and −1 indicates that the observations may be assigned to the wrong cluster