Asthma phenotypes in childhood

Abstract

Asthma is no longer thought of as a single disease, but rather a collection of varying symptoms expressing different disease patterns. One of the ongoing challenges is understanding the underlying pathophysiological mechanisms that may be responsible for the varying responses to treatment. Areas Covered: This review provides an overview of our current understanding of the asthma phenotype concept in childhood and describes key findings from both conventional and data-driven methods. Expert Commentary: With the vast amounts of data generated from cohorts, there is hope that we can elucidate distinct pathophysiological mechanisms, or endotypes. In return, this would lead to better patient stratification and disease management, thereby providing true personalised medicine.

Features of asthma which provide meaningful insights for understanding the disease heterogeneity

“…In most previous studies which used such approaches, the selection of variables used for subtype discovery was either pre‐determined by clinical advice, or by the use of statistical data reduction techniques such as principal component analysis (PCA) . Although valuable information has been gained, and there was some (but not complete) resemblance between the results, most studies reported different disease clusters; several recent reviews have summarized these findings . These inconsistencies may be explained by the inherent heterogeneity among different populations, the differences in clustering techniques used, the lack of consistency in selecting variables, their encodings and transformations, or the use of excessive numbers of variables which may result in subtype “signals” being drowned in the noise …”

“…8,12,13 Although valuable information has been gained, and there was some (but not complete) resemblance between the results, most studies reported different disease clusters; several recent reviews have summarized these findings. [14][15][16][17][18] These inconsistencies may be explained by the inherent heterogeneity among different populations, the differences in clustering techniques used, the lack of consistency in selecting variables, their encodings and transformations, or the use of excessive numbers of variables which may result in subtype "signals" being drowned in the noise. 19 When selecting the variables for unsupervised analyses, the investigators rely on the data which are available (eg in birth cohorts 10,20,21 or studies of adults and children with established disease).…”

Validation of childhood asthma predictive tools: A systematic review

“…At the present, asthma is considered an “umbrella term”, bringing together a selection of different conditions that share common clinical features such as cough and wheeze, shortness of breath and bronchial obstruction . While the concept of distinct wheezing and asthma endotypes has been proposed, existing guidelines based on clinical symptoms and predictive models are still aimed at a single disease . This may partially explain the inaccuracy of asthma prediction models; future work should consider endotypes to a larger extent in the development of predictive models.…”

Classification of Pediatric Asthma: From Phenotype Discovery to Clinical Practice

“…In contrast, data-driven classification relies on techniques and algorithms that mine the large data sets to uncover the underlying structures and patterns “hidden” in the data. Statistical methods such as cluster analysis and latent class analysis (LCA) ( 11 , 24 – 26 ), principal component analysis ( 20 , 27 ), and exploratory factor analysis ( 21 ), have been widely applied to discover homogeneous subtypes of asthma. These procedures ranged from univariate approaches (a single symptom measured over time) to more sophisticated, multivariate approaches that simultaneously model several variables, including symptoms and other clinical and environmental characteristics.…”

“…However, as the term “phenotype” has been used in this context for more than a decade ( 10 ), we will maintain this nomenclature in this review. A thorough review of the implementation of data-driven methods for phenotype discovery in pediatric asthma has been conducted recently, with a particular focus on childhood wheezing illness and different “wheezing phenotypes” at a population level ( 11 , 12 ). We will expand the discussion beyond the existing approaches to understanding phenotypic complexity in asthma, and highlight the role of clinical context and clinical experience in linking latent “phenotypes” to underlying biological mechanisms and tailored treatment approaches.…”