Characterizing wheeze phenotypes to identify endotypes of childhood asthma, and the implications for future management

Abstract

It is now a commonly held view that asthma is not a single disease, but rather a set of heterogeneous diseases sharing common symptoms. One of the major challenges in treating asthma is understanding these different asthma phenotypes and their underlying biological mechanisms. This review gives an epidemiological perspective of our current understanding of the different phenotypes that develop from birth to childhood that come under the umbrella term ‘asthma’. The review focuses mainly on publications from longitudinal birth cohort studies where the natural history of asthma symptoms is observed over time in the whole population. Identifying distinct pathophysiological mechanisms for these different phenotypes will potentially elucidate different asthma endotypes, ultimately leading to more effective treatment and management strategies.

Keywords::

• asthma phenotypes
• endotypes
• epidemiology
• longitudinal cohorts
• machine learning

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties.

No writing assistance was utilized in the production of this manuscript.

Key issues

• • Asthma is not a single disease but rather represents an umbrella term used to describe a collection of heterogeneous diseases presenting with similar symptoms.

• • Longitudinal birth cohorts have been crucial for conceptualizing the natural history of wheeze, the most common manifestation of asthma.

• • Both subjective clinician-imposed and objective data-driven approaches have been used to capture changing trajectories of wheeze profiles over time in order to identify different asthma phenotypes.

• • Computer-based objective data-driven approaches can help achieve a more refined clinician-based diagnosis by understanding the different types of asthma phenotypes and their associated genetic and environmental characteristics.

• • Further work is required to refine the phenotypes currently suggested and identify their genetic and environmental associates.

• • By understanding the different phenotypes present in this umbrella term ‘asthma’, we will better understand biological causes, thus facilitating personalized medicine including management and prevention strategies.