Phenotype and endotype based treatment of preschool wheeze
File(s)
Author(s)
Salehian, Sormeh
Fleming, Louise
Saglani, Sejal
Custovic, Adnan
Type
Journal Article
Abstract
Introduction
Preschool wheeze (PSW) is a significant public health issue, with a high presentation rate to emergency departments, recurrent symptoms, and severe exacerbations. A heterogenous condition, PSW comprises several phenotypes that may relate to a range of pathobiological mechanisms. However, treating PSW remains largely generalized to inhaled corticosteroids and a short acting beta agonist, guided by symptom-based labels that often do not reflect underlying pathways of disease.
Areas covered
We review the observable features and characteristics used to ascribe phenotypes in children with PSW and available pathobiological evidence to identify possible endotypes. These are considered in the context of treatment options and future research directions. The role of machine learning (ML) and modern analytical techniques to identify patterns of disease that distinguish phenotypes is also explored.
Expert opinion
Distinct clusters (phenotypes) of severe PSW are characterized by different underlying mechanisms, some shared and some unique. ML-based methodologies applied to clinical, biomarker, and environmental data can help design tools to differentiate children with PSW that continues into adulthood, from those in whom wheezing resolves, identifying mechanisms underpinning persistence and resolution. This may help identify novel therapeutic targets, inform mechanistic studies, and serve as a foundation for stratification in future interventional therapeutic trials.
Preschool wheeze (PSW) is a significant public health issue, with a high presentation rate to emergency departments, recurrent symptoms, and severe exacerbations. A heterogenous condition, PSW comprises several phenotypes that may relate to a range of pathobiological mechanisms. However, treating PSW remains largely generalized to inhaled corticosteroids and a short acting beta agonist, guided by symptom-based labels that often do not reflect underlying pathways of disease.
Areas covered
We review the observable features and characteristics used to ascribe phenotypes in children with PSW and available pathobiological evidence to identify possible endotypes. These are considered in the context of treatment options and future research directions. The role of machine learning (ML) and modern analytical techniques to identify patterns of disease that distinguish phenotypes is also explored.
Expert opinion
Distinct clusters (phenotypes) of severe PSW are characterized by different underlying mechanisms, some shared and some unique. ML-based methodologies applied to clinical, biomarker, and environmental data can help design tools to differentiate children with PSW that continues into adulthood, from those in whom wheezing resolves, identifying mechanisms underpinning persistence and resolution. This may help identify novel therapeutic targets, inform mechanistic studies, and serve as a foundation for stratification in future interventional therapeutic trials.
Date Issued
2023-10-01
Date Acceptance
2023-10-13
Citation
Expert Review of Respiratory Medicine, 2023, 17 (10), pp.853-864
ISSN
1747-6348
Publisher
Taylor and Francis Group
Start Page
853
End Page
864
Journal / Book Title
Expert Review of Respiratory Medicine
Volume
17
Issue
10
Copyright Statement
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Sponsor
Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Identifier
10.1080/17476348.2023.2271832
Grant Number
MR/K002449/2
MR/S025340/1
MR/T031565/1
MR/W028352/1
Subjects
asthma
atopy
CHILDHOOD ASTHMA
DOUBLE-BLIND
EARLY-LIFE
endotype
FOLLOW-UP
HIGH-RISK
infants
INHALED FLUTICASONE PROPIONATE
Life Sciences & Biomedicine
LUNG-FUNCTION
machine learning
phenotype
PLACEBO
Preschool wheeze
Respiratory System
Science & Technology
SECONDARY PREVENTION
YOUNG-CHILDREN
Publication Status
Published
Date Publish Online
2023-10-24