Journal
CURRENT ALLERGY AND ASTHMA REPORTS
Volume 15, Issue 7, Pages -Publisher
CURRENT MEDICINE GROUP
DOI: 10.1007/s11882-015-0542-0
Keywords
Asthma; Allergy; Endotypes; Phenotypes; Machine learning; Childhood asthma; Latent class analysis
Categories
Funding
- Medical Research Council
- JP Moulton Charitable Foundation
- North West Lung Research Centre Charity
- European Union
- National Institute of Health Research
- Novartis
- Thermo Fisher
- AstraZeneca
- ALK
- GlaxoSmithKline
- MRC [MR/M015181/1, MR/K006665/1, MR/K002449/1, MR/K002449/2] Funding Source: UKRI
- Medical Research Council [MR/K002449/2, 1864377, MR/M015181/1, MR/K002449/1, MC_PC_15018, MC_PC_13042, MR/K006665/1] Funding Source: researchfish
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Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as 'asthma endotypes'. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies.
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