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A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data
‘Asthma’ is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previously been based on longitudinal wheeze patterns or hypothesis-driven observational criteria. The aim of this study was to use data-driven machine learn...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936778/ https://www.ncbi.nlm.nih.gov/pubmed/31887128 http://dx.doi.org/10.1371/journal.pone.0227091 |
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author | Brew, Bronwyn K. Chiesa, Flaminia Lundholm, Cecilia Örtqvist, Anne Almqvist, Catarina |
author_facet | Brew, Bronwyn K. Chiesa, Flaminia Lundholm, Cecilia Örtqvist, Anne Almqvist, Catarina |
author_sort | Brew, Bronwyn K. |
collection | PubMed |
description | ‘Asthma’ is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previously been based on longitudinal wheeze patterns or hypothesis-driven observational criteria. The aim of this study was to use data-driven machine learning to identify asthma and wheeze phenotypes in children based on symptom and symptom history data, and, to further characterize these phenotypes. The study population included an asthma-rich population of twins in Sweden aged 9–15 years (n = 752). Latent class analysis using current and historical clinical symptom data generated asthma and wheeze phenotypes. Characterization was then performed with regression analyses using diagnostic data: lung function and immunological biomarkers, parent-reported medication use and risk-factors. The latent class analysis identified four asthma/wheeze phenotypes: early transient wheeze (15%); current wheeze/asthma (5%); mild asthma (9%), moderate asthma (10%) and a healthy phenotype (61%). All wheeze and asthma phenotypes were associated with reduced lung function and risk of hayfever compared to healthy. Children with mild and moderate asthma phenotypes were also more likely to have eczema, allergic sensitization and a family history of asthma. Furthermore, those with moderate asthma phenotype had a higher eosinophil concentration (β 0.21, 95%CI 0.12, 0.30) compared to healthy and used short-term relievers at a higher rate than children with mild asthma phenotype (RR 2.4, 95%CI 1.2–4.9). In conclusion, using a data driven approach we identified four wheeze/asthma phenotypes which were validated with further characterization as unique from one another and which can be adapted for use by the clinician or researcher. |
format | Online Article Text |
id | pubmed-6936778 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-69367782020-01-07 A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data Brew, Bronwyn K. Chiesa, Flaminia Lundholm, Cecilia Örtqvist, Anne Almqvist, Catarina PLoS One Research Article ‘Asthma’ is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previously been based on longitudinal wheeze patterns or hypothesis-driven observational criteria. The aim of this study was to use data-driven machine learning to identify asthma and wheeze phenotypes in children based on symptom and symptom history data, and, to further characterize these phenotypes. The study population included an asthma-rich population of twins in Sweden aged 9–15 years (n = 752). Latent class analysis using current and historical clinical symptom data generated asthma and wheeze phenotypes. Characterization was then performed with regression analyses using diagnostic data: lung function and immunological biomarkers, parent-reported medication use and risk-factors. The latent class analysis identified four asthma/wheeze phenotypes: early transient wheeze (15%); current wheeze/asthma (5%); mild asthma (9%), moderate asthma (10%) and a healthy phenotype (61%). All wheeze and asthma phenotypes were associated with reduced lung function and risk of hayfever compared to healthy. Children with mild and moderate asthma phenotypes were also more likely to have eczema, allergic sensitization and a family history of asthma. Furthermore, those with moderate asthma phenotype had a higher eosinophil concentration (β 0.21, 95%CI 0.12, 0.30) compared to healthy and used short-term relievers at a higher rate than children with mild asthma phenotype (RR 2.4, 95%CI 1.2–4.9). In conclusion, using a data driven approach we identified four wheeze/asthma phenotypes which were validated with further characterization as unique from one another and which can be adapted for use by the clinician or researcher. Public Library of Science 2019-12-30 /pmc/articles/PMC6936778/ /pubmed/31887128 http://dx.doi.org/10.1371/journal.pone.0227091 Text en © 2019 Brew et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Brew, Bronwyn K. Chiesa, Flaminia Lundholm, Cecilia Örtqvist, Anne Almqvist, Catarina A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data |
title | A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data |
title_full | A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data |
title_fullStr | A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data |
title_full_unstemmed | A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data |
title_short | A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data |
title_sort | modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936778/ https://www.ncbi.nlm.nih.gov/pubmed/31887128 http://dx.doi.org/10.1371/journal.pone.0227091 |
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