A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.

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  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Data Analysis* ; Machine Learning*; Asthma/*diagnosis ; Diseases in Twins/*diagnosis ; Eosinophils/*immunology; Adolescent ; Asthma/epidemiology ; Asthma/immunology ; Asthma/physiopathology ; Biomarkers/analysis ; Child ; Diseases in Twins/epidemiology ; Diseases in Twins/immunology ; Diseases in Twins/physiopathology ; Female ; Humans ; Leukocyte Count ; Male ; Medical History Taking/statistics & numerical data ; Regression Analysis ; Respiratory Sounds/physiopathology ; Risk Factors ; Severity of Illness Index ; Sweden/epidemiology ; Twins/statistics & numerical data
    • Abstract:
      '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.
      Competing Interests: FC declares that she receives salary from IQVIA, Nordics, Sweden. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials. The other authors have no conflict of interest to declare.
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    • Accession Number:
      0 (Biomarkers)
    • Publication Date:
      Date Created: 20191231 Date Completed: 20200402 Latest Revision: 20200402
    • Publication Date:
      20221213
    • Accession Number:
      PMC6936778
    • Accession Number:
      10.1371/journal.pone.0227091
    • Accession Number:
      31887128