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Comparison of hypothesis- and data-driven asthma phenotypes in NHANES 2007-2012: the importance of comprehensive data availability
Univ Porto, CINTESIS Ctr Hlth Technol & Serv Res, Fac Med, Edificio Nascente,Piso 2, P-4200450 Porto, Portugal;Porto Hlth Sch, Dept Cardiovasc & Resp Sci, Porto, Portugal.
Univ Porto, CINTESIS Ctr Hlth Technol & Serv Res, Fac Med, Edificio Nascente,Piso 2, P-4200450 Porto, Portugal;Inst & Hosp CUF, Dept Allergy, Porto, Portugal.
Univ Porto, CINTESIS Ctr Hlth Technol & Serv Res, Fac Med, Edificio Nascente,Piso 2, P-4200450 Porto, Portugal;Porto Hlth Sch, Dept Cardiovasc & Resp Sci, Porto, Portugal.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Physiology.
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2019 (English)In: Clinical and Translational Allergy, ISSN 2045-7022, E-ISSN 2045-7022, Vol. 9, article id 17Article in journal (Refereed) Published
Abstract [en]

Background

Half of the adults with current asthma among the US National Health and Nutrition Examination Survey (NHANES) participants could be classified in more than one hypothesis-driven phenotype. A data-driven approach applied to the same subjects may allow a more useful classification compared to the hypothesis-driven one.

Aim

To compare previously defined hypothesis-driven with newly derived data-driven asthma phenotypes, identified by latent class analysis (LCA), in adults with current asthma from NHANES 2007-2012.

Methods

Adults (18years) with current asthma from the NHANES were included (n=1059). LCA included variables commonly used to subdivide asthma. LCA models were derived independently according to age groups: <40 and 40years old.

Results

Two data-driven phenotypes were identified among adults with current asthma, for both age groups. The proportions of the hypothesis-driven phenotypes were similar among the two data-driven phenotypes (p>0.05). Class A <40years (n=285; 75%) and Class A 40years (n=462; 73%), respectively, were characterized by a predominance of highly symptomatic asthma subjects with poor lung function, compared to Class B <40years (n=94; 25%) and Class B 40years (n=170; 27%). Inflammatory biomarkers, smoking status, presence of obesity and hay fever did not markedly differ between the phenotypes.

Conclusion

Both data- and hypothesis-driven approaches using clinical and physiological variables commonly used to characterize asthma are suboptimal to identify asthma phenotypes among adults from the general population. Further studies based on more comprehensive disease features are required to identify asthma phenotypes in population-based studies.

Place, publisher, year, edition, pages
2019. Vol. 9, article id 17
Keywords [en]
Asthma, Phenotypes, Population-based study, Unsupervised analysis
National Category
Respiratory Medicine and Allergy
Identifiers
URN: urn:nbn:se:uu:diva-380449DOI: 10.1186/s13601-019-0258-7ISI: 000461351800001OAI: oai:DiVA.org:uu-380449DiVA, id: diva2:1300157
Available from: 2019-03-28 Created: 2019-03-28 Last updated: 2019-03-28Bibliographically approved

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Malinovschi, AndreiJanson, ChristerAlving, Kjell

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