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Multiple atopy phenotypes and their associations with asthma: similar findings from two birth cohorts

Multiple atopy phenotypes and their associations with asthma: similar findings from two birth cohorts
Multiple atopy phenotypes and their associations with asthma: similar findings from two birth cohorts
BACKGROUND: Although atopic sensitization is one of the strongest risk factors for asthma, its relationship with asthma is poorly understood. We hypothesize that 'atopy' encompasses multiple sub-phenotypes that relate to asthma in different ways.

METHODS: In two population-based birth cohorts (Manchester and Isle of Wight - IoW), we used a machine learning approach to independently cluster children into different classes of atopic sensitization in an unsupervised manner, based on skin prick and sIgE tests taken throughout childhood and adolescence. We examined the qualitative cluster properties and their relationship to asthma and lung function.

RESULTS: A five-class solution best described the data in both cohorts, with striking similarity between the classes across the two populations. Compared with nonsensitized class, children in the class with sensitivity to a wide variety of allergens (~1/3 of children atopic by conventional definition) were much more likely to have asthma (aOR [95% CI0; 20.1 [10.9-40.2] in Manchester and 11.9 [7.3-19.4] in IoW). The relationship between asthma and conventional atopy was much weaker (5.5 [3.4-8.8] in Manchester and 5.8 [4.1-8.3] in IoW). In both cohorts, children in this class had significantly poorer lung function (FEV1 /FVC lower by 4.4% in Manchester and 2.6% in IoW; P < 0.001), most reactive airways, highest eNO and most hospital admissions for asthma (P < 0.001).

CONCLUSIONS: By adopting a machine learning approach to longitudinal data on allergic sensitization from two independent unselected birth cohorts, we identified latent classes with strikingly similar patterns of atopic response and association with clinical outcomes, suggesting the existence of multiple atopy phenotypes.
asthma, atopic sensitization, birth cohort, cluster analysis, machine learning
0105-4538
764-770
Lazic, N.
94069af7-3b73-4f60-a4a7-fe490321e860
Roberts, G.
ea00db4e-84e7-4b39-8273-9b71dbd7e2f3
Custovic, A.
624645ad-f4d2-4b1f-a9b6-a8bd763a8d84
Belgrave, D.
536b8415-c468-4782-b837-c1884dbb9922
Bishop, C.M.
0427c415-8e0c-4fdc-bc09-30cb08d73b18
Winn, J.
4cb12f27-733e-4641-83aa-49c485c2dc1d
Curtin, J.A.
09a8a428-3e10-4127-9d2b-29bcc5363674
Arshad, S. Hasan
917e246d-2e60-472f-8d30-94b01ef28958
Simpson, A.
8982e6b2-6613-4de2-b7e7-545b347af6cf
Lazic, N.
94069af7-3b73-4f60-a4a7-fe490321e860
Roberts, G.
ea00db4e-84e7-4b39-8273-9b71dbd7e2f3
Custovic, A.
624645ad-f4d2-4b1f-a9b6-a8bd763a8d84
Belgrave, D.
536b8415-c468-4782-b837-c1884dbb9922
Bishop, C.M.
0427c415-8e0c-4fdc-bc09-30cb08d73b18
Winn, J.
4cb12f27-733e-4641-83aa-49c485c2dc1d
Curtin, J.A.
09a8a428-3e10-4127-9d2b-29bcc5363674
Arshad, S. Hasan
917e246d-2e60-472f-8d30-94b01ef28958
Simpson, A.
8982e6b2-6613-4de2-b7e7-545b347af6cf

Lazic, N., Roberts, G., Custovic, A., Belgrave, D., Bishop, C.M., Winn, J., Curtin, J.A., Arshad, S. Hasan and Simpson, A. (2013) Multiple atopy phenotypes and their associations with asthma: similar findings from two birth cohorts. Allergy, 68 (6), 764-770. (doi:10.1111/all.12134). (PMID:23621120)

Record type: Article

Abstract

BACKGROUND: Although atopic sensitization is one of the strongest risk factors for asthma, its relationship with asthma is poorly understood. We hypothesize that 'atopy' encompasses multiple sub-phenotypes that relate to asthma in different ways.

METHODS: In two population-based birth cohorts (Manchester and Isle of Wight - IoW), we used a machine learning approach to independently cluster children into different classes of atopic sensitization in an unsupervised manner, based on skin prick and sIgE tests taken throughout childhood and adolescence. We examined the qualitative cluster properties and their relationship to asthma and lung function.

RESULTS: A five-class solution best described the data in both cohorts, with striking similarity between the classes across the two populations. Compared with nonsensitized class, children in the class with sensitivity to a wide variety of allergens (~1/3 of children atopic by conventional definition) were much more likely to have asthma (aOR [95% CI0; 20.1 [10.9-40.2] in Manchester and 11.9 [7.3-19.4] in IoW). The relationship between asthma and conventional atopy was much weaker (5.5 [3.4-8.8] in Manchester and 5.8 [4.1-8.3] in IoW). In both cohorts, children in this class had significantly poorer lung function (FEV1 /FVC lower by 4.4% in Manchester and 2.6% in IoW; P < 0.001), most reactive airways, highest eNO and most hospital admissions for asthma (P < 0.001).

CONCLUSIONS: By adopting a machine learning approach to longitudinal data on allergic sensitization from two independent unselected birth cohorts, we identified latent classes with strikingly similar patterns of atopic response and association with clinical outcomes, suggesting the existence of multiple atopy phenotypes.

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More information

Accepted/In Press date: 18 January 2013
e-pub ahead of print date: 29 April 2013
Published date: June 2013
Keywords: asthma, atopic sensitization, birth cohort, cluster analysis, machine learning
Organisations: Human Development & Health

Identifiers

Local EPrints ID: 388737
URI: http://eprints.soton.ac.uk/id/eprint/388737
ISSN: 0105-4538
PURE UUID: 8aed5818-92ee-4147-aa30-e1c147345727
ORCID for G. Roberts: ORCID iD orcid.org/0000-0003-2252-1248

Catalogue record

Date deposited: 02 Mar 2016 16:18
Last modified: 09 Jan 2022 03:18

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Contributors

Author: N. Lazic
Author: G. Roberts ORCID iD
Author: A. Custovic
Author: D. Belgrave
Author: C.M. Bishop
Author: J. Winn
Author: J.A. Curtin
Author: S. Hasan Arshad
Author: A. Simpson

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