Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes

Detalhes bibliográficos
Autor(a) principal: Amaral, Rita
Data de Publicação: 2018
Outros Autores: Bousquet, Jean, Pereira, Ana M., Araújo, Luís M., Sá‐Sousa, Ana, Jacinto, Tiago, Almeida, Rute, Delgado, Luís, Fonseca, João A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/14334
Resumo: Background Refined phenotyping of allergic diseases may unravel novel phenotypes. Conjunctivitis as an independent disorder has never been approached. Aim To identify distinct classes of allergic respiratory diseases using latent class analysis (LCA) and distinguish each class using classification and regression tree (CART) analysis. Methods Seven hundred and twenty‐eight adults from the Portuguese general population study ICAR had a structured medical interview combined with blood collection, skin prick tests, spirometry with bronchodilation, and exhaled nitric oxide. LCA was applied to 19 variables. The CART algorithm selected the most likely variables distinguishing LCA‐classes. Results A six‐class model was obtained. Class 1 (25%): nonallergic participants without bronchial or ocular symptoms. Classes 2 (22%) and 3 (11%): nasal and ocular (low levels) symptoms without nasal impairment, monosensitized (Class 2) or polysensitized (Class 3). Class 4 (13%): polysensitized participants with high levels of nasal and ocular symptoms, and nasal impairment. Classes 5 (16%) and 6 (14%): high level of nasal, bronchial and ocular symptoms with nasal impairment (non‐allergic or polysensitized, respectively). Participants in classes 5 and 6 had more bronchial exacerbations and unscheduled medical visits (P < 0.001). Ocular symptoms were significantly higher in classes with nasal impairment, compared to those without impairment (P < 0.001) or no nasal symptom (P < 0.001). CART highlighted ocular symptoms as the most relevant variable in distinguishing LCA‐classes. Conclusion Novel severe phenotypes of participants with co‐occurrence of ocular, nasal and bronchial symptoms, and exacerbation‐prone were identified. The tree algorithm showed the importance of the ocular symptoms in the expression of allergic diseases phenotypes.
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spelling Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypesAirways symptomsAllergic sensitizationCluster analysisOcular symptomsBackground Refined phenotyping of allergic diseases may unravel novel phenotypes. Conjunctivitis as an independent disorder has never been approached. Aim To identify distinct classes of allergic respiratory diseases using latent class analysis (LCA) and distinguish each class using classification and regression tree (CART) analysis. Methods Seven hundred and twenty‐eight adults from the Portuguese general population study ICAR had a structured medical interview combined with blood collection, skin prick tests, spirometry with bronchodilation, and exhaled nitric oxide. LCA was applied to 19 variables. The CART algorithm selected the most likely variables distinguishing LCA‐classes. Results A six‐class model was obtained. Class 1 (25%): nonallergic participants without bronchial or ocular symptoms. Classes 2 (22%) and 3 (11%): nasal and ocular (low levels) symptoms without nasal impairment, monosensitized (Class 2) or polysensitized (Class 3). Class 4 (13%): polysensitized participants with high levels of nasal and ocular symptoms, and nasal impairment. Classes 5 (16%) and 6 (14%): high level of nasal, bronchial and ocular symptoms with nasal impairment (non‐allergic or polysensitized, respectively). Participants in classes 5 and 6 had more bronchial exacerbations and unscheduled medical visits (P < 0.001). Ocular symptoms were significantly higher in classes with nasal impairment, compared to those without impairment (P < 0.001) or no nasal symptom (P < 0.001). CART highlighted ocular symptoms as the most relevant variable in distinguishing LCA‐classes. Conclusion Novel severe phenotypes of participants with co‐occurrence of ocular, nasal and bronchial symptoms, and exacerbation‐prone were identified. The tree algorithm showed the importance of the ocular symptoms in the expression of allergic diseases phenotypes.WileyRepositório Científico do Instituto Politécnico do PortoAmaral, RitaBousquet, JeanPereira, Ana M.Araújo, Luís M.Sá‐Sousa, AnaJacinto, TiagoAlmeida, RuteDelgado, LuísFonseca, João A.2020-05-02T00:30:30Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/14334engAmaral, R., Bousquet, J., Pereira, A. M., Araújo, L. M., Sá-Sousa, A., Jacinto, T., Almeida, R., Delgado, L., & Fonseca, J. A. (2019). Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes. Allergy, 74(4), 698–708. https://doi.org/10.1111/all.1367010.1111/all.13670info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-17T01:47:04Zoai:recipp.ipp.pt:10400.22/14334Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:34:11.280340Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes
title Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes
spellingShingle Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes
Amaral, Rita
Airways symptoms
Allergic sensitization
Cluster analysis
Ocular symptoms
title_short Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes
title_full Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes
title_fullStr Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes
title_full_unstemmed Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes
title_sort Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes
author Amaral, Rita
author_facet Amaral, Rita
Bousquet, Jean
Pereira, Ana M.
Araújo, Luís M.
Sá‐Sousa, Ana
Jacinto, Tiago
Almeida, Rute
Delgado, Luís
Fonseca, João A.
author_role author
author2 Bousquet, Jean
Pereira, Ana M.
Araújo, Luís M.
Sá‐Sousa, Ana
Jacinto, Tiago
Almeida, Rute
Delgado, Luís
Fonseca, João A.
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Amaral, Rita
Bousquet, Jean
Pereira, Ana M.
Araújo, Luís M.
Sá‐Sousa, Ana
Jacinto, Tiago
Almeida, Rute
Delgado, Luís
Fonseca, João A.
dc.subject.por.fl_str_mv Airways symptoms
Allergic sensitization
Cluster analysis
Ocular symptoms
topic Airways symptoms
Allergic sensitization
Cluster analysis
Ocular symptoms
description Background Refined phenotyping of allergic diseases may unravel novel phenotypes. Conjunctivitis as an independent disorder has never been approached. Aim To identify distinct classes of allergic respiratory diseases using latent class analysis (LCA) and distinguish each class using classification and regression tree (CART) analysis. Methods Seven hundred and twenty‐eight adults from the Portuguese general population study ICAR had a structured medical interview combined with blood collection, skin prick tests, spirometry with bronchodilation, and exhaled nitric oxide. LCA was applied to 19 variables. The CART algorithm selected the most likely variables distinguishing LCA‐classes. Results A six‐class model was obtained. Class 1 (25%): nonallergic participants without bronchial or ocular symptoms. Classes 2 (22%) and 3 (11%): nasal and ocular (low levels) symptoms without nasal impairment, monosensitized (Class 2) or polysensitized (Class 3). Class 4 (13%): polysensitized participants with high levels of nasal and ocular symptoms, and nasal impairment. Classes 5 (16%) and 6 (14%): high level of nasal, bronchial and ocular symptoms with nasal impairment (non‐allergic or polysensitized, respectively). Participants in classes 5 and 6 had more bronchial exacerbations and unscheduled medical visits (P < 0.001). Ocular symptoms were significantly higher in classes with nasal impairment, compared to those without impairment (P < 0.001) or no nasal symptom (P < 0.001). CART highlighted ocular symptoms as the most relevant variable in distinguishing LCA‐classes. Conclusion Novel severe phenotypes of participants with co‐occurrence of ocular, nasal and bronchial symptoms, and exacerbation‐prone were identified. The tree algorithm showed the importance of the ocular symptoms in the expression of allergic diseases phenotypes.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01T00:00:00Z
2020-05-02T00:30:30Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/14334
url http://hdl.handle.net/10400.22/14334
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Amaral, R., Bousquet, J., Pereira, A. M., Araújo, L. M., Sá-Sousa, A., Jacinto, T., Almeida, R., Delgado, L., & Fonseca, J. A. (2019). Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes. Allergy, 74(4), 698–708. https://doi.org/10.1111/all.13670
10.1111/all.13670
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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