{"id":230746,"date":"2013-06-01T09:15:40","date_gmt":"2013-06-01T16:15:40","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=230746"},"modified":"2018-10-16T21:52:19","modified_gmt":"2018-10-17T04:52:19","slug":"multiple-atopy-phenotypes-associations-asthma","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/multiple-atopy-phenotypes-associations-asthma\/","title":{"rendered":"Multiple Atopy Phenotypes and Their Associations with Asthma: Similar Findings From Two Birth Cohorts"},"content":{"rendered":"<div id=\"all12134-sec-0001\" class=\"section\">\n<h4>Background<\/h4>\n<div class=\"para\">\n<p>Although atopic sensitization is one of the strongest risk factors for asthma, its relationship with asthma is poorly understood. We hypothesize that \u2018atopy\u2019 encompasses multiple sub-phenotypes that relate to asthma in different ways.<\/p>\n<\/div>\n<\/div>\n<div id=\"all12134-sec-0002\" class=\"section\">\n<h4>Methods<\/h4>\n<div class=\"para\">\n<p>In two population-based birth cohorts (Manchester and Isle of Wight \u2013 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.<\/p>\n<\/div>\n<\/div>\n<div id=\"all12134-sec-0003\" class=\"section\">\n<h4>Results<\/h4>\n<div class=\"para\">\n<p>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\u201340.2] in Manchester and 11.9 [7.3\u201319.4] in IoW). The relationship between asthma and conventional atopy was much weaker (5.5 [3.4\u20138.8] in Manchester and 5.8 [4.1\u20138.3] in IoW). In both cohorts, children in this class had significantly poorer lung function (FEV<sub>1<\/sub>\/FVC lower by 4.4% in Manchester and 2.6% in IoW; <em>P\u00a0<\/em><\u00a00.001), most reactive airways, highest eNO and most hospital admissions for asthma (<em>P\u00a0<\/em><\u00a00.001).<\/p>\n<\/div>\n<\/div>\n<div id=\"all12134-sec-0004\" class=\"section\">\n<h4>Conclusions<\/h4>\n<div class=\"para\">\n<p>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.<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Background Although atopic sensitization is one of the strongest risk factors for asthma, its relationship with asthma is poorly understood. We hypothesize that \u2018atopy\u2019 encompasses multiple sub-phenotypes that relate to asthma in different ways. Methods In two population-based birth cohorts (Manchester and Isle of Wight \u2013 IoW), we used a machine learning approach to independently [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-230746","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Allergy","msr_affiliation":"","msr_published_date":"2013-06-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"764 \u2013 770","msr_chapter":"","msr_isbn":"","msr_journal":"Allergy","msr_volume":"68","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"6","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"230749","msr_publicationurl":"","msr_doi":"10.1111\/all.12134","msr_publication_uploader":[{"type":"file","title":"Bishop-allergy-phenotypes","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/05\/Bishop-allergy-phenotypes.pdf","id":230749,"label_id":0},{"type":"doi","title":"10.1111\/all.12134","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"N. Lazic","user_id":0,"rest_url":false},{"type":"text","value":"G. Roberts","user_id":0,"rest_url":false},{"type":"text","value":"A. Custovic","user_id":0,"rest_url":false},{"type":"text","value":"D. Belgrave","user_id":0,"rest_url":false},{"type":"user_nicename","value":"cmbishop","user_id":31452,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=cmbishop"},{"type":"user_nicename","value":"jwinn","user_id":32457,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jwinn"},{"type":"text","value":"J.A. Curtin","user_id":0,"rest_url":false},{"type":"text","value":"S. Hasan Arshad","user_id":0,"rest_url":false},{"type":"text","value":"A. Simpson","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[169917],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":169917,"post_title":"Infer.NET","post_name":"infernet","post_type":"msr-project","post_date":"2008-10-15 01:55:31","post_modified":"2023-04-06 09:14:43","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/infernet\/","post_excerpt":"Infer.NET is a .NET library for machine learning. It provides state-of-the-art algorithms for probabilistic inference from data. Various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented using Infer.NET. Infer.NET is open source software under the MIT license. For more information about Infer.NET including documentation and examples please see the main Infer.NET page.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/169917"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/230746"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/230746\/revisions"}],"predecessor-version":[{"id":539805,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/230746\/revisions\/539805"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=230746"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=230746"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=230746"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=230746"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=230746"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=230746"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=230746"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=230746"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=230746"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=230746"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=230746"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=230746"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=230746"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=230746"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=230746"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=230746"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}