PittsburghV1, Main, Exploration, bibRecord, 000998

Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data

Identifieur interne : 000998 ( Main/Exploration ); précédent : 000997; suivant : 000999

Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data

Auteurs : Wei Wu [États-Unis] ; Eugene Bleecker [États-Unis] ; Wendy Moore [États-Unis] ; William W. Busse [États-Unis] ; Mario Castro [États-Unis] ; Kian Fan Chung [Royaume-Uni] ; William J. Calhoun [États-Unis] ; Serpil Erzurum [États-Unis] ; Benjamin Gaston [États-Unis] ; Elliot Israel [États-Unis] ; Douglas Curran-Everett [États-Unis] ; Sally E. Wenzel [États-Unis]

Source :

The Journal of allergy and clinical immunology [ 0091-6749 ] ; 2014.

RBID : PMC:4038417

Descripteurs français

KwdFr :
- Adulte, Adulte d'âge moyen, Asthme (), Asthme (anatomopathologie), Asthme (métabolisme), Asthme (physiopathologie), Asthme (traitement médicamenteux), Femelle, Hormones corticosurrénaliennes (administration et posologie), Humains, Indice de gravité médicale, Lavage bronchoalvéolaire, Mâle, Phénotype, Polypes du nez (), Polypes du nez (anatomopathologie), Polypes du nez (métabolisme), Polypes du nez (physiopathologie), Polypes du nez (traitement médicamenteux), Poumon (anatomopathologie), Poumon (métabolisme), Poumon (parasitologie), Âge de début, Éosinophilie (), Éosinophilie (anatomopathologie), Éosinophilie (métabolisme), Éosinophilie (physiopathologie), Éosinophilie (traitement médicamenteux).
MESH :
- administration et posologie : Hormones corticosurrénaliennes.
- anatomopathologie : Asthme, Polypes du nez, Poumon, Éosinophilie.
- métabolisme : Asthme, Polypes du nez, Poumon, Éosinophilie.
- parasitologie : Poumon.
- physiopathologie : Asthme, Polypes du nez, Éosinophilie.
- traitement médicamenteux : Asthme, Polypes du nez, Éosinophilie.
- Adulte, Adulte d'âge moyen, Asthme, Femelle, Humains, Indice de gravité médicale, Lavage bronchoalvéolaire, Mâle, Phénotype, Polypes du nez, Âge de début, Éosinophilie.

English descriptors

KwdEn :
- Adrenal Cortex Hormones (administration & dosage), Adult, Age of Onset, Asthma (complications), Asthma (drug therapy), Asthma (metabolism), Asthma (pathology), Asthma (physiopathology), Bronchoalveolar Lavage, Eosinophilia (complications), Eosinophilia (drug therapy), Eosinophilia (metabolism), Eosinophilia (pathology), Eosinophilia (physiopathology), Female, Humans, Lung (metabolism), Lung (parasitology), Lung (pathology), Male, Middle Aged, Nasal Polyps (complications), Nasal Polyps (drug therapy), Nasal Polyps (metabolism), Nasal Polyps (pathology), Nasal Polyps (physiopathology), Phenotype, Severity of Illness Index.
MESH :
- chemical , administration & dosage : Adrenal Cortex Hormones.
- complications : Asthma, Eosinophilia, Nasal Polyps.
- drug therapy : Asthma, Eosinophilia, Nasal Polyps.
- metabolism : Asthma, Eosinophilia, Lung, Nasal Polyps.
- parasitology : Lung.
- pathology : Asthma, Eosinophilia, Lung, Nasal Polyps.
- physiopathology : Asthma, Eosinophilia, Nasal Polyps.
- Adult, Age of Onset, Bronchoalveolar Lavage, Female, Humans, Male, Middle Aged, Phenotype, Severity of Illness Index.

Abstract

Background

Previous studies have identified asthma phenotypes based on small numbers of clinical, physiologic or inflammatory characteristics. However, no studies have utilized a wide range of variables using machine learning approaches.

Objectives

To identify subphenotypes of asthma utilizing blood, bronchoscopic, exhaled nitric oxide and clinical data from the Severe Asthma Research Program using unsupervised clustering, and then characterize them using supervised learning approaches.

Methods

Unsupervised clustering approaches were applied to 112 clinical, physiologic and inflammatory variables from 378 subjects. Variable selection and supervised learning techniques were employed to select relevant and nonredundant variables, address their predictive values, as well as the predictive value of the full variable set.

Results

Ten variable clusters and six subject clusters were identified, which differed and overlapped with previous clusters. Traditionally defined severe asthmatics distributed through subject Clusters 3–6. Cluster 4 identified early onset allergic asthmatics with low lung function and eosinophilic inflammation. Later onset, mostly severe asthmatics with nasal polyps and eosinophilia characterized Cluster 5. Cluster 6 asthmatics manifested persistent inflammation in blood and bronchoalveolar lavage and exacerbations despite high systemic corticosteroid use and side effects. Age of asthma onset, quality of life, symptoms, medications and health care utilization were some of the 51 nonredundant variables distinguishing subject clusters. These 51 variables classified test cases with 88% accuracy, compared to 93% accuracy with all 112 variables.

Conclusion

The unsupervised machine learning approaches used here provide unique insights into disease, confirming other approaches while revealing novel additional phenotypes.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4038417

DOI: 10.1016/j.jaci.2013.11.042
PubMed: 24589344
PubMed Central: 4038417

Affiliations:

Le document en format XML

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<author><name sortKey="Bleecker, Eugene" sort="Bleecker, Eugene" uniqKey="Bleecker E" first="Eugene" last="Bleecker">Eugene Bleecker</name>
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<author><name sortKey="Busse, William W" sort="Busse, William W" uniqKey="Busse W" first="William W." last="Busse">William W. Busse</name>
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<author><name sortKey="Castro, Mario" sort="Castro, Mario" uniqKey="Castro M" first="Mario" last="Castro">Mario Castro</name>
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<author><name sortKey="Chung, Kian Fan" sort="Chung, Kian Fan" uniqKey="Chung K" first="Kian Fan" last="Chung">Kian Fan Chung</name>
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<author><name sortKey="Calhoun, William J" sort="Calhoun, William J" uniqKey="Calhoun W" first="William J." last="Calhoun">William J. Calhoun</name>
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<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Internal Medicine, University of Texas Medical Branch, Galveston, Texas</wicri:regionArea>
<placeName><region type="state">Texas</region>
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<author><name sortKey="Erzurum, Serpil" sort="Erzurum, Serpil" uniqKey="Erzurum S" first="Serpil" last="Erzurum">Serpil Erzurum</name>
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<country xml:lang="fr">États-Unis</country>
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</placeName>
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<author><name sortKey="Gaston, Benjamin" sort="Gaston, Benjamin" uniqKey="Gaston B" first="Benjamin" last="Gaston">Benjamin Gaston</name>
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<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Division of Pediatric Pulmonology, and Allergy/Immunology, Department of Pediatrics, School of Medicine, Case Western Reserve University, Cleveland, OH</wicri:regionArea>
<placeName><region type="state">Ohio</region>
</placeName>
</affiliation>
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<author><name sortKey="Israel, Elliot" sort="Israel, Elliot" uniqKey="Israel E" first="Elliot" last="Israel">Elliot Israel</name>
<affiliation wicri:level="2"><nlm:aff id="A9">Pulmonary Division, Brigham and Women’s Hospital, Boston, MA, USA</nlm:aff>
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<author><name sortKey="Curran Everett, Douglas" sort="Curran Everett, Douglas" uniqKey="Curran Everett D" first="Douglas" last="Curran-Everett">Douglas Curran-Everett</name>
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<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>National Jewish Medical and Research Center, University of Colorado Health Sciences Center, Denver, CO</wicri:regionArea>
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<author><name sortKey="Wenzel, Sally E" sort="Wenzel, Sally E" uniqKey="Wenzel S" first="Sally E." last="Wenzel">Sally E. Wenzel</name>
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<affiliation wicri:level="4"><nlm:aff id="A11">The Asthma Institute, Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA</nlm:aff>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adrenal Cortex Hormones (administration & dosage)</term>
<term>Adult</term>
<term>Age of Onset</term>
<term>Asthma (complications)</term>
<term>Asthma (drug therapy)</term>
<term>Asthma (metabolism)</term>
<term>Asthma (pathology)</term>
<term>Asthma (physiopathology)</term>
<term>Bronchoalveolar Lavage</term>
<term>Eosinophilia (complications)</term>
<term>Eosinophilia (drug therapy)</term>
<term>Eosinophilia (metabolism)</term>
<term>Eosinophilia (pathology)</term>
<term>Eosinophilia (physiopathology)</term>
<term>Female</term>
<term>Humans</term>
<term>Lung (metabolism)</term>
<term>Lung (parasitology)</term>
<term>Lung (pathology)</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Nasal Polyps (complications)</term>
<term>Nasal Polyps (drug therapy)</term>
<term>Nasal Polyps (metabolism)</term>
<term>Nasal Polyps (pathology)</term>
<term>Nasal Polyps (physiopathology)</term>
<term>Phenotype</term>
<term>Severity of Illness Index</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Asthme ()</term>
<term>Asthme (anatomopathologie)</term>
<term>Asthme (métabolisme)</term>
<term>Asthme (physiopathologie)</term>
<term>Asthme (traitement médicamenteux)</term>
<term>Femelle</term>
<term>Hormones corticosurrénaliennes (administration et posologie)</term>
<term>Humains</term>
<term>Indice de gravité médicale</term>
<term>Lavage bronchoalvéolaire</term>
<term>Mâle</term>
<term>Phénotype</term>
<term>Polypes du nez ()</term>
<term>Polypes du nez (anatomopathologie)</term>
<term>Polypes du nez (métabolisme)</term>
<term>Polypes du nez (physiopathologie)</term>
<term>Polypes du nez (traitement médicamenteux)</term>
<term>Poumon (anatomopathologie)</term>
<term>Poumon (métabolisme)</term>
<term>Poumon (parasitologie)</term>
<term>Âge de début</term>
<term>Éosinophilie ()</term>
<term>Éosinophilie (anatomopathologie)</term>
<term>Éosinophilie (métabolisme)</term>
<term>Éosinophilie (physiopathologie)</term>
<term>Éosinophilie (traitement médicamenteux)</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="administration & dosage" xml:lang="en"><term>Adrenal Cortex Hormones</term>
</keywords>
<keywords scheme="MESH" qualifier="administration et posologie" xml:lang="fr"><term>Hormones corticosurrénaliennes</term>
</keywords>
<keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Asthme</term>
<term>Polypes du nez</term>
<term>Poumon</term>
<term>Éosinophilie</term>
</keywords>
<keywords scheme="MESH" qualifier="complications" xml:lang="en"><term>Asthma</term>
<term>Eosinophilia</term>
<term>Nasal Polyps</term>
</keywords>
<keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Asthma</term>
<term>Eosinophilia</term>
<term>Nasal Polyps</term>
</keywords>
<keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Asthma</term>
<term>Eosinophilia</term>
<term>Lung</term>
<term>Nasal Polyps</term>
</keywords>
<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Asthme</term>
<term>Polypes du nez</term>
<term>Poumon</term>
<term>Éosinophilie</term>
</keywords>
<keywords scheme="MESH" qualifier="parasitologie" xml:lang="fr"><term>Poumon</term>
</keywords>
<keywords scheme="MESH" qualifier="parasitology" xml:lang="en"><term>Lung</term>
</keywords>
<keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Asthma</term>
<term>Eosinophilia</term>
<term>Lung</term>
<term>Nasal Polyps</term>
</keywords>
<keywords scheme="MESH" qualifier="physiopathologie" xml:lang="fr"><term>Asthme</term>
<term>Polypes du nez</term>
<term>Éosinophilie</term>
</keywords>
<keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Asthma</term>
<term>Eosinophilia</term>
<term>Nasal Polyps</term>
</keywords>
<keywords scheme="MESH" qualifier="traitement médicamenteux" xml:lang="fr"><term>Asthme</term>
<term>Polypes du nez</term>
<term>Éosinophilie</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Adult</term>
<term>Age of Onset</term>
<term>Bronchoalveolar Lavage</term>
<term>Female</term>
<term>Humans</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Phenotype</term>
<term>Severity of Illness Index</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Asthme</term>
<term>Femelle</term>
<term>Humains</term>
<term>Indice de gravité médicale</term>
<term>Lavage bronchoalvéolaire</term>
<term>Mâle</term>
<term>Phénotype</term>
<term>Polypes du nez</term>
<term>Âge de début</term>
<term>Éosinophilie</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><sec id="S1"><title>Background</title>
<p id="P1">Previous studies have identified asthma phenotypes based on small
numbers of clinical, physiologic or inflammatory characteristics. However,
no studies have utilized a wide range of variables using machine learning
approaches.</p>
</sec>
<sec id="S2"><title>Objectives</title>
<p id="P2">To identify subphenotypes of asthma utilizing blood, bronchoscopic,
exhaled nitric oxide and clinical data from the Severe Asthma Research
Program using unsupervised clustering, and then characterize them using
supervised learning approaches.</p>
</sec>
<sec id="S3"><title>Methods</title>
<p id="P3">Unsupervised clustering approaches were applied to 112 clinical,
physiologic and inflammatory variables from 378 subjects. Variable selection
and supervised learning techniques were employed to select relevant and
nonredundant variables, address their predictive values, as well as the
predictive value of the full variable set.</p>
</sec>
<sec id="S4"><title>Results</title>
<p id="P4">Ten variable clusters and six subject clusters were identified, which
differed and overlapped with previous clusters. Traditionally defined severe
asthmatics distributed through subject Clusters 3–6. Cluster 4
identified early onset allergic asthmatics with low lung function and
eosinophilic inflammation. Later onset, mostly severe asthmatics with nasal
polyps and eosinophilia characterized Cluster 5. Cluster 6 asthmatics
manifested persistent inflammation in blood and bronchoalveolar lavage and
exacerbations despite high systemic corticosteroid use and side effects. Age
of asthma onset, quality of life, symptoms, medications and health care
utilization were some of the 51 nonredundant variables distinguishing
subject clusters. These 51 variables classified test cases with 88%
accuracy, compared to 93% accuracy with all 112 variables.</p>
</sec>
<sec id="S5"><title>Conclusion</title>
<p id="P5">The unsupervised machine learning approaches used here provide unique
insights into disease, confirming other approaches while revealing novel
additional phenotypes.</p>
</sec>
</div>
</front>
</TEI>
<affiliations><list><country><li>Royaume-Uni</li>
<li>États-Unis</li>
</country>
<region><li>Angleterre</li>
<li>Caroline du Nord</li>
<li>Colorado</li>
<li>Grand Londres</li>
<li>Massachusetts</li>
<li>Missouri (État)</li>
<li>Ohio</li>
<li>Pennsylvanie</li>
<li>Texas</li>
<li>Wisconsin</li>
</region>
<settlement><li>Londres</li>
<li>Pittsburgh</li>
<li>Saint-Louis (Missouri)</li>
</settlement>
<orgName><li>Université Carnegie-Mellon</li>
<li>Université de Pittsburgh</li>
<li>École de médecine (Université Washington de Saint-Louis)</li>
</orgName>
</list>
<tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Wu, Wei" sort="Wu, Wei" uniqKey="Wu W" first="Wei" last="Wu">Wei Wu</name>
</region>
<name sortKey="Bleecker, Eugene" sort="Bleecker, Eugene" uniqKey="Bleecker E" first="Eugene" last="Bleecker">Eugene Bleecker</name>
<name sortKey="Busse, William W" sort="Busse, William W" uniqKey="Busse W" first="William W." last="Busse">William W. Busse</name>
<name sortKey="Calhoun, William J" sort="Calhoun, William J" uniqKey="Calhoun W" first="William J." last="Calhoun">William J. Calhoun</name>
<name sortKey="Castro, Mario" sort="Castro, Mario" uniqKey="Castro M" first="Mario" last="Castro">Mario Castro</name>
<name sortKey="Curran Everett, Douglas" sort="Curran Everett, Douglas" uniqKey="Curran Everett D" first="Douglas" last="Curran-Everett">Douglas Curran-Everett</name>
<name sortKey="Erzurum, Serpil" sort="Erzurum, Serpil" uniqKey="Erzurum S" first="Serpil" last="Erzurum">Serpil Erzurum</name>
<name sortKey="Gaston, Benjamin" sort="Gaston, Benjamin" uniqKey="Gaston B" first="Benjamin" last="Gaston">Benjamin Gaston</name>
<name sortKey="Israel, Elliot" sort="Israel, Elliot" uniqKey="Israel E" first="Elliot" last="Israel">Elliot Israel</name>
<name sortKey="Moore, Wendy" sort="Moore, Wendy" uniqKey="Moore W" first="Wendy" last="Moore">Wendy Moore</name>
<name sortKey="Wenzel, Sally E" sort="Wenzel, Sally E" uniqKey="Wenzel S" first="Sally E." last="Wenzel">Sally E. Wenzel</name>
<name sortKey="Wenzel, Sally E" sort="Wenzel, Sally E" uniqKey="Wenzel S" first="Sally E." last="Wenzel">Sally E. Wenzel</name>
</country>
<country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Chung, Kian Fan" sort="Chung, Kian Fan" uniqKey="Chung K" first="Kian Fan" last="Chung">Kian Fan Chung</name>
</region>
</country>
</tree>
</affiliations>
</record>

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Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data

Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data

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