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Defining adult asthma endotypes by clinical features and patterns of volatile organic compounds in exhaled air.

Meyer N, Dallinga JW, Nuss SJ, Moonen EJ, van Berkel JJ, Akdis C, van Schooten FJ, Menz G - Respir. Res. (2014)

Bottom Line: Several classifications of adult asthma patients using cluster analyses based on clinical and demographic information has resulted in clinical phenotypic clusters that do not address molecular mechanisms.Volatile organic compounds (VOC) in exhaled air are released during inflammation in response to oxidative stress as a result of activated leukocytes.Cluster analysis based on VOCs in exhaled air and the clinical parameters FEV1, FEV1 change after 3 weeks of hospitalization, allergic sensitization, Junipers symptoms score and asthma medications resulted in the formation of 7 different asthma endotype clusters.

View Article: PubMed Central - PubMed

Affiliation: High Altitude Clinic (Hochgebirgsklinik) Davos, Davos-Wolfgang, Switzerland. norbert.meyer@insel.ch.

ABSTRACT

Background: Several classifications of adult asthma patients using cluster analyses based on clinical and demographic information has resulted in clinical phenotypic clusters that do not address molecular mechanisms. Volatile organic compounds (VOC) in exhaled air are released during inflammation in response to oxidative stress as a result of activated leukocytes. VOC profiles in exhaled air could distinguish between asthma patients and healthy subjects. In this study, we aimed to classify new asthma endotypes by combining inflammatory mechanisms investigated by VOC profiles in exhaled air and clinical information of asthma patients.

Methods: Breath samples were analyzed for VOC profiles by gas chromatography-mass spectrometry from asthma patients (n = 195) and healthy controls (n = 40). A total of 945 determined compounds were subjected to discriminant analysis to find those that could discriminate healthy from asthmatic subjects. 2-step cluster analysis based on clinical information and VOCs in exhaled air were used to form asthma endotypes.

Results: We identified 16 VOCs, which could distinguish between healthy and asthma subjects with a sensitivity of 100% and a specificity of 91.1%. Cluster analysis based on VOCs in exhaled air and the clinical parameters FEV1, FEV1 change after 3 weeks of hospitalization, allergic sensitization, Junipers symptoms score and asthma medications resulted in the formation of 7 different asthma endotype clusters. We identified asthma clusters with different VOC profiles but similar clinical characteristics and endotypes with similar VOC profiles, but distinct clinical characteristics.

Conclusion: This study demonstrates that both, clinical presentation of asthma and inflammatory mechanisms in the airways should be considered for classification of asthma subtypes.

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Related in: MedlinePlus

Course of sensitivity and specificity as a function of the number of VOCs involved in the discriminant analyses. The sensitivity and specificity to discriminate between asthma and healthy subjects depends on the number of VOCs (A). The correct classification of asthma and healthy subjects as a function of the VOCs is presented (B).
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Fig1: Course of sensitivity and specificity as a function of the number of VOCs involved in the discriminant analyses. The sensitivity and specificity to discriminate between asthma and healthy subjects depends on the number of VOCs (A). The correct classification of asthma and healthy subjects as a function of the VOCs is presented (B).

Mentions: To investigate, which VOCs are implicated during asthmatic airway inflammation, VOCs in exhaled air were compared between asthma patients and healthy controls. A total of 945 VOCs were found. To select the most relevant VOCs, a classification model was constructed based on 16 VOCs. With this model it was possible to distinguish between asthma patients and healthy controls with a sensitivity of 100% and specificity of 91.1% if 16 components were used. 5 components were still sufficient to achieve a sensitivity of 100% and specificity of 85.3% (Figure 1A). This model with 16 VOCs was able to classify healthy and asthma subjects 98.7% correctly. The model with 5 VOCs still classified the subjects 98.0% correctly (Figure 1B). The chemical structures of the VOCs were partly identified and are shown (Additional file 1: Table S1).Figure 1


Defining adult asthma endotypes by clinical features and patterns of volatile organic compounds in exhaled air.

Meyer N, Dallinga JW, Nuss SJ, Moonen EJ, van Berkel JJ, Akdis C, van Schooten FJ, Menz G - Respir. Res. (2014)

Course of sensitivity and specificity as a function of the number of VOCs involved in the discriminant analyses. The sensitivity and specificity to discriminate between asthma and healthy subjects depends on the number of VOCs (A). The correct classification of asthma and healthy subjects as a function of the VOCs is presented (B).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4264530&req=5

Fig1: Course of sensitivity and specificity as a function of the number of VOCs involved in the discriminant analyses. The sensitivity and specificity to discriminate between asthma and healthy subjects depends on the number of VOCs (A). The correct classification of asthma and healthy subjects as a function of the VOCs is presented (B).
Mentions: To investigate, which VOCs are implicated during asthmatic airway inflammation, VOCs in exhaled air were compared between asthma patients and healthy controls. A total of 945 VOCs were found. To select the most relevant VOCs, a classification model was constructed based on 16 VOCs. With this model it was possible to distinguish between asthma patients and healthy controls with a sensitivity of 100% and specificity of 91.1% if 16 components were used. 5 components were still sufficient to achieve a sensitivity of 100% and specificity of 85.3% (Figure 1A). This model with 16 VOCs was able to classify healthy and asthma subjects 98.7% correctly. The model with 5 VOCs still classified the subjects 98.0% correctly (Figure 1B). The chemical structures of the VOCs were partly identified and are shown (Additional file 1: Table S1).Figure 1

Bottom Line: Several classifications of adult asthma patients using cluster analyses based on clinical and demographic information has resulted in clinical phenotypic clusters that do not address molecular mechanisms.Volatile organic compounds (VOC) in exhaled air are released during inflammation in response to oxidative stress as a result of activated leukocytes.Cluster analysis based on VOCs in exhaled air and the clinical parameters FEV1, FEV1 change after 3 weeks of hospitalization, allergic sensitization, Junipers symptoms score and asthma medications resulted in the formation of 7 different asthma endotype clusters.

View Article: PubMed Central - PubMed

Affiliation: High Altitude Clinic (Hochgebirgsklinik) Davos, Davos-Wolfgang, Switzerland. norbert.meyer@insel.ch.

ABSTRACT

Background: Several classifications of adult asthma patients using cluster analyses based on clinical and demographic information has resulted in clinical phenotypic clusters that do not address molecular mechanisms. Volatile organic compounds (VOC) in exhaled air are released during inflammation in response to oxidative stress as a result of activated leukocytes. VOC profiles in exhaled air could distinguish between asthma patients and healthy subjects. In this study, we aimed to classify new asthma endotypes by combining inflammatory mechanisms investigated by VOC profiles in exhaled air and clinical information of asthma patients.

Methods: Breath samples were analyzed for VOC profiles by gas chromatography-mass spectrometry from asthma patients (n = 195) and healthy controls (n = 40). A total of 945 determined compounds were subjected to discriminant analysis to find those that could discriminate healthy from asthmatic subjects. 2-step cluster analysis based on clinical information and VOCs in exhaled air were used to form asthma endotypes.

Results: We identified 16 VOCs, which could distinguish between healthy and asthma subjects with a sensitivity of 100% and a specificity of 91.1%. Cluster analysis based on VOCs in exhaled air and the clinical parameters FEV1, FEV1 change after 3 weeks of hospitalization, allergic sensitization, Junipers symptoms score and asthma medications resulted in the formation of 7 different asthma endotype clusters. We identified asthma clusters with different VOC profiles but similar clinical characteristics and endotypes with similar VOC profiles, but distinct clinical characteristics.

Conclusion: This study demonstrates that both, clinical presentation of asthma and inflammatory mechanisms in the airways should be considered for classification of asthma subtypes.

Show MeSH
Related in: MedlinePlus