TY - JOUR
T1 - Clinical and inflammatory phenotyping by breathomics in chronic airway diseases irrespective of the diagnostic label
AU - de Vries, Rianne
AU - Dagelet, Yennece W. F.
AU - Spoor, Pien
AU - Snoey, Erik
AU - Jak, Patrick M. C.
AU - Brinkman, Paul
AU - Dijkers, Erica
AU - Bootsma, Simon K.
AU - Elskamp, Fred
AU - de Jongh, Frans H. C.
AU - Haarman, Eric G.
AU - in 't Veen, Johannes C. C. M.
AU - Maitland-van der Zee, Anke-Hilse
AU - Sterk, Peter J.
N1 - Funding Information: Support statement: This project was sponsored by the public charity Dutch Vriendenloterij. Funding information for this article has been deposited with the Crossref Funder Registry. Publisher Copyright: Copyright ©ERS 2018
PY - 2018
Y1 - 2018
N2 - Asthma and chronic obstructive pulmonary disease (COPD) are complex and overlapping diseases that include inflammatory phenotypes. Novel anti-eosinophilic/anti-neutrophilic strategies demand rapid inflammatory phenotyping, which might be accessible from exhaled breath.Our objective was to capture clinical/inflammatory phenotypes in patients with chronic airway disease using an electronic nose (eNose) in a training and validation set.This was a multicentre cross-sectional study in which exhaled breath from asthma and COPD patients (n=435; training n=321 and validation n=114) was analysed using eNose technology. Data analysis involved signal processing and statistics based on principal component analysis followed by unsupervised cluster analysis and supervised linear regression.Clustering based on eNose resulted in five significant combined asthma and COPD clusters that differed regarding ethnicity (p=0.01), systemic eosinophilia (p=0.02) and neutrophilia (p=0.03), body mass index (p=0.04), exhaled nitric oxide fraction (p<0.01), atopy (p<0.01) and exacerbation rate (p<0.01). Significant regression models were found for the prediction of eosinophilic (R2=0.581) and neutrophilic (R2=0.409) blood counts based on eNose. Similar clusters and regression results were obtained in the validation set.Phenotyping a combined sample of asthma and COPD patients using eNose provides validated clusters that are not determined by diagnosis, but rather by clinical/inflammatory characteristics. eNose identified systemic neutrophilia and/or eosinophilia in a dose-dependent manner.
AB - Asthma and chronic obstructive pulmonary disease (COPD) are complex and overlapping diseases that include inflammatory phenotypes. Novel anti-eosinophilic/anti-neutrophilic strategies demand rapid inflammatory phenotyping, which might be accessible from exhaled breath.Our objective was to capture clinical/inflammatory phenotypes in patients with chronic airway disease using an electronic nose (eNose) in a training and validation set.This was a multicentre cross-sectional study in which exhaled breath from asthma and COPD patients (n=435; training n=321 and validation n=114) was analysed using eNose technology. Data analysis involved signal processing and statistics based on principal component analysis followed by unsupervised cluster analysis and supervised linear regression.Clustering based on eNose resulted in five significant combined asthma and COPD clusters that differed regarding ethnicity (p=0.01), systemic eosinophilia (p=0.02) and neutrophilia (p=0.03), body mass index (p=0.04), exhaled nitric oxide fraction (p<0.01), atopy (p<0.01) and exacerbation rate (p<0.01). Significant regression models were found for the prediction of eosinophilic (R2=0.581) and neutrophilic (R2=0.409) blood counts based on eNose. Similar clusters and regression results were obtained in the validation set.Phenotyping a combined sample of asthma and COPD patients using eNose provides validated clusters that are not determined by diagnosis, but rather by clinical/inflammatory characteristics. eNose identified systemic neutrophilia and/or eosinophilia in a dose-dependent manner.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85053262237&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/29326334
UR - http://www.scopus.com/inward/record.url?scp=85053262237&partnerID=8YFLogxK
U2 - https://doi.org/10.1183/13993003.01817-2017
DO - https://doi.org/10.1183/13993003.01817-2017
M3 - Article
C2 - 29326334
SN - 0903-1936
VL - 51
SP - 1701817
JO - European respiratory journal
JF - European respiratory journal
IS - 1
M1 - 1701817
ER -