In April 2016 Manchester eScholar was replaced by the University of Manchester’s new Research Information Management System, Pure. In the autumn the University’s research outputs will be available to search and browse via a new Research Portal. Until then the University’s full publication record can be accessed via a temporary portal and the old eScholar content is available to search and browse via this archive.

Related resources

Full-text held externally

University researcher(s)

Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study.

Basanta, Maria; Ibrahim, Baharudin; Dockry, Rachel; Douce, David; Morris, Mike; Singh, Dave; Woodcock, Ashley; Fowler, Stephen J

Respiratory research. 2012;13:72.

Access to files

Full-text and supplementary files are not available from Manchester eScholar. Full-text is available externally using the following links:

Full-text held externally

Abstract

BACKGROUND: Non-invasive phenotyping of chronic respiratory diseases would be highly beneficial in the personalised medicine of the future. Volatile organic compounds can be measured in the exhaled breath and may be produced or altered by disease processes. We investigated whether distinct patterns of these compounds were present in chronic obstructive pulmonary disease (COPD) and clinically relevant disease phenotypes. METHODS: Breath samples from 39 COPD subjects and 32 healthy controls were collected and analysed using gas chromatography time-of-flight mass spectrometry. Subjects with COPD also underwent sputum induction. Discriminatory compounds were identified by univariate logistic regression followed by multivariate analysis: 1. principal component analysis; 2. multivariate logistic regression; 3. receiver operating characteristic (ROC) analysis. RESULTS: Comparing COPD versus healthy controls, principal component analysis clustered the 20 best-discriminating compounds into four components explaining 71% of the variance. Multivariate logistic regression constructed an optimised model using two components with an accuracy of 69%. The model had 85% sensitivity, 50% specificity and ROC area under the curve of 0.74. Analysis of COPD subgroups showed the method could classify COPD subjects with far greater accuracy. Models were constructed which classified subjects with ≥2% sputum eosinophilia with ROC area under the curve of 0.94 and those having frequent exacerbations 0.95. Potential biomarkers correlated to clinical variables were identified in each subgroup. CONCLUSION: The exhaled breath volatile organic compound profile discriminated between COPD and healthy controls and identified clinically relevant COPD subgroups. If these findings are validated in prospective cohorts, they may have diagnostic and management value in this disease.

Bibliographic metadata

Type of resource:
Content type:
Publication type:
Published date:
Journal title:
Abbreviated journal title:
ISSN:
Place of publication:
England
Volume:
13
Pagination:
72
Digital Object Identifier:
10.1186/1465-9921-13-72
Pubmed Identifier:
22916684
Pii Identifier:
1465-9921-13-72
Access state:
Active

Institutional metadata

University researcher(s):
Academic department(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:189134
Created by:
Heydon, Kirsty
Created:
6th March, 2013, 13:33:13
Last modified by:
Heydon, Kirsty
Last modified:
7th July, 2014, 18:19:19

Can we help?

The library chat service will be available from 11am-3pm Monday to Friday (excluding Bank Holidays). You can also email your enquiry to us.