Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study
<p>Abstract</p> <p>Background</p> <p>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 diseas...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2012-08-01
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| Series: | Respiratory Research |
| Subjects: | |
| Online Access: | http://respiratory-research.com/content/13/1/72 |
| _version_ | 1811316147053658112 |
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| author | Basanta Maria Ibrahim Baharudin Dockry Rachel Douce David Morris Mike Singh Dave Woodcock Ashley Fowler Stephen J |
| author_facet | Basanta Maria Ibrahim Baharudin Dockry Rachel Douce David Morris Mike Singh Dave Woodcock Ashley Fowler Stephen J |
| author_sort | Basanta Maria |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>Comparing COPD <it>versus</it> 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.</p> <p>Conclusion</p> <p>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.</p> |
| first_indexed | 2024-04-13T11:44:50Z |
| format | Article |
| id | doaj.art-34e4250c50c945febc3a50d0790c7602 |
| institution | Directory Open Access Journal |
| issn | 1465-9921 |
| language | English |
| last_indexed | 2024-04-13T11:44:50Z |
| publishDate | 2012-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Respiratory Research |
| spelling | doaj.art-34e4250c50c945febc3a50d0790c76022022-12-22T02:48:13ZengBMCRespiratory Research1465-99212012-08-011317210.1186/1465-9921-13-72Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional studyBasanta MariaIbrahim BaharudinDockry RachelDouce DavidMorris MikeSingh DaveWoodcock AshleyFowler Stephen J<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>Comparing COPD <it>versus</it> 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.</p> <p>Conclusion</p> <p>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.</p>http://respiratory-research.com/content/13/1/72Chronic obstructive pulmonary diseaseBiomarkersBreath testsMetabolomics |
| spellingShingle | Basanta Maria Ibrahim Baharudin Dockry Rachel Douce David Morris Mike Singh Dave Woodcock Ashley Fowler Stephen J Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study Respiratory Research Chronic obstructive pulmonary disease Biomarkers Breath tests Metabolomics |
| title | Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study |
| title_full | Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study |
| title_fullStr | Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study |
| title_full_unstemmed | Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study |
| title_short | Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study |
| title_sort | exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease a cross sectional study |
| topic | Chronic obstructive pulmonary disease Biomarkers Breath tests Metabolomics |
| url | http://respiratory-research.com/content/13/1/72 |
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