Plasma metabolomics and clinical predictors of survival differences in COPD patients

Plasma metabolomics profile (PMP) in COPD has been associated with clinical characteristics, but PMP's relationship to survival has not been reported. We determined PMP differences between patients with COPD who died an average of 2 years after enrollment (Non-survivors, NS) compared to those w...

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Published in:Respiratory research 2019-10, Vol.20 (1), p.219-8, Article 219
Main Authors: Pinto-Plata, Victor, Casanova, Ciro, Divo, Miguel, Tesfaigzi, Yohannes, Calhoun, Vince, Sui, Jing, Polverino, Francesca, Priolo, Carmen, Petersen, Hans, de Torres, Juan Pablo, Marin, Jose Maria, Owen, Caroline A, Baz, Rebeca, Cordova, Elizabeth, Celli, Bartolome
Format: Article
Language:English
Subjects:
Aged
Biomarkers - blood
Care and treatment
Chronic obstructive lung disease
Cohort Studies
COPD
Dyspnea
Energy Metabolism - physiology
Exercise capacity
Female
Humans
Male
Metabolomics
Metabolomics - trends
Methods
Middle Aged
Patient outcomes
Predictive Value of Tests
Prospective Studies
Pulmonary Disease, Chronic Obstructive - blood
Pulmonary Disease, Chronic Obstructive - diagnosis
Pulmonary Disease, Chronic Obstructive - mortality
Survival
Survival Rate - trends
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Summary:Plasma metabolomics profile (PMP) in COPD has been associated with clinical characteristics, but PMP's relationship to survival has not been reported. We determined PMP differences between patients with COPD who died an average of 2 years after enrollment (Non-survivors, NS) compared to those who survived (S) and also with age matched controls (C). We studied prospectively 90 patients with severe COPD and 30 controls. NS were divided in discovery and validation cohorts (30 patients each) and the results compared to the PMP of 30 S and C. All participants completed lung function tests, dyspnea scores, quality of life, exercise capacity, BODE index, and plasma metabolomics by liquid and gas chromatography / mass spectometry (LC/MS, LC/MS , GC/MS). Statistically, we used Random Forest Analysis (RFA) and Support Vector Machine (SVM) to determine metabolites that differentiated the 3 groups and compared the ability of metabolites vs. clinical characteristics to classify patients into survivors and non-survivors. There were 79 metabolites statistically different between S and NS [p 
ISSN:1465-993X
1465-9921
1465-993X
DOI:10.1186/s12931-019-1167-y