The current pilot study examined the hypothesis that cigarette smokers who developed an emphysematous phenotype of Chronic Obstructive Pulmonary Disease (COPD) were associated with distinctive patterns in their corresponding metabolomics profile as compared to those who did not. Peripheral blood plasma samples were collected from 38 subjects with different phenotypes of COPD. They were categorized into three groups: healthy non-smokers (n=16), smokers without emphysema (n=8), and smokers with emphysema (n=14). Ultra High Performance Liquid Chromatography/quadrupole-Time-of-Flight Mass Spectrometry techniques were used to identify a large number of metabolite markers (3534). Unsupervised clustering analysis accurately separated the smokers with emphysema from others without emphysema and demonstrated potentials of this metabolomics data. Subsequently predictive models were created with a supervised learning set, and these predictive models were found to be highly accurate in identifying the subjects with the emphysematous phenotype of COPD with excellent sensitivity and specificity. Our methodology provides a preliminary model that differentiates an emphysematous COPD phenotype from other COPD phenotypes on the basis of the metabolomics profiles. These results also suggest that the metabolomics profiling could potentially guide the characterization of relevant metabolites that leads to an emphysematous COPD phenotype.
Published by Elsevier Inc.