GC MS based untargeted metabolomics for understanding the pathophysiology of asthma COPD overlap (ACO)

Abstract

Background: Metabolomics is extensively used for understanding disease pathophysiology and developing reliable diagnostic biomarkers. Asthma COPD overlap (ACO) is a complex heterogeneous disease without any clear diagnostic or therapeutic guidelines. The pathophysiology of the disease, its characteristic features and existence as a unique disease entity remains unclear. In this study, we sought to determine whether ACO has a distinct metabolic profile in comparison to asthma and COPD. Methods: This pilot study involved characterization of asthma (n=32), COPD (n=31), ACO (n=33) and healthy controls (n=30) serum samples using untargeted gas chromatography mass spectrometry (GC-MS) approach. Univariate and multivariate statistics were used to help in clustering of the disease groups. One way ANOVA and VIP score plots were performed to detect the peaks that were statistically different between the groups. Results: OPLS-DA models demonstrated distinct separation between ACO and the other two diseases as well as controls. Five metabolites, mainly related to amino sugar metabolism and fatty acid biosynthesis, were found to be dysregulated in the serum of ACO patients when compared with asthma, COPD and healthy controls. Conclusions: These findings emphasize the need for investigating further the clinical identity of ACO. The metabolic profile also suggests that ACO could be associated with an enhanced metabolic burden as compared to asthma and COPD. It is anticipated that our findings will stimulate researchers to further explore ACO and unravel the pathophysiological complexities associated with the disease.