Phenotyping of Chronic Obstructive Pulmonary Disease Based on the Integration of Metabolomes and Clinical Characteristics

Kalle Kilk, Argo Aug, Aigar Ottas, Ursel Soomets, Siiri Altraja, Alan Altraja, Kalle Kilk, Argo Aug, Aigar Ottas, Ursel Soomets, Siiri Altraja, Alan Altraja

Abstract

Apart from the refined management-oriented clinical stratification of chronic obstructive pulmonary disease (COPD), the molecular pathologies behind this highly prevalent disease have remained obscure. The aim of this study was the characterization of patients with COPD, based on the metabolomic profiling of peripheral blood and exhaled breath condensate (EBC) within the context of defined clinical and demographic variables. Mass-spectrometry-based targeted analysis of serum metabolites (mainly amino acids and lipid species), untargeted profiles of serum and EBC of patients with COPD of different clinical characteristics (n = 25) and control individuals (n = 21) were performed. From the combined clinical/demographic and metabolomics data, associations between clinical/demographic and metabolic parameters were searched and a de novo phenotyping for COPD was attempted. Adjoining the clinical parameters, sphingomyelins were the best to differentiate COPD patients from controls. Unsaturated fatty acid-containing lipids, ornithine metabolism and plasma protein composition-associated signals from the untargeted analysis differentiated the Global Initiative for COPD (GOLD) categories. Hierarchical clustering did not reveal a clinical-metabolomic stratification superior to the strata set by the GOLD consensus. We conclude that while metabolomics approaches are good for finding biomarkers and clarifying the mechanism of the disease, there are no distinct co-variate independent clinical-metabolic phenotypes.

Keywords: GOLD stratification; chronic obstructive pulmonary disease; exhaled breath condensate; metabolomics; phenotyping; sphingomyelin.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The principal flowchart of the study on integrated use of non-metabolomic variables and metabolomics-based biomarkers to phenotype COPD.
Figure 2
Figure 2
Volcano plot of the fold change (x-axis) vs. the significance of the change (y-axis) in the individual parameters between the patients with chronic obstructive pulmonary disease (COPD) and the control individuals. All clinical and demographic data and metabolic profile of exhaled breath condensate and serum including targeted analyses of the metabolites in serum were included except the dichotomous variable of smoking history (statuses of current or ex-smoker). Positive change indicates higher concentration or value for the patients with COPD. The most significantly different parameters, metabolites, and m/z values from metabolite profiles are annotated. Bn—signals from serum metabolic profile in negative ionization mode followed by the mass and charge ratio value; FEV1, forced expiratory volume in one second; FVC, forced expiratory volume; SM—sphingomyelin, followed by hydrocarbon chain length and number of double bonds. % indicated percent predicted for FEV1, FVC, and FEV1/FVC.
Figure 3
Figure 3
Principal component analysis of clinical and demographic data and metabolic profile of exhaled breath condensate and serum (including targeted analyses of the metabolites in serum) of the patients with chronic obstructive pulmonary disease (COPD, empty triangles) and the control individuals (solid circles). The percentage in parenthesis indicates the fraction from total variance explained by the respective principal component. (a) Principal components 1 and 2; (b) principal components 3 and 5.
Figure 4
Figure 4
Sparse partial least squares discriminant analysis (sPLSDA), based on clinical and demographic data and metabolic profile of exhaled breath condensate and serum including targeted analyses of the metabolites in serum from the patients with chronic obstructive pulmonary disease (COPD) and the control individuals (solid circles) enrolled for the integrated metabolomics-clinical/demographic phenotyping analyses. A–D, patients with COPD at GOLD (The Global Initiative for COPD) stages A–D [5].
Figure 5
Figure 5
The flowchart of de novo phenotyping on integrated use of non-metabolomic variables and metabolomics-based biomarkers to phenotype COPD. COPD, chronic obstructive pulmonary disease; EBC, exhaled breath condensate; GOLD, global initiative for COPD; PCA, principal component analysis; sPLSDA, sparse partial least squares determinant analysis.
Figure 6
Figure 6
Hierarchical clustering of the patients with chronic obstructive pulmonary disease (COPD) (n = 25) enrolled for the integrated metabolomics-clinical/demographic phenotyping analyses, based on the 5 main principal components of the complete data. The letters A–D indicate how the individual patients with COPD at GOLD (The Global Initiative for COPD [5]) stages A–D, respectively, are allocated. The optimal number of subclasses/clusters by the Calinski-Harabasz psudo F-statistic was six.

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