Non-invasive metabolomic analysis of breath using differential mobility spectrometry in patients with chronic obstructive pulmonary disease and healthy smokers

Abstract

The rapid, accurate and non-invasive diagnosis of respiratory disease represents a challenge to clinicians, and the development of new treatments can be confounded by insufficient knowledge of lung disease phenotypes. Exhaled breath contains a complex mixture of volatile organic compounds (VOCs), some of which could potentially represent biomarkers for lung diseases. We have developed an adaptive sampling methodology for collecting concentrated samples of exhaled air from participants with impaired respiratory function, against which we employed two-stage thermal desorption gas chromatography-differential mobility spectrometry (GC-DMS) analysis, and showed that it was possible to discriminate between participants with and without chronic obstructive pulmonary disease (COPD). A 2.5 dm(3) volume of end tidal breath was collected onto adsorbent traps (Tenax TA/Carbotrap), from participants with severe COPD and healthy volunteers. Samples were thermally desorbed and analysed by GC-DMS, and the chromatograms analysed by univariate and multivariate analyses. Kruskal-Wallis ANOVA indicated several discriminatory (p < 0.01) signals, with good classification performance (receiver operator characteristic area up to 0.82). Partial least squares discriminant analysis using the full DMS chromatograms also gave excellent discrimination between groups (alpha = 19% and beta = 12.4%).

Metabolomic Profiling of Plasma from Patients with Tuberculosis by Use of Untargeted Mass Spectrometry Reveals Novel Biomarkers for Diagnosis

“…Multivariate and univariate analyses were performed to identify molecular features that discriminate TB patients from CAP patients and controls. The multivariate analysis was applied to a total of 106 LC-MS data for plasma samples collected from three groups (46,30, and 30 samples from newly diagnosed TB patients, CAP patients, and controls without active infection, respectively). The raw LC-MS data were converted into mzXML format using msConvert (ProteoWizard) and subsequently processed using the open-source XCMS package (http://www.bioconductor.org/packages/2.8/bioc/html /xcms.html) operating in R (http://www.r-project.org/), which adopted different peak detection and alignment, as well as data filtering with centWave algorithms.…”

“…The data were further processed with normalization, scaling, filtering, and statistical analysis using MetaboAnalyst 3.0 (www.metaboanalyst.ca). The data were mean centered, square root scaled, and normalized, such that the sum of squares for each chromatogram equaled on statistical analysis (30). Insignificant features between TB patients and CAP patients or controls were filtered out using both univariate and multivariate analyses.…”

Initial study of three different pathogenic microorganisms by gas chromatography-mass spectrometry

“…Finding a non-invasive and rapid method for diagnosis of infectious agents is a subject of interest, so it has been investigated in several studies 33, 42– 45 . The current study showed that using SPME fiber and GC-MS for extraction and detection of VOCs allowed detection of more specific VOCs for the three pathogenic respiratory tract organisms, E. coli , S. aureus and C. albicans , which could be used as biomarkers for their identification.…”

How long may a breath sample be stored for at −80 °C? A study of the stability of volatile organic compounds trapped onto a mixed Tenax:Carbograph trap adsorbent bed from exhaled breath

“…The reproducibility of the sampling approach has been evaluated previously where the within-subject variability was found to be significantly lower than between-subject variability ( = 6.23 ×10 -23 ) [19].…”