Maria Basanta

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

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%).

Non-invasive phenotyping using exhaled volatile organic compounds in asthma

Background Breath volatile organic compounds (VOCs) may be useful for asthma diagnosis and phenotyping, identifying patients who could benefit from personalised therapeutic strategies. The authors aimed to identify specific patterns of breath VOCs in patients with asthma and in clinically relevant disease phenotypes. Methods Breath samples were analysed by gas chromatography–mass spectrometry. The Asthma Control Questionnaire was completed, together with lung function and induced sputum cell counts. Breath data were reduced to principal components, and these principal components were used in multiple logistic regression to identify discriminatory models for diagnosis, sputum inflammatory cell profile and asthma control. Results The authors recruited 35 patients with asthma and 23 matched controls. A model derived from 15 VOCs classified patients with asthma with an accuracy of 86%, and positive and negative predictive values of 0.85 and 0.89, respectively. Models also classified patients with asthma based on the following phenotypes: sputum (obtained in 18 patients with asthma) eosinophilia ≥2% area under the receiver operating characteristics (AUROC) curve 0.98, neutrophilia ≥40% AUROC 0.90 and uncontrolled asthma (Asthma Control Questionnaire ≥1) AUROC 0.96. Conclusions Detection of characteristic breath VOC profiles could classify patients with asthma versus controls, and clinically relevant disease phenotypes based on sputum inflammatory profile and asthma control. Prospective validation of these models may lead to clinical application of non-invasive breath profiling in asthma.

Photochemistry of refractive index structures in poly(methyl methacrylate) by femtosecond laser irradiation

Femtosecond, subablation threshold photomodification of poly(methyl methacrylate) (PMMA) at 387 nm is explored to enable fabrication of optical components. Volatile fragment analysis (thermal desorption gas chromatography-mass spectrometry) and molecular weight distribution monitoring (size exclusion chromatography) suggest photochemical modification, involving direct cleavage of the polymer backbone and propagation via chain unzipping under formation of monomers, similar to the pyrolytic degradation of PMMA. Waveguides were produced in undoped, clinical-grade PMMA, showing an increased refractive index in the laser focal region (Dnmax=4x10(-3)).

An adaptive breath sampler for use with human subjects with an impaired respiratory function

An adaptive sampler for collecting 2.5 dm(3) samples of exhaled air from human subjects with an impaired respiratory function is described. Pressure in the upper respiratory tract is continuously monitored and the data used to control an automated system to collect select portions of the expired breathing cycle onto a mixed bed Tenax(trade mark) and Carbotrap(trade mark) adsorbent trap for analysis by GC-MS. The sampling approach is intended for use in metabolomic profiling of volatiles in human breath at concentrations greater than microg m(-3). The importance of experimental reproducibility in metabolomic data is emphasised and consequently a high purity air supply is used to maintain a stable exogenous volatile organic compound profile at concentrations in the range 5 to 30 microg m(-3). The results of a 90 day stability study showed that exogenous VOCs were maintained at significantly lower levels (40 times lower for isopropyl alcohol) and with significantly higher reproducibility (80 times lower standard deviation for isopropyl alcohol) than would have been be the case if ambient air had been used. The sampling system was evaluated with healthy controls alongside subjects with chronic obstructive pulmonary disease. Subjects were able to breathe normally with control subjects observed to breathe at a rate of 9 to 17 breaths per minute, compared to 16 to 30 breaths per minute for subjects with COPD. This study presents, for the first time, observations and estimates of intra-subject breath sample reproducibility from human subjects. These reproducibility studies indicated that VOCs in exhaled breath exhibit a variety of dynamic behaviours, with some species recovered with a RSD <30%, while other species were observed to have significantly more variable concentrations, 30 to 130% RSD. The approach was also demonstrated to reliably differentiate the differences in the VOC profiles between alveolar and dead space air.

Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study

BackgroundNon-invasive phenotyping of chronic respiratory diseases would be highly beneficial in the personalised medicine of the future. Volatile organic compounds can be measured in the exhaled breath and may be produced or altered by disease processes. We investigated whether distinct patterns of these compounds were present in chronic obstructive pulmonary disease (COPD) and clinically relevant disease phenotypes.MethodsBreath samples from 39 COPD subjects and 32 healthy controls were collected and analysed using gas chromatography time-of-flight mass spectrometry. Subjects with COPD also underwent sputum induction. Discriminatory compounds were identified by univariate logistic regression followed by multivariate analysis: 1. principal component analysis; 2. multivariate logistic regression; 3. receiver operating characteristic (ROC) analysis.ResultsComparing COPD versus healthy controls, principal component analysis clustered the 20 best-discriminating compounds into four components explaining 71% of the variance. Multivariate logistic regression constructed an optimised model using two components with an accuracy of 69%. The model had 85% sensitivity, 50% specificity and ROC area under the curve of 0.74. Analysis of COPD subgroups showed the method could classify COPD subjects with far greater accuracy. Models were constructed which classified subjects with ≥2% sputum eosinophilia with ROC area under the curve of 0.94 and those having frequent exacerbations 0.95. Potential biomarkers correlated to clinical variables were identified in each subgroup.ConclusionThe exhaled breath volatile organic compound profile discriminated between COPD and healthy controls and identified clinically relevant COPD subgroups. If these findings are validated in prospective cohorts, they may have diagnostic and management value in this disease.

Femtosecond laser modification of poly(methyl methacrylate): Photochemistry

Femtosecond, sub-ablation threshold UV photo-modification of poly(methyl methacrylate) is explored. Volatile fragment analysis (thermal desorption gas chromatography - mass spectrometry) and molecular weight distribution monitoring (size exclusion chromatography) suggest direct backbone cleavage and monomer formation.