Pablo Martinez-Lozano Sinues

Human Breath Analysis May Support the Existence of Individual Metabolic Phenotypes

The metabolic phenotype varies widely due to external factors such as diet and gut microbiome composition, among others. Despite these temporal fluctuations, urine metabolite profiling studies have suggested that there are highly individual phenotypes that persist over extended periods of time. This hypothesis was tested by analyzing the exhaled breath of a group of subjects during nine days by mass spectrometry. Consistent with previous metabolomic studies based on urine, we conclude that individual signatures of breath composition exist. The confirmation of the existence of stable and specific breathprints may contribute to strengthen the inclusion of breath as a biofluid of choice in metabolomic studies. In addition, the fact that the method is rapid and totally non-invasive, yet individualized profiles can be tracked, makes it an appealing approach.

Monitoring diurnal changes in exhaled human breath.

The development of noninvasive analytical techniques is of interest to the field of chronobiology, in order to reveal the human metabolome that seems to show temporal patterns and to predict internal body time. We report on the real-time mass spectrometric analysis of human breath as a potential method to be used in this field. The breath of 12 subjects was analyzed during 9 days by secondary electrospray ionization-mass spectrometry (SESI-MS). The samples were collected during four time slots: morning (8:00-11:00), before lunch (11:00-13:00), after lunch (13:00-15:00), and late afternoon (15:00-18:00). A total of 203 mass spectra were statistically analyzed. Univariate analysis revealed a number of features with a marked temporal behavior. Principal component analysis/canonical analysis showed a clear temporal evolution of the breath patterns. A blind cross-validation yielded 84% of correct classifications of the time slot at which the breath samples were collected. We conclude that this approach seems to have potential for the investigation of biological clocks, including the description of internal body time, which may have important implications for the timing of pharmacotherapy.

Analysis of the Exhalome: A Diagnostic Tool of the Future

Investigations on breath analysis have provided preliminary data on its potential in the noninvasive diagnosis of lung diseases. Although the conventional comparisons of exhaled breath in study populations (ie, diseased vs healthy) may help to identify patients with various lung diseases, we believe that the analysis of exhaled breath holds promise beyond this scenario. On the basis of preliminary findings, we hypothesize that breath analysis (1) could be applied not only to identify patients with lung disease but also to better phenotype healthy subjects at risk and patients with a particular disease, which is in-line with current efforts toward individualized medicine; (2) could be useful in estimating internal body time to determine the optimal time of drug administration, thereby maximizing drug activity and reducing toxicity (chronopharmacology); and (3) could be applied to monitor drugs or drug metabolites, thus, enhancing adherence to prescribed medications and enabling studies on pharmacokinetics.

Drug Pharmacokinetics Determined by Real-Time Analysis of Mouse Breath†

Noninvasive, real-time pharmacokinetic (PK) monitoring of ketamine, propofol, and valproic acid, and their metabolites was achieved in mice, using secondary electrospray ionization and high-resolution mass spectrometry. The PK profile of a drug influences its efficacy and toxicity because it determines exposure time and levels. The antidepressant and anaesthetic ketamine (Ket) and four Ket metabolites were studied in detail and their PK was simultaneously determined following application of different sub-anaesthetic doses of Ket. Bioavailability after oral administration vs. intraperitoneal injection was also investigated. In contrast to conventional studies that require many animals to be sacrificed even for low-resolution PK curves, this novel approach yields real-time PK curves with a hitherto unmatched time resolution (10 s), and none of the animals has to be sacrificed. This thus represents a major step forward not only in animal welfare, but also major cost and time savings.

Effects of CPAP therapy withdrawal on exhaled breath pattern in obstructive sleep apnoea

Background Obstructive sleep apnoea (OSA) is highly prevalent and associated with cardiovascular and metabolic changes. OSA is usually diagnosed by polysomnography which is time-consuming and provides little information on the patients phenotype thus limiting a personalised treatment approach. Exhaled breath contains information on metabolism which can be analysed by mass spectrometry within minutes. The objective of this study was to identify a breath profile in OSA recurrence by use of secondary-electrospray-ionization-mass spectrometry (SESI-MS). Methods Patients with OSA effectively treated with CPAP were randomised to either withdraw treatment (subtherapeutic CPAP) or continue therapeutic CPAP for 2 weeks. Exhaled breath analysis by untargeted SESI-MS was performed at baseline and 2 weeks after randomisation. The primary outcome was the change in exhaled molecular breath pattern. Results 30 patients with OSA were randomised and 26 completed the trial according to the protocol. CPAP withdrawal led to a recurrence of OSA (mean difference in change of oxygen desaturation index between groups +30.3/h; 95% CI 19.8/h,40.7/h, p<0.001) which was accompanied by a significant change in 62 exhaled features (16 metabolites identified). The panel of discriminating mass-spectral features allowed differentiation between treated and untreated OSA with a sensitivity of 92.9% and a specificity of 84.6%. Conclusion Exhaled breath analysis by SESI-MS allows rapid and accurate detection of OSA recurrence. The technique has the potential to characterise an individuals metabolic response to OSA and thus makes a comprehensible phenotyping of OSA possible. Trial registration number NCT02050425 (registered at ClinicalTrials.gov).

Breath analysis in real time by mass spectrometry in chronic obstructive pulmonary disease.

Background: It has been suggested that exhaled breath contains relevant information on health status. Objectives: We hypothesized that a novel mass spectrometry (MS) technique to analyze breath in real time could be useful to differentiate breathprints from chronic obstructive pulmonary disease (COPD) patients and controls (smokers and nonsmokers). Methods: We studied 61 participants including 25 COPD patients [Global Initiative for Obstructive Lung Disease (GOLD) stages I-IV], 25 nonsmoking controls and 11 smoking controls. We analyzed their breath by MS in real time. Raw mass spectra were then processed and statistically analyzed. Results: A panel of discriminating mass-spectral features was identified for COPD (all stages; n = 25) versus healthy nonsmokers (n = 25), COPD (all stages; n = 25) versus healthy smokers (n = 11) and mild COPD (GOLD stages I/II; n = 13) versus severe COPD (GOLD stages III/IV; n = 12). A blind classification (i.e. leave-one-out cross validation) resulted in 96% sensitivity and 72.7% specificity (COPD vs. smoking controls), 88% sensitivity and 92% specificity (COPD vs. nonsmoking controls) and 92.3% sensitivity and 83.3% specificity (GOLD I/II vs. GOLD III/IV). Acetone and indole were identified as two of the discriminating exhaled molecules. Conclusions: We conclude that real-time MS may be a useful technique to analyze and characterize the metabolome of exhaled breath. The acquisition of breathprints in a rapid manner may be valuable to support COPD diagnosis and to gain insight into the disease.

Identification of 2-alkenals, 4-hydroxy-2-alkenals, and 4-hydroxy-2,6-alkadienals in exhaled breath condensate by UHPLC-HRMS and in breath by real-time HRMS.

In recent years, breath analysis in real time has become a noninvasive alternative for the diagnosis of diseases and for molecular fingerprinting of exhaled breath. However, the techniques used lack the capabilities for proper identification of the compounds found in the exhalome. Here, we report the use of UHPLC-HRMS as a tool for the identification of several aldehydes (2-alkenals, 4-hydroxy-2-alkenals, and 4-hydroxy-2,6-alkadienals), biomarkers of lipid peroxidation, in exhaled breath condensate of three healthy subjects (N = 3). Some of the aldehydes studied have never been identified before. Their robust identification is based on retention times, on the generation of fragmentation trees from tandem mass spectra, and on the comparison of these parameters with standards. We also show that the identified compounds can be analyzed and confirmed by MS/MS in breath in real time and, therefore, they could be used as biomarkers for the rapid and noninvasive diagnosis of related diseases.

CommentaryAnalysis of the Exhalome: A Diagnostic Tool of the Future

Investigations on breath analysis have provided preliminary data on its potential in the noninvasive diagnosis of lung diseases. Although the conventional comparisons of exhaled breath in study populations (ie, diseased vs healthy) may help to identify patients with various lung diseases, we believe that the analysis of exhaled breath holds promise beyond this scenario. On the basis of preliminary findings, we hypothesize that breath analysis (1) could be applied not only to identify patients with lung disease but also to better phenotype healthy subjects at risk and patients with a particular disease, which is in-line with current efforts toward individualized medicine; (2) could be useful in estimating internal body time to determine the optimal time of drug administration, thereby maximizing drug activity and reducing toxicity (chronopharmacology); and (3) could be applied to monitor drugs or drug metabolites, thus, enhancing adherence to prescribed medications and enabling studies on pharmacokinetics.

Expanding metabolite coverage of real-time breath analysis by coupling a universal secondary electrospray ionization source and high resolution mass spectrometry--a pilot study on tobacco smokers.

Online breath analysis is an attractive approach to track exhaled compounds without sample preparation. Current commercially available real-time breath analysis platforms require the purchase of a full mass spectrometer. Here we present an ion source compatible with virtually any preexisting atmospheric pressure ionization mass spectrometer that allows real-time analysis of breath. We illustrate the capabilities of such technological development by upgrading an orbitrap mass spectrometer. As a result, we detected compounds in exhaled breath between 70 and 900 Da, with a mass accuracy of typically  <1 ppm; resolutions between m/Δm 22,000 and 70,000 and fragmentation capabilities. The setup was tested in a pilot study, comparing the breath of smokers (n  =  9) and non-smokers (n  =  10). Exogenous compounds associated to smoking, as well as endogenous metabolites suggesting increased oxidative stress in smokers, were detected and in some cases identified unambiguously. Most of these compounds correlated significantly with smoking frequency and allowed accurate discrimination of smokers and non-smokers.

Fingerprinting Breast Cancer vs. Normal Mammary Cells by Mass Spectrometric Analysis of Volatiles

There is increasing interest in the development of noninvasive diagnostic methods for early cancer detection, to improve the survival rate and quality of life of cancer patients. Identification of volatile metabolic compounds may provide an approach for noninvasive early diagnosis of malignant diseases. Here we analyzed the volatile metabolic signature of human breast cancer cell lines versus normal human mammary cells. Volatile compounds in the headspace of conditioned culture medium were directly fingerprinted by secondary electrospray ionization-mass spectrometry. The mass spectra were subsequently treated statistically to identify discriminating features between normal vs. cancerous cell types. We were able to classify different samples by using feature selection followed by principal component analysis (PCA). Additionally, high-resolution mass spectrometry allowed us to propose their chemical structures for some of the most discriminating molecules. We conclude that cancerous cells can release a characteristic odor whose constituents may be used as disease markers.