Data interpretation in breath biomarker research: pitfalls and directions

Clinical applications of volatilomic assays

The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.

An inexpensive UV-LED photoacoustic based real-time sensor-system detecting exhaled trace-acetone

In this research we present a low-cost system for breath acetone analysis based on UV-LED photoacoustic spectroscopy. We considered the end-tidal phase of exhalation, which represents the systemic concentrations of volatile organic compounds (VOCs) - providing clinically relevant information about the human health. This is achieved via the development of a CO2-triggered breath sampling system, which collected alveolar breath over several minutes in sterile and inert containers. A real-time mass spectrometer is coupled to serve as a reference device for calibration measurements and subsequent breath analysis. The new sensor system provided a 3σ detection limit of 8.3 ppbV and an NNEA of 1.4E-9 Wcm-1Hz-0.5. In terms of the performed breath analysis measurements, 12 out of 13 fell within the error margin of the photoacoustic measurement system, demonstrating the reliability of the measurements in the field.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

A novel coupling technique based on thermal desorption gas chromatography with mass spectrometry and ion mobility spectrometry for breath analysis

Exhaled breath analysis is evolving into an increasingly important non-invasive diagnostic tool. Volatile organic compounds (VOCs) in breath contain information about health status and are promising biomarkers for several diseases, including respiratory infections caused by bacteria. To monitor the composition of VOCs in breath or the emission of VOCs from bacteria, sensitive analytical techniques are required. Next to mass spectrometry, ion mobility spectrometry (IMS) is considered a promising analytical tool for detecting gaseous analytes in the parts per billion by volume to parts per trillion by volume range. This work presents a new, dual coupling of thermal desorption gas chromatography to a quadrupole mass spectrometer (MS) and an IMS by operating a simple splitter. Nearly identical retention times can be reached in the range of up to 30 min with slight deviations of 0.06 min-0.24 min. This enables the identification of unknown compounds in the IMS chromatogram using unambiguous mass spectral identification, as there are still no commercially available databases for IMS. It is also possible to discriminate one of the detectors using the splitter to improve detection limits. Using a test liquid mixture of seven ketones, namely 2-butanone, 2-pentanone, 2-hexanone, 2-heptanone, 2-octanone, 2-nonanone, and 2-decanone with a concentration of 0.01 g l-1reproducibilities ranging from 3.0% to 7.6% for MS and 2.2%-5.3%, for IMS were obtained, respectively. In order to test the system optimized here for the field of breath analysis, characteristic VOCs such as ethanol, isoprene, acetone, 2-propanol, and 1-propanol were successfully identified in exhaled air using the dual detector system due to the match of the corresponding IMS, and MS spectra. The presented results may be considered to be a starting point for the greater use of IMS in combination with MS within the medical field.

Does the last 20 years paradigm of clinical research using volatile organic compounds to non-invasively diagnose cancer need to change? Challenges and future direction

PURPOSE

Volatile organic compounds (VOCs) have shown great potential as novel biomarkers for cancer detection; however, comprehensive quantitative analysis is lacking. In this study, we performed a bibliometric analysis of non-invasive cancer diagnosis using VOCs to better characterise international trends and to predict future hotspots in this field, and then we focussed on human studies to analyse clinical characteristics for presenting the current controversies and future perspectives of further clinical work.

METHODS

Publications, from 2002 to 2022, were retrieved from the Web of Science Core Collection database. CiteSpace and VOSviewer were used to generate network maps and identify the annual publications, top countries, authors, institutions, journals, references, and keywords. Then, we further screened clinical trials, and the key information was extracted into Microsoft Excel for further systematical analysis.

RESULTS

Six hundred and forty-one articles were identified to evaluate research trends, of which 301 clinical trials were selected for further systematical analysis. Overall, the annual publications in this area increased, with an overall upward trend, while the quality of clinical research remains remarkably uneven.

CONCLUSION

The study of non-invasive cancer diagnosis using VOCs would continue to be an active field. However, without stringent clinical design criteria, most suitable acquisition and analysis devices and statistical approaches, a list of exclusive, specific, reliable and reproducible VOCs to identify a disease and these VOCs appearing in a breath at detectable levels at early stage disease, the clinical utility of VOC tests will be difficult to have any breakthroughs.

Advanced setup for safe breath sampling and patient monitoring under highly infectious conditions in the clinical environment

Being the proximal matrix, breath offers immediate metabolic outlook of respiratory infections. However, high viral load in exhalations imposes higher transmission risk that needs improved methods for safe and repeatable analysis. Here, we have advanced the state-of-the-art methods for real-time and offline mass-spectrometry based analysis of exhaled volatile organic compounds (VOCs) under SARS-CoV-2 and/or similar respiratory conditions. To reduce infection risk, the general experimental setups for direct and offline breath sampling are modified. Certain mainstream and side-stream viral filters are examined for direct and lab-based applications. Confounders/contributions from filters and optimum operational conditions are assessed. We observed immediate effects of infection safety mandates on breath biomarker profiles. Main-stream filters induced physiological and analytical effects. Side-stream filters caused only systematic analytical effects. Observed substance specific effects partly depended on compound's origin and properties, sampling flow and respiratory rate. For offline samples, storage time, -conditions and -temperature were crucial. Our methods provided repeatable conditions for point-of-care and lab-based breath analysis with low risk of disease transmission. Besides breath VOCs profiling in spontaneously breathing subjects at the screening scenario of COVID-19/similar test centres, our methods and protocols are applicable for moderately/severely ill (even mechanically-ventilated) and highly contagious patients at the intensive care.

Profiling of exhaled volatile organics in the screening scenario of a COVID-19 test center

Breath volatile organics (VOCs) may provide immediate information on infection mechanisms and host response. We conducted real-time mass spectrometry-based breath profiling in 708 non-preselected consecutive subjects in the screening scenario of a COVID-19 test center. Recruited subjects were grouped based on PCR-confirmed infection status and presence or absence of flu-like symptoms. Exhaled VOC profiles of SARS-CoV-2-positive cases (n = 36) differed from healthy (n = 256) and those with other respiratory infections (n = 416). Concentrations of most VOCs were suppressed in COVID-19. VOC concentrations also differed between symptomatic and asymptomatic cases. Breath markers mirror effects of infections onto host's cellular metabolism and microbiome. Downregulation of specific VOCs was attributed to suppressive effects of SARS-CoV-2 onto gut or pulmonary microbial metabolism. Breath analysis holds potential for monitoring SARS-CoV-2 infections rather than for primary diagnosis. Breath profiling offers unconventional insight into host-virus cross-talk and infection microbiology and enables non-invasive assessment of disease manifestation.

Volatile compounds in human breath: critical review and meta-analysis

Volatile compounds contained in human breath reflect the inner workings of the body. A large number of studies have been published that link individual components of breath to disease, but diagnostic applications remain limited, in part due to inconsistent and conflicting identification of breath biomarkers. New approaches are therefore required to identify effective biomarker targets. Here, volatile organic compounds have been identified in the literature from four metabolically and physiologically distinct diseases and grouped into chemical functional groups (e.g. - methylated hydrocarbons or aldehydes; based on known metabolic and enzymatic pathways) to support biomarker discovery and provide new insight on existing data. Using this functional grouping approach, principal component analysis doubled explanatory capacity from 19.1% to 38% relative to single individual compound approaches. Random forest and linear discriminant analysis reveal 93% classification accuracy for cancer. This review and meta-analysis provides insight for future research design by identifying volatile functional groups associated with disease. By incorporating our understanding of the complexities of the human body, along with accounting for variability in methodological and analytical approaches, this work demonstrates that a suite of targeted, functional volatile biomarkers, rather than individual biomarker compounds, will improve accuracy and success in diagnostic research and application.

Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications

Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.

Discriminant Profiles of Volatile Compounds in the Alveolar Air of Patients with Squamous Cell Lung Cancer, Lung Adenocarcinoma or Colon Cancer

The objective of the present work was to analyze volatile compounds in alveolar air in patients with squamous cell lung cancer, lung adenocarcinoma or colon cancer, to prepare algorithms able to discriminate such specific pathological conditions. The concentration of 95 volatile compounds was measured in the alveolar air of 45 control subjects, 36 patients with lung adenocarcinoma, 25 patients with squamous cell lung cancer and 52 patients with colon cancer. Volatile compounds were measured with ion molecule reaction mass spectrometry (IMR-MS). An iterated least absolute shrinkage and selection operator multivariate logistic regression model was used to generate specific algorithms and discriminate control subjects from patients with different kinds of cancer. The final predictive models reached the following performance: by using 11 compounds, patients with lung adenocarcinoma were identified with a sensitivity of 86% and specificity of 84%; nine compounds allowed us to identify patients with lung squamous cell carcinoma with a sensitivity of 88% and specificity of 84%; patients with colon adenocarcinoma could be identified with a sensitivity of 96% and a specificity of 73% using a model comprising 13 volatile compounds. The different alveolar profiles of volatile compounds, obtained from patients with three different kinds of cancer, suggest dissimilar biological–biochemistry conditions; each kind of cancer has probably got a specific alveolar profile.

Analytical Tools for Disease Diagnosis in Animals via Fecal Volatilome

Volatilome analysis is growing in attention for the diagnosis of diseases in animals and humans. In particular, volatilome analysis in fecal samples is starting to be proposed as a fast, easy and noninvasive method for disease diagnosis. Volatilome comprises volatile organic compounds (VOCs), which are produced during both physiological and patho-physiological processes. Thus, VOCs from a pathological condition often differ from those of a healthy state and therefore the VOCs profile can be used in the detection of some diseases. Due to their strengths and advantages, feces are currently being used to obtain information related to health status in animals. However, they are complex samples, that can present problems for some analytical techniques and require special consideration in their use and preparation before analysis. This situation demands an effort to clarify which analytic options are currently being used in the research context to analyze the possibilities these offer, with the final objectives of contributing to develop a standardized methodology and to exploit feces potential as a diagnostic matrix. The current work reviews the studies focused on the diagnosis of animal diseases through fecal volatilome in order to evaluate the analytical methods used and their advantages and limitations. The alternatives found in the literature for sampling, storage, sample pretreatment, measurement and data treatment have been summarized, considering all the steps involved in the analytical process.

Non-invasive cancer detection using volatile biomarkers: Is urine superior to breath?

In recent years numerous reports have highlighted the options of chemical breath analysis with regard to non-invasive cancer detection. Certain volatile organic compounds (VOC) supposedly present in higher amounts or in characteristic patterns have been suggested as potential biomarkers. However, so far no clinical application based on a specific set of compounds appears to exist. Numerous reports on the capability of sniffer dogs and sensor arrays or electronic noses to distinguish breath of cancer patients and healthy controls supports the concept of genuine cancer-related volatile profiles. However, the actual compounds responsible for the scent are completely unknown and there is no correlation with the potential biomarkers suggested on basis of chemical trace analysis. It is outlined that specific features connected with the VOC analysis in breath - namely small concentrations of volatiles, interfering background concentrations, considerable sampling effort and sample instability, impracticability regarding routine application - stand in the way of substantial progress. The underlying chemical-analytical challenge can only be met considering the severe susceptibility of VOC determination to these adverse conditions. Therefore, the attention is drawn to the needs for appropriate quality assurance/quality control as the most important feature for the reliable quantification of volatiles present in trace concentration. Consequently, the advantages of urine as an alternative matrix for volatile biomarker search in the context of diagnosing lung and other cancers are outlined with specific focus on quality assurance and practicability in clinical chemistry. The headspace over urine samples as the VOC source allows adapting gas chromatographical procedures well-established in water analysis. Foremost, the selection of urine over breath as non-invasive matrix should provide considerably more resilience to adverse effects during sampling and analysis. The most important advantage of urine over breath is seen in the option to partition, dispense, mix, spike, store, and thus to dispatch taylor-made urine samples on demand for quality control measures. Although it is still open at this point if cancer diagnosis supported by non-invasively sampled VOC profiles will ultimately reach clinical application the advantages of urine over breath should significantly facilitate urgently required steps beyond the current proof-of-concept stage and towards standardisation.

A volatile biomarker in breath predicts lung cancer and pulmonary nodules

BACKGROUND

previous studies have reported volatile organic compounds (VOCs) in the breath as apparent biomarkers of lung cancer. We tested the hypothesis that a robust breath VOC biomarker of lung cancer should also predict pulmonary nodules in chest CT images.

METHODS

Biomarker discovery study (unblinded): 301 subjects were screened for lung cancer with low dose chest CT (LDCT), and donated duplicate samples of alveolar breath for analysis with gas chromatography mass spectrometry (GC MS). Monte Carlo analysis of breath chromatograms revealed a mass ion as a biomarker that identified biopsy-proven lung cancer as well as suspicious pulmonary nodules on LDCT. The biomarker was termed Mass Abnormalities in Gaseous Ions with Imaging Correlates (MAGIIC). The chemical structure of MAGIIC was tentatively identified from the NIST library of mass spectra; the best-fit compounds included C4 and C5 alkane derivatives that were consistent with metabolic products of oxidative stress. Blinded validation of MAGIIC: the abundance of the MAGIIC biomarker was determined in a different group of 161 subjects undergoing screening with LDCT. They donated duplicate alveolar breath VOC samples that were analyzed at two independent laboratories. The study was blinded and monitored with Good Clinical Practice. The abundance of MAGIIC in breath predicted biopsy-proven lung cancer with 84% accuracy, sensitivity = 75.4% and specificity = 85.0%. MAGIIC also predicted pulmonary nodules in LDCT with 80.5% accuracy, sensitivity = 80.1% and specificity = 75.0%. Breath MAGIIC abundance was not significantly affected by tobacco smoking history.

CONCLUSIONS

in a blinded study, breath VOC MAGIIC accurately predicted lung cancer confirmed on a tissue biopsy, as well as suspicious pulmonary nodules observed on LDCT. MAGIIC may have been a product of oxidative stress and it could potentially be employed as an ancillary to LDCT to predict the likelihood that a pulmonary nodule is malignant.

Critical Review of Volatile Organic Compound Analysis in Breath and In Vitro Cell Culture for Detection of Lung Cancer

Breath analysis is a promising technique for lung cancer screening. Despite the rapid development of breathomics in the last four decades, no consistent, robust, and validated volatile organic compound (VOC) signature for lung cancer has been identified. This review summarizes the identified VOC biomarkers from both exhaled breath analysis and in vitro cultured lung cell lines. Both clinical and in vitro studies have produced inconsistent, and even contradictory, results. Methodological issues that lead to these inconsistencies are reviewed and discussed in detail. Recommendations on addressing specific issues for more accurate biomarker studies have also been made.

Breath analysis by two-dimensional gas chromatography with dual flame ionisation and mass spectrometric detection - Method optimisation and integration within a large-scale clinical study

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New method for the analysis of exhaled breath VOCs by TD-GC × GC-FID/qMS.

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Optimisation of flow modulation and dual detection alongside clinical requirements.

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Addresses key challenges of using GC × GC for large-scale breath metabolomics.

Precision medicine has spurred new innovations in molecular pathology leading to recent advances in the analysis of exhaled breath as a non-invasive diagnostic tool. Volatile organic compounds (VOCs) detected in exhaled breath have the potential to reveal a wealth of chemical and metabolomic information. This study describes the development of a method for the analysis of breath, based on automated thermal desorption (TD) combined with flow modulated comprehensive two-dimensional gas chromatography (GC×GC) with dual flame ionisation and quadrupole mass spectrometric detection (FID and qMS). The constrained optimisation and analytical protocol was designed to meet the practical demands of a large-scale multi-site clinical study, while maintaining analytical rigour to produce high fidelity data. The results demonstrate a comprehensive method optimisation for the collection and analysis of breath VOCs by GC×GC, integral to the standardisation and integration of breath analysis within large clinical studies.

Exhaled-breath Testing for Prostate Cancer Based on Volatile Organic Compound Profiling Using an Electronic Nose Device (Aeonose™): A Preliminary Report

BACKGROUND

Prostate biopsy, an invasive examination, is the gold standard for diagnosing prostate cancer (PCa). There is a need for a novel noninvasive diagnostic tool that achieves a significantly high pretest probability for PCa, reducing unnecessary biopsy numbers. Recent studies have shown that volatile organic compounds (VOCs) in exhaled breath can be used to detect different types of cancers via training of an artificial neural network (ANN).

OBJECTIVE

To determine whether exhaled-breath analysis using a handheld electronic nose device can be used to discriminate between VOC patterns between PCa patients and healthy individuals.

DESIGN, SETTING, AND PARTICIPANTS

This prospective pilot study was conducted in the outpatient urology clinic of the Maastricht University Medical Center, the Netherlands. Patients with histologically proven PCa were already included before initial biopsy or during follow-up, with no prior treatment for their PCa. Urological patients with negative biopsies in the past year or patients with prostate enlargement (PE) with low or stable serum prostate-specific antigen were used as controls. Exhaled breath was probed from 85 patients: 32 with PCa and 53 controls (30 having negative biopsies and 23 PE).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Patient characteristics were statistically analyzed using independent sample t test and Pearson's chi-square test. Data analysis was performed by Aethena software after data compression using the TUCKER3 algorithm. ANN models were trained and evaluated using the leave-10%-out cross-validation method.

RESULTS AND LIMITATIONS

Our trained ANN showed an accuracy of 0.75, with an area under the curve of 0.79 with sensitivity and specificity of 0.84 (95% confidence interval [CI] 0.66-0.94) and 0.70 (95% CI 0.55-0.81) respectively, comparing PCa with control individuals. The negative predictive value was found to be 0.88. The main limitation is the relatively small sample size.

CONCLUSIONS

Our findings imply that the Aeonose allows us to discriminate between patients with untreated, histologically proven primary PCa and control patients based on exhaled-breath analysis.

PATIENT SUMMARY

We explored the possibility of exhaled-breath analysis using an electronic nose, to be used as a noninvasive tool in clinical practice, as a pretest for diagnosing prostate cancer. We found that the electronic nose was able to discriminate between prostate cancer patients and control individuals.

Metabolomics of tracheal wash samples and exhaled breath condensates in healthy horses and horses affected by equine asthma

The present work characterized the metabolomic profile of tracheal wash (TW) and exhaled breath condensate (EBC) in healthy horses and horses with respiratory disease. Six asthma-affected horses (group A) and six healthy controls (group H) underwent clinical, endoscopic and cytologic examinations of upper airways to confirm the active phase of asthma. TW and EBC samples were collected from each animal and investigated by proton nuclear magnetic resonance (¹H-NMR) metabolomic analysis. A total of ten out of 38 metabolites found in the TW were significantly different between the groups (p < 0.05). Higher concentrations of histamine and oxidant agents, such as glutamate, valine, leucine and isoleucine, as well as lower levels of ascorbate, methylamine, dimethylamine and O-phosphocholine, were found in group A compared to group H. Eight metabolites were found in equine EBC, namely methanol, ethanol, formate, trimethylamine, acetone, acetate, lactate and butanone, previously observed also in human EBC. Despite the fact that this was a pilot study, the results showed that the metabolomic analysis of TW and EBC has the potentiality to serve as a basis for diagnostic tools in horses with asthma.

Miniaturization of breath sampling with silicon chip: application to volatile tobacco markers tracking

This work presents the performances of silicon micro-preconcentrators chips for breath sampling. The silicon chips were coupled to a handheld battery powered system for breath sampling and direct injection in a laboratory gas chromatography mass spectrometry system through thermal desorption (TD). Performances of micro-preconcentrators were first compared to commercial TD for benzene trapping. Similar chromatographic peaks after gas chromatographic separation were observed while the volume of sample needed was reduced by a factor of 5. Repeatability and day to day variability of the micro-preconcentrators were then studied for a 500 ppb synthetic model mixture injected three times a day four days in a row: 8% and 12% were measured respectively. Micro-preconcentrator to micro-preconcentrator variability was not significant compared to day to day variability. In addition, micro-preconcentrators were tested for breath samples collected in Tedlar® bags. Three analyses of the same breath sample displayed relative standard deviations values below 16% for eight of the ten most intense peaks. Finally, the performances of micro-preconcentrators for breath sampling on a single expiration were illustrated with the example of volatile tobacco markers tracking. The signals of three smoking markers in breath, benzene, 2,5-dimethylfuran, and toluene were studied. Concentrations of benzene and toluene were found to be 10 to 100 higher in the breath of smokers. 2,5-dimethylfuran was only found in the breath of smokers. The elimination kinetics of the markers were followed as well during 4 h: a fast decrease of the signal of the three markers in breath was observed 20 min after smoking in good agreement with what is described in the literature. Those results demonstrate the efficiency of silicon chips for breath sampling, compared to the state of the art techniques. Thanks to miniaturization and lower sample volumes needed, micro-preconcentrators could be in the future a key technology towards portable breath sampling and analysis.

Laser spectroscopy for breath analysis: towards clinical implementation

Detection and analysis of volatile compounds in exhaled breath represents an attractive tool for monitoring the metabolic status of a patient and disease diagnosis, since it is non-invasive and fast. Numerous studies have already demonstrated the benefit of breath analysis in clinical settings/applications and encouraged multidisciplinary research to reveal new insights regarding the origins, pathways, and pathophysiological roles of breath components. Many breath analysis methods are currently available to help explore these directions, ranging from mass spectrometry to laser-based spectroscopy and sensor arrays. This review presents an update of the current status of optical methods, using near and mid-infrared sources, for clinical breath gas analysis over the last decade and describes recent technological developments and their applications. The review includes: tunable diode laser absorption spectroscopy, cavity ring-down spectroscopy, integrated cavity output spectroscopy, cavity-enhanced absorption spectroscopy, photoacoustic spectroscopy, quartz-enhanced photoacoustic spectroscopy, and optical frequency comb spectroscopy. A SWOT analysis (strengths, weaknesses, opportunities, and threats) is presented that describes the laser-based techniques within the clinical framework of breath research and their appealing features for clinical use.

Natural menstrual rhythm and oral contraception diversely affect exhaled breath compositions

Natural menstrual cycle and/or oral contraception diversely affect women metabolites. Longitudinal metabolic profiling under constant experimental conditions is thereby realistic to understand such effects. Thus, we investigated volatile organic compounds (VOCs) exhalation throughout menstrual cycles in 24 young and healthy women with- and without oral contraception. Exhaled VOCs were identified and quantified in trace concentrations via high-resolution real-time mass-spectrometry, starting from a menstruation and then repeated follow-up with six intervals including the next bleeding. Repeated measurements within biologically comparable groups were employed under optimized measurement setup. We observed pronounced and substance specific changes in exhaled VOC concentrations throughout all cycles with low intra-individual variations. Certain blood-borne volatiles changed significantly during follicular and luteal phases. Most prominent changes in endogenous VOCs were observed at the ovulation phase with respect to initial menstruation. Here, the absolute median abundances of alveolar ammonia, acetone, isoprene and dimethyl sulphide changed significantly (P-value ≤ 0.005) by 18.22↓, 13.41↓, 18.02↑ and 9.40↓%, respectively. These VOCs behaved in contrast under the presence of combined oral contraception; e.g. isoprene decreased significantly by 30.25↓%. All changes returned to initial range once the second bleeding phase was repeated. Changes in exogenous benzene, isopropanol, limonene etc. and smoking related furan, acetonitrile and orally originated hydrogen sulphide were rather nonspecific and mainly exposure dependent. Our observations could apprehend a number of known/pre-investigated metabolic effects induced by monthly endocrine regulations. Potential in vivo origins (e.g. metabolic processes) of VOCs are crucial to realize such effects. Despite ubiquitous confounders, we demonstrated the true strength of volatolomics for metabolic monitoring of menstrual cycle and contraceptives. These outcomes may warrant further studies in this direction to enhance our fundamental and clinical understanding on menstrual metabolomics and endocrinology. Counter-effects of contraception can be deployed for future noninvasive assessment of birth control pills. Our findings could be translated toward metabolomics of pregnancy, menopause and post-menopausal complications via breath analysis.

Instrumental drift removal in GC-MS data for breath analysis: the short-term and long-term temporal validation of putative biomarkers for COPD

Breath analysis holds the promise of a non-invasive technique for the diagnosis of diverse respiratory conditions including chronic obstructive pulmonary disease (COPD) and lung cancer. Breath contains small metabolites that may be putative biomarkers of these conditions. However, the discovery of reliable biomarkers is a considerable challenge in the presence of both clinical and instrumental confounding factors. Among the latter, instrumental time drifts are highly relevant, as since question the short and long-term validity of predictive models. In this work we present a methodology to counter instrumental drifts using information from interleaved blanks for a case study of GC-MS data from breath samples. The proposed method includes feature filtering, and additive, multiplicative and multivariate drift corrections, the latter being based on component correction. Biomarker discovery was based on genetic algorithms in a filter configuration using Fisher's ratio computed in the partial least squares-discriminant analysis subspace as a figure of merit. Using our protocol, we have been able to find nine peaks that provide a statistically significant area under the ROC curve of 0.75 for COPD discrimination. The method developed has been successfully validated using blind samples in short-term temporal validation. However, the attempt to use this model for patient screening six months later was not successful. This negative result highlights the importance of increasing validation rigor when reporting biomarker discovery results.

Can Recognition of Spinal Ischemia Be Improved? Application of Motor-Evoked Potentials, Serum Markers, and Breath Gas Analysis in an Acutely Instrumented Pig Model

BACKGROUND

Paraplegia due to spinal cord ischemia (SCI) is a serious complication after repair of thoracoabdominal aortic aneurysms. For prevention and early treatment of spinal ischemia, intraoperative monitoring of spinal cord integrity is essential. This study was intended to improve recognition of SCI through a combination of transcranial motor-evoked potentials (tc-MEPs), serum markers, and innovative breath analysis.

METHODS

In 9 female German Landrace pigs, tc-MEPs were captured, markers of neuronal damage were determined in blood, and volatile organic compounds (VOCs) were analyzed in exhaled air. After thoraco-phrenico-laparotomy, SCI was initiated through sequential clamping (n = 4) or permanently ligating (n = 5) SAs of the abdominal and thoracic aorta in caudocranial orientation until a drop in the tc-MEPs to at least 25% of the baseline was recorded. VOCs in breath were determined by means of solid-phase microextraction coupled with gas chromatography-mass spectrometry. After waking up, clinical and neurological status was evaluated (Tarlov score). Spinal cord histology was obtained in postmortem.

RESULTS

Permanent vessel ligature induced a worse neurological outcome and a higher number of necrotic motor neurons compared to clamping. Changes of serum markers remained unspecific. After laparotomy, exhaled acetone and isopropanol showed highest concentrations, and pentane and hexane increased during ischemia-reperfusion injury.

CONCLUSIONS

To mimic spinal ischemia occurring in humans during aortic aneurysm repair, animal models have to be meticulously evaluated concerning vascular anatomy and function. Volatiles from breath indicated metabolic stress during surgery and oxidative damage through ischemia reperfusion. Breath VOCs may provide complimentary information to conventional monitoring methods.

What is the real utility of breath ammonia concentration measurements in medicine and physiology?

Much effort continues to be devoted to the development of devices to analyse breath ammonia with the anticipation that breath ammonia analyses will be useful in clinical practice. In this perspective we refer to the analytical techniques that have been used to measure breath ammonia, focusing on selected ion flow tube mass spectrometry, SIFT-MS, of which we have special knowledge and understanding. From the collected data obtained using the different techniques, we exam the origins of mouth- and nose-exhaled ammonia and conclude that mouth-exhaled ammonia is always elevated above a concentration that would be equilibrated with blood ammonia and is largely produced by the action of enzymes on salivary urea. Support to this conclusion is given by the reasonable correlation between blood urea concentration and mouth-exhaled ammonia concentration. Further, it is discussed that nose-exhaled ammonia largely originates at the alveolar interface and so its concentration more closely relates to the expected alveolar blood ammonia concentration. Ingestion of proteins results in increased blood/saliva urea and ultimately mouth-exhaled ammonia as does the generation of urease by H. pylori infection. It is also concluded that when mouth-exhaled ammonia is elevated then it may be due to either abnormally high blood urea, a high pH of the saliva/mouth/airways mucosa, poor oral hygiene or a combinations of these.

Breathomics from exhaled volatile organic compounds in pediatric asthma

Asthma is the most common chronic disease in children, and is characterized by airway inflammation, bronchial hyperresponsiveness, and airflow obstruction. Asthma diagnosis, phenotyping, and monitoring are still challenging with currently available methods, such as spirometry, F_E NO or sputum analysis. The analysis of volatile organic compounds (VOCs) in exhaled breath could be an interesting non-invasive approach, but has not yet reached clinical practice. This review describes the current status of breath analysis in the diagnosis and monitoring of pediatric asthma. Furthermore, features of an ideal breath test, different breath analysis techniques, and important methodological issues are discussed. Although only a (small) number of studies have been performed in pediatric asthma, of which the majority is focusing on asthma diagnosis, these studies show moderate to good prediction accuracy (80-100%, with models including 6-28 VOCs), thereby qualifying breathomics for future application. However, standardization of procedures, longitudinal studies, as well as external validation are needed in order to further develop breathomics into clinical tools. Such a non-invasive tool may be the next step toward stratified and personalized medicine in pediatric respiratory disease.

Strategies for the identification of disease-related patterns of volatile organic compounds: prediction of paratuberculosis in an animal model using random forests

Modern statistical methods which were developed for pattern recognition are increasingly being used for data analysis in studies on emissions of volatile organic compounds (VOCs). With the detection of disease-related VOC profiles, novel non-invasive diagnostic tools could be developed for clinical applications. However, it is important to bear in mind that not all statistical methods are equally suitable for the investigation of VOC profiles. In particular, univariate methods are not able to discover VOC patterns as they consider each compound separately. The present study demonstrates this fact in practice. Using VOC samples from a controlled animal study on paratuberculosis, the random forest classification method was applied for pattern recognition and disease prediction. This strategy was compared with a prediction approach based on single compounds. Both methods were framed within a cross-validation procedure. A comparison of both strategies based on these VOC data reveals that random forests achieves higher sensitivities and specificities than predictions based on single compounds. Therefore, it will most likely be more fruitful to further investigate VOC patterns instead of single biomarkers for paratuberculosis. All methods used are thoroughly explained to aid the transfer to other data analyses.

Exhaled Volatile Organic Compounds of Infection: A Systematic Review

With heightened global concern of microbial drug resistance, advanced methods for early and accurate diagnosis of infection are urgently needed. Analysis of exhaled breath volatile organic compounds (VOCs) toward detecting microbial infection potentially allows a highly informative and noninvasive alternative to current genomics and culture-based methods. We performed a systematic review of research literature reporting human and animal exhaled breath VOCs related to microbial infections. In this Review, we find that a wide range of breath sampling and analysis methods are used by researchers, which significantly affects interstudy method comparability. Studies either perform targeted analysis of known VOCs relating to an infection, or non-targeted analysis to obtain a global profile of volatile metabolites. In general, the field of breath analysis is still relatively immature, and there is much to be understood about the metabolic production of breath VOCs, particularly in a host where both commensal microflora as well as pathogenic microorganisms may be manifested in the airways. We anticipate that measures to standardize high throughput sampling and analysis, together with an increase in large scale collaborative international trials, will bring routine breath VOC analysis to improve diagnosis of infection closer to reality.

Comparison of volatile organic compounds from lung cancer patients and healthy controls-challenges and limitations of an observational study

This paper outlines the design and performance of an observational study on the profiles of volatile organic compounds (VOCs) in the breath of 37 lung cancer patients and 23 healthy controls of similar age. The need to quantify each VOC considered as a potential disease marker on the basis of individual calibration is elaborated, and the quality control measures required to maintain reproducibility in breath sampling and subsequent instrumental trace VOC analysis using solid phase microextraction-gas chromatography-mass spectrometry over a study period of 14 months are described. Twenty-four VOCs were quantified on the basis of their previously suggested potential as cancer markers. The concentration of aromatic compounds in the breath was increased, as expected, in smokers, while lung cancer patients displayed significantly increased levels of oxygenated VOCs such as aldehydes, 2-butanone and 1-butanol. Although sets of selected oxygenated VOCs displayed sensitivities and specificities between 80% and 90% using linear discriminant analysis (LDA) with leave-one-out cross validation, the effective selectivity of the breath VOC approach with regard to cancer detection is clearly limited. Results are discussed against the background of the literature on volatile cancer marker investigations and the prospects of linking increased VOC levels in patients' breath with approaches that employ sniffer dogs. Experience from this study and the literature suggests that the currently available methodology is not able to use breath VOCs to reliably discriminate between cancer patients and healthy controls. Observational studies often tend to note significant differences in levels of certain oxygenated VOCs, but without the resolution required for practical application. Any step towards the exploitation of differences in VOC profiles for illness detection would have to solve current restrictions set by the low and variable VOC concentrations. Further challenges are the technical complexity of studies involving breath sampling and possibly the limited capability of current analytical procedures to detect unstable marker candidates.

Noninvasive strategies for breast cancer early detection

Breast cancer screening and presurgical diagnosis are currently based on mammography, ultrasound and more sensitive imaging technologies; however, noninvasive biomarkers represent both a challenge and an opportunity for early detection of cancer. An extensive number of potential breast cancer biomarkers have been discovered by microarray hybridization or sequencing of circulating DNA, noncoding RNA and blood cell RNA; multiplex analysis of immune-related molecules and mass spectrometry-based approaches for high-throughput detection of protein, endogenous peptides, circulating and volatile metabolites. However, their medical relevance and their translation to clinics remain to be exploited. Once they will be fully validated, cancer biomarkers, used in combination with the current and emerging imaging technologies, represent an avenue to a personalized breast cancer diagnosis.

Metabolite profiling of the ripening of Mangoes Mangifera indica L. cv. 'Tommy Atkins' by real-time measurement of volatile organic compounds

Real-time profiling of mango ripening based on proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) of small molecular weight volatile organic compounds (VOCs), is demonstrated using headspace measurements of 'Tommy Atkins' mangoes. VOC metabolites produced during the ripening process were sampled directly, which enabled simultaneous and rapid detection of a wide range of compounds. Headspace measurements of 'Keitt' mangoes were also conducted for comparison. A principle component analysis of the results indicated that several mass channels were not only key to the ripening process but could also be used to distinguish between mango cultivars. The identities of 22 of these channels, tentatively speciated using contemporaneous GC-MS measurements of sorbent tubes, are rationalized through examination of the biochemical pathways that produce volatile flavour components. Results are discussed with relevance to the potential of headspace analysers and electronic noses in future fruit ripening and quality studies.

Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening

Background

Breath volatile organic compounds (VOCs) have been reported as biomarkers of lung cancer, but it is not known if biomarkers identified in one group can identify disease in a separate independent cohort. Also, it is not known if combining breath biomarkers with chest CT has the potential to improve the sensitivity and specificity of lung cancer screening.

Methods

Model-building phase (unblinded): Breath VOCs were analyzed with gas chromatography mass spectrometry in 82 asymptomatic smokers having screening chest CT, 84 symptomatic high-risk subjects with a tissue diagnosis, 100 without a tissue diagnosis, and 35 healthy subjects. Multiple Monte Carlo simulations identified breath VOC mass ions with greater than random diagnostic accuracy for lung cancer, and these were combined in a multivariate predictive algorithm. Model-testing phase (blinded validation): We analyzed breath VOCs in an independent cohort of similar subjects (n = 70, 51, 75 and 19 respectively). The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening.

Results

Unblinded model-building phase. The algorithm identified lung cancer with sensitivity 74.0%, specificity 70.7% and C-statistic 0.78. Blinded model-testing phase: The algorithm identified lung cancer at Laboratory A with sensitivity 68.0%, specificity 68.4%, C-statistic 0.71; and at Laboratory B with sensitivity 70.1%, specificity 68.0%, C-statistic 0.70, with linear correlation between replicates (r = 0.88). In a projected outcome model, breath biomarkers increased the sensitivity, specificity, and positive and negative predictive values of chest CT for lung cancer when the tests were combined in series or parallel.

Conclusions

Breath VOC mass ion biomarkers identified lung cancer in a separate independent cohort, in a blinded replicated study. Combining breath biomarkers with chest CT could potentially improve the sensitivity and specificity of lung cancer screening.

Trial Registration

ClinicalTrials.gov NCT00639067

A Raman cell based on hollow core photonic crystal fiber for human breath analysis

PURPOSE

Breath analysis has a potential prospect to benefit the medical field based on its perceived advantages to become a point-of-care, easy to use, and cost-effective technology. Early studies done by mass spectrometry show that volatile organic compounds from human breath can represent certain disease states of our bodies, such as lung cancer, and revealed the potential of breath analysis. But mass spectrometry is costly and has slow-turnaround time. The authors' goal is to develop a more portable and cost effective device based on Raman spectroscopy and hollow core-photonic crystal fiber (HC-PCF) for breath analysis.

METHODS

Raman scattering is a photon-molecular interaction based on the kinetic modes of an analyte which offers unique fingerprint type signals that allow molecular identification. HC-PCF is a novel light guide which allows light to be confined in a hollow core and it can be filled with a gaseous sample. Raman signals generated by the gaseous sample (i.e., human breath) can be guided and collected effectively for spectral analysis.

RESULTS

A Raman-cell based on HC-PCF in the near infrared wavelength range was developed and tested in a single pass forward-scattering mode for different gaseous samples. Raman spectra were obtained successfully from reference gases (hydrogen, oxygen, carbon dioxide gases), ambient air, and a human breath sample. The calculated minimum detectable concentration of this system was ∼15 parts per million by volume, determined by measuring the carbon dioxide concentration in ambient air via the characteristic Raman peaks at 1286 and 1388 cm(-1).

CONCLUSIONS

The results of this study were compared to a previous study using HC-PCF to trap industrial gases and backward-scatter 514.5 nm light from them. The authors found that the method presented in this paper has an advantage to enhance the signal-to-noise ratio (SNR). This SNR advantage, coupled with the better transmission of HC-PCF in the near-IR than in the visible wavelengths led to an estimated seven times improvement in the detection sensitivity. The authors' prototype device also demonstrated a 100-fold improvement over a recently reported detection limit of a reflective capillary fiber-based Raman cell for breath analysis. Continued development is underway to increase the detection sensitivity further to reach practical clinical applications.

Physiological variability in volatile organic compounds (VOCs) in exhaled breath and released from faeces due to nutrition and somatic growth in a standardized caprine animal model

Physiological effects may change volatile organic compound (VOC) concentrations and may therefore act as confounding factors in the definition of VOCs as disease biomarkers. To evaluate the extent of physiological background variability, this study assessed the effects of feed composition and somatic growth on VOC patterns in a standardized large animal model. Fifteen clinically healthy goats were followed during their first year of life. VOCs present in the headspace over faeces, exhaled breath and ambient air inside the stable were repeatedly assessed in parallel with the concentrations of glucose, protein, and albumin in venous blood. VOCs were collected and analysed using solid-phase or needle-trap microextraction and gas chromatograpy together with mass spectroscopy. The concentrations of VOCs in exhaled breath and above faeces varied significantly with increasing age of the animals. The largest variations in volatiles detected in the headspace over faeces occurred with the change from milk feeding to plant-based diet. VOCs above faeces and in exhaled breath correlated significantly with blood components. Among VOCs exhaled, the strongest correlations were found between exhaled nonanal concentrations and blood concentrations of glucose and albumin. Results stress the importance of a profound knowledge of the physiological backgrounds of VOC composition before defining reliable and accurate marker sets for diagnostic purposes.

Volatile S-nitrosothiols and the typical smell of cancer

An unconventional approach to investigations into the identification of typical volatile emissions during illnesses gives rise to the proposal of a new class of cancer markers. Until now, cancer markers seem not to have been conclusively identified, though the obvious behavior of dogs points to their existence. The focus has been directed towards molecules containing sulfurous functionalities. Among such compounds, S-nitrosothiols (SNOs) are known to be involved in important physiological processes in living organisms and they are described as being typically elevated in cancer. Volatile SNOs (vSNOs) are proposed to be the source of the significant smell of cancer. Synthetic vSNOs are known to have lifetimes of between some minutes and several hours, which may be the main reason as to why they have been ignored until now, and also for the inability of analytics to detect them in vivo. Based on typical structures occurring in the volatile sulfur organics being emitted from human breath, four vSNOs have been synthesized and characterized by tandem mass spectrometry and gas chromatography/mass spectrometry. Simulating the relatively fatty consistency of cancer tissue by diluting the samples in n-decane, surprisingly reduces their tendency to decompose to lifetimes of weeks even at room temperature. A sniffer dog was trained with the synthetic vSNOs, and the results of the tests indicate that synthetic and cancer smells are very similar or even the same. The findings can be a clue for further target-oriented systematic optimization of existing sensitive measurement methods to prove vSNOs as cancer emissions and finally establish future methods for cancer diagnosis based on screening for this new class of volatile illness markers.

Breath analysis as a potential and non-invasive frontier in disease diagnosis: an overview

Currently, a small number of diseases, particularly cardiovascular (CVDs), oncologic (ODs), neurodegenerative (NDDs), chronic respiratory diseases, as well as diabetes, form a severe burden to most of the countries worldwide. Hence, there is an urgent need for development of efficient diagnostic tools, particularly those enabling reliable detection of diseases, at their early stages, preferably using non-invasive approaches. Breath analysis is a non-invasive approach relying only on the characterisation of volatile composition of the exhaled breath (EB) that in turn reflects the volatile composition of the bloodstream and airways and therefore the status and condition of the whole organism metabolism. Advanced sampling procedures (solid-phase and needle traps microextraction) coupled with modern analytical technologies (proton transfer reaction mass spectrometry, selected ion flow tube mass spectrometry, ion mobility spectrometry, e-noses, etc.) allow the characterisation of EB composition to an unprecedented level. However, a key challenge in EB analysis is the proper statistical analysis and interpretation of the large and heterogeneous datasets obtained from EB research. There is no standard statistical framework/protocol yet available in literature that can be used for EB data analysis towards discovery of biomarkers for use in a typical clinical setup. Nevertheless, EB analysis has immense potential towards development of biomarkers for the early disease diagnosis of diseases.

Changes in the concentration of breath ammonia in response to exercise: a preliminary investigation

Breath ammonia has proven to be a difficult compound to measure accurately. The goal of this study was to evaluate the effects that the physiological intervention, exercise, had on the levels of breath ammonia. The effects of vigorous exercise (4000 m indoor row) in 13 participants were studied and increases in breath ammonia were observed in all participants. Mean pre-exercise concentrations of ammonia were 670 pmol ml(-1) CO2 (SD, 446) and these concentrations increased to post-exercise maxima of 1499 pmol ml(-1) CO2 (SD, 730), p < 0.0001. The mean increase in ammonia concentrations from pre-exercise to maximum achieved in conditioned (1362 pmol ml(-1) CO2) versus non-conditioned rowers (591 pmol ml(-1) CO2) were found to be statistically different, p = 0.029. Taken together, these results demonstrate our ability to repeatedly measure the influence of exercise on the concentration of breath ammonia.