Monitoring breath during oral glucose tolerance tests

The reactive pyruvate metabolite dimethylglyoxal mediates neurological consequences of diabetes

Complications of diabetes are often attributed to glucose and reactive dicarbonyl metabolites derived from glycolysis or gluconeogenesis, such as methylglyoxal. However, in the CNS, neurons and endothelial cells use lactate as energy source in addition to glucose, which does not lead to the formation of methylglyoxal and has previously been considered a safer route of energy consumption than glycolysis. Nevertheless, neurons and endothelial cells are hotspots for the cellular pathology underlying neurological complications in diabetes, suggesting a cause that is distinct from other diabetes complications and independent of methylglyoxal. Here, we show that in clinical and experimental diabetes plasma concentrations of dimethylglyoxal are increased. In a mouse model of diabetes, ilvb acetolactate-synthase-like (ILVBL, HACL2) is the enzyme involved in formation of increased amounts of dimethylglyoxal from lactate-derived pyruvate. Dimethylglyoxal reacts with lysine residues, forms Nε-3-hydroxy-2-butanonelysine (HBL) as an adduct, induces oxidative stress more strongly than other dicarbonyls, causes blood-brain barrier disruption, and can mimic mild cognitive impairment in experimental diabetes. These data suggest dimethylglyoxal formation as a pathway leading to neurological complications in diabetes that is distinct from other complications. Importantly, dimethylglyoxal formation can be reduced using genetic, pharmacological and dietary interventions, offering new strategies for preventing CNS dysfunction in diabetes.

Breath analysis combined with cardiopulmonary exercise testing and echocardiography for monitoring heart failure patients: the AEOLUS protocol

This paper describes the AEOLUS pilot study which combines breath analysis with cardiopulmonary exercise testing (CPET) and an echocardiographic examination for monitoring heart failure (HF) patients. Ten consecutive patients with a prior clinical diagnosis of HF with reduced left ventricular ejection fraction were prospectively enrolled together with 15 control patients with cardiovascular risk factors, including hypertension, type II diabetes or chronic ischemic heart disease. Breath samples were collected at rest and during CPET coupled with exercise stress echocardiography (CPET-ESE) protocol by means of needle trap micro-extraction and were analyzed through gas-chromatography coupled with mass spectrometry. The protocol also involved using of a selected ion flow tube mass spectrometer for a breath-by-breath isoprene and acetone analysis during exercise. At rest, HF patients showed increased breath levels of acetone and pentane, which are related to altered oxidation of fatty acids and oxidative stress, respectively. A significant positive correlation was observed between acetone and the gold standard biomarker NT-proBNP in plasma (r= 0.646,p< 0.001), both measured at rest. During exercise, some exhaled volatiles (e.g., isoprene) mirrored ventilatory and/or hemodynamic adaptation, whereas others (e.g., sulfide compounds and 3-hydroxy-2-butanone) depended on their origin. At peak effort, acetone levels in HF patients differed significantly from those of the control group, suggesting an altered myocardial and systemic metabolic adaptation to exercise for HF patients. These preliminary data suggest that concomitant acquisition of CPET-ESE and breath analysis is feasible and might provide additional clinical information on the metabolic maladaptation of HF patients to exercise. Such information may refine the identification of patients at higher risk of disease worsening.

Inter-Day Variation in the Fasting Plasma Lipopolysaccharide Concentration in the Morning Is Associated with Inter-Day Variation in Appetite in Japanese Males: A Short-Term Cohort Study

Injection of lipopolysaccharide (LPS), a product of gut bacteria, into the blood increases blood triglycerides and cortisol, an appetite-stimulating hormone. Meanwhile, small amounts of LPS derived from gut bacteria are thought to enter the bloodstream from the gut in daily basis. This study aimed to investigate the effect of LPS influx on appetite or lipid metabolism in humans in everyday life. We measured the fasting plasma LPS concentration before breakfast and the corresponding days’ appetite and fat-burning markers for 10 days in four Japanese males (28–31 years) and analyzed the correlation of their inter-day variation. The LPS concentration was negatively correlated with fullness, and positively correlated with the carbohydrate intake. Against our hypothesis, the LPS concentration was positively correlated with the fasting breath acetone concentration, a fat-burning marker. There was a positive correlation between the LPS concentration and fasting body mass index (BMI), but the inter-day variation in BMI was slight. The results suggest that the LPS influx in everyday life is at least associated with appetite in the day.

Effects of long-term vegan diet on breath composition

The composition of exhaled breath derives from an intricate combination of normal and abnormal physiological processes that are modified by the consumption of food and beverages, circadian rhythms, bacterial infections, and genetics as well as exposure to xenobiotics. This complexity, which results wide intra- and inter-individual variability and is further influenced by sampling conditions, hinders the identification of specific biomarkers and makes it difficult to differentiate between pathological and nominally healthy subjects. The identification of a "normal" breath composition and the relative influence of the aforementioned parameters would make breath analyses much faster for diagnostic applications. We thus compared, for the first time, the breath composition of age-matched volunteers following a vegan and a Mediterranean omnivorous diet in order to evaluate the impact of diet on breath composition. Mixed breath was collected from 38 nominally healthy volunteers who were asked to breathe into a two-liter handmade Nalophan bag. Exhalation flow rate and carbon dioxide values were monitored during breath sampling. An aliquot (100 mL) of breath was loaded into a sorbent tube (250 mg of Tenax GR, 60/80 mesh) before being analyzed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Breath profiling using TD-GC-MS analysis identified five compounds (methanol, 1-propanol, pentane, hexane, and hexanal), thus enabling differentiation between samples collected from the different group members . Principal component analysis showed a clear separation between groups, suggesting that breath analysis could be used to study the influence of dietary habits in the fields of nutrition and metabolism. Surprisingly, one Italian woman and her brother showed extremely low breath isoprene levels (about 5 ppbv), despite their normal lipidic profile and respiratory data, such as flow rate and pCO2. Further investigations to reveal the reasons behind low isoprene levels in breath would help reveal the origin of isoprene in breath.

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection

Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall.

A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome

This paper comprises an updated version of the 2014 review which reported 1846 volatile organic compounds (VOCs) identified from healthy humans. In total over 900 additional VOCs have been reported since the 2014 review and the VOCs from semen have been added. The numbers of VOCs found in breath and the other bodily fluids are: blood 379, breath 1488, faeces 443, milk 290, saliva 549, semen 196, skin 623 and urine 444. Compounds were assigned CAS registry numbers and named according to a common convention where possible. The compounds have been included in a single table with the source reference(s) for each VOC, an update on our 2014 paper. VOCs have also been grouped into tables according to their chemical class or functionality to permit easy comparison. Careful use of the database is needed, as a number of the identified VOCs only have level 2-putative assignment, and only a small fraction of the reported VOCs have been validated by standards. Some clear differences are observed, for instance, a lack of esters in urine with a high number in faeces and breath. However, the lack of compounds from matrices such a semen and milk compared to breath for example could be due to the techniques used or reflect the intensity of effort e.g. there are few publications on VOCs from milk and semen compared to a large number for breath. The large number of volatiles reported from skin is partly due to the methodologies used, e.g. by collecting skin sebum (with dissolved VOCs and semi VOCs) onto glass beads or cotton pads and then heating to a high temperature to desorb VOCs. All compounds have been included as reported (unless there was a clear discrepancy between name and chemical structure), but there may be some mistaken assignations arising from the original publications, particularly for isomers. It is the authors' intention that this work will not only be a useful database of VOCs listed in the literature but will stimulate further study of VOCs from healthy individuals; for example more work is required to confirm the identification of these VOCs adhering to the principles outlined in the metabolomics standards initiative. Establishing a list of volatiles emanating from healthy individuals and increased understanding of VOC metabolic pathways is an important step for differentiating between diseases using VOCs.

Sport in Town: The Smart Healthy ENV Project, a Pilot Study of Physical Activity with Multiparametric Monitoring

Background: Increasing evidence links meteorological characteristics and air pollution to physiological responses during sports activities in urban areas with different traffic levels. Objective: The main objective of the Smart Healthy ENV (SHE, “Smart Monitoring Integrated System For A Healthy Urban Environment In Smart Cities”) project was to identify the specific responses of a group of volunteers during physical activity, by monitoring their heart rates and collecting breath samples, combined with data on meteorological determinants and pollution substances obtained through fixed sensor nodes placed along city routes and remotely connected to a dedicated data acquisition server. Methods: Monitoring stations were placed along two urban routes in Pisa, each two km long, with one located within the park beside the Arno river (green route) and the other in a crowded traffic zone (red route). Our sample participants were engaged in sports activities (N = 15, with different levels of ability) and were monitored through wearable sensors. They were first asked to walk back and forth (4 km) and then to run the same route. The experimental sessions were conducted over one day per route. A breath sample was also collected before each test. A questionnaire concerning temperature and fatigue perception was administered for all of the steps of the study over the two days. Results: The heart rates of the participants were monitored in the baseline condition, during walking, and while running, and were correlated with meteorological and pollutant data and with breath composition. Changes in the heart rates and breath composition were detected during the experimental sessions. These variations were related to the physical activity and to the meteorological conditions and air pollution levels. Conclusions: The SHE project can be considered a proof-of-concept study aimed at monitoring physiological and environmental variables during physical activity in urban areas, and can be used in future studies to provide useful information to those involved in sports and the broader community.

Determination of peppermint compounds in breath by needle trap micro-extraction coupled with gas chromatography-tandem mass spectrometry

Breath analysis is an alternative approach for disease diagnosis and for monitoring therapy. The lack of standardized procedures for collecting and analysing breath samples currently limits its use in clinical practice. In order to overcome this limitation, the "Peppermint Consortium" was established within the breath community to carry out breath wash-out experiments and define reference values for a panel of compounds contained in the peppermint oil capsule. Here, we present a needle trap micro-extraction technique coupled with gas chromatography and tandem mass spectrometry for a rapid and accurate determination of alpha-pinene, beta-pinene, limonene, eucalyptol, menthofuran, menthone, menthol and menthyl acetate in mixed breath samples. Detection limits between 1 and 20 pptv were observed when 25 mL of a humidified standard gas mixture were loaded into a needle trap device at a flow rate of 10 mL/min. Inter- and intra-day precisions were lower than 15%, thus confirming the reliability of the assay. Our procedure was used to analyse breath samples taken from a nominally healthy volunteer who were invited to swallow a 200 mg capsule of peppermint oil. Six samples were collected at various times within six hours of ingestion. Analyte concentrations were not affected by the sampling mode (i.e. mixed vs. end-tidal fraction), whereas respiratory rate and exhalation flow rate values slightly influenced the concentration of the target compounds in breath samples.

Validation and Application of a Derivatization-Free RP-HPLC-DAD Method for the Determination of Low Molecular Weight Salivary Metabolites

Saliva is an interesting, non-conventional, valuable diagnostic fluid. It can be collected using standardized sampling device; thus, its sampling is easy and non-invasive, it contains a variety of organic metabolites that reflect blood composition. The aim of this study was to validate a user-friendly method for the simultaneous determination of low molecular weight metabolites in saliva. We have optimized and validated a high throughput, direct, low-cost reversed phase liquid chromatographic method with diode array detection method without any pre- or post-column derivatization. We indexed salivary biomolecules in 35 whole non-stimulated saliva samples collected in 8 individuals in different days, including organic acids and amino acids and other carbonyl compounds. Among these, 16 whole saliva samples were collected by a single individual over three weeks before, during and after treatment with antibiotic in order to investigate the dynamics of metabolites. The concentrations of the metabolites were compared with the literature data. The multianalyte method here proposed requires a minimal sample handling and it is cost-effectiveness as it makes possible to analyze a high number of samples with basic instrumentation. The identification and quantitation of salivary metabolites may allow the definition of potential biomarkers for non-invasive "personal monitoring" during drug treatments, work out, or life habits over time.

A sampler prototype for the simultaneous collection of exhaled air and breath condensate

Exhaled air and breath condensate contain a large number of health biomarkers, such as volatile and semi-volatile organic compounds, proteins and lipids. Nowadays, the collection of breath samples is carried out by commercial or lab-made sampling systems that collect only one type of sample (e.g. gaseous or condensate phase), thus limiting the diagnostic capability of breath tests. This work presents a portable prototype optimized for the simultaneous collection of gaseous exhaled breath and exhaled breath condensate within five minutes. The system is fully portable and has a total weight of about 1 Kg. An illustrative determination of ethanol, isoprene, acetone, isopropyl alcohol, 1-propanol, 2-butanone, 2-pentanone, toluene and xylenes in breath, and cortisol and 8-iso-prostaglandin F_2α in breath condensate is discussed.

Experimental Design and Sample Preparation in Forest Tree Metabolomics

Appropriate experimental design and sample preparation are key steps in metabolomics experiments, highly influencing the biological interpretation of the results. The sample preparation workflow for plant metabolomics studies includes several steps before metabolite extraction and analysis. These include the optimization of laboratory procedures, which should be optimized for different plants and tissues. This is particularly the case for trees, whose tissues are complex matrices to work with due to the presence of several interferents, such as oleoresins, cellulose. A good experimental design, tree tissue harvest conditions, and sample preparation are crucial to ensure consistency and reproducibility of the metadata among datasets. In this review, we discuss the main challenges when setting up a forest tree metabolomics experiment for mass spectrometry (MS)-based analysis covering all technical aspects from the biological question formulation and experimental design to sample processing and metabolite extraction and data acquisition. We also highlight the importance of forest tree metadata standardization in metabolomics studies.

The Potential Benefits of Therapeutic Treatment Using Gaseous Terpenes at Ambient Low Levels

Biological volatile organic compounds (BVOCs) are emitted abundantly from the flora. Among BVOCs, monoterpenes (MTs) have been used for aromatherapy (e.g., forest bathing) to treat human physiological and psychological conditions. However, relatively little is known about the therapeutic effects of MTs at low part per billion by volume (ppbv) levels. The effects of artificial MTs on human subjects were assessed by both olfactory and therapeutic parameters (brain alpha waves and stress index). Gaseous standards of three monoterpenes (i.e., α-pinene, β-pinene, and d-limonene) prepared at low ppbv levels were used individually and as mixtures. Fifty-nine healthy and non-asthmatics volunteer university students were selected for the test. All human subjects inhaled low ppbv levels of monoterpene in the testing room. Brain alpha waves and stress index were investigated during the inhalation time. Questionnaires were also used after testing. It was found that the detection threshold of MTs was close to 5 ppbv. When the MT levels increased from 0 to 20 ppbv, the mean values of brain alpha waves derived from all participants increased from 9.8 to 15.1. In contrast, the stress index values declined from 46.2 to 34.7. The overall results suggest that MTs have great potential to positively affect the relaxation state of subjects in a manner similar to forest bathing in terms of short-term effect. They can thus be applied as potential therapeutic media for mental health care.

A Novel Medical E-Nose Signal Analysis System

It has been proven that certain biomarkers in people’s breath have a relationship with diseases and blood glucose levels (BGLs). As a result, it is possible to detect diseases and predict BGLs by analysis of breath samples captured by e-noses. In this paper, a novel optimized medical e-nose system specified for disease diagnosis and BGL prediction is proposed. A large-scale breath dataset has been collected using the proposed system. Experiments have been organized on the collected dataset and the experimental results have shown that the proposed system can well solve the problems of existing systems. The methods have effectively improved the classification accuracy.

Significance of Exhaled Breath Test in Clinical Diagnosis: A Special Focus on the Detection of Diabetes Mellitus

Analysis of volatile organic compounds (VOCs) emanating from human exhaled breath can provide deep insight into the status of various biochemical processes in the human body. VOCs can serve as potential biomarkers of physiological and pathophysiological conditions related to several diseases. Breath VOC analysis, a noninvasive and quick biomonitoring approach, also has potential for the early detection and progress monitoring of several diseases. This paper gives an overview of the major VOCs present in human exhaled breath, possible biochemical pathways of breath VOC generation, diagnostic importance of their analysis, and analytical techniques used in the breath test. Breath analysis relating to diabetes mellitus and its characteristic breath biomarkers is focused on. Finally, some challenges and limitations of the breath test are discussed.

A breath sampling system assessing the influence of respiratory rate on exhaled breath composition

This work presents a computerized system to monitor mouth pressure, tidal volume, exhaled airflow, respiration rate and end-tidal partial pressure of CO2 during breath collection. The system was used to investigate the effect of different respiratory rates on the volatile organic compounds (VOCs) concentrations in exhaled breath. For this purpose, VOCs with well-defined biochemical pathways and different chemical and physical properties were selected as biomarkers related to metabolism (acetone and isopropyl alcohol), cholesterol synthesis (isoprene) and intestinal microflora activity (ethanol). Mixed breath was collected from a nominally healthy volunteer in resting conditions by filling a Nalophan bag. The subject followed a regimented breathing pattern at different respiratory rates (10, 30 and 50 breaths per minute). Results highlight that ventilation pattern strongly influences the concentration of the selected compounds. The proposed system allows exhaled breath to be collected also in patients showing dyspnea such as in case of chronic heart failure, asthma and pulmonary diseases.

Vacuum ultraviolet absorption spectroscopy in combination with comprehensive two-dimensional gas chromatography for the monitoring of volatile organic compounds in breath gas: A feasibility study

Vacuum ultraviolet (VUV) absorption spectroscopy was recently introduced as a new detection system for one, as well as comprehensive two-dimensional gas chromatography (GC×GC) and successfully applied to the analysis of various analytes in several matrices. In this study, its suitability for the analysis of breath metabolites was investigated and the impact of a finite volume of the absorption cell and makeup gas pressure was evaluated for volatile analytes in terms of sensitivity and chromatographic resolution. A commercial available VUV absorption spectrometer was coupled to GC×GC and applied to the analysis of highly polar volatile organic compounds (VOCs). Breath gas samples were acquired by needle trap micro extraction (NTME) during a glucose challenge and analysed by the applied technique. Regarding qualitative and quantitative information, the VGA-100 is compatible with common GC×GC detection systems like FID and even TOFMS. Average peak widths of 300ms and LODs in the lower ng range were achieved using GC×GC-VUV. Especially small oxygenated breath metabolites show intense and characteristic absorption patterns in the VUV region. Challenge responsive VOCs could be identified and monitored during a glucose challenge. The new VUV detection technology might especially be of benefit for applications in clinical research.

Fiber content of diet affects exhaled breath volatiles in fasting and postprandial state in a pilot crossover study

Our pilot study examined the potential of exhaled breath analysis in studying the metabolic effects of dietary fiber (DF). We hypothesized that a high-fiber diet (HFD) containing whole grain rye changes volatile organic compound (VOC) levels in exhaled breath and that consuming a single meal affects these levels. Seven healthy men followed a week-long low-fiber diet (17 g/d) and HFD (44 g/d) in a randomized crossover design. A test meal containing 50 g of the available carbohydrates from wheat bread was served as breakfast after each week. Alveolar exhaled breath samples were analyzed at fasting state and 30, 60, and 120 minutes after this meal parallel to plasma glucose, insulin, and serum lipids. We used solid-phase microextraction and gas chromatography-mass spectrometry for detecting changes in 15 VOCs. These VOCs were acetone, ethanol, 1-propanol, 2-propanol, 1-butanol, acetic acid, propionic acid, butyric acid, valeric acid, isovaleric acid, 2-methylbutyric acid, hexanoic acid, acetoin, diacetyl, and phenol. Exhaled breath 2-methylbutyric acid in the fasting state and 1-propanol at 120 minutes decreased (P = .091 for both) after an HFD. Ingestion of the test meal increased ethanol, 1-propanol, acetoin, propionic acid, and butyric acid levels while reducing acetone, 1-butanol, diacetyl, and phenol levels. Both DF diet content and having a single meal affected breathVOCs. Exploring exhaled breath further could help to develop tools for monitoring the metabolic effects of DF.

A dual mode breath sampler for the collection of the end-tidal and dead space fractions

This work presents a breath sampler prototype automatically collecting end-tidal (single and multiple breaths) or dead space air fractions (multiple breaths). This result is achieved by real time measurements of the CO2 partial pressure and airflow during the expiratory and inspiratory phases. Suitable algorithms, used to control a solenoid valve, guarantee that a Nalophan(®) bag is filled with the selected breath fraction even if the subject under test hyperventilates. The breath sampler has low pressure drop (<0.5 kPa) and uses inert or disposable components to avoid bacteriological risk for the patients and contamination of the breath samples. A fully customisable software interface allows a real time control of the hardware and software status. The performances of the breath sampler were evaluated by comparing (a) the CO2 partial pressure calculated during the sampling with the CO2 pressure measured off-line within the Nalophan(®) bag; (b) the concentrations of four selected volatile organic compounds in dead space, end-tidal and mixed breath fractions. Results showed negligible deviations between calculated and off-line CO2 pressure values and the distributions of the selected compounds into dead space, end-tidal and mixed breath fractions were in agreement with their chemical-physical properties.

Determination of sevoflurane and isopropyl alcohol in exhaled breath by thermal desorption gas chromatography-mass spectrometry for exposure assessment of hospital staff

Volatile anaesthetics and disinfection chemicals pose ubiquitous inhalation and dermal exposure risks in hospital and clinic environments. This work demonstrates specific non-invasive breath biomonitoring methodology for assessing staff exposures to sevoflurane (SEV) anaesthetic, documenting its metabolite hexafluoroisopropanol (HFIP) and measuring exposures to isopropanol (IPA) dermal disinfection fluid. Methods are based on breath sample collection in Nalophan bags, followed by an aliquot transfer to adsorption tube, and subsequent analysis by thermal desorption gas chromatography-mass spectrometry (TD-GC-MS). Ambient levels of IPA were also monitored. These methods could be generalized to other common volatile chemicals found in medical environments. Calibration curves were linear (r(2)=0.999) in the investigated ranges: 0.01-1000 ppbv for SEV, 0.02-1700 ppbv for IPA, and 0.001-0.1 ppbv for HFIP. The instrumental detection limit was 10 pptv for IPA and 5 pptv for SEV, both estimated by extracted ion-TIC chromatograms, whereas the HFIP minimum detectable concentration was 0.5 pptv as estimated in SIM acquisition mode. The methods were applied to hospital staff working in operating rooms and clinics for blood draws. SEV and HFIP were present in all subjects at concentrations in the range of 0.7-18, and 0.002-0.024 ppbv for SEV and HFIP respectively. Correlation between IPA ambient air and breath concentration confirmed the inhalation pathway of exposure (r=0.95, p<0.001) and breath-borne IPA was measured as high as 1500 ppbv. The methodology is easy to implement and valuable for screening exposures to common hospital chemicals. Although the overall exposures documented were generally below levels of health concern in this limited study, outliers were observed that indicate potential for acute exposures.

Breath analysis in disease diagnosis: methodological considerations and applications

Breath analysis is a promising field with great potential for non-invasive diagnosis of a number of disease states. Analysis of the concentrations of volatile organic compounds (VOCs) in breath with an acceptable accuracy are assessed by means of using analytical techniques with high sensitivity, accuracy, precision, low response time, and low detection limit, which are desirable characteristics for the detection of VOCs in human breath. "Breath fingerprinting", indicative of a specific clinical status, relies on the use of multivariate statistics methods with powerful in-built algorithms. The need for standardisation of sample collection and analysis is the main issue concerning breath analysis, blocking the introduction of breath tests into clinical practice. This review describes recent scientific developments in basic research and clinical applications, namely issues concerning sampling and biochemistry, highlighting the diagnostic potential of breath analysis for disease diagnosis. Several considerations that need to be taken into account in breath analysis are documented here, including the growing need for metabolomics to deal with breath profiles.

Design of a breath analysis system for diabetes screening and blood glucose level prediction

It has been reported that concentrations of several biomarkers in diabetics' breath show significant difference from those in healthy people's breath. Concentrations of some biomarkers are also correlated with the blood glucose levels (BGLs) of diabetics. Therefore, it is possible to screen for diabetes and predict BGLs by analyzing one's breath. In this paper, we describe the design of a novel breath analysis system for this purpose. The system uses carefully selected chemical sensors to detect biomarkers in breath. Common interferential factors, including humidity and the ratio of alveolar air in breath, are compensated or handled in the algorithm. Considering the intersubject variance of the components in breath, we build subject-specific prediction models to improve the accuracy of BGL prediction. A total of 295 breath samples from healthy subjects and 279 samples from diabetic subjects were collected to evaluate the performance of the system. The sensitivity and specificity of diabetes screening are 91.51% and 90.77%, respectively. The mean relative absolute error for BGL prediction is 21.7%. Experiments show that the system is effective and that the strategies adopted in the system can improve its accuracy. The system potentially provides a noninvasive and convenient method for diabetes screening and BGL monitoring as an adjunct to the standard criteria.

Diabetes and the metabolic syndrome: possibilities of a new breath test in a dolphin model

Diabetes type-2 and the metabolic syndrome are prevalent in epidemic proportions and result in significant co-morbid disease. Limitations in understanding of dietary effects and cholesterol metabolism exist. Current methods to assess diabetes are essential, though many are invasive; for example, blood glucose and lipid monitoring require regular finger sticks and blood draws. A novel method to study these diseases may be non-invasive breath testing of exhaled compounds. Currently, acetone and lipid peroxidation products have been seen in small scale studies, though other compounds may be significant. As Atlantic bottlenose dolphins (Tursiops truncatus) have been proposed as a good model for human diabetes, applications of dietary manipulations and breath testing in this population may shed important light on how to design human clinical studies. In addition, ongoing studies indicate that breath testing in dolphins is feasible, humane, and yields relevant metabolites. By studying the metabolic and cholesterol responses of dolphins to dietary modifications, researchers may gain insight into human diabetes, improve the design of costly human clinical trials, and potentially discover biomarkers for non-invasive breath monitoring.