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.

Indole as a new tentative marker in exhaled breath for non-invasive blood glucose monitoring of diabetic subjects

Blood glucose monitoring (BGM) is the most important part of diabetes management. In classical BGM, glucose measurement by test strips involves invasive finger pricking. We present results of a clinical study that focused on a non-invasive approach based on volatile organic compounds (VOCs) in exhaled breath. Main objective was the discovery of markers for prediction of blood glucose levels (BGL) in diabetic patients. Exhaled breath was measured repeatedly in 60 diabetic patients (30 type 1, 30 type 2) in fasting state and after a standardized meal. Proton Transfer Reaction Time of Flight Mass Spectrometry (PTR-ToF-MS) was used to sample breath every 15 minutes for a total of six hours. BGLs were tested in parallel via BGM test strips. VOC signals were plotted against glucose trends for each subject to identify correlations. Exhaled indole (a bacterial metabolite of tryptophan) showed significant mean correlation to BGL (with negative trend) and significant individual correlation in 36 patients. The type of diabetes did not affect this result. Additional experiments of one healthy male subject by ingestion of lactulose and 13C-labeled glucose (n=3) revealed that exhaled indole does not directly originate from food digestion by intestinal microbiota. As indole has been linked to human glucose metabolism, it might be a tentative marker in breath for non-invasive BGM. Clinical studies with greater diversity are required for confirmation of such results and further investigation of metabolic pathways.

Inactivation of SARS-CoV-2 in clinical exhaled breath condensate samples for metabolomic analysis

Exhaled breath condensate (EBC) is routinely collected and analyzed in breath research. Because it contains aerosol droplets, EBC samples from SARS-CoV-2 infected individuals harbor the virus and pose the threat of infectious exposure. We report for the first time a safe and consistent method to fully inactivate SARS-CoV-2 in EBC samples and make EBC samples safe for processing and analysis. EBC samples containing infectious SARS-CoV-2 were treated with several concentrations of acetonitrile. The most commonly used 10% acetonitrile treatment for EBC processing failed to completely inactivate the virus in samples and viable virus was detected by the assay of SARS-CoV-2 infection of Vero E6 cells in a BSL-3 laboratory. Treatment with either 50% or 90% acetonitrile was effective to completely inactivate the virus, resulting in safe, non-infectious EBC samples that can be used for metabolomic analysis. Our study provides SARS-CoV-2 inactivation protocol for the collection and processing of EBC samples in the clinical setting and for advancing to metabolic assessments in health and disease.

Levels of end-tidal carbon dioxide are low despite normal respiratory rate in individuals with Long COVID

N/A - Letter to the Editor

Differentiation of aspirated nasal air from room air using analysis with a differential mobility spectrometry-based electronic nose: a proof-of-concept study

Over the last few decades, breath analysis using electronic nose technology has become a topic of intense research, as it is both non-invasive and painless, and is suitable for point-of-care use. To date, however, only a few studies have examined nasal air. As the air in the oral cavity and the lungs differs from the air in the nasal cavity, it is unknown whether aspirated nasal air could be exploited with electronic nose technology. Compared to traditional electronic noses, differential mobility spectrometry uses an alternating electrical field to discriminate the different molecules of gas mixtures, providing analogous information. This study reports the collection of nasal air by aspiration and the subsequent analysis of the collected air using a differential mobility spectrometer. We collected nasal air from ten volunteers into breath collecting bags and compared them to bags of room air and the air aspirated through the device. Distance and dissimilarity metrics between the sample types were calculated and statistical significance evaluated with Kolmogorov-Smirnov test. After leave-one-day-out cross-validation, a shrinkage linear discriminant classifier was able to correctly classify 100% of the samples. The nasal air differed (p < 0.05) from the other sample types. The results show the feasibility of collecting nasal air by aspiration and subsequent analysis using differential mobility spectrometry, and thus increases the potential of the method to be used in disease detection studies.

Breath biomarkers of total body irradiation in non-human primates

Background: Radiation exposure causes oxidative stress, eliciting production of metabolites that are exhaled in the breath as volatile organic compounds (VOCs). We evaluated breath VOCs as potential biomarkers for use in radiation biodosimetry. Methods: Five anesthetized non-human primates receive total body irradiation (TBI) of three daily fractions of 120 cGy per day for three days, resulting in a cumulative dose of 10.8 Gy. Breath samples were collected prior to irradiation and after each radiation fraction, and analyzed with gas chromatography mass spectrometry. Results: TBI elicited a prompt and statistically significant increase in the abundance of several hundred VOCs in the breath, including some that were increased more than five-fold, with100% sensitivity and 100% specificity for radiation exposure. The most significant breath VOC biomarkers of radiation mainly comprised straight-chain n-alkanes (e.g. hexane), as well as methylated alkanes (e.g. 3-methyl-pentane) and alkane derivatives (e.g. 2-butyl-1-octanol), consistent with metabolic products of oxidative stress. An unidentified breath VOC biomarker increased more than ten-fold following TBI, and rose linearly with the total cumulative dose of radiation (R2=0.92). Conclusions: TBI of non-human primates elicited increased production of breath VOCs consistent with increased oxidative stress. These findings provide a rational basis for further evaluation of breath VOC biomarkers in human radiation biodosimetry.

Noninvasive real-time breath test for controlling hormonal background of the human body: Detection of serotonin and melatonin with quantum point-contact sensors

Significant progress in development of noninvasive diagnostic tools based on breath analysis can be expected if one employs a real-time detection method based on finding a spectral breath profile which would contain some energy characteristics of the analyzed gas mixture. Using the fundamental energy parameters of a quantum system, it is possible to determine with a high accuracy its quantitative and qualitative composition. Among the most efficient tools to measure energy characteristics of quantum systems are sensors based on Yanson point contacts. This paper reports the results of serotonin and melatonin detection as an example of testing the human hormonal background with point-contact sensors, which have already demonstrated their high efficiency in detecting carcinogenic strains of Helicobacter pylori and selective detection of complex gas mixtures. When comparing the values of serotonin and melatonin with the characteristic parameters of the spectral profile of the exhaled breath of each patient, high correlation dependences of the concentration of serotonin and melatonin with a number of characteristic parameters of the response curve of the point-contact sensor were found. The performed correlation analysis was complemented with the regression analysis. As a result, empiric regression relations were proposed to realize in practice the new non-invasive breath test for evaluation of the human hormonal background. Registration of the patient’s breath profile using point-contact sensors makes it possible to easily monitor the dynamics of changes in the human hormonal background and perform a quantitative evaluation of serotonin and melatonin levels in the human body in real time without invasive interventions (blood collection) and expensive equipment or reagents.

Clinical implications of concentration of alveolar nitric oxide in asthmatic and non-asthmatic subacute cough

Background and objective: Asthma is one of the important causes of subacute cough. Concentration of alveolar nitric oxide (CANO) is a sensitive inflammatory indicator of peripheral airways, which has received much less attention than fraction of exhaled nitric oxide (FeNO50). The main objective of this study was to explore the correlation between CANO and clinical parameters in asthmatic and non-asthmatic subacute cough, which might promote understanding the clinical utility of CANO in these special patient population. Materials and methods: 155 patients with subacute cough were included consecutively, of which 25 were diagnosed as asthma. Data for demographic characteristics, FeNO50, CANO, baseline spirometry, bronchial provocation test (or bronchodilation test) and response dose ratio (RDR) was collected. Differences between asthmatic and non-asthmatic group were analyzed. Spearman’s correlation coefficient (rho) was used to evaluate the correlation between FeNO50, CANO and other clinical parameters. Results: In patients with subacute cough, baseline CANO values did not differ between asthmatic and non-asthmatic patients (4.4(1.3, 11.4) versus 4.0(2.1, 6.8) ppb, P>0.05). Besides, CANO exhibited stronger association with pulmonary function parameters when compared with FeNO50. For asthmatic subacute cough, CANO was inversely correlated with FEV1/FVC (rho=-0.69, P<0.01) and small airway parameters including MEF25 (rho=-0.47, P<0.05) and MMEF (rho=-0.45, P<0.05). For non-asthmatic subacute cough, CANO was inversely correlated with MEF25 (rho=-0.19, P<0.05) and RDR (rho=-0.21, P<0.05). Conclusion: In subacute cough, asthmatic and non-asthmatic patients had similar values of baseline CANO. In both asthmatic and non-asthmatic subacute cough, CANO exhibited stronger association with pulmonary function parameters when compared with FeNO50. A low CANO value in non-asthmatic subacute cough corresponded to a higher value of RDR, which implied stronger tendency towards airway responsiveness.

Breath acetone concentration: too heterogeneous to constitute a diagnosis or prognosis biomarker in heart failure? A systematic review and meta-analysis

Introduction: Exhaled breath acetone (ExA) has been investigated as a biomarker for heart failure (HF). Yet, barriers to its use in the clinical field have not been identified. The aim of this systematic review and meta-analysis was to assess the ExA heterogeneity and factors of variability in healthy controls (HC), to identify its relations with HF diagnosis and prognostic factors and to assess its diagnosis and prognosis accuracy in HF patients. Methods: A systematic search was conducted in PUBMED and Web of Science database. All studies with HC and HF patients with a measured ExA were included and studies providing ExA’s diagnosis and prognosis accuracy were identified. Results: Out of 971 identified studies, 18 studies involving 833 HC and 1009 HF patients were included in the meta-analysis. In HC, ExA showed an important heterogeneity (I²=99%). Variability factors were fasting state, sampling type and analytical method. The mean ExA was 1.89 times higher in HF patients vs. HC (782 [531-1032] vs. 413 [347-478] ppbv; p<0.001). One study showed excellent diagnosis accuracy, and one showed a good prognosis value. ExA correlated with New York Heart Association (NYHA) dyspnea (p<0.001) and plasma brain natriuretic peptide (p<0.001). Studies showed a poor definition and reporting of included subjects. Discussion: Despite the between-study heterogeneity in HC, the evidence of an excellent diagnosis and prognosis value of ExA in HF from single studies can be extended to clinical populations worldwide. Factors of variability (ExA procedure and breath sampling) could further improve the diagnosis and prognosis values of this biomarker in HF patients.

The variability of volatile organic compounds in the indoor air of clinical environments

The development of clinical breath-analysis is confounded by the variability of background volatile organic compounds (VOC). Reliable interpretation of clinical breath-analysis at individual, and cohort levels requires characterisation of clinical-VOC levels and exposures. Active-sampling with thermal-desorption/gas chromatography-mass spectrometry recorded and evaluated VOC concentrations in 245 samples of indoor air from three sites in a large NHS provider trust in the UK over 27 months. Data deconvolution, alignment and clustering isolated 7344 features attributable to VOC and described the variability (composition and concentration) of respirable clinical VOC. 328 VOC were observed in more than 5% of the samples and 68 VOC appeared in more than 30% of samples. Common VOC were associated with exogenous and endogenous sources and 17 VOC were identified as seasonal differentiators. The presence of metabolites from the anaesthetic sevoflurane, and putative-disease biomarkers in room air, indicated that exhaled VOC were a source of background-pollution in clinical breath-testing activity. With the exception of solvents, and PPE waxes, exhaled VOC concentrations above 3 µg m-3 are unlikely to arise from room air contamination, and in the absence of extensive survey-data, this level could be applied as a threshold for inclusion in studies, removing a potential environmental confounding-factor in developing breath-based diagnostics.