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.

P-OGC39 The smell of oesophageal adenocarcinoma: opportunities for tests and treatments

Background Exhaled breath analysis is a promising approach for oesophageal adenocarcinoma (OAC) early detection. The biomarkers of interest are low molecular weight metabolites including volatile aldehydes. In this translational study we investigated whether these metabolites originated from a tumoral source, and how this might impact the diagnosis and treatment of OAC patients. Methods The investigative strategy was directed by an unbiased informatics screen of metabolic reprogramming in OAC, and validated using complimentary gene expression assays (n = 638, including controls). Mass spectrometric methods were used to quantify corresponding metabolites and putative source compounds at a tissue level (n = 158), and also in exhaled breath for correlative purposes. Targeted in vitro experiments were performed to demonstrate the cause and effect of the proposed model of metabolic reprogramming in OAC. Results The unbiased screen and subsequent validation found that reduced aldehyde detoxification is an OAC hallmark. In vitro and in vivo this was associated with endogenous aldehyde accumulation. OAC tissue was generally enriched for volatile aldehydes, including the genotoxins formaldehyde, acetaldehyde, 4-hydroxy-2-nonenal and 2-butenal, and the exhaled biomarker decanal (all P < 0.0001). Decanal concentrations correlated with exhaled concentrations. Considering potential aldehyde sources, the OAC phospholipidome was characterised by desaturated and longer lipid acyls, and these spontaneously generated biomarker aldehyde species at ambient conditions. Enriched genotoxic aldehydes were detectable in base-pairing positions in DNA; this genotoxicity was therapeutically targetable with aldehyde scavengers in vitro. Conclusions These data support a model for enriched exhaled aldehydes based on increased production from an altered lipid phenotype, and reduced detoxification. Some aldehydes are non-reactive and thus support non-invasive detection. Others react with DNA and increase local genotoxicity; this process is druggable. These findings have implications for OAC early diagnosis and chemoprevention.

Exhaled Breath Analysis for Diabetes Diagnosis and Monitoring: Relevance, Challenges and Possibilities

With the global population prevalence of diabetes surpassing 463 million cases in 2019 and diabetes leading to millions of deaths each year, there is a critical need for feasible, rapid, and non-invasive methodologies for continuous blood glucose monitoring in contrast to the current procedures that are either invasive, complicated, or expensive. Breath analysis is a viable methodology for non-invasive diabetes management owing to its potential for multiple disease diagnoses, the nominal requirement of sample processing, and immense sample accessibility; however, the development of functional commercial sensors is challenging due to the low concentration of volatile organic compounds (VOCs) present in exhaled breath and the confounding factors influencing the exhaled breath profile. Given the complexity of the topic and the skyrocketing spread of diabetes, a multifarious review of exhaled breath analysis for diabetes monitoring is essential to track the technological progress in the field and comprehend the obstacles in developing a breath analysis-based diabetes management system. In this review, we consolidate the relevance of exhaled breath analysis through a critical assessment of current technologies and recent advancements in sensing methods to address the shortcomings associated with blood glucose monitoring. We provide a detailed assessment of the intricacies involved in the development of non-invasive diabetes monitoring devices. In addition, we spotlight the need to consider breath biomarker clusters as opposed to standalone biomarkers for the clinical applicability of exhaled breath monitoring. We present potential VOC clusters suitable for diabetes management and highlight the recent buildout of breath sensing methodologies, focusing on novel sensing materials and transduction mechanisms. Finally, we portray a multifaceted comparison of exhaled breath analysis for diabetes monitoring and highlight remaining challenges on the path to realizing breath analysis as a non-invasive healthcare approach.

Simultaneous measurement of inhaled air and exhaled breath by “Double-MCC/IMS”. A new method for breath analysis. Results of a feasibility study

The high sensitivity of methods, which are applied in breath analysis, entails a high risk of detecting analytes which do not derive from endogenous production. Consequentially, it appears useful to have knowledge about the composition of inhaled air and to include alveolar gradients into interpretation.The current study aimed to standardise sampling procedures in breath analysis, especially with multicapillary column ion-mobility spectrometry (MCC-IMS), by applying a simultaneous registration of inhaled air and exhaled breath.A “Double MCC-IMS” device, which for the first time allows simultaneous analysis of inhaled air and exhaled breath, was developed and tested in 18 healthy individuals. For this two BreathDiscoverys® (BDs) were coupled with each other.Measurements of inhaled air and exhaled breath in 18 healthy individuals (mean age 46±10.9 years; 9 men, 9 women) identified 35 different volatile organic compounds (VOCs) for further analysis. Not all out of these had positive alveolar gradients and could be regarded as endogenous VOCs; 16 VOCs had a positive alveolar gradient in mean, 19 VOCs a negative one. 12 VOCs were positive in more than 12 of the healthy subjects.For the first time in our understanding a method is described, which enables simultaneous measurement of inhaled air and exhaled breath. This facilitates the calculation of alveolar gradients and selection of endogenous VOCs for exhaled breath analysis. Only a part of VOCs in exhaled breath are truly endogenous VOCs. The observation of different and varying polarities of the alveolar gradients needs further analysis.

MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.