Search Results (151)

Background: Type 2 diabetes (T2D) and insulin resistance (IR) are major global health challenges. Volatile organic compounds (VOCs) in exhaled breath offer a non-invasive window into metabolic dysregulation. This study aimed to predict HOMA-IR using machine learning (ML) by integrating biochemical markers and VOC profiles in a male cohort. Methods: This cross-sectional study included 1258 male participants from the Taiwan MJ cohort. Four ML algorithms (Elastic Net, MARS, Random Forest, and XGBoost) were trained to predict HOMA-IR. Model performance was evaluated using R², RMSE, and MAE. SHAP analysis was used to interpret feature contributions. Results: Ensemble tree-based approaches (Random Forest and XGBoost) demonstrated better predictive performance than Elastic Net and MARS. Random Forest achieved the highest predictive performance on the test set (R² = 0.323). SHAP analysis identified BMI (mean |SHAP| = 1.326) as the strongest predictor, followed by TG (1.005) and HDL-C (0.445). Notably, specific breath VOCs, including methanol and formic acid, ranked among the top 20 predictors, capturing distinct aspects of metabolic dysregulation orthogonal to standard blood tests. Conclusions: Integrating VOC profiles with clinical markers provides acceptable predictive performance for IR in men. While traditional metabolic markers dominate the prediction, specific VOCs capture distinct metabolic information, highlighting the potential of breath analysis as a complementary early screening tool. Full article

Non-invasive blood glucose estimation from exhaled breath has been proposed as a painless alternative to repeated capillary measurements; however, performance evaluation remains challenging in small-sample settings. This study investigates the estimation of blood glucose from human breath using volatile organic compound (VOC) signals acquired with an electronic nose. Responses from three metal-oxide sensor channels sensitive to CO, alcohol, and acetone were collected from 58 individuals, with one measurement per subject, and analyzed using strictly patient-level five-fold cross-validation, in which test folds comprised only real subjects. Two experimental factors were examined. First, model performance was evaluated with and without an additional interpretable alcohol–acetone log-ratio capturing relative variation between compounds. Second, model training was performed using either real data only or fold-wise tabular synthetic augmentation generated via a Gaussian copula fitted exclusively on training subjects, while evaluation remained strictly real-only. Under real-only training, classical machine learning models achieved the lowest prediction errors (approximately 6–7 mg/dL), whereas under synthetic augmentation FTTransformer was the best-performing deep learning model. This findings should be understood as a constrained proof-of-concept analysis rather than as evidence of diagnostic capability or clinical readiness. Full article

Student mental health and academic stress are increasingly addressed through digital monitoring, yet evidence for personalized physiological thresholds based on exhaled VOCs, their in-the-wild feasibility, and their trigger–experience correspondence in everyday student life remains limited. This study examines whether exhaled breath signals can support personalized, real-world stress monitoring in university students using a two-stage design that moves from laboratory calibration to daily life validation. A total of 24 university students took part in the laboratory phase (Study 1; N = 24). Under two stress tasks, a social-conflict video task and a Stroop task, we derived an individualized breath-trigger threshold (θ_i) for each participant. We then invited 21 of them to join a three-day field deployment (Study 2; N = 21). Each participant’s θ_i from Study 1 was used directly as the trigger threshold for daily monitoring in order to test the association between trigger events and subjectively noticeable emotional deviations and to assess preliminary trigger–experience correspondence in daily life. The results show that 78.6% of paired trigger–EMA records were rated as subjectively salient, with 93.9% of these rated at medium-to-high intensity. These events occurred most frequently during study/work activities (60.6%), in dorm/home settings (57.6%), and when participants were alone (63.6%), suggesting that the triggers captured personally meaningful emotional episodes embedded in routine academic life rather than random physiological fluctuations. Overall, this study presents a portable breath-based emotion sampling device for student academic contexts and a reproducible protocol that combines laboratory thresholding with daily life validation. The findings provide preliminary and exploratory indications of the feasibility and within-person transferability of VOC-based emotion detection in students, and offer methodological support for future digital emotion monitoring and intervention design based on breath signals. Full article

Diagnostic latency limits time-sensitive care and early detection, and exhaled breath provides a rapid, repeatable window into metabolic and inflammatory chemistry. We review real-time breath sampling and analytical technologies and evaluate their readiness for clinical adoption, with emphasis on molecular pathways reflected in the breath volatilome and in exhaled breath condensate. Real-time mass spectrometry enables kinetic VOC profiling and targeted quantification, while humidity-aware sensors and wearable condensate platforms extend monitoring beyond the laboratory. Pathway-anchored interpretation links breath readouts to ketone handling, isoprenoid metabolism, nitric oxide signaling, lipid peroxidation, uremic nitrogen handling, and microbiome–host co-metabolism, but performance remains vulnerable to confounding, drift, and non-representative comparators. Translation requires standardized breath fraction control, traceable features, robust quality systems, and governed device algorithm stacks so that breath outputs inform decisions and outcomes. Full article

Chronic obstructive and inflammatory lung diseases share overlapping clinical manifestations and spirometric features, complicating differential diagnosis and monitoring. In this study, we performed an integrative real-time proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOF-MS) breathomics analysis to assess whether exhaled volatile organic compound (VOC) profiles enable multiclass discrimination among bronchial asthma (BA), chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), and lymphangioleiomyomatosis (LAM), with healthy individuals as controls. Breath VOC data from 843 subjects were analyzed using a stratified 70/30 train/test split. An ensemble feature selection strategy based on gradient boosting (XGBoost with SMOTE within cross-validation) identified stable VOC panels (top 25% selection probability), yielding 29 VOCs and 31 features including clinical covariates. On the independent test set, the VOC-only model achieved a macro-averaged one-vs-one (OvO) AUC of 0.866 (95% CI 0.829–0.903), while the combined model improved to 0.888 (95% CI 0.853–0.919), indicating modest value of clinical variables. Pairwise analysis demonstrated highest discrimination for CF (AUC up to 0.988), whereas BA and LAM showed lower sensitivity (<0.60), likely reflecting heterogeneity and limited sample size. Given differences in age, sex, BMI, and smoking status across cohorts, confounding effects were assessed, confirming that VOC signatures retain independent diagnostic information. Disease-specific VOC interaction networks revealed distinct remodeling patterns, with central metabolites not captured by univariate analysis. Overall, PTR-TOF-MS breathomics demonstrates proof-of-concept multiclass discrimination across chronic lung diseases. Full article

Secondary electrospray ionization mass spectrometry (SESI-MS) has emerged as a powerful technique for the real-time, non-invasive analysis of volatile organic compounds (VOCs) in complex matrices, such as exhaled breath and microbial volatilomes. However, its transition to routine application is hindered by significant challenges in absolute quantification, unambiguous identification, and standardization. This review provides a comprehensive overview of these limitations and the emerging solutions proposed to overcome them. Matrix effects, including gas-phase ion suppression and C-trap competition, are examined alongside mitigation strategies such as spectral stitching and standard addition. To enhance quantification stability, advanced standard delivery systems and dynamic quality control protocols are evaluated. The identification bottleneck—stemming from the absence of chromatographic separation—is addressed through the use of curated databases and advanced fragmentation techniques, such as incremental quadrupole acquisition to resolve overlapping spectra (IQAROS), to resolve isobaric interferences. Furthermore, the role of chemometrics in extracting biological fingerprints is discussed. Finally, the need for harmonized reporting standards and multicenter validation is emphasized to ensure cross-study reproducibility. Resolving these methodological gaps is essential for the clinical and industrial translation of SESI-MS. Full article

Exhaled breath analysis has gained considerable interest as a noninvasive diagnostic tool capable of detecting volatile organic compounds (VOCs) and inorganic gases that serve as biomarkers for various diseases. Polymer-based gas sensors have garnered significant attention due to their high sensitivity, room-temperature operation, excellent flexibility, and tunable chemical properties. This review comprehensively summarized recent advancements in polymer-based gas sensors for the detection of disease biomarkers in exhaled breath. The gas-sensing mechanism of polymers, along with novel gas-sensitive materials such as conductive polymers, polymer composites, and functionalized polymers was examined in detail. Moreover, key applications in diagnosing diseases, including asthma, chronic kidney disease, lung cancer, and diabetes, were highlighted through detecting specific biomarkers. Furthermore, current challenges related to sensor selectivity, stability, and interference from environmental humidity were discussed, and potential solutions were proposed. Future perspectives were offered on the development of next-generation polymer-based sensors, including the integration of machine learning for data analysis and the design of electronic-nose (e-nose) sensor arrays. Full article

Current gene-based PCR diagnostics involving reverse-transcription polymerase chain reaction (RT-PCR) require at least several hours, expensive tools, and complicated sample collection methods to obtain results. A test for detecting volatile organic compounds (VOCs) in exhaled breath is advantageous as a simple, non-invasive, and fast screening method. In this study, a VOC detection system of array sensors was applied for the classification of breath control and COVID-19 virus infection. The ability to classify VOCs in the breath with COVID-19 virus infection has been studied with two metal-oxide (MOX) gas sensor arrays, commercially available sensors, and in-house sensors. The dataset of gas response signals from the array-type semiconductive gas sensors of the VOC detection system was analyzed using machine learning; principal component analysis (PCA) was used as a dimensionality-reduction method, and random forest (RF) and a convolutional neural network (CNN) were used as classification methods for the VOC concentration patterns in each breath. For the RF model, the accuracy results for the classification by two gas sensor arrays was 0.917 and this was improved by CO₂ calibration to 0.967, and the feature importance analysis revealed the importance of specific gas sensors. For the CNN, an input layer of a transformed gray-scale image with the shape of 12 data points × 8 sensors was used, and its accuracy reached 100% within a relatively small number of epochs, demonstrating a short training time, which is beneficial for breath detectors or e-nose devices. Full article

Gas sensors provide versatile opportunities for detecting volatile organic compounds (VOCs) such as acetone, methanol, ethanol, propanol, isoprene, and aldehydes in exhaled breath (EB) associated with COVID-19 respiratory infections. These VOCs provide valuable information about metabolic markers linked with COVID-19. They have opened opportunities to develop sensors for COVID-19 screening based on breath analysis. These sensors have the potential to provide the rapid detection of viruses in healthcare settings. RT-PCR, as a conventionally adopted diagnostic method, has a detection limit around 10–100 RNA copies/mL, with an accuracy of around 95%. Gas sensors have demonstrated VOC detection limits at the ppm level in COVID-19 EB and have displayed a sensitivity and specificity of 98.2% and 74.3%, respectively. Multiple gas sensors combined with machine learning algorithms have the potential to enhance the specificity of VOC detection. In addition to having an accuracy similar to that of the PCR method, the VOC-based diagnosis of COVID-19 offers unique advantages in terms of non-invasive and rapid detection. This review provides an overview of state-of-the-art gas sensors developed for COVID-19 detection. Despite there being significant developments in this field, there are certain challenges that still need to be addressed—these include the impact of environmental factors, the specificity of detection, the sensing range, and precision limitations, leading to accuracy issues. Despite these existing challenges, the integration of gas sensors with machine learning methods can enhance the accuracy of the detection of COVID-19. Future research directions are proposed to validate and standardize the application of gas sensors for COVID-19 in clinical settings. Full article

Environmental exposures throughout the life of the subjects (exposome) could have a negative effect on their health outcomes. From this perspective, analysis of volatile organic compounds (VOCs) in human exhaled breath is emerging as a non-invasive tool to identify and check exposure to harmful agents. Breath analysis is also a helpful technique for human metabolism assessment, which allows for examining the impact of environmental exposures on organisms (biomonitoring). In this paper, a comprehensive review has been carried out to assess the use of breath analysis by mass spectrometry-based technologies for monitoring environmental exposures. Records of the last 20 years from three databases (PubMed/Medline, Scopus, and Web of Science) have been evaluated independently by two reviewers. A total of 38 studies fulfilled the criteria for eligibility. It has been compiled information about environmental exposures that have been monitored by breath analysis using mass spectrometry-based analytical platforms, as well as the most commonly used protocols of breath sampling, analytical techniques, and statistical methods. In addition, special emphasis has been placed on the huge range of VOCs selected as potential markers of environmental exposures. Despite the potential of breath analysis for monitoring human exposure, further research is needed to identify useful markers to establish it as a routine tool. Full article

Exhaled breath analysis is emerging as one of the most promising non-invasive strategies for the early detection of life-threatening diseases, especially lung cancer, where rapid and reliable diagnosis remains a major clinical challenge. In this study, we designed and optimized an electronic nose (e-nose) platform composed of quantum resistive vapor sensors (vQRSs) engineered by polymer-carbon nanotube nanocomposites via spray layer-by-layer assembly. Each sensor was tailored through specific polymer functionalization to tune selectivity and enhance sensitivity toward volatile organic compounds (VOCs) of medical relevance. The sensor array, combined with linear discriminant analysis (LDA), demonstrated the ability to accurately discriminate between cancer-related biomarkers in synthetic blends, even when present at trace concentrations within complex volatile backgrounds. Beyond artificial mixtures, the system successfully distinguished real exhaled breath samples collected under challenging conditions, including before and after smoking and alcohol consumption. These results not only validate the robustness and reproducibility of the vQRS-based array but also highlight its potential as a versatile diagnostic tool. Overall, this work underscores the relevance of nanocomposite chemo-resistive arrays for breathomics and paves the way for their integration into future portable e-nose devices dedicated to telemedicine, continuous monitoring, and early-stage disease diagnosis. Full article

Background: Early cancer detection remains a critical challenge in clinical oncology, requiring further development of innovative diagnostic methods with improved sensitivity and specificity. This study addresses the issue by investigating the potential of exhaled air metabolome analysis, using highly porous carbon material for sample collection, as a promising approach for the early diagnosis of laryngeal cancer. Volatile organic compounds (VOCs) present in exhaled breath are known to reflect underlying metabolic changes. This research explores the feasibility of using VOC-derived metabolomic signatures as non-invasive biomarkers for cancer detection. Methods: The primary objective was to evaluate exhaled air metabolome analysis as a diagnostic tool for individuals at risk of respiratory tract malignancies. The study involved 36 participants, including 13 patients diagnosed with laryngeal cancer and 23 healthy individuals serving as a control group. Breath samples were collected using a highly porous carbon material, selected for its superior sorption properties, enabling efficient capture and stabilization of VOCs. These samples were subsequently analyzed using gas chromatography-mass spectrometry (GC-MS) to identify and compare VOC patterns between the two groups. Results: Preliminary analysis revealed apparent differences in VOC profiles between cancer patients and healthy individuals, with cancer patients exhibiting elevated peak intensities for specific metabolites such as diethyl phthalate, nonadecane, and trimethyl-dodecane. Multivariate analysis using principal component analysis (PCA) demonstrated separation between the two groups, reflecting systematic differences in exhaled VOC signatures. Conclusions: This initial study supports the potential of breath VOC profiling for laryngeal cancer detection, laying the groundwork for further validation and refinement of this diagnostic approach. The use of porous carbon material facilitated efficient VOC capture, supporting its role in non-invasive breath analysis. Full article

Background: Asthma and chronic obstructive pulmonary disease (COPD) are prevalent obstructive lung diseases with distinct inflammatory pathways but overlapping clinical features. Bronchodilator responsiveness (BDR) is a key diagnostic criterion, yet its metabolic determinants are poorly understood. Objective: This cross-sectional study investigated whether integrated profiling of exhaled volatile organic compounds (VOCs) and clinical biomarkers can differentiate BA, COPD, and health, and predict BDR. Methods: Exhaled breath from 160 BA patients, 128 COPD patients, and 254 healthy controls was analyzed in real-time using proton-transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) during tidal and forced expiration. Clinical assessment included spirometry, fractional exhaled nitric oxide (FeNO), blood eosinophil count, and total IgE. Machine-learning (XGBoost) was employed for feature selection and model development. Results: Distinct VOC signatures effectively discriminated disease groups from controls and from each other. The model for distinguishing asthma from healthy controls achieved an AUC of 0.747 during normal quiet breathing and 0.710 during forced exhale. For discriminating COPD from healthy controls, the model performance was higher, with an AUC of 0.821 for normal quiet breathing and 0.856 for forced exhale. A model integrating VOC profiles with clinical biomarkers (FeNO, eosinophils, IgE) demonstrated very high accuracy in internal validation in predicting BDR (AUC = 1.000 for tidal breathing; AUC = 0.970 for forced expiration). Specific mass spectral features (m/z 79, m/z 101) were significantly associated with a positive BDR test. Conclusions: This study delineates disease-specific VOC signatures and underscores the profound synergy between exhaled metabolomics and clinical immunology for identifying associations treatment response, advocating for the integration of real-time breath analysis into personalized management strategies for obstructive lung diseases. Full article

Neurodegenerative diseases (NDDs) are a group of progressive diseases affecting neuronal cells in specific areas of the brain, causing cognitive decline and movement impairment. Nowadays, NDDs play a significant role in the global burden of disease, and their incidence is increasing, particularly due to population aging. NDD onset is multi-factorial; based on the current knowledge, genetic, environmental, and cellular factors are believed to contribute to their occurrence and progression. Taking into account that at an early stage, the symptoms are not clearly defined, and diagnosis may be delayed, the development of innovative and non-invasive methodological approaches for early diagnosis of NDDs is strategic for timely and tailored disease management, as well as for the overall improvement of patients’ quality of life. The present review aims to provide, in the first part, an overview based on the current level of knowledge on the environmental risk factors that can explicate a role in the onset of the most common NDDs and on the main pathogenic mechanisms involved in disease initiation and progression. The second part aims to define the current state of the art regarding the significance of Volatile Organic Compounds (VOCs) in the volatome of different human biological matrices (exhaled breath, feces, and skin sebum) as candidate biomarkers of specific NDDs, with the aim of developing non-invasive diagnostic approaches for the early diagnosis and personalized management of the patients. A critical synthesis and discussion on the applied methodological approaches and on the relevant outcomes obtained across the studies is reported. Full article

Exhaled breath (EB) contains numerous volatile organic compounds (VOCs) that can reflect pathological metabolic processes, making breath analysis a promising non-invasive diagnostic approach. In particular, volatile aldehydes and ketones have been identified as disease biomarkers in EB. Gas sensors are expected to play a crucial role in the diagnosis of numerous diseases at an early stage. Among the various available approaches, sensors stand out as especially attractive tools for diagnosing diseases such as lung cancer (LC) and diabetes, due to their affordability and operational simplicity. There is an urgent need in the field of disease detection for the development of affordable, non-invasive, and user-friendly sensors capable of detecting various biomarkers. Devices of the new generation should also demonstrate high repeatability of measurements and extended operational stability of the employed sensors. Due to these demands, the past few years have seen significant advancements in the development and implementation of electronic noses (ENs), which are composed of an array of sensors for the determination of VOCs present in EB. To meet these requirements, the development and integration of advanced receptor coatings on sensor transducers is essential. These coatings include nanostructured materials, molecularly imprinted polymers, and bioreceptors, which collectively enhance selectivity, sensitivity, and operational stability. However, reliable biomarker detection in point-of-care (PoC) mode remains a significant challenge, constrained by several factors. This review provides a comprehensive and critical evaluation of recent studies demonstrating that the detection of VOCs using gas sensor platforms enables disease detection and can be implemented in PoC mode. Full article