Journal of breath research最新文献

Interstitial lung disease (ILD) encompasses pulmonary disorders characterised by varying degrees of inflammation and/or fibrosis. The presence and extent of these pulmonary abnormalities on (high-resolution) computed tomography (CT) have consequences for diagnosis and treatment; however, inter-observer assessment varies. Analysis of exhaled volatile organic compounds (VOCs) through gas chromatography-mass spectrometry (GC-MS) offers a noninvasive approach to biomarker discovery and pathophysiology understanding. Our study aims to explore the ability of GC-MS-driven exhaled breath analysis to differentiate ILD patients with predominant fibrotic, inflammatory, or a combination of fibrotic and inflammatory pulmonary abnormalities in a training and an external validation cohort. In a multicentre cross-sectional study, patients diagnosed with ILD were recruited. After central review of chest CT scans by independent radiologists, patients were categorised as fibrotic, inflammatory or mixed phenotype group based on the percentage of chest CT scan abnormalities. Breath samples were collected and analysed via GC-MS. Significantly different VOC fragments between groups were selected and used to differentiate groups in the training cohort with sparse partial least squares discriminant analysis. Analyses were validated with patients from an external cohort. 53 patients were included, 21 patients in the fibrotic, 14 in the inflammatory and 18 in the mixed phenotype group. Area under the curve (AUCs) for discrimination between groups ranged from 0.89-1.00 in training cohorts. An attempt to confirm these findings in our external validation cohort resulted in AUCs of 0.63-0.84. Re-evaluation of the training model led to an AUC of 0.78-0.83. This study shows that GC-MS driven exhaled breath analysis towards differentiation of ILD phenotypes is challenging. Current findings emphasise the importance of predefined validation steps during the process of biomarker discovery.

Chronic obstructive pulmonary disease (COPD) is a complex, progressive inflammatory disorder characterized by airflow limitation and respiratory symptoms. Its heterogeneity is manifested at etiological, pathological and clinical levels, and leads to different phenotypes: chronic bronchitis, emphysema, asthma-COPD overlap, frequent exacerbator and eosinophilic phenotypes. COPD is also associated with systemic manifestations including cardiovascular diseases, muscle dysfunction, osteoporosis and mental-health issues, which require a comprehensive management approach. Key risk factors are tobacco smoke and air pollution, both of which induce oxidative stress and airway remodeling. Although there is still no definitive cure for COPD, an early diagnosis and a multidisciplinary treatment are essential to prevent or slow the disease progression and reduce the mortality rate. Molecular biomarkers, particularly those identified through metabolomics, show promise for early detection, phenotyping and precision therapies. Challenges in biomarker discovery include specimen variability and stability. Overall, metabolomics provides valuable insights into COPD's molecular pathways, supporting improved diagnosis, prognosis and tailored treatments. In this tutorial, we will explore metabolomics findings from different COPD matrices and their clinical implications for diagnosis, treatment and prognosis.

Background: Despite major progress in diagnosis and treatment, cardiovascular disease (CVD) continues to be the leading cause of death worldwide, responsible for roughly 19.8 million lives lost each year. A key challenge in preventive cardiology is still the early detection of those at elevated risk of serious heart complications. Aims: Assess the ability of the machine learning model to stratify CVD risk using exhaled breath analysis. Materials and methods: A single-center study involved 80 participants with vs. without stress-induced myocardial perfusion defect. All participants underwent a single resting breath sample collection in PTR-TOF-MS-1000, single blood sample intake, and stress computed tomography myocardial perfusion imaging with vasodilation test. Statistical analyses were performed using Statistica 12 (StatSoft, Inc., 2014), IBM SPSS Statistics v29.0.1.1 (IBM Corp., 2024). The threshold for statistical significance was p < 0.05. Machine learning models were developed using Google Colab with Python 3. Results: The gradient-boosting model demonstrated the best performance and was therefore selected for further evaluation. The model showed an AUC of 0.77 [95% CI; 0.4976 - 1.0000] to differentiate participants with low CVD risk, moderate risk 0.55 [95% CI; 0.3345 - 0.7875], and high risk 0.66 [95% CI; 0.3765 - 0.8661]. Conclusion: The gradient boosting machine learning model provides initial evidence that rest exhaled breath analysis can differentiate cardiovascular risk strata through identifiable concentration patterns of specific volatile organic compounds. However, substantial challenges remain regarding model performance and the confounding effects of class imbalance within a limited sample. .

To evaluate the therapeutic efficacy of probiotics in managing halitosis and to determine the optimal intervention strategies. An extensive search was conducted in PubMed, Web of Science, Cochrane Library, and Embase up to December 2024, focusing on studies evaluating probiotic interventions for halitosis. Sensitivity and subgroup analyses were undertaken to assess the robustness of the results and to explore potential sources of heterogeneity. All analyses were performed through Review Manager 5.4 and STATA 15.0. Of the 194 records initially identified, 10 studies met the predefined criteria. The pooled results demonstrated a significant reduction in volatile sulfur compounds (VSCs) levels in the probiotic group compared to controls (SMD = - 1.01, 95% CI [-1.93, -0.09],P= 0.03). Likewise, the organoleptic test (OLT) scores showed a marked improvement in the probiotic group (RR = 1.31, 95% CI [1.22, 1.41],P< 0.00001). Nevertheless, no substantial differences were observed between groups in oral health-related quality of life (SMD = 0.21, 95% CI [-0.06, 0.49],P= 0.12), subjective oral health status (SMD = - 0.04, 95% CI [-0.35, 0.28],I²= 0%,p= 0.74), depression (SMD = 0.03, 95% CI [-0.29, 0.35],I²= 0%,p= 0.85), self-esteem (SMD = - 0.07, 95% CI [-0.39, 0.25],I²= 0%,p= 0.67), OLT scores (SMD = - 0.24, 95% CI [-0.64, 0.16],I²= 0%,p= 0.24), or plaque index (SMD = - 0.06, 95% CI [-0.57, 0.46],I²= 0%,p= 0.82). The findings suggest that probiotic therapy, when combined with conventional treatments, may be more effective in enhancing OLT scores and reducing VSC levels in individuals with halitosis than using probiotic alone. Nonetheless, potential publication bias, limited sample sizes, and heterogeneity among the included clinical trials may affect the reliability of these conclusions.

Silicosis is a prevalent chronic occupational disease, causing incurable damage to the lungs. The conventional methods for diagnosing silicosis are costly and complex. Noninvasive biomarker studies based on exhalation metabolomics have potential in the early diagnosis of silicosis, but existing studies remain scarce and especially lack the biomarkers for staging diagnosis. Exhaled breath from 74 healthy controls and 112 patients, including 28 stage Ⅰ silicosis patients (SILs), 22 stage Ⅱ SILs, 52 stage ࡲ SILs and 10 coal workers' pneumoconiosis patients, were detected using solid-phase microextraction incorporating gas chromatography-mass spectrometry for the identification of the volatile metabolites. The univariate statistical analysis and orthogonal partial least squares-discriminant analysis were employed to screen potential biomarkers of SILs, with diagnostic performance assessed with the receiver-operating characteristic (ROC) curve and decision tree model. Fourteen volatile metabolites were found to distinguish different stages of SILs from healthy controls, and 8 metabolites differentiating stage Ⅰ and ࡲ as well as 3 metabolites distinguishing stage Ⅱ and ࡲ. The ROC analysis of silicosis based on the biomarkers exhibited an area under curve (AUC) of more than 0.9, with the largest AUC of 0.986 in stage Ⅰ SILs and healthy controls. Mechanistic exploration showed that these biomarkers were associated with inflammatory response, oxidative stress, and fibrosis in silicosis, respectively. This study preliminarily screened out the biomarkers of exhaled breath for different stages of SILs, and explored the metabolic pathways of biomarkers, which can provide basic data for the early, staging and specific diagnosis of SILs.

Several investigations have identified volatile organic compounds (VOCs) as potential biomarkers for the detection and identification of microbial contamination of metabolically active mammalian cell cultures. In this study, we showed that emitted VOCs discriminate between uncontaminated mesenchymal stromal cells (MSCs) and those contaminated with the bacteriumStaphylococcus epidermidisor fungusAspergillus Fumigatusseparately,in vitro, using a methodology based on an adapted cell culture and thermal desorption-gas chromatography-mass spectrometry. In addition, we elucidated a set of discriminatory volatile compounds from the MSC cultures and media alone across a time series experiment. Partial least squares-discriminant analysis-variable importance in projection confirmed putative identifications of 18, 16, and 26 VOCs that showed relevant changes in a bacterial, fungal, and universal pathogen model, respectively, with an accuracy of 100% in the fungal model. Among these metabolites, octane, 2,5,6-trimethyl- overlapped between the three groups. Furthermore, a total of 15 VOCs were found most relevant to cell culture expansion over three days based on cluster analysis. This novel study goes a step further in identifying distinct VOC signatures of MSCs contaminated withS. epidermidisorA. fumigatus, and in monitoring MSCs proliferation over time. This pilot study shows preliminary results that indicate that VOC headspace analysis could serve as a suitable, rapid, non-invasive, and non-destructive tool for the metabolic and growth monitoring of MSCs in a dynamic cell culture bioreactor system.

Background.Increased exhaled breath acetone (EBA) concentrations might reflect impaired myocardial energetics and haemodynamic stress. We investigated the relation of EBA and cardiac structure, function, and exercise capacity in patients with or at risk of heart failure (HF).Methods.We enrolled outpatients with HF and reduced (<50%, HFrEF) or preserved (>50%, HFpEF) left ventricular ejection fraction (LVEF) and subjects with cardiovascular risk factors and/or structural heart disease without established HF. All participants underwent clinical and laboratory evaluation, resting transthoracic echocardiography, and a combined cardiopulmonary-echocardiographic stress test with EBA monitoring at rest (EBA_rest) and during exercise (EBA_ex).Results.Patients with HFpEF (n= 62) were older and more often female than those at risk of HF (n= 50) or with HFrEF (n= 41). EBA_rest(1.5, interquartile range (IQR) 1.0-3.1 vs 0.9, IQR 0.7-1.2 mcg l^-1) and EBA_ex(2.4, IQR 1.5-4.4 vs 1.1, IQR 0.9-2.1 mcg l^-1; allp< 0.0001) were significantly higher in patients with HF compared to others. Among HF patients, those in the highest EBA_resttertile had lower LVEF, greater echocardiographic signs of congestion, higher NT-proBNP levels, and lower peak oxygen consumption, indicating impaired exercise capacity. In multivariate regression, NT-proBNP (p= 0.0004) and the slope of minute ventilation to carbon dioxide production (p= 0.0013) were independent predictors of EBA_rest(adjustedR²= 0.458).Conclusions.EBA concentrations are higher in patients with HF compared to those without, regardless of LVEF, and are associated with markers of disease severity. Further studies are needed to determine whether EBA measurement can aid in HF diagnosis and management.

Atmospheric microplastics and nanoplastics (MPs/NPs) have become an increasing concern, with health impacts that remain insufficiently characterised and measured. Indoor airborne MP/NPs have raised greater alarm due to their origins in routine domestic activities and sources such as synthetic textiles, bed linen, face masks, electric dryers, and household laundry, posing a high inhalation risk that can lead to serious health consequences. These particles can enter the human body through various routes, with inhalation being the main pathway for both short- and long-term health effects. Additionally, they are engulfed by epithelial and immune cells, causing multiple pathological effects on the lungs, which can subsequently lead to or contribute to various disease entities. This narrative review thoroughly explores potential cellular, genetic, and immunological mechanisms by which MP/NPs impact the respiratory system, emphasising immune mediators and cytosolic pathways involved, and linking these mechanisms to various pulmonary diseases.

Millions of people worldwide are exposed to environmental arsenic in drinking water, resulting in both malignant and nonmalignant diseases. Interestingly, early life exposure by itself is sufficient to produce higher incidences of these diseases later in life. Based on the delayed onset of disease, we hypothesized that early life arsenic exposure would also induce long-term alterations in the metabolic profile. The objective of this study was to examine metabolomic biomarkers in exhaled breath condensate (EBC) of individuals exposed to arsenic in drinking water early in life, but not later. One hundred and fifty subjects (75 males and 75 females) were initially recruited from Antofagasta, Chile, some of whom were exposed to high water arsenic levels (⩾870µg l^-1; HighAE group), and others, low water arsenic levels (⩽110µg l^-1; LowAE group) early in life (1958-1970). EBC samples were collected for targeted and untargeted metabolomic biomarker analysis. The results showed significantly shorter individuals and reduced pulmonary functions (forced vital capacity, FVC and forced expiratory volume in 1 s, FEV₁) in both males and females in the high-arsenic groups. Males exposed to high arsenic levels also had reduced red blood cell concentrations, as well as higher concentrations of the oxidative stress metabolites 8-OH-2dG and 8-iso-PGF2α. Females in the high-arsenic group showed reductions in 8-OH-2dG. Untargeted analysis revealed metabolomic markers that differentiated the HighAE group from the LowAE group, with a subgroup of markers whose concentrations were proportional to the level of arsenic exposure. Targeted and untargeted analyses of EBC using liquid chromatography-mass spectrometry indicated that adults exposed to high arsenic levels in drinking water in utero and during early childhood retained a modified metabolic profile 47 years after the end of exposure.

Adenoid hypertrophy (AH) is a common condition among pediatric and adolescent populations. The clinical diagnosis primarily relies on rhinoscopy, with a notable absence of noninvasive early diagnostic methods. This study sought to identify potential biomarkers to facilitate the early diagnosis of AH. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS) was employed to analyze and compare urine samples from 40 patients with AH and 30 healthy controls. To validate and enhance the findings from untargeted metabolomics, targeted metabolomics was conducted using UHPLC-QqQ-MS/MS, aiming to elucidate the relationship between AH and metabolic pathways. The untargeted metabolomics analysis, utilizing multivariate techniques, identified significant differences in the levels of 20 endogenous metabolites in urine samples between the AH and healthy groups. Further investigation of metabolic pathways indicated that sphingolipid and riboflavin metabolism are implicated in the pathogenesis of AH. Riboflavin and phytosphingosine were identified as potential biomarkers using targeted metabolomics. In this study, a comprehensive approach involving both untargeted and targeted metabolomics was employed to investigate diagnostic biomarkers of AH. The abnormal expression levels of riboflavin and phytosphingosine may be related to inflammation, oxidative damage, and immunomodulatory dysfunction in the pathogenesis of AH. The results showed that the identified biomarkers may serve as a novel tool for early diagnosis and tracking of disease progression.