Journal of breath research最新文献

The non-invasive biological marker exhaled nitric oxide (FE_NO) is increasingly used in asthma, but its clinical role in COPD is less established. FE_NOhas been reported to be both high and low outside the COPD exacerbation period. The study aimed to follow FE_NOvalues over two years during stable conditions in a cohort of COPD subjects participating in the TIE-study (Tools Identifying Exacerbation). The follow-up study included 353 subjects who attended three visits one year apart. The subjects that were ex-smokers (n= 265) had higher FE_NO,50values (median and IQR) compared with current smokers (n= 88), at inclusion 15 (10, 24) versus 9 (7, 15) ppb, after one year 15 (10, 24) versus 10 (7, 18) ppb, and after two years 14 (9, 22) versus 10 (7, 17) ppb, allp< 0.001. All subjects were further divided into two FE_NOgroups: <20 ppb (72%) and ⩾20 ppb (28%). After one year, 81% of the participants remained in the low group and 65% in the high FE_NOgroup. After two years, 71% remained in the low group and 52% in the high FE_NOgroup. The persistent low FE_NOgroup had statistically significantly lower FEV₁%pred and FVC%pred compared to the high FE_NOgroup for all three visits. Among the ex-smokers, the proportion of subjects reporting dyspnoea (mMRC ⩾ 2) was higher in the persistent low FE_NOgroup than in the persistent high FE_NOgroup at all three visits. In conclusion, good consistency in FE_NOover two years is promising for monitoring FE_NOduring stable disease. COPD subjects with persistent low FE_NOhad poorer lung function and reported more dyspnoea than subjects with persistent high FE_NO.

The rapid transfer of volatiles from alveolar blood into the lungs and then out of the body in exhaled breath leads to the common and natural conclusion that these volatiles provide information on health and metabolic processes, with considerable potential as biomarkers for use in the screening, diagnosis and monitoring of diseases. Whilst these exhaled volatiles could well serve as biomarkers for human metabolic processes, thereby providing insights into the clinical and nutritional status of individuals, there exist various confounding factors that limit their easy application. A major confounding factor is the introduction of microbially produced oral volatiles into the exhaled breath, yet these volatiles are often ignored in discovery volatile research studies. Here, we provide a comparative cross-sectional study of selected volatiles commonly found in exhaled breath. Using gas chromatography-ion mobility spectrometry, we monitored these selected volatiles in nasal and oral end-tidal exhaled breath samples from twenty-one volunteers. The signal intensities from untargeted volatile detection were analysed for variances using principal component analysis (PCA), revealing a clear separation correlated with the sampling method. Four compounds representing sampling method-independent (acetone, isoprene, methanol, and 2-pentanone) and four corresponding to sampling method-dependent (1-propanol, 2-propanol, ethanol, and acetoin) were identified and selected based on their high PCA loadings. These compounds are further analysed and discussed to illustrate the extent to which the oral microbiome can influence volatile concentrations in exhaled breath. An additional noteworthy finding of this study is that the nasally sampled selected exhaled volatiles are little influenced by the inhalation route (oral or nasal). The outcome from this study is clear, namely that in order to reduce the influence of the oral microbiome on untargeted discovery breath research studies, end-tidal exhaled nasal breath samples should be taken for endogenous volatile analysis, otherwise oral microbial volatiles could be falsely identified as biomarkers. This is particularly important given the continuous rise in the use of machine learning algorithms and artificial intelligence to identify variations in volatilomes. The development and commercialisation of simple, user-friendly and comfortable end-tidal exhaled nasal sample collection devices are required for nasal sampling to become widely adopted.

Breath contains numerous classes of compounds and biomolecules that could potentially be used as biomarkers for infectious disease as well as a range of other respiratory conditions or states. The goal of this work was to develop a testbed for simultaneous, multi-modal breath measurements. To validate the capabilities of this testbed, a pilot human-subjects research study was conducted to gather a wide range of correlated breath measurements. Seventeen healthy subjects provided breath samples at baseline respiratory rate for particle size, lipid composition and bacterial nucleic acid composition analysis. The majority of the particles the participants exhaled at baseline were smaller than 5μm, consistent with previous literature. A deviation from baseline was detected in one participant immediately prior to COVID-19 symptom onset. This feature persisted for weeks after infection. The exhaled breath particulate contained lipids found in lung surfactant, indicating origin in the lung. Although bacterial DNA was not significantly higher in the exhaled breath particulate than in the environmental background, the metagenome of the breath was distinct from the environment, oral cavity and nasal passages of the participants. The low abundance of the breath microbiome limited analysis. No assertions of statistical significance are offered due to the limited nature of the study scope, the multi-modal breath testbed has promise for discovery of breath biomarkers and as a reference for biomarkers of different classes that are currently being used.

Obtaining multiple sample types from the same exhaled breath condensate (EBC) sample can reduce the number of samples needed for diagnostics purposes, allowing for sampling to be completed quicker and making it even easier to collect breath from patients. In this study, we performed analysis for volatile organic compounds (VOCs) and proteins from the same EBC sample. Pooled EBC samples were split into two groups: three samples that utilized immersion thin film-solid phase microextraction (TF-SPME) sampling for VOC analysis and three samples that did not undergo TF-SPME sampling (non-TF-SPME). All six EBC samples were analyzed using liquid chromatography with tandem mass spectrometry (LC-MS/MS) for proteomics analysis. VOCs were analyzed via two-dimensional gas chromatography-mass spectrometry (GC x GC-MS). One hundred and eighty-four VOCs were found to be more abundant in EBC samples compared to blank or controls. There was no significant difference in the number of proteins detected in the TF-SPME samples compared to the non-TF-SPME samples and 144 of the 206 total unique proteins detected were found in both sample groups. These results indicate that TF-SPME sampling does not negatively affect the number of proteins that can be detected in EBC. This work is a step towards linking VOC and protein data together to obtain multi-omics breath data from a single breath sample. EBC samples were collected as part of a vaccination clinical trial (NCT05346302).

There is an unmet need for breath-based markers for pulmonary vascular disease (PVD). We developed a fully-automatic algorithm to analyze expiratory CO₂flow from resting ventilation and evaluated the clinical associations of our readouts. We enrolled patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), pulmonary arterial hypertension (PAH) and healthy controls and evaluated fractionated volumes for dead space, mixed space (MSV) and alveolar space, their respective CO₂volumes and ventilatory equivalents for CO₂(EqCO₂) and the maximum slope of the first derivative of the cumulative expiratory CO₂volume over expired volume (MSV-slope) as primary readouts. Differences between groups were analyzed using non-parametric tests. Associations were analyzed by Spearman correlation. The discriminatory power was determined with receiver operating characteristic analysis. Eleven COPD (median (IQR) age 64 (63-69) years), 10 ILD (61 (54-77) years), 10 PAH (64 (61-73) years) and 21 healthy controls (56 (52-61) years) were investigated. Patients vs healthy controls showed increased MSV and mixed space CO₂(221 (164-270) ml vs 144 (131-167) ml, and 3.9 (3.2-4.9) ml vs 3.0 (2.7-3.4) ml,p< 0.001 andp= 0.002) and EqCO₂(38 (34-42) vs 30 (29-35),p< 0.001), and decreased MSV-slopes (0.16 (0.12-0.21) vs 0.27 (0.23-0.32) l CO₂l^-2,p< 0.001). Area under the curve (AUC) for MSV and MSV-slope for disease prediction was 0.81 (95% CI 0.69-0.93) and 0.84 (0.73-0.95), respectively. MSV and mixed space CO₂were only strongly increased in COPD and ILD but not PAH, resulting in a significant difference between PAH and COPD&ILD (AUC 0.74 (95% CI: 0.56-0.92). MSV and MSV-slope were significantly correlated with DLCO (ρ=-0.69 andρ= 0.72, respectively; bothp< 0.001). Fully-automatic high-fidelity expiratory CO₂flow analysis is technically feasible, easy and safe to perform, and may represent a novel approach to detect PVD with or without structural changes of the airways and lung parenchyma. Prospective studies with larger sample size are needed to validate these findings.

Chios mastic gum, derived from Pistacia lentiscus variation chia, has emerged as a significant natural remedy to improve oral health and mitigate halitosis. This study aimed to examine the effect of mastic toothpaste on halitosis, plaque, and gingival indices in adolescents undergoing orthodontic treatment with fixed appliances. This study was a double-blind, placebo-controlled, parallel-group, randomized clinical trial. Thirty-two patients were randomly divided into two groups: A) mastic-toothpaste group and B) placebo-toothpaste group. Participants in both groups used the assigned toothpaste three times daily for 14 d. The primary outcome was objective hydrogen sulfide (H₂S) levels in breath, measured using a gas chromatograph. The secondary outcomes were dimethyl sulfide and methyl-mercaptan levels, as well as the Silness and Löe Gingival Index (GI) and the Modified Silness and Löe Plaque Index (PI-M). Assessments were conducted at baseline and after two weeks. Data were analyzed using the Mann-WhitneyUtest and Student'st-test. A statistically significant difference was found between interventions, in favor of the mastic group's H₂S (p= 0.001). The H₂S median levels decreased from 158 parts per billion (ppb) to 26 ppb. Neither treatment group experienced a different decline in the levels of the other two components. Statistically significant differences were observed in the periodontal parameters, favoring the mastic group. The GI index decreased from 1.8 to 1 [p< 0.001, 95% CI: -0.7, -0.4], whereas the PI-M index decreased from 1.2 to 0.8 [p< 0.001, (95% CI: -0.5, -0.2)]. Mastic toothpaste may be an alternative option to reduce halitosis in adolescents undergoing orthodontic treatment with fixed appliances. Regular use of this toothpaste may lead to a clinically meaningful reduction in plaque and gingival indices (ClinicalTrials.gov, NCT06766097).

Nonalcoholic fatty liver disease (NAFLD) is now the leading cause of global chronic liver disease, affecting approximately 32.4% of the population in various regions and imposing healthcare and economic burdens. The gold standard for the diagnosis of NAFLD, such as liver biopsy, has numerous limitations in large-scale screening. Recent studies have explored the use of machine learning to diagnose NAFLD. In this study, we investigated the effect of the lactulose breath test (LBT) on a machine-learning model for predicting NAFLD. The input variables for machine learning included three combination sets to assess the effect of the LBT results: anthropometric characteristics and blood test results; anthropometric characteristics and LBT results; and anthropometric characteristics, blood test results, and LBT results. The machine learning models developed in this study included linear regression, support vector machine, K-nearest neighbour, Random forest, and extreme gradient boosting (XGBoost) with 536 participants. The model performance was evaluated using six metrics: Accuracy, Area Under the Receiver Operating Characteristic curve (AUROC), specificity, sensitivity, precision, and F1 score. Among the six models, XGBoost had the highest AUROC at 0.88. The AUROC results from the three combination variable sets indicate that the LBT results significantly improve the model performance. LBT results improve the NAFLD prediction model and provide an opportunity for additional NAFLD screening in patients receiving LBT.

Volatile organic compounds (VOCs) offer potential for non-invasive diagnosis as biomarkers of disease and metabolism. In complex biological matrices, such as breath however, identifying useful biomarkers from hundreds, or even thousands of VOCs can be challenging. Models of disease, such as cellular or animal models, offer a means to elucidate VOC metabolisms, for accurate targeted studies in patient samples. Neurodegenerative conditions, such as Parkinson's have been associated with changed VOCs, offering a potential for early diagnostics and interventions improving treatments and outcomes for patients. Here, three separate models including; human HEK-293t cells, isolated primary rat glial cells, andDrosophilafruit flies (wild type and a mutant of the Parkinson's associated gene,DJ-1β) were grown for an extended period and levels of the VOC chloroform investigated using custom static headspace sampling chambers. Samples were analysed using targeted gas chromatography mass spectroscopy with selected ion monitoring mode, measuring chloroform at masses 83/85. Chloroform levels were shown to dramatically increase in all models over time. HEK-293t cells revealed a 60-fold increase after 10 weeks, glial cells revealed a 10-fold increase after 3-4 weeks andDJ-1βmutant flies revealed significant increases compared to control flies at 4 weeks. These results, taken together, suggest that chloroform release is related to ageing in these models and may provide a target for neurodegenerative studies. We present here the first evidence of chloroform being actively produced by human and rat cells and the first observation of volatile metabolisms inDrosophila. Recent clinical studies have also identified increased chloroform flux in the breath of patients, supporting the translational potential of our findings.

Chronic kidney disease (CKD) is a long-term progressive disease. The key to treatment lies in early diagnosis and timely intervention. How to achieve early diagnosis of CKD has always been an important challenge. Exhaled breath sample analysis, as an emerging method, has attracted much attention due to its non-invasiveness and the convenience of sample collection. Compared with the complex traditional detection methods, it is more suitable for large-scale screening. The main purpose of this review is to extensively collect relevant literature on the research of exhaled breath biomarkers for CKD, summarize the potential biomarkers discovered in these studies, and compare the similarities and differences. Through in-depth analysis of the causes of these differences and commonalities, this review aims to explore whether these potential exhaled breath biomarkers could serve as reliable indicators for the early diagnosis of CKD.

Halitosis is known to be associated with oral bacteria; however, its specific relationship with particular bacterial species within the oral microbiota remains uncertain. Our objective was to identify oral bacterial species associated with volatile sulfur compound (VSC) production that contribute to halitosis in a community-based Japanese population. This study included 1018 participants. Tongue plaque samples were collected and the oral microbiome was analyzed via 16S rDNA amplicon sequencing. Participants with VSC levels greater than 250 ppb were categorized as having oral malodor. Linear discriminant analysis effect size was used to compare bacterial compositions between participants with and without halitosis. In this study, we identified 37 bacterial genera in tongue plaque samples. Significant differences in bacterial composition were found between the malodor and control groups.Porphyromonas, Fusobacterium, andSolobacteriumwere more abundant in the malodor group, whereasStreptococcusandRothiawere more prevalent in the control group. Multiple regression analysis further revealed thatPorphyromonasandSolobacteriumabundances were positively correlated with oral malodor. We found that halitosis in this Japanese population is associated primarily with pathogenic periodontal bacteria (members of the red and orange complexes) andSolobacterium moorei. The bacterial community composition of individuals with halitosis differs significantly from that of healthy individuals, emphasizing the role of specific bacterial species in oral malodor development. These findings increase our understanding of the microbial basis of halitosis and suggest that targetingSolobacterium, along with treating periodontal disease, may be effective in combating halitosis.