Non-invasive phenotyping using exhaled volatile organic compounds in asthma

Outcome of liver disease in children is mainly determined by severity and progression of liver fibrosis. Liver biopsy is the accepted standard for evaluating fibrosis but is limited by the need for sedation in children, sampling error, and risks including bleeding. The aim of the present study was to compare tools for noninvasive assessment of liver fibrosis in a paediatric cohort. Children undergoing liver biopsy for chronic liver disease were recruited and underwent transient elastography (TE). Liver biopsies were scored by a hepatohistopathologist from F0 (no fibrosis) to F4 (cirrhosis). TE was compared with biopsy score. During the study period, 104 children (62 boys) were enrolled (median age 13.6 years). Diagnosis was autoimmune liver disease in 27; nonalcoholic fatty liver disease in 37; posttransplant in 16; hepatitis B/C in 8; Wilson disease in 5; and the remainder, miscellaneous. TE was successful in all but 7 patients and was a good discriminator of significant fibrosis (≥ F2) (P < 0.001), severe fibrosis (≥ F3) (P < 0.001), and cirrhosis (F4) (P = 0.003). The area under the receiver operating characteristic curve for the prediction of ≥ F2, ≥ F3, and F4 using TE was 0.78, 0.79, and 0.96, respectively. TE performed best in children with autoimmune liver disease and in those posttransplant. The present study demonstrates that TE is a reliable tool in distinguishing different stages of liver fibrosis in paediatric patients. Thus, TE may serve as a useful adjunct to liver biopsy for diagnostic purposes providing a reliable method of noninvasively monitoring liver disease progression in children.

Indoor volatile organic compounds (VOCs) have been associated with asthma, but there is little epidemiologic work on ambient exposures, and no data on relationships between respiratory health and exhaled breath VOCs, which is a biomarker of VOC exposure. We recruited 26 Hispanic children with mild asthma in a Los Angeles community with high VOC levels near major freeways and trucking routes. Two dropped out, three had invalid peak expiratory flow (PEF) or breath VOC data, leaving 21. Children filled out symptom diaries and performed PEF maneuvers daily, November 1999-January 2000. We aimed to collect breath VOC samples on asthma episode and baseline symptom-free days, but six subjects only gave samples on symptom-free days. We analyzed 106 breath samples by GC-MS. Eight VOCs were quantifiable in >75% of breath samples (benzene, methylene chloride, styrene, tetrachloroethylene, toluene, m,p-xylene, o-xylene, and p-dichlorobenzene). Generalized estimating equation and mixed linear regression models for VOC exposure-response relationships controlled for temperature and respiratory infections. We found marginally positive associations between bothersome or more severe asthma symptoms and same day breath concentrations of benzene [odds ratio (OR) 2.03, 95% confidence interval (CI) 0.80, 5.11] but not other breath VOCs. Ambient petroleum-related VOCs measured on the same person-days as breath VOCs showed notably stronger associations with symptoms, including toluene, m,p-xylene, o-xylene, and benzene (OR 5.93, 95% CI 1.64, 21.4). On breath sample days, symptoms were also associated with 1-h ambient NO(2), OR 8.13 (1.52, 43.4), and SO(2), OR 2.36 (1.16, 4.81). Consistent inverse relationships were found between evening PEF and the same ambient VOCs, NO(2), and SO(2). There were no associations with O(3). Given the high traffic density of the region, stronger associations for ambient than for breath VOCs suggest that ambient VOC measurements were better markers for daily exposure to combustion-related compounds thought to be causally related to acute asthma. Alternatively, the low sample size of symptom responses (15-21 responses per 108 breath samples) may have led to the nonsignificant results for breath VOCs.

BackgroundExisting biomarkers including cerebrospinal fluid and positron emission tomography for Alzheimer’s disease (AD) diagnosis are relatively invasive and expensive. Application of exhaled breath collection and volatile organic compound (VOC) detection for AD diagnosis remains unclear.MethodIn this cross‐sectional study, high‐pressure photon ionization time‐of‐flight mass spectrometry (HPPI‐ToF‐MS) was used to detect VOCs from breath in three datasets and patients diagnosed as Parkinson’s disease (PD). Plasma p‐tau181 was quantified using ultra‐sensitive Simoa technology. GC x GC TOF‐MS was used to detect VOCs in plasma. Near‐infrared spectroscopy was carried out to elucidate alterations of brain metabolism.ResultA total of 861 participants were enrolled. Patients with AD were diagnosed according to the 2011 NIA‐AA diagnosis criteria and further confirmed in dataset 1 (n=30) based on the A+T+N+ framework. Datasets 1 and 2 were used to recognize the different VOCs between patients with AD and controls. The diagnostic power of VOCs for AD was confirmed separately in train datasets 1 and 2 and independent test dataset 3. The specific VOCs in AD diagnosis were further compared with patients with PD. Among all participants, no statistical difference was observed in demographic characteristics for dataset 1. Six VOCs, including ethanol (m/z = 46), isopropanol (m/z = 60), chloroethane (m/z = 64), pyrrole/3‐butenenitrile (m/z = 67), 1‐butanol (m/z = 74), and benzene (m/z = 78), were significantly different between the two groups in dataset 1 and were verified in dataset 2 and dataset 3. Ethanol (m/z=46) and pyrrole/3‐butenenitrile (m/z=67) presented areas under the ROC curve (AUCs) values of 0.907 and 0.895, respectively, in datasets 1 and 2 and 0.849 and 0.974, respectively, in dataset 3 (community). The six VOCs were associated with cognitive decline as reflected by neuropsychological tests, five (m/z = 46, 64, 67, 74, and 78) were correlated with plasma p‐tau181 levels, and five (m/z = 46, 60, 67, 74, and 78) showed consistent alteration in plasma. Moreover, five (m/z = 46, 60, 67, 74, and 78) were associated with altered brain connectivity.ConclusionSpecific breath VOCs may be used as simple biomarkers for AD detection.

Rationale: Analysis of exhaled volatile organic compounds (VOCs) by sensor-driven electronic nose (eNose) technology is a widely suggested measure for non-invasive monitoring of chronic airway diseases. While this technology allows probabilistic (clinical) advise, it is incapable of providing details regarding involved metabolites. Modelling sensor deflections by matching Gas Chromatography-Mass Spectrometry (GC-MS) samples could help to ascertain which VOCs induce a sensor response. Objective: To determine the association between eNose sensor deflections and exhaled VOCs measured by GC-MS. Methods: Paired samples of breath from asthma patients (n=22) were collected on sorbent tubes. One tube was analysed by four different eNoses and the second one by GC-MS. Pooling of resulting datasets consisted of 1) Partial Least Square Regression (PLSR) analysis and 2) clustered heat-mapping of PLSR loadings. Results: Matching data was available for 158 eNose sensors and 1025 GC-MS features, whereby PLSR resulted in R2‘s from 0.25 to 0.72. Figure 1 shows clustered heat-mapping outcomes. Conclusion: This explorative analysis revealed distinctive and associated patterns of exhaled VOCs between eNose and GC-MS. This data matching could help to identify which VOCs are responsible for clinically relevant results obtained by eNose and will facilitate valorisation of breathomics into clinical tests.

Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.

Ovarian reserve index outperforms AMH, Inhibin B, and FSH in predicting poor egg supply

Cigarette smoking harms nearly every organ of the body and causes many diseases. The analysis of exhaled breath for exogenous and endogenous volatile organic compounds (VOCs) can provide fundamental information on active smoking and insight into the health damage that smoke is creating. Various exhaled VOCs have been reported as typical of smoking habit and recent tobacco consumption, but to date, no eligible biomarkers have been identified. Aiming to identify such potential biomarkers, in this pilot study we analyzed the chemical patterns of exhaled breath from 26 volunteers divided into groups of nonsmokers and subgroups of smokers sampled at different periods of withdrawal from smoking. Solid-phase microextraction technique and gas chromatography/mass spectrometry methods were applied. Many breath VOCs were identified and quantified in very low concentrations (ppbv range), but only a few (toluene, pyridine, pyrrole, benzene, 2-butanone, 2-pentanone and 1-methyldecyclamine) were found to be statistically significant variables by Mann-Whitney test. In our analysis, we did not consider the predictive power of individual VOCs, as well as the criterion of uniqueness for biomarkers suggests, but we used the patterns of the only statistically significant compounds. Probit prediction model based on statistical relevant VOCs-patterns showed that assessment of smoking status is heavily time dependent. In a two-class classifier model, it is possible to predict with high specificity and sensitivity if a subject is a smoker who respected 1 hour of abstinence from smoking (short-term exposure to tobacco) or a smoker (labelled "blank smoker") after a night out of smoking (long-term exposure to tobacco). On the other side, in our study "blank smokers" are more like non-smokers so that the two classes cannot be well distinguished and the corresponding prediction results showed a good sensitivity but low selectivity.

As current follow-up modalities for colorectal carcinoma (CRC) have restricted sensitivity, novel diagnostic tools are needed. The presence of CRC changes the endogenous metabolism, resulting in the release of a specific volatile organic compounds (VOC) pattern that can be detected with an electronic nose or AeonoseTM. To evaluate the use of an electronic nose in the follow-up of CRC, we studied the effect of curative surgery on the VOC pattern recognition using AeonoseTM. A prospective cohort study was performed, in which 47 patients diagnosed with CRC were included, all of whom underwent curative surgical resection. Breath testing was performed before and after surgery using the AeonoseTM. A machine learning model was developed by discerning between the 94 pre-and postoperative breath samples. The training model differentiated between the pre-and postoperative CRC breath samples with a sensitivity and specificity of 0.78 (95%CI 0.61-0.90) and 0.73 (95%CI 0.56-0.86), respectively, with an accuracy of 0.76 (95%CI 0.66-0.85), and an area under the curve of 0.79 (95%CI 0.68-0.89). The internal validation of the test set resulted in an accuracy of 0.75 (95%CI 0.51-0.91) and AUC of 0.82 (95%CI 0.61-1). In conclusion, our results suggest that the VOC pattern of CRC patients is altered by curative surgery in a short period, indicating that the exhaled VOCs might be closely related to the presence of CRC. However, to use AeonoseTM as a potential diagnostic tool in the clinical follow-up of CRC patients, the performance of the models needs to be improved through further large-scale prospective research.

To compare the circumpapillary and macular vessel density (cpVD/mVD) of eyes with temporal visual field (VF) defect and band atrophy (BA) of the optic nerve and normal controls using OCTA and to verify the association of VD parameters with circumpapillary retinal nerve fibre layer (cpRNFL) thickness, macular ganglion cell complex (mGCC) thickness and VF loss. Thirty-three eyes of 26 patients with BA and 42 eyes of 22 age-matched normal controls underwent OCT + OCTA scanning. cpVD and cpRNFL were expressed as average and sector measurements. mVD and mGCC were calculated as averages and in quadrants and hemiretinas. VF loss was estimated using the 24-2 and the 10-2 protocols. Generalized estimated equation models were used for comparisons and area under the receiver operating characteristics (AROC) were calculated. Compared with controls, BA eyes displayed smaller average cpVD and mVD values (p < 0.001 and AROC = 0.91 for both). Sectorial measurements were also reduced, especially the nasotemporal sector average cpVD (p < 0.001 and AROC = 0.96) and the nasal retina mVD measurements (p < 0.001 and AROC = 0.93). cpVD and mVD correlated strongly with corresponding cpRNFL and mGCC thickness measurements in affected regions (r range: 0.67-0.78 and 0.56-0.76, respectively). Similarly, cpVD and mVD parameters correlated significantly with corresponding VF loss (r range: 0.45-0.68). cpVD and mVD are significantly reduced in BA eyes compared with controls and are strongly correlated with retinal neural and VF loss. cpVD and mVD reduction on OCTA could serve as a surrogate for retinal neural loss in compressive optic neuropathy and might be useful in its management.

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

Background: Breath analysis is a promising noninvasive screening technique that offers a wide range of probable possibilities to facilitate early diagnosis of lung cancer (LC). Method: Exhaled breath samples of 352 subjects were analyzed to detect C6∼C30 VOCs through thermal desorption coupled with gas chromatography-mass spectrometry (TD-GC-MS). Data were divided into three categories Lung Cancer (LC), Benign Pulmonary Disease (BPD) and Healthy control (HC). Statistical classification models were used to analyze VOCs to distinguish between HC, BPD and LC patient's early and advanced stages. Receiver operator characteristics (ROC) curves were used to select different VOCs to evaluate the accuracy, specificity, and sensitivity of the diagnostic model. Results: The analysis resulted that 10,9,11 and 10 VOCs have a significant correlation to discriminate Histology, stage, BPD from LC and HC from LC. The diagnostic model predicted accuracy, sensitivity, and specificity of NSCLC vs. SCLC is 92%, 96% and 90% with area under curve AUC&amp;gt;0.913. As of ROC curve with AUC&amp;gt;0.819 has distinguished Stage I-II vs. stage III-IV with accuracy, sensitivity, and specificity 72%, 84%, and 80%. Whereas the ROC curve with AUC&amp;gt;0.795 has distinguished BPD from LC with accuracy, sensitivity, and specificity 83%, 89%, and 84%. While ROC curve with AUC&amp;gt;0.945, accuracy, sensitivity and specificity 92%, 96%, and 90% have distinguished HC from LC. Conclusion: This study confirms that TD GC-MS is a sensitive and dependable method to distinguish the VOCs in exhaled breath. We have witnessed significant differences in breath of BPD, HC and LC subjects. Where 10,8,11 and 10 VOCs have distinguished between early from the advance stage, NSCLC from SCLC, BPD from LC and HC from LC. These VOCs has the potential to help improve LC diagnosis and prognosis. However, with the expansion of multi centers to other regions could result in more vital findings and will help improve breath analysis and the diagnosis standard. Keywords: Breath Analysis, VOC markers, lung cancer, TD-GC-MS. Citation Format: Kanhar Ghulam Muhammad, Channa Madeeha, Fu Wei, Xu Lin Xin, Yanjie Hu, Kejing Ying, Jun Liu, Xing Chen. Volatile organic compounds (VOCs) from exhaled Breath for lung cancer Staging using TD-GC-MS [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 744.

Exhaled breath is coming to the forefront of non-invasive biomarker discovery efforts. Concentration of exhaled breath volatile organic compounds (VOCs) on thermal desorption (TD) tubes with subsequent analysis by gas chromatography-mass spectrometry (GC-MS) has dominated this field. As discovery experimentation increases in frequency, the need to evaluate the long-term storage stability of exhaled breath VOCs on thermal desorption adsorbent material is critical. To address this gap, exhaled breath was loaded on Tenax TA thermal desorption tubes and stored at various temperature conditions. 74 VOCs, 56 of which have been previously uncharacterized, were monitored using GC-MS over a period of 31 d. The results suggest that storage of exhaled breath at cold temperatures (4 °C) provides the most consistent retention of exhaled breath VOCs temporally. Samples were determined to be stable up to 14 d across storage conditions prior to gaining or losing 1–2 standard deviations in abundance. Through gene set enrichment analysis (GSEA), certain chemical classes were found to be positively (acids) or negatively (sulfur-containing) enriched temporally. By means of field sample collections, the effect of storage and shipping was found to be similar to those studies preformed in the laboratory at 4 °C. Collectively this study not only provides recommendations for proper storage conditions and storage length, but also illustrates the use of GSEA to exhaled breath based GC-MS data.

<p id="p00015">Volatile organic compounds (VOCs) in human exhaled breath can act as biochemical indicators for the objective determination and evaluation of physiological or pathological conditions. In humans, the pathophysiology associated with malignant tumors causes metabolic changes that alter the types and concentrations of VOCs in exhaled breath. However, as exhaled breath has a complex composition and low VOC content, meeting the direct detection requirements of analytical instruments is difficult. Therefore, the direct detection of VOCs in exhaled breath requires the development of a simple, fast, and efficient enrichment technique. In this study, graphitic carbon nitride (g-C₃N₄) and the metal-organic framework UiO-66 were combined to prepare a three-dimensional nanoflower-like g-C₃N₄/UiO-66 solid-phase microextraction (SPME) coating. The g-C₃N₄/UiO-66 composite was characterized using Fourier transform infrared spectroscopy, thermogravimetric analysis, X-ray diffraction, and scanning electron microscopy. UiO-66, which exhibited a regular ortho-octahedral structure with sharp corners, was embedded among g-C₃N₄ nanoflowers, and the surface was encapsulated in g-C₃N₄ to form a hydrophobic barrier. Five biomarkers (benzene, toluene, ethylbenzene, <italic>o</italic>-xylene, and decanal) in the exhaled breath of patients with lung cancer were enriched using the developed SPME fiber and then detected using gas chromatography-mass spectrometry. The optimum extraction time and temperature of the nanoflower-like SPME fiber were 10 min and 60 ℃, respectively, whereas the optimum desorption time and temperature were 3 min and 260 ℃, respectively. The adsorption efficiencies of the g-C₃N₄/UiO-66 composite coating, a single UiO-66 coating, and a 30 μm polydimethylsiloxane commercial coating were compared under optimal conditions. The five VOCs showed good linearity in the investigated concentration ranges, with correlation coefficients of 0.9919-0.9977, limits of detection of 0.16-4.50 μg/L, and limits of quantification of 0.54-9.18 μg/L. The intra- and inter-day precisions for six parallel determinations with the SPME fiber were 3.3%-7.3% and 4.0%-6.6%, respectively. Owing to the presence of nanoflower-like interlayer channels and g-C₃N₄ micropores in g-C₃N₄/UiO-66, the composite coating exhibited a high enrichment efficiency and good repeatability. The spiked recoveries of the five VOCs were in the range of 84.6%-113.2%. Finally, the method was applied to analyze exhaled breath for the detection of lung cancer. The benzene contents in the exhaled breath of both healthy individuals and patients with lung cancer were generally low. However, the toluene contents differed significantly, with patients with lung cancer having much higher levels than healthy individuals. The established method is expected to provide a reference for the noninvasive early diagnosis of lung cancer.

Midgut neuroendocrine tumors (NET) derive from enterochromaffin cells, which have a close interrelationship with intestinal microbiota. Recently, we have utilized 16S rRNA sequencing to uncover that midgut NET patients have a depleted gut microbiome and a specific fecal microbial signature. This study aims to validate these findings and to further characterize the role of microbes and microbial metabolic pathways in midgut NET patients with and without carcinoid syndrome (CS). Fecal samples from 60 midgut NET patients and 20 household-matched controls were subjected to whole metagenome sequencing. The gut microbial community composition of midgut NET patients differed from that of controls, with 2 genera, 17 species and 9 microbial pathways showing differential abundance (P < 0.001). No differences in the microbial composition were observed between midgut NET patients with and without CS (P > 0.05). However, we did observe changes in inter-genus correlations of Bacteroides, Odoribacter, Parasutterella, Klebsiella, Ruminococcus and Proteobacteria when comparing these two patient groups. A signature of 16 microbial species (area under the receiver operating characteristics (AUROC) curve 0.892) or 18 microbial pathways (AUROC 0.909) accurately predicted the presence of a midgut NET. Furthermore, a microbial signature consisting of 14 functional microbial pathways distinguished CS patients from non-CS patients (AUROC 0.807). Thus, this study confirms that the gut microbiome of midgut NET patients is altered at the metagenomic level, which is not related to the presence of CS. A fecal microbial signature could constitute a novel biomarker for the diagnosis of midgut NET or CS.

BackgroundNew Injury Severity Score (NISS) and Glasgow Coma Scale, Age and Pressure (GAP) scoring systems have cutoffs to define severe injury and identify high-risk patients. This is important in trauma quality monitoring and improvement. The overall aim was to explore if GAP scoring system can be a complement or an alternative to the traditional NISS scoring system. MethodsAdults exposed to trauma between 2017 and 2021 were included in the study, using data from The Swedish Trauma Registry. The performance of NISS and GAP scores in predicting mortality, and ICU admissions were assessed using the area under the receiver operator characteristics (AUROC) in all patients and in subgroups (blunt, penetrating trauma and older (≥65 years) trauma patients). Patients were classified as severely injured by NISS >15 as Severely Injured NISS (SIN) or with a high-risk for mortality, by GAP 3–18 as High Risk GAP (HRG). Undertriage was calculated based on the cutoffs HRG and SIN. ResultsOverall, 37,017 patients were included. The AUROC (95 % CI) for mortality using NISS was 0.84 (0.83–0.85) and for GAP 0.92 (0.91–0.93) (p-value <0.001), the AUROC (95 % CI) for ICU-admissions was 0.82 (0.82–0.83) using NISS and for GAP 0.70 (0.70–0.71) p-value <0.001, in the overall cohort. In older patients the AUROC (95 % CI) for mortality was 0.76 (0.75–0.78) using NISS and 0.79 (0.78–0.81) using GAP, p-value <0.001. Overall, 8,572 (23.2 %) and 2,908 (7.9 %) were classified as SIN and HRG, respectively, with mortality rates of 13.7 % and 34.3 %. In the HRG group low-energy falls dominated and in the SIN group most patients were exposed to MVCs. In the SIN and HRG groups the rate of Emergency Trauma Interventions according to Utstein guidelines (ETIU) and ICU admission was 14.0 vs 9.5 % and 47.0 vs 62.5 % respectively. ConclusionOur findings suggest that the GAP score and its cutoff 3–18 can be used to define severe trauma as complement to NISS >15 and can be a valuable tool in trauma quality monitoring and improvement. However, both scoring systems were less accurate in predicting mortality for the older trauma patients and should be explored further.