Accuracy and Methodologic Challenges of Volatile Organic Compound-Based Exhaled Breath Tests for Cancer Diagnosis: A Systematic Review and Meta-analysis

Early Screening and Subtype Identification of High-Risk Lung Nodules via Breathprint by Graphene eNose Platform: A Large Cohort Study

Early screening of individuals with high-risk lung nodules can significantly improve the prognosis of lung cancer patients, and accurate identification of lung nodule subtypes can provide guidance for medical treatment. Exhaled breath (EB) analysis via eNoses offers a quick and noninvasive approach, but current eNose technology lacks quality control and solid validation in large population studies. Herein, an eNose platform integrated with a metal ion-decorated graphene sensor array and a breath sampling accessory was established. EB samples from 427 healthy subjects and 2586 subjects with lung nodules, including various benign and malignant subtypes, were collected through the breath sampling accessory for quality control. The large-cohort clinical EB samples were analyzed by the eNose platform to acquire the cross-reactive resistance response. Breathprint analysis for high-risk lung nodules using SVM and age-matched training sets yielded strong and robust performance. Combined with baseline data, the model achieved an AUC of 0.93 (95% CI, 0.89-0.96) on the external test set, with 97% sensitivity and 73% specificity. Moreover, dimensionality reduction analysis of breathprints demonstrated separability across different lung nodule subtypes. This study demonstrates the reliability of the graphene eNose platform to identify high-risk lung nodules and classify lung nodule subtypes in a noninvasive and rapid method.

Accurate Diagnosis of Alzheimer's Disease Using Specific Breath Volatile Organic Compounds

Whether volatile organic compounds (VOCs) from exhaled breath can be used as a novel biomarker for Alzheimer's disease (AD) diagnosis is unclear. To determine the significantly distinctive VOCs for AD, a total of 970 participants were enrolled, including 60 individuals in data set 1 (AD, 30; controls, 30), 164 individuals in data set 2 (AD, 82; controls, 82), 637 individuals in data set 3 (AD, 31; controls, 606), and 109 individuals in data set 4 (frontotemporal dementia, 19; vascular dementia, 21; Parkinson's disease, 69). The participants in data sets 1, 2, and 4 were from Xiangya Hospital, Central South University. Participants in data set 3 were from a two-year follow-up cohort. VOCs in breath and plasma, neuropsychological scores, plasma p-tau181 levels, metabolites in plasma, and brain functional connectivity were detected. We found that six VOCs were significantly different between the two groups in data set 1 and were verified in data set 2 and data set 3. Ethanol (m/z = 46) and pyrrole (m/z = 67) presented AUC values of 0.907 and 0.895 in data sets 1 and 2 (clinical data sets) and 0.849 and 0.974 in data set 3 (community data set), respectively. The six VOCs were associated with cognitive decline as reflected by neuropsychological tests; five of them were correlated with plasma p-tau181, and these five plasma VOCs were consistently altered as breath VOCs. Correlation between metabolites and five VOCs in plasma was noted, and the five VOCs may originate from blood metabolites. Moreover, four breath VOCs were associated with altered brain connectivity. In conclusion, specific breath VOCs may be used as biomarkers for AD detection.

Design of Multi-Cancer VOCs Profiling Platform via a Deep Learning-Assisted Sensing Library Screening Strategy

The efficiency of sensor arrays in parallel discrimination of multianalytes is fundamentally influenced by the quantity and performance of the sensor elements. The advent of combinational design has notably accelerated the generation of chemical libraries, offering numerous candidates for the development of robust sensor arrays. However, screening elements with superior cross-responsiveness remains challenging, impeding the development of high-performance sensor arrays. Herein, we propose a new deep learning-assisted, two-step screening strategy to identify the optimal combination of minimal sensor elements, using a designed volatile organic compounds (VOCs)-targeted sensor library. 400 sensing elements constructed by pairing 20 ionizable cationic elements and 20 anionic dyes in the sensor library were employed for various VOCs, generating plentiful color variation data. By employing a feedforward neural network─random forest-recursive feature elimination (FRR) algorithm, sensing elements were effectively screened, resulting in the rapidly producing 8-element and 10-element arrays for two VOC models, both achieving 100% discrimination accuracy. Furthermore, a smartphone-based point-of-care testing (POCT) platform achieved cancer discrimination in a simulated cancer VOC model, using image-based deep learning, demonstrating the rationality and practicality of deep learning in the assembly of sensor elements for parallel sensing platforms.

Chemical Nose-Based Non-Invasive Detection of Breast Cancer Using Exhaled Breath

Breast cancer (BC) is the most commonly occurring cancer in women and one of the leading causes of cancer death in women worldwide. BC mortality is related to early tumor detection, highlighting the importance of early detection methods. This work aims to develop a robust, accurate and highly reliable, non-invasive, low-cost screening method for early detection of BC in routine screening using exhaled breath (EB) analysis. For this, exhaled breath samples were collected from 267 women: 131 breast cancer patients and 136 healthy women. After collection, the samples were measured using a commercially available electronic nose. The signals obtained for each sample were first processed and then went through a feature extraction step. An SVM model was then optimized with respect to the accuracy matrix using a validation set by applying a Monte Carlo cross-validation with 100 iterations, with each iteration containing 20% of the data. The validation set results were 80, 94, 88, and 95% for recall, precision, accuracy, and specificity, correspondingly. Once model optimization had concluded, 22 unknown samples were analyzed by the model, and an accuracy, precision, and specificity of 91% was achieved.

Engineering Multiplexed Synthetic Breath Biomarkers as Diagnostic Probes

Breath biopsy is emerging as a rapid and non-invasive diagnostic tool that links exhaled chemical signatures with specific medical conditions. Despite its potential, clinical translation remains limited by the challenge of reliably detecting endogenous, disease-specific biomarkers in breath. Synthetic biomarkers represent an emerging paradigm for precision diagnostics such that they amplify activity-based biochemical signals associated with disease fingerprints. However, their adaptation to breath biopsy has been constrained by the limited availability of orthogonal volatile reporters that are detectable in exhaled breath. Here, we engineer multiplexed breath biomarkers that couple aberrant protease activities to exogenous volatile reporters. We designed novel intramolecular reactions that leverage protease-mediated aminolysis, enabling the sensing of a broad spectrum of proteases, and that each release a unique reporter in breath. This approach was validated in a mouse model of influenza to establish baseline sensitivity and specificity in a controlled inflammatory setting, and subsequently applied to diagnose lung cancer using an autochthonous Alk -mutant model. We show that combining multiplexed reporter signals with machine learning algorithms enables tumor progression tracking, treatment response monitoring, and detection of relapse after 30 minutes. Our multiplexed breath biopsy platform highlights a promising avenue for rapid, point-of-care diagnostics across diverse disease states.

Diagnostic accuracy of breath tests based on volatile organic compounds for cancer: A systematic review and meta-analysis

Exhaled volatile organic compounds (VOCs) are being extensively studied for the purposes of noninvasive cancer diagnoses. This systematic review and meta-analysis aims to evaluate the diagnostic accuracy of breath tests based on VOCs for cancer detection, and to propose potential cancer biomarkers. This study was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Relevant studies up to February 2024 were retrieved from public databases, including PubMed, EMBASE, and Web of Science. A total of 114 articles were included, covering 125 non-duplicate studies involving 8768 cancer patients. Meta-analysis showed that the VOC breath test demonstrated a sensitivity of 87% and a specificity of 81% in cancer diagnosis, with an area under the receiver operating characteristic curve (AUC) of 0.93. Subgroup analyses based on cancer types and breath detection techniques also showed high sensitivity and specificity in diagnosing cancer patients. These suggest that breath analysis for VOCs has excellent diagnostic accuracy for cancer. The breath test based on VOCs, as a non-invasive detection method, shows great potential for cancer diagnosis.

Minimally invasive biomarkers for triaging lung nodules-challenges and future perspectives

CT chest scans are commonly performed worldwide, either in routine clinical practice for a wide range of indications or as part of lung cancer screening programs. Many of these scans detect lung nodules, which are small, rounded opacities measuring 8-30 mm. While the concern about nodules is that they may represent early lung cancer, in screening programs, only 1% of such nodules turn out to be cancer. This leads to a series of complex decisions and, at times, unnecessary biopsies for nodules that are ultimately determined to be benign. Additionally, patients may be anxious about the status of detected lung nodules. The high rate of false positive lung nodule detections has driven advancements in biomarker-based research aimed at triaging lung nodules (benign versus malignant) to identify truly malignant nodules better. Biomarkers found in biofluids and breath hold promise owing to their minimally invasive sampling methods, ease of use, and cost-effectiveness. Although several biomarkers have demonstrated clinical utility, their sensitivity and specificity are still relatively low. Combining multiple biomarkers could enhance the characterisation of small pulmonary nodules by addressing the limitations of individual biomarkers. This approach may help reduce unnecessary invasive procedures and accelerate diagnosis in the future. This review offers a thorough overview of emerging minimally invasive biomarkers for triaging lung nodules, emphasising key challenges and proposing potential solutions for biomarker-based nodule differentiation. It focuses on diagnosis rather than screening, analysing research published primarily in the past five years with some exceptions. The incorporation of biomarkers into clinical practice will facilitate the early detection of malignant nodules, leading to timely interventions and improved outcomes. Further efforts are needed to increase the cost-effectiveness and practicality of many of these applications in clinical settings. However, the range of technologies is advancing rapidly, and they may soon be implemented in clinics in the near future.

Optimized breath analysis: customized analytical methods and enhanced workflow for broader detection of VOCs

INTRODUCTION

Breath Volatile organic compounds (VOCs) are promising biomarkers for clinical purposes due to their unique properties. Translation of VOC biomarkers into the clinic depends on identification and validation: a challenge requiring collaboration, well-established protocols, and cross-comparison of data. Previously, we developed a breath collection and analysis method, resulting in 148 breath-borne VOCs identified.

OBJECTIVES

To develop a complementary analytical method for the detection and identification of additional VOCs from breath. To develop and implement upgrades to the methodology for identifying features determined to be "on-breath" by comparing breath samples against paired background samples applying three metrics: standard deviation, paired t-test, and receiver-operating-characteristic (ROC) curve.

METHODS

A thermal desorption (TD)-gas chromatography (GC)-mass spectrometry (MS)-based analytical method utilizing a PEG phase GC column was developed for the detection of biologically relevant VOCs. The multi-step VOC identification methodology was upgraded through several developments: candidate VOC grouping schema, ion abundance correlation based spectral library creation approach, hybrid alkane-FAMES retention indexing, relative retention time matching, along with additional quality checks. In combination, these updates enable highly accurate identification of breath-borne VOCs, both on spectral and retention axes.

RESULTS

A total of 621 features were statistically determined as on-breath by at least one metric (standard deviation, paired t-test, or ROC). A total of 38 on-breath VOCs were able to be confidently identified from comparison to chemical standards.

CONCLUSION

The total confirmed on-breath VOCs is now 186. We present an updated methodology for high-confidence VOC identification, and a new set of VOCs commonly found on-breath.

Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study

BACKGROUND

Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring the need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile organic compounds (VOCs) shows promise, yet its effectiveness in assessing the malignancy of pulmonary nodules remains underexplored.

METHODS

Employing a prospective study design from June 2023 to January 2024 at the Affiliated Hospital of Yangzhou University, we assessed the malignancy of pulmonary nodules using the Mayo Clinic model and collected exhaled breath samples alongside lifestyle and health examination data. We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics.

RESULTS

A total of 267 participants were enrolled, including 210 with low-risk and 57 with moderate-risk pulmonary nodules. Univariate analysis identified 11 exhaled VOCs associated with nodule malignancy, alongside two lifestyle factors (smoke index and sites of tobacco smoke inhalation) and one clinical metric (nodule diameter) as independent predictors for moderate-risk nodules. The logistic regression model integrating lifestyle and health data achieved an AUC of 0.91 (95% CI: 0.8611-0.9658), while the random forest model incorporating exhaled VOCs achieved an AUC of 0.99 (95% CI: 0.974-1.00). Calibration curves indicated strong concordance between predicted and observed risks. Decision curve analysis confirmed the net benefit of these models over traditional methods. A nomogram was developed to aid clinicians in assessing nodule malignancy based on VOCs, lifestyle, and health data.

CONCLUSIONS

The integration of ML algorithms with exhaled biomarkers and clinical data provides a robust framework for noninvasive assessment of pulmonary nodules. These models offer a safer alternative to traditional methods and may enhance early detection and management of pulmonary nodules. Further validation through larger, multicenter studies is necessary to establish their generalizability.

TRIAL REGISTRATION

Number ChiCTR2400081283.

Ultrasensitive In2O3-Based Nanoflakes for Lung Cancer Diagnosis and the Sensing Mechanism Investigated by Operando Spectroscopy

Rapid gas sensing with high sensitivity and selectivity is pivotal in advanced production, in smart living, and increasingly in medical health applications. This study presents a novel Pt@InNiOx nanoflake isoprene sensor that achieves an exceptionally low limit of detection (LOD) at 2 ppb, the lowest reported for isoprene sensors to date. Notably, it exhibits high selectivity and remarkable antihumidity capacity, thus meeting the stringent requirements for lung cancer screening. To unravel the sensing mechanism, we fabricate an operando DRIFTS-Raman cell coupled to online electrical measurements. It reveals that the ultrasensitive performance of Pt@InNiOx nanoflakes stems from the activated conjugated structure of isoprene by Pt nanoclusters and from the enhanced isoprene adsorption and electron interaction due to the nanoflake morphology. The p-n junction constructed by doping Ni maintains Fermi level equilibrium, shielding it from humidity interference. Practically, we integrate these ultrasensitive Pt@InNiOx nanoflakes into a miniaturized portable electronic device that successfully distinguishes lung cancer patients with expiratory isoprene below 40 ppb, from the healthy population with isoprene above 60 ppb, enabling an accurate diagnosis in clinics. Our work not only provides a breakthrough in low-cost, noninvasive cancer screening through breath analysis but also advances the rational design of cutting-edge gas sensing materials.

Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis

Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and Methods: A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. Results: The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models.

A Multifunctional Peptide Nucleic Acid/Peptide Copolymer-Based Dual-Mode Biosensor with Macrophage-Hitchhiking for Enhanced Tumor Imaging and Urinalysis

Biosensors are capable of diagnosing tumors through imaging in vivoor liquid biopsy, but they face the challenges of inefficient delivery into tumor sites and the lack of reliable tumor-associated biomarkers. Herein, we constructed a dual-mode biosensor based on a multifunctional peptide nucleic acid (PNA)/peptide copolymer and DNA tetrahedron for tumor imaging and urinalysis. The biosensor could enter the cancer cells to initiate a microRNA-21-specific catalytic hairpin assembly reaction after cleavage by matrix-metalloprotease (MMP) in the tumor microenvironment, and the MMP cleavage product was released into the bloodstream and then was filtered out by the kidney. As PNA was a synthetic DNA analogue that could not be degraded by nucleases and proteases, it could serve as a reliable synthetic biomarker and be easily detected by high-performance liquid chromatography in urine. Importantly, the biosensor was hitchhiked on the macrophage membrane to realize efficient delivery in the depth of tumor utilizing the macrophage ability of actively homing to the tumor site and infiltrating into the tumor. The results indicated that the signal output of the biosensor was improved remarkably and mice with a tumor volume as little as 30-40 mm3 could be reliably discriminated through urine assay. This innovative macrophage-hitchhiking dual-mode biosensor holds a great potential as a non-invasive and convenient tool for tumor diagnosis and tumor progression evaluation.

Development of a new breath collection method for analyzing volatile organic compounds from intubated mouse models

A new pre-clinical method for capturing breath samples from intubated mice is presented. This method significantly reduces background levels, allowing more accurate measurements of VOCs originating from the breath ("on-breath") as opposed to background contamination. The method was developed by integrating industry-standard volatile-capturing sorbent tubes with respiratory mechanics measurement equipment (flexiVent®), resulting in a mouse breath sample that can be transported and analyzed by TD-GC-MS and other central lab technologies. Using the methodology, the discrimination between on-breath VOCs from background compounds provides a cleaner dataset, which can accelerate the validation of VOCs identified from mouse models and their translation to clinical trials. Three metrics were developed to identify on-breath VOCs, with 22 identified using Type 1 (50% of the breath samples exceeding three standard deviations above the mean signal of the system blanks), 34 with Type 2 (P-value ≤ .05 between paired breath and blank samples), and 61 with Type 3 (ROC-AUC value ≥ 0.8 to differentiate between breath and blank samples). The number of compounds seen at elevated levels on mouse breath was quantified and compared to the levels seen on human breath samples to compare methodologies.

Associations of urinary volatile organic compounds with cardiovascular disease among the general adult population

This study was to estimate the associations of volatile organic compounds (VOCs) exposure with the prevalence of total and specific cardiovascular disease (CVD) among the general adult population. This cross-sectional study analyzed 15 urinary VOC metabolites in the general population using the 2011-2016 National Health and Nutrition Examination Survey (n = 5,213). The weighted study population with 47.0 years median age, was primarily female (51.2%). The prevalence of total CVD in the overall population was 7.9%. The single-exposure analyzes of AAMA, ATCA, CEMA, CYMA, DHBMA, 3HPMA, and 3MHA +4MHA were significantly associated with increased prevalence of total CVD. Qgcomp regression consistently showed that urinary VOCs-mixed exposure was positively correlated with the prevalence of total and specific CVDs (chronic heart failure, angina, and stroke), and highlighted each VOCs metabolite weights and direction. The similar results were observed for the WQS regression using mixed analysis methods. In conclusion, exposure to VOCs increases CVD prevalence and advances the identification of risk factors for CVD for environmental study.

Ion-sensitive field effect transistor biosensors for biomarker detection: current progress and challenges

The ion-sensitive field effect transistor (ISFET) has emerged as a crucial sensor device, owing to its numerous benefits such as label-free operation, miniaturization, high sensitivity, and rapid response time. Currently, ISFET technology excels in detecting ions, nucleic acids, proteins, and cellular components, with widespread applications in early disease screening, condition monitoring, and drug analysis. Recent advancements in sensing techniques, coupled with breakthroughs in nanomaterials and microelectronics, have significantly improved sensor performance. These developments are steering ISFETs toward a promising future characterized by enhanced sensitivity, seamless integration, and multifaceted detection capabilities. This review explores the structure and operational principles of ISFETs, highlighting recent research in ISFET biosensors for biomarker detection. It also examines the limitations of these sensors, proposes potential solutions, and anticipates their future trajectory. This review aims to provide a valuable reference for advancing ISFETs in the field of biomarker measurement.

Identification potential biomarkers for diagnosis, and progress of breast cancer by using high-pressure photon ionization time-of-flight mass spectrometry

BACKGROUND

In this study, exhaled breath testing has been considered a promising method for the detection and monitoring of breast cancer (BC).

METHODS

A high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform was used to detect volatile organic compounds (VOCs) in breath samples. Then, machine learning (ML) models were constructed on VOCs for the diagnosis of BC and its progression monitoring. Ultimately, 1981 women with useable breath samples were included in the study, of whom 937 (47.3 %) had been diagnosed with BC. VOC panels were used for ML model construction for BC detection and progression monitoring.

RESULTS

On the blinded testing cohort, this VOC-based model successfully differentiated patients with and without BC with sensitivity, specificity, and area under receiver operator characteristic curve (AUC) values of 85.9 %, 90.4 %, and 0.946. The corresponding AUC values when differentiating between patients with and without lymph node metastasis (LNM) or between patients with tumor-node-metastasis (TNM) stage 0/I/II or III/IV disease were 0.840 and 0.708, respectively. While developed VOC-based models exhibited poor performance when attempting to differentiate between patients based on pathological patterns (Ductal carcinoma in situ (DCIS) vs Invasive BC (IBC)) or molecular subtypes (Luminal vs Human epidermal growth factor receptor 2 (HER2+) vs Triple-negative BC (TNBC)) of BC.

CONCLUSION

Collectively, the HPPI-TOFMS-based breathomics approaches may offer value for the detection and progression monitoring of BC. Additional research is necessary to explore the fundamental mechanisms of the identified VOCs.

Miniature mass spectrometers and their potential for clinical point-of-care analysis

Mass spectrometry (MS) has become a powerful technique for clinical applications with high sensitivity and specificity. Different from conventional MS diagnosis in laboratory, point-of-care (POC) analyses in clinics require mass spectrometers and analytical procedures to be friendly for novice users and applicable for on-site clinical diagnosis. The recent decades have seen the progress in the development of miniature mass spectrometers, providing a promising solution for clinical POC applications. In this review, we report recent advances of miniature mass spectrometers and their exploration in clinical applications, mainly including the rapid analysis of illegal drugs, on-site monitoring of therapeutic drugs, and detection of biomarkers. With improved analytical performance, miniature mass spectrometers are also expected to apply to more and more clinical applications. Some promising POC analyses that can be performed by miniature mass spectrometers in the future are discussed. Lastly, we also provide our perspectives on the challenges in technical development of miniature mass spectrometers for clinical POC analysis.

Prospect and Challenges of Volatile Organic Compound Breath Testing in Non-Cancer Gastrointestinal Disorders

Breath analysis, despite being an overlooked biomatrix, has a rich history in disease diagnosis. However, volatile organic compounds (VOCs) have yet to establish themselves as clinically validated biomarkers for specific diseases. As focusing solely on late-stage or malignant disease biomarkers may have limited relevance in clinical practice, the objective of this review is to explore the potential of VOC breath tests for the diagnosis of non-cancer diseases: (1) Precancerous conditions like gastro-esophageal reflux disease (GERD) and Barrett's esophagus (BE), where breath tests can complement endoscopic screening; (2) endoluminal diseases associated with autoinflammation and dysbiosis, such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and coeliac disease, which currently rely on biopsy and symptom-based diagnosis; (3) chronic liver diseases like cirrhosis, hepatic encephalopathy, and non-alcoholic fatty liver disease, which lack non-invasive diagnostic tools for disease progression monitoring and prognostic assessment. A literature search was conducted through EMBASE, MEDLINE, and Cochrane databases, leading to an overview of 24 studies. The characteristics of these studies, including analytical platforms, disorder type and stage, group size, and performance evaluation parameters for diagnostic tests are discussed. Furthermore, how VOCs can be utilized as non-invasive diagnostic tools to complement existing gold standards is explored. By refining study designs, sampling procedures, and comparing VOCs in urine and blood, we can gain a deeper understanding of the metabolic pathways underlying VOCs. This will establish breath analysis as an effective non-invasive method for differential diagnosis and disease monitoring.

Neonatal Exhaled Breath Sampling for Infrared Spectroscopy: Biomarker Analysis

Monitoring health conditions in neonates for early therapeutic intervention in case deviations from physiological conditions is crucial for their long-term development. Due to their immaturity preterm born neonates are dependent on particularly careful physical and neurological diagnostic methods. Ideally, these should be noninvasive, noncontact, and radiation free. Infrared spectroscopy was used to analyze exhaled breath from 71 neonates with a special emphasis on preterm infants, as a noninvasive, noncontact, and radiation-free diagnostic tool. Passive sample collection was performed by skilled clinicians. Depending on the mode of respiratory support of infants, four different sampling procedures were adapted to collect exhaled breath. With the aid of appropriate reference samples, infrared spectroscopy has successfully demonstrated its effectiveness in the analysis of breath samples of neonates. The discernible increase in concentrations of carbon dioxide, carbon monoxide, and methane in collected samples compared to reference samples served as compelling evidence of the presence of exhaled breath. With regard to technical hurdles and sample analysis, samples collected from neonates without respiratory support proved to be more advantageous compared to those obtained from intubated infants and those with CPAP (continuous positive airway pressure). The main obstacle lies in the significant dilution of exhaled breath in the case of neonates receiving respiratory support. Metabolic analysis of breath samples holds promise for the development of noninvasive biomarker-based diagnostics for both preterm and sick neonates provided an adequate amount of breath is collected.

Exhaled breath analysis for the discrimination of asthma and chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) and asthma are the most common chronic respiratory diseases. In middle-aged and elderly patients, it is difficult to distinguish between COPD and asthma based on clinical symptoms and pulmonary function examinations in clinical practice. Thus, an accurate and reliable inspection method is required. In this study, we aimed to identify breath biomarkers and evaluate the accuracy of breathomics-based methods for discriminating between COPD and asthma. In this multi-center cross-sectional study, exhaled breath samples were collected from 89 patients with COPD and 73 with asthma and detected on a high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform from 20 October 2022, to 20 May 2023, in four hospitals. Data analysis was performed from 15 June 2023 to 16 August 2023. The sensitivity, specificity, and accuracy were calculated to assess the overall performance of the volatile organic component (VOC)-based COPD and asthma discrimination models. Potential VOC markers related to COPD and asthma were also analyzed. The age of all participants ranged from to 18-86 years, and 54 (33.3%) were men. The age [median (minimum, maximum)] of COPD and asthma participants were 66.0 (46.0, 86.0), and 44.0 (17.0, 80.0). The male and female ratio of COPD and asthma participants were 14/75 and 40/33, respectively. Based on breathomics feature selection, ten VOCs were identified as COPD and asthma discrimination biomarkers via breath testing. The joint panel of these ten VOCs achieved an area under the curve of 0.843, sensitivity of 75.9%, specificity of 87.5%, and accuracy of 80.0% in COPD and asthma discrimination. Furthermore, the VOCs detected in the breath samples were closely related to the clinical characteristics of COPD and asthma. The VOC-based COPD and asthma discrimination model showed good accuracy, providing a new strategy for clinical diagnosis. Breathomics-based methods may play an important role in the diagnosis of COPD and asthma.

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

BACKGROUND

The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review.

AIM OF REVIEW

This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

Distinguish Esophageal Cancer Cells through VOCs Induced by Methionine Regulation

Detection of exhaled volatile organic compounds (VOCs) is promising for noninvasive screening of esophageal cancer (EC). Cellular VOC analysis can be used to investigate potential biomarkers. Considering the crucial role of methionine (Met) during cancer development, exploring associated abnormal metabolic phenotypes becomes imperative. In this work, we employed headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) to investigate the volatile metabolic profiles of EC cells (KYSE150) and normal esophageal epithelial cells (HEECs) under a Met regulation strategy. Using untargeted approaches, we analyzed the metabolic VOCs of the two cell types and explored the differential VOCs between them. Subsequently, we utilized targeted approaches to analyze the differential VOCs in both cell types under gradient Met culture conditions. The results revealed that there were five/six differential VOCs between cells under Met-containing/Met-free culture conditions. And the difference in levels of two characteristic VOCs (1-butanol and ethyl 2-methylbutyrate) between the two cell types intensified with the increase of the Met concentration. Notably, this is the first report on VOC analysis of EC cells and the first to consider the effect of Met on volatile metabolic profiles. The present work indicates that EC cells can be distinguished through VOCs induced by Met regulation, which holds promise for providing novel insights into diagnostic strategies.

Exhaled VOC detection in lung cancer screening: a comprehensive meta-analysis

BACKGROUND

Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined.

METHODS

Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies' titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity.

RESULTS

This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research.

CONCLUSION

This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.

A multi-omics method for breast cancer diagnosis based on metabolites in exhaled breath, ultrasound imaging, and basic clinical information

Background and aims

Through a nested cohort study, we evaluated the diagnostic performance of breath-omics in differentiating between benign and malignant breast lesions, and assessed the diagnostic performance of a multi-omics approach that combines breath-omics, ultrasound radiomics, and clinic-omics in distinguishing between benign and malignant breast lesions.

Materials and methods

We recruited 1,723 consecutive patients who underwent an automated breast volume scanner (ABVS) examination. Breath samples were collected and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOF-MS) to obtain breath-omics features. 238 of 1,723 enrolled participants have received pathological confirmation of breast nodules finally. The breast lesions of the 238 participants were contoured manually based on ABVS images for ultrasound radiomics feature calculation. Then, single- and multi-omics models were constructed and evaluated for breast nodules diagnosis via five-fold cross-validation.

Results

The area under the curve (AUC) of the breath-omics model was 0.855. In comparison, the multi-omics model demonstrated superior diagnostic performance for breast cancer, with sensitivity, specificity, and AUC of 84.1 %, 89.9 %, and 0.946, respectively. The multi-omics performance was comparable to that of the Breast Imaging Reporting and Data System (BI-RADS) classification via senior ultrasound physician evaluation.

Conclusion

The multi-omics approach combining metabolites in exhaled breath, ultrasound imaging, and basic clinical information exhibits superior diagnostic performance and promises to be a non-invasive and reliable tool for breast cancer diagnosis.

AI Applied to Volatile Organic Compound (VOC) Profiles from Exhaled Breath Air for Early Detection of Lung Cancer

Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.

Exhaled volatile organic compounds in the detection of colorectal cancer: a systematic review and meta-analysis

There is an apparent need for novel non-invasive colorectal cancer (CRC) screening tests that are more acceptable to patients and can reliably detect CRC or reduce the number of unnecessary colonoscopies performed in cancer-free patients. An emerging number of studies demonstrate the potential value of exhaled volatile organic compounds (VOCs) as a diagnostic and triaging test for CRC. A systematic appraisal and meta-analysis of the published evidence was done to determine whether exhaled VOCs can be used in the detection and screening of CRC. Nine electronic databases were searched from inception of the databases until August 2020. Quantitative and descriptive data of CRC patients and healthy control (HC) participants who underwent VOCs breath analysis was extracted. In addition, where possible, sampling methods, analytical platforms, processors, and specific breath biomarkers found in each study were recorded. Fourteen articles were included in the systematic review with 491 colorectal patients and 754 HC participants (n=1245). Sub-group meta-analysis was conducted on nine of those articles and the pooled sensitivity was estimated to be 0.89 (95 % CI = 0.80-0.99) whereas specificity was 0.83 (95 % CI = 0.74-0.92). Heterogeneity of pooled sensitivity and specificity was estimated as I2=11.11 %. Although this study was limited by small sample size and different analytical platforms, the proposed future framework resolves such limitations and standardizes future research. It is reasonable to deduce that VOCs breath analysis is certainly a field of research that can progress to replace traditional methods within the framework of CRC screening and diagnosis.

Diagnostic performance of volatile organic compounds analysis and electronic noses for detecting colorectal cancer: a systematic review and meta-analysis

Introduction

The detection of Volatile Organic Compounds (VOCs) could provide a potential diagnostic modality for the early detection and surveillance of colorectal cancers. However, the overall diagnostic accuracy of the proposed tests remains uncertain.

Objective

This systematic review is to ascertain the diagnostic accuracy of using VOC analysis techniques and electronic noses (e-noses) as noninvasive diagnostic methods for colorectal cancer within the realm of clinical practice.

Methods

A systematic search was undertaken on PubMed, EMBASE, Web of Science, and the Cochrane Library to scrutinize pertinent studies published from their inception to September 1, 2023. Only studies conducted on human subjects were included. Meta-analysis was performed using a bivariate model to obtain summary estimates of sensitivity, specificity, and positive and negative likelihood ratios. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was deployed for quality assessment. The protocol for this systematic review was registered in PROSPERO, and PRISMA guidelines were used for the identification, screening, eligibility, and selection process.

Results

This review encompassed 32 studies, 22 studies for VOC analysis and 9 studies for e-nose, one for both, with a total of 4688 subjects in the analysis. The pooled sensitivity and specificity of VOC analysis for CRC detection were 0.88 (95% CI, 0.83-0.92) and 0.85 (95% CI, 0.78-0.90), respectively. In the case of e-nose, the pooled sensitivity was 0.87 (95% CI, 0.83-0.90), and the pooled specificity was 0.78 (95% CI, 0.62-0.88). The area under the receiver operating characteristic analysis (ROC) curve for VOC analysis and e-noses were 0.93 (95% CI, 0.90-0.95) and 0.90 (95% CI, 0.87-0.92), respectively.

Conclusion

The outcomes of this review substantiate the commendable accuracy of VOC analysis and e-nose technology in detecting CRC. VOC analysis has a higher specificity than e-nose for the diagnosis of CRC and a sensitivity comparable to that of e-nose. However, numerous limitations, including a modest sample size, absence of standardized collection methods, lack of external validation, and a notable risk of bias, were identified. Consequently, there exists an imperative need for expansive, multi-center clinical studies to elucidate the applicability and reproducibility of VOC analysis or e-nose in the noninvasive diagnosis of colorectal cancer.

Systematic review registration

https://www.crd.york.ac.uk/prospero/#recordDetails, identifier CRD42023398465.

Established and emerging biomarkers of immunotherapy in renal cell carcinoma

Immunotherapies, such as immune checkpoint inhibitors, have heralded impressive progress for patient care in renal cell carcinoma (RCC). Despite this success, some patients' disease fails to respond, and other patients experience significant side effects. Thus, development of biomarkers is needed to ensure that patients can be selected to maximize benefit from immunotherapies. Improving clinicians' ability to predict which patients will respond to immunotherapy and which are most at risk of adverse events - namely through clinical biomarkers - is indispensable for patient safety and therapeutic efficacy. Accordingly, an evolving suite of therapeutic biomarkers continues to be investigated. This review discusses biomarkers for immunotherapy in RCC, highlighting current practices and emerging innovations, aiming to contribute to improved outcomes for patients with RCC.

Real-Time Monitoring of Air Pollution Health Impacts Using Breath-Borne Gaseous Biomarkers from Rats

Offline techniques are adopted for studying air pollution health impacts, thus failing to provide in situ observations. Here, we have demonstrated their real-time monitoring by online analyzing an array of gaseous biomarkers from rats' exhaled breath using an integrated exhaled breath array sensor (IEBAS) developed. The biomarkers include total volatile organic compounds (TVOC), CO2, CO, NO, H2S, H2O2, O2, and NH3. Specific breath-borne VOCs were also analyzed by a gas chromatography-ion mobility spectrometer (GC-IMS). After real-life ambient air pollution exposures (2 h), the pollution levels of PM2.5 and O3 were both found to significantly affect the relative levels of multiple gaseous biomarkers in rats' breath. Eleven biomarkers, especially NO, H2S, and 1-propanol, were detected as significantly correlated with PM2.5 concentration, while heptanal was shown to be significantly correlated with O3. Likewise, significant changes were also detected in multiple breath-borne biomarkers from rats under lab-controlled O3 exposures with levels of 150, 300, and 1000 μg/m3 (2 h), compared to synthetic air exposure. Importantly, heptanal was experimentally confirmed as a reliable biomarker for O3 exposure, with a notable dose-response relationship. In contrast, conventional biomarkers of inflammation and oxidative stress in rat sera exhibited insignificant differences after the 2 h exposures. The results imply that breath-borne gaseous biomarkers can serve as an early and sensitive indicator for ambient pollutant exposure. This work pioneered a new research paradigm for online monitoring of air pollution health impacts while obtaining important candidate biomarker information for PM2.5 and O3 exposures.

Association of volatile organic compound exposure with metabolic syndrome and its components: a nationwide cross-sectional study

BACKGROUND

Metabolic syndrome (MetS) is a health issue consisting of multiple metabolic abnormalities. The impact of exposure to volatile organic compounds (VOCs) on MetS and its components remains uncertain. This study aimed to assess the associations of individual urinary metabolites of VOC (mVOCs) and mVOC mixtures with MetS and its components among the general adult population in the United States.

METHODS

A total of 5345 participants with eligible data were filtered from the 2011-2020 cycles of the National Health and Nutrition Examination Survey. Multivariate logistic regression models were applied to assess the associations of individual mVOCs with MetS and its components. The least absolute shrinkage and selection operator (LASSO) regression models were constructed to identify more relevant mVOCs. The weight quantile sum regression model was applied to further explore the links between mVOC co-exposure and MetS and its components.

RESULTS

The results indicated positive associations between multiple mVOCs and MetS, including CEMA, DHBMA, and HMPMA. CEMA was found to be positively correlated with all components of MetS. HMPMA was associated with elevated triglyceride (TG), reduced high-density lipoprotein, and fasting blood glucose (FBG) impairment; 3HPMA was associated with an elevated risk of high TG and FBG impairment; and DHBMA had positive associations with elevated TG and high blood pressure. The co-exposure of LASSO-selected mVOCs was associated with an increased risk of elevated TG, high blood pressure, and FBG impairment.

CONCLUSION

Positive associations of certain individual urinary mVOCs and mVOC mixtures with MetS and its components were observed by utilizing multiple statistical models and large-scale national data. These findings may serve as the theoretical basis for future experimental and mechanistic studies and have important implications for public health.

A meta-analysis for the diagnostic accuracy of SelectMDx in prostate cancer

To overview the diagnostic accuracy of SelectMDx for the detection of clinically significant prostate cancer and to review sources of methodologic variability. Four electronic databases, including PubMed, Embase, Web of Science, and Cochrane Library were searched for eligible studies investigating the diagnostic value of SelectMDx compared with the gold standard. The pooled sensitivity, specificity, and positive and negative predictive values were calculated. Included studies were assessed according to the Standards for Quality Assessment of Diagnostic Accuracy Studies 2 tool. The review identified 14 relevant publications with 2579 patients. All reports constituted phase 1 biomarker studies. Pooled analysis of findings found an area under the receiver operating characteristic analysis curve of 70% [95% CI, 66%-74%], a sensitivity of 81% [95% CI, 69%-89%], and a specificity of 52% [95% CI, 41%-63%]. The positive likelihood ratio was 1.68, and the negative predictive value is 0.37. Factors that may influence variability in test results included the breath collection method, the patient's physiologic condition, the test environment, and the method of analysis. Considerable heterogeneity was observed among the studies owing to the difference in the sample size. SelectMDx appears to have moderate to good diagnostic accuracy in differentiating patients with clinically significant prostate cancer from people at high risk of developing prostate cancer. Higher-quality clinical studies assessing the diagnostic accuracy of SelectMDx for clinically significant cancer are still needed.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

Emerging Strategies in Lung Cancer Screening: Blood and Beyond

BACKGROUND

Although low dose computed tomography (LDCT)-based lung cancer screening (LCS) can decrease lung cancer-related mortality among high-risk individuals, it remains an imperfect and substantially underutilized process. LDCT-based LCS may result in false-positive findings, which can lead to invasive procedures and potential morbidity. Conversely, current guidelines may fail to capture at-risk individuals, particularly those from under-represented minority populations. To address these limitations, numerous biomarkers have emerged to complement LDCT and improve early lung cancer detection.

CONTENT

This review focuses primarily on blood-based biomarkers, including protein, microRNAs, circulating DNA, and methylated DNA panels, in current clinical development for LCS. We also examine other emerging biomarkers-utilizing airway epithelia, exhaled breath, sputum, and urine-under investigation. We highlight challenges and limitations of biomarker testing, as well as recent strategies to integrate molecular strategies with imaging technologies.

SUMMARY

Multiple biomarkers are under active investigation for LCS, either to improve risk-stratification after nodule detection or to optimize risk-based patient selection for LDCT-based screening. Results from ongoing and future clinical trials will elucidate the clinical utility of biomarkers in the LCS paradigm.

Breathomics Detect the Cardiovascular Disease: Delusion or Dilution of the Metabolomic Signature

Volatile organic compounds (VOCs) can be subdivided into exogenous and endogenous categories based on their origin. Analyzing the endogenous VOCs can provide insights into maintaining the internal organs' homeostasis. Despite the ongoing development and the current understanding, studies have suggested a link between cardiovascular metabolic alterations in patients with ischemic heart disease and elevated levels of ethane and isoprene detectable through exhaled breath analysis. Conversely, patients with chronic heart failure exhibit elevated acetone and pentane in their exhaled air. These substances originate from disturbances in the heart tissue, including cellular and subcellular modulations. Hypothetically, ethane levels in the exhaled breath analysis can demonstrate the severity of ischemic heart disease and, consequently, the risk of death in the next 10 years due to cardiovascular disease (CVD). Real-time direct mass spectrometry is the preferred method for assessing VOCs in exhaled breath analysis. The accuracy of this analysis depends on several factors, including the selection of the relevant breath fraction, the type of breath collection container (if used), and the pre-concentration technique.

Knowledge of Non-Invasive Biomarkers of Breast Cancer, Risk Factors, and BSE Practices Among Nursing Undergraduates in Farasan Island, KSA

Background of the Study

Mammograms are sometimes met with issues of overdiagnosis and underdiagnosis; therefore, they are less reliable in identifying cancer in women with dense breasts. As a result, it is critical to be aware of other sensitive screening techniques for the early diagnosis of breast cancer.

Aim

The ultimate objective of this study was to assess the knowledge of nursing undergraduates regarding non-invasive biomarkers, such as volatile organic compounds in breath, nipple aspirate fluid, sweat, urine, and tears, for the early detection of breast cancer to help improve patient care, determine the risk factors, and encourage practice of breast self-examination.

Methods

Cross-sectional research was done in the Department of Nursing at Farasan campus using a self-structured questionnaire as the study tool. A total of 260 students willingly participated. The study tool had evaluation questions focused on the non-invasive biomarkers of breast cancer, risk factors, and breast self-examination practices to collect data. The data were subjected to descriptive and inferential statistics. The statistical significance was calculated at P < .05. Data analyses were done using Microsoft Excel (2013).

Results

A significant knowledge gap existed among the study participants about the non-invasive biomarkers of breast cancer. A lesser percentage of students (25%) stated that they do breast self-examination on a monthly basis. The most common reasons for not doing the breast self-examination were "not knowing how to do the breast self-examination" (77.3%), fear of a positive diagnosis (53.9%), thinking that they are not at risk as all were in their teens and hence not required (44.7%), and lack of time (48.7%). Age and frequency of breast self-examination were significantly associated (P < .05) as those few students (22.7%) who were doing breast self-examination practices every 2-4 months belonged to a higher study year. Furthermore, knowledge regarding incidence rates and health care expenditure by the government on breast cancer was also significantly low (P < .05).

Conclusions

Outcomes would help prioritize actions to help future nurses better understand breast cancer, allowing them to extend patient care in the best way possible.

Volatilome is Inflammasome- and Lipidome-dependent in Ischemic Heart Disease

Ischemic heart disease (IHD) is a pathology of global interest because it is widespread and has high morbidity and mortality. IHD pathophysiology involves local and systemic changes, including lipidomic, proteomic, and inflammasome changes in serum plasma. The modulation in these metabolites is viable in the pre-IHD, during the IHD period, and after management of IHD in all forms, including lifestyle changes and pharmacological and surgical interventions. Therefore, these biochemical markers (metabolite changes; lipidome, inflammasome, proteome) can be used for early prevention, treatment strategy, assessment of the patient's response to the treatment, diagnosis, and determination of prognosis. Lipidomic changes are associated with the severity of inflammation and disorder in the lipidome component, and correlation is related to disturbance of inflammasome components. Main inflammasome biomarkers that are associated with coronary artery disease progression include IL-1β, Nucleotide-binding oligomerization domain- like receptor family pyrin domain containing 3 (NLRP3), and caspase-1. Meanwhile, the main lipidome biomarkers related to coronary artery disease development involve plasmalogen lipids, lysophosphatidylethanolamine (LPE), and phosphatidylethanolamine (PE). The hypothesis of this paper is that the changes in the volatile organic compounds associated with inflammasome and lipidome changes in patients with coronary artery disease are various and depend on the severity and risk factor for death from cardiovascular disease in the time span of 10 years. In this paper, we explore the potential origin and pathway in which the lipidome and or inflammasome molecules could be excreted in the exhaled air in the form of volatile organic compounds (VOCs).

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

Breath Analysis for Lung Cancer Early Detection-A Clinical Study

This clinical study presents a comprehensive investigation into the utility of breath analysis as a non-invasive method for the early detection of lung cancer. The study enrolled 14 lung cancer patients, 14 non-lung cancer controls with diverse medical conditions, and 3 tuberculosis (TB) patients for biomarker discovery. Matching criteria including age, gender, smoking history, and comorbidities were strictly followed to ensure reliable comparisons. A systematic breath sampling protocol utilizing a BIO-VOC sampler was employed, followed by VOC analysis using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). The resulting VOC profiles were subjected to stringent statistical analysis, including Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA), Kruskal-Wallis test, and Receiver Operating Characteristic (ROC) analysis. Notably, 13 VOCs exhibited statistically significant differences between lung cancer patients and controls. The combination of eight VOCs (hexanal, heptanal, octanal, benzaldehyde, undecane, phenylacetaldehyde, decanal, and benzoic acid) demonstrated substantial discriminatory power with an area under the curve (AUC) of 0.85, a sensitivity of 82%, and a specificity of 76% in the discovery set. Validation in an independent cohort yielded an AUC of 0.78, a sensitivity of 78%, and a specificity of 64%. Further analysis revealed that elevated aldehyde levels in lung cancer patients' breath could be attributed to overactivated Alcohol Dehydrogenase (ADH) pathways in cancerous tissues. Addressing methodological challenges, this study employed a matching of physiological and pathological confounders, controlled room air samples, and standardized breath sampling techniques. Despite the limitations, this study's findings emphasize the potential of breath analysis as a diagnostic tool for lung cancer and suggest its utility in differentiating tuberculosis from lung cancer. However, further research and validation are warranted for the translation of these findings into clinical practice.

Volatile organic compounds analysis as promising biomarkers for Parkinson's disease diagnosis: A systematic review and meta-analysis

OBJECTIVE

Researchers are investigating the potential of volatile organic compounds (VOCs) obtained from exhaled breath and sebum as non-invasive tools for early Parkinson's disease (PD) diagnosis. The present study aims to assess the feasibility of using VOC analysis for PD diagnosis and determine the overall diagnostic accuracy of the proposed tests.

METHODS

We performed systematic searches based on the PRISMA guidelines to identify relevant studies on VOCs in PD diagnosis using exhaled breath or sebum samples. The selected articles were described, and meta-analysis was conducted on those that provided the sensitivity and specificity data.

RESULTS

Out of 1268 articles initially identified, 8 met the inclusion criteria and provided specific sensitivity and specificity data for PD, which were included in the current meta-analysis. The pooled analysis of these findings showed a mean area under the receiver operating characteristic curve of 0.85, a sensitivity of 0.81 (95% confidence interval (CI): 0.72, 0.88), and a specificity of 0.76 (95% CI: 0.66, 0.84).

CONCLUSION

The analysis of VOCs in exhaled breath and sebum has shown promise as a new avenue for non-invasive diagnosis of PD. VOCs' ability to distinguish PD from healthy controls suggests their potential clinical application in screening for the disease. Consequently, VOCs hold significant potential as biomarkers for PD diagnosis and offer a promising novel approach to identifying and diagnosing the condition.

Global Urinary Volatolomics with (GC×)GC-TOF-MS

Urinary volatolomics offers a noninvasive approach for disease detection and monitoring. Herein we present an improved methodology for global volatolomic profiling. Wide coverage was achieved by utilizing a multiphase sorbent for volatile organic compound (VOC) extraction. A single, midpolar column gas chromatography (GC) assay yielded substantially higher numbers of monitored VOCs compared to our previously reported single-sorbent method. Multidimensional GC (GC×GC) enhanced further biomarker discovery while data analysis was simplified by using a tile-based approach. At the same time, the required urine volume was reduced 5-fold from 2 to 0.4 mL. The applicability of the methodology was demonstrated in a pancreatic ductal adenocarcinoma cohort where previous findings were confirmed while a series of additional VOCs with diagnostic potential were discovered.

Adult-Onset Transcriptomic Effects of Developmental Exposure to Benzene in Zebrafish (Danio rerio): Evaluating a Volatile Organic Compound of Concern

Urban environments are afflicted by mixtures of anthropogenic volatile organic compounds (VOCs). VOC sources that drive human exposure include vehicle exhaust, industrial emissions, and oil spillage. The highly volatile VOC benzene has been linked to adverse health outcomes. However, few studies have focused on the later-in-life effects of low-level benzene exposure during the susceptible window of early development. Transcriptomic responses during embryogenesis have potential long-term consequences at levels equal to or lower than 1 ppm, therefore justifying the analysis of adult zebrafish that were exposed during early development. Previously, we identified transcriptomic alteration following controlled VOC exposures to 0.1 or 1 ppm benzene during the first five days of embryogenesis using a zebrafish model. In this study, we evaluated the adult-onset transcriptomic responses to this low-level benzene embryogenesis exposure (n = 20/treatment). We identified key genes, including col1a2 and evi5b, that were differentially expressed in adult zebrafish in both concentrations. Some DEGs overlapped at the larval and adult stages, specifically nfkbiaa, mecr, and reep1. The observed transcriptomic results suggest dose- and sex-dependent changes, with the highest impact of benzene exposure to be on cancer outcomes, endocrine system disorders, reproductive success, neurodevelopment, neurological disease, and associated pathways. Due to molecular pathways being highly conserved between zebrafish and mammals, developmentally exposed adult zebrafish transcriptomics is an important endpoint for providing insight into the long term-effects of VOCs on human health and disease.

A feasibility study on exhaled breath analysis using UV spectroscopy to detect COVID-19

A 23-subject feasibility study is reported to assess how UV absorbance measurements on exhaled breath samples collected from silicon microreactors can be used to detect COVID-19. The silicon microreactor technology chemoselectively preconcentrates exhaled carbonyl volatile organic compounds and subsequent methanol elution provides samples for analysis. The underlying scientific rationale that viral infection will induce an increase in exhaled carbonyls appears to be supported by the results of the feasibility study. The data indicate statistically significant differences in measured UV absorbance values between healthy and symptomatic COVID-19 positive subjects in the wavelength range from 235 nm to 305 nm. Factors such as subject age were noted as potential confounding variables.

Detection of urological cancers by the signature of organic volatile compounds in urine, from dogs to electronic noses

PURPOSE OF REVIEW

Urine volatile organic compound (VOC) testing for early detection of urological cancers is a minimally invasive and promising method. The objective of this review was to present the results of recently published work on this subject.

RECENT FINDINGS

Organic volatile compounds are produced through oxidative stress and peroxidation of cell membranes, and they are eliminated through feces, urine, and sweat. Studies looking for VOCs in urine for the diagnosis of urological cancers have mostly focused on bladder and prostate cancers. However, the number of patients included in the studies was small. The electronic nose was the most widely used means of detecting VOCs in urine for the detection of urological cancers. MOS sensors and pattern recognition machine learning were more used for the composition of electronic noses. Early detection of urological cancers by detection of VOCs in urine is a method with encouraging results with sensitivities ranging from 27 to 100% and specificities ranging from 72 to 94%.

SUMMARY

The olfactory signature of urine from patients with urological cancers is a promising biomarker for the early diagnosis of urological cancers. The electronic nose with its ability to recognize complex odors is an excellent alterative to canine diagnosis and analytical techniques. Nevertheless, additional research improving the technology of Enoses and the methodology of the studies is necessary for its implementation in daily clinical practice.

Association between urinary metabolites of volatile organic compounds and cardiovascular disease in the general population from NHANES 2011-2018

BACKGROUND

Volatile organic compounds (VOCs) contain hundreds of chemicals and human exposure to VOCs is pervasive. However, most studies have considered only a single chemical or a class of similar chemicals.

OBJECTIVE

We aimed to investigate the association between urinary volatile organic compound metabolites (mVOCs) and the risk of cardiovascular disease (CVD) in the general population.

METHODS

The data in this study were collected from the National Health and Nutrition Examination Survey in 2011-2018. Eligible patients were aged ≥20 years for whom complete data for 20 types of urinary mVOCs and CVD outcomes were available. Multivariate logistic regression models were used to elucidate the association between mVOCs and CVD. Generalized additive models were used to examine the nonlinear relationships between mVOCs and CVD.

RESULTS

6814 indiviuals were included in the final analysis, of whom 508 had CVD. Higher urinary concentrations of N-acetyl-S-(2-carboxyethyl)-L-cysteine (CEMA) and N-Acetyl-S-(2-cyanoethyl)-l-cysteine (CYMA) and a lower urinary concentration of 2-aminothiazoline-4-carboxylic acid (ATCA) were associated with CVD outcomes after the adjustment for potential confounding factors. A nonlinear relationship and a threshold effect were only observed between N-acetyl-S-(N-methylcarbamoyl)-l-cysteine (AMCC) and CVD among 20 types of mVOCs. There was a significantly positive correlation between AMCC and CVD when AMCC concentration was >2.32 g/mL.

CONCLUSION

The findings of this study suggested a significant correlation between urinary VOC metabolites and CVD. Urinary mVOCs may indicate hazardous exposure or distinct metabolic traits in patients with CVD.

Association of volatile organic compounds exposure with the risk of depression in U.S. adults: a cross‑sectional study from NHANES 2013-2016

BACKGROUND

Volatile organic compounds (VOCs) are a broad class of chemicals, and previous studies showed that VOCs could increase the risk of central nervous system disorders. However, few studies have comprehensively explored their association with depression among general adults.

OBJECTIVE

We aimed to explore the association between blood VOCs and depression risk based on a large cross-sectional study of the National Health and Nutrition Examination Survey (NHANES).

METHODS

We analyzed data from 3449 American adults in the NHANES 2013-2016. Survey-weighted logistic regression model was used to explore the association of ten blood VOCs with depression. Subsequently, the relative importance of the selected VOCs was determined using the XGBoost model. The weighted quantile sum (WQS) regression model was used to explore the overall association of 10 blood VOCs with depression. Subgroup analyses were performed to identify high-risk populations. Finally, restricted cubic spline (RCS) analysis was utilized to explore the dose-response relationship between blood VOCs and the risk of depression.

RESULTS

XGBoost Algorithm model identified blood 2,5-dimethylfuran was the most critical variable in depression. The logistic regression model showed that blood benzene, blood 2,5-dimethylfuran, and blood furan showed a positive correlation with depression. In subgroup analysis, we found that the effects of the above VOCs on depression existed among the female, young middle-aged, and overweight-obese population. Mixture VOCs exposure was positively associated with depression risk (OR = 2.089, 95% CI: 1.299-3.361), and 2,5-dimethylfuran had the largest weights in WQS regression. RCS displayed that blood benzene, blood 2,5-dimethylfuran, and blood furan were positively associated with depression.

CONCLUSION

The results of this study indicated that VOCs exposure was associated with an increased prevalence of depression in U.S. adults. Women, young and middle-aged, and overweight-obese populations are more vulnerable to VOCs.

A systematic review on the detection of volatile organic compounds in exhaled breath in experimental animals in the context of gastrointestinal and hepatic diseases

BACKGROUND

Analysis of volatile organic compounds (VOCs) in exhaled breath has the potential to serve as an accurate diagnostic tool for gastro-intestinal diseases. Animal studies could be instrumental as a preclinical base and subsequent clinical translation to humans, as they are easier to standardize and better equipped to relate specific VOCs to metabolic and pathological processes. This review provides an overview of the study design, characteristics and methodological quality of previously published animal studies on analysis of exhaled breath in gastrointestinal and hepatic diseases. Guidelines are provided for standardization in study design and breath collection methods to improve comparability, avoid duplication of research and reduce discomfort of animals in future studies.

METHODS

PubMed and Embase database were searched for animal studies using exhaled breath analysis to detect gastro-intestinal diseases. Risk of bias was assessed using the SYRCLE's risk of bias tool for animal studies. Information on study design, standardization methods, animal models, breath collection methods and identified VOCs were extracted from the included studies.

RESULTS

10 studies were included (acute liver failure n = 1, non-alcoholic steatohepatitis n = 1, hepatic ischemia n = 2, mesenteric ischemia n = 2, sepsis and peritonitis n = 3, colitis n = 1). Rats were used in most of the studies. Exhaled breath was mostly collected using invasive procedures as tracheal cannulation or tracheostomy. Poor reporting on standardization, breath collection methods, analytical techniques, as well as heterogeneity of the studies, complicate comparison of the different studies.

CONCLUSION

Poor reporting of essential methodological details impaired comprehensive summarizing the various studies on exhaled breath in gastrointestinal and hepatic diseases. Potential pitfalls in study design, and suggestions for improvement of study design are discussed which, when applied, lead to consistent and generalizable results and a reduction in the use of laboratory animals. Refining the methodological quality of animal studies has the potential to improve subsequent clinical trial design.

Does the last 20 years paradigm of clinical research using volatile organic compounds to non-invasively diagnose cancer need to change? Challenges and future direction

PURPOSE

Volatile organic compounds (VOCs) have shown great potential as novel biomarkers for cancer detection; however, comprehensive quantitative analysis is lacking. In this study, we performed a bibliometric analysis of non-invasive cancer diagnosis using VOCs to better characterise international trends and to predict future hotspots in this field, and then we focussed on human studies to analyse clinical characteristics for presenting the current controversies and future perspectives of further clinical work.

METHODS

Publications, from 2002 to 2022, were retrieved from the Web of Science Core Collection database. CiteSpace and VOSviewer were used to generate network maps and identify the annual publications, top countries, authors, institutions, journals, references, and keywords. Then, we further screened clinical trials, and the key information was extracted into Microsoft Excel for further systematical analysis.

RESULTS

Six hundred and forty-one articles were identified to evaluate research trends, of which 301 clinical trials were selected for further systematical analysis. Overall, the annual publications in this area increased, with an overall upward trend, while the quality of clinical research remains remarkably uneven.

CONCLUSION

The study of non-invasive cancer diagnosis using VOCs would continue to be an active field. However, without stringent clinical design criteria, most suitable acquisition and analysis devices and statistical approaches, a list of exclusive, specific, reliable and reproducible VOCs to identify a disease and these VOCs appearing in a breath at detectable levels at early stage disease, the clinical utility of VOC tests will be difficult to have any breakthroughs.