Biomarkers In Exhaled Breath Research Articles

P2.11A.04 Exhaled Breath Biomarkers of Response to Immunotherapy in Advanced Non-Small Lung Cancer

P2.11A.04 Exhaled Breath Biomarkers of Response to Immunotherapy in Advanced Non-Small Lung Cancer

Portable and Hand-Held Ammonia Gas Sensor Enables Noninvasive Prediagnosis of Helicobacter pylori Infection.

Disease diagnosis of Helicobacter pylori (Hp) through human exhaled breath analysis has attracted considerable attention. However, conventional methods, such as carbon 13 (13C) breath test and infrared spectrometers, are facing the challenge of achieving portability and reliability synchronously. Herein, we report a portable and hand-held Hp analyzer using a bimetallic PtRu@SnO2-based gas sensor for the prediagnosis of Hp infection, which is based on detecting ammonia (NH3) as a potential biomarker in exhaled breath. Owing to the surface functionalization through highly catalytically active bimetallic PtRu nanoparticles (NPs) prepared by a photochemical reduction strategy, the PtRu@SnO2-based sensor exhibits high sensitivity and selectivity toward trace-level (200 ppb) NH3 even at high-humidity surroundings (80% RH). Consequently, the designed portable and hand-held Hp analyzer makes the accurate determination of NH3 at 800 ppb in exhaled breath. The tuning of energy band structure and electrical characteristics and the catalytic modulation of NH3 oxidation by PtRu NPs are proposed to be the reasons behind the enhanced NH3 gas-sensing performance, as confirmed by in situ analysis using an online MKS MultiGas 2030 FTIR gas analyzer. This work paves the way for the prediagnosis of Hp infection using a metal oxide gas sensor.

Advancing Lung Cancer Diagnosis through NH2-MON-SPME-GC-MS/MS: Enhanced Sensitivity in Aldehyde Biomarker Detection from Exhaled Breath.

The sensitive detection of trace biomarkers in exhaled breath for lung cancer diagnosis represents a critical area of research in life analytical chemistry, with profound implications for early disease detection, therapeutic intervention, and prognosis monitoring. Despite its potential, the analytical process faces significant challenges due to the ultratrace levels of disease biomarkers present and the complex, high-humidity composition of exhaled breath. This study introduces a highly sensitive method for detecting aldehyde biomarkers in exhaled breath by integrating the use of amino-functionalized microporous organic networks (NH2-MON) as a solid-phase microextraction (SPME) fiber coating with gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) analysis. The method innovatively combines sample collection and extraction, achieving a dual-step enrichment process that significantly enhances both the enrichment efficiency and reproducibility of biomarker detection while effectively mitigating the interference caused by water vapor in exhaled breath. The NH2-MON, utilized as an SPME fiber coating, demonstrates exceptional enrichment capacity for five key aldehyde biomarkers, facilitating the development of a highly sensitive detection approach for these biomarkers in exhaled breath. Compared to previously reported methods, the proposed technique exhibits significantly lower limits of quantification, ranging from 0.77 to 11.89 pg mL-1, and achieves substantially higher enrichment factors, ranging from 9156- to 35723-fold. The practicality and feasibility of the method were validated through the analysis of exhaled breath samples from lung cancer patients, underscoring its potential application in the early diagnosis and monitoring of lung cancer.

A comprehensive non-targeted approach for the analysis of biomarkers in exhaled breath across different nicotine product categories

In the context of the evolving landscape of nicotine consumption, the assessment of biomarkers plays a crucial role in understanding the health impact of different product categories. Exhaled breath (EB) emerges as a promising, non-invasive matrix for biomarker analysis, complementary to conventional urine and plasma data. This study explores distinctive EB biomarker profiles among users of combustible cigarettes (CC), heated tobacco products (HTP), electronic cigarettes (EC), smokeless/oral tobacco (OT), and oral/dermal nicotine products (NRT). We have successfully developed and validated a non-targeted GC-TOF-MS method for the analysis of EB samples across the aforementioned product categories. A total of 66 compounds were identified, with significantly elevated levels in at least one study group. The study found that CC users had higher levels of established VOCs associated with smoking, which supports the proof-of-concept of the method. Breathomic analysis identified increased levels of p-cymene and α-pinene in EC users, while HTP users showed potential biomarker candidates like γ-butyrolactone. This study underscores the utility of EB biomarkers for a comprehensive evaluation of diverse nicotine products. The unique advantages offered by EB analysis position it as a valuable tool for understanding the relationship between exposure and health outcomes.

The detection of acetone in exhaled breath using gas Pre-Concentrator by modified Metal-Organic framework nanoparticles

Volatile organic compounds (VOCs) from exhaled breath are promising indicators for the diagnostics of human health conditions. However, the composition of breath is rather complex, including water vapors and various tiny amounts of gases and biomarkers. Pre-concentrator plays a significant role in capturing and concentrating target gas in the gas mixture. In this work, the MIL-101 (Cr) nanoparticles are coated with polydimethylsiloxane (PDMS) through physical vapor deposition process to concentrate acetone under high humidity and room temperature. Experimentally, the newly developed material exhibits significant improvement in signal intensity that is 76.3 times greater than a commercial reference material under relative humility of 80 %. The detection range is also proven to be desirable (from 100 ppb to 2500 ppb), capturing the spectrum of acetone concentrations typically found in human exhaled breath. Moreover, following a thermal desorption process, the device demonstrates a high degree of linearity with a coefficient of 0.9956. The modified metal–organic framework materials are made into a flexible thin-film-shape device, that could be embedded in a facemask. The proposed device could meet the critical requirements for sampling, concentrating and detecting of exhaled breath biomarkers, thereby opening a new opportunity as part of the flexible electronic systems on face masks to facilitate quantitative breath analyses.

Wearable Masks Integrated with Conducting Polymer and Carbon-Based Nanomaterials for VOC and Breath Monitoring

There has been considerable effort to develop wearable electronics from life-supporting devices for solders to fashion accessories such as smartwatches. The research of gas sensors has also attempted to adapt wearables with the power of nanotechnology. On the wearable platform, miniature gas sensors will provide real-time information about the atmosphere to protect each personnel from possible hazardous chemical attacks. In addition, wearable gas sensors can be facilitated to monitor human’s breath as medical applications. [1] Continuous monitoring of respiratory rate in real-time can act as a crucial benchmark for non-invasively detecting cardiac or arterial vascular function. Anomalies in respiratory rate serve as a significant indicator of a patient's health deterioration. Furthermore, it has been reported that aberrations in respiratory rate, as a physiological parameter, can be utilized to monitor common COVID-19 symptoms [2]. The analysis of breath through identifying biomarkers in exhaled breath proves to be an effective method for assessing metabolic disorders or dysfunctions in the human body. Conducting polymers (CPs) have generated significant interest in constructing flexible gas sensors. The inherent issue with pure CPs lies in their tendency to deprotonate in the presence of air, leading to a notable decline in long-term stability. Furthermore, limited gas response on diverse gaseous species demands the exploration of hybridizing CPs and other nanomaterials. When nanomaterials are hybridized with CPs, a synergistic effect, which comes from the benefits of each material, can improve sensing performance.In this study, polyaniline (PANI) conducting polymer and carbon-based nanomaterials, including carbon nanotubes (CNTs), graphene, or MXene were synthesized in a composite form and deposited into disposable masks. The developed gas sensor demonstrates remarkable stability attributed to the presence of van der Waals forces and π–π interactions between carbon-based nanomaterials and polyaniline (PANI). This stability is crucial for its reliable performance over time. The sensor's sensitivity is notably improved due to enhanced charge transfer channels and a porous structure that facilitates the adsorption of target gas. Additionally, the composite sensors detect breathing patterns in real-time, which demonstrates noninvasive human breath monitoring. A noteworthy aspect is that a significant portion of breath signals originates from the presence of target gases and moisture in exhaled breath. Although volatile sulfur compounds, CO2, and volatile organic compounds in the exhaled breath have a minimal influence on the sensing signals, the sensor remains highly responsive to the key components, particularly target gas by selecting carbon-based nanomaterials [3, 4]. The proposed gas sensing mechanism of the CP-based composites is discussed.

A rational design of metal doped C20 fullerene based sensor for the selective detection of ethyl butyrate as COVID-19 biomarker

The detection of ethyl butyrate as a COVID-19 biomarker in exhaled breath could be a novel, rapid, less expensive, painless and non-invasive technique compared to a costly, painful and time-consuming PCR test. The potential of alkaline earth metals doped C20 fullerenes (M@C20) was explored as sensing materials to detect ethyl butyrate selectively. The ethyl butyrate (EB) exhibited the physisorption on pristine C20 fullerene with adsorption energy of -4.16 kcal/mol, while chemisorption was observed with M@C20 fullerenes. The frontier molecular orbital analysis demonstrates enhanced stability of complexes, attributed to widened Eg gaps during EB adsorption onto M@C20 fullerenes. The notable shift in λmax values in the visible region suggests using M@C20 fullerenes as UV–visible-based naked-eye sensors. The infrared analysis showed a red shift in the carbonyl bond stretching frequency of ethyl butyrate upon adsorption onto M@C20 fullerenes. The red shift resulted from charge transfer from ethyl butyrate to M@C20. These findings establish the foundation for utilizing M@C20 fullerenes in IR-based handheld sensing devices. The minimum recovery time (6.8 s) demonstrates Ca@C20 fullerene as a promising, reusable sensor for detecting ethyl butyrate at human body temperature. Our results pave the way to design rapid, reusable sensors for detecting ethyl butyrate.

Combination of real-time and hyphenated mass spectrometry for improved characterisation of exhaled breath biomarkers in clinical research.

Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption-two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research.

Influence of Silsesquioxane-Containing Ultra-Thin Polymer Films on Metal Oxide Gas Sensor Performance for the Tunable Detection of Biomarkers

Certain biomarkers in exhaled breath are indicators of diseases in the human body. The non-invasive detection of such biomarkers in human breath increases the demand for simple and cost-effective gas sensors to replace state-of-the-art gas chromatography (GC) machines. The use of metal oxide (MOX) gas sensors based on thin-film structures solves the current limitations of breath detectors. However, the response at high humidity levels, i.e., in the case of exhaled human breath, significantly decreases the sensitivity of MOX sensors, making it difficult to detect small traces of biomarkers. We have introduced, in previous work, the concept of a hybrid gas sensor, in which thin-film-based MOX gas sensors are combined with an ultra-thin (20–30 nm) polymer top layer deposited by solvent-free initiated chemical vapor deposition (iCVD). The hydrophobic top layer enables sensor measurement in high-humidity conditions as well as the precise tuning of selectivity and sensitivity. In this paper, we present a way to increase the hydrogen (H2) sensitivity of hybrid sensors through chemical modification of the polymer top layer. A poly(1,3,5,7-tetramethyl-tetravinylcyclotetrasiloxane) (PV4D4) thin film, already applied in one of our previous studies, is transformed into a silsesquioxane-containing top layer by a simple heating step. The transformation results in a significant increase in the gas response for H2 ~709% at an operating temperature of 350 °C, which we investigate based on the underlying sensing mechanism. These results reveal new pathways in the biomedical application field for the analysis of exhaled breath, where H2 indicates gastrointestinal diseases.

Potential application of C-MoS2 monolayer for identifying lung cancer biomarkers in exhaled breath: a DFT study

Potential application of C-MoS2 monolayer for identifying lung cancer biomarkers in exhaled breath: a DFT study

SERS-based AI diagnosis of lung and gastric cancer via exhaled breath

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

New perspectives on ‘Breathomics’: metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS

Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. “Breathomics” as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.

Alternative electrolyte solutions for untargeted breath metabolomics using secondary-electrospray ionization high-resolution mass spectrometry.

Secondary-electrospray ionization (SESI) coupled with high-resolution mass spectrometry is a powerful tool for the discovery of biomarkers in exhaled breath. A primary electrospray consisting of aqueous formic acid (FA) is currently used to charge the volatile organic compounds in breath. To investigate whether alternate electrospray compositions could enable different metabolite coverage and sensitivities, the electrospray dopants NaI and AgNO3 were tested. In a proof-of-principle manner, the exhaled breath of one subject was analyzed repeatedly with different electrospray solutions and with the help of a spectral stitching technique. Capillary diameter and position were optimized to achieve proper detection of exhaled breath. The detected features were then compared using formula annotation. Using an evaporation-based gas standard system, the signal response of the different solutions was probed. Principal component analysis revealed a substantial difference in features detected with AgNO3 . With silver, more sulfur-containing features and more unsaturated hydrocarbon compounds were detected. Furthermore, more primary amines were potentially ionized, as indicated by van Krewelen diagrams. In total, twice as many features were unique to AgNO3 than for other electrospray dopants. Using gas standards at known concentrations, the high sensitivity of FA as a dopant was demonstrated but also indicated alternate sensitivities of the other electrospray solutions. This work demonstrated the potential of AgNO3 as a complementary dopant for further biomarker discovery in SESI-based breath analysis.

Potential exhaled breath biomarkers identified in chlorine-exposed mice.

Exhaled breath (EB) contains various volatile organic compounds (VOCs) that can indicate specific biological or pathological processes in the body. Analytical techniques like gas chromatography-mass spectrometry (GC-MS) can be used to detect and measure these exhaled biomarkers. In this study, the objective was to develop a non-invasive method of EB sampling in animals that were awake, as well as to analyze EB for volatile biomarkers specific for chlorine exposure and/or diagnostic biomarkers for chlorine-induced acute lung injury (ALI). To achieve this, a custom-made sampling device was used to collect EB samples from 19 female Balb/c mice. EB was sampled both pre-exposure (serving as internal control) and 30 min after exposure to chlorine. EB was collected on thermal desorption tubes and subsequently analyzed for VOCs by GC-MS. The following day, the extent of airway injury was assessed in the animals by examining neutrophils in the bronchoalveolar lavage fluid. VOC analysis revealed alterations in the EB biomarker pattern post-chlorine exposure, with eight biomarkers displaying increased levels and six exhibiting decreased levels following exposure. Four chlorinated compounds: trichloromethane, chloroacetone, 1,1-dichloroacetone and dichloroacetonitrile, were increased in chlorine-exposed mice, suggesting their specificity as chlorine EB biomarkers. Furthermore, chlorine-exposed mice displayed a neutrophilic inflammatory response and body weight loss 24 h following exposure. In conclusion, all animals developed an airway inflammation characterized by neutrophil infiltration and a specific EB pattern that could be extracted after chlorine exposure. Monitoring EB samples can readily and non-invasively provide valuable information on biomarkers for diagnosis of chlorine-induced ALI, confirming chlorine exposures.

Real-time monitoring of breath biomarkers using magnonic wireless sensor based on magnetic nanoparticles

In this paper, an innovative device with gas remote-sensing capability is proposed, which is based on the interaction between magnetic nanoparticles and gases associated with exhaled breath biomarkers that can have a metabolic origin. Magnetite (Fe₃O₄) nanoparticles of around 30 nm have been used. The gas molecules adsorbed on surface modulate the magnetization of the nanoparticles and magnetostatic surface spin waves (MSSW) propagated on an yttrium iron garnet (YIG) thin film are used to detect this modulation by the induced frequency shift. The optimization of the remote gas sensor has been carried out through simulations of a magnetic model. Simulations show the feasibility of developing a high-performance remote sensor by encapsulating the nanostructures in a polytetrafluoroethylene (PTFE) tube and detecting part per million changes in their magnetization. The results show the possibility of developing new, inexpensive, reusable, contactless magnetic gas sensors employing spin waves as transductor. The developed sensor shows a high sensitivity and selectivity to concentrations as low as 50 ppm of different breath biomarkers.

Machine Learning Approaches: Detecting the Disease Variants in Human-Exhaled Breath Biomarkers.

In recent days, the development of sensor-based medical devices has been found to be very effective for the prediction and analysis of the onset of diseases. The instigation of an electronic nose (eNose) device is profound and very useful in diverse applications. The analysis of exhaled breath biomarkers using eNose sensors has attained wider attention among researchers, and the prediction of multiple disease variants using the same is still an open research problem. In this work, an enhanced XGBooster classifier-based prediction mechanism was introduced to identify the disease variants based on the responses of commercially available metal oxide-based Figaro (Japan) sensors including TGS826, TGS822, TGS2600, and TGS2602. The implemented model secured 98.36% prediction accuracy in multiclass disease prediction and classification. The homemade one-dimensional metal oxide sensing elements such as ZnO, Cr-doped ZnO, and ZnO/NiO were integrated with the aforementioned sensor array for the specific detection of the three biomarkers of interest. This model has attained a classification accuracy of 99.77, 94.91, and 96.56% toward ammonia, ethanol, and acetone, respectively. And the multiclass disease biomarker classification accuracy of the readymade and homemade eNose prototype models was compared, and the results are summarized.

Au- and Pd-Doped SnS2 Monolayers for Lung Cancer Biomarkers (C3H6O, C6H6, and C5H8) Detection: A Density Functional Theory Investigation.

An efficient and noninvasive method of sensing lung cancer at an early stage is through detecting its biomarkers in the patient's exhaled breath. Acetone (C3H6O), benzene (C6H6), and isoprene (C5H8) emerged as crucial biomarkers, which were significantly elevated in lung cancer patients. Here, we investigated the adsorption behaviors of the three gas molecules on pristine and transition metal (TM)-doped (Au and Pd) SnS2 monolayers using the density functional theory (DFT) method. Our findings indicate that both Au- and Pd-doped SnS2 display higher adsorption energies (-0.53 to -1.313 eV) than that of the pure SnS2 monolayer (0.031 to 0.066 eV). Specifically, Pd-SnS2 exhibits smaller adsorption energy compared to that of Au-SnS2 when capturing C3H6O, C6H6, and C5H8. The estimated recovery times for Pd-SnS2 (8.016 × 10-4 to 16.02 s) are shorter compared to those of Au-SnS2 (1.11 to 1.14 × 1010 s), indicating the superior capability of Pd-SnS2 over Au-SnS2 as a reversible sensor. Afterward, calculations of band structure, projected density of states (PDOS), and charge transfer were performed, which further substantiates the more promising potentials for Pd-doped SnS2 monolayer as gas sensors over the others. Overall, our results suggest that Pd-SnS2 is a better candidate for C3H6O, C6H6, and C5H8 detection over Au-SnS2 and pristine SnS2.

A clinical proteomics study of exhaled breath condensate and biomarkers for pulmonary embolism

Pulmonary embolism (PE) can be a diagnostic challenge. Current diagnostic markers for PE are unspecific and new diagnostic tools are needed. The air we exhale is a possible new source for biomarkers which can be tapped into by analysing the exhaled breath condensate (EBC). We analysed the EBC from patients with PE and controls to investigate if the EBC is a useful source for new diagnostic biomarkers of PE. We collected and analysed EBC samples from patients with suspected PE and controls matched on age and sex. Patients in whom PE was ruled out after diagnostic work-up were included in the control group to increase the sensitivity and generalizability of the identified markers. EBC samples were collected using an RTube™. The protein composition of the EBCs were analysed using data dependent label-free quantitative nano liquid chromatography–tandem mass spectrometry. EBC samples from 28 patients with confirmed PE, and 49 controls were analysed. A total of 928 EBC proteins were identified in the 77 EBC samples. As expected, a low protein concentration was determined which resulted in many proteins with unmeasurable levels in several samples. The levels of HSPA5, PEBP1 and SFTPA2 were higher and levels of POF1B, EPPK1, PSMA4, ALDOA, and CFL1 were lower in PE compared with controls. In conclusion, the human EBC contained a variety of endogenous proteins and may be a source for new diagnostic markers of PE and other diseases.

Gas sensors based on Co3O4/TiO2 core-shell nanofibers prepared by coaxial electrospinning for breath marker acetone detection

Gas sensors play an important role in environmental detection and exhaled breath biomarker detection. In this paper, Co3O4/TiO2 core-shell NFs were synthesized by coaxial electrostatic spinning method. The morphology, structure and composition of the composites were analyzed by SEM, TEM, EDS, XRD and XPS. The results of conventional static gas sensitivity performance tests showed that the Co3O4/TiO2 sensors exhibited a P-type response with excellent sensing performance to acetone. The higher response values and better selectivity of Co3O4/TiO2 sensors for acetone were observed compared to Co3O4 NFS and TiO2 NFS sensors, and the shortcomings that cannot be recovered from TiO2 NFS have also been significantly improved. In addition, the breath simulation experiments showed that the Co3O4/TiO2 sensors have a lower acetone detection limit down to 500 ppb. The initial resistance of the Co3O4/TiO2 sensor does not change significantly with relative humidity increasing, and the response value fluctuates by 1.5% at 300 °C. The enhanced acetone sensing performance of Co3O4/TiO2 is ascribed to the heterojunction and porous structure which result in more adsorbed active sites on the material surface. With the advantages of low detection limit and high anti-humidity, Co3O4/TiO2 gas sensors are expected to become a potential candidate for the detection of acetone in exhaled gas of patients with diabetes in clinical non-invasive testing.

Selection of consistent breath biomarkers of abnormal liver function using feature selection: a pilot study

PurposeBreath profiling has gained importance in recent years as it is a non-invasive technique to identify biomarkers for various diseases. Breath profiling of abnormal liver function in individuals for identifying potential biomarkers in exhaled breath could be a useful diagnostic tool. The objective of this study was to identify potential biomarkers in exhaled breath that remain stable and consistent during different physiological states, including rest and brief workouts, intending to develop a non-invasive diagnostic tool for detecting abnormal liver function.MethodOur study employed a gas chromatography and mass-spectrometer quantified dataset for analysis. Machine learning techniques, including feature selection and model training, were used to rank and evaluate potential biomarkers' contributions to the model's performance. Statistical methods were applied to filter significant and consistent biomarkers. The final selected biomarkers were iterated for all possible combinations using machine learning algorithms to determine their accuracy range. Furthermore, classification models were used to evaluate the performance metrics of the biomarkers and compare models.ResultThe final selected biomarkers, including 2-Myristynoyl Pantetheine, Pterin-6 Carboxylic Acid, Methyl Mercaptan, N-Acetyl Cysteine, and Butyric Acid, exhibited stable levels in exhaled breath during different physiological states. They showed high accuracy and precision in detecting abnormal liver function. Our machine learning models achieved an accuracy rate ranging from 0.7 to 0.95 in all conditions, with precision, recall, prediction probability, and a 95% confidence interval ranging from 0.84 to 0.94, using various combinations of these biomarkers.ConclusionOur statistical and machine learning analysis identified significant and potential biomarkers that contribute to the detection of abnormal liver function. These biomarkers were consistent across different physiological states of the body in both patient and healthy groups. The use of breath samples and feature selection machine learning methods proved to be an accurate and reliable approach for identifying these biomarkers. Our findings provide valuable insights for future research in this field and can inform the development of non-invasive and cost-effective diagnostic tests for liver disease.