Implementation of quality controls is essential to prevent batch effects in breathomics data and allow for cross-study comparisons

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.

Common Strategies and Factors Affecting Off-Line Breath Sampling and Volatile Organic Compounds Analysis Using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS)

An analysis of exhaled breath enables specialists to noninvasively monitor biochemical processes and to determine any pathological state in the human body. Breath analysis holds the greatest potential to remold and personalize diagnostics; however, it requires a multidisciplinary approach and collaboration of many specialists. Despite the fact that breath is considered to be a less complex matrix than blood, it is not commonly used as a diagnostic and prognostic tool for early detection of disordered conditions due to its problematic sampling, analysis, and storage. This review is intended to determine, standardize, and marshal experimental strategies for successful, reliable, and especially, reproducible breath analysis.

Detecting Colorectal Adenomas and Cancer Using Volatile Organic Compounds in Exhaled Breath: A Proof-of-Principle Study to Improve Screening

INTRODUCTION

Early detection of colorectal cancer (CRC) by screening programs is crucial because survival rates worsen at advanced stages. However, the currently used screening method, the fecal immunochemical test (FIT), suffers from a high number of false-positives and is insensitive for detecting advanced adenomas (AAs), resulting in false-negatives for these premalignant lesions. Therefore, more accurate, noninvasive screening tools are needed. In this study, the utility of analyzing volatile organic compounds (VOCs) in exhaled breath in a FIT-positive population to detect the presence of colorectal neoplasia was studied.

METHODS

In this multicenter prospective study, breath samples were collected from 382 FIT-positive patients with subsequent colonoscopy participating in the national Dutch bowel screening program (n = 84 negative controls, n = 130 non-AAs, n = 138 AAs, and n = 30 CRCs). Precolonoscopy exhaled VOCs were analyzed using thermal desorption-gas chromatography-mass spectrometry, and the data were preprocessed and analyzed using machine learning techniques.

RESULTS

Using 10 discriminatory VOCs, AAs could be distinguished from negative controls with a sensitivity and specificity of 79% and 70%, respectively. Based on this biomarker profile, CRC and AA combined could be discriminated from controls with a sensitivity and specificity of 77% and 70%, respectively, and CRC alone could be discriminated from controls with a sensitivity and specificity of 80% and 70%, respectively. Moreover, the feasibility to discriminate non-AAs from controls and AAs was shown.

DISCUSSION

VOCs in exhaled breath can detect the presence of AAs and CRC in a CRC screening population and may improve CRC screening in the future.

Changes of breath volatile organic compounds in healthy volunteers following segmental and inhalation endotoxin challenge

Background It is still unclear how airway inflammation affects the breath volatile organic compounds (VOC) profile in exhaled air. We therefore analyzed breath following well-defined pulmonary endotoxin (lipopolysaccharide, LPS) challenges. Methods Breath was collected from 10 healthy non-smoking subjects at eight time points before and after segmental and whole lung LPS inhalation challenge. Four Tenax-TA® adsorption tubes were simultaneously loaded from an aluminum reservoir cylinder and independently analyzed by two research groups using gas chromatography - mass spectrometry. Airway inflammation was assessed in bronchoalveolar lavage (BAL) and in sputum after segmental and inhaled LPS challenge, respectively. Results Segmental LPS challenge significantly increased the median (interquartile range, IQR) percentage of neutrophils in BAL from 3.0 (4.2) % to 64.0 (7.3) %. The inhalation challenge increased sputum neutrophils from 33.9 (26.8) % to 78.3 (13.5) %. We observed increases in breath aldehydes at both time points after segmental and inhaled LPS challenge. These results were confirmed by an independent laboratory. The longitudinal breath analysis also revealed distinct VOC patterns related to environmental exposures, clinical procedures, and to metabolic changes after food intake. Conclusions Changes in breath aldehydes suggest a relationship to LPS induced inflammation compatible with lipid peroxidation processes within the lung. Findings from our longitudinal data highlight the need for future studies to better consider the potential impact of the multiple VOCs from detergents, hygiene or lifestyle products a subject is continuously exposed to. We suspect that this very individual "owncloud" exposure is contributing to an increased variability of breath aldehydes, which might limit a use as inflammatory markers in daily clinical practice.

Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88–92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Breath Biopsy and Discovery of Exclusive Volatile Organic Compounds for Diagnosis of Infectious Diseases

Exhaled breath contains thousand metabolites and volatile organic compounds (VOCs) that originated from both respiratory tract and internal organ systems and their microbiomes. Commensal and pathogenic bacteria and virus of microbiomes are capable of producing VOCs of different chemical classes, and some of them may serve as biomarkers for installation and progression of various common human diseases. Here we describe qualitative and quantitative methods for measuring VOC fingerprints generated by cellular and microbial metabolic and pathologic pathways. We describe different chemical classes of VOCs and their role in the host cell-microbial interactions and their impact on infection disease pathology. We also update on recent progress on VOC signatures emitted by isolated bacterial species and microbiomes, and VOCs identified in exhaled breath of patients with respiratory tract and gastrointestinal diseases, and inflammatory syndromes, including the acute respiratory distress syndrome and sepsis. The VOC curated databases and instrumentations have been developed through statistically robust breathomic research in large patient populations. Scientists have now the opportunity to find potential biomarkers for both triage and diagnosis of particular human disease.