Mangilal Agarwal

Introduction: Volatile organic compounds (VOCs) in breath are potential biomarkers for medical conditions that may be used for non-invasive health monitoring. One challenge that still exists is determining the fidelity of reported VOC biomarkers. The lack of universally accepted sampling methods makes it difficult to identify reliable candidates, thus allowing for the potential of false discovery. Objectives: The purpose of this study was to robustly profile VOCs in breath samples collected from relatively healthy participants using two offline methods for collection/analysis via solid phase microextraction (SPME) coupled to gas chromatography – mass spectrometry (GC-MS). Methods: 158 cross-sectional volunteers provided one-time samples using two methods, one which directly sampled breath via SPME and another which collected breath in Tedlar bags. Using both methods, 10 volunteers provided an additional nine longitudinal samples. Ambient air samples were collected routinely, and a robust data processing schematic was used to ensure high quality reporting of on-breath VOCs. Results: Data screening and processing led to the identification of > 30 unique VOCs in both methods. Hierarchical clustering and correlation analyses demonstrated volatile terpene/-oids showed homologous trends in both data sets. Of the 12 VOCs identified using both methods, 11 analytes displayed statistically significant correlations ( p < 0.05) in healthy breath samples. Finally, both methods were benchmarked regarding VOC reproducibility, and analyses showed that longitudinally collected samples were more reproducible compared to cross-sectional. Conclusions: The quantitative results from both sampling methods mirrored each other, thus increasing the reliability and fidelity of VOCs reported along with the results from biostatistical analysis.

Harnessing the potential of exhaled breath analysis is an emerging frontier in medical diagnostics, given breath is a rich source of volatile organic compound (VOC) biomarkers for different medical conditions. A current downfall in this field, however, is the lack of standardized and widely available methods for offline sampling of exhaled VOCs. Herein, strides are taken toward the standardization of breath sampling in Tedlar bags by exploring several factors that can impact VOC heterogeneity, including tubing material, chemical composition of collection bags, breath fractionation, exhalation volume, and transfer flow rate. After bag-based sampling standardization, performance was benchmarked using two offline breath sampling methods, Tedlar bags and the Respiration Collector for In Vitro Analysis (ReCIVA). Three volunteers from the laboratory with no known respiratory diseases donated ≥ n = 5 samples collected onto adsorption tubes via each method, which were analyzed through thermal desorption (TD) coupled with gas chromatography-mass spectrometry (GC–MS). Data processing revealed a set of 15 highly reliable on-breath VOCs detected across volunteers, and most analytes (except indole) demonstrated higher sensitivity using Tedlar bags. Calculating relative standard deviation (RSD) values showed Tedlar bags were also significantly more reproducible compared to the ReCIVA (p < 0.03). Agreement between the two methods was demonstrated through correlating VOC signals with high statistical significance (R ² = 0.70), indicating both devices are well situated for biomarker discovery applications. Graphical Abstract

Persons with type 1 diabetes (T1D) must track/control their blood glucose (BG) levels to avoid hypoglycemic events (BG < 70 mg/dL), which in the most severe cases can lead to seizures or even death. Canines may lead the way toward innovative testing solutions, as they can be trained to identify hypoglycemia simply and noninvasively by smelling exhaled volatile organic compounds (VOCs). To identify breath-based biomarkers of hypoglycemia, samples were collected during two consecutive summers at a diabetes camp (Cohort 1 and Cohort 2), and VOCs were analyzed by gas chromatography-mass spectrometry. Conserved VOCs between the two cohorts were identified, but individual VOCs alone had low accuracies for detection. Therefore, supervised multivariate statistical analysis was undertaken to identify a biosignature in the training data set (Cohort 1) that could detect hypoglycemia with higher accuracy (sensitivity = 94.8%/specificity = 95.0%). When this model was blindly tested on Cohort 2, hypoglycemia was classified with sensitivity = 90.0%/specificity = 89.9%. Ultimately, this study makes strides toward clinical validation through verifying biomarkers of hypoglycemia in hundreds of breath samples. These results may be translated to design a sensor array that could be integrated into a portable breathalyzer to increase glycemic control in persons with T1D.

Volatile organic compounds (VOCs) in urine headspace are potential biomarkers for different medical conditions, as canines can detect human diseases simply by smelling VOCs. Because dogs can detect disease-specific VOCs, gas chromatography–mass spectrometry (GC–MS) systems may be able to differentiate medical conditions with enhanced accuracy and precision, given they have unprecedented efficiency in separating, quantifying, and identifying VOCs in urine. Advancements in instrumentation have permitted the development of portable GC–MS systems that analyze VOCs at the point of care, but these are designed for environmental monitoring, emergency response, and manufacturing/processing. The purpose of this study is to repurpose the HAPSITE^® ER portable GC–MS for identifying urinary VOC biomarkers. Method development focused on optimizing sample preparation, off-column conditions, and instrumental parameters that may affect performance. Once standardized, the method was used to analyze a urine standard (n = 10) to characterize intra-day reproducibility. To characterize inter-day performance, n = 3 samples each from three volunteers (and the standard) were analyzed each day for a total of four days (n = 48 samples). Results showed the method could detect VOC signals with adequate reproducibility and distinguish VOC profiles from different volunteers with 100% accuracy.