Julian King

This review addresses several important confounding factors that are often overlooked in the analysis of volatiles contained in exhaled breath, which, if ignored, will significantly limit the interpretation of volatile data from exhaled breath and thus prevent meaningful outcomes. Crucial confounding factors that tend to be neglected are those that influence the alveolar volatile concentrations according to the Farhi equation, namely cardiac output, alveolar ventilation, blood:air partition coefficients and mixed-venous blood volatile concentrations. Another potential confounding factor is associated with the contributions of volatiles produced in the oral cavity through microbial activity. In addition, the concentration of an exhaled breath volatile will be affected if that volatile is also present in the ambient inhaled air. The purpose of this review is to show how these confounding factors can be accounted for. We will demonstrate how mathematical modeling and an understanding of the Farhi equation aid in the interpretation of the exhaled breath volatile concentration measurements. We will discuss the limitations of the alveolar gradient method used to determine the effects of inhaled volatiles. An alternative method is presented that correctly allows for any inhaled volatile contribution to the exhaled concentration of that volatile. The review concludes with suggested recommendations that, if adopted, will improve the quality of breath data leading to an improved interpretation of exhaled volatiles.

In the breath research community’s search for volatile organic compounds that can act as non-invasive biomarkers for various diseases, hundreds of endogenous volatiles have been discovered. Whilst these systemic chemicals result from normal and abnormal metabolic activities or pathological disorders, to date very few are of any use for the development of clinical breath tests that could be used for disease diagnosis or to monitor therapeutic treatments. The reasons for this lack of application are manifold and complex, and these complications either limit or ultimately inhibit the analytical application of endogenous volatiles for use in the medical sciences. One such complication is a lack of knowledge on the biological origins of the endogenous volatiles. A major exception to this is isoprene. Since 1984, i.e. for 40 years, it has been generally accepted that the pathway to the production of human isoprene, and hence the origin of isoprene in exhaled breath, is through cholesterol biosynthesis via the mevalonate (MVA) pathway within the liver. However, various studies between 2001 and 2012 provide compelling evidence that human isoprene is produced in skeletal muscle tissue. A recent multi-omic investigation of genes and metabolites has revealed that this proposal is correct by showing that human isoprene predominantly results from muscular lipolytic cholesterol metabolism. Despite the overwhelming proof for a muscular pathway to isoprene production in the human body, breath research papers still reference the hepatic MVA pathway. The major aim of this perspective is to review the evidence that leads to a correct interpretation for the origins of human isoprene, so that the major pathway to human isoprene production is understood and appropriately disseminated. This is important, because an accurate attribution to the endogenous origins of isoprene is needed if exhaled isoprene levels are to be correctly interpreted and for assessing isoprene as a clinical biomarker.

We summarize the history and review the literature on isoprene in exhaled breath and discuss the current evidence and models that describe its endogenous origin and consequence for understanding isoprene levels and their variations in exhaled breath.

Researchers looking for biomarkers from different sources, such as breath, urine, or blood, frequently search for specific patterns of volatile organic compounds (VOCs), often using pattern recognition or machine learning techniques. However, they are not generally aware that these patterns change depending on the source they use. Therefore, we have created a simple model to demonstrate that the distribution patterns of VOCs in fat, mixed venous blood, alveolar air, and end-tidal breath are different. Our approach follows well-established models for the description of dynamic real-time breath concentration profiles. We start with a uniform distribution of end-tidal concentrations of selected VOCs and calculate the corresponding target concentrations. For this, we only need partition coefficients, mass balance, and the assumption of an equilibrium state, which avoids the need to know the volatiles’ metabolic rates and production rates within the different compartments.

Real-time measurements of the differences in inhaled and exhaled, unlabeled and fully deuterated acetone concentration levels, at rest and during exercise, have been conducted using proton transfer reaction mass spectrometry. A novel approach to continuously differentiate between the inhaled and exhaled breath acetone concentration signals is used. This leads to unprecedented fine grained data of inhaled and exhaled concentrations. The experimental results obtained are compared with those predicted using a simple three compartment model that theoretically describes the influence of inhaled concentrations on exhaled breath concentrations for volatile organic compounds with high blood:air partition coefficients, and hence is appropriate for acetone. An agreement between the predicted and observed concentrations is obtained. Our results highlight that the influence of the upper airways cannot be neglected for volatiles with high blood:air partition coefficients, i.e. highly water soluble volatiles.

Objective: With the overall goal to harmonize prospective effectiveness assessment of active safety systems, the specific objective of this study is to identify and evaluate sources of variation in virtual precrash simulations and to suggest topics for harmonization resulting in increased comparability and thus trustworthiness of virtual simulation-based prospective effectiveness assessment. Methods: A round-robin assessment of the effectiveness of advanced driver assistance systems was performed using an array of state-of-the-art virtual simulation tools on a set of standard test cases. The results were analyzed to examine reasons for deviations in order to identify and assess aspects that need to be harmonized and standardized. Deviations between results calculated by independent engineering teams using their own tools should be minimized if the research question is precisely formulated regarding input data, models, and postprocessing steps. Results: Two groups of sources of variations were identified; one group (mostly related to the implementation of the system under test) can be eliminated by using a more accurately formulated research question, whereas the other group highlights further harmonization needs because it addresses specific differences in simulation tool setups. Time-to-collision calculations, vehicle dynamics, especially braking behavior, and hit-point position specification were found to be the main sources of variation. Conclusions: The study identified variations that can arise from the use of different simulation setups in assessment of the effectiveness of active safety systems. The research presented is a first of its kind and provides significant input to the overall goal of harmonization by identifying specific items for standardization. Future activities aim at further specification of methods for prospective assessments of the effectiveness of active safety, which will enhance comparability and trustworthiness in this kind of studies and thus contribute to increased traffic safety.

In a recent paper (Unterkofler et al 2015 J. Breath Res. 9 036002) we presented a simple two compartment model which describes the influence of inhaled concentrations on exhaled breath concentrations for volatile organic compounds (VOCs) with small Henry constants. In this paper we extend this investigation concerning the influence of inhaled concentrations on exhaled breath concentrations for VOCs with higher Henry constants. To this end we extend our model with an additional compartment which takes into account the influence of the upper airways on exhaled breath VOC concentrations.

We develop a simple three compartment model based on mass balance equations which quantitatively describes the dynamics of breath methane concentration profiles during exercise on an ergometer. With the help of this model it is possible to estimate the endogenous production rate of methane in the large intestine by measuring breath gas concentrations of methane.

We develop a simple three compartment model based on mass balance equations which quantitatively describes the dynamics of breath methane concentration profiles during exercise on an ergometer. With the help of this model it is possible to estimate the endogenous production rate of methane in the large intestine by measuring breath gas concentrations of methane.