Matthew A. Stiegel

Clinical breath analysis: discriminating between human endogenous compounds and exogenous (environmental) chemical confounders

Volatile organic compounds (VOCs) in exhaled breath originate from current or previous environmental exposures (exogenous compounds) and internal metabolic (anabolic and catabolic) production (endogenous compounds). The origins of certain VOCs in breath presumed to be endogenous have been proposed to be useful as preclinical biomarkers of various undiagnosed diseases including lung cancer, breast cancer, and cardio-pulmonary disease. The usual approach is to develop difference algorithms comparing VOC profiles from nominally healthy controls to cohorts of patients presenting with a documented disease, and then to apply the resulting rules to breath profiles of subjects with unknown disease status. This approach to diagnosis has a progression of sophistication; at the most rudimentary level, all measurable VOCs are included in the model. The next level corrects exhaled VOC concentrations for current inspired air concentrations. At the highest level, VOCs exhibiting discriminatory value also require a plausible biochemical pathway for their production before inclusion. Although these approaches have all shown some level of success, there is concern that pattern recognition is prone to error from environmental contamination and between-subject variance. In this paper, we explore the underlying assumptions for the interpretation and assignment of endogenous compounds with probative value for assessing changes. Specifically, we investigate the influence of previous exposures, elimination mechanisms and partitioning of exogenous compounds as confounders of true endogenous compounds. We provide specific examples based on a simple classical pharmacokinetic approach to identify potential misinterpretations of breath data and propose some remedies.

Analysis of inflammatory cytokines in human blood, breath condensate, and urine using a multiplex immunoassay platform

Abstract A change in the expression of cytokines in human biological media indicates an inflammatory response to external stressors and reflects an early step along the adverse outcome pathway (AOP) for various health endpoints. To characterize and interpret this inflammatory response, methodology was developed for measuring a suite of 10 different cytokines in human blood, exhaled breath condensate (EBC), and urine using an electrochemiluminescent multiplex Th1/Th2 cytokine immunoassay platform. Measurement distributions and correlations for eight interleukins (IL) (1β, 2, 4, 5, 8, 10, 12p70 and 13), interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α) were evaluated using 90 blood plasma, 77 EBC, and 400 urine samples collected from nominally healthy adults subjects in North Carolina in 2008–2012. The in vivo results show that there is sufficient sensitivity for characterizing all 10 cytokines at levels of 0.05–0.10 ρg/ml with a dynamic range up to 100 ng/ml across all three of these biological media. The measured in vivo results also show that the duplicate analysis of blood, EBC and urine samples have average estimated fold ranges of 2.21, 3.49, and 2.50, respectively, which are similar to the mean estimated fold range (2.88) for the lowest concentration (0.610 ρg/ml) from a series of spiked control samples; the cytokine method can be used for all three biological media. Nine out of the 10 cytokines measured in EBC were highly correlated within one another with Spearman ρ coefficients ranging from 0.679 to 0.852, while the cytokines measured in blood had a mix of negative and positive correlations, ranging from −0.620 to 0.836. Almost all correlations between EBC and blood were positive. This work also represents the first successful within- and between-person evaluation of ultra trace-level inflammatory markers in blood, EBC, and urine.

Inflammatory Cytokines and White Blood Cell Counts Response to Environmental Levels of Diesel Exhaust and Ozone Inhalation Exposures

Epidemiological observations of urban inhalation exposures to diesel exhaust (DE) and ozone (O3) have shown pre-clinical cardiopulmonary responses in humans. Identifying the key biological mechanisms that initiate these health bioindicators is difficult due to variability in environmental exposure in time and from person to person. Previously, environmentally controlled human exposure chambers have been used to study DE and O3 dose-response patterns separately, but investigation of co-exposures has not been performed under controlled conditions. Because a mixture is a more realistic exposure scenario for the general public, in this study we investigate the relationships of urban levels of urban-level DE exposure (300 μg/m3), O3 (0.3 ppm), DE + O3 co-exposure, and innate immune system responses. Fifteen healthy human volunteers were studied for changes in ten inflammatory cytokines (interleukins 1β, 2, 4, 5, 8, 10, 12p70 and 13, IFN-γ, and TNF-α) and counts of three white blood cell types (lymphocytes, monocytes, and neutrophils) following controlled exposures to DE, O3, and DE+O3. The results show subtle cytokines responses to the diesel-only and ozone-only exposures, and that a more complex (possibly synergistic) relationship exists in the combination of these two exposures with suppression of IL-5, IL-12p70, IFN-γ, and TNF-α that persists up to 22-hours for IFN-γ and TNF-α. The white blood cell differential counts showed significant monocyte and lymphocyte decreases and neutrophil increases following the DE + O3 exposure; lymphocytes and neutrophils changes also persist for at least 22-hours. Because human studies must be conducted under strict safety protocols at environmental levels, these effects are subtle and are generally only seen with detailed statistical analysis. This study indicates that the observed associations between environmental exposures and cardiopulmonary effects are possibly mediated by inflammatory response mechanisms.

Estimating common parameters of lognormally distributed environmental and biomonitoring data: Harmonizing disparate statistics from publications

The progression of science is driven by the accumulation of knowledge and builds upon published work of others. Another important feature is to place current results into the context of previous observations. The published literature, however, often does not provide sufficient direct information for the reader to interpret the results beyond the scope of that particular article. Authors tend to provide only summary statistics in various forms, such as means and standard deviations, median and range, quartiles, 95% confidence intervals, and so on, rather than providing measurement data. Second, essentially all environmental and biomonitoring measurements have an underlying lognormal distribution, so certain published statistical characterizations may be inappropriate for comparisons. The aim of this study was to review and develop direct conversions of different descriptions of data into a standard format comprised of the geometric mean (GM) and the geometric standard deviation (GSD) and then demonstrate how, under the assumption of lognormal distribution, these parameters are used to answer questions of confidence intervals, exceedance levels, and statistical differences among distributions. A wide variety of real-world measurement data sets was reviewed, and it was demonstrated that these data sets are indeed of lognormal character, thus making them amenable to these methods. Potential errors incurred from making retrospective estimates from disparate summary statistics are described. In addition to providing tools to interpret “other people’s data,” this review should also be seen as a cautionary tale for publishing one’s own data to make it as useful as possible for other researchers.

Linking physiological parameters to perturbations in the human exposome: Environmental exposures modify blood pressure and lung function via inflammatory cytokine pathway

ABSTRACT Human biomonitoring is an indispensable tool for evaluating the systemic effects derived from external stressors including environmental pollutants, chemicals from consumer products, and pharmaceuticals. The aim of this study was to explore consequences of environmental exposures to diesel exhaust (DE) and ozone (O3) and ultimately to interpret these parameters from the perspective of in vitro to in vivo extrapolation. In particular, the objective was to use cytokine expression at the cellular level as a biomarker for physiological systemic responses such as blood pressure and lung function at the systemic level. The values obtained could ultimately link in vivo behavior to simpler in vitro experiments where cytokines are a measured parameter. Human exposures to combinations of DE and O3 and the response correlations between forced exhaled volume in 1 second (FEV1), forced vital capacity (FVC), systolic and diastolic blood pressure (SBP and DBP, respectively), and 10 inflammatory cytokines in blood (interleukins 1β, 2, 4, 5, 8, 10, 12p70 and 13, IFN-γ, and TNF-α) were determined in 15 healthy human volunteers. Results across all exposures revealed that certain individuals displayed greater inflammatory responses compared to the group and, generally, there was more between-person variation in the responses. Evidence indicates that individuals are more stable within themselves and are more likely to exhibit responses independent of one another. Data suggest that in vitro findings may ultimately be implemented to elucidate underlying adverse outcome pathways (AOP) for linking high-throughput toxicity tests to physiological in vivo responses. Further, this investigation supports assessing subjects based upon individual responses as a complement to standard longitudinal (pre vs. post) intervention grouping strategies. Ultimately, it may become possible to predict a physiological (systemic) response based upon cellular-level (in vitro) observations.

Kidney injury biomarkers and urinary creatinine variability in nominally healthy adults

Abstract Environmental exposure diagnostics use creatinine concentrations in urine aliquots as the internal standard for dilution normalization of all other excreted metabolites when urinary excretion rate data are not available. This is a reasonable approach for healthy adults as creatinine is a human metabolite that is continually produced in skeletal muscles and presumably excreted in the urine at a stable rate. However, creatinine also serves as a biomarker for glomerular filtration rate (efficiency) of the kidneys, so undiagnosed kidney function impairment could affect this commonly applied dilution calculation. The United States Environmental Protection Agency (US EPA) has recently conducted a study that collected approximately 2600 urine samples from 50 healthy adults, aged 19–50 years old, in North Carolina in 2009–2011. Urinary ancillary data (creatinine concentration, total void volume, elapsed time between voids), and participant demographic data (race, gender, height, and body weight) were collected. A representative subset of 280 urine samples from 29 participants was assayed using a new kidney injury panel (KIP). In this article, we investigated the relationships of KIP biomarkers within and between subjects and also calculated their interactions with measured creatinine levels. The aims of this work were to document the analytical methods (procedures, sensitivity, stability, etc.), provide summary statistics for the KIP biomarkers in “healthy” adults without diagnosed disease (distribution, fold range, central tendency, variance), and to develop an understanding as to how urinary creatinine level varies with respect to the individual KIP proteins. Results show that new instrumentation and data reduction methods have sufficient sensitivity to measure KIP levels in nominally healthy urine samples, that linear regression between creatinine concentration and urinary excretion explains only about 68% of variability, that KIP markers are poorly correlated with creatinine (r2 ∼ 0.34), and that statistical outliers of KIP markers are not random, but are clustered within certain subjects. In addition, we interpret these new adverse outcome pathways based in vivo biomarkers for their potential use as intermediary chemicals that may be diagnostic of kidney adverse outcomes to environmental exposure.

Human biomarker interpretation: the importance of intra-class correlation coefficients (ICC) and their calculations based on mixed models, ANOVA, and variance estimates

ABSTRACT Human biomonitoring is the foundation of environmental toxicology, community public health evaluation, preclinical health effects assessments, pharmacological drug development and testing, and medical diagnostics. Within this framework, the intra-class correlation coefficient (ICC) serves as an important tool for gaining insight into human variability and responses and for developing risk-based assessments in the face of sparse or highly complex measurement data. The analytical procedures that provide data for clinical and public health efforts are continually evolving to expand our knowledge base of the many thousands of environmental and biomarker chemicals that define human systems biology. These chemicals range from the smallest molecules from energy metabolism (i.e., the metabolome), through larger molecules including enzymes, proteins, RNA, DNA, and adducts. In additiona, the human body contains exogenous environmental chemicals and contributions from the microbiome from gastrointestinal, pulmonary, urogenital, naso-pharyngeal, and skin sources. This complex mixture of biomarker chemicals from environmental, human, and microbiotic sources comprise the human exposome and generally accessed through sampling of blood, breath, and urine. One of the most difficult problems in biomarker assessment is assigning probative value to any given set of measurements as there are generally insufficient data to distinguish among sources of chemicals such as environmental, microbiotic, or human metabolism and also deciding which measurements are remarkable from those that are within normal human variability. The implementation of longitudinal (repeat) measurement strategies has provided new statistical approaches for interpreting such complexities, and use of descriptive statistics based upon intra-class correlation coefficients (ICC) has become a powerful tool in these efforts. This review has two parts; the first focuses on the history of repeat measures of human biomarkers starting with occupational toxicology of the early 1950s through modern applications in interpretation of the human exposome and metabolic adverse outcome pathways (AOPs). The second part reviews different methods for calculating the ICC and explores the strategies and applications in light of different data structures.

Predicting polycyclic aromatic hydrocarbons using a mass fraction approach in a geostatistical framework across North Carolina

Currently in the United States there are no regulatory standards for ambient concentrations of polycyclic aromatic hydrocarbons (PAHs), a class of organic compounds with known carcinogenic species. As such, monitoring data are not routinely collected resulting in limited exposure mapping and epidemiologic studies. This work develops the log-mass fraction (LMF) Bayesian maximum entropy (BME) geostatistical prediction method used to predict the concentration of nine particle-bound PAHs across the US state of North Carolina. The LMF method develops a relationship between a relatively small number of collocated PAH and fine Particulate Matter (PM2.5) samples collected in 2005 and applies that relationship to a larger number of locations where PM2.5 is routinely monitored to more broadly estimate PAH concentrations across the state. Cross validation and mapping results indicate that by incorporating both PAH and PM2.5 data, the LMF BME method reduces mean squared error by 28.4% and produces more realistic spatial gradients compared to the traditional kriging approach based solely on observed PAH data. The LMF BME method efficiently creates PAH predictions in a PAH data sparse and PM2.5 data rich setting, opening the door for more expansive epidemiologic exposure assessments of ambient PAH.

Field-Testing Method for Loose-Fitting Powered Air-Purifying Respirators Equipped with HEPA Filters:

Respiratory protection is a key component of the biosafety program for the Regional Biocontainment Laboratory at Duke University. Two types of loose-fitting powered air-purifying respirators (PAPRs) equipped with high-efficiency particulate air (HEPA) filters have been approved for use in the facility. Although both respirator systems were previously certified by the National Institute of Occupational Safety and Health for use in occupational environments that pose a risk of respiratory exposure to infectious agents, there are currently no regulatory requirements for routine field-testing of units in situ. This report describes a method for conducting on-site total leak testing for each HEPA-filtered PAPR used in a biocontainment setting. This testing evaluates the integrity of the filter itself and whether it is seated properly in the filter housing. On-site routine performance testing such as this provides an enhancement to the safety procedures governing research activities assigned to biosafety level 3 or animal biosafety level 3. The outlined test method is currently used in the Regional Biocontainment Laboratory at Duke University to verify proper filtration efficiency prior to placing newly purchased units into service and for annual reverification. Results from on-site testing with this method have demonstrated that some PAPR/HEPA filter systems fail to perform at the expected level of filtration efficiency, thus emphasizing the need for including this enhancement and regular testing in a comprehensive biosafety program that includes respiratory protection at biosafety level 3 or animal biosafety level 3 (ABSL-3).