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

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 (O-3) 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 O-3 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 1, 2, 4, 5, 8, 10, 12p70 and 13, IFN-, and TNF-) 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.