The mechanisms by which air pollution has multiple systemic effects in humans are not fully elucidated, but appear to include inflammation and thrombosis. This study examines whether concentrations of ozone and components of fine particle mass are associated with changes in methylation on tissue factor (F3), interferon gamma (IFN-γ), interleukin 6 (IL-6), toll-like receptor 2 (TLR-2), and intercellular adhesion molecule 1 (ICAM-1). We investigated associations between air pollution exposure and gene-specific methylation in 777 elderly men participating in the Normative Aging Study (1999-2009). We repeatedly measured methylation at multiple CpG sites within each gene's promoter region and calculated the mean of the position-specific measurements. We examined intermediate-term associations between primary and secondary air pollutants and mean methylation and methylation at each position with distributed-lag models. Increase in air pollutants concentrations was significantly associated with F3, ICAM-1, and TLR-2 hypomethylation, and IFN-γ and IL-6 hypermethylation. An interquartile range increase in black carbon concentration averaged over the four weeks prior to assessment was associated with a 12% reduction in F3 methylation (95% CI: -17% to -6%). For some genes, the change in methylation was observed only at specific locations within the promoter region. DNA methylation may reflect biological impact of air pollution. We found some significant mediated effects of black carbon on fibrinogen through a decrease in F3 methylation, and of sulfate and ozone on ICAM-1 protein through a decrease in ICAM-1 methylation.

Background: Previous studies have observed associations between air pollution and heart disease. Susceptibility to air pollution effects has been examined mostly with a test of effect modification, but little evidence is available whether air pollution distorts cardiovascular risk factor distribution. Objectives: This paper aims to examine distributional and heterogeneous effects of air pollution on known cardiovascular biomarkers. Methods: A total of 1,112 men from the Normative Aging Study and residents of the greater Boston, Massachusetts, area with mean age of 69 years at baseline were included in this study during the period 1995–2013. We used quantile regression and random slope models to investigate distributional effects and heterogeneity in the traffic-related responses on blood pressure, heart rate variability, repolarization, lipids, and inflammation. We considered 28-day averaged exposure to particle number, PM2.5 black carbon, and PM2.5 mass concentrations (measured at a single monitor near the site of the study visits). Results: We observed some evidence suggesting distributional effects of traffic-related pollutants on systolic blood pressure, heart rate variability, corrected QT interval, low density lipoprotein (LDL) cholesterol, triglyceride, and intercellular adhesion molecule-1 (ICAM-1). For example, among participants with LDL cholesterol below 80 mg/dL, an interquartile range increase in PM2.5 black carbon exposure was associated with a 7-mg/dL (95% CI: 5, 10) increase in LDL cholesterol, while among subjects with LDL cholesterol levels close to 160 mg/dL, the same exposure was related to a 16-mg/dL (95% CI: 13, 20) increase in LDL cholesterol. We observed similar heterogeneous associations across low versus high percentiles of the LDL distribution for PM2.5 mass and particle number. Conclusions: These results suggest that air pollution distorts the distribution of cardiovascular risk factors, and that, for several outcomes, effects may be greatest among individuals who are already at high risk. Citation: Bind MA, Peters A, Koutrakis P, Coull B, Vokonas P, Schwartz J. 2016. Quantile regression analysis of the distributional effects of air pollution on blood pressure, heart rate variability, blood lipids, and biomarkers of inflammation in elderly American men: the Normative Aging Study. Environ Health Perspect 124:1189–1198; http://dx.doi.org/10.1289/ehp.1510044

Mediation analysis is a valuable approach to examine pathways in epidemiological research. Prospective cohort studies are often conducted to study biological mechanisms and often collect longitudinal measurements on each participant. Mediation formulae for longitudinal data have been developed. Here, we formalize the natural direct and indirect effects using a causal framework with potential outcomes that allows for an interaction between the exposure and the mediator. To allow different types of longitudinal measures of the mediator and outcome, we assume two generalized mixed-effects models for both the mediator and the outcome. The model for the mediator has subject-specific random intercepts and random exposure slopes for each cluster, and the outcome model has random intercepts and random slopes for the exposure, the mediator, and their interaction. We also expand our approach to settings with multiple mediators and derive the mediated effects, jointly through all mediators. Our method requires the absence of time-varying confounding with respect to the exposure and the mediator. This assumption is achieved in settings with exogenous exposure and mediator, especially when exposure and mediator are not affected by variables measured at earlier time points. We apply the methodology to data from the Normative Aging Study and estimate the direct and indirect effects, via DNA methylation, of air pollution, and temperature on intercellular adhesion molecule 1 (ICAM-1) protein levels. Our results suggest that air pollution and temperature have a direct effect on ICAM-1 protein levels (i.e. not through a change in ICAM-1 DNA methylation) and that temperature has an indirect effect via a change in ICAM-1 DNA methylation.

Background: Previous studies have found relationships between DNA methylation and various environmental contaminant exposures. Associations with weather have not been examined. Because temperature and humidity are related to mortality even on non-extreme days, we hypothesized that temperature and relative humidity may affect methylation. Methods: We repeatedly measured methylation on long interspersed nuclear elements (LINE-1), Alu, and 9 candidate genes in blood samples from 777 elderly men participating in the Normative Aging Study (1999–2009). We assessed whether ambient temperature and relative humidity are related to methylation on LINE-1 and Alu, as well as on genes controlling coagulation, inflammation, cortisol, DNA repair, and metabolic pathway. We examined intermediate-term associations of temperature, relative humidity, and their interaction with methylation, using distributed lag models. Results: Temperature or relative humidity levels were associated with methylation on tissue factor (F3), intercellular adhesion molecule 1 (ICAM-1), toll-like receptor 2 (TRL-2), carnitine O-acetyltransferase (CRAT), interferon gamma (IFN-γ), inducible nitric oxide synthase (iNOS), and glucocorticoid receptor, LINE-1, and Alu. For instance, a 5°C increase in 3-week average temperature in ICAM-1 methylation was associated with a 9% increase (95% confidence interval: 3% to 15%), whereas a 10% increase in 3-week average relative humidity was associated with a 5% decrease (−8% to −1%). The relative humidity association with ICAM-1 methylation was stronger on hot days than mild days. Conclusions: DNA methylation in blood cells may reflect biological effects of temperature and relative humidity. Temperature and relative humidity may also interact to produce stronger effects.

The field of environmental health has been dominated by modeling associations, especially by regressing an observed outcome on a linear or nonlinear function of observed covariates. Readers interested in advances in policies for improving environmental health are, however, expecting to be informed about health effects resulting from, or more explicitly caused by, environmental exposures. The quantification of health impacts resulting from the removal of environmental exposures involves causal statements. Therefore, when possible, causal inference frameworks should be considered for analyzing the effects of environmental exposures on health outcomes.