Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications.
Rationale: Exposure to particulate air pollution has been related to increased hospitalization and death, particularly from cardiovascular disease. Lower blood DNA methylation content is found in processes related to cardiovascular outcomes, such as oxidative stress, aging, and atherosclerosis. Objectives: We evaluated whether particulate pollution modifies DNA methylation in heavily methylated sequences with high representation throughout the human genome. Methods: We measured DNA methylation of long interspersed nucleotide element (LINE)-1 and Alu repetitive elements by quantitative polymerase chain reaction-pyrosequencing of 1,097 blood samples from 718 elderly participants in the Boston area Normative Aging Study. We used covariate-adjusted mixed models to account for within-subject correlation in repeated measures. We estimated the effects on DNA methylation of ambient particulate pollutants (black carbon, particulate matter with aerodynamic diameter < 2.5 mm [PM 2.5 ], or sulfate) in multiple time windows (4 h to 7 d) before the examination. We estimated standardized regression coefficients (b) expressing the fraction of a standard deviation change in DNA methylation associated with a standard deviation increase in exposure. Measurements and Main Results: Repetitive element DNA methylation varied in association with time-related variables, such as day of the week and season. LINE-1 methylation decreased after recent exposure to higher black carbon (b 5 20.11; 95% confidence interval [CI], 20.18 to 20.04; P 5 0.002) and PM 2.5 (b 5 20.13; 95% CI, 20.19 to 20.06; P , 0.001 for the 7-d moving average). In two-pollutant models, only black carbon, a tracer of traffic particles, was significantly associated with LINE-1 methylation (b 5 20.09; 95% CI, 20.17 to 20.01; P 5 0.03). No association was found with Alu methylation (P . 0.12). Conclusions: We found decreased repeated-element methylation after exposure to traffic particles. Whether decreased methylation mediates exposure-related health effects remains to be determined.
Loss of genomic DNA methylation has been found in a variety of common human age-related diseases. Whether DNA methylation decreases over time as individuals age is unresolved. We measured DNA methylation in 1,097 blood DNA samples from 718 elderly subjects between 55-92 years of age (1-3 samples/subjects), who have been repeatedly evaluated over an 8-year time span in the Boston area Normative Aging Study. DNA methylation was measured using quantitative PCRPyrosequencing analysis in Alu and LINE-1 repetitive elements, heavily methylated sequences with high representation throughout the human genome. Age at the visit was negatively associated with Alu element methylation (β=−.12 %5mC/year, p=0.0005). A weaker association was observed with LINE-1 elements (β=−.06 %5mC/year, p=0.049). We observed a significant decrease in average Alu methylation over time, with a −0.2 %5mc change (p=0.012) compared to blood samples collected up to 8 years earlier. The longitudinal decline in Alu methylation was linear and highly correlated with time since the first measurement (β=−.089 %5mC/year, p<0.0001). In contrast, average LINE-1 methylation did not vary over time [p=0.51]. Our results demonstrate a progressive loss of DNA methylation in repetitive elements dispersed throughout the genome.
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