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      Long-term Air Pollution Exposure, Genome-wide DNA Methylation and Lung Function in the LifeLines Cohort Study

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          Abstract

          Background:

          Long-term air pollution exposure is negatively associated with lung function, yet the mechanisms underlying this association are not fully clear. Differential DNA methylation may explain this association.

          Objectives:

          Our main aim was to study the association between long-term air pollution exposure and DNA methylation.

          Methods:

          We performed a genome-wide methylation study using robust linear regression models in 1,017 subjects from the LifeLines cohort study to analyze the association between exposure to nitrogen dioxide ( NO 2 ) and particulate matter ( PM 2.5 , fine particulate matter with aerodynamic diameter 2.5 μ m ; PM 10 , particulate matter with aerodynamic diameter 10 μ m ) and PM 2.5 absorbance , indicator of elemental carbon content (estimated with land-use-regression models) with DNA methylation in whole blood (Illumina® HumanMethylation450K BeadChip). Replication of the top hits was attempted in two independent samples from the population-based Cooperative Health Research in the Region of Augsburg studies (KORA).

          Results:

          Depending on the p-value threshold used, we found significant associations between NO 2 exposure and DNA methylation for seven CpG sites (Bonferroni corrected threshold p < 1.19 × 10 7 ) or for 4,980 CpG sites (False Discovery Rate < 0.05 ). The top associated CpG site was annotated to the PSMB9 gene (i.e., cg04908668). None of the seven Bonferroni significant CpG-sites were significantly replicated in the two KORA-cohorts. No associations were found for PM exposure.

          Conclusions:

          Long-term NO 2 exposure was genome-wide significantly associated with DNA methylation in the identification cohort but not in the replication cohort. Future studies are needed to further elucidate the potential mechanisms underlying NO 2 - exposure –related respiratory disease. https://doi.org/10.1289/EHP2045

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          Most cited references49

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          High density DNA methylation array with single CpG site resolution.

          We have developed a new generation of genome-wide DNA methylation BeadChip which allows high-throughput methylation profiling of the human genome. The new high density BeadChip can assay over 480K CpG sites and analyze twelve samples in parallel. The innovative content includes coverage of 99% of RefSeq genes with multiple probes per gene, 96% of CpG islands from the UCSC database, CpG island shores and additional content selected from whole-genome bisulfite sequencing data and input from DNA methylation experts. The well-characterized Infinium® Assay is used for analysis of CpG methylation using bisulfite-converted genomic DNA. We applied this technology to analyze DNA methylation in normal and tumor DNA samples and compared results with whole-genome bisulfite sequencing (WGBS) data obtained for the same samples. Highly comparable DNA methylation profiles were generated by the array and sequencing methods (average R2 of 0.95). The ability to determine genome-wide methylation patterns will rapidly advance methylation research. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project.

            Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.
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              Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – The ESCAPE project

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                Author and article information

                Journal
                Environ Health Perspect
                Environ. Health Perspect
                EHP
                Environmental Health Perspectives
                Environmental Health Perspectives
                0091-6765
                1552-9924
                06 February 2018
                February 2018
                : 126
                : 2
                : 027004
                Affiliations
                [ 1 ]Department of Epidemiology, University of Groningen, University Medical Center Groningen , Groningen, Netherlands
                [ 2 ]Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen , Groningen, Netherlands
                [ 3 ]Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen , Netherlands
                [ 4 ]Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen , Netherlands
                [ 5 ]Department of Epidemiology, Erasmus Medical Center , Rotterdam, Netherlands
                [ 6 ]Institute of Epidemiology II, Helmholtz Zentrum München , Neuherberg, Germany
                [ 7 ]Environmental Public Health Division, U.S. Environmental Protection Agency , Chapel Hill, North Carolina, USA
                [ 8 ]Molecular Epidemiology Unit, Helmholtz Zentrum München , Neuherberg, Germany
                [ 9 ]Institute for Risk Assessment Sciences, Utrecht University , Utrecht, Netherlands
                [ 10 ]Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht , Netherlands
                Author notes
                Address correspondence to J.M. Vonk, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, Netherlands. Telephone: +31 50 3610934. Email: j.m.vonk@ 123456umcg.nl
                Article
                EHP2045
                10.1289/EHP2045
                6047358
                29410382
                5871f196-722e-404d-9d94-04e88538a981

                EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted.

                History
                : 12 April 2017
                : 13 December 2017
                : 13 December 2017
                Categories
                Research

                Public health
                Public health

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