Abstract
Large-scale epigenome-wide association meta-analyses have identified multiple ‘signatures’’ of smoking. Drawing on these findings, we describe the construction of a polyepigenetic DNA methylation score that indexes smoking behavior and that can be utilized for multiple purposes in population health research. To validate the score, we use data from two birth cohort studies: The Dunedin Longitudinal Study, followed to age-38 years, and the Environmental Risk Study, followed to age-18 years. Longitudinal data show that changes in DNA methylation accumulate with increased exposure to tobacco smoking and attenuate with quitting. Data from twins discordant for smoking behavior show that smoking influences DNA methylation independently of genetic and environmental risk factors. Physiological data show that changes in DNA methylation track smoking-related changes in lung function and gum health over time. Moreover, DNA methylation changes predict corresponding changes in gene expression in pathways related to inflammation, immune response, and cellular trafficking. Finally, we present prospective data about the link between adverse childhood experiences (ACEs) and epigenetic modifications; these findings document the importance of controlling for smoking-related DNA methylation changes when studying biological embedding of stress in life-course research. We introduce the polyepigenetic DNA methylation score as a tool both for discovery and theory-guided research in epigenetic epidemiology.
Highlights
Tobacco smoking is the greatest health hazard in the modern world
We computed a smoking methylation polyepigenetic score (SmPEGS) for tobacco exposure based on the 2623 CpG probes identified by Joehanes et al.[2,11] in their epigenome-wide meta-analysis of current vs. never smoking (Supplementary Table S1)
The resulting SmPEGSs were differentially distributed in never, former- and currentsmokers in the Dunedin and Environmental Risk (E-Risk) cohorts
Summary
Tobacco smoking is the greatest health hazard in the modern world. Smoking is associated with practically every risk factor known to impact health and with numerous clinical endpoints[1]. We examine the confounding effects of tobacco smoking in DNA methylation research and demonstrate the potential of utilizing the SmPEGS in lieu of observational smoking data as a covariate to reduce spurious associations in studies of the effects of early adversity on health. E-Risk Study members gave blood for DNA analysis at the most recent assessment, aged 18 years.
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