Understanding the role of epigenetic modifications, e.g. DNA methylation, in the process of aging requires the characterization of methylation patterns in large cohorts. We analysed >480 000 CpG sites using Infinium HumanMethylation450 BeadChip (Illumina) in whole blood DNA of 965 participants of a population-based cohort study aged between 50 and 75 years. In an exploratory analysis in 400 individuals, 200 CpG sites with the highest Spearman correlation coefficients for the association between methylation and age were identified. Of these 200 CpGs, 162 were significantly associated with age, which was verified in an independent cohort of 498 individuals using mixed linear regression models adjusted for gender, smoking behaviour, age-related diseases and random batch effect and corrected for multiple testing by Bonferroni. In another independent cohort of 67 individuals without history of major age-related diseases and with a follow-up of 8 years, we observed a gain in methylation at 96% (52%, significant) of the positively age-associated CpGs and a loss at all (89%, significant) of the negatively age-associated CpGs in each individual while getting 8 years older. A regression model for age prediction based on 17 CpGs as predicting variables explained 71% of the variance in age with an average accuracy of 2.6 years. In comparison with cord blood samples obtained from the Ulm Birth Cohort Study, we observed a more than 2-fold change in mean methylation levels from birth to older age at 86 CpGs. We were able to identify 65 novel CpG sites with significant association of methylation with age.
Cg19693031, which is located within the 3'-untranslated region of TXNIP, might play a role in the pathophysiology of type 2 diabetes. This result appears biologically plausible given that thioredoxin-interacting protein is overexpressed in diabetic animals and humans and 3'-untranslated regions are known to play a regulatory role in gene expression.
BackgroundWith epigenome-wide mapping of DNA methylation, a number of novel smoking-associated loci have been identified.ObjectivesWe aimed to assess dose–response relationships of methylation at the top hits from the epigenome-wide methylation studies with smoking exposure as well as with total and cause-specific mortality.MethodsIn a population-based prospective cohort study in Germany, methylation was quantified in baseline blood DNA of 1,000 older adults by the Illumina 450K assay. Deaths were recorded during a median follow-up of 10.3 years. Dose–response relationships of smoking exposure with methylation at nine CpGs were modeled by restricted cubic spline regression. Associations of individual and aggregate methylation patterns with all-cause, cardiovascular, and cancer mortality were assessed by multiple Cox regression.ResultsClear dose–response relationships with respect to current and lifetime smoking intensity were consistently observed for methylation at six of the nine CpGs. Seven of the nine CpGs were also associated with mortality outcomes to various extents. A methylation score based on the top two CpGs (cg05575921 and cg06126421) showed the strongest associations with all-cause, cardiovascular, and cancer mortality, with adjusted hazard ratios (95% CI) of 3.59 (2.10, 6.16), 7.41 (2.81, 19.54), and 2.48 (1.01, 6.08), respectively, for participants with methylation levels in the lowest quartile at both CpGs. Adding methylation at those two CpGs into a model that included the variables of the Systematic Coronary Risk Evaluation chart for fatal cardiovascular risk prediction improved the predictive discrimination.ConclusionThe novel methylation biomarkers are highly informative for both smoking exposure and smoking-related mortality outcomes. In particular, these biomarkers may substantially improve cardiovascular risk prediction. Nevertheless, the findings of the present study need to be further validated in additional large longitudinal studies.CitationZhang Y, Schöttker B, Florath I, Stock C, Butterbach K, Holleczek B, Mons U, Brenner H. 2016. Smoking-associated DNA methylation biomarkers and their predictive value for all-cause and cardiovascular mortality. Environ Health Perspect 124:67–74; http://dx.doi.org/10.1289/ehp.1409020
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