Methylomic Aging as a Window onto the Influence of Lifestyle: Tobacco and Alcohol Use Alter the Rate of Biological Aging

Abstract

Objectives To examine the effect of the relationship between alcohol and cigarette consumption on biological aging using deoxyribonucleic acid (DNA) methylation-based indices. Design We examined the association between DNA methylation indices of smoking and alcohol to those for biological aging in two independent cohorts using the epigenetic “clock” provided by Hannum and colleagues. Setting Longitudinal studies of aging and the effect of psychosocial stress. Participants Publicly available genome-wide methylation data from participants in two ethnically informative cohorts (n=656 white, n=180 black). Measurements Deviation of biological age from chronological age as a result of smoking and alcohol consumption. Results Greater cigarette consumption was associated with accelerated biological aging, with strong effects evident at even low levels of exposure. In contrast, alcohol consumption was associated with a mixed effect on biological aging and pronounced nonlinear effects. At low and heavy levels of alcohol consumption, there was accelerated biological aging, whereas at intermediate levels of consumption there was a relative decelerating effect. The decelerating effects of alcohol were particularly notable at loci for which methylation increased with age. Conclusion These data support prior epidemiological studies indicating that moderate alcohol use is associated with healthy aging, but we urge caution in interpreting these results. Conversely, smoking has strong negative effects at all levels of consumption. These results also support the use of methylomic indices as a tool for assessing the impact of lifestyle on aging.

Relationship of tobacco smoking and smoking-related DNA methylation with epigenetic age acceleration

“…However, none of the genomic regions that become either hypermethylated or hypomethylated with aging has been identified in smoking EWASs [4, 7], even though the AA derived according to Hannum et al's algorithm is linked closely to one CpG site, cg05575921, which had been identified as an epigenetic indicator of smoking exposure in previous EWASs [15, 24, 25]. Our study confirmed this locus and additionally identified 65 loci that were associated with both smoking and AA as well.…”

“…In more detail, we computed the mean β value ( μ c ) and standard deviation ( σ c ) across the never smokers of the given dataset, and then defined the SI as $\text{SI}\left(s\right)=\frac{1}{n}{\displaystyle \sum _c^n{W}_c\frac{{\beta }_{\mathit{\text{cs}}}-{\mu }_c}{{\sigma }_c}}$ where W c is +1(−1) if the smoking-associated CpG, c , is hypermethylated (hypomethylated) in smokers and where β c is the β value of this CpG in samples s [6]. We calculated the SI for each participant in both panels based on the validated AA associated loci, and then compared it with the single epigenetic smoking indicator cg05575921 ( AHRR ) used in the study by Beach et al, [15] and the SI estimated based on 1501 loci identified in the study by Teschendorff et al (Teschendorff SI) [6]. Mutual correlations of these indicators and AA were assessed by Spearman's correlation coefficients, and the associations of the smoking indicators with AA were assessed by mixed linear regression (Models 1 and 2).…”

“…However, the few studies assessing this relationship have reported conflicting findings. Horvath et al and Marioni et al did not find significant associations of self-reported smoking with DNA methylation age determined in peripheral blood samples [11, 14], while Beach et al recently reported such an association for the most robust smoking-related locus, cg05575921 ( AHRR ), as a biomarker of smoking exposure [15]. To further explore a possible role of smoking in AA, we conducted a comprehensive analysis of the associations of self-reported smoking, serum cotinine levels (an established biomarker of current smoking exposure) and smoking-associated methylation signatures with AA in a large population-based study.…”

Economic hardship and biological weathering: The epigenetics of aging in a U.S. sample of black women

“…Nearly all 71 markers in their model lay within or near genes with known functions associated with age-related conditions, including Alzheimer’s disease, cancer, tissue degradation, DNA damage, and oxidative stress (Hannum et al 2013). Despite the fact that Hannum et al’s measure was developed using a White sample, their findings were recently replicated among a sample of Black Americans (Beach et al, in press). …”

Shifting the focus of aging research into earlier decades of life

“…One possibility is that the biological processes that determine differential rates of telomere shortening had already taken place in the subjects studied. The question of how best to precisely quantify biological aging is a focus of intensive research (Beach et al, 2015;Hertel et al, 2015). Future studies will develop therapeutic approaches aimed at decelerated loss of physiologic integrity earlier in the life course and extending healthy life span.…”