Epigenetic clocks are based on DNA methylation (DNAm). It has been suggested that these clocks are useable markers of biological aging and premature mortality. Because genetic factors explain variations in both epigenetic aging and mortality, this association could also be explained by shared genetic factors. We investigated the influence of genetic and lifestyle factors (smoking, alcohol consumption, physical activity, chronic diseases, body mass index) and education on the association of accelerated epigenetic aging with mortality using a longitudinal twin design. Utilizing a publicly available online tool, we calculated the epigenetic age using two epigenetic clocks, Horvath DNAmAge and DNAm GrimAge, in 413 Finnish twin sisters, aged 63–76 years, at the beginning of the 18-year mortality follow-up. Epigenetic age acceleration was calculated as the residuals from a linear regression model of epigenetic age estimated on chronological age (AAHorvath, AAGrimAge, respectively). Cox proportional hazard models were conducted for individuals and twin pairs. The results of the individual-based analyses showed an increased mortality hazard ratio (HR) of 1.31 (CI95: 1.13–1.53) per one standard deviation (SD) increase in AAGrimAge. The results indicated no significant associations of AAHorvath with mortality. Pairwise mortality analyses showed an HR of 1.50 (CI95: 1.02–2.20) per 1 SD increase in AAGrimAge. However, after adjusting for smoking, the HR attenuated substantially and was statistically non-significant (1.29; CI95: 0.84–1.99). Similarly, in multivariable adjusted models the HR (1.42–1.49) was non-significant. In AAHorvath, the non-significant HRs were lower among monozygotic pairs in comparison to dizygotic pairs, while in AAGrimAge there were no systematic differences by zygosity. Further, the pairwise analysis in quartiles showed that the increased within pair difference in AAGrimAge was associated with a higher all-cause mortality risk. In conclusion, the findings suggest that DNAm GrimAge is a strong predictor of mortality independent of genetic influences. Smoking, which is known to alter DNAm levels and is built into the DNAm GrimAge algorithm, attenuated the association between epigenetic aging and mortality risk.

中文翻译：

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表观遗传时钟 GrimAge 是否预测独立于遗传影响的死亡率：一项针对老年女性双胞胎的 18 年随访研究

表观遗传时钟基于 DNA 甲基化 (DNAm)。有人提出，这些时钟是生物衰老和过早死亡的有用标志。因为遗传因素解释了表观遗传衰老和死亡率的变化，这种关联也可以用共同的遗传因素来解释。我们使用纵向双胞胎设计调查了遗传和生活方式因素（吸烟、饮酒、体育活动、慢性病、体重指数）和教育对加速表观遗传衰老与死亡率之间关系的影响。利用公开可用的在线工具，我们在 18 年死亡率随访开始时，使用两个表观遗传时钟 Horvath DNAmAge 和 DNAm GrimAge 计算了 413 名芬兰双胞胎姐妹的表观遗传年龄，年龄为 63-76 岁。表观遗传年龄加速度计算为根据实际年龄估计的表观遗传年龄线性回归模型的残差（分别为 AAHorvath、AAGrimAge）。对个体和双胞胎进行 Cox 比例风险模型。基于个体的分析结果显示，AAGrimAge 每增加一个标准差 (SD)，死亡率风险比 (HR) 就会增加 1.31 (CI95: 1.13–1.53)。结果表明AAHorvath与死亡率没有显着关联。成对死亡率分析显示，AAGrimAge 每增加 1 个 SD，HR 为 1.50（CI95：1.02-2.20）。然而，在调整吸烟后，HR 显着减弱并且在统计学上不显着（1.29；CI95：0.84-1.99）。同样，在多变量调整模型中，HR（1.42-1.49）不显着。在 AAHorvath，与异卵对相比，单卵对的非显着性 HR 较低，而在 AAGrimAge 中，结合性没有系统差异。此外，四分位数的成对分析表明，AAGrimAge 的成对内差异增加与更高的全因死亡风险相关。总之，研究结果表明 DNAm GrimAge 是独立于遗传影响的死亡率的强有力预测因子。众所周知，吸烟会改变 DNAm 水平，并且内置于 DNAm GrimAge 算法中，它减弱了表观遗传衰老与死亡风险之间的关联。四分位数的成对分析表明，AAGrimAge 的成对内差异增加与更高的全因死亡风险相关。总之，研究结果表明 DNAm GrimAge 是独立于遗传影响的死亡率的强有力预测因子。众所周知，吸烟会改变 DNAm 水平，并且内置于 DNAm GrimAge 算法中，它减弱了表观遗传衰老与死亡风险之间的关联。四分位数的成对分析表明，AAGrimAge 的成对内差异增加与更高的全因死亡风险相关。总之，研究结果表明 DNAm GrimAge 是独立于遗传影响的死亡率的强有力预测因子。众所周知，吸烟会改变 DNAm 水平，并且内置于 DNAm GrimAge 算法中，它减弱了表观遗传衰老与死亡风险之间的关联。