Exploring Epigenetic Aging and Cognitive Change in Middle Adulthood

Daisy V Zavala, Sharon H Kim, John M Felt, Zita Oravecz, Martin J Sliwinski, Jennifer E Graham-Engeland, Christopher G Engeland, Kristin Bernard, Krishna R Veeramah, Stacey B Scott
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Abstract

Greater age deviation (AgeDev), in which a person’s biological age is older than their chronological age, is associated with worse cognitive performance. Most findings, however, rely on lab-administered cognitive measures and do not account for time-ordered effects of practice. The present study leveraged a measurement burst dataset to examine six distinct features of cognitive performance (peak performance, change in peak performance, intraindividual variability, forgetting, retest-related gains), and explored whether individual differences in these features can be predicted by AgeDev. In a sample of 139 adults aged 25-65, baseline AgeDev (Horvath1, Horvath2, Hannum, PhenoAge, GrimAge), DunedinPACE, and SystemsAge were derived from DNA methylation. Across four annual high-frequency assessment bursts, processing speed and working memory were assessed via a Symbol Search and N-Back task, respectively. Using Bayesian Double Exponential Models (BDEMs), we found that baseline peak performance was lower for individuals with greater AgeDev, DunedinPACE, and chronological age. Interestingly, individuals with greater AgeDev and faster DunedinPACE showed greater retest-related gains within and across bursts. Neither AgeDev nor chronological age predicted differences in cognitive change across time. Individuals with greater AgeDev showed more intraindividual variability in processing speed. Finally, greater chronological age (but not AgeDev) was linked with more forgetting between bursts in both tasks. These findings underscore the value of multidimensional indices of cognitive performance. Importantly, even in a sample spanning young adulthood through early old age, such indices may provide unique information regarding the nature of longitudinal patterns in cognition associated with chronological or biological aging.
探索成年中期的表观遗传衰老和认知变化
较大的年龄偏差(AgeDev),即一个人的生理年龄大于其实际年龄,与较差的认知表现有关。然而,大多数研究结果依赖于实验室管理的认知测量,并没有考虑到练习的时间顺序效应。本研究利用测量突发数据集来检查认知表现的六个不同特征(峰值表现,峰值表现变化,个体内部变异性,遗忘,重测相关收益),并探索这些特征的个体差异是否可以通过AgeDev来预测。在139名25-65岁的成年人样本中,基线AgeDev (Horvath1, Horvath2, Hannum, PhenoAge, GrimAge), DunedinPACE和SystemsAge来自DNA甲基化。在四次年度高频评估爆发中,分别通过符号搜索和N-Back任务评估处理速度和工作记忆。使用贝叶斯双指数模型(bdem),我们发现年龄越大、DunedinPACE越大和实足年龄越大的个体,其基线峰值表现越低。有趣的是,AgeDev越高、DunedinPACE越快的人,在爆发内和爆发间的重测相关收益越高。AgeDev和实足年龄都不能预测不同时间认知变化的差异。AgeDev越大的个体在处理速度上表现出更大的个体差异。最后,更大的实足年龄(而不是年龄)与两项任务之间的遗忘次数有关。这些发现强调了认知表现多维指数的价值。重要的是,即使在跨越青年到老年早期的样本中,这些指数也可能提供关于与时间或生物衰老相关的认知纵向模式性质的独特信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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