Borrus Daniel S, Sehgal Raghav, Armstrong Jenel Fraij, Kasamoto Jessica, Gonzalez John, Higgins-Chen Albert
Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Program in Computational Biology and Bioinformatics, Yale University School of Medicine, New Haven, CT, USA.
bioRxiv. 2024 Oct 25:2024.10.22.619720. doi: 10.1101/2024.10.22.619720.
Recent human studies have suggested that aging interventions can reduce aging biomarkers related to morbidity and mortality risk. Such biomarkers may potentially serve as early, rapid indicators of effects on healthspan. An increasing number of studies are measuring intervention effects on epigenetic clocks, commonly used aging biomarkers based on DNA methylation profiles. However, with dozens of clocks to choose from, different clocks may not agree on the effect of an intervention. Furthermore, changes in some clocks may simply be the result of technical noise causing a false positive result. To address these issues, we measured the variability between 6 popular epigenetic clocks across a range of longitudinal datasets containing either an aging intervention or an age-accelerating event. We further compared them to the same clocks re-trained to have high test-retest reliability. We find the newer generation of clocks, trained on mortality or rate-of-aging, capture aging events more reliably than those clocks trained on chronological age, as these show consistent effects (or lack thereof) across multiple clocks including high-reliability versions, and including after multiple testing correction. In contrast, clocks trained on chronological age frequently show sporadic changes that are not replicable when using high-reliability versions of those same clocks, or when using newer generations of clocks and these results do not survive multiple-testing correction. These are likely false positive results, and we note that some of these clock changes were previously published, suggesting the literature should be re-examined. This work lays the foundation for future clinical trials that aim to measure aging interventions with epigenetic clocks, by establishing when to attribute a given change in biological age to a change in the aging process.
最近的人体研究表明,衰老干预措施可以减少与发病和死亡风险相关的衰老生物标志物。这些生物标志物可能潜在地作为对健康寿命影响的早期、快速指标。越来越多的研究正在测量干预措施对表观遗传时钟的影响,表观遗传时钟是基于DNA甲基化谱的常用衰老生物标志物。然而,有几十种时钟可供选择,不同的时钟可能对干预效果的看法不一致。此外,一些时钟的变化可能仅仅是技术噪声导致假阳性结果的结果。为了解决这些问题,我们在一系列包含衰老干预或年龄加速事件的纵向数据集中测量了6种流行的表观遗传时钟之间的变异性。我们进一步将它们与重新训练以具有高重测可靠性的相同时钟进行比较。我们发现,基于死亡率或衰老率训练的新一代时钟比基于实足年龄训练的时钟更可靠地捕捉衰老事件,因为这些时钟在包括高可靠性版本在内的多个时钟上显示出一致的效果(或没有效果),并且在多次测试校正后也是如此。相比之下,基于实足年龄训练的时钟经常显示出零星的变化,当使用这些相同时钟的高可靠性版本时,或者当使用新一代时钟时,这些变化无法复制,并且这些结果在多次测试校正后无法保留。这些可能是假阳性结果,我们注意到其中一些时钟变化以前已经发表,这表明该文献应该重新审视。这项工作为未来旨在用表观遗传时钟测量衰老干预措施的临床试验奠定了基础,通过确定何时将生物年龄的特定变化归因于衰老过程的变化。