Artificial Intelligence Research Center, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
Mriya Life Institute, National Academy of Active Longevity, Moscow 124489, Russia.
Ageing Res Rev. 2024 Sep;100:102418. doi: 10.1016/j.arr.2024.102418. Epub 2024 Jul 14.
We present a systematic analysis of epigenetic age acceleration based on by far the largest collection of publicly available DNA methylation data for healthy samples (93 datasets, 23 K samples), focusing on the geographic (25 countries) and ethnic (31 ethnicities) aspects around the world. We employed the most popular epigenetic tools for assessing age acceleration and examined their quality metrics and ability to extrapolate to epigenetic data from different tissue types and age ranges different from the training data of these models. In most cases, the models proved to be inconsistent with each other and showed different signs of age acceleration, with the PhenoAge model tending to systematically underestimate and different versions of the GrimAge model tending to systematically overestimate the age prediction of healthy subjects. Referring to data availability and consistency, most countries and populations are still not represented in GEO, moreover, different datasets use different criteria for determining healthy controls. Because of this, it is difficult to fully isolate the contribution of "geography/environment", "ethnicity" and "healthiness" to epigenetic age acceleration. Among the explored metrics, only the DunedinPACE, which measures aging rate, appears to adequately reflect the standard of living and socioeconomic indicators in countries, although it has a limited application to blood methylation data only. Invariably, by epigenetic age acceleration, males age faster than females in most of the studied countries and populations.
我们基于迄今为止最大的公开健康样本 DNA 甲基化数据集（93 个数据集，23K 个样本），对表观遗传年龄加速进行了系统分析，重点关注全球的地理（25 个国家）和种族（31 个种族）方面。我们使用了最流行的评估年龄加速的表观遗传工具，并检查了它们的质量指标和将这些模型的训练数据以外的不同组织类型和年龄范围的表观遗传数据外推的能力。在大多数情况下，这些模型彼此不一致，表现出不同的年龄加速迹象，PhenoAge 模型往往会系统地低估，而不同版本的 GrimAge 模型往往会系统地高估健康受试者的年龄预测。参考数据的可用性和一致性，大多数国家和人群在 GEO 中仍然没有得到代表，此外，不同的数据集使用不同的标准来确定健康对照。因此，很难完全将“地理/环境”、“种族”和“健康状况”对表观遗传年龄加速的贡献隔离开来。在所探索的指标中，只有测量衰老速度的 DunedinPACE 似乎能够充分反映国家的生活水平和社会经济指标，尽管它仅适用于血液甲基化数据。不变的是，通过表观遗传年龄加速，在大多数研究的国家和人群中，男性比女性衰老得更快。
