The mechanism of aging is not yet fully understood. It has been recognized that there are age-dependent changes in the DNA methylation pattern of the whole genome. To date, there are several DNA methylation-based estimators of the chronological age. A majority of the estimators use the DNA methylation data from a single tissue type, such as blood. In 2013, for the first time, Steve Horvath reported the DNA methylation-based age estimator (353 CpGs were used) that could be applied to multiple tissues. A refined, more sensitive version that uses 391 CpGs was subsequently developed and validated in human cells, including fibroblasts. In this review, the age predicted by DNA methylation-based age estimator is referred to as DNAmAge, and the biological process controlling the progression of DNAmAge is referred to as the epigenetic aging in this minireview. The concepts of DNAmAge and epigenetic aging provide us opportunities to discover previously unrecognized biological events controlling aging. In this article, we discuss the frequently asked questions regarding DNAmAge and the epigenetic aging by introducing recent studies of ours and others. We focus on addressing the following questions: (1) Is there any synchronization of DNAmAge between cells in a human body?, (2) Can we use in vitro (cell culture) systems to study the epigenetic aging?, (3) Is there an age limit of DNAmAge?, and (4) Is it possible to change the speed and direction of the epigenetic aging? We describe our current understandings to these questions and outline potential future directions.

Impact statement

Aging is associated with DNA methylation (DNAm) changes. Recent advancement of the whole-genome DNAm analysis technology allowed scientists to develop DNAm-based age estimators. A majority of these estimators use DNAm data from a single tissue type such as blood. In 2013, a multi-tissue age estimator using DNAm pattern of 353 CpGs was developed by Steve Horvath. This estimator was named “epigenetic clock”, and the improved version using DNAm pattern of 391 CpGs was developed in 2018. The estimated age by epigenetic clock is named DNAmAge. DNAmAge can be used as a biomarker of aging predicting the risk of age-associated diseases and mortality. Although the DNAm-based age estimators were developed, the mechanism of epigenetic aging is still enigmatic. The biological significance of epigenetic aging is not well understood, either. This minireview discusses the current understanding of the mechanism of epigenetic aging and the future direction of aging research.

中文翻译：

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体内外表观遗传老化分析：控制速度和方向的因素。

衰老的机制尚未完全了解。人们已经认识到，整个基因组的 DNA 甲基化模式存在年龄依赖性变化。迄今为止，有几种基于 DNA 甲基化的年代估计器。大多数估计器使用来自单一组织类型（例如血液）的 DNA 甲基化数据。2013 年，Steve Horvath 首次报道了可应用于多种组织的基于 DNA 甲基化的年龄估计器（使用了 353 个 CpG）。随后开发了一种使用 391 CpG 的精制、更灵敏的版本，并在包括成纤维细胞在内的人体细胞中进行了验证。在这篇综述中，基于 DNA 甲基化的年龄估计器预测的年龄被称为 DNAmAge，在这篇小综述中，控制 DNAmAge 进展的生物学过程被称为表观遗传衰老。DNAmAge 和表观遗传衰老的概念为我们提供了发现以前未被认识的控制衰老的生物事件的机会。在本文中，我们通过介绍我们和其他人的最新研究来讨论有关 DNAmAge 和表观遗传衰老的常见问题。我们重点解决以下问题：（1）人体内细胞之间是否存在DNAmAge同步？，（2）我们可以使用体外（细胞培养）系统来研究表观遗传老化？，（3）DNAmAge 是否有年龄限制？，以及（4）是否有可能改变表观遗传老化的速度和方向？我们描述了我们目前对这些问题的理解，并概述了未来的潜在方向。

影响陈述

衰老与 DNA 甲基化 (DNAm) 变化有关。全基因组 DNAm 分析技术的最新进展使科学家能够开发基于 DNAm 的年龄估计器。大多数这些估计器使用来自单一组织类型（如血液）的 DNAm 数据。2013 年，Steve Horvath 开发了一种使用 353 个 CpG 的 DNAm 模式的多组织年龄估计器。这个估计器被命名为“表观遗传时钟”，2018年开发了使用391个CpG的DNAm模式的改进版本。通过表观遗传时钟估计的年龄被命名为DNAmAge。DNAmAge 可用作衰老的生物标志物，预测与年龄相关的疾病和死亡的风险。尽管开发了基于 DNAm 的年龄估计器，但表观遗传衰老的机制仍然是个谜。表观遗传衰老的生物学意义也不是很清楚。