AbstractThe emergence of epigenetic predictors was a pivotal moment in geroscience, propelling the measurement and concept of biological aging into a quantitative era; however, while current epigenetic clocks show strong predictive power, they are data-driven in nature and are not based on the underlying biological mechanisms driving methylation dynamics.

摘要表观遗传预测因子的出现是老年科学的关键时刻，推动了生物衰老的测量和概念进入定量时代；然而，虽然目前的表观遗传时钟显示出强大的预测能力，但它们本质上是数据驱动的，并且不基于驱动甲基化动力学的潜在生物学机制。

We show that predictions of these clocks are susceptible to several confounding non-age-related phenomena that make interpretation of these estimates and associations difficult. To address these limitations, we developed a probabilistic model describing methylation transitions at the cellular level.

我们表明，这些时钟的预测容易受到几种混杂的非年龄相关现象的影响，这些现象使得对这些估计和关联的解释变得困难。为了解决这些限制，我们开发了一个概率模型，描述了细胞水平的甲基化转变。

Our approach reveals two measurable components, acceleration and bias, which directly reflect perturbations of the underlying cellular dynamics. Acceleration is the proportional increase in the speed of methylation transitions across CpG sites, whereas bias corresponds to global changes in methylation levels.

我们的方法揭示了两个可测量的组件，加速度和偏差，它们直接反映了潜在细胞动力学的扰动。加速是CpG位点甲基化转变速度的成比例增加，而偏差对应于甲基化水平的整体变化。

Using data from 15,900 participants from the Generation Scotland study, we develop a robust inference framework and show that these are two distinct processes confounding current epigenetic predictors. Our results show improved associations of acceleration and bias with physiological traits known to impact healthy aging, such as smoking and alcohol consumption, respectively.

使用来自苏格兰一代研究的15900名参与者的数据，我们开发了一个强大的推理框架，并表明这是混淆当前表观遗传预测因子的两个不同过程。我们的研究结果表明，加速和偏倚与已知影响健康衰老的生理特征（例如吸烟和饮酒）之间的关联有所改善。

Furthermore, a genome-wide association study of epigenetic age acceleration identified seven genomic loci..

此外，表观遗传年龄加速的全基因组关联研究确定了七个基因组位点。。

MainThe role of age as the predominant risk factor for cancer, neurodegenerative disease and cardiovascular disease has motivated research into its underlying cellular mechanisms. Until recently, a major challenge in this field was the lack of a reliable method for accurately measuring biological age.

主要年龄作为癌症，神经退行性疾病和心血管疾病的主要危险因素的作用激发了对其潜在细胞机制的研究。直到最近，该领域的一个主要挑战是缺乏准确测量生物年龄的可靠方法。

A tipping point was the development of the first comprehensive epigenetic age predictors1,2,3,4. These models quantified biological age based on the presence of age-related changes in the DNA methylome of individuals. This development naturally led to the concept of epigenetic age acceleration, which is commonly defined, for an individual within a cohort, as the residual from the epigenetic clock’s predicted age and their chronological age3,5.

一个转折点是第一个全面的表观遗传年龄预测因子1,2,3,4的发展。这些模型基于个体DNA甲基化组中年龄相关变化的存在来量化生物学年龄。这种发展自然产生了表观遗传年龄加速的概念，对于队列中的个体来说，表观遗传年龄加速通常被定义为表观遗传时钟预测年龄及其时间年龄的残余3,5。

The use of epigenetic clocks to measure the rate of epigenetic aging is now widely employed as they have been shown to capture the impact of various diseases and environmental factors in a single metric5,6.In recent years, advances in the field have led to numerous improvements. First, population-based cohorts used for training have increased from modest sizes to include and combine large cross-sectional studies with thousands of participants1,2,7,8.

使用表观遗传时钟来测量表观遗传衰老的速度现在被广泛使用，因为它们已被证明可以在单个指标中捕捉各种疾病和环境因素的影响5,6。近年来，该领域的进步导致了许多改进。首先，用于培训的基于人群的队列已经从适度规模增加到包括并结合了数千名参与者的大型横断面研究1,2,7,8。

Second, the complexity of models has advanced on two fronts. Machine learning techniques are now used increasingly to develop epigenetic clocks that capture nonlinear and interaction dynamics9,10. Furthermore, ‘composite’ or ‘second-generation’ clocks have been directly trained on quantitative markers tracking health and longevity.

其次，模型的复杂性在两个方面都有所提高。机器学习技术现在越来越多地用于开发捕获非线性和相互作用动态的表观遗传时钟9,10。。

As a result, these clocks have increased associations with a number of diseases as well as overall mortality11,12,13.While more sophisticated algorithms and larger cohort sizes have improved the accuracy of epigenetic clocks in predicting chronological age, th.

。

Data availability

数据可用性

According to the terms of consent for GS participants, access to data must be reviewed by the GS Access Committee. Applications should be made to access@generationscotland.org. Applications are reviewed usually within 6–8 weeks of submission, but typically sooner. Applicants are notified of the decision no later than 2 weeks after the meeting.

根据GS参与者的同意条款，数据的访问必须由GS访问委员会审查。申请应提交至access@generationscotland.org.申请通常在提交后6-8周内进行审查，但通常更快。不迟于会议后2周通知申请人该决定。

The dataset ‘Sample information of DNA methylation profiles of male and female in 24 tissues’ used to check the associations for tissue-specific sites is publicly available at the EWAS Datahub at https://download.cncb.ac.cn/ewas/datahub/download/sex_methylation_v1.zip. The Down syndrome dataset is publicly available in the Gene Expression Omnibus under accession no.GSE52588.

用于检查组织特异性位点关联的数据集“24种组织中男性和女性DNA甲基化谱的样本信息”可在EWAS Datahub上公开获得https://download.cncb.ac.cn/ewas/datahub/download/sex_methylation_v1.zip.唐氏综合症数据集可在Gene Expression Omnibus中公开获得，登录号为GSE52588。

The Hannum dataset is publicly available in the Gene Expression Omnibus under accession no. GSE40279..

。。

Code availability

代码可用性

All code used in this manuscript is available at https://github.com/zuberek/probage. The inference of the posterior distribution of model parameters was implemented in Python v.3.9 with dependencies on PyMC v.5.0.2 (ref. 40), Numpy v.1.24.1 (ref. 44), Anndata v.0.8.0 (ref. 45) and Pandas v.1.5.3 (ref.

本手稿中使用的所有代码均可在https://github.com/zuberek/probage.模型参数后验分布的推断是在Python v.3.9中实现的，依赖于PyMC v.5.0.2（参考文献40），Numpy v.1.24.1（参考文献44），Anndata v.0.8.0（参考文献45）和Pandas v.1.5.3（参考文献45）。

46). Cox proportional hazards regression was conducted using the survival package v.3.4.0 (refs. 47,48), while linear and logistic regression were carried out using the stats base package under R base v.4.2.2. GWAS summary statistics were generated using GCTA v.1.93.2. Functional analysis of the GWAS results was conducted using FUMA v.1.5.1.

46）。使用survival package v.3.4.0（参考文献47,48）进行Cox比例风险回归，而使用R base v.4.2.2下的stats base package进行线性和逻辑回归。GWAS汇总统计数据是使用GCTA v.1.93.2生成的。使用FUMA v.1.5.1对GWAS结果进行功能分析。

The human reference genome assembly used for GWAS is at http://www.ncbi.nlm.nih.gov/assembly/2758/. GENCODE annotation release 39 is at https://www.gencodegenes.org/human/release_39.html..

用于GWAS的人类参考基因组组件位于http://www.ncbi.nlm.nih.gov/assembly/2758/.GENCODE注释版本39位于https://www.gencodegenes.org/human/release_39.html..

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Therneau, T. A package for survival analysis in S. R package version 2 (2015).Download referencesAcknowledgementsThe authors thank all of the participants of the GS: Scottish Family Health Study as well as study team members for their previous and ongoing contribution to this study. GS received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006).

Therneau，T。S.R软件包第2版（2015）中的生存分析软件包。下载参考文献致谢作者感谢GS：苏格兰家庭健康研究的所有参与者以及研究团队成员之前和正在进行的对这项研究的贡献。GS获得了苏格兰政府卫生局首席科学家办公室（CZD/16/6）和苏格兰资助委员会（HR03006）的核心支持。

DNA methylation profiling of the GS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK, the Brain & Behavior Research Foundation (ref. 27404) and the Wellcome Trust (Wellcome Trust Strategic Award ‘STratifying Resilience and Depression Longitudinally’ ((STRADL) ref.

GS样品的DNA甲基化分析由苏格兰爱丁堡惠康信托临床研究机构的遗传学核心实验室进行，并由英国医学研究委员会，大脑与行为研究基金会（参考文献27404）和惠康信托基金会（惠康信托战略奖“纵向分层弹性和抑郁”（（STRADL）参考文献）资助。

104036/Z/14/Z)). We thank C. P. Ponting for critical reading of the manuscript. We thank N. Olova and all members of the Chandra laboratory for their input. J.K.D. is supported by a UK Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics.

104036/Z/14/Z））。我们感谢C.P.Ponting对稿件的批判性阅读。我们感谢N.Olova和钱德拉实验室的所有成员的投入。J、 K.D.得到了英国研究与创新（资助EP/S02431X/1），爱丁堡大学信息学院生物医学人工智能博士培训中心的支持。

E.J.Y. is funded through core funding to the MRC Human Genetics Unit (MC_ST_U16003) S.J.C.C. is supported by the Wellcome Trust Hosts, Pathogens & Global Health Programme (grant no. 226831/Z/22/Z). E.L.C. is supported by an EHA Bilateral grant no. BCG-202209-02649 to K.K. E.L.C. has also been supported by a Medical Research Council grant (MC_UU_00009/2).

E、 J.Y.由MRC人类遗传学部门（MC\U ST\U U16003）的核心资金资助。J.C.C.由惠康信托基金宿主，病原体和全球健康计划（批准号226831/Z/22/Z）支持。E、 L.C.得到了英国EHA双边拨款BCG-202209-02649的支持。E.L.C.也得到了医学研究委员会拨款（MC\U UU\U 00009/2）的支持。

T.C. was partly supported through a Chancellor’s Fellow at the University of Edinburgh and the MRC Human Genetics Unit. Research reported in this publication was supported by the Mayo Clinic Robert and Arlene Kogod Center on Aging. Blood Cancer UK provide.

T、 C.得到了爱丁堡大学校长研究员和MRC人类遗传学部门的部分支持。本出版物中报道的研究得到了梅奥诊所罗伯特和阿琳·科戈德衰老中心的支持。英国提供的血癌。

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PubMed Google ScholarContributionsE.L.C. and T.C. conceived and supervised the study. J.K.D., E.J.Y., S.J.C.C., E.L.C. and T.C. wrote the manuscript. J.K.D. and E.L.C. developed the modeling framework. J.K.D., E.J.Y., S.J.C.C., D.J.S. and T.C. conducted data analysis. R.F.H., D.L.M.

PubMed谷歌学术贡献。五十、 C.和T.C.构思并监督了这项研究。J、 。J、 K.D.和E.L.C.开发了建模框架。J.K.D.，E.J.Y.，S.J.C.C.，D.J.S.和T.C.进行了数据分析。R、 F.H.，D.L.M。

and R.E.M. curated GS, gave access to data and advised on the study.Corresponding authorsCorrespondence to.

和R.E.M.策划的GS，提供了数据并为研究提供了建议。通讯作者通讯。

Eric Latorre-Crespo or Tamir Chandra.Ethics declarations

埃里克·拉托雷·克雷斯波或塔米尔·钱德拉。道德宣言

Competing interests

相互竞争的利益

R.E.M. is an advisor to the Epigenetic Clock Development Foundation. R.E.M. and R.F.H. are advisors to Optima Partners. The other authors declare no competing interests.

R、 E.M.是表观遗传时钟发展基金会的顾问。R、 E.M.和R.F.H.是Optima Partners的顾问。其他作者声明没有利益冲突。

Peer review

同行评审

Peer review information

同行评审信息

Nature Aging thanks João Pedro de Magsalhães, Kejun Ying and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

自然老化感谢João Pedro de Magsalhães，Kejun Ying和其他匿名审稿人对这项工作的同行评审做出的贡献。

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Analysis of current limitations in epigenetic clocks.a. Distribution of ANOVA statistics between six example tissues, for each site included in the Horvath clock when calculated in EWAS Datahub dataset.

Additional informationPublisher的注释Springer Nature在已发布的地图和机构隶属关系中的管辖权主张方面保持中立。。

b. Methylation level vs age for a single CpG included in the first-generation epigenetic clock of Horvath. Each point is a cell and is colored by the tissue type it was taken from (all six tissue types were included in the training dataset of the original Horvath clock). The CpG displays negligible association between methylation and age, but strong association between tissue type, in this case liver.

b、 。每个点都是一个细胞，并根据其所取的组织类型进行着色（所有六种组织类型都包含在原始Horvath时钟的训练数据集中）。CpG显示甲基化与年龄之间的关联可以忽略不计，但组织类型（在这种情况下是肝脏）之间的关联很强。

Marginal boxplot shows the median and exclusive interquartile range of methylation levels in six different tissues. c. Comparison between the association of methylation levels with smoking and age on each CpG site included in a training size bootstrap experiment. Each point corresponds to a site included in a clock, colored by the size of the dataset used to train the epigenetic predictor and sized proportionally to the smoking coefficient value.

边缘箱线图显示了六种不同组织中甲基化水平的中位数和排他性四分位间距。c、 在训练规模引导实验中包括的每个CpG位点上甲基化水平与吸烟和年龄的关联之间的比较。每个点对应于时钟中包含的一个位点，由用于训练表观遗传预测因子的数据集的大小着色，并与吸烟系数值成比例。

Age association displayed as adjusted R2 from linear regressions for each CpG of the form: methylation ~ age. Smoking association shown as the absolute value of the coefficient of smoking (dichotomized as a weighted smoking value of greater than 0.25) from regressions for each CpG of the form: methylation ~ smoking + age + sex.

年龄关联显示为形式为甲基化〜年龄的每个CpG的线性回归调整后的R2。吸烟关联显示为吸烟系数的绝对值（二分法为加权吸烟值大于0.25），来自每种CpG的回归形式：甲基化~吸烟+年龄+性别。

Weighted smoking is defined as log(1 + pack years)/exp(ever smoke). Points are displayed with a random jitter to avoid overlap. d. Acceleration obtained from bootstrapped lasso linear regressions trained on chronological.

加权吸烟定义为log（1+包年）/exp（曾经吸烟）。点以随机抖动显示，以避免重叠。d、 从按时间顺序训练的自举套索线性回归获得的加速度。

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Reprints and permissionsAbout this articleCite this articleDabrowski, J.K., Yang, E.J., Crofts, S.J.C. et al. Probabilistic inference of epigenetic age acceleration from cellular dynamics.

转载和许可本文引用本文Dabrowski，J.K.，Yang，E.J.，Crofts，S.J.C.等人。从细胞动力学推断表观遗传年龄加速的概率。

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