Huang Ching-Chun, Pan Shih-Chun, Chen Pau-Chung, Guo Yue Leon
Environmental and Occupational Medicine, College of Medicine, National Taiwan University and National Taiwan University Hospital, Taipei, Taiwan; Environmental and Occupational Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan.
Environmental and Occupational Medicine, College of Medicine, National Taiwan University and National Taiwan University Hospital, Taipei, Taiwan.
Environ Res. 2025 Jul 15;277:121542. doi: 10.1016/j.envres.2025.121542. Epub 2025 Apr 3.
Most epigenetic clocks have been developed in populations of European or Hispanic descent; therefore, population-specific models are needed for Asian cohorts to enhance predictive accuracy and generalizability. This study aims to develop epigenetic clocks in a Taiwanese cohort and examine the association between long-term air pollution exposure and epigenetic age acceleration (EAA). The Taiwan Biobank (TWB) has been recruiting community-based adults aged 30-70 years since 2012, enrolling 173,806 participants by the end of 2022. Among them, 2,469 participants were selected for serum DNA methylation (DNAm) analysis. Epigenetic ages were estimated using penalized elastic net regression, with residuals defined as TWB-based epigenetic age acceleration (TWBEAA) and healthy-subset-based acceleration (TWBhEAA). Additionally, four previously established EAAs were obtained using Horvath's online DNA Methylation Age Calculator: DNAmEAA, DNAmSBEAA, PhenoEAA, and GrimEAA. Air pollution exposure levels at participants' residential townships were estimated from pre-1 day to pre-1 year using a kriging-based spatial interpolation method. Associations were assessed using multiple linear regression models, with robustness verified through Bayesian Kernel Machine Regression (BKMR). The TWBAge (325 CpG sites) and TWBhAge (179 CpG sites) prediction models demonstrated high accuracy (R = 0.95) in predicting chronological age. In the single-pollutant model, pre-1 year PM exposure was significantly associated with TWBhEAA (β = 0.67 [0.14-1.19], year) and DNAmEAA (β = 0.93 [0.03-1.83], year), while O exposure showed a positive association with DNAmSBEAA (β = 0.53 [0.29-0.77], year) and a negative association with GrimEAA (β = -0.44 [-0.70 to -0.17], year). BKMR analysis confirmed these findings. This study is among the first attempts to develop epigenetic clocks tailored for Asian population, providing evidence of air pollution's role in accelerating biological aging. Our findings highlight PM and O exposure as major contributors to EAA, emphasizing the need for air pollution mitigation strategies to promote healthier aging.
大多数表观遗传时钟是在欧洲或西班牙裔人群中开发的;因此,亚洲队列需要特定人群的模型来提高预测准确性和通用性。本研究旨在开发台湾队列中的表观遗传时钟,并研究长期空气污染暴露与表观遗传年龄加速(EAA)之间的关联。台湾生物银行(TWB)自2012年以来一直在招募30至70岁的社区成年人,截至2022年底共招募了173,806名参与者。其中,2469名参与者被选作血清DNA甲基化(DNAm)分析。使用惩罚弹性网络回归估计表观遗传年龄,残差定义为基于TWB的表观遗传年龄加速(TWBEAA)和基于健康子集的加速(TWBhEAA)。此外,使用Horvath的在线DNA甲基化年龄计算器获得了四个先前建立的EAA:DNAmEAA、DNAmSBEAA、PhenoEAA和GrimEAA。使用基于克里金法的空间插值方法估计参与者居住乡镇前1天到前1年的空气污染暴露水平。使用多元线性回归模型评估关联,并通过贝叶斯核机器回归(BKMR)验证稳健性。TWBAge(325个CpG位点)和TWBhAge(179个CpG位点)预测模型在预测实际年龄方面显示出高精度(R = 0.95)。在单污染物模型中,前1年的PM暴露与TWBhEAA(β = 0.67 [0.14 - 1.19],年)和DNAmEAA(β = 0.93 [0.03 - 1.83],年)显著相关,而O暴露与DNAmSBEAA呈正相关(β = 0.53 [0.29 - 0.77],年),与GrimEAA呈负相关(β = -0.44 [-0.70至-0.17],年)。BKMR分析证实了这些发现。本研究是为亚洲人群量身定制表观遗传时钟的首批尝试之一,提供了空气污染在加速生物衰老中作用的证据。我们的研究结果强调了PM和O暴露是EAA的主要促成因素,强调需要采取减轻空气污染的策略来促进更健康的衰老。