DNA methylation clocks for estimating biological age in Chinese cohorts
Zikai Zheng, Jiaming Li, Tianzi Liu, Yanling Fan, Qiaocheng Zhai, Muzhao Xiong, Qiaoran Wang, Xiaoyan Sun, Qi-Wen Zheng, Shanshan Che, Beier Jiang, Quan Zheng, Cui Wang, Lixiao Liu, Jiale Ping, Si Wang, Dandan Gao, Jinlin Ye, Kuan Yang, Yuesheng Zuo, Shuai Ma, Yun‐Gui Yang, Jing Qu, Feng Zhang, Peilin Jia, Guang‐Hui Liu, Weiqi Zhang
Abstract
Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNAm changes. Additionally, a DNA methylation aging clock (iCAS-DNAmAge) and a group of DNAm-based multi-modal clocks for Chinese individuals were developed, with most of them demonstrating strong predictive capabilities for chronological age. The clocks were further employed to predict factors influencing aging rates. The DNAm aging clock, derived from multi-modal aging features (compositeAge-DNAmAge), exhibited a close association with multi-omics changes, lifestyles, and disease status, underscoring its robust potential for precise biological age assessment. Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace, providing the basis for evaluating aging intervention strategies.