Time marches on, and aging is an unstoppable force of nature. Yet, the pace of aging is not uniform—even among people of the same age, there can be significant differences in the rate of physiological decline and organ aging. These variations suggest that an individual's biological age, reflecting their physiological state, may not always align with their chronological age. How old we are, how quickly we age, and how close we might be to disease depend on our biological age. Therefore, developing precise biological age clocks to assess an individual's aging status holds great value for understanding the mechanisms of aging and advancing personalized aging interventions. Epigenetic clocks, as an emerging tool that analyzes age-dependent methylation changes in specific genomic regions, offer a new approach for assessing an individual's biological age. However, despite the increased accuracy in predicting age by training methylation clocks with chronological age, this approach neglects the connection to aging biology phenotypes, making it difficult to reflect the true health status associated with aging. Moreover, existing models fail to integrate genomic methylation data with multimodal data within the same population cohort, lacking a systematic revelation of the links between epigenetic and other physiological aging biomarkers. Thus, the application of existing methylation clocks in assessing physiological status is limited. Another issue is the accuracy of methylation clock models highly depends on the training datasets used. Considering most clocks are developed based on Western populations, their applicability in Chinese populations has not been fully validated. To date, there's been a lack of systematic studies on epigenetic clocks for the Chinese population, limiting precise assessments of aging health and preemptive interventions for aging-related diseases in Chinese individuals.
Recently, multiple research groups from the Aging Biomarker Consortium of China (ABC) collaborated closely and published a study titled "DNA methylation clocks for estimating biological age in Chinese cohorts" in the journal Protein & Cell. This research, based on two independent population cohorts from northern and southern China, systematically analyzed the changes in the genomic DNA methylation landscape of the Chinese population during aging. It constructed a methylation clock model highly correlated with human multimodal data. This model can accurately predict the biological age of the Chinese population, track the dynamics of aging, and further aid in the scientific assessment of various aging intervention effects. This study establishes a new tool for measuring the aging index of Chinese individuals and offers a new perspective on understanding the epigenetic mechanisms of aging.
The research team first focused on a population cohort from Quzhou, Zhejiang, finding that the overall DNA methylation levels in whole blood decrease with age, accompanied by an increase in entropy (disorder level), especially around the age of 35. Building on this, the researchers employed machine learning methods to construct the Index of Chinese Aging Score – DNA methylation Clock (iCAS-DNAmAge) and verified its accuracy using a cohort from the Chinese Academy of Sciences professional population. This methylation clock includes 58 unique DNA methylation sites and shows higher precision in predicting the actual age of the Chinese population compared to other clocks such as Horvath, Hannum, and PhenoAge. Additionally, iCAS-DNAmAge shares seven CpG sites with other aging clocks, six of which are located in aging-related gene regions of ELOVL2, FHL2, KLF14, NHLRC1, TRIM59, and MKLN1. Based on these core sites, researchers further constructed a streamlined methylation clock model that can predict individual age, offering a convenient option for the widespread adoption of aging clocks.
Chinese Population Composite Methylation Aging Clock
Furthermore, researchers have constructed methylation clocks that reflect multimodal physiological characteristics by using multimodal ages (such as composite age reflecting various levels of aging features, facial age trained based on facial features, immune age trained based on immune-related molecular features, etc.) as training labels. The research results indicate that the CompositeAge-DNAmAge composite methylation clock trained on multimodal ages can more accurately reflect the physiological status in biological age. By comparing individuals with accelerated aging and delayed aging, researchers found that this composite methylation clock can effectively assess the negative impact of unhealthy lifestyles (such as insufficient sleep and consumption of excessive pickled and fried foods) on the aging process, and it suggests that individuals with chronic diseases such as hypertension have a faster aging rate. Additionally, the study also revealed an association between cytomegalovirus antibody titers and individual aging rates. These findings confirm the potential applications of composite methylation clocks in assessing biological age and monitoring health status. These clock tools have been integrated into the Human Aging and Longevity Landscape (HALL) database of the National Genomics Data Center (https://ngdc.cncb.ac.cn/hall/index), providing functional services for researchers to calculate DNA methylation age online.
Based on a Chinese population cohort, this study systematically investigated the patterns of DNA methylation changes associated with human aging and established high-precision multimodal methylation clocks. These clocks can predict the biological age of Chinese individuals more accurately, reflecting the degree and speed of aging, thus bringing new tools for assessing and warning about aging and providing quantitative basis for clinical practice in aging intervention. This research was conducted under the framework of the Aging Biomarker Consortium (ABC) in China and is another innovative achievement of the consortium after establishing the Composite Aging Clock for Chinese women, providing a new paradigm for multicenter research on aging clocks for Chinese people.
Dr. Weiqi Zhang from the Beijing Institute of Genomics, Chinese Academy of Sciences (National Genomics Data Center), Dr. Guanghui Liu from the Institute of Zoology, Chinese Academy of Sciences, Dr. Peilin Jia from the Beijing Institute of Genomics, Chinese Academy of Sciences (National Genomics Data Center), Dr. Feng Zhang from Quzhou People's Hospital, Dr. Jing Qu from the Institute of Zoology, Chinese Academy of Sciences, Dr. Yungui Yang from the Beijing Institute of Genomics, Chinese Academy of Sciences (National Genomics Data Center) are the corresponding authors of the article. Zikai Zheng, Jiaming Li, Tian Liu, and Yanling Fan from the Beijing Institute of Genomics, Chinese Academy of Sciences (National Genomics Data Center), Qiaocheng Zhai from Quzhou People's Hospital, and Muzhao Xiong, a graduate student from the Beijing Institute of Genomics, Chinese Academy of Sciences (National Genomics Data Center), are the co-first authors of the article.
