DNA Methylation Age Mediates Effect of Metabolic Profile on Cardiovascular and General Aging

Jiahui Si; Yu Ma; Canqing Yu; Dianjianyi Sun; Yuanjie Pang; Pei Pei; Ling Yang; Iona Y Millwood; Robin G Walters; Yiping Chen; Huaidong Du; Xiaoyan Zheng; Daniel Avery; Junshi Chen; Zhengming Chen; Liming Liang; Liming Li; Jun Lv; China Kadoorie Biobank Collaborative Group

doi:10.1161/CIRCRESAHA.124.325066

DNA Methylation Age Mediates Effect of Metabolic Profile on Cardiovascular and General Aging

Circ Res. 2024 Sep 23. doi: 10.1161/CIRCRESAHA.124.325066. Online ahead of print.

Authors

Jiahui Si¹, Yu Ma², Canqing Yu^{2

3

4}, Dianjianyi Sun^{2

3

4}, Yuanjie Pang^{2

3

4}, Pei Pei³, Ling Yang⁵, Iona Y Millwood⁵, Robin G Walters⁵, Yiping Chen⁵, Huaidong Du⁵, Xiaoyan Zheng⁶, Daniel Avery⁵, Junshi Chen⁷, Zhengming Chen⁵, Liming Liang⁸, Liming Li^{2

3

4}, Jun Lv^{2

3

4

9}; China Kadoorie Biobank Collaborative Group

Collaborators

China Kadoorie Biobank Collaborative Group:
Rory Collins, Richard Peto, Ruth Boxall, Derrick Bennett, Yumei Chang, Robert Clarke, Simon Gilbert, Alex Hacker, Michael Holmes, Andri Iona, Christiana Kartsonaki, Rene Kerosi, Ling Kong, Om Kurmi, Garry Lancaster, Sarah Lewington, Kuang Lin, John McDonnell, Winnie Mei, Qunhua Nie, Jayakrishnan Radhakrishnan, Sajjad Rafiq, Paul Ryder, Sam Sansome, Dan Schmidt, Paul Sherliker, Rajani Sohoni, Iain Turnbull, Jenny Wang, Lin Wang, Xiaoming Yang, Zheng Bian, Ge Chen, Yu Guo, Can Hou, Jun Lv, Shuzhen Qu, Yunlong Tan, Canqing Yu, Zengchang Pang, Ruqin Gao, Shaojie Wang, Yongmei Liu, Ranran Du, Yajing Zang, Liang Cheng, Xiaocao Tian, Hua Zhang, Silu Lv, Junzheng Wang, Wei Hou, Jiyuan Yin, Ge Jiang, Xue Zhou, Liqiu Yang, Hui He, Bo Yu, Yanjie Li, Huaiyi Mu, Qinai Xu, Meiling Dou, Jiaojiao Ren, Shanqing Wang, Ximin Hu, Hongmei Wang, Jinyan Chen, Yan Fu, Zhenwang Fu, Xiaohuan Wang, Min Weng, Xiangyang Zheng, Yilei Li, Huimei Li, Yanjun Wang, Ming Wu, Jinyi Zhou, Ran Tao, Jie Yang, Chuanming Ni, Jun Zhang, Yihe Hu, Yan Lu, Liangcai Ma, Aiyu Tang, Shuo Zhang, Jianrong Jin, Jingchao Liu, Zhenzhu Tang, Naying Chen, Ying Huang, Mingqiang Li, Jinhuai Meng, Rong Pan, Qilian Jiang, Weiyuan Zhang, Yun Liu, Liuping Wei, Liyuan Zhou, Ningyu Chen, Hairong Guan, Xianping Wu, Ningmei Zhang, Xiaofang Chen, Xuefeng Tang, Guojin Luo, Jianguo Li, Xiaofang Chen, Xunfu Zhong, Jiaqiu Liu, Qiang Sun, Pengfei Ge, Xiaolan Ren, Caixia Dong, Hui Zhang, Enke Mao, Xiaoping Wang, Tao Wang, Xi Zhang, Ding Zhang, Gang Zhou, Shixian Feng, Liang Chang, Lei Fan, Yulian Gao, Tianyou He, Huarong Sun, Pan He, Chen Hu, Qiannan Lv, Xukui Zhang, Min Yu, Ruying Hu, Hao Wang, Yijian Qian, Chunmei Wang, Kaixue Xie, Lingli Chen, Yidan Zhang, Dongxia Pan, Yuelong Huang, Biyun Chen, Li Yin, Donghui Jin, Huilin Liu, Zhongxi Fu, Qiaohua Xu, Xin Xu, Hao Zhang, Youping Xiong, Huajun Long, Xianzhi Li, Libo Zhang, Zhe Qiu

Affiliations

¹ CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China (J.S.).
² Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China (Y.M., C.Y., D.S., Y.P., L. Li, J.L.).
³ Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (C.Y., D.S., Y.P., P.P., L. Li, J.L.).
⁴ Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China (C.Y., D.S., Y.P., L. Li, J.L.).
⁵ Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom (L.Y., I.Y.M., R.G.W., Y.C., H.D., D.A., Z.C.).
⁶ NCDs Prevention and Control Department, Licang CDC (X.Z.).
⁷ China National Center for Food Safety Risk Assessment, Beijing, China (J.C.).
⁸ Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (L. Liang).
⁹ State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China (J.L.).

PMID: 39308399
DOI: 10.1161/CIRCRESAHA.124.325066

Abstract

Background: Alterations in lipid metabolism and DNA methylation are 2 hallmarks of aging. Connecting metabolomic, epigenomic, and aging outcomes help unravel the complex mechanisms underlying aging. We aimed to assess whether DNA methylation clocks mediate the association of circulating metabolites with incident atherosclerotic cardiovascular disease (ASCVD) and frailty.

Methods: The China Kadoorie Biobank is a prospective cohort study with a baseline survey from 2004 to 2008 and a follow-up period until December 31, 2018. We used the Infinium Methylation EPIC BeadChip to measure the methylation levels of 988 participants' baseline blood leukocyte DNA. Metabolite profiles, including lipoprotein particles, lipid constituents, and various circulating metabolites, were measured using quantitative nuclear magnetic resonance. The pace of DNA methylation age acceleration (AA) was calculated using 5 widely used epigenetic clocks (the first generation: Horvath, Hannum, and Li; the second generation: Grim and Pheno). Incident ASCVD was ascertained through linkage with local death and disease registries and national health insurance databases, supplemented by active follow-up. The frailty index was constructed using medical conditions, symptoms, signs, and physical measurements collected at baseline.

Results: A total of 508 incident cases of ASCVD were documented during a median follow-up of 9.5 years. The first generation of epigenetic clocks was associated with the risk of ASCVD (P<0.05). For each SD increment in LiAA, HorvathAA, and HannumAA, the corresponding hazard ratios for ASCVD risk were 1.16 (1.05-1.28), 1.10 (1.00-1.22), and 1.17 (1.04-1.31), respectively. Only LiAA mediated the association of various metabolites (lipids, fatty acids, histidine, and inflammatory biomarkers) with ASCVD, with the mediating proportion reaching up to 15% for the diameter of low-density lipoprotein (P=1.2×10^-²). Regarding general aging, a 1-SD increase in GrimAA was associated with an average increase of 0.10 in the frailty index (P=2.0×10^-³), and a 33% and 63% increased risk of prefrailty and frailty at baseline (P=1.5×10^-² and 5.8×10^-²), respectively; this association was not observed with other clocks. GrimAA mediated the effect of various lipids, fatty acids, glucose, lactate, and inflammatory biomarkers on the frailty index, with the mediating proportion reaching up to 22% for triglycerides in very small-sized very low-density lipoprotein (P=6.0×10^-³).

Conclusions: These findings suggest that epigenomic mechanisms may play a role in the associations between circulating metabolites and the aging process. Different mechanisms underlie the first and second generations of DNA methylation age in cardiovascular and general aging.

Keywords: aging; atherosclerosis; leukocyte; lipid; lipoprotein.