{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T13:44:52Z","timestamp":1790862292857,"version":"4.1.0"},"reference-count":40,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"9","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circulation Research"],"published-print":{"date-parts":[[2024,10,11]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>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\u2019 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      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 (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.05). For each SD increment in LiAA, HorvathAA, and HannumAA, the corresponding hazard ratios for ASCVD risk were 1.16 (1.05\u22121.28), 1.10 (1.00\u22121.22), and 1.17 (1.04\u22121.31), 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 (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      =1.2\u00d710\n                      <jats:sup>\u22122<\/jats:sup>\n                      ). Regarding general aging, a 1-SD increase in GrimAA was associated with an average increase of 0.10 in the frailty index (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      =2.0\u00d710\n                      <jats:sup>\u22123<\/jats:sup>\n                      ), and a 33% and 63% increased risk of prefrailty and frailty at baseline (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      =1.5\u00d710\n                      <jats:sup>\u22122<\/jats:sup>\n                      and 5.8\u00d710\n                      <jats:sup>\u22122<\/jats:sup>\n                      ), 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 (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      =6.0\u00d710\n                      <jats:sup>\u22123<\/jats:sup>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circresaha.124.325066","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T05:00:31Z","timestamp":1727067631000},"page":"954-966","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":30,"title":["DNA Methylation Age Mediates Effect of Metabolic Profile on Cardiovascular and General Aging"],"prefix":"10.1161","volume":"135","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0827-4973","authenticated-orcid":true,"given":"Jiahui","family":"Si","sequence":"first","affiliation":[{"name":"CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China (J.S.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2141-6146","authenticated-orcid":true,"given":"Yu","family":"Ma","sequence":"additional","affiliation":[{"name":"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.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0019-0014","authenticated-orcid":true,"given":"Canqing","family":"Yu","sequence":"additional","affiliation":[{"name":"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.)."},{"name":"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.)."},{"name":"Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China (C.Y., D.S., Y.P., L. Li, J.L.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3651-6693","authenticated-orcid":true,"given":"Dianjianyi","family":"Sun","sequence":"additional","affiliation":[{"name":"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.)."},{"name":"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.)."},{"name":"Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China (C.Y., D.S., Y.P., L. 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