{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,8]],"date-time":"2026-11-08T10:38:41Z","timestamp":1794134321984,"version":"4.8.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med"],"DOI":"10.1186\/s12916-025-04410-6","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T13:07:54Z","timestamp":1761570474000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Long-term BMI trajectories and epigenetic age acceleration: the role of genetic risk for obesity"],"prefix":"10.1186","volume":"23","author":[{"given":"Yue","family":"Wen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanling","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhuo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongmei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"issue":"21","key":"4410_CR1","doi-asserted-by":"publisher","first-page":"e984","DOI":"10.1161\/CIR.0000000000000973","volume":"143","author":"TM Powell-Wiley","year":"2021","unstructured":"Powell-Wiley TM, Poirier P, Burke LE, Despr\u00e9s JP, Gordon-Larsen P, Lavie CJ, et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2021;143(21):e984-1010.","journal-title":"Circulation"},{"issue":"12","key":"4410_CR2","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1016\/S2213-8587(18)30288-2","volume":"6","author":"K Bhaskaran","year":"2018","unstructured":"Bhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3\u00b76 million adults in the UK. Lancet Diabetes Endocrinol. 2018;6(12):944\u201353.","journal-title":"Lancet Diabetes Endocrinol"},{"issue":"10","key":"4410_CR3","doi-asserted-by":"publisher","first-page":"2240","DOI":"10.1002\/oby.21615","volume":"24","author":"TS J\u00f8rgensen","year":"2016","unstructured":"J\u00f8rgensen TS, Osler M, \u00c4ngquist LH, Zimmermann E, Christensen GT, S\u00f8rensen TI. The u-shaped association of body mass index with mortality: influence of the traits height, intelligence, and education. Obesity. 2016;24(10):2240\u20137.","journal-title":"Obesity"},{"key":"4410_CR4","doi-asserted-by":"publisher","DOI":"10.1186\/s13148-016-0301-7","volume":"9","author":"T Nevalainen","year":"2017","unstructured":"Nevalainen T, Kananen L, Marttila S, Jylh\u00e4v\u00e4 J, Mononen N, K\u00e4h\u00f6nen M, et al. Obesity accelerates epigenetic aging in middle-aged but not in elderly individuals. Clin Epigenetics. 2017;9:20.","journal-title":"Clin Epigenetics"},{"key":"4410_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2021.101348","volume":"69","author":"L Oblak","year":"2021","unstructured":"Oblak L, van der Zaag J, Higgins-Chen AT, Levine ME, Boks MP. A systematic review of biological, social and environmental factors associated with epigenetic clock acceleration. Ageing Res Rev. 2021;69:101348.","journal-title":"Ageing Res Rev"},{"issue":"2","key":"4410_CR6","doi-asserted-by":"publisher","first-page":"513","DOI":"10.18632\/aging.204492","volume":"15","author":"CA Foster","year":"2023","unstructured":"Foster CA, Barker-Kamps M, Goering M, Patki A, Tiwari HK, Mrug S. Epigenetic age acceleration correlates with BMI in young adults. Aging (Albany NY). 2023;15(2):513\u201323.","journal-title":"Aging (Albany NY)"},{"issue":"1","key":"4410_CR7","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1093\/lifemeta\/loac002","volume":"1","author":"JR Speakman","year":"2022","unstructured":"Speakman JR, Elmquist JK. Obesity: an evolutionary context. Life Metab. 2022;1(1):10\u201324.","journal-title":"Life Metab"},{"issue":"7","key":"4410_CR8","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1016\/j.cmet.2024.06.004","volume":"36","author":"MS Kim","year":"2024","unstructured":"Kim MS, Shim I, Fahed AC, Do R, Park WY, Natarajan P, et al. Association of genetic risk, lifestyle, and their interaction with obesity and obesity-related morbidities. Cell Metab. 2024;36(7):1494-503.e3.","journal-title":"Cell Metab"},{"issue":"7","key":"4410_CR9","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.18632\/aging.101508","volume":"10","author":"S Horvath","year":"2018","unstructured":"Horvath S, Oshima J, Martin GM, Lu AT, Quach A, Cohen H, et al. Epigenetic clock for skin and blood cells applied to Hutchinson Gilford progeria syndrome and ex vivo studies. Aging (Albany NY). 2018;10(7):1758\u201375.","journal-title":"Aging (Albany NY)"},{"issue":"2","key":"4410_CR10","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.molcel.2012.10.016","volume":"49","author":"G Hannum","year":"2013","unstructured":"Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359\u201367.","journal-title":"Mol Cell"},{"issue":"5","key":"4410_CR11","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/s41576-024-00807-w","volume":"26","author":"AE Teschendorff","year":"2025","unstructured":"Teschendorff AE, Horvath S. Epigenetic ageing clocks: statistical methods and emerging computational challenges. Nat Rev Genet. 2025;26(5):350\u201368.","journal-title":"Nat Rev Genet"},{"issue":"4","key":"4410_CR12","doi-asserted-by":"publisher","first-page":"573","DOI":"10.18632\/aging.101414","volume":"10","author":"ME Levine","year":"2018","unstructured":"Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573\u201391.","journal-title":"Aging (Albany NY)"},{"issue":"2","key":"4410_CR13","doi-asserted-by":"publisher","first-page":"303","DOI":"10.18632\/aging.101684","volume":"11","author":"AT Lu","year":"2019","unstructured":"Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, et al. DNA methylation grimAge strongly predicts lifespan and healthspan. Aging (Albany NY). 2019;11(2):303\u201327.","journal-title":"Aging (Albany NY)"},{"key":"4410_CR14","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.54870","author":"DW Belsky","year":"2020","unstructured":"Belsky DW, Caspi A, Arseneault L, Baccarelli A, Corcoran DL, Gao X, et al. Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. Elife. 2020. https:\/\/doi.org\/10.7554\/eLife.54870.","journal-title":"Elife"},{"key":"4410_CR15","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.73420","author":"DW Belsky","year":"2022","unstructured":"Belsky DW, Caspi A, Corcoran DL, Sugden K, Poulton R, Arseneault L, et al. DunedinPACE, a DNA methylation biomarker of the pace of aging. Elife. 2022. https:\/\/doi.org\/10.7554\/eLife.73420.","journal-title":"Elife"},{"issue":"1","key":"4410_CR16","doi-asserted-by":"publisher","DOI":"10.1186\/s13148-021-01035-3","volume":"13","author":"F Ammous","year":"2021","unstructured":"Ammous F, Zhao W, Ratliff SM, Mosley TH, Bielak LF, Zhou X, et al. Epigenetic age acceleration is associated with cardiometabolic risk factors and clinical cardiovascular disease risk scores in African Americans. Clin Epigenetics. 2021;13(1):55.","journal-title":"Clin Epigenetics"},{"issue":"1","key":"4410_CR17","doi-asserted-by":"publisher","DOI":"10.1186\/s13148-023-01476-y","volume":"15","author":"Y Pan","year":"2023","unstructured":"Pan Y, Sun X, Huang Z, Zhang R, Li C, Anderson AH, et al. Effects of epigenetic age acceleration on kidney function: a Mendelian randomization study. Clin Epigenetics. 2023;15(1):61.","journal-title":"Clin Epigenetics"},{"issue":"2","key":"4410_CR18","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1210\/jc.2017-01382","volume":"103","author":"P Petrus","year":"2018","unstructured":"Petrus P, Bialesova L, Checa A, Kerr A, Naz S, B\u00e4ckdahl J, et al. Adipocyte expression of SLC19A1 links DNA hypermethylation to adipose tissue inflammation and insulin resistance. J Clin Endocrinol Metab. 2018;103(2):710\u201321.","journal-title":"J Clin Endocrinol Metab"},{"issue":"10","key":"4410_CR19","doi-asserted-by":"publisher","first-page":"1731","DOI":"10.1002\/oby.23255","volume":"29","author":"WY Lin","year":"2021","unstructured":"Lin WY, Wang YC, Teng IH, Liu C, Lou XY. Associations of five obesity metrics with epigenetic age acceleration: evidence from 2,474 Taiwan Biobank participants. Obesity. 2021;29(10):1731\u20138.","journal-title":"Obesity"},{"issue":"8","key":"4410_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cdnut.2024.104411","volume":"8","author":"SN Cheung","year":"2024","unstructured":"Cheung SN, Lieberman HR, Pasiakos SM, Fulgoni VL, Berryman CE. Associations between essential amino acid intake and functional health outcomes in older adults: analysis of the National Health and Nutrition Examination Survey, 2001\u20132018. Curr Dev Nutr. 2024;8(8):104411.","journal-title":"Curr Dev Nutr"},{"issue":"43","key":"4410_CR21","doi-asserted-by":"publisher","first-page":"15538","DOI":"10.1073\/pnas.1412759111","volume":"111","author":"S Horvath","year":"2014","unstructured":"Horvath S, Erhart W, Brosch M, Ammerpohl O, von Sch\u00f6nfels W, Ahrens M, et al. Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci U S A. 2014;111(43):15538\u201343.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"1","key":"4410_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/s13148-019-0754-6","volume":"11","author":"J de Toro-Mart\u00edn","year":"2019","unstructured":"de Toro-Mart\u00edn J, Gu\u00e9nard F, Tchernof A, Hould FS, Lebel S, Julien F, et al. Body mass index is associated with epigenetic age acceleration in the visceral adipose tissue of subjects with severe obesity. Clin Epigenetics. 2019;11(1):172.","journal-title":"Clin Epigenetics"},{"key":"4410_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines12091985","author":"TM Ke","year":"2024","unstructured":"Ke TM, Lophatananon A, Muir KR. Exploring the relationships between lifestyle patterns and epigenetic biological age measures in men. Biomedicines. 2024. https:\/\/doi.org\/10.3390\/biomedicines12091985.","journal-title":"Biomedicines"},{"issue":"7","key":"4410_CR24","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2024.27889","volume":"7","author":"KM Harris","year":"2024","unstructured":"Harris KM, Levitt B, Gaydosh L, Martin C, Meyer JM, Mishra AA, et al. Sociodemographic and lifestyle factors and epigenetic aging in US young adults: NIMHD social epigenomics program. JAMA Netw Open. 2024;7(7):e2427889.","journal-title":"JAMA Netw Open"},{"issue":"5","key":"4410_CR25","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1093\/aje\/kwad080","volume":"193","author":"Y Luo","year":"2024","unstructured":"Luo Y, Liu Z, Luo J, Li R, Wei Z, Yang L, et al. BMI trajectories in late middle age, genetic risk, and incident diabetes in older adults: evidence from a 26-year longitudinal study. Am J Epidemiol. 2024;193(5):685\u201394.","journal-title":"Am J Epidemiol"},{"issue":"1","key":"4410_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-022-02400-6","volume":"20","author":"D You","year":"2022","unstructured":"You D, Wang D, Wu Y, Chen X, Shao F, Wei Y, et al. Associations of genetic risk, BMI trajectories, and the risk of non-small cell lung cancer: a population-based cohort study. BMC Med. 2022;20(1):203.","journal-title":"BMC Med"},{"issue":"1","key":"4410_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/workar\/wax032","volume":"4","author":"GG Fisher","year":"2018","unstructured":"Fisher GG, Ryan LH. Overview of the health and retirement study and introduction to the special issue. Work Aging Retire. 2018;4(1):1\u20139.","journal-title":"Work Aging Retire"},{"key":"4410_CR28","unstructured":"Health and Retirement Study, (RAND HRS Longitudinal File 2020 (V1)) public use dataset. Produced and distributed by the\u00a0university of michigan with funding from the national institute on Aging (grant number NIA U01AG009740). Ann Arbor, MI. 2023."},{"key":"4410_CR29","unstructured":"RAND HRS Longitudinal File 2020 (V1). Produced by the RAND Center for the study of aging, with funding from the nationalinstitute on aging and the social security administration. Santa Monica, CA. 2023."},{"key":"4410_CR30","doi-asserted-by":"crossref","unstructured":"Heeringa SG, Connor JH. Technical description of the Health and Retirement Study sample design. Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan; 1995 [Available from: https:\/\/hrs.isr.umich.edu\/sites\/default\/files\/biblio\/HRSSAMP.pdf.","DOI":"10.7826\/ISR-UM.06.585031.001.05.0001.1995"},{"key":"4410_CR31","unstructured":"Crimmins E, Faul J, Thyagarajan B, Weir D. HRS documentation report: venous blood collection and assay protocol in the 2016 Health and Retirement Study: 2016 Venous Blood Study (VBS) 2017 [Available from: https:\/\/hrs.isr.umich.edu\/sites\/default\/files\/biblio\/HRS2016VBSDD_0.pdf."},{"key":"4410_CR32","unstructured":"Faul JD, Crimmins E, Munro S, Thyagarajan B, Weir DR. HRS DNA methylation \u2013 VBS 2016: Survey Research Center, Institute for Social Research, University of Michigan; 2023 [Available from: https:\/\/hrs.isr.umich.edu\/sites\/default\/files\/genetic\/HRS_DNAm_OCT2023.pdf."},{"issue":"6","key":"4410_CR33","first-page":"1117","volume":"76","author":"EM Crimmins","year":"2021","unstructured":"Crimmins EM, Thyagarajan B, Levine ME, Weir DR, Faul J. Associations of age, sex, race\/ethnicity, and education with 13 epigenetic clocks in a nationally representative U.S. sample: the health and retirement study. The Journals of Gerontology: Series A. 2021;76(6):1117\u201323.","journal-title":"The Journals of Gerontology: Series A"},{"issue":"1","key":"4410_CR34","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1038\/oby.2007.504","volume":"15","author":"MA McAdams","year":"2007","unstructured":"McAdams MA, Van Dam RM, Hu FB. Comparison of self-reported and measured BMI as correlates of disease markers in US adults. Obesity (Silver Spring). 2007;15(1):188\u201396.","journal-title":"Obesity (Silver Spring)"},{"issue":"4","key":"4410_CR35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0231229","volume":"15","author":"JM Hodge","year":"2020","unstructured":"Hodge JM, Shah R, McCullough ML, Gapstur SM, Patel AV. Validation of self-reported height and weight in a large, nationwide cohort of U.S. adults. PLoS ONE. 2020;15(4):e0231229.","journal-title":"PLoS ONE"},{"key":"4410_CR36","unstructured":"Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i-xii, 1\u2013253."},{"issue":"9403","key":"4410_CR37","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/S0140-6736(03)15268-3","volume":"363","author":"WE Consultation","year":"2004","unstructured":"Consultation WE. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157\u201363.","journal-title":"Lancet"},{"key":"4410_CR38","unstructured":"Crimmins E, Kim JK, Fisher J, Faul JD. HRS Epigenetic Clocks \u2013 Release 1. Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan. 2020."},{"issue":"10","key":"4410_CR39","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2013-14-10-r115","volume":"14","author":"S Horvath","year":"2013","unstructured":"Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.","journal-title":"Genome Biol"},{"issue":"2","key":"4410_CR40","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2014-15-2-r24","volume":"15","author":"CI Weidner","year":"2014","unstructured":"Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15(2):R24.","journal-title":"Genome Biol"},{"key":"4410_CR41","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2016.00126","volume":"7","author":"L Vidal-Bralo","year":"2016","unstructured":"Vidal-Bralo L, Lopez-Golan Y, Gonzalez A. Simplified assay for epigenetic age estimation in whole blood of adults. Front Genet. 2016;7:126.","journal-title":"Front Genet"},{"issue":"6","key":"4410_CR42","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0014821","volume":"6","author":"S Bocklandt","year":"2011","unstructured":"Bocklandt S, Lin W, Sehl ME, S\u00e1nchez FJ, Sinsheimer JS, Horvath S, et al. Epigenetic predictor of age. PLoS ONE. 2011;6(6):e14821.","journal-title":"PLoS ONE"},{"issue":"6","key":"4410_CR43","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1111\/acel.12005","volume":"11","author":"P Garagnani","year":"2012","unstructured":"Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D, et al. Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell. 2012;11(6):1132\u20134.","journal-title":"Aging Cell"},{"issue":"2","key":"4410_CR44","doi-asserted-by":"publisher","first-page":"394","DOI":"10.18632\/aging.100908","volume":"8","author":"Q Lin","year":"2016","unstructured":"Lin Q, Weidner CI, Costa IG, Marioni RE, Ferreira MR, Deary IJ, et al. DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging (Albany NY). 2016;8(2):394\u2013401.","journal-title":"Aging (Albany NY)"},{"key":"4410_CR45","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms14617","volume":"8","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Wilson R, Heiss J, Breitling LP, Saum KU, Sch\u00f6ttker B, et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat Commun. 2017;8:14617.","journal-title":"Nat Commun"},{"issue":"1","key":"4410_CR46","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-016-1064-3","volume":"17","author":"Z Yang","year":"2016","unstructured":"Yang Z, Wong A, Kuh D, Paul DS, Rakyan VK, Leslie RD, et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 2016;17(1):205.","journal-title":"Genome Biol"},{"issue":"2","key":"4410_CR47","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1093\/ije\/dyu067","volume":"43","author":"A Sonnega","year":"2014","unstructured":"Sonnega A, Faul JD, Ofstedal MB, Langa KM, Phillips JW, Weir DR. Cohort profile: the health and retirement study (HRS). Int J Epidemiol. 2014;43(2):576\u201385.","journal-title":"Int J Epidemiol"},{"issue":"20","key":"4410_CR48","doi-asserted-by":"publisher","first-page":"3641","DOI":"10.1093\/hmg\/ddy271","volume":"27","author":"L Yengo","year":"2018","unstructured":"Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in \u223c700000 individuals of European ancestry. Hum Mol Genet. 2018;27(20):3641\u20139.","journal-title":"Hum Mol Genet"},{"issue":"9","key":"4410_CR49","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1093\/bioinformatics\/btu848","volume":"31","author":"J Euesden","year":"2015","unstructured":"Euesden J, Lewis CM, O\u2019Reilly PF. PRSice: polygenic risk score software. Bioinformatics. 2015;31(9):1466\u20138.","journal-title":"Bioinformatics"},{"key":"4410_CR50","unstructured":"Ware EB, Gard AM, Schmitz LL, Faul JD. HRS Polygenic Scores \u2013 Release 4. Ann Arbor, MI: Survey Research Center, Institutefor Social Research, University of Michigan. 2020."},{"key":"4410_CR51","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16173141","author":"W Zhao","year":"2019","unstructured":"Zhao W, Ammous F, Ratliff S, Liu J, Yu M, Mosley TH, et al. Education and lifestyle factors are associated with DNA methylation clocks in older African Americans. Int J Environ Res Public Health. 2019. https:\/\/doi.org\/10.3390\/ijerph16173141.","journal-title":"Int J Environ Res Public Health"},{"issue":"2","key":"4410_CR52","doi-asserted-by":"publisher","first-page":"419","DOI":"10.18632\/aging.101168","volume":"9","author":"A Quach","year":"2017","unstructured":"Quach A, Levine ME, Tanaka T, Lu AT, Chen BH, Ferrucci L, et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging (Albany NY). 2017;9(2):419\u201346.","journal-title":"Aging (Albany NY)"},{"issue":"6","key":"4410_CR53","doi-asserted-by":"publisher","DOI":"10.1111\/acel.13828","volume":"22","author":"FAU Fox","year":"2023","unstructured":"Fox FAU, Liu D, Breteler MMB, Aziz NA. Physical activity is associated with slower epigenetic ageing-findings from the Rhineland study. Aging Cell. 2023;22(6):e13828.","journal-title":"Aging Cell"},{"issue":"3","key":"4410_CR54","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1177\/1099800420966648","volume":"23","author":"T Liu","year":"2021","unstructured":"Liu T, Canon MD, Shen L, Marples BA, Colton JP, Lo WJ, et al. The influence of the BDNF Val66Met polymorphism on the association of regular physical activity with cognition among individuals with diabetes. Biol Res Nurs. 2021;23(3):318\u201330.","journal-title":"Biol Res Nurs"},{"issue":"6","key":"4410_CR55","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0165025409343765","volume":"33","author":"N Ram","year":"2009","unstructured":"Ram N, Grimm KJ. Growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. Int J Behav Dev. 2009;33(6):565\u201376.","journal-title":"Int J Behav Dev"},{"issue":"3","key":"4410_CR56","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1177\/0049124101029003005","volume":"29","author":"B Jones","year":"2001","unstructured":"Jones B, Nagin D, Roeder K. A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res. 2001;29(3):374\u201393.","journal-title":"Sociol Methods Res"},{"key":"4410_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101429","volume":"48","author":"ZJ Ward","year":"2022","unstructured":"Ward ZJ, Willett WC, Hu FB, Pacheco LS, Long MW, Gortmaker SL. Excess mortality associated with elevated body weight in the USA by state and demographic subgroup: a modelling study. eClinicalMedicine. 2022;48:101429.","journal-title":"eClinicalMedicine"},{"issue":"10046","key":"4410_CR58","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1016\/S0140-6736(16)30175-1","volume":"388","author":"E Di Angelantonio","year":"2016","unstructured":"Global BMI Mortality Collaboration, Di Angelantonio E, Bhupathiraju SN, Wormser D, Gao P, Kaptoge S, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388(10046):776\u201386.","journal-title":"Lancet"},{"issue":"1","key":"4410_CR59","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1001\/jama.2012.113905","volume":"309","author":"KM Flegal","year":"2013","unstructured":"Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309(1):71\u201382.","journal-title":"JAMA"},{"key":"4410_CR60","volume":"58","author":"E Ojalehto","year":"2023","unstructured":"Ojalehto E, Zhan Y, Jylh\u00e4v\u00e4 J, Reynolds CA, Dahl Aslan AK, Karlsson IK. Genetically and environmentally predicted obesity in relation to cardiovascular disease: a nationwide cohort study. eClin Med. 2023;58:101943.","journal-title":"eClin Med"},{"key":"4410_CR61","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.14151.1","author":"MJ M\u00fcller","year":"2018","unstructured":"M\u00fcller MJ, Geisler C, Heymsfield SB, Bosy-Westphal A. Recent advances in understanding body weight homeostasis in humans. F1000Res. 2018. https:\/\/doi.org\/10.12688\/f1000research.14151.1.","journal-title":"F1000Res"},{"issue":"1","key":"4410_CR62","doi-asserted-by":"publisher","DOI":"10.1186\/s13148-020-00952-z","volume":"13","author":"N Robinson","year":"2021","unstructured":"Robinson N, Brown H, Antoun E, Godfrey KM, Hanson MA, Lillycrop KA, et al. Childhood DNA methylation as a marker of early life rapid weight gain and subsequent overweight. Clin Epigenetics. 2021;13(1):8.","journal-title":"Clin Epigenetics"},{"issue":"1","key":"4410_CR63","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-30527-w","volume":"13","author":"SM Jeong","year":"2023","unstructured":"Jeong SM, Lee DH, Rezende LFM, Giovannucci EL. Different correlation of body mass index with body fatness and obesity-related biomarker according to age, sex and race-ethnicity. Sci Rep. 2023;13(1):3472.","journal-title":"Sci Rep"},{"issue":"1","key":"4410_CR64","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-023-01063-w","volume":"23","author":"S Mouchti","year":"2023","unstructured":"Mouchti S, Orliacq J, Reeves G, Chen Z. Assessment of correlation between conventional anthropometric and imaging-derived measures of body fat composition: a systematic literature review and meta-analysis of observational studies. BMC Med Imaging. 2023;23(1):127.","journal-title":"BMC Med Imaging"},{"issue":"1","key":"4410_CR65","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0245436","volume":"16","author":"JC Wong","year":"2021","unstructured":"Wong JC, O\u2019Neill S, Beck BR, Forwood MR, Khoo SK. Comparison of obesity and metabolic syndrome prevalence using fat mass index, body mass index and percentage body fat. PLoS ONE. 2021;16(1):e0245436.","journal-title":"PLoS ONE"}],"container-title":["BMC Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12916-025-04410-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12916-025-04410-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12916-025-04410-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T14:03:50Z","timestamp":1761573830000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedicine.biomedcentral.com\/articles\/10.1186\/s12916-025-04410-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["4410"],"URL":"https:\/\/doi.org\/10.1186\/s12916-025-04410-6","relation":{},"ISSN":["1741-7015"],"issn-type":[{"value":"1741-7015","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"24 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The original HRS was approved by the University of Michigan Institutional Review Board. All participants provided informed consent. The current project was approved by the institutional review board at UT Southwestern Medicine Center (STU-2024\u20131276).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"589"}}