Näytä suppeat kuvailutiedot

dc.contributor.authorFöhr, Tiina
dc.contributor.authorWaller, Katja
dc.contributor.authorViljanen, Anne
dc.contributor.authorRantanen, Taina
dc.contributor.authorKaprio, Jaakko
dc.contributor.authorOllikainen, Miina
dc.contributor.authorSillanpää, Elina
dc.date.accessioned2023-07-21T11:14:35Z
dc.date.available2023-07-21T11:14:35Z
dc.date.issued2023
dc.identifier.citationFöhr, T., Waller, K., Viljanen, A., Rantanen, T., Kaprio, J., Ollikainen, M., & Sillanpää, E. (2023). Mortality associations with DNA methylation-based biological aging and physical functioning measures across a 20-year follow-up period. <i>Journals of Gerontology Series A : Biological Sciences and Medical Sciences</i>, <i>78</i>(8), 1489-1496. <a href="https://doi.org/10.1093/gerona/glad026" target="_blank">https://doi.org/10.1093/gerona/glad026</a>
dc.identifier.otherCONVID_176438294
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/88431
dc.description.abstractBackground Measures of biological aging range from DNA methylation (DNAm)-based estimates to measures of physical abilities. The purpose of this study was to compare DNAm- and physical functioning-based measures of biological aging in predicting mortality. Methods We studied 63- to 76-year-old women (N = 395) from the Finnish Twin Study on Aging (FITSA). Participants’ biological age (epigenetic clocks DNAm GrimAge and DunedinPACE) was estimated using blood DNAm data. Tests of physical functioning conducted under standardized laboratory conditions included the Timed Up and Go (TUG) test and 10-m walk test. Mortality hazard ratios (HRs) were calculated per every one standard deviation (SD) increase in the predictor. Cox regression models were conducted for individuals and twin pairs, the latter controlling for underlying genetic effects. The models were adjusted for known lifestyle predictors of mortality. Results During the follow-up period (mean 17.0 years, range 0.2–20.3), 187 participants died. In both the individual-based and pairwise analyses, GrimAge and both functional biomarkers of aging were associated with mortality independent of family relatedness, chronological age, physical activity, body mass index, smoking, education, or chronic diseases. In a model including both the DNAm-based measures and functional biomarkers of aging, GrimAge and TUG remained predictive. Conclusions The findings suggest that DNAm GrimAge and the TUG test are strong predictors of mortality independent of each other’s and genetic influences. DNAm-based measures and functional tests capture different aspects of the aging process and thus complement each other as measures of biological aging in predicting mortality.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherOxford University Press (OUP)
dc.relation.ispartofseriesJournals of Gerontology Series A : Biological Sciences and Medical Sciences
dc.rightsCC BY 4.0
dc.subject.otherepigenetic clock
dc.subject.otherTimed Up and Go test
dc.subject.otherwalking speed
dc.subject.othertwins
dc.titleMortality associations with DNA methylation-based biological aging and physical functioning measures across a 20-year follow-up period
dc.typeresearch article
dc.identifier.urnURN:NBN:fi:jyu-202307214556
dc.contributor.laitosLiikuntatieteellinen tiedekuntafi
dc.contributor.laitosFaculty of Sport and Health Sciencesen
dc.contributor.oppiaineLiikuntalääketiedefi
dc.contributor.oppiaineGerontologia ja kansanterveysfi
dc.contributor.oppiaineSports and Exercise Medicineen
dc.contributor.oppiaineGerontology and Public Healthen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1489-1496
dc.relation.issn1079-5006
dc.relation.numberinseries8
dc.relation.volume78
dc.type.versionpublishedVersion
dc.rights.copyright© 2023 the Authors
dc.rights.accesslevelopenAccessfi
dc.type.publicationarticle
dc.relation.grantnumber346509
dc.relation.grantnumber341750
dc.subject.ysokuolleisuus
dc.subject.ysoikääntyminen
dc.subject.ysokävely
dc.subject.ysokaksoset
dc.subject.ysoepigenetiikka
dc.subject.ysoperimä
dc.subject.ysoepigeneettinen periytyminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p5003
jyx.subject.urihttp://www.yso.fi/onto/yso/p5056
jyx.subject.urihttp://www.yso.fi/onto/yso/p3706
jyx.subject.urihttp://www.yso.fi/onto/yso/p9845
jyx.subject.urihttp://www.yso.fi/onto/yso/p24631
jyx.subject.urihttp://www.yso.fi/onto/yso/p8862
jyx.subject.urihttp://www.yso.fi/onto/yso/p21979
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1093/gerona/glad026
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramResearch costs of Academy Research Fellow, AoFen
jyx.fundingprogramAcademy Research Fellow, AoFen
jyx.fundingprogramAkatemiatutkijan tutkimuskulut, SAfi
jyx.fundingprogramAkatemiatutkija, SAfi
jyx.fundinginformationThis work was supported by the Academy of Finland (grant 251723 to T.R., grants 265240, 263278, 308248, 312073, 336823 to J.K., 297908 and 251316 to M.O., 341750, 346509 to E.S.), EC MC ITN Project EPITRAIN (J.K. and M.O.), University of Helsinki Research Funds (M.O.), the Sigrid Juselius Foundation (to J.K. and M.O.), the Juho Vainio Foundation (T.F. and E.S.), the Päivikki and Sakari Sohlberg foundation (E.S.), and the Yrjö Jahnsson Foundation (E.S.).
dc.type.okmA1


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