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      Marital and living status and biological ageing trajectories: a longitudinal cohort study with a 20-year follow-up

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          Abstract

          Biomarkers of ageing (BA) can predict health risks beyond chronological age, but little is known about how marital/living status affects longitudinal changes in BA. We examined the association between marital/living status and BA over time using the-Swedish-Adoption/Twin-Study-of-Aging (SATSA) cohort. Four BAs were analyzed: telomere length (TL) (638 individuals; 1603 measurements), DNAmAge (535 individuals; 1392 measurements), cognition (823 individuals; 3218 measurements), and frailty index (FI) (1828 individuals; 9502 measurements). Individuals were born between 1900 and 1948, and data on marital/living status, BAs, and covariates were collected through nine waves of questionnaires and in-person testing from 1986 to 2014. Mixed linear regression with random effects at twin-pair and individual levels were used to assess BA changes for constant marital/living status. Conditional generalized estimating equation assessed within-individual BA changes for varying marital/living status. Results showed that individuals who were consistently unmarried/non-cohabiting (β = 0.291, 95%CI = 0.189–0.393) or living alone (β = 0.203, 95%CI = 0.090–0.316) were more frail, and experienced accelerated frailty (p-for-interaction with age < 0.001 for marital status; p-for-interaction = 0.002 for living status) and cognitive decline (p-for-interaction < 0.001), compared to those married/cohabiting or living with someone Among individuals whose marital/living status changed, frailty was higher when living alone (β = 0.089, 95%CI = 0.017–0.162) and frailty accelerated when they became unmarried/non-cohabiting or were living alone (p-for-interaction < 0.001). Cognitive decline also accelerated when living alone (p-for-interaction = 0.020). No associations were observed for TL and DNAmAge. In conclusion, being unmarried/non-cohabiting or living alone from mid-to-old age is linked to accelerated cognitive decline and frailty. These findings highlight the potential importance of social support networks and living arrangements for healthy ageing.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10522-024-10171-1.

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          DNA methylation age of human tissues and cell types

          Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
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            Genome-wide methylation profiles reveal quantitative views of human aging rates.

            The ability to measure human aging from molecular profiles has practical implications in many fields, including disease prevention and treatment, forensics, and extension of life. Although chronological age has been linked to changes in DNA methylation, the methylome has not yet been used to measure and compare human aging rates. Here, we build a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101. This model measures the rate at which an individual's methylome ages, which we show is impacted by gender and genetic variants. We also show that differences in aging rates help explain epigenetic drift and are reflected in the transcriptome. Moreover, we show how our aging model is upheld in other human tissues and reveals an advanced aging rate in tumor tissue. Our model highlights specific components of the aging process and provides a quantitative readout for studying the role of methylation in age-related disease. Copyright © 2013 Elsevier Inc. All rights reserved.
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              Effects of stress throughout the lifespan on the brain, behaviour and cognition.

              Chronic exposure to stress hormones, whether it occurs during the prenatal period, infancy, childhood, adolescence, adulthood or aging, has an impact on brain structures involved in cognition and mental health. However, the specific effects on the brain, behaviour and cognition emerge as a function of the timing and the duration of the exposure, and some also depend on the interaction between gene effects and previous exposure to environmental adversity. Advances in animal and human studies have made it possible to synthesize these findings, and in this Review a model is developed to explain why different disorders emerge in individuals exposed to stress at different times in their lives.
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                Author and article information

                Contributors
                weiyao.yin.2@ki.se
                Journal
                Biogerontology
                Biogerontology
                Biogerontology
                Springer Netherlands (Dordrecht )
                1389-5729
                1573-6768
                8 January 2025
                8 January 2025
                2025
                : 26
                : 1
                : 34
                Affiliations
                [1 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, ( https://ror.org/056d84691) Box 210, 171 77 Stockholm, Sweden
                [2 ]Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, ( https://ror.org/00726et14) Chengdu, China
                [3 ]School of Public Health and Emergency Management, Southern University of Science and Technology, ( https://ror.org/049tv2d57) Shenzhen, Guangdong China
                [4 ]School of Public Health (Shenzhen), Sun Yat-Sen University, ( https://ror.org/0064kty71) Shenzhen, China
                [5 ]Institute of Environmental Medicine, Karolinska Institutet, ( https://ror.org/056d84691) Stockholm, Sweden
                [6 ]Faculty of Medicine and Health Technology and Gerontology Research Center (GEREC), University of Tampere, ( https://ror.org/033003e23) Tampere, Finland
                [7 ]Tampere Institute for Advanced Study, Tampere, Finland
                Article
                10171
                10.1007/s10522-024-10171-1
                11711563
                39775304
                1bc705d9-51d4-4924-bac5-0ee413ad10b9
                © The Author(s) 2025

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 November 2024
                : 12 December 2024
                Funding
                Funded by: the Strategic Research Program in Epidemiology at Karolinska Institutet
                Funded by: the Academy of Finland
                Award ID: 349335
                Award Recipient :
                Funded by: NIH
                Award ID: AG04563, AG10175, AG028555
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100006636, Forskningsrådet om Hälsa, Arbetsliv och Välfärd;
                Award ID: 97:0147:1B, 2009-0795, 2013-2292
                Award Recipient :
                Funded by: the MacArthur Foundation Research Network on Successful Aging
                Funded by: the Loo & Hans Osterman Foundation
                Funded by: the Foundation for Geriatric Diseases
                Funded by: the Magnus Bergwall Foundation
                Funded by: the Sigrid Jusélius Foundation
                Funded by: Yrjö Jahnsson Foundation and King Gustaf V:s and Queen Victorias Freemason Foundation
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: 825-2007-7460, 825-2009- 6141, 521-2013-8689, 2015-03255, 2018-02077, 2017-00641
                Award Recipient :
                Funded by: Karolinska Institute
                Categories
                Research Article
                Custom metadata
                © Springer Nature B.V. 2025

                Geriatric medicine
                biological ageing,ageing trajectories,frailty,cognitive function,living status,marital status,telomere,cognition,longitudinal

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