1
|
Ryan CP, Lee NR, Carba DB, MacIsaac JL, Lin DTS, Atashzay P, Belsky DW, Kobor MS, Kuzawa CW. Pregnancy is linked to faster epigenetic aging in young women. Proc Natl Acad Sci U S A 2024; 121:e2317290121. [PMID: 38588424 PMCID: PMC11032455 DOI: 10.1073/pnas.2317290121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 02/13/2024] [Indexed: 04/10/2024] Open
Abstract
A central prediction of evolutionary theory is that energy invested into reproduction comes at the expense of somatic maintenance and repair, accelerating biological aging. Supporting this prediction are findings that high fertility among women predicts shorter lifespan and poorer health later in life. However, biological aging is thought to begin before age-related health declines, limiting the applicability of morbidity and mortality for studying the aging process earlier in life. Here, we examine the relationship between reproductive history and biological aging in a sample of young (20 to 22yo) men and women from the Cebu Longitudinal Health and Nutrition Survey, located in the Philippines (n = 1,735). We quantify biological aging using six measures, collectively known as epigenetic clocks, reflecting various facets of cellular aging, health, and mortality risk. In a subset of women, we test whether longitudinal changes in gravidity between young and early-middle adulthood (25 to 31yo) are associated with changes in epigenetic aging during that time. Cross-sectionally, gravidity was associated with all six measures of accelerated epigenetic aging in women (n = 825). Furthermore, longitudinal increases in gravidity were linked to accelerated epigenetic aging in two epigenetic clocks (n = 331). In contrast, the number of pregnancies a man reported fathering was not associated with epigenetic aging among same-aged cohort men (n = 910). These effects were robust to socioecological, environmental, and immunological factors, consistent with the hypothesis that pregnancy accelerates biological aging and that these effects can be detected in young women in a high-fertility context.
Collapse
Affiliation(s)
- Calen P. Ryan
- Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY10032
| | - Nanette R. Lee
- USC-Office of Population Studies Foundation, University of San Carlos, Talamban, Cebu City6000, Philippines
| | - Delia B. Carba
- USC-Office of Population Studies Foundation, University of San Carlos, Talamban, Cebu City6000, Philippines
| | - Julie L. MacIsaac
- BC Children’s Hospital Research Institute, University of British Columbia, Vancouver, BCV5Z 4H4, Canada
| | - David T. S. Lin
- BC Children’s Hospital Research Institute, University of British Columbia, Vancouver, BCV5Z 4H4, Canada
| | - Parmida Atashzay
- BC Children’s Hospital Research Institute, University of British Columbia, Vancouver, BCV5Z 4H4, Canada
| | - Daniel W. Belsky
- Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY10032
- Department of Epidemiology, Columbia University Mailman School of Public Health, Columbia University, New York, NY10032
- Child and Brain Development Program, Canadian Institute for Advanced Research, TorontoONM5G 1M1, Canada
| | - Michael S. Kobor
- BC Children’s Hospital Research Institute, University of British Columbia, Vancouver, BCV5Z 4H4, Canada
- Child and Brain Development Program, Canadian Institute for Advanced Research, TorontoONM5G 1M1, Canada
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 2A1, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, BCV5Z 4H4, Canada
| | | |
Collapse
|
2
|
Harada S, Iida M, Miyagawa N, Hirata A, Kuwabara K, Matsumoto M, Okamura T, Edagawa S, Kawada Y, Miyake A, Toki R, Akiyama M, Kawai A, Sugiyama D, Sato Y, Takemura R, Fukai K, Ishibashi Y, Kato S, Kurihara A, Sata M, Shibuki T, Takeuchi A, Kohsaka S, Sawano M, Shoji S, Izawa Y, Katsumata M, Oki K, Takahashi S, Takizawa T, Maruya H, Nishiwaki Y, Kawasaki R, Hirayama A, Ishikawa T, Saito R, Sato A, Soga T, Sugimoto M, Tomita M, Komaki S, Ohmomo H, Ono K, Otsuka-Yamasaki Y, Shimizu A, Sutoh Y, Hozawa A, Kinoshita K, Koshiba S, Kumada K, Ogishima S, Sakurai-Yageta M, Tamiya G, Takebayashi T. Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS). J Epidemiol 2024:JE20230192. [PMID: 38191178 DOI: 10.2188/jea.je20230192] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study used an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability, functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.
Collapse
Affiliation(s)
- Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
- Institute for Advanced Biosciences, Keio University
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
- Institute for Advanced Biosciences, Keio University
| | - Naoko Miyagawa
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Minako Matsumoto
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Shun Edagawa
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Yoko Kawada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Atsuko Miyake
- Department of Obstetrics and Gynecology, Keio University School of Medicine
| | - Ryota Toki
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Miki Akiyama
- Institute for Advanced Biosciences, Keio University
- Faculty of Environment and Information Studies, Keio University
| | - Atsuki Kawai
- Institute for Advanced Biosciences, Keio University
| | - Daisuke Sugiyama
- Faculty of Nursing and Medical Care and Graduate School of Health Management, Keio University
| | - Yasunori Sato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
- Biostatistics Unit, Clinical and Translational Research Center, Keio University Hospital
| | - Ryo Takemura
- Biostatistics Unit, Clinical and Translational Research Center, Keio University Hospital
| | - Kota Fukai
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Yoshiki Ishibashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Suzuka Kato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Ayako Kurihara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Mizuki Sata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Takuma Shibuki
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Ayano Takeuchi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Shun Kohsaka
- Department of Cardiology, Keio University School of Medicine
| | - Mitsuaki Sawano
- Department of Cardiology, Keio University School of Medicine
| | - Satoshi Shoji
- Department of Cardiology, Keio University School of Medicine
- Duke Clinical Research Institute
| | | | | | - Koichi Oki
- Department of Neurology, Keio University School of Medicine
- Department of Neurology, Tokyo Saiseikai Central Hospital
| | - Shinichi Takahashi
- Department of Neurology, Keio University School of Medicine
- Department of Neurology and Stroke, Saitama Medical University International Medical Center
| | | | | | - Yuji Nishiwaki
- Department of Environmental and Occupational Health, School of Medicine, Toho University
| | - Ryo Kawasaki
- Division of Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University
| | | | | | | | - Asako Sato
- Institute for Advanced Biosciences, Keio University
| | | | | | | | - Shohei Komaki
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences of Iwate Medical University
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University
| | - Hideki Ohmomo
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences of Iwate Medical University
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University
| | - Kanako Ono
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University
| | - Yayoi Otsuka-Yamasaki
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences of Iwate Medical University
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University
| | - Atsushi Shimizu
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences of Iwate Medical University
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University
| | - Yoichi Sutoh
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences of Iwate Medical University
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University
| | - Atsushi Hozawa
- Tohoku Medical Megabank Organization, Tohoku University
- Graduate School of Medicine, Tohoku University
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University
- Graduate School of Information Sciences, Tohoku University
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University
- Institute of Development, Aging and Cancer, Tohoku University
| | - Seizo Koshiba
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kazuki Kumada
- Tohoku Medical Megabank Organization, Tohoku University
| | | | | | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University
- Graduate School of Medicine, Tohoku University
- Center for Advanced Intelligence Project, RIKEN
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
- Institute for Advanced Biosciences, Keio University
| |
Collapse
|
3
|
Moqri M, Herzog C, Poganik JR, Justice J, Belsky DW, Higgins-Chen A, Moskalev A, Fuellen G, Cohen AA, Bautmans I, Widschwendter M, Ding J, Fleming A, Mannick J, Han JDJ, Zhavoronkov A, Barzilai N, Kaeberlein M, Cummings S, Kennedy BK, Ferrucci L, Horvath S, Verdin E, Maier AB, Snyder MP, Sebastiano V, Gladyshev VN. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell 2023; 186:3758-3775. [PMID: 37657418 PMCID: PMC11088934 DOI: 10.1016/j.cell.2023.08.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 09/03/2023]
Abstract
With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.
Collapse
Affiliation(s)
- Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Chiara Herzog
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
| | - Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jamie Justice
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Daniel W Belsky
- Department of Epidemiology, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | - Alexey Moskalev
- Institute of Biogerontology, Lobachevsky University, Nizhny Novgorod, Russia
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany; School of Medicine, University College Dublin, Dublin, Ireland
| | - Alan A Cohen
- Department of Environmental Health Sciences, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Ivan Bautmans
- Gerontology Department, Vrije Universiteit Brussel, Brussels, Belgium; Frailty in Ageing Research Department, Vrije Universiteit Brussel, Brussels, Belgium
| | - Martin Widschwendter
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria; Department of Women's Cancer, EGA Institute for Women's Health, University College London, London, UK; Department of Women's and Children's Health, Division of Obstetrics and Gynaecology, Karolinska Institutet, Stockholm, Sweden
| | - Jingzhong Ding
- Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | | | - Jing-Dong Jackie Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology, Peking University, Beijing, China
| | - Alex Zhavoronkov
- Insilico Medicine Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Nir Barzilai
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Steven Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Brian K Kennedy
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | | | - Eric Verdin
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Andrea B Maier
- Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA.
| | - Vittorio Sebastiano
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA.
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
4
|
Poganik JR, Zhang B, Baht GS, Tyshkovskiy A, Deik A, Kerepesi C, Yim SH, Lu AT, Haghani A, Gong T, Hedman AM, Andolf E, Pershagen G, Almqvist C, Clish CB, Horvath S, White JP, Gladyshev VN. Biological age is increased by stress and restored upon recovery. Cell Metab 2023; 35:807-820.e5. [PMID: 37086720 PMCID: PMC11055493 DOI: 10.1016/j.cmet.2023.03.015] [Citation(s) in RCA: 49] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/22/2022] [Accepted: 03/20/2023] [Indexed: 04/24/2023]
Abstract
Aging is classically conceptualized as an ever-increasing trajectory of damage accumulation and loss of function, leading to increases in morbidity and mortality. However, recent in vitro studies have raised the possibility of age reversal. Here, we report that biological age is fluid and exhibits rapid changes in both directions. At epigenetic, transcriptomic, and metabolomic levels, we find that the biological age of young mice is increased by heterochronic parabiosis and restored following surgical detachment. We also identify transient changes in biological age during major surgery, pregnancy, and severe COVID-19 in humans and/or mice. Together, these data show that biological age undergoes a rapid increase in response to diverse forms of stress, which is reversed following recovery from stress. Our study uncovers a new layer of aging dynamics that should be considered in future studies. The elevation of biological age by stress may be a quantifiable and actionable target for future interventions.
Collapse
Affiliation(s)
- Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Bohan Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Gurpreet S Baht
- Department of Orthopaedic Surgery, Duke University, Durham, NC 27701, USA; Duke Molecular Physiology Institute, Duke University, Durham, NC 27701, USA
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA 01241, USA
| | - Csaba Kerepesi
- Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network, Budapest, 1111, Hungary
| | - Sun Hee Yim
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Altos Labs, San Diego, CA, USA
| | - Amin Haghani
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Altos Labs, San Diego, CA, USA
| | - Tong Gong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anna M Hedman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ellika Andolf
- Department of Clinical Sciences, Division of Obstetrics and Gynaecology, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Göran Pershagen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 01241, USA
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Altos Labs, San Diego, CA, USA; Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - James P White
- Duke Molecular Physiology Institute, Duke University, Durham, NC 27701, USA; Department of Medicine, Duke University School of Medicine, Durham, NC 27701, USA.
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 01241, USA.
| |
Collapse
|
5
|
Pinel C, Green S, Svendsen MN. Slowing down decay: biological clocks in personalized medicine. Front Sociol 2023; 8:1111071. [PMID: 37139225 PMCID: PMC10149663 DOI: 10.3389/fsoc.2023.1111071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 03/27/2023] [Indexed: 05/05/2023]
Abstract
This article discusses so-called biological clocks. These technologies, based on aging biomarkers, trace and measure molecular changes in order to monitor individuals' "true" biological age against their chronological age. Drawing on the concept of decay, and building on ethnographic fieldwork in an academic laboratory and a commercial firm, we analyze the implications of the development and commercialization of biological clocks that can identify when decay is "out of tempo." We show how the building of biological clocks rests on particular forms of knowing decay: In the academic laboratory, researchers focus on endo-processes of decay that are internal to the person, but when the technology moves to the market, the focus shifts as staff bracket decay as exo-processes, which are seen as resulting from a person's lifestyle. As the technology of biological clocks travels from the laboratory to the market of online testing of the consumer's biological age, we observe shifting visions of aging: from an inevitable trajectory of decline to a malleable and plastic one. While decay is an inevitable trajectory starting at birth and ending with death, the commercialization of biological clocks points to ways of stretching time between birth and death as individuals "optimize" their biological age through lifestyle changes. Regardless of admitted uncertainties about what is measured and the connection between maintenance and future health outcomes, the aging person is made responsible for their decaying body and for enacting maintenance to slow down decay. We show how the biological clock's way of "knowing" decay turns aging and its maintenance into a life-long concern and highlight the normative implications of framing decay as malleable and in need of intervention.
Collapse
Affiliation(s)
- Clémence Pinel
- Centre for Medical Science and Technology Studies, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- *Correspondence: Clémence Pinel
| | - Sara Green
- Section for History of Philosophy of Science, Department of Science Education, University of Copenhagen, Copenhagen, Denmark
| | - Mette N. Svendsen
- Centre for Medical Science and Technology Studies, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
6
|
Petrovic D, Carmeli C, Sandoval JL, Bodinier B, Chadeau-Hyam M, Schrempft S, Ehret G, Dhayat NA, Ponte B, Pruijm M, Vineis P, Gonseth-Nusslé S, Guessous I, McCrory C, Bochud M, Stringhini S. Life-course socioeconomic factors are associated with markers of epigenetic aging in a population-based study. Psychoneuroendocrinology 2023; 147:105976. [PMID: 36417838 DOI: 10.1016/j.psyneuen.2022.105976] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022]
Abstract
Adverse socioeconomic circumstances negatively affect the functioning of biological systems, but the underlying mechanisms remain only partially understood. Here, we explore the associations between life-course socioeconomic factors and four markers of epigenetic aging in a population-based setting. We included 684 participants (52 % women, mean age 52.6 ± 15.5 years) from a population and family-based Swiss study. We used nine life-course socioeconomic indicators as the main exposure variables, and four blood-derived, second generation markers of epigenetic aging as the outcome variables (Levine's DNAmPhenoAge, DunedinPoAm38, GrimAge epigenetic age acceleration (EAA), and the mortality risk score (MS)). First, we investigated the associations between socioeconomic indicators and markers of epigenetic aging via mixed-effect linear regression models, adjusting for age, sex, participant's recruitment center, familial structure (random-effect covariate), seasonality of blood sampling, and technical covariates. Second, we implemented counterfactual mediation analysis to investigate life-course and intermediate mechanisms underlying the socioeconomic gradient in epigenetic aging. Effect-size estimates were assessed using regression coefficients and counterfactual mediation parameters, along with their respective 95 % confidence intervals. Individuals reporting a low father's occupation, adverse financial conditions in childhood, a low income, having financial difficulties, or experiencing unfavorable socioeconomic trajectories were epigenetically older and had a higher mortality risk score than their more advantaged counterparts. Specifically, this corresponded to an average increase of 1.1-1.5 years for Levine's epigenetic age (β and 95 %CI range, β (minimum and maximum): 1.1-1.5 95 %CI[0.0-0.2; 2.3-3.0]), 1.1-1.5 additional years for GrimAge (β: 1.1-1.5 95 %CI[0.2-0.6; 1.9-3.0]), a 1-3 % higher DunedinPoAm38 age acceleration (β: 0.01-0.03 95 %CI[0.00; 0.03-0.04]), and a 10-50 % higher MS score (β: 0.1-0.4 95 %CI[0.0-0.2; 0.3-0.4]) for the aforementioned socioeconomic indicators. By exploring the life-course mechanisms underlying the socioeconomic gradient in epigenetic aging, we found that both childhood and adulthood socioeconomic factors contributed to epigenetic aging, and that detrimental lifestyle factors mediated the relation between socioeconomic circumstances in adulthood and EAA (31-89 % mediated proportion). This study provides emerging evidence for an association between disadvantaged life-course socioeconomic circumstances and detrimental epigenetic aging patterns, supporting the "sensitive-period" life-course model. Counterfactual mediation analyses further indicated that the effect of socioeconomic factors in adulthood operates through detrimental lifestyle factors, whereas associations involving early-life socioeconomic factors were less clear.
Collapse
Affiliation(s)
- Dusan Petrovic
- Department of Epidemiology and Health Systems (DESS), University Center for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland; Centre for Environment and Health, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
| | - Cristian Carmeli
- Population Health Laboratory, University of Fribourg, Fribourg, Switzerland
| | - José Luis Sandoval
- Department of Oncology, Geneva University Hospitals, Geneva, Switzerland
| | - Barbara Bodinier
- Centre for Environment and Health, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Marc Chadeau-Hyam
- Centre for Environment and Health, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Stephanie Schrempft
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Georg Ehret
- Department of Cardiology, Geneva University Hospitals, Geneva, Switzerland
| | - Nasser Abdalla Dhayat
- Nephrology & Renal Care Center, B. Braun Medical Care AG, Hochfelden, Zurich, Switzerland
| | - Belén Ponte
- Department of Nephrology and Hypertension, Geneva University Hospitals, Geneva, Switzerland
| | - Menno Pruijm
- Department of Nephrology and Hypertension, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Paolo Vineis
- Department of Medical Gerontology, Trinity College Dublin, Dublin, Ireland
| | - Sémira Gonseth-Nusslé
- Department of Epidemiology and Health Systems (DESS), University Center for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
| | - Idris Guessous
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Cathal McCrory
- Department of Medical Gerontology, Trinity College Dublin, Dublin, Ireland
| | - Murielle Bochud
- Department of Epidemiology and Health Systems (DESS), University Center for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
| | - Silvia Stringhini
- Department of Epidemiology and Health Systems (DESS), University Center for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland; Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| |
Collapse
|