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Warner B, Ratner E, Datta A, Lendasse A. A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans. Aging (Albany NY) 2024; 16:12414-12427. [PMID: 39215995 PMCID: PMC11424583 DOI: 10.18632/aging.206098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 07/15/2024] [Indexed: 09/04/2024]
Abstract
Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This review aimed to systematically survey all proposed epigenetic and phenotypic clocks to date, excluding mitotic clocks (i.e., cancer risk clocks) and those that were modeled using non-human samples. We reported the predictive performance of 33 clocks and outlined the statistical or machine learning techniques used. We also reported the most influential clinical measurements used in the included phenotypic clocks. Our findings provide a systematic reporting of the last decade of biological clock research and indicate possible avenues for future research.
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Affiliation(s)
| | | | | | - Amaury Lendasse
- Department of IST, University of Houston, Houston, TX 77004, USA
- Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
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2
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Fong S, Pabis K, Latumalea D, Dugersuren N, Unfried M, Tolwinski N, Kennedy B, Gruber J. Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention. NATURE AGING 2024; 4:1137-1152. [PMID: 38898237 PMCID: PMC11333290 DOI: 10.1038/s43587-024-00646-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 05/08/2024] [Indexed: 06/21/2024]
Abstract
Clocks that measure biological age should predict all-cause mortality and give rise to actionable insights to promote healthy aging. Here we applied dimensionality reduction by principal component analysis to clinical data to generate a clinical aging clock (PCAge) identifying signatures (principal components) separating healthy and unhealthy aging trajectories. We found signatures of metabolic dysregulation, cardiac and renal dysfunction and inflammation that predict unsuccessful aging, and we demonstrate that these processes can be impacted using well-established drug interventions. Furthermore, we generated a streamlined aging clock (LinAge), based directly on PCAge, which maintains equivalent predictive power but relies on substantially fewer features. Finally, we demonstrate that our approach can be tailored to individual datasets, by re-training a custom clinical clock (CALinAge), for use in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study of caloric restriction. Our analysis of CALERIE participants suggests that 2 years of mild caloric restriction significantly reduces biological age. Altogether, we demonstrate that this dimensionality reduction approach, through integrating different biological markers, can provide targets for preventative medicine and the promotion of healthy aging.
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Affiliation(s)
- Sheng Fong
- Department of Geriatric Medicine, Singapore General Hospital, Singapore, Singapore
- Clinical and Translational Sciences PhD Program, Duke-NUS Medical School, Singapore, Singapore
| | - Kamil Pabis
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Center for Healthy Longevity, National University Health System, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Djakim Latumalea
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Center for Healthy Longevity, National University Health System, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Maximilian Unfried
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Center for Healthy Longevity, National University Health System, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Nicholas Tolwinski
- Science Division, Yale-NUS College, Singapore, Singapore
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, Singapore
| | - Brian Kennedy
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Center for Healthy Longevity, National University Health System, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Jan Gruber
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Center for Healthy Longevity, National University Health System, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Science Division, Yale-NUS College, Singapore, Singapore.
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Yusri K, Kumar S, Fong S, Gruber J, Sorrentino V. Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks. Int J Mol Sci 2024; 25:6793. [PMID: 38928497 PMCID: PMC11203944 DOI: 10.3390/ijms25126793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.
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Affiliation(s)
- Khalishah Yusri
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Sanjay Kumar
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Sheng Fong
- Department of Geriatric Medicine, Singapore General Hospital, Singapore 169608, Singapore
- Clinical and Translational Sciences PhD Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Jan Gruber
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Science Division, Yale-NUS College, Singapore 138527, Singapore
| | - Vincenzo Sorrentino
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Department of Medical Biochemistry, Amsterdam UMC, Amsterdam Gastroenterology Endocrinology Metabolism and Amsterdam Neuroscience Cellular & Molecular Mechanisms, University of Amsterdam, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
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4
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Wu Y, Xu R, Li S, Ming Wong E, Southey MC, Hopper JL, Abramson MJ, Li S, Guo Y. Epigenome-wide association study of short-term temperature fluctuations based on within-sibship analyses in Australian females. ENVIRONMENT INTERNATIONAL 2023; 171:107655. [PMID: 36476687 DOI: 10.1016/j.envint.2022.107655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/26/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Temperature fluctuations can affect human health independent of the effect of mean temperature. However, no study has evaluated whether short-term temperature fluctuations could affect DNA methylation. METHODS Peripheral blood DNA methylation for 479 female siblings of 130 families were analysed. Gridded daily temperatures data were obtained, linked to each participant's home address, and used to calculate nine different metrics of short-term temperature fluctuations: temperature variabilities (TVs) within the day of blood draw and preceding one to seven days (TV 0-1 to TV 0-7), diurnal temperature range (DTR), and temperature change between neighbouring days (TCN). Within-sibship design was used to perform epigenome-wide association analyses, adjusting for daily mean temperatures, and other important covariates (e.g., smoking, alcohol use, cell-type proportions). Differentially methylated regions (DMRs) were further identified. Multiple-testing comparisons with a significant threshold of 0.01 for cytosine-guanine dinucleotides (CpGs) and 0.05 for DMRs were applied. RESULTS Among 479 participants (mean age ± SD, 56.4 ± 7.9 years), we identified significant changes in methylation levels in 14 CpGs and 70 DMRs associated with temperature fluctuations. Almost all identified CpGs were associated with exposure to temperature fluctuations within three days. Differentially methylated signals were mapped to 68 genes that were linked to human diseases such as cancer (e.g., colorectal carcinoma, breast carcinoma, and metastatic neoplasms) and mental disorder (e.g., schizophrenia, mental depression, and bipolar disorder). The top three most significantly enriched gene ontology terms were Response to bacterium (TV 0-3), followed by Hydrolase activity, acting on ester bonds (TCN), and Oxidoreductase activity (TV 0-3). CONCLUSIONS Short-term temperature fluctuations were associated with differentially methylated signals across the human genome, which provides evidence on the potential biological mechanisms underlying the health impact of temperature fluctuations. Future studies are needed to further clarify the roles of DNA methylation in diseases associated with temperature fluctuations.
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Affiliation(s)
- Yao Wu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Rongbin Xu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia; Cancer Epidemiology Division, Cancer Council Victoria, VIC 3004, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Michael J Abramson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shuai Li
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3052, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
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5
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Xu R, Li S, Wu Y, Yue X, Wong EM, Southey MC, Hopper JL, Abramson MJ, Li S, Guo Y. Wildfire-related PM 2.5 and DNA methylation: An Australian twin and family study. ENVIRONMENT INTERNATIONAL 2023; 171:107704. [PMID: 36542997 DOI: 10.1016/j.envint.2022.107704] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/24/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Wildfire-related fine particulate matter (PM2.5) has many adverse health impacts, but its impacts on human epigenome are unknown. We aimed to evaluate the associations between long-term exposure to wildfire-related PM2.5 and blood DNA methylation, and whether the associations differ from those with non-wildfire-related PM2.5. METHODS We studied 479 Australian women comprising 132 twin pairs and 215 of their sisters. Blood-derived DNA methylation was measured using the HumanMethylation450 BeadChip array. Data on 3-year (year of blood collection and previous two years) average wildfire-related and non-wildfire-related PM2.5 at 0.01°×0.01° spatial resolution were created by combining information from satellite observations, chemical transport models, and ground-based observations. Exposure data were linked to each participant's home address, assuming the address did not change during the exposure window. For DNA methylation of each cytosine-guanine dinucleotide (CpG), and for global DNA methylation represented by the average of all measured CpGs or CpGs in repetitive elements, we evaluated their associations with wildfire- or non-wildfire-related PM2.5 using a within-sibship analysis controlling for factors shared between siblings and other important covariates. Differentially methylated regions (DMRs) were defined by comb-p and DMRcate. RESULTS The 3-year average wildfire-related PM2.5 (range: 0.3 to 7.6 µg/m3, mean: 1.6 µg/m3) was negatively, but not significantly (p-values greater than 0.05) associated with all seven global DNA methylation measures. There were 26 CpGs and 33 DMRs associated with wildfire-related PM2.5 (Bonferroni adjusted p-value < 0.05) mapped to 47 genes enriched for pathways related to inflammatory regulation and platelet activation. These genes have been related to many human diseases or phenotypes e.g., cancer, mental disorders, diabetes, obesity, asthma, blood pressure. These CpGs, DMRs and enriched pathways did not overlap with the 1 CpG and 7 DMRs associated with non-wildfire-related PM2.5. CONCLUSIONS Long-term exposure to wildfire-related PM2.5 was associated with various blood DNA methylation signatures in Australian women, and these were distinct from those associated with non-wildfire-related PM2.5.
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Affiliation(s)
- Rongbin Xu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yao Wu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Xu Yue
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia; Cancer Epidemiology Division, Cancer Council Victoria, VIC 3004, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Michael J Abramson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shuai Li
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3052, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
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6
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Epigenetic clock: A promising biomarker and practical tool in aging. Ageing Res Rev 2022; 81:101743. [PMID: 36206857 DOI: 10.1016/j.arr.2022.101743] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 09/13/2022] [Accepted: 09/30/2022] [Indexed: 01/31/2023]
Abstract
As a complicated process, aging is characterized by various changes at the cellular, subcellular and nuclear levels, one of which is epigenetic aging. With increasing awareness of the critical role that epigenetic alternations play in aging, DNA methylation patterns have been employed as a measure of biological age, currently referred to as the epigenetic clock. This review provides a comprehensive overview of the epigenetic clock as a biomarker of aging and a useful tool to manage healthy aging. In this burgeoning scientific field, various kinds of epigenetic clocks continue to emerge, including Horvath's clock, Hannum's clock, DNA PhenoAge, and DNA GrimAge. We hereby present the most classic epigenetic clocks, as well as their differences. Correlations of epigenetic age with morbidity, mortality and other factors suggest the potential of epigenetic clocks for risk prediction and identification in the context of aging. In particular, we summarize studies on promising age-reversing interventions, with epigenetic clocks employed as a practical tool in the efficacy evaluation. We also discuss how the lack of higher-quality information poses a major challenge, and offer some suggestions to address existing obstacles. Hopefully, our review will help provide an appropriate understanding of the epigenetic clocks, thereby enabling novel insights into the aging process and how it can be manipulated to promote healthy aging.
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7
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Marttila S, Tamminen H, Rajić S, Mishra PP, Lehtimäki T, Raitakari O, Kähönen M, Kananen L, Jylhävä J, Hägg S, Delerue T, Peters A, Waldenberger M, Kleber ME, März W, Luoto R, Raitanen J, Sillanpää E, Laakkonen EK, Heikkinen A, Ollikainen M, Raitoharju E. Methylation status of VTRNA2-1/ nc886 is stable across populations, monozygotic twin pairs and in majority of tissues. Epigenomics 2022; 14:1105-1124. [PMID: 36200237 DOI: 10.2217/epi-2022-0228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aims & methods: The aim of this study was to characterize the methylation level of a polymorphically imprinted gene, VTRNA2-1/nc886, in human populations and somatic tissues.48 datasets, consisting of more than 30 tissues and >30,000 individuals, were used. Results: nc886 methylation status is associated with twin status and ethnic background, but the variation between populations is limited. Monozygotic twin pairs present concordant methylation, whereas ∼30% of dizygotic twin pairs present discordant methylation in the nc886 locus. The methylation levels of nc886 are uniform across somatic tissues, except in cerebellum and skeletal muscle. Conclusion: The nc886 imprint may be established in the oocyte, and, after implantation, the methylation status is stable, excluding a few specific tissues.
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Affiliation(s)
- Saara Marttila
- Molecular Epidemiology, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland.,Gerontology Research Center, Tampere University, Tampere, 33014, Finland
| | - Hely Tamminen
- Molecular Epidemiology, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland
| | - Sonja Rajić
- Molecular Epidemiology, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland.,Finnish Cardiovascular Research Center Tampere, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland.,Fimlab Laboratories, Arvo Ylpön katu 4, Tampere, 33520, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland.,Finnish Cardiovascular Research Center Tampere, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland.,Fimlab Laboratories, Arvo Ylpön katu 4, Tampere, 33520, Finland
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku & Turku University Hospital, Turku, 20014, Finland.,Research Centre of Applied & Preventive Cardiovascular Medicine, University of Turku, Turku, 20014, Finland.,Department of Clinical Physiology & Nuclear Medicine, Turku University Hospital, Turku, 20014, Finland
| | - Mika Kähönen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland.,Department of Clinical Physiology, Tampere University Hospital, Tampere, 33521, Finland
| | - Laura Kananen
- Faculty of Medicine & Health Technology, & Gerontology Research Center, Tampere University, Arvo Ylpön katu 34, Tampere, 33520,Finland.,Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden.,Faculty of Social Sciences (Health Sciences), & Gerontology Research Center, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland
| | - Juulia Jylhävä
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden.,Faculty of Social Sciences (Health Sciences), & Gerontology Research Center, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland
| | - Sara Hägg
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden
| | - Thomas Delerue
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, D-85764,, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, D-85764, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, D-85764,, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Marcus E Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany.,SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany.,Competence Cluster for Nutrition & Cardiovascular Health (nutriCARD) Halle-Jena-Leipzig, Jena, 07743, Germany.,SYNLAB Academy, SYNLAB Holding Deutschland GmbH, Augsburg, 86156, Germany.,Clinical Institute of Medical & Chemical Laboratory Diagnostics, Medical University of Graz, Graz, 8010, Austria
| | - Riitta Luoto
- The Social Insurance Institute of Finland (Kela), Helsinki, 00250, Finland.,The UKK Institute for Health Promotion Research, Kaupinpuistonkatu 1, Tampere, 33500, Finland
| | - Jani Raitanen
- The UKK Institute for Health Promotion Research, Kaupinpuistonkatu 1, Tampere, 33500, Finland.,Faculty of Social Sciences (Health Sciences), Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland
| | - Elina Sillanpää
- Gerontology Research Center & Faculty of Sport & Health Sciences, University of Jyväskylä, Jyväskylä, 40014, Finland.,Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00014, Finland
| | - Eija K Laakkonen
- Gerontology Research Center & Faculty of Sport & Health Sciences, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Aino Heikkinen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00014, Finland
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00014, Finland
| | - Emma Raitoharju
- Molecular Epidemiology, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland.,Finnish Cardiovascular Research Center Tampere, Faculty of Medicine & Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland
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8
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Li S, Ye Z, Mather KA, Nguyen TL, Dite GS, Armstrong NJ, Wong EM, Thalamuthu A, Giles GG, Craig JM, Saffery R, Southey MC, Tan Q, Sachdev PS, Hopper JL. Early life affects late-life health through determining DNA methylation across the lifespan: A twin study. EBioMedicine 2022; 77:103927. [PMID: 35301182 PMCID: PMC8927831 DOI: 10.1016/j.ebiom.2022.103927] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 02/07/2022] [Accepted: 02/24/2022] [Indexed: 12/22/2022] Open
Abstract
Background Previous findings for the genetic and environmental contributions to DNA methylation variation were for limited age ranges only. We investigated the lifespan contributions and their implications for human health for the first time. Methods 1,720 monozygotic twin (MZ) pairs and 1,107 dizygotic twin (DZ) pairs aged 0-92 years were included. Familial correlations (i.e., correlations between twins) for 353,681 methylation sites were estimated and modelled as a function of twin pair cohabitation history. Findings The methylome average familial correlation was around zero at birth (MZ pair: -0.01; DZ pair: -0.04), increased with the time of twins living together during childhood at rates of 0.16 (95%CI: 0.12-0.20) for MZ pairs and 0.13 (95%CI: 0.07-0.20) for DZ pairs per decade, and decreased with the time of living apart during adulthood at rates of 0.026 (95%CI: 0.019-0.033) for MZ pairs and 0.027 (95%CI: 0.011-0.043) for DZ pairs per decade. Neither the increasing nor decreasing rate differed by zygosity (both P>0.1), consistent with cohabitation environment shared by twins, rather than genetic factors, influencing the methylation familial correlation changes. Familial correlations for 6.6% (23,386/353,681) sites changed with twin pair cohabitation history. These sites were enriched for high heritability, proximal promoters, and epigenetic/genetic associations with various early-life factors and late-life health conditions. Interpretation Early life strongly influences DNA methylation variation across the lifespan, and the effects are stronger for heritable sites and sites biologically relevant to the regulation of gene expression. Early life could affect late-life health through influencing DNA methylation. Funding Victorian Cancer Agency, Cancer Australia, Cure Cancer Foundation.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia; Genetic Technologies Ltd, Fitzroy, Victoria, Australia
| | - Nicola J Armstrong
- Mathematics and Statistics, Curtin University, Western Australia, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia; Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Jeffrey M Craig
- The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Faculty of Health, Deakin University, Waurn Ponds, Victoria, Australia; Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Richard Saffery
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia; Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Qihua Tan
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
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9
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Lin WY. Genome-wide association study for four measures of epigenetic age acceleration and two epigenetic surrogate markers using DNA methylation data from Taiwan biobank. Hum Mol Genet 2021; 31:1860-1870. [PMID: 34962275 DOI: 10.1093/hmg/ddab369] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 12/17/2021] [Accepted: 12/21/2021] [Indexed: 11/15/2022] Open
Abstract
To highlight the genetic architecture for epigenetic aging, McCartney et al. recently identified 137 significant SNPs based on genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and two epigenetic surrogate markers. However, none Asian ancestry studies have been included in this or previous meta-analyses. I performed a GWAS on blood DNA methylation (DNAm) levels of 2309 Taiwan Biobank (TWB) participants. Owing to the fact that the sample size of an individual GWAS of DNAm data is still not large, I adopted the "prioritized subset analysis" (PSA) method to boost the power of a GWAS. The four epigenetic clocks and the two epigenetic surrogate markers were investigated, respectively. I replicated 21 out of the 137 aging-associated genetic loci by applying the PSA method to the TWB DNAm data. Moreover, I identified five novel loci, including rs117530284 that was associated with the "epigenetic age acceleration" (EAA) according to Lu et al.'s GrimAge (called "GrimEAA"). Considering 16 covariates (sex, BMI, smoking status, drinking status, regular exercise, educational attainment, and the first 10 ancestry principal components), each "A" allele of rs117530284 in the IBA57 gene was found to be associated with a 1.5943-year GrimEAA (95% C.I. = [1.0748, 2.1138]). IBA57 is a protein coding gene and is associated with multiple mitochondrial dysfunctions syndromes. A decline in mitochondrial activity and quality is associated with aging and many age-related diseases. This is one of the first DNAm GWAS for individuals of Asian ancestry.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Master of Public Health Degree Program, College of Public Health, National Taiwan University, Taipei, Taiwan.,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
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10
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Xu R, Li S, Li S, Wong EM, Southey MC, Hopper JL, Abramson MJ, Guo Y. Ambient temperature and genome-wide DNA methylation: A twin and family study in Australia. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 285:117700. [PMID: 34380236 DOI: 10.1016/j.envpol.2021.117700] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 06/13/2023]
Abstract
Little is known about the association between ambient temperature and DNA methylation, which is a potential biological process through which ambient temperature affects health. This study aimed to evaluate the association between ambient temperature and DNA methylation across human genome. We included 479 Australian women, including 132 twin pairs and 215 sisters of these twins. Blood-derived DNA methylation was measured using the HumanMethylation450 BeadChip array. Data on average ambient temperature during eight different exposure windows [lag0d (the blood draw day), lag0-7d (the current day and previous seven days prior to blood draw), lag0-14d, lag0-21d, lag0-28d, lag0-90d, lag0-180d, and lag0-365d)] was linked to each participant's home address. For each cytosine-guanine dinucleotide (CpG), we evaluated the association between its methylation level and temperature using generalized estimating equations (GEE), adjusting for important covariates. We used comb-p and DMRcate to identify differentially methylated regions (DMRs). We identified 31 CpGs at which blood DNA methylation were significantly associated with ambient temperature with false discovery rate [FDR] < 0.05. There were 82 significant DMRs identified by both comb-p (Sidak p-value < 0.01) and DMRcate (FDR < 0.01). Most of these CpGs and DMRs only showed association with temperature during one specific exposure window. These CpGs and DMRs were mapped to 85 genes. These related genes have been related to many human chronic diseases or phenotypes (e.g., diabetes, arthritis, breast cancer, depression, asthma, body height) in previous studies. The signals of short-term windows (lag0d and lag0-21d) showed enrichment in biological processes related to cell adhesion. In conclusion, short-, medium-, and long-term exposures to ambient temperature were all associated with blood DNA methylation, but the target genomic loci varied by exposure window. These differential methylation signals may serve as potential biomarkers to understand the health impacts of temperature.
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Affiliation(s)
- Rongbin Xu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3800, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3800, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3800, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, 3010, Australia; Cancer Epidemiology Division, Cancer Council Victoria, VIC, 3004, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Michael J Abramson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.
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11
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Xu R, Li S, Li S, Wong EM, Southey MC, Hopper JL, Abramson MJ, Guo Y. Residential surrounding greenness and DNA methylation: An epigenome-wide association study. ENVIRONMENT INTERNATIONAL 2021; 154:106556. [PMID: 33862401 DOI: 10.1016/j.envint.2021.106556] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/26/2021] [Accepted: 03/31/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND DNA methylation is a potential biological mechanism through which residential greenness affects health, but little is known about its association with greenness and whether the association could be modified by genetic background. We aimed to evaluate the association between surrounding greenness and genome-wide DNA methylation and potential gene-greenness interaction effects on DNA methylation. METHODS We measured blood-derived DNA methylation using the HumanMethylation450 BeadChip array (Illumina) for 479 Australian women, including 66 monozygotic, 66 dizygotic twin pairs, and 215 sisters of these twins. Surrounding greenness was represented by Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) within 300, 500, 1000 or 2000 m surrounding participants' home addresses. For each cytosine-guanine dinucleotide (CpG), the associations between its methylation level and NDVI or EVI were evaluated by generalized estimating equations, after adjusting for age, education, marital status, area-level socioeconomic status, smoking behavior, cell-type proportions, and familial clustering. We used comb-p and DMRcate to identify significant differentially methylated regions (DMRs). For each significant CpG, we evaluated the interaction effects of greenness and single-nucleotide polymorphisms (SNPs) within ±1 Mb window on its methylation level. RESULTS We found associations between surrounding greenness and blood DNA methylation for one CpG (cg04720477, mapped to the promoter region of CNP gene) with false discovery rate [FDR] < 0.05, and for another 9 CpGs with 0.05 ≤ FDR < 0.10. For two of these CpGs, we found 33 SNPs significantly (FDR < 0.05) modified the greenness-methylation association. There were 35 significant DMRs related to surrounding greenness that were identified by both comb-p (Sidak p-value < 0.01) and DMRcate (FDR < 0.01). Those CpGs and DMRs were mapped to genes related to many human diseases, such as mental health disorders and neoplasms as well as nutritional and metabolic diseases. CONCLUSIONS Surrounding greenness was associated with blood DNA methylation of many loci across human genome, and this association could be modified by genetic variations.
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Affiliation(s)
- Rongbin Xu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia; Cancer Epidemiology Division, Cancer Council Victoria, VIC 3004, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Michael J Abramson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
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12
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Piniewska-Róg D, Heidegger A, Pośpiech E, Xavier C, Pisarek A, Jarosz A, Woźniak A, Wojtas M, Phillips C, Kayser M, Parson W, Branicki W. Impact of excessive alcohol abuse on age prediction using the VISAGE enhanced tool for epigenetic age estimation in blood. Int J Legal Med 2021; 135:2209-2219. [PMID: 34405265 PMCID: PMC8523459 DOI: 10.1007/s00414-021-02665-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 07/06/2021] [Indexed: 12/13/2022]
Abstract
DNA methylation-based clocks provide the most accurate age estimates with practical implications for clinical and forensic genetics. However, the effects of external factors that may influence the estimates are poorly studied. Here, we evaluated the effect of alcohol consumption on epigenetic age prediction in a cohort of extreme alcohol abusers. Blood samples from deceased alcohol abusers and age- and sex-matched controls were analyzed using the VISAGE enhanced tool for age prediction from somatic tissues that enables examination of 44 CpGs within eight age markers. Significantly altered DNA methylation was recorded for alcohol abusers in MIR29B2CHG. This resulted in a mean predicted age of 1.4 years higher compared to the controls and this trend increased in older individuals. The association of alcohol abuse with epigenetic age acceleration, as determined by the prediction analysis performed based on MIR29B2CHG, was small but significant (β = 0.190; P-value = 0.007). However, the observed alteration in DNA methylation of MIR29B2CHG had a non-significant effect on age estimation with the VISAGE age prediction model. The mean absolute error in the alcohol-abusing cohort was 3.1 years, compared to 3.3 years in the control group. At the same time, upregulation of MIR29B2CHG expression may have a biological function, which merits further studies.
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Affiliation(s)
- Danuta Piniewska-Róg
- Jagiellonian University Medical College, Faculty of Medicine, Department of Forensic Medicine, Grzegórzecka 16, 31-531, Krakow, Poland
| | - Antonia Heidegger
- Institute of Legal Medicine, Medical University of Innsbruck, Muellerstrasse 44, 6020, Innsbruck, Austria
| | - Ewelina Pośpiech
- Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7A, 30-348, Krakow, Poland
| | - Catarina Xavier
- Institute of Legal Medicine, Medical University of Innsbruck, Muellerstrasse 44, 6020, Innsbruck, Austria
| | - Aleksandra Pisarek
- Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7A, 30-348, Krakow, Poland
| | - Agata Jarosz
- Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7A, 30-348, Krakow, Poland
| | - Anna Woźniak
- Central Forensic Laboratory of the Police, Aleje Ujazdowskie 7, 00-583, Warsaw, Poland
| | - Marta Wojtas
- Jagiellonian University Medical College, Faculty of Medicine, Department of Forensic Medicine, Grzegórzecka 16, 31-531, Krakow, Poland
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, R/ San Francisco s/n, 15782, Santiago de Compostela, Spain
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Walther Parson
- Institute of Legal Medicine, Medical University of Innsbruck, Muellerstrasse 44, 6020, Innsbruck, Austria
- Forensic Science Program, The Pennsylvania State University, 13 Thomas Building, University Park, PA, 16802, USA
| | - Wojciech Branicki
- Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7A, 30-348, Krakow, Poland.
- Central Forensic Laboratory of the Police, Aleje Ujazdowskie 7, 00-583, Warsaw, Poland.
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13
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Xu R, Li S, Li S, Wong EM, Southey MC, Hopper JL, Abramson MJ, Guo Y. Surrounding Greenness and Biological Aging Based on DNA Methylation: A Twin and Family Study in Australia. ENVIRONMENTAL HEALTH PERSPECTIVES 2021; 129:87007. [PMID: 34460342 PMCID: PMC8404778 DOI: 10.1289/ehp8793] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 08/06/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND High surrounding greenness has many health benefits and might contribute to slower biological aging. However, very few studies have evaluated this from the perspective of epigenetics. OBJECTIVES We aimed to evaluate the association between surrounding greenness and biological aging based on DNA methylation. METHODS We derived Horvath's DNA methylation age (DNAmAge), Hannum's DNAmAge, PhenoAge, and GrimAge based on DNA methylation measured in peripheral blood samples from 479 Australian women in 130 families. Measures of DNAmAge acceleration (DNAmAgeAC) were derived from the residuals after regressing each DNAmAge metric on chronological age. Greenness was represented by satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) metrics within 300-, 500-, 1,000-, and 2,000-m buffers surrounding participant addresses. Greenness-DNAmAgeAC associations were estimated using a within-sibship design fitted by linear mixed effect models, adjusting for familial clustering and important covariates. RESULTS Greenness metrics were associated with significantly lower DNAmAgeAC based on GrimAge acceleration, suggesting slower biological aging with higher greenness based on both NDVI and EVI in 300-2,000m buffer areas. For example, each interquartile range increase in NDVI within 1,000m was associated with a 0.59 (95% CI: 0.18, 1.01)-year decrease in GrimAge acceleration. Greenness was also inversely associated with three of the eight components of GrimAge, specifically, DNA methylation-based surrogates of serum cystatin-C, serum growth differentiation factor 15, and smoking pack years. Associations between greenness and biological aging measured by Horvath's and Hannum's DNAmAgeAC were less consistent, and depended on neighborhood socioeconomic status. No significant associations were estimated for PhenoAge acceleration. DISCUSSION Higher surrounding greenness was associated with slower biological aging, as indicated by GrimAge age acceleration, in Australian women. Associations were also evident for three individual components of GrimAge, but were inconsistent for other measures of biological aging. Additional studies are needed to confirm our results. https://doi.org/10.1289/EHP8793.
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Affiliation(s)
- Rongbin Xu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Victoria, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Michael J. Abramson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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14
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Teeuw J, Ori APS, Brouwer RM, de Zwarte SMC, Schnack HG, Hulshoff Pol HE, Ophoff RA. Accelerated aging in the brain, epigenetic aging in blood, and polygenic risk for schizophrenia. Schizophr Res 2021; 231:189-197. [PMID: 33882370 DOI: 10.1016/j.schres.2021.04.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 03/02/2021] [Accepted: 04/07/2021] [Indexed: 02/07/2023]
Abstract
Schizophrenia patients show signs of accelerated aging in cognitive and physiological domains. Both schizophrenia and accelerated aging, as measured by MRI brain images and epigenetic clocks, are correlated with increased mortality. However, the association between these aging measures have not yet been studied in schizophrenia patients. In schizophrenia patients and healthy subjects, accelerated aging was assessed in brain tissue using a longitudinal MRI (N = 715 scans; mean scan interval 3.4 year) and in blood using two epigenetic age clocks (N = 172). Differences ('gaps') between estimated ages and chronological ages were calculated, as well as the acceleration rate of brain aging. The correlations between these aging measures as well as with polygenic risk scores for schizophrenia (PRS; N = 394) were investigated. Brain aging and epigenetic aging were not significantly correlated. Polygenic risk for schizophrenia was significantly correlated with brain age gap, brain age acceleration rate, and negatively correlated with DNAmAge gap, but not with PhenoAge gap. However, after controlling for disease status and multiple comparisons correction, these effects were no longer significant. Our results imply that the (accelerated) aging observed in the brain and blood reflect distinct biological processes. Our findings will require replication in a larger cohort.
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Affiliation(s)
- Jalmar Teeuw
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Anil P S Ori
- Center for Neurobehavioral Genetics, University of California, Los Angeles, United States
| | - Rachel M Brouwer
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Sonja M C de Zwarte
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hugo G Schnack
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Roel A Ophoff
- Center for Neurobehavioral Genetics, University of California, Los Angeles, United States; Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
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15
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Li S, Bui M, Hopper JL. Inference about causation from examination of familial confounding (ICE FALCON): a model for assessing causation analogous to Mendelian randomization. Int J Epidemiol 2021; 49:1259-1269. [PMID: 32500139 PMCID: PMC7750993 DOI: 10.1093/ije/dyaa065] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 03/27/2020] [Indexed: 12/25/2022] Open
Abstract
Background We developed a method to make Inference about Causation from Examination of FAmiliaL CONfounding (ICE FALCON) using observational data for related individuals and considering changes in a pair of regression coefficients. ICE FALCON has some similarities to Mendelian randomization (MR) but uses in effect all the familial determinants of the exposure, not just those captured by measured genetic variants, and does not require genetic data nor make strong assumptions. ICE FALCON can assess tracking of a measure over time, an issue often difficult to assess using MR due to lack of a valid instrumental variable. Methods We describe ICE FALCON and present two empirical applications with simulations. Results We found evidence consistent with body mass index (BMI) having a causal effect on DNA methylation at the ABCG1 locus, the same conclusion as from MR analyses but providing about 2.5 times more information per subject. We found evidence that tracking of BMI is consistent with longitudinal causation, as well as familial confounding. The simulations supported the validity of ICE FALCON. Conclusions There are conceptual similarities between ICE FALCON and MR, but empirically they are giving similar conclusions with possibly more information per subject from ICE FALCON. ICE FALCON can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge and/or data available to create a valid instrumental variable, or when the assumptions underlying MR analysis are suspect. ICE FALCON could provide insights into causality for a wide range of public health questions.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.,Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
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16
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Li S, Nguyen TL, Wong EM, Dugué PA, Dite GS, Armstrong NJ, Craig JM, Mather KA, Sachdev PS, Saffery R, Sung J, Tan Q, Thalamuthu A, Milne RL, Giles GG, Southey MC, Hopper JL. Genetic and environmental causes of variation in epigenetic aging across the lifespan. Clin Epigenetics 2020; 12:158. [PMID: 33092643 PMCID: PMC7583207 DOI: 10.1186/s13148-020-00950-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/13/2020] [Indexed: 12/14/2022] Open
Abstract
Background DNA methylation-based biological age (DNAm age) is an important biomarker for adult health. Studies in specific age ranges have found widely varying results about its genetic and environmental causes of variation. However, these studies are not able to provide a comprehensive view of the causes of variation over the lifespan.
Results In order to investigate the genetic and environmental causes of DNAm age variation across the lifespan, we pooled genome-wide DNA methylation data for 4217 people aged 0–92 years from 1871 families. DNAm age was calculated using the Horvath epigenetic clock. We estimated familial correlations in DNAm age for monozygotic (MZ) twin, dizygotic (DZ) twin, sibling, parent–offspring, and spouse pairs by cohabitation status. Genetic and environmental variance components models were fitted and compared. We found that twin pair correlations were − 0.12 to 0.18 around birth, not different from zero (all P > 0.29). For all pairs of relatives, their correlations increased with time spent living together (all P < 0.02) at different rates (MZ > DZ and siblings > parent–offspring; P < 0.001) and decreased with time spent living apart (P = 0.02) at similar rates. These correlation patterns were best explained by cohabitation-dependent shared environmental factors, the effects of which were 1.41 (95% confidence interval [CI] 1.16 to 1.66) times greater for MZ pairs than for DZ and sibling pairs, and the latter were 2.03 (95% CI 1.13 to 9.47) times greater than for parent–offspring pairs. Genetic factors explained 13% (95% CI − 10 to 35%) of variation (P = 0.27). Similar results were found for another two epigenetic clocks, suggesting that our observations are robust to how DNAm age is measured. In addition, results for the other clocks were consistent with there also being a role for prenatal environmental factors in determining their variation. Conclusions Variation in DNAm age is mostly caused by environmental factors, including those shared to different extents by relatives while living together and whose effects persist into old age. The equal environment assumption of the classic twin study might not hold for epigenetic aging.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.,Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,Precision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia.,Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.,Precision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | | | - Jeffrey M Craig
- Centre for Molecular and Medical Research, School of Medicine, Faculty of Health, Deakin University, Waurn Ponds, VIC, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Richard Saffery
- Murdoch Childrens Research Institute, Parkville, VIC, Australia
| | - Joohon Sung
- Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea
| | - Qihua Tan
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.,Precision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.,Precision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia.,Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
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17
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DNA methylation-based biological age, genome-wide average DNA methylation, and conventional breast cancer risk factors. Sci Rep 2019; 9:15055. [PMID: 31636290 PMCID: PMC6803691 DOI: 10.1038/s41598-019-51475-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 09/25/2019] [Indexed: 12/12/2022] Open
Abstract
DNA methylation-based biological age (DNAm age), as well as genome-wide average DNA methylation, have been reported to predict breast cancer risk. We aimed to investigate the associations between these DNA methylation-based risk factors and 18 conventional breast cancer risk factors for disease-free women. A sample of 479 individuals from the Australian Mammographic Density Twins and Sisters was used for discovery, a sample of 3354 individuals from the Melbourne Collaborative Cohort Study was used for replication, and meta-analyses pooling results from the two studies were conducted. DNAm age based on three epigenetic clocks (Hannum, Horvath and Levine) and genome-wide average DNA methylation were calculated using the HumanMethylation 450 K BeadChip assay data. The DNAm age measures were positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche (all nominal P < 0.05). Genome-wide average DNA methylation was negatively associated with smoking and number of live births, and positively associated with age at first live birth (all nominal P < 0.05). The association of DNAm age with BMI was also evident in within-twin-pair analyses that control for familial factors. This study suggests that some lifestyle and hormonal risk factors are associated with these DNA methylation-based breast cancer risk factors, and the observed associations are unlikely to be due to familial confounding but are likely causal. DNA methylation-based risk factors could interplay with conventional risk factors in modifying breast cancer risk.
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18
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Abstract
Identifying and validating molecular targets of interventions that extend the human health span and lifespan has been difficult, as most clinical biomarkers are not sufficiently representative of the fundamental mechanisms of ageing to serve as their indicators. In a recent breakthrough, biomarkers of ageing based on DNA methylation data have enabled accurate age estimates for any tissue across the entire life course. These 'epigenetic clocks' link developmental and maintenance processes to biological ageing, giving rise to a unified theory of life course. Epigenetic biomarkers may help to address long-standing questions in many fields, including the central question: why do we age?
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19
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Fransquet PD, Wrigglesworth J, Woods RL, Ernst ME, Ryan J. The epigenetic clock as a predictor of disease and mortality risk: a systematic review and meta-analysis. Clin Epigenetics 2019; 11:62. [PMID: 30975202 PMCID: PMC6458841 DOI: 10.1186/s13148-019-0656-7] [Citation(s) in RCA: 169] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 03/25/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Ageing is one of the principal risk factors for many chronic diseases. However, there is considerable between-person variation in the rate of ageing and individual differences in their susceptibility to disease and death. Epigenetic mechanisms may play a role in human ageing, and DNA methylation age biomarkers may be good predictors of age-related diseases and mortality risk. The aims of this systematic review were to identify and synthesise the evidence for an association between peripherally measured DNA methylation age and longevity, age-related disease, and mortality risk. METHODS A systematic search was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Using relevant search terms, MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsychINFO databases were searched to identify articles meeting the inclusion criteria. Studies were assessed for bias using Joanna Briggs Institute critical appraisal checklists. Data was extracted from studies measuring age acceleration as a predictor of age-related diseases, mortality or longevity, and the findings for similar outcomes compared. Using Review Manager 5.3 software, two meta-analyses (one per epigenetic clock) were conducted on studies measuring all-cause mortality. RESULTS Twenty-three relevant articles were identified, including a total of 41,607 participants. Four studies focused on ageing and longevity, 11 on age-related disease (cancer, cardiovascular disease, and dementia), and 11 on mortality. There was some, although inconsistent, evidence for an association between increased DNA methylation age and risk of disease. Meta-analyses indicated that each 5-year increase in DNA methylation age was associated an 8 to 15% increased risk of mortality. CONCLUSION Due to the small number of studies and heterogeneity in study design and outcomes, the association between DNA methylation age and age-related disease and longevity is inconclusive. Increased epigenetic age was associated with mortality risk, but positive publication bias needs to be considered. Further research is needed to determine the extent to which DNA methylation age can be used as a clinical biomarker.
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Affiliation(s)
- Peter D Fransquet
- Department of Epidemiology and Preventive Medicine, Monash University, ASPREE, Level 5, The Alfred Centre, 99 Commercial Road, Melbourne, Victoria, 3004, Australia.,Disease Epigenetics, Murdoch Childrens Research Institute, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Jo Wrigglesworth
- Department of Epidemiology and Preventive Medicine, Monash University, ASPREE, Level 5, The Alfred Centre, 99 Commercial Road, Melbourne, Victoria, 3004, Australia
| | - Robyn L Woods
- Department of Epidemiology and Preventive Medicine, Monash University, ASPREE, Level 5, The Alfred Centre, 99 Commercial Road, Melbourne, Victoria, 3004, Australia
| | - Michael E Ernst
- Department of Pharmacy Practice and Science, College of Pharmacy, The University of Iowa, Iowa City, IA, USA.,Department of Family Medicine, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Joanne Ryan
- Department of Epidemiology and Preventive Medicine, Monash University, ASPREE, Level 5, The Alfred Centre, 99 Commercial Road, Melbourne, Victoria, 3004, Australia. .,Disease Epigenetics, Murdoch Childrens Research Institute, The University of Melbourne, Parkville, Victoria, 3052, Australia. .,INSERM, U1061, Neuropsychiatrie, Recherche Clinique et Epidémiologique, Neuropsychiatry: Research Epidemiological and Clinic, Université Montpellier, 34000, Montpellier, France.
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20
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Li S, Dugué PA, Baglietto L, Severi G, Wong EM, Nguyen TL, Stone J, English DR, Southey MC, Giles GG, Hopper JL, Milne RL. Genome-wide association study of peripheral blood DNA methylation and conventional mammographic density measures. Int J Cancer 2019; 145:1768-1773. [PMID: 30694562 DOI: 10.1002/ijc.32171] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 01/18/2019] [Accepted: 01/22/2019] [Indexed: 02/06/2023]
Abstract
Age- and body mass index (BMI)-adjusted mammographic density is one of the strongest breast cancer risk factors. DNA methylation is a molecular mechanism that could underlie inter-individual variation in mammographic density. We aimed to investigate the association between breast cancer risk-predicting mammographic density measures and blood DNA methylation. For 436 women from the Australian Mammographic Density Twins and Sisters Study and 591 women from the Melbourne Collaborative Cohort Study, mammographic density (dense area, nondense area and percentage dense area) defined by the conventional brightness threshold was measured using the CUMULUS software, and peripheral blood DNA methylation was measured using the HumanMethylation450 (HM450) BeadChip assay. Associations between DNA methylation at >400,000 sites and mammographic density measures adjusted for age and BMI were assessed within each cohort and pooled using fixed-effect meta-analysis. Associations with methylation at genetic loci known to be associated with mammographic density were also examined. We found no genome-wide significant (p < 10-7 ) association for any mammographic density measure from the meta-analysis, or from the cohort-specific analyses. None of the 299 methylation sites located at genetic loci associated with mammographic density was associated with any mammographic density measure after adjusting for multiple testing (all p > 0.05/299 = 1.7 × 10-4 ). In summary, our study did not find evidence for associations between blood DNA methylation, as measured by the HM450 assay, and conventional mammographic density measures that predict breast cancer risk.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Gianluca Severi
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Centre de Recherche en Épidémiologie et Santé des Populations (INSERM U1018), Université Paris-Saclay, Université Paris-Sud, Université Versailles Saint-Quentin-en-Yvelines, Institut Gustave Roussy, Villejuif, France
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Genetic Epidemiology Laboratory, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, WA, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Genetic Epidemiology Laboratory, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
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21
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Longitudinal changes in the genetic and environmental influences on the epigenetic clocks across old age: Evidence from two twin cohorts. EBioMedicine 2019; 40:710-716. [PMID: 30704927 PMCID: PMC6413471 DOI: 10.1016/j.ebiom.2019.01.040] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 01/11/2019] [Accepted: 01/22/2019] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Measures based on DNA methylation, epigenetic clocks, have recently gained attraction as predictors of mortality and age-related pathologies. However, the origins of variation in these measures are not well understood. METHODS In a pooled sample of 104 Swedish and Danish twin pairs, we estimated, at the mean age of 70 (baseline) and 79 years (follow-up), the genetic and environmental influences on the Horvath and Levine clocks. FINDINGS A model incorporating additive genetic (A) and person-specific environmental (E) influences best explained the variation in both clocks. Heritability was estimated at 55% at baseline and at 51% at follow-up for the Horvath clock and 34% at baseline and 41% at follow-up for the Levine clock. For the Horvath clock, new sources of A influences emerged at follow-up, whereas for the Levine clock, the same A influences accounted for the genetic variance at both measurement occasions. The cross-time phenotypic correlations, 0·52 for the Horvath clock and 0·36 for the Levine clock, were mediated primarily by genetic factors, whereas the person-specific environmental factors were completely different at the two measurement occasions. INTERPRETATION For both clocks, new sources of person-specific environmental influences emerge with age. The epigenetic clocks might thus be responsive to new environmental stimuli even at old age. FUND: NIH (R01;AG04563;AG10175;AG028555) the MacArthur Foundation Research Network on Successful Aging, FAS/FORTE (97:0147:1B;2009-0795), Swedish Research Council (825-2007-7460;825-2009-6141;521-2013-8689;2015-03255;2015-06796;2018-02077), FORTE (2013-2292), the Strategic Research Program in Epidemiology at KI, VELUX FOUNDATION, NIA (P01-AG08761), the EU (FP7/2007-2011;259679) and The Danish National Program for Research Infrastructure 2007 (9-063256).
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22
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Li S, Wong EM, Nguyen TL, Joo JHE, Stone J, Dite GS, Giles GG, Saffery R, Southey MC, Hopper JL. Causes of blood methylomic variation for middle-aged women measured by the HumanMethylation450 array. Epigenetics 2018; 12:973-981. [PMID: 29099274 DOI: 10.1080/15592294.2017.1384891] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
To address the limitations in current classic twin/family research on the genetic and/or environmental causes of human methylomic variation, we measured blood DNA methylation for 479 women (mean age 56 years) including 66 monozygotic (MZ), 66 dizygotic (DZ) twin pairs and 215 sisters of twins, and 11 random technical duplicates using the HumanMethylation450 array. For each methylation site, we estimated the correlation for pairs of duplicates, MZ twins, DZ twins, and siblings, fitted variance component models by assuming the variation is explained by genetic factors, by shared and individual environmental factors, and by independent measurement error, and assessed the best fitting model. We found that the average (standard deviation) correlations for duplicate, MZ, DZ, and sibling pairs were 0.10 (0.35), 0.07 (0.21), -0.01 (0.14) and -0.04 (0.07). At the genome-wide significance level of 10-7, 93.3% of sites had no familial correlation, and 5.6%, 0.1%, and 0.2% of sites were correlated for MZ, DZ, and sibling pairs. For 86.4%, 6.9%, and 7.1% of sites, the best fitting model included measurement error only, a genetic component, and at least one environmental component. For the 13.6% of sites influenced by genetic and/or environmental factors, the average proportion of variance explained by environmental factors was greater than that explained by genetic factors (0.41 vs. 0.37, P value <10-15). Our results are consistent with, for middle-aged woman, blood methylomic variation measured by the HumanMethylation450 array being largely explained by measurement error, and more influenced by environmental factors than by genetic factors.
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Affiliation(s)
- Shuai Li
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia
| | - Ee Ming Wong
- b Genetic Epidemiology Laboratory, Department of Pathology , University of Melbourne , Parkville , Victoria , Australia.,c Precision Medicine, School of Clinical Sciences at Monash Health , Monash University , Clayton , Victoria , Australia
| | - Tuong L Nguyen
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia
| | - Ji-Hoon Eric Joo
- b Genetic Epidemiology Laboratory, Department of Pathology , University of Melbourne , Parkville , Victoria , Australia.,c Precision Medicine, School of Clinical Sciences at Monash Health , Monash University , Clayton , Victoria , Australia
| | - Jennifer Stone
- d Centre for Genetic Origins of Health and Disease , Curtin University and the University of Western Australia , Perth , Western Australia , Australia
| | - Gillian S Dite
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia
| | - Graham G Giles
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia.,e Cancer Epidemiology and Intelligence Division , Cancer Council Victoria , Melbourne , Victoria , Australia
| | - Richard Saffery
- f Murdoch Children's Research Institute , Royal Children's Hospital , Parkville , Victoria , Australia.,g Department of Paediatrics , University of Melbourne , Parkville , Victoria , Australia
| | - Melissa C Southey
- b Genetic Epidemiology Laboratory, Department of Pathology , University of Melbourne , Parkville , Victoria , Australia.,c Precision Medicine, School of Clinical Sciences at Monash Health , Monash University , Clayton , Victoria , Australia
| | - John L Hopper
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia
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23
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Li S, Wong EM, Bui M, Nguyen TL, Joo JHE, Stone J, Dite GS, Dugué PA, Milne RL, Giles GG, Saffery R, Southey MC, Hopper JL. Inference about causation between body mass index and DNA methylation in blood from a twin family study. Int J Obes (Lond) 2018; 43:243-252. [PMID: 29777239 DOI: 10.1038/s41366-018-0103-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 03/19/2018] [Accepted: 04/04/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND Several studies have reported DNA methylation in blood to be associated with body mass index (BMI), but few have investigated causal aspects of the association. We used a twin family design to assess this association at two life points and applied a novel analytical approach to appraise the evidence for causality. METHODS The methylation profile of DNA from peripheral blood was measured for 479 Australian women from 130 twin families. Linear regression was used to estimate the associations of DNA methylation at ~410,000 cytosine-guanine dinucleotides (CpGs), and of the average DNA methylation at ~20,000 genes, with current BMI, BMI at age 18-21 years, and the change between the two (BMI change). A novel regression-based methodology for twins, Inference about Causation through Examination of Familial Confounding (ICE FALCON), was used to assess causation. RESULTS At a 5% false discovery rate, nine, six and 12 CpGs at 24 loci were associated with current BMI, BMI at age 18-21 years and BMI change, respectively. The average DNA methylation of the BHLHE40 and SOCS3 loci was associated with current BMI, and of the PHGDH locus with BMI change. From the ICE FALCON analyses with BMI as the predictor and DNA methylation as the outcome, a woman's DNA methylation level was associated with her co-twin's BMI, and the association disappeared after conditioning on her own BMI, consistent with BMI causing DNA methylation. To the contrary, using DNA methylation as the predictor and BMI as the outcome, a woman's BMI was not associated with her co-twin's DNA methylation level, consistent with DNA methylation not causing BMI. CONCLUSION For middle-aged women, peripheral blood DNA methylation at several genomic locations is associated with current BMI, BMI at age 18-21 years and BMI change. Our study suggests that BMI has a causal effect on peripheral blood DNA methylation.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Ee Ming Wong
- Genetic Epidemiology Laboratory, Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Ji-Hoon Eric Joo
- Genetic Epidemiology Laboratory, Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, WA, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Richard Saffery
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia.,Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
| | - Melissa C Southey
- Genetic Epidemiology Laboratory, Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
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24
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Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, Whitsel EA, Wilson JG, Reiner AP, Aviv A, Lohman K, Liu Y, Ferrucci L, Horvath S. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 2018; 10:573-591. [PMID: 29676998 PMCID: PMC5940111 DOI: 10.18632/aging.101414] [Citation(s) in RCA: 1454] [Impact Index Per Article: 242.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 04/08/2018] [Indexed: 04/08/2023]
Abstract
Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.
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Affiliation(s)
- Morgan E. Levine
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Ake T. Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Austin Quach
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Brian H. Chen
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA. Baltimore, MD 21224, USA
| | | | | | - Lifang Hou
- Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Andrea A. Baccarelli
- Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences and Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA
| | - James D. Stewart
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Yun Li
- Department of Genetics, Department of Biostatistics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eric A. Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
- Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Alex P Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Abraham Aviv
- Center of Human Development and Aging, New Jersey Medical School, Rutgers State University of New Jersey, Newark, NJ 07103, USA
| | - Kurt Lohman
- Center of Human Development and Aging, New Jersey Medical School, Rutgers State University of New Jersey, Newark, NJ 07103, USA
| | - Yongmei Liu
- Department of Epidemiology & Prevention, Division of Public Health Sciences, Wake Forrest School of Medicine, Winston-Salem, NC 27157, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA. Baltimore, MD 21224, USA
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
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25
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Li S, Wong EM, Dugué PA, McRae AF, Kim E, Joo JHE, Nguyen TL, Stone J, Dite GS, Armstrong NJ, Mather KA, Thalamuthu A, Wright MJ, Ames D, Milne RL, Craig JM, Saffery R, Montgomery GW, Song YM, Sung J, Spector TD, Sachdev PS, Giles GG, Southey MC, Hopper JL. Genome-wide average DNA methylation is determined in utero. Int J Epidemiol 2018. [PMID: 29518222 PMCID: PMC6005037 DOI: 10.1093/ije/dyy028] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Investigating the genetic and environmental causes of variation in genome-wide average DNA methylation (GWAM), a global methylation measure from the HumanMethylation450 array, might give a better understanding of genetic and environmental influences on methylation. METHODS We measured GWAM for 2299 individuals aged 0 to 90 years from seven twin and/or family studies. We estimated familial correlations, modelled correlations with cohabitation history and fitted variance components models for GWAM. RESULTS The correlation in GWAM for twin pairs was ∼0.8 at birth, decreased with age during adolescence and was constant at ∼0.4 throughout adulthood, with no evidence that twin pair correlations differed by zygosity. Non-twin first-degree relatives were correlated, from 0.17 [95% confidence interval (CI): 0.05-0.30] to 0.28 (95% CI: 0.08-0.48), except for middle-aged siblings (0.01, 95% CI: -0.10-0.12), and the correlation increased with time living together and decreased with time living apart. Spouse pairs were correlated in all studies, from 0.23 (95% CI: 0.3-0.43) to 0.31 (95% CI: 0.05-0.52), and the correlation increased with time living together. The variance explained by environmental factors shared by twins alone was 90% (95% CI: 74-95%) at birth, decreased in early life and plateaued at 28% (95% CI: 17-39%) in middle age and beyond. There was a cohabitation-related environmental component of variance. CONCLUSIONS GWAM is determined in utero by prenatal environmental factors, the effects of which persist throughout life. The variation of GWAM is also influenced by environmental factors shared by family members, as well as by individual-specific environmental factors.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics
| | - Ee Ming Wong
- Genetic Epidemiology Laboratory, University of Melbourne, Parkville, VIC, Australia.,Precision Medicine, Monash University, Clayton, VIC, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Allan F McRae
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Eunae Kim
- Complex Disease and Genome Epidemiology Branch, Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Ji-Hoon Eric Joo
- Genetic Epidemiology Laboratory, University of Melbourne, Parkville, VIC, Australia.,Precision Medicine, Monash University, Clayton, VIC, Australia
| | | | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, WA, Australia
| | | | | | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - David Ames
- National Ageing Research Institute and University of Melbourne Academic Unit for Psychiatry of Old Age, Parkville, VIC, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Jeffrey M Craig
- Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, VIC, Australia.,Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia.,School of Medicine, Deakin University, Geelong, VIC, Australia
| | - Richard Saffery
- Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, VIC, Australia.,Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
| | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Yun-Mi Song
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Joohon Sung
- Complex Disease and Genome Epidemiology Branch, Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Republic of Korea.,Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Melissa C Southey
- Genetic Epidemiology Laboratory, University of Melbourne, Parkville, VIC, Australia.,Precision Medicine, Monash University, Clayton, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics.,Complex Disease and Genome Epidemiology Branch, Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Republic of Korea.,Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea
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26
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Dugué PA, Bassett JK, Joo JE, Baglietto L, Jung CH, Wong EM, Fiorito G, Schmidt D, Makalic E, Li S, Moreno-Betancur M, Buchanan DD, Vineis P, English DR, Hopper JL, Severi G, Southey MC, Giles GG, Milne RL. Association of DNA Methylation-Based Biological Age With Health Risk Factors and Overall and Cause-Specific Mortality. Am J Epidemiol 2018; 187:529-538. [PMID: 29020168 DOI: 10.1093/aje/kwx291] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 07/26/2017] [Indexed: 11/13/2022] Open
Abstract
Measures of biological age based on blood DNA methylation, referred to as age acceleration (AA), have been developed. We examined whether AA was associated with health risk factors and overall and cause-specific mortality. At baseline (1990-1994), blood samples were drawn from 2,818 participants in the Melbourne Collaborative Cohort Study (Melbourne, Victoria, Australia). DNA methylation was determined using the Infinium HumanMethylation450 BeadChip array (Illumina Inc., San Diego, California). Mixed-effects models were used to examine the association of AA with health risk factors. Cox models were used to assess the association of AA with mortality. A total of 831 deaths were observed during a median 10.7 years of follow-up. Associations of AA were observed with male sex, Greek nationality (country of birth), smoking, obesity, diabetes, lower education, and meat intake. AA measures were associated with increased mortality, and this was only partly accounted for by known determinants of health (hazard ratios were attenuated by 20%-40%). Weak evidence of heterogeneity in the association was observed by sex (P = 0.06) and cause of death (P = 0.07) but not by other factors. DNA-methylation-based AA measures are associated with several major health risk factors, but these do not fully explain the association between AA and mortality. Future research should investigate what genetic and environmental factors determine AA.
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Affiliation(s)
- Pierre-Antoine Dugué
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Julie K Bassett
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - JiHoon E Joo
- Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Laura Baglietto
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Department of Clinical and Experimental Medicine, School of Medicine, University of Pisa, Pisa, Italy
| | - Chol-Hee Jung
- Melbourne Bioinformatics, University of Melbourne, Victoria, Australia
| | - Ee Ming Wong
- Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, Victoria, Australia
| | | | - Daniel Schmidt
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Enes Makalic
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Shuai Li
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Margarita Moreno-Betancur
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
| | - Daniel D Buchanan
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, Victoria, Australia
- Genetic Medicine and Familial Cancer Center, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Paolo Vineis
- Italian Institute for Genomic Medicine, Turin, Italy
- MRC-PHE Center for Environment and Health, Imperial College London, London, United Kingdom
| | - Dallas R English
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Gianluca Severi
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Centre de Recherche en Épidémiologie et Santé des Populations
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Melissa C Southey
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
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27
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Li S, Wong EM, Bui M, Nguyen TL, Joo JHE, Stone J, Dite GS, Giles GG, Saffery R, Southey MC, Hopper JL. Causal effect of smoking on DNA methylation in peripheral blood: a twin and family study. Clin Epigenetics 2018; 10:18. [PMID: 29456763 PMCID: PMC5810186 DOI: 10.1186/s13148-018-0452-9] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 02/01/2018] [Indexed: 11/10/2022] Open
Abstract
Background Smoking has been reported to be associated with peripheral blood DNA methylation, but the causal aspects of the association have rarely been investigated. We aimed to investigate the association and underlying causation between smoking and blood methylation. Methods The methylation profile of DNA from the peripheral blood, collected as dried blood spots stored on Guthrie cards, was measured for 479 Australian women including 66 monozygotic twin pairs, 66 dizygotic twin pairs, and 215 sisters of twins from 130 twin families using the Infinium HumanMethylation450K BeadChip array. Linear regression was used to estimate associations between methylation at ~ 410,000 cytosine-guanine dinucleotides (CpGs) and smoking status. A regression-based methodology for twins, Inference about Causation through Examination of Familial Confounding (ICE FALCON), was used to assess putative causation. Results At a 5% false discovery rate, 39 CpGs located at 27 loci, including previously reported AHRR, F2RL3, 2q37.1 and 6p21.33, were found to be differentially methylated across never, former and current smokers. For all 39 CpG sites, current smokers had the lowest methylation level. Our study provides the first replication for two previously reported CpG sites, cg06226150 (SLC2A4RG) and cg21733098 (12q24.32). From the ICE FALCON analysis with smoking status as the predictor and methylation score as the outcome, a woman’s methylation score was associated with her co-twin’s smoking status, and the association attenuated towards the null conditioning on her own smoking status, consistent with smoking status causing changes in methylation. To the contrary, using methylation score as the predictor and smoking status as the outcome, a woman’s smoking status was not associated with her co-twin’s methylation score, consistent with changes in methylation not causing smoking status. Conclusions For middle-aged women, peripheral blood DNA methylation at several genomic locations is associated with smoking. Our study suggests that smoking has a causal effect on peripheral blood DNA methylation, but not vice versa. Electronic supplementary material The online version of this article (10.1186/s13148-018-0452-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Shuai Li
- 1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria Australia
| | - Ee Ming Wong
- 2Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, Victoria Australia.,3Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria Australia
| | - Minh Bui
- 1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria Australia
| | - Tuong L Nguyen
- 1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria Australia
| | - Ji-Hoon Eric Joo
- 2Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, Victoria Australia.,3Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria Australia
| | - Jennifer Stone
- 4Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Western Australia Australia
| | - Gillian S Dite
- 1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria Australia
| | - Graham G Giles
- 1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria Australia.,5Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria Australia
| | - Richard Saffery
- 6Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria Australia.,7Department of Paediatrics, University of Melbourne, Parkville, Victoria Australia
| | - Melissa C Southey
- 2Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, Victoria Australia.,3Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria Australia
| | - John L Hopper
- 1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria Australia
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28
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Ward-Caviness CK, Nwanaji-Enwerem JC, Wolf K, Wahl S, Colicino E, Trevisi L, Kloog I, Just AC, Vokonas P, Cyrys J, Gieger C, Schwartz J, Baccarelli AA, Schneider A, Peters A. Long-term exposure to air pollution is associated with biological aging. Oncotarget 2018; 7:74510-74525. [PMID: 27793020 PMCID: PMC5342683 DOI: 10.18632/oncotarget.12903] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 10/13/2016] [Indexed: 11/28/2022] Open
Abstract
Long-term exposure to air pollution is associated with age-related diseases. We explored the association between accelerated biological aging and air pollution, a potential mechanism linking air pollution and health. We estimated long-term exposure to PM10, PM2.5, PM2.5 absorbance/black carbon (BC), and NOx via land-use regression models in individuals from the KORA F4 cohort. Accelerated biological aging was assessed using telomere length (TeloAA) and three epigenetic measures: DNA methylation age acceleration (DNAmAA), extrinsic epigenetic age acceleration (correlated with immune cell counts, EEAA), and intrinsic epigenetic age acceleration (independent of immune cell counts, IEAA). We also investigated sex-specific associations between air pollution and biological aging, given the published association between sex and aging measures. In KORA an interquartile range (0.97 μg/m3) increase in PM2.5 was associated with a 0.33 y increase in EEAA (CI = 0.01, 0.64; P = 0.04). BC and NOx (indicators or traffic exposure) were associated with DNAmAA and IEAA in women, while TeloAA was inversely associated with BC in men. We replicated this inverse BC-TeloAA association in the Normative Aging Study, a male cohort based in the USA. A multiple phenotype analysis in KORA F4 combining all aging measures showed that BC and PM10 were broadly associated with biological aging in men. Thus, we conclude that long-term exposure to air pollution is associated with biological aging measures, potentially in a sex-specific manner. However, many of the associations were relatively weak and further replication of overall and sex-specific associations is warranted.
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Affiliation(s)
- Cavin K Ward-Caviness
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Bavaria, Germany
| | | | - Kathrin Wolf
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Bavaria, Germany
| | - Simone Wahl
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Bavaria, Germany.,Research Unit Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Bavaria, Germany
| | - Elena Colicino
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Letizia Trevisi
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Allan C Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pantel Vokonas
- VA Normative Aging Study, Veterans Affairs Boston Healthcare System and the Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Josef Cyrys
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Bavaria, Germany
| | - Christian Gieger
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Bavaria, Germany.,Research Unit Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Bavaria, Germany
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Alexandra Schneider
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Bavaria, Germany
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Bavaria, Germany
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29
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Declerck K, Vanden Berghe W. Back to the future: Epigenetic clock plasticity towards healthy aging. Mech Ageing Dev 2018; 174:18-29. [PMID: 29337038 DOI: 10.1016/j.mad.2018.01.002] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 01/08/2018] [Accepted: 01/10/2018] [Indexed: 12/22/2022]
Abstract
Aging is the most important risk factor for major human lifestyle diseases, including cancer, neurological and cardiometabolic disorders. Due to the complex interplay between genetics, lifestyle and environmental factors, some individuals seem to age faster than others, whereas centenarians seem to have a slower aging process. Therefore, a biochemical biomarker reflecting the relative biological age would be helpful to predict an individual's health status and aging disease risk. Although it is already known for years that cumulative epigenetic changes occur upon aging, DNA methylation patterns were only recently used to construct an epigenetic clock predictor for biological age, which is a measure of how well your body functions compared to your chronological age. Moreover, the epigenetic DNA methylation clock signature is increasingly applied as a biomarker to estimate aging disease susceptibility and mortality risk. Finally, the epigenetic clock signature could be used as a lifestyle management tool to monitor healthy aging, to evaluate preventive interventions against chronic aging disorders and to extend healthy lifespan. Dissecting the mechanism of the epigenetic aging clock will yield valuable insights into the aging process and how it can be manipulated to improve health span.
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Affiliation(s)
- Ken Declerck
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp (UA), Belgium
| | - Wim Vanden Berghe
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp (UA), Belgium.
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30
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Dugué PA, Bassett JK, Joo JE, Jung CH, Ming Wong E, Moreno-Betancur M, Schmidt D, Makalic E, Li S, Severi G, Hodge AM, Buchanan DD, English DR, Hopper JL, Southey MC, Giles GG, Milne RL. DNA methylation-based biological aging and cancer risk and survival: Pooled analysis of seven prospective studies. Int J Cancer 2017; 142:1611-1619. [PMID: 29197076 DOI: 10.1002/ijc.31189] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 11/03/2017] [Accepted: 11/21/2017] [Indexed: 12/23/2022]
Abstract
The association between aging and cancer is complex. Recent studies have developed measures of biological aging based on DNA methylation and called them "age acceleration." We aimed to assess the associations of age acceleration with risk of and survival from seven common cancers. Seven case-control studies of DNA methylation and colorectal, gastric, kidney, lung, prostate and urothelial cancer and B-cell lymphoma nested in the Melbourne Collaborative Cohort Study were conducted. Cancer cases, vital status and cause of death were ascertained through linkage with cancer and death registries. Conditional logistic regression and Cox models were used to estimate odds ratios (OR) and hazard ratios (HR) and 95% confidence intervals (CI) for associations of five age acceleration measures derived from the Human Methylation 450 K Beadchip assay with cancer risk (N = 3,216 cases) and survival (N = 1,726 deaths), respectively. Epigenetic aging was associated with increased cancer risk, ranging from 4% to 9% per five-year age acceleration for the 5 measures considered. Heterogeneity by study was observed, with stronger associations for risk of kidney cancer and B-cell lymphoma. An associated increased risk of death following cancer diagnosis ranged from 2% to 6% per five-year age acceleration, with no evidence of heterogeneity by cancer site. Cancer risk and mortality were increased by 15-30% for the fourth versus first quartile of age acceleration. DNA methylation-based measures of biological aging are associated with increased cancer risk and shorter cancer survival, independently of major health risk factors.
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Affiliation(s)
- Pierre-Antoine Dugué
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Julie K Bassett
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - JiHoon E Joo
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Chol-Hee Jung
- Melbourne Bioinformatics, The University of Melbourne, Parkville, Victoria, Australia
| | - Ee Ming Wong
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Margarita Moreno-Betancur
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Daniel Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Gianluca Severi
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Centre de Recherche en Épidémiologie et Santé des Populations (CESP, Inserm U1018), Université Paris-Saclay, UPS, USQ, Gustave Roussy, Villejuif, France.,Human Genetics Foundation (HuGeF), Turin, Italy
| | - Allison M Hodge
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Daniel D Buchanan
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia.,Genetic Medicine and Familial Cancer Centre, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
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31
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Abstract
We asked if twin birth influences the DNA methylation of subsequent siblings. We measured whole blood methylation using the HumanMethylation450 array for siblings from two twin and family studies in Australia and Korea. We compared the means and correlations in methylation between pairs of siblings born before a twin birth (BT siblings), born on either side of a twin birth (B/AT pairs) and born after a twin birth (AT siblings). For the genome-wide average DNA methylation, the correlation for AT pairs (rAT) was larger than the correlation for BT pairs (rBT) in both studies, and from the meta-analysis, rAT = 0.46 (95% CI: 0.26, 0.63) and rBT = -0.003 (95% CI: -0.30, 0.29) (P = 0.02). B/AT pairs were not correlated (from the meta-analysis rBAT = 0.08; 95% CI: -0.31, 0.45). Similar results were found for the average methylation of several genomic regions, e.g., CpG shelf and gene body. BT and AT pairs were differentially correlated in methylation for 15 probes (all P < 10-7), and the top 152 differentially correlated probes (at P < 10-4) were enriched in cell signalling and breast cancer regulation pathways. Our observations are consistent with a twin birth changing the intrauterine environment such that siblings both born after a twin birth are correlated in DNA methylation.
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32
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Starnawska A, Tan Q, Lenart A, McGue M, Mors O, Børglum AD, Christensen K, Nyegaard M, Christiansen L. Blood DNA methylation age is not associated with cognitive functioning in middle-aged monozygotic twins. Neurobiol Aging 2016; 50:60-63. [PMID: 27889677 DOI: 10.1016/j.neurobiolaging.2016.10.025] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 09/23/2016] [Accepted: 10/25/2016] [Indexed: 10/20/2022]
Abstract
The epigenetic clock, also known as DNA methylation age (DNAmAge), represents age-related changes of DNA methylation at multiple sites of the genome and is suggested to be a biomarker for biological age. Elevated blood DNAmAge is associated with all-cause mortality, with the strongest effects reported in a recent intrapair twin study where epigenetically older twins had increased mortality risk in comparison to their co-twins. In the study presented here, we hypothesize that DNAmAge in blood is associated with cross-sectional and longitudinal cognitive abilities in middle-aged individuals. In 486 monozygotic twins, we investigated the association of DNAmAge, difference between DNAmAge and chronological age and age acceleration with cognition. Despite using a powerful paired twin design, we found no evidence for association of blood DNAmAge with cognitive abilities. This observation was confirmed in unpaired analyses, where DNAmAge initially correlated with cognitive abilities, until adjusting for chronological age. Overall, our study shows that for middle-aged individuals DNAmAge calculated in blood does not correlate with cognitive abilities.
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Affiliation(s)
- A Starnawska
- Department of Biomedicine, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Center for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark.
| | - Q Tan
- The Danish Aging Research Center, and The Danish Twin Registry, Institute of Public Health, University of Southern Denmark, Odense, Denmark; Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - A Lenart
- Max Planck Odense Center on the Biodemography of Aging, Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - M McGue
- The Danish Aging Research Center, and The Danish Twin Registry, Institute of Public Health, University of Southern Denmark, Odense, Denmark; Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - O Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
| | - A D Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Center for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark
| | - K Christensen
- The Danish Aging Research Center, and The Danish Twin Registry, Institute of Public Health, University of Southern Denmark, Odense, Denmark; Department of Clinical Genetics, Odense University Hospital, Odense, Denmark; Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - M Nyegaard
- Department of Biomedicine, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Center for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark
| | - L Christiansen
- The Danish Aging Research Center, and The Danish Twin Registry, Institute of Public Health, University of Southern Denmark, Odense, Denmark
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