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      An epigenetic clock for gestational age at birth based on blood methylation data

      research-article
      1 , 2 , 3 , 4 , 4 , 4 , 4 , 5 , 4 , 5 , 6 , 7 , 7 , 8 , 1 , 9 , 10 , 11 , 12 , 2 , 3 , 2 , 2 , 3 , 2 , 13 , 14 , 14 , 15 , 14 , 16 , 1 , 10 , 17 , 17 , 17 , 18 , 18 , 19 , 20 , 18 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 28 , 10 , 28 , 29 , 30 , 31 , 32 , 33 , 32 , 34 , 1 , 9 , 1 , 10 , 28 ,
      Genome Biology
      BioMed Central
      Developmental age, Aging, Epigenetic clock, DNA methylation, Preterm birth, Cord blood, Fetus, Blood spot, Biomarker, Medicaid, Socioeconomic status, Birthweight

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          Abstract

          Background

          Gestational age is often used as a proxy for developmental maturity by clinicians and researchers alike. DNA methylation has previously been shown to be associated with age and has been used to accurately estimate chronological age in children and adults. In the current study, we examine whether DNA methylation in cord blood can be used to estimate gestational age at birth.

          Results

          We find that gestational age can be accurately estimated from DNA methylation of neonatal cord blood and blood spot samples. We calculate a DNA methylation gestational age using 148 CpG sites selected through elastic net regression in six training datasets. We evaluate predictive accuracy in nine testing datasets and find that the accuracy of the DNA methylation gestational age is consistent with that of gestational age estimates based on established methods, such as ultrasound. We also find that an increased DNA methylation gestational age relative to clinical gestational age is associated with birthweight independent of gestational age, sex, and ancestry.

          Conclusions

          DNA methylation can be used to accurately estimate gestational age at or near birth and may provide additional information relevant to developmental stage. Further studies of this predictor are warranted to determine its utility in clinical settings and for research purposes. When clinical estimates are available this measure may increase accuracy in the testing of hypotheses related to developmental age and other early life circumstances.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-016-1068-z) contains supplementary material, which is available to authorized users.

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          Most cited references53

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          DNA methylation age of blood predicts all-cause mortality in later life

          Background DNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age. Results Here we test whether differences between people’s chronological ages and estimated ages, DNA methylation age, predict all-cause mortality in later life. The difference between DNA methylation age and chronological age (Δage) was calculated in four longitudinal cohorts of older people. Meta-analysis of proportional hazards models from the four cohorts was used to determine the association between Δage and mortality. A 5-year higher Δage is associated with a 21% higher mortality risk, adjusting for age and sex. After further adjustments for childhood IQ, education, social class, hypertension, diabetes, cardiovascular disease, and APOE e4 status, there is a 16% increased mortality risk for those with a 5-year higher Δage. A pedigree-based heritability analysis of Δage was conducted in a separate cohort. The heritability of Δage was 0.43. Conclusions DNA methylation-derived measures of accelerated aging are heritable traits that predict mortality independently of health status, lifestyle factors, and known genetic factors. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0584-6) contains supplementary material, which is available to authorized users.
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            Aging of blood can be tracked by DNA methylation changes at just three CpG sites

            Background Human aging is associated with DNA methylation changes at specific sites in the genome. These epigenetic modifications may be used to track donor age for forensic analysis or to estimate biological age. Results We perform a comprehensive analysis of methylation profiles to narrow down 102 age-related CpG sites in blood. We demonstrate that most of these age-associated methylation changes are reversed in induced pluripotent stem cells (iPSCs). Methylation levels at three age-related CpGs - located in the genes ITGA2B, ASPA and PDE4C - were subsequently analyzed by bisulfite pyrosequencing of 151 blood samples. This epigenetic aging signature facilitates age predictions with a mean absolute deviation from chronological age of less than 5 years. This precision is higher than age predictions based on telomere length. Variation of age predictions correlates moderately with clinical and lifestyle parameters supporting the notion that age-associated methylation changes are associated more with biological age than with chronological age. Furthermore, patients with acquired aplastic anemia or dyskeratosis congenita - two diseases associated with progressive bone marrow failure and severe telomere attrition - are predicted to be prematurely aged. Conclusions Our epigenetic aging signature provides a simple biomarker to estimate the state of aging in blood. Age-associated DNA methylation changes are counteracted in iPSCs. On the other hand, over-estimation of chronological age in bone marrow failure syndromes is indicative for exhaustion of the hematopoietic cell pool. Thus, epigenetic changes upon aging seem to reflect biological aging of blood.
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              Epigenetic Predictor of Age

              From the moment of conception, we begin to age. A decay of cellular structures, gene regulation, and DNA sequence ages cells and organisms. DNA methylation patterns change with increasing age and contribute to age related disease. Here we identify 88 sites in or near 80 genes for which the degree of cytosine methylation is significantly correlated with age in saliva of 34 male identical twin pairs between 21 and 55 years of age. Furthermore, we validated sites in the promoters of three genes and replicated our results in a general population sample of 31 males and 29 females between 18 and 70 years of age. The methylation of three sites—in the promoters of the EDARADD, TOM1L1, and NPTX2 genes—is linear with age over a range of five decades. Using just two cytosines from these loci, we built a regression model that explained 73% of the variance in age, and is able to predict the age of an individual with an average accuracy of 5.2 years. In forensic science, such a model could estimate the age of a person, based on a biological sample alone. Furthermore, a measurement of relevant sites in the genome could be a tool in routine medical screening to predict the risk of age-related diseases and to tailor interventions based on the epigenetic bio-age instead of the chronological age.
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                Author and article information

                Contributors
                404-712-9582 , alicia.smith@emory.edu
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                7 October 2016
                7 October 2016
                2016
                : 17
                : 206
                Affiliations
                [1 ]Genetics and Molecular Biology Program, Emory University, Atlanta, GA USA
                [2 ]Murdoch Childrens Research Institute and Department of Paediatrics, University of Melbourne, Parkville, Victoria 3052 Australia
                [3 ]The Royal Women’s Hospital, Murdoch Childrens Research Institute and University of Melbourne, Parkville, Victoria 3052 Australia
                [4 ]Section of Neonatal Genetics, Danish Centre for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Artillerivej 5, DK-2300 Copenhagen S, Denmark
                [5 ]The Danish Neonatal Screening Biobank, Department for Congenital Disorders, Statens Serum Institut, Artillerivej 5, DK-2300 Copenhagen S, Denmark
                [6 ]National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
                [7 ]Institute of Biological Psychiatry, Sct. Hans Mental Health Center, Copenhagen Mental Health Services, iPSYCH - The Lundbeck Foundation’s Initiative for Integrative Psychiatric Research, Boserupvej, DK-4000 Roskilde, Denmark
                [8 ]Department of Psychology, Emory University, Atlanta, GA USA
                [9 ]Department of Human Genetics, Emory University School of Medicine, Atlanta, GA USA
                [10 ]Department of Psychiatry & Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
                [11 ]Departments of Psychiatry & Behavioral Sciences and Obstetrics & Gynecology, University of Miami Miller School of Medicine, Miami, FL USA
                [12 ]Departments of Psychiatry & Behavioral Sciences, Pediatrics, and Obstetrics & Gynecology, University of Arkansas for Medical Sciences, Little Rock, AR USA
                [13 ]Laboratory of Environmental Precision Biosciences, Columbia University Mailman School of Public Health, New York, NY USA
                [14 ]Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
                [15 ]Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Morelos Mexico
                [16 ]Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA USA
                [17 ]Department of Translational Research in Psychiatry, Max-Planck Institute of Psychiatry, Munich, Germany
                [18 ]Institute of Behavioral Sciences, University of Helsinki, 00014 Helsinki, Finland
                [19 ]Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland
                [20 ]Folkhälsan Research Centre, Helsinki, Finland
                [21 ]National Institute for Health and Welfare, Children’s Hospital, Helsinki University Hospital, 00271 Helsinki, Finland
                [22 ]University of Helsinki, 00029 Helsinki, Finland
                [23 ]Department of Obstetrics and Gynecology, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
                [24 ]Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, 00014 Helsinki, Finland
                [25 ]Medical and Clinical Genetics, and Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, 00014 Helsinki, Finland
                [26 ]Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland
                [27 ]HUSLAB and Department of Clinical Chemistry, Helsinki University Central Hospital, 00014 Helsinki, Finland
                [28 ]Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA US
                [29 ]Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, TX US
                [30 ]Department of Human Genetics, David Geffen School of Medicine University of California Los Angeles, Los Angeles, CA 90095 US
                [31 ]Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095 US
                [32 ]Department of Psychiatry, University of California, San Francisco, CA US
                [33 ]Department of Pediatrics, University of California, San Francisco, CA US
                [34 ]Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN US
                Author information
                http://orcid.org/0000-0002-8537-5156
                Article
                1068
                10.1186/s13059-016-1068-z
                5054584
                27717399
                25cfac69-5541-49cb-9d85-0bc2af193cf6
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 19 April 2016
                : 20 September 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100006545, National Institute on Minority Health and Health Disparities;
                Award ID: R01MD009064
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: 1083779
                Award ID: 491246
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: T32GM008490
                Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: K99 ES023450
                Award ID: R01ES021357
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: 1U34AG051425
                Award ID: U34AG051425
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002341, Suomen Akatemia;
                Award ID: 281335
                Award ID: 140278
                Award Recipient :
                Funded by: Early Career Fellowship
                Award ID: 1053787
                Award Recipient :
                Funded by: Centre of Research Excellence Grants
                Award ID: 546519
                Award Recipient :
                Funded by: Centre of Research Excellence Grants
                Award ID: 1060733
                Funded by: FundRef http://dx.doi.org/http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: T32ES012870
                Funded by: FundRef http://dx.doi.org/10.13039/501100006306, Sigrid Juséliuksen Säätiö;
                Funded by: FundRef http://dx.doi.org/10.13039/501100005744, Lastentautien Tutkimussäätiö;
                Funded by: Novo Nordisk Foundation
                Funded by: Finnish Medical Society Duodecim
                Funded by: FundRef http://dx.doi.org/10.13039/501100004012, Jane ja Aatos Erkon Säätiö;
                Funded by: FundRef http://dx.doi.org/10.13039/501100004212, Päivikki ja Sakari Sohlbergin Säätiö;
                Funded by: University of Helsinki Research Funds
                Funded by: National Institute of Public Health/Ministry of Health of Mexico
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: P50 MH077928
                Award ID: RC1 MH088609
                Award Recipient :
                Funded by: Urban Child Institute
                Funded by: Georgia Experimental Agriculture Station
                Award ID: GEO00706
                Award Recipient :
                Funded by: Interdisciplinary Proposal Developmental Program at the University of Georgia
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: K99ES012870
                Award Recipient :
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                © The Author(s) 2016

                Genetics
                developmental age,aging,epigenetic clock,dna methylation,preterm birth,cord blood,fetus,blood spot,biomarker,medicaid,socioeconomic status,birthweight

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