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      An epigenetic biomarker of aging for lifespan and healthspan


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          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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          Most cited references65

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          GCTA: a tool for genome-wide complex trait analysis.

          For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
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            DNA methylation age of human tissues and cell types

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

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

                Author and article information

                Aging (Albany NY)
                Aging (Albany NY)
                Aging (Albany NY)
                Impact Journals
                April 2018
                17 April 2018
                : 10
                : 4
                : 573-591
                [1 ]Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles , Los Angeles, , CA, 90095, USA
                [2 ]Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA. Baltimore, , MD, 21224, USA
                [3 ]Department of Medicine, Stanford University School of Medicine , Stanford, , CA, 94305, USA
                [4 ]Geriatric Unit, Azienda Toscana Centro , Florence, , Italy
                [5 ]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
                [6 ]Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences and Epidemiology, Columbia University Mailman School of Public Health , New York, , NY, 10032, USA
                [7 ]Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , Chapel Hill, , NC, 27599, USA
                [8 ]Department of Genetics, Department of Biostatistics, Department of Computer Science, University of North Carolina , Chapel Hill, , NC, 27599, USA
                [9 ]Department of Medicine, School of Medicine, University of North Carolina , Chapel Hill, , NC, 27599, USA
                [10 ]Department of Physiology and Biophysics, University of Mississippi Medical Center , Jackson, , MS, 39216, USA
                [11 ]Public Health Sciences Division, Fred Hutchinson Cancer Research Center , Seattle, , WA, 98109, USA
                [12 ]Center of Human Development and Aging, New Jersey Medical School, Rutgers State University of New Jersey , Newark, , NJ, 07103, USA
                [13 ]Department of Biostatistics, Division of Public Health Sciences, Wake Forrest School of Medicine , Winston-Salem, , NC, 27157, USA
                [14 ]Department of Epidemiology & Prevention, Division of Public Health Sciences, Wake Forrest School of Medicine , Winston-Salem, , NC, 27157, USA
                [15 ]Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles , Los Angeles, , CA, 90095, USA
                Author notes

                Co-senior authors

                Correspondence to: Steve Horvath; email: shorvath@ 123456mednet.ucla.edu
                Copyright © 2018 Levine et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 20 March 2018
                : 08 April 2018
                Research Paper

                Cell biology
                epigenetic clock,dna methylation,biomarker,healthspan
                Cell biology
                epigenetic clock, dna methylation, biomarker, healthspan


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