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      An epigenetic biomarker of aging for lifespan and healthspan

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          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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          Most cited references 42

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          Inflamm-aging. An evolutionary perspective on immunosenescence.

          In this paper we extend the "network theory of aging," and we argue that a global reduction in the capacity to cope with a variety of stressors and a concomitant progressive increase in proinflammatory status are major characteristics of the aging process. This phenomenon, which we will refer to as "inflamm-aging," is provoked by a continuous antigenic load and stress. On the basis of evolutionary studies, we also argue that the immune and the stress responses are equivalent and that antigens are nothing other than particular types of stressors. We also propose to return macrophage to its rightful place as central actor not only in the inflammatory response and immunity, but also in the stress response. The rate of reaching the threshold of proinflammatory status over which diseases/disabilities ensue and the individual capacity to cope with and adapt to stressors are assumed to be complex traits with a genetic component. Finally, we argue that the persistence of inflammatory stimuli over time represents the biologic background (first hit) favoring the susceptibility to age-related diseases/disabilities. A second hit (absence of robust gene variants and/or presence of frail gene variants) is likely necessary to develop overt organ-specific age-related diseases having an inflammatory pathogenesis, such as atherosclerosis, Alzheimer's disease, osteoporosis, and diabetes. Following this perspective, several paradoxes of healthy centenarians (increase of plasma levels of inflammatory cytokines, acute phase proteins, and coagulation factors) are illustrated and explained. In conclusion, the beneficial effects of inflammation devoted to the neutralization of dangerous/harmful agents early in life and in adulthood become detrimental late in life in a period largely not foreseen by evolution, according to the antagonistic pleiotropy theory of aging.
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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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                Author and article information

                Journal
                Aging (Albany NY)
                Aging (Albany NY)
                Aging
                Aging (Albany NY)
                Impact Journals
                1945-4589
                April 2018
                17 April 2018
                : 10
                : 4
                : 573-591
                Affiliations
                [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
                Article
                101414
                10.18632/aging.101414
                5940111
                29676998
                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.

                Categories
                Research Paper

                Cell biology

                epigenetic clock, dna methylation, biomarker, healthspan

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