DNA methylation GrimAge strongly predicts lifespan and healthspan

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Abstract

It was unknown whether plasma protein levels can be estimated based on DNA methylation (DNAm) levels, and if so, how the resulting surrogates can be consolidated into a powerful predictor of lifespan. We present here, seven DNAm-based estimators of plasma proteins including those of plasminogen activator inhibitor 1 (PAI-1) and growth differentiation factor 15. The resulting predictor of lifespan, DNAm GrimAge (in units of years), is a composite biomarker based on the seven DNAm surrogates and a DNAm-based estimator of smoking pack-years. Adjusting DNAm GrimAge for chronological age generated novel measure of epigenetic age acceleration, AgeAccelGrim.

Using large scale validation data from thousands of individuals, we demonstrate that DNAm GrimAge stands out among existing epigenetic clocks in terms of its predictive ability for time-to-death (Cox regression P=2.0E-75), time-to-coronary heart disease (Cox P=6.2E-24), time-to-cancer (P= 1.3E-12), its strong relationship with computed tomography data for fatty liver/excess visceral fat, and age-at-menopause (P=1.6E-12). AgeAccelGrim is strongly associated with a host of age-related conditions including comorbidity count (P=3.45E-17). Similarly, age-adjusted DNAm PAI-1 levels are associated with lifespan (P=5.4E-28), comorbidity count (P= 7.3E-56) and type 2 diabetes (P=2.0E-26). These DNAm-based biomarkers show the expected relationship with lifestyle factors including healthy diet and educational attainment.

Overall, these epigenetic biomarkers are expected to find many applications including human anti-aging studies.

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An epigenetic biomarker of aging for lifespan and healthspan

Morgan E Levine, Ake T. Lu, Austin Quach … (2018)

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

Riccardo E Marioni, Sonia Shah, Allan McRae … (2015)

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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Biological Age Predictors

Juulia Jylhävä, Nancy Pedersen, Sara Hägg (2017)

The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.

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Journal

Journal ID (nlm-ta): Aging (Albany NY)

Journal ID (iso-abbrev): Aging (Albany NY)

Journal ID (publisher-id): Aging

Title: Aging (Albany NY)

Publisher: Impact Journals

ISSN (Electronic): 1945-4589

Publication date Collection: January 2019

Publication date (Electronic): 21 January 2019

Volume: 11

Issue: 2

Pages: 303-327

Affiliations

[1 ]Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles , Los Angeles, , CA, 90095, USA

[2 ]Department of Physiology and Biophysics, University of Mississippi Medical Center , Jackson, , MS, 39216, USA

[3 ]Public Health Sciences Division, Fred Hutchinson Cancer Research Center , Seattle, , WA, 98109, USA

[4 ]Center of Development and Aging, New Jersey Medical School, Rutgers State University of New Jersey , Newark, , NJ, 07103, USA

[5 ]Radiation Effects Department, Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Chilton, Didcot, , Oxfordshire, , OX11 0RQ, United Kingdom

[6 ]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

[7 ]Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences Epidemiology, Columbia University Mailman School of Public Health , New York, , NY, 10032, USA

[8 ]Departments of Genetics, Biostatistics, Computer Science, University of North Carolina , Chapel Hill, , NC, 27599, USA

[9 ]Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , Chapel Hill, , NC, 27599, USA

[10 ]Department of Medicine, School of Medicine, University of North Carolina , Chapel Hill, , NC, 27516, USA

[11 ]Department of Medicine (Division of Cardiovascular Medicine), Stanford University School of Medicine, Stanford, CA 94305, USA

[12 ]VA Palo Alto Health Care System, Palo Alto, , CA, 94304, USA

[13 ]Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA, Baltimore, , MD, 21224, USA

[14 ]Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles , Los Angeles, , CA, 90095, USA

Author notes

Correspondence to: Steve Horvath; email: shorvath@ 123456mednet.ucla.edu

Article

Publisher ID: 101684

DOI: 10.18632/aging.101684

PMC ID: 6366976

PubMed ID: 30669119

SO-VID: 8cba238d-c4b8-4717-a873-b9dc589ddfb5

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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.

DNA methylation GrimAge strongly predicts lifespan and healthspan

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Most cited references 35

An epigenetic biomarker of aging for lifespan and healthspan

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

Biological Age Predictors

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