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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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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Regularization and variable selection via the elastic net

Hui Zou, Trevor J. Hastie (2005)

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DNA methylation age of human tissues and cell types

Steve Horvath, Ilias Lagkouvardos (2014)

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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Author and article information

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

License:

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.

History

Date received : 24 August 2018

Date accepted : 22 November 2018

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DNA methylation GrimAge strongly predicts lifespan and healthspan

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Most cited references 63

WGCNA: an R package for weighted correlation network analysis

Regularization and variable selection via the elastic net

DNA methylation age of human tissues and cell types

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Cited by 646

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