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DNA methylation GrimAge strongly predicts lifespan and healthspan

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 acti...

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Autores principales: Lu, Ake T., Quach, Austin, Wilson, James G., Reiner, Alex P., Aviv, Abraham, Raj, Kenneth, Hou, Lifang, Baccarelli, Andrea A., Li, Yun, Stewart, James D., Whitsel, Eric A., Assimes, Themistocles L., Ferrucci, Luigi, Horvath, Steve
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366976/
https://www.ncbi.nlm.nih.gov/pubmed/30669119
http://dx.doi.org/10.18632/aging.101684
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author Lu, Ake T.
Quach, Austin
Wilson, James G.
Reiner, Alex P.
Aviv, Abraham
Raj, Kenneth
Hou, Lifang
Baccarelli, Andrea A.
Li, Yun
Stewart, James D.
Whitsel, Eric A.
Assimes, Themistocles L.
Ferrucci, Luigi
Horvath, Steve
author_facet Lu, Ake T.
Quach, Austin
Wilson, James G.
Reiner, Alex P.
Aviv, Abraham
Raj, Kenneth
Hou, Lifang
Baccarelli, Andrea A.
Li, Yun
Stewart, James D.
Whitsel, Eric A.
Assimes, Themistocles L.
Ferrucci, Luigi
Horvath, Steve
author_sort Lu, Ake T.
collection PubMed
description 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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spelling pubmed-63669762019-02-15 DNA methylation GrimAge strongly predicts lifespan and healthspan Lu, Ake T. Quach, Austin Wilson, James G. Reiner, Alex P. Aviv, Abraham Raj, Kenneth Hou, Lifang Baccarelli, Andrea A. Li, Yun Stewart, James D. Whitsel, Eric A. Assimes, Themistocles L. Ferrucci, Luigi Horvath, Steve Aging (Albany NY) Research Paper 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. Impact Journals 2019-01-21 /pmc/articles/PMC6366976/ /pubmed/30669119 http://dx.doi.org/10.18632/aging.101684 Text en Copyright © 2018 Lu et al. http://creativecommons.org/licenses/by/3.0/ 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.
spellingShingle Research Paper
Lu, Ake T.
Quach, Austin
Wilson, James G.
Reiner, Alex P.
Aviv, Abraham
Raj, Kenneth
Hou, Lifang
Baccarelli, Andrea A.
Li, Yun
Stewart, James D.
Whitsel, Eric A.
Assimes, Themistocles L.
Ferrucci, Luigi
Horvath, Steve
DNA methylation GrimAge strongly predicts lifespan and healthspan
title DNA methylation GrimAge strongly predicts lifespan and healthspan
title_full DNA methylation GrimAge strongly predicts lifespan and healthspan
title_fullStr DNA methylation GrimAge strongly predicts lifespan and healthspan
title_full_unstemmed DNA methylation GrimAge strongly predicts lifespan and healthspan
title_short DNA methylation GrimAge strongly predicts lifespan and healthspan
title_sort dna methylation grimage strongly predicts lifespan and healthspan
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366976/
https://www.ncbi.nlm.nih.gov/pubmed/30669119
http://dx.doi.org/10.18632/aging.101684
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