Research Article: DNA methylation GrimAge strongly predicts lifespan and healthspan

Date Published: January 31, 2019

Publisher: Impact Journals

Author(s): Ake T. Lu, Austin Quach, James G. Wilson, Alex P. Reiner, Abraham Aviv, Kenneth Raj, Lifang Hou, Andrea A. Baccarelli, Yun Li, James D. Stewart, Eric A. Whitsel, Themistocles L. Assimes, Luigi Ferrucci, Steve Horvath.

http://doi.org/10.18632/aging.101684

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.

Partial Text

DNAm levels have been used to build accurate composite biomarkers of chronological age [1–4]. DNAm-based age (epigenetic age) estimators, include the pan tissue epigenetic clock by Horvath 2013 [1], based on 353 CpGs, and an estimator developed by Hannum 2013 [2], based on 71 CpGs in leukocytes. These estimators predict lifespan after adjusting for chronological age and other risk factors [5–9]. Moreover, they are also associated with a large host of age-related conditions [10–20]. Recently, DNAm-based biomarkers for lifespan (time-to-death due to all-cause mortality) have been developed [21,22]. For example, Zhang et al (2017) combined mortality associated CpGs [21] into an overall mortality risk score, while Levine et al (2018) developed a lifespan predictor, DNAm PhenoAge, by regressing a phenotypic measure of mortality risk on CpGs [22].

Several articles have previously described DNAm-based biomarkers for measuring tissue age and for predicting lifespan [10,40]. This work shows that DNAm GrimAge, which is as a linear combination of chronological age, sex, and DNAm-based surrogate biomarkers for seven plasma proteins and smoking pack-years, outperforms all other DNAm-based biomarkers, on a variety of health-related metrics. An age-adjusted version of DNAm GrimAge, which can be regarded as a new measure of epigenetic age acceleration (AgeAccelGrim), is associated with a host of age-related conditions, lifestyle factors, and clinical biomarkers. Using large scale validation data from three ethnic groups, we demonstrate that AgeAccelGrim stands out among pre-existing epigenetic clocks in terms of its predictive ability for time-to-death, time-to-coronary heart disease, time-to-cancer, its association with computed tomography data for fatty liver/excess fat, and early age at menopause.

 

Source:

http://doi.org/10.18632/aging.101684

 

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