DNA methylation-based measures of biological age: meta-analysis predicting time to death

By Brian H. Chen, R. Marioni, E. Colicino, M. Peters, C. Ward‐Caviness, P. Tsai, Nicholas S. Roetker, A. Just, E. Demerath, W. Guan and 56 more. Aging, 2016

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Estimates of biological age based on DNA methylation patterns, often referred to as “epigenetic age”, “DNAm age”, have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2×10−9), independent of chronological age, even after adjusting for additional risk factors (p<5.4×10−4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5×10−43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality.

Subfields: Health, Toxicology and Mutagenesis, Molecular Biology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/64e948aa71ef80a95ecdf6eaee248ac15f9a7000

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Citations in the corpus

An epigenetic biomarker of aging for lifespan and healthspan (2018, background): “2, the DNAm age based measures from Hannum and Horvath also related to all-cause mortality, consistent with what has been reported previously [15, 19, 23, 33, 34].”
Measuring biological age using omics data (2022, background): “However, mortality risk, which is widely used as a measure of ‘global’ biological age, has shown an over-all weak and variable association with these clocks 46,58 , which raises questions as to what biology they actually measure.”
More than a feeling: A unified view of stress measurement for population science (2018, background): “, 2001), and cellular aging such as inflammation, telomere length, and epigenetic clock (Chen et al., 2016; Codd et al., 2013; Emerging Risk Factors Collaboration et al., 2010; Li et al., 2017; Zhang et al., 2017; Zhou et al., 2016) that can be measured during mid-life, and serve as markers, risk fa”
DNA methylation aging clocks: challenges and recommendations (2019, background): “While these first clocks are markers capturing these effects to a greater or lesser extent [42], both can predict all-cause mortality at a population, but not individual level, even after correcting for known risk factors [27].”
Measuring biological aging in humans: A quest (2019, background): “Consistent with this notion, “epigenetically older” individuals have a higher risk of developing several age-related diseases and premature mortality for all causes and cardiovascular diseases (Chen et al., 2016; Marioni et al., 2015).”
Epigenetic clock analysis of diet, exercise, education, and lifestyle factors (2017, positive): “For instance, the epigenetic age of blood has been found to be predictive of all-cause mortality [40-43], frailty [44], lung cancer [45], and cognitive and physical functioning [46], while the blood of the offspring of Italian semi-supercentenarians (i.”