Refining epigenetic prediction of chronological and biological age

Abstract

Background Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. Methods First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study). Results Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10−60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. Conclusions The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.

DNA Methylation Age Acceleration Is Not Associated with Age of Onset in Parkinson's Disease

“…However, the CpGs of the clock have themselves no known direct biological relevance and a limited capability in terms of being an accurate measure of biological age. 20 The Horvath methylation clock was trained on data obtained by using the Illumina 27K and 450K platforms, 3 whereas the studies reporting an inverse relationship between DNAm AA and age of onset used data from both the Illumina 450K 14 and EPIC 13,15,16 array. The Illumina EPIC array, released in 2015, 21 has largely replaced the older platforms (27K and 450K), and although it lacks 19 of the 353 CpGs used in the Horvath clock, this does not appear to greatly affect DNAm age predictions.…”

“…First‐generation epigenetic clocks are based on the observation that the methylated fraction of certain CpGs is highly correlated with chronological age. However, the CpGs of the clock have themselves no known direct biological relevance and a limited capability in terms of being an accurate measure of biological age 20 . The Horvath methylation clock was trained on data obtained by using the Illumina 27K and 450K platforms, 3 whereas the studies reporting an inverse relationship between DNAm AA and age of onset used data from both the Illumina 450K 14 and EPIC 13,15,16 array.…”

Nurturing longevity through natural compounds: Where do we stand, and where do we go?

“…Additionally, they shed light on the relationship between epigenetic alterations and aging, as a recent study confirms that the induction of changes in the epigenome in mice accelerated DNA methylation by almost 50% (J. H. Yang et al., 2023). Thus, by monitoring DNA methylation, biological age, also referred to as physiological age, can be calculated (Bernabeu et al., 2023). This further explains why some individuals may have shorter lifespans and acquire age‐related diseases earlier, as their biological age is higher than their chronological age (Bell et al., 2019).…”

Effects of Human LAV-BPIFB4 Gene Therapy on the Epigenetic Clock and Health of Aged Mice

“…In addition, there is increased methylation of some genes that are important for the oxidative stress response and DNA damage repair, which may contribute to the accumulation of cellular damage and the onset of age-related diseases [ 8 ]. In humans, epigenetic clocks have been shown to accurately estimate chronological and biological age and predict survival [ 9 , 10 , 11 , 12 ]. In mice, a three CpG clock, analyzing methylation sites in Prima1, Hsf4 and Kcns1 genes, was recently demonstrated to be highly correlated with chronological age [ 13 , 14 ].…”