DNAm can precisely track ageing through predictors termed “epigenetic clocks”. DNAm is dynamic, tissue-specific, and is influenced by both genetic and environmental factors. These predictors can aid disease risk stratification and are based on associations between CpG DNA methylation (DNAm) and age, health, and lifestyle outcomes. The development and application of epigenetic predictors for healthcare research has grown dramatically over the last decade. 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. Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HR GrimAge = 1.47 with p = 1.08 × 10 −52, and HR bAge = 1.52 with p = 2.20 × 10 −60). 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. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study). 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. 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). 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 19, and 8 other cohorts with publicly available data. Methodsįirst, we perform large-scale ( N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. As cohort sample sizes increase, estimates of cAge and bAge become more precise. The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. Epigenetic clocks can track both chronological age (cAge) and biological age (bAge).
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