Dataset Information

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.

ABSTRACT: Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n?=?1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n?=?1047; testing: n?=?350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n?=?1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n?=?1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

SUBMITTER: Shireby GL

PROVIDER: S-EPMC7805794 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.

Shireby Gemma L GL Davies Jonathan P JP Francis Paul T PT Burrage Joe J Walker Emma M EM Neilson Grant W A GWA Dahir Aisha A Thomas Alan J AJ Love Seth S Smith Rebecca G RG Lunnon Katie K Kumari Meena M Schalkwyk Leonard C LC Morgan Kevin K Brookes Keeley K Hannon Eilis E Mill Jonathan J

Brain : a journal of neurology 20201201 12

Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We ai ...[more]

PMID: 33300551

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Project description:BackgroundAgeing is associated with DNA methylation changes in all human tissues, and epigenetic markers can estimate chronological age based on DNA methylation patterns across tissues. However, the construction of the original pan-tissue epigenetic clock did not include skeletal muscle samples and hence exhibited a strong deviation between DNA methylation and chronological age in this tissue.MethodsTo address this, we developed a more accurate, muscle-specific epigenetic clock based on the genome-wide DNA methylation data of 682 skeletal muscle samples from 12 independent datasets (18-89 years old, 22% women, 99% Caucasian), all generated with Illumina HumanMethylation (HM) arrays (HM27, HM450, or HMEPIC). We also took advantage of the large number of samples to conduct an epigenome-wide association study of age-associated DNA methylation patterns in skeletal muscle.ResultsThe newly developed clock uses 200 cytosine-phosphate-guanine dinucleotides to estimate chronological age in skeletal muscle, 16 of which are in common with the 353 cytosine-phosphate-guanine dinucleotides of the pan-tissue clock. The muscle clock outperformed the pan-tissue clock, with a median error of only 4.6 years across datasets (vs. 13.1 years for the pan-tissue clock, P < 0.0001) and an average correlation of ρ = 0.62 between actual and predicted age across datasets (vs. ρ = 0.51 for the pan-tissue clock). Lastly, we identified 180 differentially methylated regions with age in skeletal muscle at a false discovery rate < 0.005. However, gene set enrichment analysis did not reveal any enrichment for gene ontologies.ConclusionsWe have developed a muscle-specific epigenetic clock that predicts age with better accuracy than the pan-tissue clock. We implemented the muscle clock in an r package called Muscle Epigenetic Age Test available on Bioconductor to estimate epigenetic age in skeletal muscle samples. This clock may prove valuable in assessing the impact of environmental factors, such as exercise and diet, on muscle-specific biological ageing processes.

Project description:BackgroundEpigenetic 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.MethodsFirst, 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).ResultsThrough 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.ConclusionsThe 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.

Dataset Information

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.

Publications

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.

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