Abstract
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.
Highlights
Ageing is the normal, progressive decline of function occurring at the cellular, tissue and organismal levels over the lifespan.[1]
We gathered skeletal muscle methylomes from 12 datasets generated with three different platforms: HM27, HM450 and the more recent HMEPIC, totalling n = 682 samples (Figure 2, Data S1)
Eight of the 12 datasets were paired designs, meaning that some of the 682 muscle samples were taken from healthy individuals at baseline or after a control diet, while other samples were taken after an exercise intervention, a high-fat diet, sleep deprivation, insulin stimulation, or were from individuals with type 2 diabetes
Summary
Progressive decline of function occurring at the cellular, tissue and organismal levels over the lifespan.[1]. Changes in epigenetic patterns constitute a primary hallmark of ageing in all tissues of the human body.[2] Epigenetic marks are cellular properties conferring the ability to remember a previous biological event,[3] and some of these marks are sensitive to environmental stimuli such as diet, sleep,[4] and exercise training.[5,6] Epigenetic changes with age are well characterised at the DNA methylation level,[7,8] including skeletal muscle.[9]. DNA methylation changes in all human tissues, and epigenetic markers can estimate chronological age based on DNA methylation patterns across tissues. The construction of the original pan-tissue epigenetic clock did not include skeletal muscle samples and exhibited a strong deviation between DNA methylation and chronological age in this tissue. We took advantage of the large number of samples to conduct an epigenome-wide association study of age-associated DNA methylation patterns in skeletal muscle
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