Search

Abstract Background Few studies have explored the association between DNA methylation and physical activity. The aim of this study was to evaluate the association of objectively measured hours of sedentary behavior (SB) and moderate physical activity (MPA) with DNA methylation. We further aimed to explore the association between SB or MPA related CpG sites and cardiometabolic traits, gene expression, and genetic variation. Results For discovery, we performed cross sectional analyses in pregnant women from the Epigenetics in pregnancy (EPIPREG) sample with both DNA methylation (Illumina MethylationEPIC BeadChip) and objectively measured physical activity data (SenseWear™ Pro 3 armband) (European = 244, South Asian = 109). For EWAS of SB and MPA, two main models were designed: model (1) a linear mixed model adjusted for age, smoking, blood cell composition, including ancestry as random intercept, and model (2) which was additionally adjusted for the total number of steps per day. In model 1, we did not identify any CpG sites associated with neither SB nor MPA. In model 2, SB was positively associated (false discovery rate, FDR

Abstract Background Epigenetic clocks have been recognized for their precise prediction of chronological age, age-related diseases, and all-cause mortality. Existing epigenetic clocks are based on CpGs from the Illumina HumanMethylation450 BeadChip (450 K) which has now been replaced by the latest platform, Illumina MethylationEPIC BeadChip (EPIC). Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age. Results We developed three blood-based epigenetic clocks for human adults using EPIC-based DNA methylation (DNAm) data from the Norwegian Mother, Father and Child Cohort Study (MoBa) and the Gene Expression Omnibus (GEO) public repository: 1) an Adult Blood-based EPIC Clock (ABEC) trained on DNAm data from MoBa (n = 1592, age-span: 19 to 59 years), 2) an extended ABEC (eABEC) trained on DNAm data from MoBa and GEO (n = 2227, age-span: 18 to 88 years), and 3) a common ABEC (cABEC) trained on the same training set as eABEC but restricted to CpGs common to 450 K and EPIC. Our clocks showed high precision (Pearson correlation between chronological and epigenetic age (r) > 0.94) in independent cohorts, including GSE111165 (n = 15), GSE115278 (n = 108), GSE132203 (n = 795), and the Epigenetics in Pregnancy (EPIPREG) study of the STORK Groruddalen Cohort (n = 470). This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set. Conclusions Our ABECs predicted adults’ chronological age precisely in independent cohorts. As EPIC is now the dominant platform for measuring DNAm, these clocks will be useful in further predictions of chronological age, age-related diseases, and mortality.