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Objective: Obesity is driven by modifiable lifestyle factors whose effects may be mediated by epigenetics. Therefore, we investigated lifestyle effects on blood DNA methylation in participants of the LIFE‐Adult study, a well‐characterised population‐based cohort from Germany. Research design and methods: Lifestyle scores (LS) based on diet, physical activity, smoking and alcohol intake were calculated in 4107 participants of the LIFE‐Adult study. Fifty subjects with an extremely healthy lifestyle and 50 with an extremely unhealthy lifestyle (5th and 95th percentiles LS) were selected for genome‐wide DNA methylation analysis in blood samples employing Illumina Infinium® Methylation EPIC BeadChip system technology. Results: Differences in DNA methylation patterns between body mass index groups (<25 vs. >30 kg/m2) were rather marginal compared to inter‐lifestyle differences (0 vs. 145 differentially methylated positions [DMPs]), which identified 4682 differentially methylated regions (DMRs; false discovery rate [FDR <5%) annotated to 4426 unique genes. A DMR annotated to the glutamine‐fructose‐6‐phosphate transaminase 2 (GFPT2) locus showed the strongest hypomethylation (∼6.9%), and one annotated to glutamate rich 1 (ERICH1) showed the strongest hypermethylation (∼5.4%) in healthy compared to unhealthy lifestyle individuals. Intersection analysis showed that diet, physical activity, smoking and alcohol intake equally contributed to the observed differences, which affected, among others, pathways related to glutamatergic synapses (adj. p <.01) and axon guidance (adj. p <.05). We showed that methylation age correlates with chronological age and waist‐to‐hip ratio with lower DNA methylation age (DNAmAge) acceleration distances in participants with healthy lifestyles. Finally, two identified top DMPs for the alanyl aminopeptidase (ANPEP) locus also showed the strongest expression quantitative trait methylation in blood. Conclusions: DNA methylation patterns help discriminate individuals with a healthy versus unhealthy lifestyle, which may mask subtle methylation differences derived from obesity. [ABSTRACT FROM AUTHOR]

The epigenetic clock parameter DNAm age acceleration is a promising biomarker of aging. We have recently described an epigenetic clock based on only seven cytosine-phosphate-guanine sites, which is highly associated with chronological age. The aim of this study was to examine this epigenetic clock with respect to its relationship with cardiovascular health (CVH) in older adults. We used data from the Berlin Aging Study II (BASE-II; 1,671 participants; 68.8 ± 3.7 years old). CVH was operationalized using two different CVH scores, the Framingham Risk Score (FRS), and the Life's simple 7 (LS7). To adjust for potential confounding, e.g. by sex, we performed regression analyses. The LS7 score was higher, i.e. more favorable, in woman than in men (8.8 ± 2 vs. 8.2 ± 2, p < 0.001). DNAm age acceleration was associated with the FRS (β = 0.122, p = 0.028) and with the LS7 (β = -0.804, p = 0.032). In more detail, physical activity (β = -0.461, p = 0.05), HDL-cholesterol (β = 0.343, p = 0.03) and total cholesterol (β = -0.364, p = 0.002) were associated with epigenetic age acceleration. We present evidence suggesting that better CVH is associated with decelerated biological aging measured by the epigenetic clock., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Three DNA methylation (DNAm) based algorithms, DNAm PhenoAge acceleration (AgeAccelPheno), DNAm GrimAge acceleration (AgeAccelGrim), and mortality risk score (MRscore), based on methylation in 513, 1030, and 10 CpGs, respectively, were established to predict health outcomes and mortality. We aimed to compare and validate the predictive ability of these scores and frailty in relation to mortality in a population-based cohort from Germany., Methods: DNA methylation in whole blood was measured by the Infinium Methylation EPIC BeadChip kit (EPIC, Illumina, San Diego, CA, USA) in two random subsets of the ESTHER cohort study (n = 741 and n = 1030). AgeAccelPheno, AgeAccelGrim, and a revised MRscore to adapt EPIC, the MRscore with 8 CpGs (MRscore-8CpGs), were calculated. Frailty was assessed by a frailty index (FI)., Findings: During 17 years of follow-up, 458 deaths were observed. All DNAm algorithms and FI were positively correlated with each other. AgeAccelPheno, AgeAccelGrim, MRscore, and FI showed independent associations with all-cause mortality [hazard ratio (95% CI) per SD increase = 1·32 (1·19-1·46), 1·47 (1·32-1·64), 1·73 (1·49-2·01), and 1·31 (1·20-1·43), respectively]. Harrell's C-statistic was 0·710 for a model predicting mortality by age, sex, and leukocyte composition and increased to 0·759 in a model including MRscore-8CpGs and FI. The predictive performance was further improved (Harrell's C-statistic = 0·766) when additionally including AgeAccelPheno and AgeAccelGrim into the model., Interpretation: The combination of a DNA methylation score based on 8 CpGs only and an easy to ascertain frailty index may strongly enhance mortality prediction beyond age and sex., Funding: The ESTHER study was funded by grants from the Baden-Württemberg state Ministry of Science, Research and Arts (Stuttgart, Germany), the Federal Ministry of Education and Research (Berlin, Germany), the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (Berlin, Germany), and the Saarland State Ministry of Health, Social Affairs, Women and the Family (Saarbrücken, Germany). The work of Xiangwei Li was supported by a grant from Fondazione Cariplo (Bando Ricerca Malattie invecchiamento, #2017-0653)., Competing Interests: Declaration of Competing Interest All authors confirmed the full access to all the data in the study and accepted responsibility to submit for publication. No potential conflicts of interest were disclosed., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)