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Senescent cells emerge with aging and injury. The contribution of senescent cells to DNA methylation age (DNAmAGE) in vivo is uncertain. Furthermore, stem cell therapy can mediate “rejuvenation”, but how tissue regeneration controlled by resident stem cells affects whole tissue DNAmAGE is unclear. We assessed DNAmAGE with or without senolytics (BI01) in aged male mice (24–25 months) 35 days following muscle healing (BaCl2‐induced regeneration versus non‐injured). Young injured mice (5–6 months) without senolytics were comparators. DNAmAGE was decelerated by up to 68% after injury in aged muscle. DNAmAGE was modestly but further significantly decelerated by injury recovery with senolytics. ~1/4 of measured CpGs were altered by injury then recovery regardless of senolytics in aged muscle. Specific methylation changes caused by senolytics included differential regulation of Col, Hdac, Hox, and Wnt genes, which likely contributed to improved regeneration. Altered extracellular matrix remodeling using histological analysis aligned with the methylomic findings with senolytics. Without senolytics, regeneration had a contrasting effect in young mice and tended not to influence or modestly accelerate DNAmAGE. Comparing young to old injury recovery without senolytics using methylome‐transcriptome integration, we found a more coordinated molecular profile in young and differential regulation of genes implicated in muscle stem cell performance: Axin2, Egr1, Fzd4, Meg3, and Spry1. Muscle injury and senescent cells affect DNAmAGE and aging influences the transcriptomic‐methylomic landscape after resident stem cell‐driven tissue reformation. Our data have implications for understanding muscle plasticity with aging and developing therapies aimed at collagen remodeling and senescence. Injury then recovery markedly rewires the DNA methylome in aged skeletal muscle.The addition of senolytics during muscle regeneration decelerates DNAmAGE more than regeneration alone as well as targets collagen remodeling and stem cell function‐related genes. Regeneration in young adult mice has a less pronounced effect on the methylome‐transcriptome landscape than in aged skeletal muscle, but still elicits a distinct molecular profile versus aged skeletal muscle after injury.

Background Telomere length (TL), mitochondrial DNA copy number (mtDNAcn), and DNA methylation age (DNAmAge) are common aging biomarkers. However, research on the associations between these three markers at birth and subsequent metabolic status was limited. This study aimed to evaluate the association between TL, mtDNAcn, and DNAmAge in newborns and the variation in metabolic hormones of children at 3 years old. Methods This research involved 895 mother–child pairs from a birth cohort in China, with TL and mtDNAcn measured using quantitative real-time PCR, DNA methylation (DNAm) assessed using Infinium MethylationEPIC Beadchip, and DNAm age (DNAmAge) determined using Horvath’s epigenetic clock. Insulin and leptin levels were measured via electrochemiluminescence assay. Multivariable adjusted linear regression and restricted cubic spline (RCS) analysis were utilized to examine the association between aging markers and metabolic hormones. Results The linear regression analysis indicated the percentage change of metabolism hormones for per doubling of aging biomarkers alterations and found significant associations between DNAmAge and insulin levels (adjusted percent change (95% CI), − 13.22 (− 23.21 to − 1.94)), TL and leptin levels (adjusted percent change (95% CI), 15.32 (1.32 to 31.24)), and mtDNAcn and leptin levels (adjusted percent change (95% CI), − 14.13 (− 21.59 to − 5.95)). The RCS analysis revealed significant non-linear associations between TL (Ln transformed) and insulin (Ln transformed) ( P  = 0.024 for nonlinearity), as well as DNAmAge (Ln transformed) and leptin (Ln transformed) ( P  = 0.043 for nonlinearity). Specifically, for TL and insulin, a positive association was observed when TL (Ln transformed) was less than − 0.05, which transitioned to an inverse association when TL (Ln transformed) was greater than − 0.05. Regarding DNAmAge and leptin, there was a sharp decline when DNAmAge (Ln transformed) was less than − 1.35, followed by a plateau between − 1.35 and − 0.67 and then a further decline when DNAmAge (Ln transformed) was greater than − 0.67. Conclusions In this prospective birth cohort study, variation in metabolic hormones of children at 3 years old was associated with TL, mtDNAcn, and DNAmAge at birth. These findings suggested that TL, mtDNAcn, and DNAmAge might play a role in the biological programming of metabolic health from birth.

To examine the relationships between two epigenetic clocks, aging and exceptional longevity. Participants were from three adult cohorts with blood DNA methylation data (Illumina 450 K, n = 275, 34-103 years). Epigenetic age (DNAmage) and age acceleration measures were calculated using the Hannum and Horvath epigenetic clocks. Across all cohorts, DNAmage was correlated with chronological age. In the long-lived cohort (Sydney Centenarian Study; 95+, n = 23), DNAmage was lower than chronological age for both clocks. Mean Sydney Centenarian Study Hannum age acceleration was negative, while the converse was observed for the Horvath model. Long-lived individuals have a young epigenetic age compared with their chronological age.

Attention Deficit/Hyperactivity Disorder (ADHD) is a common behavioral syndrome that begins in childhood and affects 3.4% of children worldwide. Due to its etiological complexity, there are no consistent biomarkers for ADHD, however the high heritability presented by the disorder indicates a genetic/epigenetic influence. The main epigenetic mechanism is DNA methylation, a process with an important role in gene expression and in many psychiatric disorders. Thus, our study sought to identify epi-signatures biomarkers in 29 children clinically diagnosed with ADHD. After DNA extraction and bisulfite conversion, we performed methylation array experiment for differential methylation, ontological and biological age analysis. The biological response in ADHD patients was not sufficient to determine a conclusive epi-signature in our study. However, our results highlighted the interaction of energy metabolism and oxidative stress pathways in ADHD patients detected by differential methylation patterns. Furthermore, we were able to identify a marginal association between the DNAmAge and ADHD. Our study present new methylation biomarkers findings associated with energy metabolism and oxidative stress pathways, in addition to DNAmAge in ADHD patients. However, we propose that further multiethnic studies, with larger cohorts and including maternal conditions, are necessary to demonstrate a definitive association between ADHD and these methylation biomarkers. •There is still no conclusive episignature for ADHD.•Oxidative stress and energy metabolism play a crucial role in the pathophysiology of ADHD.•Biological methylation age (DNAmAge) acceleration has a marginal association with clinical status of ADHD.

Background The present study investigates whether epigenetic differences emerge in the heart of patients undergoing cardiac surgery for an aortic valvular replacement (AVR) or coronary artery bypass graft (CABG). An algorithm is also established to determine how the pathophysiological condition might influence the human biological cardiac age. Results Blood samples and cardiac auricles were collected from patients who underwent cardiac procedures: 94 AVR and 289 CABG. The CpGs from three independent blood-derived biological clocks were selected to design a new blood- and the first cardiac-specific clocks. Specifically, 31 CpGs from six age-related genes, ELOVL2, EDARADD, ITGA2B, ASPA, PDE4C, and FHL2, were used to construct the tissue-tailored clocks. The best-fitting variables were combined to define new cardiac- and blood-tailored clocks validated through neural network analysis and elastic regression. In addition, telomere length (TL) was measured by qPCR. These new methods revealed a similarity between chronological and biological age in the blood and heart; the average TL was significantly higher in the heart than in the blood. In addition, the cardiac clock discriminated well between AVR and CABG and was sensitive to cardiovascular risk factors such as obesity and smoking. Moreover, the cardiac-specific clock identified an AVR patient's subgroup whose accelerated bioage correlated with the altered ventricular parameters, including left ventricular diastolic and systolic volume. Conclusion This study reports on applying a method to evaluate the cardiac biological age revealing epigenetic features that separate subgroups of AVR and CABG.

The epigenetic clock, defined as the DNA methylome age (DNAmAge), is a candidate biomarker of ageing. In this study, we aimed to characterize the behaviour of this marker during the human lifespan in more detail using two follow-up cohorts (the Young Finns study, calendar age i.e. cAge range at baseline 15–24 years, 25-year-follow-up, N  = 183; The Vitality 90+ study, cAge range at baseline 19–90 years, 4-year-follow-up, N  = 48). We also aimed to assess the relationship between DNAmAge estimate and the blood cell distributions, as both of these measures are known to change as a function of age. The subjects’ DNAmAges were determined using Horvath’s calculator of epigenetic cAge. The estimate of the DNA methylome age acceleration (Δ-cAge-DNAmAge) demonstrated remarkable stability in both cohorts: the individual rank orders of the DNAmAges remained largely unchanged during the follow-ups. The blood cell distributions also demonstrated significant intra-individual correlation between the baseline and follow-up time points. Interestingly, the immunosenescence-associated features (CD8+CD28− and CD4+CD28− cell proportions and the CD4/CD8 cell ratio) were tightly associated with the estimate of the DNA methylome age. In summary, our data demonstrate that the general level of Δ-cAge-DNAmAge is fixed before adulthood and appears to be quite stationary thereafter, even in the oldest-old ages. Moreover, the blood DNAmAge estimate seems to be tightly associated with ageing-associated shifts in blood cell composition, especially with those that are the hallmarks of immunosenescence. Overall, these observations contribute to the understanding of the longitudinal aspects of the DNAmAge estimate.