Explore the vast range of titles available.

Background DNA methylation-based biological age (DNAm age) is an important biomarker for adult health. Studies in specific age ranges have found widely varying results about its genetic and environmental causes of variation. However, these studies are not able to provide a comprehensive view of the causes of variation over the lifespan. Results In order to investigate the genetic and environmental causes of DNAm age variation across the lifespan, we pooled genome-wide DNA methylation data for 4217 people aged 0–92 years from 1871 families. DNAm age was calculated using the Horvath epigenetic clock. We estimated familial correlations in DNAm age for monozygotic (MZ) twin, dizygotic (DZ) twin, sibling, parent–offspring, and spouse pairs by cohabitation status. Genetic and environmental variance components models were fitted and compared. We found that twin pair correlations were − 0.12 to 0.18 around birth, not different from zero (all P  > 0.29). For all pairs of relatives, their correlations increased with time spent living together (all P  < 0.02) at different rates (MZ > DZ and siblings > parent–offspring; P  < 0.001) and decreased with time spent living apart ( P  = 0.02) at similar rates. These correlation patterns were best explained by cohabitation-dependent shared environmental factors, the effects of which were 1.41 (95% confidence interval [CI] 1.16 to 1.66) times greater for MZ pairs than for DZ and sibling pairs, and the latter were 2.03 (95% CI 1.13 to 9.47) times greater than for parent–offspring pairs. Genetic factors explained 13% (95% CI − 10 to 35%) of variation ( P  = 0.27). Similar results were found for another two epigenetic clocks, suggesting that our observations are robust to how DNAm age is measured. In addition, results for the other clocks were consistent with there also being a role for prenatal environmental factors in determining their variation. Conclusions Variation in DNAm age is mostly caused by environmental factors, including those shared to different extents by relatives while living together and whose effects persist into old age. The equal environment assumption of the classic twin study might not hold for epigenetic aging.

Background Global but predictable changes impact the DNA methylome as we age, acting as a type of molecular clock. This clock can be hastened by conditions that decrease lifespan, raising the question of whether it can also be slowed, for example, by conditions that increase lifespan. Mice are particularly appealing organisms for studies of mammalian aging; however, epigenetic clocks have thus far been formulated only in humans. Results We first examined whether mice and humans experience similar patterns of change in the methylome with age. We found moderate conservation of CpG sites for which methylation is altered with age, with both species showing an increase in methylome disorder during aging. Based on this analysis, we formulated an epigenetic-aging model in mice using the liver methylomes of 107 mice from 0.2 to 26.0 months old. To examine whether epigenetic aging signatures are slowed by longevity-promoting interventions, we analyzed 28 additional methylomes from mice subjected to lifespan-extending conditions, including Prop1 df/df dwarfism, calorie restriction or dietary rapamycin. We found that mice treated with these lifespan-extending interventions were significantly younger in epigenetic age than their untreated, wild-type age-matched controls. Conclusions This study shows that lifespan-extending conditions can slow molecular changes associated with an epigenetic clock in mice livers.

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health‐related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell‐type deconvolution with EAA predicted by six widely‐used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging‐related diseases, precision medicine, and therapeutic interventions. DNA methylation‐based epigenetic clocks in combination with the advanced 12‐immune‐cell‐deconvolution in human blood offers new opportunities to study intrinsic epigenetic age acceleration and insights into biological implications of immune profile in epigenetic age acceleration in human health and disease.

Background Cigarette smoke is a major public health concern. Epigenetic aging may be an important pathway by which exposure to cigarette smoke affects health. However, little is known about how exposure to smoke at different life stages affects epigenetic aging, especially in older adults. This study examines how three epigenetic aging measures (GrimAge, PhenoAge, and DunedinPoAm38) are associated with parental smoking, smoking in youth, and smoking in adulthood, and whether these epigenetic aging measures mediate the link between smoke exposure and morbidity and mortality. This study utilizes data from the Health and Retirement Study (HRS) Venous Blood Study (VBS), a nationally representative sample of US adults over 50 years old collected in 2016. 2978 participants with data on exposure to smoking, morbidity, and mortality were included. Results GrimAge is significantly increased by having two smoking parents, smoking in youth, and cigarette pack years in adulthood. PhenoAge and DunedinPoAm38 are associated with pack years. All three mediate some of the effect of pack years on cancer, high blood pressure, heart disease, and mortality and GrimAge and DunedinPoAm38 mediate this association on lung disease. Conclusions Results suggest epigenetic aging is one biological mechanism linking lifetime exposure to smoking with development of disease and earlier death in later life. Interventions aimed at reducing smoking in adulthood may be effective at weakening this association.

Research on maternal-fetal epigenetic programming argues that adverse exposures to the intrauterine environment can have long-term effects on adult morbidity and mortality. However, causal research on epigenetic programming in humans at a population level is rare and is often unable to separate intrauterine effects from conditions in the postnatal period that may continue to impact child development. In this study, we used a quasi-natural experiment that leverages state-year variation in economic shocks during the Great Depression to examine the causal effect of environmental exposures in early life on late-life accelerated epigenetic aging for 832 participants in the US Health and Retirement Study (HRS). HRS is the first population-representative study to collect epigenome-wide DNA methylation data that has the sample size and geographic variation necessary to exploit quasi-random variation in state environments, which expands possibilities for causal research in epigenetics. Our findings suggest that exposure to changing economic conditions in the 1930s had lasting impacts on next-generation epigenetic aging signatures that were developed to predict mortality risk (GrimAge) and physiological decline (DunedinPoAm). We show that these effects are localized to the in utero period specifically as opposed to the preconception, postnatal, childhood, or early adolescent periods. After evaluating endogenous shifts in mortality and fertility related to Depression-era birth cohorts, we conclude that these effects likely represent lower bound estimates of the true impacts of the economic shock on long-term epigenetic aging.

The elegant work by Maejima et al. recently published in Aging Cell reveals a previously unrecognized mechanism linking age‐related oxytocin (OXT) decline to epigenetic remodeling, mitochondrial dysfunction, and systemic inflammation (Maejima et al. 2025). Beyond documenting this relationship, the authors demonstrate its remarkable reversibility through nasal OXT administration. These findings provide the first molecular evidence supporting what has long been proposed: that the OXT system functions as a fundamental long‐term regulator of health across the entire lifespan, from early development through aging (Moberg 2024, 2003; Uvnas‐Moberg 1998). The current work now gives a tantalizing glimpse into the epigenetic mechanism behind this life course regulation. The elegant work by Maejima et al. (Aging Cell, 2025) reveals a previously unrecognized mechanism linking age‐related oxytocin (OXT) decline to epigenetic remodeling, mitochondrial dysfunction, and systemic inflammation, reversible through exogenous OXT administration. This provides molecular evidence for the long‐proposed role of the OXT system as a regulator of health across the entire lifespan, from early development through aging, here, we extend this tantalizing glimpse into the epigenetic mechanism underlying life course regulation.

Mammalian oocyte quality reduces with age. We show that prior to the occurrence of significant aneuploidy (9M in mouse), heterochromatin histone marks are lost, and oocyte maturation is impaired. This loss occurs in both constitutive and facultative heterochromatin marks but not in euchromatic active marks. We show that heterochromatin loss with age also occurs in human prophase I‐arrested oocytes. Moreover, heterochromatin loss is accompanied in mouse oocytes by an increase in RNA processing and associated with an elevation in L1 and IAP retrotransposon expression and in DNA damage and DNA repair proteins nuclear localization. Artificial inhibition of the heterochromatin machinery in young oocytes causes an elevation in retrotransposon expression and oocyte maturation defects. Inhibiting retrotransposon reverse‐transcriptase through azidothymidine (AZT) treatment in older oocytes partially rescues their maturation defects and activity of the DNA repair machinery. Moreover, activating the heterochromatin machinery via treatment with the SIRT1 activating molecule SRT‐1720, or overexpression of Sirt1 or Ezh2 via plasmid electroporation into older oocytes causes an upregulation in constitutive heterochromatin, downregulation of retrotransposon expression, and elevated maturation rates. Collectively, our work demonstrates a significant process in oocyte aging, characterized by the loss of heterochromatin‐associated chromatin marks and activation of specific retrotransposons, which cause DNA damage and impair oocyte maturation. Our work has shown that (a) with age, oocytes’ chromatin becomes less, and less heterochromatic. Silencing and compacting modifications (Presented as colorful hexagons) are gradually lost, changing transcriptional regulation and chromatin structure. (b) The loss of silencing and compaction of heterochromatin results in the loss of transcriptional regulation, when genome accessibility enabling the upregulation of genes that are regularly silent (in blue) and retrotransposons (in red). (c) Retrotransposon activity causes retrotransposition and DNA damage. (d) We suggest that the early stages of reproductive aging are the outcome of the gradual loss of repression. The harmful activity of activated retrotransposons eventually leads to the loss of oocyte functionality and a reduction in fertility.

Gerontological research reveals considerable interindividual variability in aging phenotypes, and emerging evidence suggests that high impact chronic pain may be associated with various accelerated biological aging processes. In particular, epigenetic aging is a robust predictor of health-span and disability compared to chronological age alone. The current study aimed to determine whether several epigenetic aging biomarkers were associated with high impact chronic pain in middle to older age adults (44–78 years old). Participants (n = 213) underwent a blood draw, demographic, psychosocial, pain and functional assessments. We estimated five epigenetic clocks and calculated the difference between epigenetic age and chronological age, which has been previously reported to predict overall mortality risk, as well as included additional derived variables of epigenetic age previously associated with pain. There were significant differences across Pain Impact groups in three out of the five epigenetic clocks examined (DNAmAge, DNAmPhenoAge and DNAmGrimAge), indicating that pain-related disability during the past 6 months was associated with markers of epigenetic aging. Only DNAmPhenoAge and DNAmGrimAge were associated with higher knee pain intensity during the past 48 h. Finally, pain catastrophizing, depressive symptomatology and more neuropathic pain symptoms were significantly associated with an older epigenome in only one of the five epigenetic clocks (i.e. DNAmGrimAge) after correcting for multiple comparisons (corrected p’s < 0.05). Given the scant literature in relation to epigenetic aging and the complex experience of pain, additional research is needed to understand whether epigenetic aging may help identify people with chronic pain at greater risk of functional decline and poorer health outcomes.

Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unverifiable because the true biological age of reprogrammed cells remains unknown. We present an analytical framework to consider rejuvenation predictions from the uncertainty perspective. Our analysis reveals that the DNA methylation profiles across reprogramming are poorly represented in the aging data used to train clock models, thus introducing high epistemic uncertainty in age estimations. Moreover, predictions of different published clocks are inconsistent, with some even suggesting zero or negative rejuvenation. While not questioning the possibility of age reversal, we show that the high clock uncertainty challenges the reliability of rejuvenation effects observed during in vitro reprogramming before pluripotency and throughout embryogenesis. Conversely, our method reveals a significant age increase after in vivo reprogramming. We recommend including uncertainty estimation in future aging clock models to avoid the risk of misinterpreting the results of biological age prediction. Epigenetic aging clocks are used to validate rejuvenation during cellular reprogramming, but their predictions are unverifiable due to the unknown true biological ages of reprogrammed cells. Our framework tests the reliability of these predictions by introducing the concept of uncertainty and finds them unreliable. We recommend incorporating uncertainty estimation in future aging clock models to improve the reliability of biological age predictions.

For most animals, events that occur early in life can have a lasting impact on individuals’ health. In humans, adolescence is a particularly vulnerable time when rapid growth and development collide with growing independence and experimentation. An unhealthy lifestyle during this period of rapid cell growth can contribute to later health problems like heart disease, lung disease, and premature death. This is due partly to accelerated biological aging, where the body deteriorates faster than what would be expected for an individual’s chronological age. One way to track the effects of lifestyle on biological aging is by measuring epigenetic changes. Epigenetic changes consist on adding or removing chemical ‘tags’ on genes. These tags can switch the genes on or off without changing their sequences. Scientists can measure certain epigenetic changes by measuring the levels of methylated DNA – DNA with a chemical ‘tag’ known as a methyl group – in blood samples. Several algorithms – known as ‘epigenetic clocks’ – are available that estimate how fast an individual is aging biologically based on DNA methylation. Kankaanpää et al. show that unhealthy lifestyles during adolescence may lead to accelerated aging in early adulthood. For their analysis, Kankaanpää et al. used data on the levels of DNA methylation in blood samples from 824 twins between 21 and 25 years old. The twins were participants in the FinnTwin12 study and had completed a survey about their lifestyles at ages 12, 14, and 17. Kankaanpää et al. classified individuals into five groups depending on their lifestyles. The first three groups, which included most of the twins, contained individuals that led relatively healthy lives. The fourth group contained individuals with a higher body mass index based on their height and weight. Finally, the last group included individuals with unhealthy lifestyles who binge drank, smoked and did not exercise. After estimating the biological ages for all of the participants, Kankaanpää et al. found that both the individuals with higher body mass indices and those in the group with unhealthy lifestyles aged faster than those who reported healthier lifestyles. However, the results varied depending on which epigenetic clock Kankaanpää et al. used to measure biological aging: clocks that had been developed earlier showed fewer differences in aging between groups; while newer clocks consistently found that individuals in the higher body mass index and unhealthy groups were older. Kankaanpää et al. also showed that shared genetic factors explained both unhealthy lifestyles and accelerated biological aging. The experiments performed by Kankaanpää et al. provide new insights into the vital role of an individual’s genetics in unhealthy lifestyles and cellular aging. These insights might help scientists identify at risk individuals early in life and try to prevent accelerated aging.