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Age-associated changes in DNA methylation have been characterized across various animals, but not yet in amphibians, which are of particular interest because they include widely studied model organisms. In this study, we present clear evidence that the aquatic vertebrate species Xenopus tropicalis displays patterns of age-associated changes in DNA methylation. We have generated whole-genome bisulfite sequencing (WGBS) profiles from skin samples of nine frogs representing young, mature, and old adults and characterized the gene- and chromosome-scale DNA methylation changes with age. Many of the methylation features and changes we observe are consistent with what is known in mammalian species, suggesting that the mechanism of age-related changes is conserved. Moreover, we selected a few thousand age-associated CpG sites to build an assay based on targeted DNA methylation analysis (TBSseq) to expand our findings in future studies involving larger cohorts of individuals. Preliminary results of a pilot TBSeq experiment recapitulate the findings obtained with WGBS setting the basis for the development of an epigenetic clock assay. The results of this study will allow us to leverage the unique resources available for Xenopus to study how DNA methylation relates to other hallmarks of ageing.

International audience; Aging is a progressive time-dependent biological process affecting differentially individuals, who can sometimes present exceptional longevity. Epigenetic alterations are one of the hallmarks of aging, which comprise the epigenetic drift and clock at DNA methylation level. In the present study, we estimated the DNA methylation-based age (DNAmage) using four epigenetic clocks based on a small number of CpGs in French centenarians and semi-supercentenarians (CSSC, n=214) as well as nonagenarians' and centenarians' offspring (NCO, n=143) compared to individuals from the French general population (CG, n=149). DNA methylation analysis of the nine CpGs included in the epigenetic clocks showed high correlation with chronological age (-0.66>R>0.54) and also the presence of an epigenetic drift for four CpGs that was only visible in CSSC. DNAmage analysis showed that CSSC and to a lesser extend NCO present a younger DNAmage than their chronological age (15-28.5 years for CSSC, 4.4-11.5 years for NCO and 4.2-8.2 years for CG), which were strongly significant in CSSC compared to CG (p-values

Background: Obesity is a heritable complex phenotype that can increase the risk of age-related outcomes. Biological age can be estimated from DNA methylation (DNAm) using various “epigenetic clocks.” Previous work suggests individuals with elevated weight also display accelerated aging, but results vary by epigenetic clock and population. Here, we utilize the new epigenetic clock GrimAge, which closely correlates with mortality. Objectives: We aimed to assess the cross-sectional association of body mass index (BMI) with age acceleration in twins to limit confounding by genetics and shared environment. Methods and results: Participants were from the Finnish Twin Cohort (FTC; n = 1424), including monozygotic (MZ) and dizygotic (DZ) twin pairs, and DNAm was measured using the Illumina 450K array. Multivariate linear mixed effects models including MZ and DZ twins showed an accelerated epigenetic age of 1.02 months (p-value = 6.1 × 10–12) per one-unit BMI increase. Additionally, heavier twins in a BMI-discordant MZ twin pair (ΔBMI >3 kg/m2) had an epigenetic age 5.2 months older than their lighter cotwin (p-value = 0.0074). We also found a positive association between log (homeostatic model assessment of insulin resistance) and age acceleration, confirmed by a meta-analysis of the FTC and two other Finnish cohorts (overall effect = 0.45 years, p-value = 4.1 × 10–25) from adjusted models. Conclusion: We identified significant associations of BMI and insulin resistance with age acceleration based on GrimAge, which were not due to genetic effects on BMI and aging. Overall, these results support a role of BMI in aging, potentially in part due to the effects of insulin resistance. publishedVersion

The opossum (Monodelphis domestica), with its sequenced genome, ease of laboratory care and experimental manipulation, and unique biology, is the most used laboratory marsupial. Using the mammalian methylation array, we generated DNA methylation data from n = 100 opossum samples from the ear, liver, and tail. We contrasted postnatal development and later aging effects in the opossum methylome with those in mouse (Mus musculus, C57BL/6 J strain) and other marsupial species such as Tasmanian devil, kangaroos, and wallabies. While the opossum methylome is similar to that of mouse during postnatal development, it is distinct from that shared by other mammals when it comes to the age-related gain of methylation at target sites of polycomb repressive complex 2. Our immunohistochemical staining results provide additional support for the hypothesis that PRC2 activity increases with later aging in mouse tissues but remains constant in opossum tissues. We present several epigenetic clocks for opossums that are distinguished by their compatibility with tissue type (pan-tissue and blood clock) and species (opossum and human). Two dual-species human-opossum pan-tissue clocks accurately measure chronological age and relative age, respectively. The human-opossum epigenetic clocks are expected to provide a significant boost to the attractiveness of opossum as a biological model. Additional epigenetic clocks for Tasmanian devil, red kangaroos and other species of the genus Macropus may aid species conservation efforts.

Epigenetic clocks based on patterns of DNA methylation have great importance in understanding aging and disease; however, there are basic questions to be resolved in their application. It remains unknown whether epigenetic age acceleration (EAA) within an individual shows strong correlation between different primary tissue sites, the extent to which tissue pathology and clinical illness correlate with EAA in the target organ, and if EAA variability across tissues differs according to sex. Considering the outsized role of age-related illness in Human Immunodeficiency Virus-1 (HIV), these questions were pursued in a sample enriched for tissue from HIV-infected individuals. We used a custom methylation array to generate DNA methylation data from 661 samples representing 11 human tissues (adipose, blood, bone marrow, heart, kidney, liver, lung, lymph node, muscle, spleen and pituitary gland) from 133 clinically characterized, deceased individuals, including 75 infected with HIV. We developed a multimorbidity index based on the clinical disease history. Epigenetic age was moderately correlated across tissues. Blood had the greatest number and degree of correlation, most notably with spleen and bone marrow. However, blood did not correlate with epigenetic age of liver. EAA in liver was weakly correlated with EAA in kidney, adipose, lung and bone marrow. Clinically, hypertension was associated with EAA in several tissues, consistent with the multiorgan impacts of this illness. HIV infection was associated with positive age acceleration in kidney and spleen. Male sex was associated with increased epigenetic acceleration in several tissues. Preliminary evidence indicates that amyotrophic lateral sclerosis is associated with positive EAA in muscle tissue. Finally, greater multimorbidity was associated with greater EAA across all tissues. Blood alone will often fail to detect EAA in other tissues. While hypertension is associated with increased EAA in several tissues, many pathologies are associated with organ-specific age acceleration.

Ageing is seen as one of the greatest challenges of the coming decades, as it leads to enormous financial and social burden (Prince et al., 2015). Globally, the gap in lifespan and health span between high and low socioeconomic classes has been growing for the last decades (Partridge et al., 2018). The main differences between these classes are found in lifestyle and environment, strengthening the observation that genetics only explain a minor proportion of lifespan variation (Fong et al., 2021; Kaplanis et al., 2018). The heterogeneity in the ageing process (and consequential susceptibility to diseases) between individuals renders chronological age unequipped to accurately represent the ageing process (Levine et al., 2018). As determining causes of accelerated or decelerated ageing rates has potential social, economic, and public health benefits, researchers have been studying alternative methods that can more accurately represent the ageing process. To date, the most accurate biomarker of human ageing are so called epigenetic clocks (Levine et al., 2018), which represents one’s biological age. Epigenetic clocks estimate a person’s biological age by analyzing the degree of methylation on a set of CpG sites determined by machine learning (Horvath & Raj, 2018). Briefly, DNA methylation is the process that involves the transfer of a methyl-group onto the C5 position of cytosine, forming 5-methylcytosine (5mC). This process, like other epigenetic modifications, regulates gene expression without changing the DNA sequence (Abdul et al., 2017). Interestingly, epigenetic clocks show excellent correlations with chronological age, are able to predict illness and death and can be ‘rewound’ by healthy lifestyle interventions, such as a Mediterranean diet (Kresovich et al., 2022; Lu et al., 2019). Additionally, a study from Quach et al. (2017) investigated the relationships between epigenetic age acceleration in blood and dietary variables, education, alcohol, and exercise by using data from older women within the Women's Health Initiative (WHI) from two large epidemiological cohorts. The main findings showed a relatively strong correlation between higher intake of fish, vegetables, fruits and poultry and decelerated epigenetic ageing, reflecting a potential health benefit by specific dietary elements (Quach et al., 2017). These results are only the first examples of an emerging new field in human ageing research: making use of epigenetic clocks to identify dietary habits that protect against age-acceleration. A limitation of the study method of Quach et al. (2017) and other studies investigating epigenetic ageing, is the hypothesis-driven approach, since it limits the amount of nutrients that can be identified to play a role in biological ageing. With an entirely data-driven approach in a suitable dataset, the importance of specific dietary habits in light of ageing can be identified in a more objective and open-minded fashion. After all, although various studies have shown the causal relationship between a healthy lifestyle and slowing down of DNA methylation-based biomarkers of biological ageing (Fiorito et al., 2021; Fitzgerald et al., 2021), the effects of specific nutrients that could slow down ageing are still unclear. With a data-driven approach, which considers the full range of nutrients, new doors in the field of nutrition and ageing research can be opened. These leads can be further unraveled in hypothesis-driven, observational or interventional study designs. For optimal data-driven analyses, the data should be of high quality, with epigenetic age data of many participants, and with extensive information on diet, physical activity, smoking and drinking habits, as well as general covariates. Therefore, using the extensive data of the WHI in which these high-quality data are available in a large sample of older women would be of great value for our research. We propose to use Copula Graphical Modeling (CGM), which is a network analysis method that copes with non-gaussian, ordinal and count data, as well as with complex inter-nutrient relationships. This flexibility makes CGM highly applicable in nutritional epidemiology. CGM allows to identify direct relationships between dietary factors and biological ageing, while adjusting for every other variable present in the dataset (Lauritzen, 1996; Behrouzi et al., 2020), thereby preventing subjective confounder selection while allowing us to include all measured nutrient intake in the networks. In previous unpublished work, we successfully carried out CGM analyses on a small dataset with lifestyle factors and biological age. For this study, we aim to repeat the methodology in a large subsample of the WHI, to discover nutritional and lifestyle factors that influence the rate of epigenetic ageing. References . Abdul, Q. A., et al. (2017). Epigenetic modifications of gene expression by lifestyle and environment. Archives of Pharmacal Research 40(11): 1219-1237. https://doi.org/10.1007/s12272-017-0973-3 Behrouzi, P., Grootswagers, P., Keizer, P. L. C., Smeets, E. T. H. C., Feskens, E. J. M., de Groot, L. C. P. G. M., van Eeuwijk, F. A. (2020). Dietary Intakes of Vegetable Protein, Folate, and Vitamins B-6 and B-12 Are Partially Correlated with Physical Functioning of Dutch Older Adults Using Copula Graphical Models. Journal of Nutrition, 150(3). https://doi.org/10.1093/jn/nxz269 Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biology. 2019;20(1):249. Fiorito, G., et al. (2021). "DNA methylation-based biomarkers of aging were slowed down in a two-year diet and physical activity intervention trial: the DAMA study." Aging Cell 20(10): e13439. https://doi.org/10.1111/acel.13439 Fong, B. Y. F., et al. (2021). A Review Study of a Green Diet and Healthy Ageing. International Journal of Environmental Research and Public Health 18(15): 8024. https://doi.org/10.3390/ijerph18158024 Horvath, S. and K. Raj (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 19(6): 371-384. https://doi.org/10.1038/s41576-018-0004-3 Levine, M. E., et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 10(4): 573-591. http://doi.org/10.18632/aging.101414 Kresovich, J. K., et al. (2022). Healthy eating patterns and epigenetic measures of biological age. Am J Clin Nutr 115(1): 171-179. http://doi.org/10.1093/ajcn/nqab307 Kaplanis, J., et al. (2018). Quantitative analysis of population-scale family trees with millions of relatives. Science 360(6385): 171-175. https://www.science.org/doi/abs/10.1126/science.aam9309 Lauritzen, S. L. (1996). Graphical models. Claren- don Press. Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., Whitsel, E. A., Wilson, J. G., Reiner, A. P., Aviv, A., Lohman, K., Liu, Y., Ferrucci, L., & Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging, 10(4), 573–591. https://doi.org/10.18632/aging.101414 Lu, A. T., et al. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY) 11(2): 303-327. http://doi.org/10.18632/aging.101684 Partridge, L., et al. (2018). Facing up to the global challenges of ageing. Nature 561(7721): 45-56. https://doi.org/10.1038/s41586-018-0457-8 Prince, M. J., et al. (2015). The burden of disease in older people and implications for health policy and practice. The Lancet 385(9967): 549-562. https://doi.org/10.1016/S0140-6736(14)61347-7 Quach A, Levine ME, Tanaka T, Lu AT, Chen BH, Ferrucci L, Ritz B, Bandinelli S, Neuhouser ML, Beasley JM, Snetselaar L, Wallace RB, Tsao PS, Absher D, Assimes TL, Stewart JD, Li Y, Hou L, Baccarelli AA, Whitsel EA, Horvath S. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging (Albany NY). 2017 Feb 14;9(2):419-446. doi: 10.18632/aging.101168. PMID: 28198702; PMCID: PMC5361673.

DNA methylation-based biomarkers of aging have been developed for humans and many other mammals and could be used to assess how stress factors impact aging. Deer mice (Peromyscus) are long-living rodents that have emerged as an informative model to study aging, adaptation to extreme environments, and monogamous behavior. In the present study, we have undertaken an exhaustive, genome-wide analysis of DNA methylation in Peromyscus, spanning different species, stocks, sexes, tissues, and age cohorts. We describe DNA methylation-based estimators of age for different species of deer mice based on novel DNA methylation data generated on highly conserved mammalian CpGs measured with a custom array. The multi-tissue epigenetic clock for deer mice was trained on 3 tissues (tail, liver, and brain). Two human-Peromyscus clocks accurately measure age and relative age, respectively. We present CpGs and enriched pathways that relate to different conditions such as chronological age, high altitude, and monogamous behavior. Overall, this study provides a first step towards studying the epigenetic correlates of monogamous behavior and adaptation to high altitude in Peromyscus. The human-Peromyscus epigenetic clocks are expected to provide a significant boost to the attractiveness of Peromyscus as a biological model.

Epigenetic clocks based on DNA methylation (DNAm) can accurately predict chronological age and are thought to capture biological aging. A variety of epigenetic clocks have been developed for different tissue types and age ranges, but none have focused on postnatal age prediction for preterm infants. Epigenetic estimators of biological age might be especially informative in epidemiologic studies of neonates since DNAm is highly dynamic during the neonatal period and this is a key developmental window. Additionally, markers of biological aging could be particularly important for those born preterm since they are at heightened risk of developmental impairments. We aimed to fill this gap by developing epigenetic clocks for neonatal aging in preterm infants. As part of the Neonatal Neurobehavior and Outcomes in Very Preterm Infants (NOVI) study, buccal cells were collected at NICU discharge to profile DNAm levels in 542 very preterm infants. We applied elastic net regression to identify four epigenetic clocks (NEOage Clocks) predictive of post-menstrual and postnatal age, compatible with the Illumina EPIC and 450K arrays. We observed high correlations between predicted and reported ages (0.93 – 0.94) with root mean squared errors (1.28 - 1.63 weeks). Epigenetic estimators of neonatal aging in preterm infants can be useful tools to evaluate biological maturity and associations with neonatal and long-term morbidities.

We previously described a DNA methylation (DNAm) based biomarker of human mortality risk DNAm GrimAge. Here we describe version 2 of GrimAge (trained on individuals aged between 40 and 92) which leverages two new DNAm based estimators of (log transformed) plasma proteins: high sensitivity C-reactive protein (logCRP) and hemoglobin A1C (logA1C). We evaluate GrimAge2 in 13,399 blood samples across nine study cohorts. After adjustment for age and sex, GrimAge2 outperforms GrimAge in predicting mortality across multiple racial/ethnic groups (meta P=3.6x10-167 versus P=2.6x10-144) and in terms of associations with age related conditions such as coronary heart disease, lung function measurement FEV1 (correlation= -0.31, P=1.1x10-136), computed tomography based measurements of fatty liver disease. We present evidence that GrimAge version 2 also applies to younger individuals and to saliva samples where it tracks markers of metabolic syndrome. DNAm logCRP is positively correlated with morbidity count (P=1.3x10-54). DNAm logA1C is highly associated with type 2 diabetes (P=5.8x10-155). DNAm PAI-1 outperforms the other age-adjusted DNAm biomarkers including GrimAge2 in correlating with triglyceride (cor=0.34, P=9.6x10-267) and visceral fat (cor=0.41, P=4.7x10-41). Overall, we demonstrate that GrimAge version 2 is an attractive epigenetic biomarker of human mortality and morbidity risk.

The past decades, studies indicated that night shift work is associated with adverse health effects, however, molecular mechanisms underlying these effects are poorly understood. A few previous studies have hypothesized a role for DNA-methylation (DNAm) in this relationship. We performed a cross-sectional epigenome-wide association study, to investigate if night shift work is associated with genome-wide DNAm changes and DNAm-based biological age acceleration, based on previously developed so-called 'epigenetic clocks.' Short term (2-6 years) and intermediate term (10-16 years) night shift workers, along with age and sex matched dayworkers (non-shift workers) were selected from the Lifelines Cohort Study. For genome-wide methylation analysis the Infinium Methylation EPIC array (Ilumina) was used. Linear regression analyses were used to detect differences in methylation at individual CpG-sites associated with night shift work. Pathway analysis was performed based on KEGG pathways and predictions of age acceleration in night shift workers were performed based on four previously developed epigenetic age calculators. Only in women, differences in methylation at individual CpG-sites were observed between night shift workers and non-shift workers. Most of these differentially methylated positions (DMPs) were observed in intermediate term night shift workers. Pathway analysis shows involvement of pathways related to circadian rhythm and cellular senescence. Increased age acceleration was observed only in short-term night shift workers (men and women). This might be indicative of adaptation to night shift work or a so-called healthy worker effect. In conclusion, these results show that DNA methylation changes are associated with night shift work, specifically in women.

BackgroundObesity is a multifactorial and chronic condition of growing universal concern. It has recently been reported that bariatric surgery is a more successful treatment for severe obesity than other noninvasive interventions, resulting in rapid significant weight loss and associated chronic disease remission. The identification of distinct epigenetic patterns in patients who are obese or have metabolic imbalances has suggested a potential role for epigenetic alterations in causal or mediating pathways in the development of obesity-related pathologies. Specific changes in the epigenome (DNA methylome), associated with metabolic disorders, can be detected in the blood. We investigated whether such epigenetic changes are reversible after weight loss using genome-wide DNA methylome analysis of blood samples from individuals with severe obesity (mean BMI ~ 45) undergoing bariatric surgery.ResultsOur analysis revealed 41 significant (Bonferronip p p SMAD6andPFKFB3genes were the top hypermethylated and hypomethylated regions, respectively. Pathway enrichment analysis of the DMR-associated genes showed that functional pathways related to immune function and type 1 diabetes were significant. Weight loss due to bariatric surgery also significantly decelerated epigenetic age 12 months after the intervention (mean = − 4.29;p = 0.02).ConclusionsWe identified weight loss-associated DNA-methylation alterations targeting immune and inflammatory gene pathways in blood samples from bariatric-surgery patients. The top hits were replicated in samples from an independent cohort of BMI-discordant monozygotic twins following a hypocaloric diet. Energy restriction and bariatric surgery thus share CpGs that may represent early indicators of response to the metabolic effects of weight loss. The analysis of bariatric surgery-associated DMRs suggests that epigenetic regulation of genes involved in endothelial and adipose tissue function is key in the pathophysiology of obesity.

DNA-methylation profiles have been used successfully to develop highly accurate biomarkers of age, epigenetic clocks, for many species. Using a custom methylation array, we generated DNA methylation data from n = 238 porcine tissues including blood, bladder, frontal cortex, kidney, liver, and lung, from domestic pigs (Sus scrofa domesticus) and minipigs (Wisconsin Miniature Swine™). Samples used in this study originated from Large White X Landrace crossbred pigs, Large White X Minnesota minipig crossbred pigs, and Wisconsin Miniature Swine™. We present 4 epigenetic clocks for pigs that are distinguished by their compatibility with tissue type (pan-tissue and blood clock) and species (pig and human). Two dual-species human-pig pan-tissue clocks accurately measure chronological age and relative age, respectively. We also characterized CpGs that differ between minipigs and domestic pigs. Strikingly, several genes implicated by our epigenetic studies of minipig status overlap with genes (ADCY3, TFAP2B, SKOR1, and GPR61) implicated by genetic studies of body mass index in humans. In addition, CpGs with different levels of methylation between the two pig breeds were identified proximal to genes involved in blood LDL levels and cholesterol synthesis, of particular interest given the minipig’s increased susceptibility to cardiovascular disease compared to domestic pigs. Thus, breed-specific differences of domestic and minipigs may potentially help to identify biological mechanisms underlying weight gain and aging-associated diseases. Our porcine clocks are expected to be useful for elucidating the role of epigenetics in aging and obesity, and the testing of anti-aging interventions. Supplementary Information The online version contains supplementary material available at 10.1007/s11357-021-00439-6.

Human DNA methylation profiles have been used successfully to develop highly accurate biomarkers of aging (“epigenetic clocks”). Although these human epigenetic clocks are not immediately applicable to all species of the animal kingdom, the principles underpinning them appear to be conserved even in animals that are evolutionarily far removed from humans. This is exemplified by recent development of epigenetic clocks for mice and other mammalian species. Here, we describe epigenetic clocks for the domestic cat (Felis catus), based on methylation profiles of CpGs with flanking DNA sequences that are highly conserved between multiple mammalian species. Methylation levels of these CpGs are measured using a custom-designed Infinium array (HorvathMammalMethylChip40). From these, we present 3 epigenetic clocks for cats; of which, one applies only to blood samples from cats, while the remaining two dual-species human-cat clocks apply both to cats and humans. We demonstrate that these domestic cat clocks also lead to high age correlations in cheetahs, tigers, and lions. It is expected that these epigenetic clocks for cats possess the potential to be further developed for monitoring feline health as well as being used for identifying and validating anti-aging interventions. Supplementary Information The online version contains supplementary material available at 10.1007/s11357-021-00445-8.

Biological aging is an umbrella term that refers to a complex biological process which entails the aging of cells, their organelles and DNA (Hägg & Jylhävä, 2021). Biological aging processes have been associated with a variety of diseases, including cancer and atherosclerosis (Adams & White, 2004; Dugué et al., 2018), and are a good predictor for higher morbidity and mortality rates (Perna et al., 2016). Therefore, a growing body of research is focused on potential factors affecting biological aging. Biological aging can be operationalized by different biomarkers, including telomere length and epigenetic clocks (Belsky et al., 2018; Pearce et al., 2022). Telomeres are repetitive TTAGGG sequences at the end of chromosomes that protect the chromosomes from shortening during cell division. Telomeres shorten throughout the lifespan, and accelerated telomere shortening is an index of accelerated biological aging (Tempaku et al., 2015; Vaiserman & Krasnienkov, 2021). Furthermore, the epigenetic clock concerns DNA methylation levels, namely the accumulation of methyl groups to DNA molecules, and is an estimator of chronical age (Chen et al., 2016; Jylhävä et al., 2017). These clocks capture methylation of certain sets of CpG sites in the DNA, and methylation levels correlate with age. Accordingly, the biological age of the cells can be calculated in years (Chen et al., 2016; Horvath et al., 2012; McEwen et al., 2020). It has been found that, besides genetic factors (Gibson et al., 2019), also environmental factors, such as smoking and diet, are associated with both of the previous biomarkers of accelerated biological aging (Gao et al., 2016; Horvath et al., 2014; Huang et al., 2019; Vaiserman & Krasnienkov, 2021). Accelerated biological aging in terms of accelerated telomere attrition and advanced epigenetic age has also been associated with sleeping problems in adults (Carskadon et al., 2019; Carroll & Prather, 2021; Cribbet et al., 2014; Gao et al., 2022; James et al., 2017; Jin et al., 2022). Sleep is a complex behavioral and homeostatically regulated state of all mammals (Zielinski et al., 2016). Sleep has many vital functions, including development, modulation of immune responses, memory consolidation and endocrine functions (Krueger et al., 2016; Zielinski et al., 2016). Previous research has demonstrated associations between poor quality of sleep, shorter sleep duration, and delayed sleep onset with shortened telomere lengths (Cribbet et al., 2014; Wynchank et al., 2019). Additionally, reduced sleep duration and poor sleep quality have also been linked to the acceleration of the epigenetic clock (Carskadon et al., 2019; Gao et al., 2022) This association between sleep and accelerated biological aging was not only observed in adults. James and colleagues (2017) found an association of shorter telomeres with shorter sleep durations in nine-year-old children, indicating that the relationship between biological aging and sleep already emerges early in development (James et al., 2017). However, to our knowledge, this is the only study that has looked at children’s sleep in relation to markers for biological aging. Therefore, to date, there is a lack of research on the association of sleep quality with biological aging in children. Additionally, the previously mentioned study on this association in children was cross-sectional, hence the directionality of the relationship between biological aging and sleep quality in children remains unclear. On the one hand, previous studies in adults have largely assumed that more sleeping problems cause alterations in neuro-chemical processes, which, in turn, cause accelerated aging. But on the other hand, studies on the elderly population suggest that accelerated aging can cause significant alterations in the sleep stages and in the quantity and quality of sleep (Tempaku et al., 2015), indicating that aging could also bring about sleep changes. Accordingly, Carrol and Prather (2021) suggest that there is also a possibility that accelerated biological aging causes alterations in brain areas that regulate sleep states, hereby causing sleep difficulties (Carroll & Prather, 2021). Since previous research on the association of sleep and biological aging in children was solely cross-sectional, the directionality of the association remains to be assessed. In the current study we assess the association of sleep with accelerated biological aging in children bi-directionally between chronological ages 6 years (T1) and 10 years (T2). As telomere length and epigenetic clocks have been shown to capture different biological aging processes (Belsky et al., 2018; Pearce et al., 2022), both will be used in this study to measure biological aging. In previous studies, the Pediatric-Buccal-Epigenetic (PedBE) clock has been reported to be the most reliable epigenetic clock for pediatrics (McEwen et al., 2020), and will hence be used in the current study. In this study, we will assess the following goals: 1) Our first goal is to assess cross-sectional associations at T1 and T2 between our biomarkers of biological aging (telomere length and PedBE clock) with child sleeping problems. 2) Our second goal is to determine whether sleeping problems at T1 forecasts biological aging, defined as a) telomere length at T2; b) telomere erosion between T1 – and T2; c) PedBE clock at T2. 3) Our third goal is to determine whether our biomarkers of biological aging (a, b & c) predict sleeping problems at T2. a) telomere length; b) telomere erosion from T1 to T2; c) PedBE clock at T1. 4) Lastly, we will assess the directionality between sleeping problems and biological aging by comparing the explained variance between question 2 and 3. Note that we do not include the change in PedBE from T1 to T2, as the PedBE clock already incorporates the difference between biological aging and chronological aging as an age acceleration. Given that telomere length and/or erosion varies by sex (Barrett & Richardson, 2011), z-score body mass index index (zBMI; Clemente et al., 2019; Gielen et al., 2018), and maternal education (Broer et al., 2013), we will statistically control for these aspects in our analyses. This will be done by using child sex determined at birth, child zBMI at T2 and maternal education defined as the highest level of education completed when assessed at T2 (Beijers, Hartman, et al., 2020; Shalev et al., 2013).

Methylation of the DNA (DNAm) is an epigenetic mechanism of transcriptional regulation and is influenced by stress and psychosocial adjustment (Aristizabal et al., 2020; Palma-Gudiel et al., 2020). Composite scores of DNAm across tens or hundreds of genomic loci have been developed as indices of biological ageing – the so-called “epigenetic clocks” (Horvath & Raj, 2018). Under stressful contexts, such as negative interpersonal experiences or stress-inducing mental health difficulties, the body is thought to age faster, resulting in a difference between chronological age and epigenetic age (Epigenetic Age Acceleration, EAA). Empirical studies have examined associations of epigenetic age with stress, such as abuse and neglect, and psychosocial problems, indicating that those who experience more stress, develop in more adverse environments, or experience worse mental health tend to also show epigenetic clock acceleration (e.g., Oblak et al., 2021; Zannas et al., 2015). However, most existing research has used cross-sectional or retrospective designs, or has combined longitudinal stress or mental health data with only a single-timepoint measure of epigenetic age. Thus, we understand little regarding whether epigenetic clocks change following changes in the psychosocial environment, and whether epigenetic clocks reflect or precede changes in mental health. In the current longitudinal study (ages 13 to 25), negative parenting and adolescent mental health throughout adolescence will be used to predict late adolescence EAA, and changes in EAA from late adolescence to young adulthood. EAA will be tested as a predictor of changes in mental health from adolescence to young adulthood.

Accumulating evidence suggests that the “epigenetic clock,” or DNA methylation (DNAm) “age” biomarkers may be good predictors of age-related diseases and mortality risk because these epigenetic processes may have a role in cell development and differentiation, tissue homeostasis, and disease susceptibility. Recently, multiple studies have demonstrated that the ticking rate of the epigenetic clock can be altered during early life such that a discrepancy between the individual’s epigenetic and chronological age can be programmed in early development. A recent study demonstrated that accelerated epigenetic aging at birth (a greater gap between epigenetic and chronological age) is associated with child psychiatric problems at age 3-4, suggesting a potential mechanism for the development of brain and behavioral disorders. Studies have also shown that childhood trauma is correlated with accelerated epigenetic aging. But it is not clear whether altered epigenetic aging from childhood trauma persists and can be transmitted to the next generation. We will test whether mothers’ childhood trauma is correlated with accelerated epigenetic aging during pregnancy and in their newborn offspring.

A systematic review on studies that have compared epigenetic age acceleration between participants grouped by a particular psychiatric disorder compared to an unaffected (or control) group.

Aging and cancer are clearly associated processes, at both the epidemiological and molecular level. Epigenetic mechanisms are good candidates to explain the molecular links between the two phenomena, but recent reports have also revealed considerable differences, particularly regarding the loss of DNA methylation in the two processes. The large-scale generation and availability of genome-wide epigenetic data now permits systematic studies to be undertaken which may help clarify the similarities and differences between aging and cancer epigenetic alterations. In addition, the development of epigenetic clocks provides a new dimension in which to investigate diseases at the molecular level. Here, we examine current and future questions about the roles of DNA methylation mechanisms as causal factors in the processes of aging and cancer so that we may better understand if and how aging-associated epigenetic alterations lead to tumorigenesis. It seems certain that comprehending the molecular mechanisms underlying epigenetic clocks, especially with regard to somatic stem cell aging, combined with applying single-cell epigenetic-age profiling technologies to aging and cancer cohorts, and the integration of existing and upcoming epigenetic evidence within the genetic damage models of aging will prove to be crucial to improving understanding of these two interrelated phenomena., Asturias Government (PCTI) cofunding 2018-2022/ FEDER, Grant/Award Number: IDI/2018/146 and BP17-114; Centro de Investigación Biomédica en Red de Enfermedades Raras CIBERER; Fundación Bancaria Caja de Ahorros de Asturias; Fundación Científica Asociación Española Contra el Cáncer, Grant/Award Number: PROYE18061FERN; Fundación General CSIC, Grant/Award Number: 0348_CIE_6_E; Instituto de Salud Carlos III, Grant/Award Number: PI21/01067; Ministerio de Ciencia e Innovación, Grant/ Award Number: IJC2018-36825- I and SGL2021-03- 039/ 40

ObjectiveSubjective social status (SSS), an individual's assessment of their own social status in relation to others, is associated with health and mortality independently of objective SES; however, no studies have tested whether SSS influences epigenetic aging. The current study examines if SSS is associated with epigenetic age acceleration in both Black and White women, independently of objective SES measured during both childhood and adulthood.MethodFor 9- and 10-year-old Black and White girls, parental education and annual household income was obtained.At ages 39-42, 361 participants (175 Black, 186 White)reported their current education, household income, and SSS, and provided saliva to assess age acceleration using the GrimAge epigenetic clock. Linear regression estimated the association of SSS with epigenetic age acceleration, controlling for objective SES (current education, current income, parents' education, income during childhood), smoking, and counts of cell types.ResultsWhen all objective SES variables were included in the model, SSS remained significantly associated with epigenetic age acceleration, b=-0.31, p=.003, ß=-0.15. Black women had significantly greater age acceleration than White women, (t(359)=5.20, p>.001, d=0.55) but race did not moderate the association between SSS and epigenetic age acceleration.ConclusionsWomen who rated themselves lower in SSS had greater epigenetic age acceleration, regardless of income and education. There was no difference by race for this association.

Age‐based demography is fundamental to management of wild fish populations. Age estimates for individuals can determine rates of change in key life‐history parameters such as length, maturity, mortality and fecundity. These age‐based characteristics are critical for population viability analysis in endangered species and for developing sustainable harvest strategies. For teleost fish, age has traditionally been determined by counting increments formed in calcified structures such as otoliths. However, the collection of otoliths is lethal and therefore undesirable for threatened species. At a molecular level, age can be predicted by measuring DNA methylation. Here, we use previously identified age‐associated sites of DNA methylation in zebrafish (Danio rerio) to develop two epigenetic clocks for three threatened freshwater fish species. One epigenetic clock was developed for the Australian lungfish (Neoceratodus forsteri) and the second for the Murray cod (Maccullochella peelii) and Mary River cod (Maccullochella mariensis). Age estimation models were calibrated using either known‐age individuals, ages derived from otoliths or bomb radiocarbon dating of scales. We demonstrate a high Pearson's correlation between the chronological and predicted age in both the Lungfish clock (cor = .98) and Maccullochella clock (cor = .92). The median absolute error rate for both epigenetic clocks was also low (Lungfish = 0.86 years; Maccullochella = 0.34 years). This study demonstrates the transferability of DNA methylation sites for age prediction between highly phylogenetically divergent fish species. Given the method is nonlethal and suited to automation, age prediction by DNA methylation has the potential to improve fisheries and other wildlife management settings.

Age is a fundamental parameter in wildlife management as it is used to determine the risk of extinction, manage invasive species, and regulate sustainable harvest. In a broad variety of vertebrates species, age can be determined by measuring DNA methylation. Animals with known ages are initially required during development, calibration, and validation of these epigenetic clocks. However, wild animals with known ages are frequently difficult to obtain. Here, we perform Monte‐Carlo simulations to determine the optimal sample size required to create an accurate calibration model for age estimation by elastic net regression modelling of cytosine‐phosphate‐guanine methylation data. Our results suggest a minimum calibration population size of 70, but ideally 134 individuals or more for accurate and precise models. We also provide estimates to the extent a model can be extrapolated beyond a distribution of ages that was used during calibration. The findings can assist researchers to better design age estimation models and decide if their model is adequate for determining key population attributes.

DNA methylation age acceleration, the discrepancy between epigenetic age and chronological age, is associated with mortality and chronic diseases, including diabetes, hypertension, and hyperlipidemia. In this study, we investigate whether medications commonly used to treat these diseases in 15 drug categories are associated with four epigenetic age acceleration measures: HorvathAge acceleration (HorvathAA), HannumAge acceleration (HannumAA), PhenoAge acceleration, and GrimAge acceleration (GrimAA) using cross-sectional (Phase 1, N=1,100) and longitudinal (Phases 1 and 2, N=266) data from African Americans in the Genetic Epidemiology Network of Arteriopathy (GENOA) study. In cross-sectional analyses, the use of calcium channel blockers was associated with 1.27 years lower HannumAA after adjusting for covariates including hypertension (p=0.001). Longitudinal analyses showed that, compared to those who never used antihypertensives, those who started to take antihypertensives after Phase 1 had a 0.97-year decrease in GrimAA (p=0.007). In addition, compared to those who never used NSAID analgesics, those who started to take them after Phase 1 had a 2.61-year increase in HorvathAA (p=0.0005). Our study demonstrates that three commonly used medications are associated with DNAm age acceleration in African Americans and sheds light on the potential epigenetic effects of pharmaceuticals on aging at the cellular level.

Astronauts exhibit an assortment of clinical abnormalities in their eyes during long-duration spaceflight. The purpose of this study was to determine whether spaceflight induces epigenomic and transcriptomic reprogramming in the retina or alters the epigenetic clock. The mice were flown for 37 days in animal enclosure modules on the International Space Station; ground-based control animals were maintained under similar housing conditions. Mouse retinas were isolated and both DNA methylome and transcriptome were determined by deep sequencing. We found that a large number of genes were differentially methylated with spaceflight, whereas there were fewer differentially expressed genes at the transcriptome level. Several biological pathways involved in retinal diseases such as macular degeneration were significantly altered. Our results indicated that spaceflight decelerated the retinal epigenetic clock. This study demonstrates that spaceflight impacts the retina at the epigenomic and transcriptomic levels, and such changes could be involved in the etiology of eye-related disorders among astronauts.

We recently demonstrated that adolescents perinatally infected with HIV-1 (PHIV+) have accelerated aging as measured by a highly accurate epigenetic biomarker of aging known as the epigenetic clock. However, whether epigenetic age acceleration in PHIV+ impacts brain development at the macro- and microstructural levels of brain anatomy has not been studied. We report on a cross-sectional study of PHIV+ enrolled in the Cape Town Adolescent Antiretroviral Cohort (CTAAC). The Illumina Infinium Methylation EPIC array was used to generate DNA methylation data from the blood samples of 180 PHIV+ aged 9 to 12years. The epigenetic clock software and method was used to estimate two measures, epigenetic age acceleration (AgeAccelerationResidual) and extrinsic epigenetic age acceleration (EEAA). Each participant underwent T1 structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). In order to investigate the associations of chronological age, sex, epigenetic age acceleration and treatment variables (CNS penetration effectiveness score (CPE)) of antiretroviral regimen on brain structure in PHIV+, we developed stepwise multiple regression models in R (version 3.4.3, 2017) including grey and white matter volumes, cortical thickness, cortical surface area and DTI measures of white matter microstructural integrity. The mean DNAm age (16.01years) of the participants was higher than their mean chronological age (10.77years). Epigenetic age acceleration contributed more to regional alterations of brain volumes, cortical thickness, cortical surface areas and neuronal microstructure than chronological age, in a range of regions. CPE positively contributed to volume of the brain stem. Understanding the drivers of epigenetic age acceleration could lead to valuable insights into structural brain alterations, and the persistence of neurocognitive disorders in seen in PHIV+ .

DNA methylation-based biomarkers of aging have been developed for many mammals but not yet for the vervet monkey (Chlorocebus sabaeus), which is a valuable non-human primate model for biomedical studies. We generated novel DNA methylation data from vervet cerebral cortex, blood, and liver using highly conserved mammalian CpGs represented on a custom array (HorvathMammalMethylChip40). We present six DNA methylation-based estimators of age: vervet multi-tissue epigenetic clock and tissue-specific clocks for brain cortex, blood, and liver. In addition, we developed two dual species clocks (human-vervet clocks) for measuring chronological age and relative age, respectively. Relative age was defined as ratio of chronological age to maximum lifespan to address the species differences in maximum lifespan. The high accuracy of the human-vervet clocks demonstrates that epigenetic aging processes are evolutionary conserved in primates. When applying these vervet clocks to tissue samples from another primate species, rhesus macaque, we observed high age correlations but strong offsets. We characterized CpGs that correlate significantly with age in the vervet. CpG probes that gain methylation with age across tissues were located near the targets of Polycomb proteins SUZ12 and EED and genes possessing the trimethylated H3K27 mark in their promoters. The epigenetic clocks are expected to be useful for anti-aging studies in vervets.

Background Although aging is the strongest risk factor for the development of Alzheimer's disease (AD), it remains uncertain if the blood DNA methylation clock, which reflects the effect of biological aging on DNA methylation (DNAme) status of blood cells, may be used as a surrogate biomarker for AD pathology in the central nervous system (CNS). Objective We aimed to develop a practical model to predict for A/T/N-based AD biomarkers as the prediction targets using the aging acceleration of blood cells. Methods We obtained data of North American ADNI study participants (n = 317) whose blood DNA methylation microarray (Illumina HumanMethylation EPIC Beadchips) and cerebrospinal fluid (CSF) AD biomarkers (Aβ, t-tau, and p-tau) were recorded simultaneously. Methylation clock was calculated to conduct machine learning, in order to predict binary statuses (+ or -) for A (corresponding to the lowered CSF Aβ), T (the elevated CSF p-tau), or N (the elevated CSF t-tau). The predictive performance of the models was evaluated by area under curve (AUC) in the test subset within ADNI. Results Among the 317 included samples, 194 (61.2%) were A+, 247 (77.9%) were T+, and 104 (32.8%) were N+. The degree of blood aging acceleration showed weak positive correlation with the CSF Aβ levels, even after adjustment with APOE genotype and other covariates. However, the contribution of aging acceleration to improve the predictive performance of models was not significant for any of A+, T+, or N+. Conclusion Our exploratory attempts could not demonstrate the substantial utility of the peripheral blood cells' methylation clock as a predictor for A/T/N-based CSF biomarkers of AD, and further additional work should be conducted to determine whether the blood DNAme signatures including methylation clock have substantial utility in detecting underlying amyloid, tau or neurodegeneration pathology of AD.

The severity and outcome of coronavirus disease 2019 (COVID-19) largely depends on a patient's age. Adults over 65 years of age represent 80% of hospitalizations and have a 23-fold greater risk of death than those under 65. In the clinic, COVID-19 patients most commonly present with fever, cough and dyspnea, and from there the disease can progress to acute respiratory distress syndrome, lung consolidation, cytokine release syndrome, endotheliitis, coagulopathy, multiple organ failure and death. Comorbidities such as cardiovascular disease, diabetes and obesity increase the chances of fatal disease, but they alone do not explain why age is an independent risk factor. Here, we present the molecular differences between young, middle-aged and older people that may explain why COVID-19 is a mild illness in some but life-threatening in others. We also discuss several biological age clocks that could be used in conjunction with genetic tests to identify both the mechanisms of the disease and individuals most at risk. Finally, based on these mechanisms, we discuss treatments that could increase the survival of older people, not simply by inhibiting the virus, but by restoring patients' ability to clear the infection and effectively regulate immune responses.

Lung function, strongly associated with morbidity and mortality, decreases with age. This study examines whether poor adult lung function is associated with age accelerations (AAs). DNA methylation (DNAm) based AAs, lifespan predictors (GrimAge and plasminogen activator inhibitor 1-PAI1) and their related age-adjusted measures were estimated from peripheral blood at two time points (8-to-11 years apart) in adults from two cohorts: SAPALDIA (n=987) and ECRHS (n=509). Within each cohort and stratified by gender (except for estimators from GrimAge and PAI1), AAs were used as predictors in multivariate linear regression with cross-sectional lung function parameters, and in covariate-adjusted mixed linear regression with longitudinal change in lung function and meta-analysed. AAs were found cross-sectionally associated with lower mean FEV1 (Forced Expiratory Volume in one second) (AA-residuals:P-value=4x10-4; Intrinsic Epigenetic AA:P-value=2x10-4) in females at the follow-up time point only, and the same trend was observed for FVC (Forced Vital Capacity). Both lifespan and plasma level predictors were observed strongly associated with lung function decline and the decline was stronger in the follow-up time points (strongest association between FEV1 and DNAmAge GrimAge:P-value=1.25x10-17). This study suggests that DNAm based lifespan and plasma level predictors can be utilised as important factors to assess lung health in adults. publishedVersion

Background & Aims The epithelia of the intestine and colon turn over rapidly and are maintained by adult stem cells at the base of crypts. Although the small intestine and colon have distinct, well-characterized physiological functions, it remains unclear if there are fundamental regional differences in stem cell behavior or region-dependent degenerative changes during aging. Mesenchyme-free organoids provide useful tools for investigating intestinal stem cell biology in vitro and have started to be used for investigating age-related changes in stem cell function. However, it is unknown whether organoids maintain hallmarks of age in the absence of an aging niche. We tested whether stem cell–enriched organoids preserved the DNA methylation-based aging profiles associated with the tissues and crypts from which they were derived. Methods To address this, we used standard human methylation arrays and the human epigenetic clock as a biomarker of age to analyze in vitro–derived, 3-dimensional, stem cell–enriched intestinal organoids. Results We found that human stem cell–enriched organoids maintained segmental differences in methylation patterns and that age, as measured by the epigenetic clock, also was maintained in vitro. Surprisingly, we found that stem cell–enriched organoids derived from the small intestine showed striking epigenetic age reduction relative to organoids derived from colon. Conclusions Our data validate the use of organoids as a model for studying human intestinal aging and introduce methods that can be used when modeling aging or age-onset diseases in vitro., Graphical abstract

Contains fulltext : 219496.pdf (Publisher’s version ) (Closed access) DNA methylation is the most studied modification in human population epigenetics. Its information content can be explored in 2 principal ways - epigenome-wide association studies and epigenetic age. The latter likely reflects cellular/biological age and works with impressive accuracy across most tissues. In adults, it associates with various environments and health. However, current epigenetic clocks are not very accurate in the pediatric age range perhaps because DNA methylation changes much faster in children. Addressing this crucial gap, we created a precise tool to estimate DNA methylation age specific to pediatric buccal epithelial cells. This tool has the potential to become the standard reference for epigenetic studies broadly relevant to child development across the spectrum from health to disease.The development of biological markers of aging has primarily focused on adult samples. Epigenetic clocks are a promising tool for measuring biological age that show impressive accuracy across most tissues and age ranges. In adults, deviations from the DNA methylation (DNAm) age prediction are correlated with several age-related phenotypes, such as mortality and frailty. In children, however, fewer such associations have been made, possibly because DNAm changes are more dynamic in pediatric populations as compared to adults. To address this gap, we aimed to develop a highly accurate, noninvasive, biological measure of age specific to pediatric samples using buccal epithelial cell DNAm. We gathered 1,721 genome-wide DNAm profiles from 11 different cohorts of typically developing individuals aged 0 to 20 y old. Elastic net penalized regression was used to select 94 CpG sites from a training dataset (n = 1,032), with performance assessed in a separate test dataset (n = 689). DNAm at these 94 CpG sites was highly predictive of age in the test cohort (median absolute error = 0.35 y). The Pediatric-Buccal-Epigenetic (PedBE) clock was characterized in additional cohorts, showcasing the accuracy in longitudinal data, the performance in nonbuccal tissues and adult age ranges, and the association with obstetric outcomes. The PedBE tool for measuring biological age in children might help in understanding the environmental and contextual factors that shape the DNA methylome during child development, and how it, in turn, might relate to child health and disease. 7 p.

DNA methylation age (DNAm age, epigenetic clock) is a novel and promising biomarker of aging. It is calculated from the methylation fraction of specific cytosine phosphate guanine sites (CpG sites) of genomic DNA. Several groups have proposed epigenetic clock algorithms and these differ mostly regarding the number and location of the CpG sites considered and the method used to assess the methylation status. Most epigenetic clocks are based on a large number of CpGs, e.g. as measured by DNAm microarrays. We have recently evaluated an epigenetic clock based on the methylation fraction of seven CpGs that were determined by methylation-sensitive single nucleotide primer extension (MS-SNuPE). This method is more cost-effective when compared to array-based technologies as only a few CpGs need to be examined. However, there is only little data on the correspondence in epigenetic age estimation using the 7-CpG clock and other algorithms. To bridge this gap, in this study we measured the 7-CpG DNAm age using two methods, via MS-SNuPE and via the MethylationEPIC array, in a sample of 1,058 participants of the Berlin Aging Study II (BASE-II), assessed as part of the GendAge study. On average, participants were 75.6 years old (SD: 3.7, age range: 64.9���90.0, 52.6% female). Agreement between methods was assessed by Bland-Altman plots. DNAm age was highly correlated between methods (Pearson���s r = 0.9) and Bland-Altman plots showed a difference of 3.1 years. DNAm age by the 7-CpG formula was 71.2 years (SD: 6.9 years, SNuPE) and 68.1 years (SD: 6.4 years, EPIC array). The mean of difference in methylation fraction between methods for the seven individual CpG sites was between 0.7 and 13 percent. To allow direct conversion of DNAm age obtained from both methods we developed an adjustment formula with a randomly selected training set of 529 participants using linear regression. After conversion of the Illumina data in a second and independent validation set, the adjusted DNAm age was 71.44 years (SD: 6.1 years, n = 529). In summary, we found the results of DNAm clocks to be highly comparable. Furthermore, we developed an adjustment formula that allows for direct conversion of DNAm age estimates between methods and enables one singular clock to be used in studies that employ either the Illumina or the SNuPE method.

(1) Background: Higher levels of physical fitness are believed to increase the physiological quality of life and impact the aging process with a wide range of adaptive mechanisms, including the regulation of the expression of the age-associated klotho (KL) gene and protein levels. (2) Methods: Here, we tested the relationship between the DNA methylation-based epigenetic biomarkers PhenoAge and GrimAge and methylation of the promoter region of the KL gene, the circulating level of KL, and the stage of physical fitness and grip force in two groups of volunteer subjects, trained (TRND) and sedentary (SED), aged between 37 and 85 years old. (3) Results: The circulating KL level is negatively associated with chronological age in the TRND group (r = −0.19; p = 0.0295) but not in the SED group (r = −0.065; p = 0.5925). The age-associated decrease in circulating KL is partly due to the increased methylation of the KL gene. In addition, higher plasma KL is significantly related to epigenetic age-deceleration in the TRND group, assessed by the biomarker of PhenoAge (r = −0.21; p = 0.0192). (4) Conclusions: The level of physical fitness, on the other hand, does not relate to circulating KL levels, nor to the rate of the methylation of the promoter region of the KL gene, only in males.

Objectives: Age-related hearing loss (ARHL) is highly prevalent among older adults, but the potential mechanisms and predictive markers for ARHL are lacking. Epigenetic age acceleration has been shown to be predictive of many age-associated diseases and mortality. However, the association between epigenetic age acceleration and hearing remains unknown. Our study aims to investigate the relationship between epigenetic age acceleration and audiometric hearing in the Baltimore Longitudinal Study of Aging (BLSA).Methods: Participants with both DNA methylation and audiometric hearing measurements were included. The main independent variables are epigenetic age acceleration measures, including intrinsic epigenetic age acceleration—“IEAA,” Hannum age acceleration—“AgeAccelerationResidualHannum,” PhenoAge acceleration—“AgeAccelPheno,” GrimAge acceleration—“AgeAccelGrim,” and methylation-based pace of aging estimation—“DunedinPoAm.” The main dependent variable is speech-frequency pure tone average. Linear regression was used to assess the association between epigenetic age acceleration and hearing.Results: Among the 236 participants (52.5% female), after adjusting for age, sex, race, time difference between measurements, cardiovascular factors, and smoking history, the effect sizes were 0.11 995% CI: (–0.00, 0.23), p = 0.054] for Hannum’s clock, 0.08 [95% CI: (–0.03, 0.19), p = 0.143] for Horvath’s clock, 0.10 [95% CI: (–0.01, 0.21), p = 0.089] for PhenoAge, 0.20 [95% CI: (0.06, 0.33), p = 0.004] for GrimAge, and 0.21 [95% CI: (0.09, 0.33), p = 0.001] for DunedinPoAm.Discussion: The present study suggests that some epigenetic age acceleration measurements are associated with hearing. Future research is needed to study the potential subclinical cardiovascular causes of hearing and to investigate the longitudinal relationship between DNA methylation and hearing.

Extensively grazed cattle are often mustered only once a year. Therefore, birthdates are typically unknown or inaccurate. Birthdates would be useful for deriving important traits (growth rate; calving interval), breed registrations, and making management decisions. Epigenetic clocks use methylation of DNA to predict an individual’s age. An epigenetic clock for cattle could provide a solution to the challenges of industry birthdate recording. Here we derived the first epigenetic clock for tropically adapted cattle using portable sequencing devices from tail hair, a tissue which is widely used in industry for genotyping. Cattle (n= 66) with ages ranging from 0.35 to 15.7 years were sequenced using Oxford Nanopore Technologies MinION and methylation was called at CpG sites across the genome. Sites were then filtered and used to calculate a covariance relationship matrix based on methylation state. Best linear unbiased prediction was used with 10-fold cross validation to predict age. A second methylation relationship matrix was also calculated that contained sites associated with genes used in the dog and human epigenetic clocks. The correlation between predicted age and actual age was 0.71 for all sites and 0.60 for dog and human gene epigenetic clock sites. The mean absolute deviation was 1.4 years for animals aged less than 3 years of age, and 1.5 years for animals aged 3–10 years. This is the first reported epigenetic clock using industry relevant samples in cattle.

Human DNA methylation data have previously been used to develop highly accurate biomarkers of aging (“epigenetic clocks”). Subsequent studies demonstrate that similar epigenetic clocks can also be developed for mice and many other mammals. Here, we describe epigenetic clocks for common marmosets (Callithrix jacchus) based on novel DNA methylation data generated from highly conserved mammalian CpGs that were profiled using a custom Infinium array (HorvathMammalMethylChip40). From these, we developed and present here two epigenetic clocks for marmosets that are applicable to whole blood samples. We find that the human-marmoset clock for relative age exhibits moderately high age correlations in two other non-human primate species: vervet monkeys and rhesus macaques. In a separate cohort of marmosets, we tested whether intervention with rapamycin, a drug shown to extend lifespan in mice, would alter the epigenetic age of marmosets, as measured by the marmoset epigenetic clocks. These clocks did not detect significant effects of rapamycin on the epigenetic age of marmoset blood. The common marmoset stands out from other mammals in that it is not possible to build accurate estimators of sex based on DNA methylation data: the accuracy of a random forest predictor of sex (66%) was substantially lower than that observed for other mammals (which is close to 100%). Overall, the epigenetic clocks developed here for the common marmoset are expected to be useful for age estimation of wild-born animals and for anti-aging studies in this species. Supplementary Information The online version contains supplementary material available at 10.1007/s11357-021-00438-7.

We evaluated associations between nine epigenetic age acceleration (EAA) scores and 18 cardio-metabolic phenotypes using an Eastern European ageing population cohort richly annotated for a diverse set of phenotypes (subsample, n = 306; aged 45-69 years). This was implemented by splitting the data into groups with positive and negative EAAs. We observed strong association between all epigenetic age acceleration scores and sex, suggesting that any analysis of EAAs should be adjusted by sex. We found that some sex-adjusted EAA scores were significantly associated with several phenotypes such as blood levels of gamma-glutamyl transferase and low-density lipoprotein, smoking status, annual alcohol consumption, multiple carotid plaques, and incident coronary heart disease status (not necessarily the same phenotypes for different EAAs). We demonstrated that even after adjusting EAAs for sex, EAA-phenotype associations remain sex-specific, which should be taken into account in any downstream analysis involving EAAs. The obtained results suggest that in some EAA-phenotype associations, negative EAA scores (i.e. epigenetic age below chronological age) indicated more harmful phenotype values, which is counter-intuitive. Among all considered epigenetic clocks, GrimAge was significantly associated with more phenotypes than any other EA scores in this Russian sample.

Accurate identification of individual ages within wild bottlenose dolphins (Tursiops truncatus) is critical for determining population health and the development of population management strategies. As such, we analyzed DNA methylation (DNAm) patterns by applying a custom methylation array (HorvathMammalMethyl40) to both blood (n= 140) and skin samples (n= 87) from known age or approximate age (0–57 years) bottlenose dolphins. We present three bottlenose dolphin specific age estimation clocks using combined blood and skin [48 CpGs,R= 0.93, median absolute error (MAE) = 2.13 years], blood only (64 CpGs, R = 0.97, error = 1.46 years) and skin only (39 CpGs,R= 0.95, error = 2.53). We characterized individual cytosines that correlate with sex and age in dolphins and developed a sex estimator based on 71 CpGs that predicts the sex of any odontocete species with 99.5% accuracy. The presented epigenetic clocks are expected to be useful for conservation efforts and for determining if anthropogenic events affect aging rates in wild populations.

Background In the first study of its kind, we examine the association between growth and development in early life and DNAm age biomarkers in mid-life. Methods Participants were from the Medical Research Council National Survey of Health and Development (n = 1376). Four DNAm age acceleration (AgeAccel) biomarkers were measured when participants were aged 53 years: AgeAccelHannum; AgeAccelHorvath; AgeAccelLevine; and AgeAccelGrim. Exposure variables included: relative weight gain (standardised residuals from models of current weight z-score on current height, and previous weight and height z-scores); and linear growth (standardised residuals from models of current height z-score on previous height and weight z-scores) during infancy (0–2 years, weight gain only), early childhood (2–4 years), middle childhood (4–7 years) and late childhood to adolescence (7–15 years); age at menarche; and pubertal stage for men at 14–15 years. The relationship between relative weight gain and linear growth and AgeAccel was investigated using conditional growth models. We replicated analyses from the late childhood to adolescence period and pubertal timing among 240 participants from The National Child and Development Study (NCDS). Results A 1SD increase in relative weight gain in late childhood to adolescence was associated with 0.50 years (95% CI 0.20, 0.79) higher AgeAccelGrim. Although the CI includes the null, the estimate was similar in NCDS [0.57 years (95% CI − 0.01, 1.16)] There was no strong evidence that relative weight gain and linear growth in childhood was associated with any other AgeAccel biomarker. There was no relationship between pubertal timing in men and AgeAccel biomarkers. Women who reached menarche ≥ 12 years had 1.20 years (95% CI 0.15, 2.24) higher AgeAccelGrim on average than women who reached menarche Conclusions Our findings generally do not support an association between growth and AgeAccel biomarkers in mid-life. However, we found rapid weight gain during pubertal development, previously related to higher cardiovascular disease risk, to be associated with older AgeAccelGrim. Given this is an exploratory study, this finding requires replication.

DNA methylation at CpG sites is an essential epigenetic mark that regulates gene expression during mammalian development and diseases. Methylome refers to the entire set of methylation modifications present in the whole genome. Over the last several years, an increasing number of reports on brain DNA methylome reported the association between aberrant methylation and the abnormalities in the expression of critical genes known to have critical roles during aging and neurodegenerative diseases. Consequently, the role of methylation in understanding neurodegenerative diseases has been under focus. This review outlines the current knowledge of the human brain DNA methylomes during aging and neurodegenerative diseases. We describe the differentially methylated genes from fetal stage to old age and their biological functions. Additionally, we summarize the key aspects and methylated genes identified from brain methylome studies on neurodegenerative diseases. The brain methylome studies could provide a basis for studying the functional aspects of neurodegenerative diseases. [BMB Reports 2019; 52(10): 577-588].

DNA methylation (DNAm) overwrites information about multiple extrinsic factors on the genome. Age is one of these factors. Age causes characteristic DNAm changes that are thought to be not only major drivers of normal ageing but also precursors to diseases, cancer being one of these. Although there is still much to learn about the relationship between ageing, age-related diseases and DNAm, we now know how to interpret some of the effects caused by age in the form of changes in methylation marks at specific loci. In fact, these changes form the basis of the so called "epigenetic clocks", which translate the genomic methylation profile into an "epigenetic age". Epigenetic age does not only estimate chronological age but can also predict the risk of chronic diseases and mortality. Epigenetic age is believed to be one of the most accurate metrics of biological age. Initial evidence has recently been gathered pointing to the possibility that the rate of epigenetic ageing can be slowed down or even reversed. In this review, we discuss some of the most relevant advances in this field. Expected outcome is that this approach can provide insights into how to preserve health and reduce the impact of ageing diseases in humans.

BackgroundBiological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.ResultsLeveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels.ConclusionThis study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

Age‐associated DNA‐methylation profiles have been used successfully to develop highly accurate biomarkers of age ("epigenetic clocks") in humans, mice, dogs, and other species. Here we present epigenetic clocks for African and Asian elephants. These clocks were developed using novel DNA methylation profiles of 140 elephant blood samples of known age, at loci that are highly conserved between mammalian species, using a custom Infinium array (HorvathMammalMethylChip40). We present epigenetic clocks for Asian elephants (Elephas maximus), African elephants (Loxodonta africana), and both elephant species combined. Two additional human‐elephant clocks were constructed by combining human and elephant samples. Epigenome‐wide association studies identified elephant age‐related CpGs and their proximal genes. The products of these genes play important roles in cellular differentiation, organismal development, metabolism, and circadian rhythms. Intracellular events observed to change with age included the methylation of bivalent chromatin domains, and targets of polycomb repressive complexes. These readily available epigenetic clocks can be used for elephant conservation efforts where accurate estimates of age are needed to predict demographic trends., DNA methylation aging clocks for African and Asian elephants. Some of these clocks apply to humans as well. Parts of the graphic were created by Hollis Burbank‐Hammerlund, Sandra Oviedo, and BioRender.com.

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 CpG sites, respectively, were recently 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 of older adults (50-75 years at baseline) from Germany. Methods: DNA methylation in whole blood was measured by the Infinium Methylation EPIC BeadChip kit (EPIC, Illumina, San Diego, CA, USA) and used to calculate the three methylation algorithms in two random subsets of the ESTHER cohort study (n = 741 and n = 1030). Frailty was assessed by a frailty index (FI) based on percentage of 34 selected deficits. Findings: During 14 years of follow-up, a total of 346 deaths were observed. AgeAccelPheno, AgeAccelGrim, MRscore and FI were positively correlated. Although each of them was strongly and independently associated with mortality, the association was strongest for MRscore [adjusted hazard ratio (95% CI) per SD increase = 1·73 (1·46-2·06]. Harrell’s C-statistic was 0·706 for a model predicting mortality by age and sex alone and increased to 0·752 in a model including MRscore and the FI. Interpretation: The combination of a DNA methylation score based on 10 CpGs only and an easy to ascertain frailty index may strongly enhance mortality prediction beyond age and sex. Funding Information: The ESTHER study was funded by grants from the Baden-Wurttemberg 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 (Saarbrucken, Germany). Declaration of Interests: No potential conflicts of interest were disclosed. Ethics Approval Statement: The ESTHER study was approved by the ethics committees of the medical faculty of the University of Heidelberg and the medical board of the state of Saarland. Written informed consent was obtained from each participant.

Background Circulating folate, vitamin B12 and homocysteine concentrations during fetal development have been associated with health outcomes in childhood. Changes in fetal DNA methylation may be an underlying mechanism. This may be reflected in altered epigenetic aging of the fetus, as compared to chronological aging. The difference between gestational age derived in clinical practice and gestational age predicted from neonatal DNA methylation data is referred to as gestational age acceleration. Differences in circulating folate, vitamin B12 and homocysteine concentrations during fetal development may be associated with gestational age acceleration. Results Up to 1346 newborns participating in the Generation R Study, a population-based prospective cohort study, had both cord blood DNA methylation data available and information on plasma folate, serum total and active B12 and plasma homocysteine concentrations, measured in early pregnancy and/or in cord blood. A subgroup of 380 newborns had mothers with optimal pregnancy dating based on a regular menstrual cycle and a known date of last menstrual period. For comparison, gestational age acceleration was calculated based the method of both Bohlin and Knight. In the total study population, which was more similar to Bohlin’s training population, one standard deviation score (SDS) higher maternal plasma homocysteine concentrations was nominally associated with positive gestational age acceleration [0.07 weeks, 95% confidence interval (CI) 0.02, 0.13] by Bohlin’s method. In the subgroup with pregnancy dating based on last menstrual period, the method that was also used in Knight’s training population, one SDS higher cord serum total and active B12 concentrations were nominally associated with negative gestational age acceleration [(− 0.16 weeks, 95% CI − 0.30, − 0.02) and (− 0.15 weeks, 95% CI − 0.29, − 0.01), respectively] by Knight’s method. Conclusions We found some evidence to support associations of higher maternal plasma homocysteine concentrations with positive gestational age acceleration, suggesting faster epigenetic than clinical gestational aging. Cord serum vitamin B12 concentrations may be associated with negative gestational age acceleration, indicating slower epigenetic than clinical gestational aging. Future studies could examine whether altered fetal epigenetic aging underlies the associations of circulating homocysteine and vitamin B12 blood concentrations during fetal development with long-term health outcomes.

Cytosine methylation is one of the best studied epigenetic modifications. In mammals, DNA methylation patterns vary among cells and is mainly found in the CpG context. DNA methylation is involved in important processes during development and differentiation and its dysregulation can lead to or is associated with diseases, such as cancer, loss-of-imprinting syndromes and neurological disorders. It has been also shown that DNA methylation at the cellular, tissue and organism level varies with age. To overcome the costs of Whole-Genome Bisulfite Sequencing, the gold standard method to detect 5-methylcytosines at a single base resolution, DNA methylation arrays have been developed and extensively used. This method allows one to assess the status of a fraction of the CpG sites present in the genome of an organism. In order to combine the relatively low cost of Methylation Arrays and digital signals of bisulfite sequencing, we developed a Targeted Bisulfite Sequencing method that can be applied to biomarker discovery for virtually any phenotype. Here we describe a comprehensive step-by-step protocol to build a DNA methylation-based epigenetic clock.

Background Epigenetic clocks are based on DNA methylation (DNAm). It has been suggested that these clocks are useable markers of biological aging and premature mortality. Because genetic factors explain variations in both epigenetic aging and mortality, this association could also be explained by shared genetic factors. We investigated the influence of genetic and lifestyle factors (smoking, alcohol consumption, physical activity, chronic diseases, body mass index) and education on the association of accelerated epigenetic aging with mortality using a longitudinal twin design. Utilizing a publicly available online tool, we calculated the epigenetic age using two epigenetic clocks, Horvath DNAmAge and DNAm GrimAge, in 413 Finnish twin sisters, aged 63–76 years, at the beginning of the 18-year mortality follow-up. Epigenetic age acceleration was calculated as the residuals from a linear regression model of epigenetic age estimated on chronological age (AAHorvath, AAGrimAge, respectively). Cox proportional hazard models were conducted for individuals and twin pairs. Results The results of the individual-based analyses showed an increased mortality hazard ratio (HR) of 1.31 (CI95: 1.13–1.53) per one standard deviation (SD) increase in AAGrimAge. The results indicated no significant associations of AAHorvath with mortality. Pairwise mortality analyses showed an HR of 1.50 (CI95: 1.02–2.20) per 1 SD increase in AAGrimAge. However, after adjusting for smoking, the HR attenuated substantially and was statistically non-significant (1.29; CI95: 0.84–1.99). Similarly, in multivariable adjusted models the HR (1.42–1.49) was non-significant. In AAHorvath, the non-significant HRs were lower among monozygotic pairs in comparison to dizygotic pairs, while in AAGrimAge there were no systematic differences by zygosity. Further, the pairwise analysis in quartiles showed that the increased within pair difference in AAGrimAge was associated with a higher all-cause mortality risk. Conclusions In conclusion, the findings suggest that DNAm GrimAge is a strong predictor of mortality independent of genetic influences. Smoking, which is known to alter DNAm levels and is built into the DNAm GrimAge algorithm, attenuated the association between epigenetic aging and mortality risk. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01112-7.

Cattle are an attractive animal model of fertility in women due to their high degree of similarity relative to follicle selection, embryo cleavage, blastocyst formation, and gestation length. To facilitate future studies of the epigenetic underpinnings of aging effects in the female reproductive axis, several DNA methylation‐based biomarkers of aging (epigenetic clocks) for bovine oocytes are presented. One such clock was germane to only oocytes, while a dual‐tissue clock was highly predictive of age in both oocytes and blood. Dual species clocks that apply to both humans and cattle were also developed and evaluated. These epigenetic clocks can be used to accurately estimate the biological age of oocytes. Both epigenetic clock studies and epigenome‐wide association studies revealed that blood and oocytes differ substantially with respect to aging and the underlying epigenetic signatures that potentially influence the aging process. The rate of epigenetic aging was found to be slower in oocytes compared to blood; however, oocytes appeared to begin at an older epigenetic age. The epigenetic clocks for oocytes are expected to address questions in the field of reproductive aging, including the central question: how to slow aging of oocytes., Cattle are an attractive animal model of fertility in women due to their high degree of similarity relative to follicle selection, embryo cleavage, blastocyst formation, and gestation length. To facilitate future studies of the epigenetic underpinnings of aging effects in the female reproductive axis, several DNA methylation‐based biomarkers of aging (epigenetic clocks) for bovine oocytes are presented. One such clock was germane to only oocytes, while a dual‐tissue clock was highly predictive of age in both oocytes and blood.