An epigenetic clock for human skeletal muscle

Epigenetic biomarkers in aging and longevity: Current and future application

The aging process has been one of the most necessary research fields in the current century, and knowing different theories of aging and the role of different genetic, epigenetic, molecular, and environmental modulating factors in increasing the knowledge of aging mechanisms and developing appropriate diagnostic, therapeutic, and preventive ways would be helpful. One of the most conserved signs of aging is epigenetic changes, including DNA methylation, histone modifications, chromatin remodeling, noncoding RNAs, and extracellular RNAs. Numerous biological processes and hallmarks are vital in aging development, but epigenomic alterations are especially notable because of their importance in gene regulation and cellular identity. The mounting evidence points to a possible interaction between age-related epigenomic alterations and other aging hallmarks, like genome instability. To extend a healthy lifespan and possibly reverse some facets of aging and aging-related diseases, it will be crucial to comprehend global and locus-specific epigenomic modifications and recognize corresponding regulators of health and longevity. In the current study, we will aim to discuss the role of epigenomic mechanisms in aging and the most recent developments in epigenetic diagnostic biomarkers, which have the potential to focus efforts on reversing the destructive signs of aging and extending the lifespan.

The benefits of exercise on aging: focus on muscle biomarkers

The focus on maintaining health and vitality (e.g., good healthspan) in later life has become increasingly important as the world's population is getting older. In the last decade, advances in aging research have identified biomarkers like DNA methylation (DNAm) and gene expression, offering insights into both chronological and biological aging. This understanding opens up possibilities for interventions that can slow down molecular aspects of the aging process. Exploring the impact of exercise on these biomarkers in human skeletal muscle (a critical tissue for metabolism, thermogenesis and movement) reveals its potential to foster healthier aging.

Development of an epigenetic clock resistant to changes in immune cell composition

Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG methylation values to predict chronological or biological age. Increases in predicted epigenetic age relative to chronological age (epigenetic age acceleration) are connected to aging-associated pathologies, and changes in epigenetic age are linked to canonical aging hallmarks. However, epigenetic clocks rely on training data from bulk tissues whose cellular composition changes with age. Here, we found that human naive CD8+ T cells, which decrease in frequency during aging, exhibit an epigenetic age 15-20 years younger than effector memory CD8+ T cells from the same individual. Importantly, homogenous naive T cells isolated from individuals of different ages show a progressive increase in epigenetic age, indicating that current epigenetic clocks measure two independent variables, aging and immune cell composition. To isolate the age-associated cell intrinsic changes, we created an epigenetic clock, the IntrinClock, that did not change among 10 immune cell types tested. IntrinClock shows a robust predicted epigenetic age increase in a model of replicative senescence in vitro and age reversal during OSKM-mediated reprogramming.

A multi-epigenomic map of endurance exercise training

The Molecular Transducers of Physical Activity Consortium (MoTrPAC) aims to comprehensively map molecular alterations in response to acute exercise and chronic training. In one of a recent series of papers from MoTrPAC, Nair et al. provide the first multi-epigenomic and transcriptomic integration across eight tissues in both sexes following adaptation to endurance exercise training (EET).

DNA methylation of skeletal muscle function-related secretary factors identifies FGF2 as a potential biomarker for sarcopenia

BACKGROUND

Sarcopenia is characterized by progressive loss of muscle mass and function due to aging. DNA methylation has been identified to play important roles in the dysfunction of skeletal muscle. The aim of our present study was to explore the whole blood sample-based methylation changes of skeletal muscle function-related factors in patients with sarcopenia.

METHODS

The overall DNA methylation levels were analysed by using MethlTarget™ DNA Methylation Analysis platform in a discovery set consistent of 50 sarcopenic older adults (aged ≥65 years) and 50 age- and sex-matched non-sarcopenic individuals. The candidate differentially methylated regions (DMRs) were further validated by Methylation-specific PCR (MSP) in another two independent larger sets and confirmed by pyrosequencing. Receiver operating characteristic (ROC) curve analysis was used to determine the optimum cut-off levels of fibroblast growth factor 2 (FGF2)_30 methylation best predicting sarcopenia and area under the ROC curve (AUC) was measured. The correlation between candidate DMRs and the risk of sarcopenia was investigated by univariate analysis and multivariate logistic regression analysis.

RESULTS

Among 1149 cytosine-phosphate-guanine (CpG) sites of 27 skeletal muscle function-related secretary factors, 17 differentially methylated CpG sites and 7 differentially methylated regions (DMRs) were detected between patients with sarcopenia and control subjects in the discovery set. Further methylation-specific PCR identified that methylation of fibroblast growth factor 2 (FGF2)_30 was lower in patients with sarcopenia and the level was decreased as the severity of sarcopenia increased, which was confirmed by pyrosequencing. Correlation analysis demonstrated that the methylation level of FGF2_30 was positively correlated to ASMI (r = 0.372, P < 0.001), grip strength (r = 0.334, P < 0.001), and gait speed (r = 0.411, P < 0.001). ROC curve analysis indicated that the optimal cut-off value of FGF2_30 methylation level that predicted sarcopenia was 0.15 with a sensitivity of 84.6% and a specificity of 70.1% (AUC = 0.807, 95% CI = 0.756-0.858, P < 0.001). Multivariate logistic regression analyses showed that lower FGF2_30 methylation level (<0.15) was significantly associated with increased risk of sarcopenia even after adjustment for potential confounders including age, sex, and BMI (adjusted OR = 9.223, 95% CI: 6.614-12.861, P < 0.001).

CONCLUSIONS

Our results suggest that lower FGF2_30 methylation is correlated with the risk and severity of sarcopenia in the older adults, indicating that FGF2 methylation serve as a surrogate biomarker for the screening and evaluation of sarcopenia.

Sarcopenic obesity in older adults: a clinical overview

Sarcopenic obesity is characterized by a concurrent decline in muscle mass and function, along with increased adipose tissue. Sarcopenic obesity is a growing concern in older adults owing to significant health consequences, including implications for mortality, comorbidities and risk of developing geriatric syndromes. A 2022 consensus statement established a new definition and diagnostic criteria for sarcopenic obesity. The pathophysiology of this condition involves a complex interplay between muscle, adipose tissue, hormonal changes, inflammation, oxidative stress and lifestyle factors, among others. Sarcopenic obesity is treated with a range of management approaches, such as lifestyle interventions, exercise, nutrition and medical therapies. Emerging therapies that were developed for treating other conditions may be relevant to sarcopenic obesity, including novel pharmacological agents and personalized approaches such as precision medicine. In this Review, we synthesize the current knowledge of the clinical importance of sarcopenic obesity, its assessment and diagnosis, along with current and emerging management strategies.

Strategies to deal with genetic analyzer-specific DNA methylation measurements

Targeted bisulfite sequencing using single-base extension (SBE) can be used to measure DNA methylation via capillary electrophoresis on genetic analyzers in forensic labs. Several accurate age prediction models have been reported using this method. However, using different genetic analyzers with different software settings can generate different methylation values, leading to significant errors in age prediction. To address this issue, the study proposes and compares four methods as follows: (1) adjusting methylation values using numerous actual body fluid DNA samples, (2) adjusting methylation values using control DNAs with varying methylation ratios, (3) constructing new age prediction models for each genetic analyzer type, and (4) constructing new age prediction models that could be applied to all types of genetic analyzers. To test the methods for adjusting values using actual body fluid DNA samples, previously reported adjusting equations were used for blood/saliva DNA age prediction markers (ELOVL2, FHL2, KLF14, MIR29B2CHG/C1orf132, and TRIM59). New equations were generated for semen DNA age prediction markers (TTC7B, LOC401324/cg12837463, and LOC729960/NOX4) by drawing polynomial regression lines between the results of the three types of genetic analyzers (3130, 3500, and SeqStudio). The same method was applied to obtain adjustment equations using 11 control DNA samples. To develop new age prediction models for each genetic analyzer type, linear regression analysis was conducted using DNA methylation data from 150 blood, 150 saliva, and 62 semen samples. For the genetic analyzer-independent models, control DNAs were used to formulate equations for calibrating the bias of the data from each genetic analyzer, and linear regression analysis was performed using calibrated body fluid DNA data. In the comparison results, the genetic analyzer-specific models showed the highest accuracy. However, genetic analyzer-independent models through bias adjustment also provided accurate age prediction results, suggesting its use as an alternative in situations with multiple constraints.

Where are we in the implementation of tissue-specific epigenetic clocks?

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense. Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set. Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when-unsurprisingly-the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR. Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks.

Deciphering the role of immune cell composition in epigenetic age acceleration: Insights from cell-type deconvolution applied to human blood epigenetic clocks

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 as a window into female reproductive aging

People with ovaries experience reproductive aging as their reproductive function and system declines. This has significant implications for both fertility and long-term health, with people experiencing an increased risk of cardiometabolic disorders after menopause. Reproductive aging can be assessed through markers of ovarian reserve, response to fertility treatment or molecular biomarkers, including DNA methylation. Changes in DNA methylation with age associate with poorer reproductive outcomes, and epigenome-wide studies can provide insight into genes and pathways involved. DNA methylation-based epigenetic clocks can quantify biological age in reproductive tissues and systemically. This review provides an overview of hallmarks and theories of aging in the context of the reproductive system, and then focuses on studies of DNA methylation in reproductive tissues.

Application of array-based age prediction models to post-mortem tissue samples

Since DNA methylation at specific CpG sites exhibits a strong age association, researchers have developed numerous age prediction models based on the methylation BeadChip array. These models harness epigenetic clocks that hold the potential to narrow down the search range for unknown suspects and unidentified victims. This study collected 180 post-mortem tissue samples comprising nine tissue types (blood, brain, heart, lung, liver, kidney, muscle, epidermis, and dermis) from autopsies of 20 Koreans aged 18-78. Subsequently, DNA methylation profiling was conducted using the Infinium MethylationEPIC array. We tested several array-based age prediction models using the data obtained from various tissues. The pan-tissue clock exhibited a moderately accurate prediction across all nine tissue types (MAE = 8.7 years, r = 0.88). Notably, the DNAm ages of the Hannum clock, the skin & blood clock, and the Zhang clock strongly correlated with the actual age in blood samples (MAE < approximately 5 years, r > 0.9). PhenoAge yielded an MAE of 10.1 years and an r-value of 0.92. The muscle-specific epigenetic clock, the MEAT package, demonstrated high prediction accuracy in muscle samples (MAE = 4.7 years, r = 0.93). Those previously reported array-based age prediction models were mainly constructed in Europeans but performed well in Koreans. In addition, tests involving various quantities of DNA and fragmented DNA have shown that DNA quantity and quality affected methylation measurements and age prediction results. However, robust age prediction models exist under low amounts of DNA and fragmented DNA conditions.

Exercise is associated with younger methylome and transcriptome profiles in human skeletal muscle

Exercise training prevents age-related decline in muscle function. Targeting epigenetic aging is a promising actionable mechanism and late-life exercise mitigates epigenetic aging in rodent muscle. Whether exercise training can decelerate, or reverse epigenetic aging in humans is unknown. Here, we performed a powerful meta-analysis of the methylome and transcriptome of an unprecedented number of human skeletal muscle samples (n = 3176). We show that: (1) individuals with higher baseline aerobic fitness have younger epigenetic and transcriptomic profiles, (2) exercise training leads to significant shifts of epigenetic and transcriptomic patterns toward a younger profile, and (3) muscle disuse "ages" the transcriptome. Higher fitness levels were associated with attenuated differential methylation and transcription during aging. Furthermore, both epigenetic and transcriptomic profiles shifted toward a younger state after exercise training interventions, while the transcriptome shifted toward an older state after forced muscle disuse. We demonstrate that exercise training targets many of the age-related transcripts and DNA methylation loci to maintain younger methylome and transcriptome profiles, specifically in genes related to muscle structure, metabolism, and mitochondrial function. Our comprehensive analysis will inform future studies aiming to identify the best combination of therapeutics and exercise regimes to optimize longevity.

Skeletal muscle DNA methylation: Effects of exercise and HIV

Aging, human immunodeficiency virus (HIV) infection, and antiretroviral therapy modify the epigenetic profile and function of cells and tissues, including skeletal muscle (SkM). In some cells, accelerated epigenetic aging begins very soon after the initial HIV infection, potentially setting the stage for the early onset of frailty. Exercise imparts epigenetic modifications in SkM that may underpin some health benefits, including delayed frailty, in people living with HIV (PWH). In this first report of exercise-related changes in SkM DNA methylation among PWH, we investigated the impact of 24 weeks of aerobic and resistance exercise training on SkM (vastus lateralis) DNA methylation profiles and epigenetic age acceleration (EAA) in older, virally suppressed PWH (n = 12) and uninfected controls (n = 18), and associations of EAA with physical function at baseline. We identified 983 differentially methylated positions (DMPs) in PWH and controls at baseline and 237 DMPs after training. The influence of HIV serostatus on SkM methylation was more pronounced than that of exercise training. There was little overlap in the genes associated with the probes most significantly differentiated by exercise training within each group. Baseline EAA (mean ± SD) was similar between PWH (-0.4 ± 2.5 years) and controls (0.2 ± 2.6 years), and the exercise effect was not significant (p = 0.79). EAA and physical function at baseline were not significantly correlated (all p ≥ 0.10). This preliminary investigation suggests HIV-specific epigenetic adaptations in SkM with exercise training but confirmation in a larger study that includes transcriptomic analysis is warranted.

The ticking of aging clocks

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points.

Hallmarks of ageing in human skeletal muscle and implications for understanding the pathophysiology of sarcopenia in women and men

Ageing is a complex biological process associated with increased morbidity and mortality. Nine classic, interdependent hallmarks of ageing have been proposed involving genetic and biochemical pathways that collectively influence ageing trajectories and susceptibility to pathology in humans. Ageing skeletal muscle undergoes profound morphological and physiological changes associated with loss of strength, mass, and function, a condition known as sarcopenia. The aetiology of sarcopenia is complex and whilst research in this area is growing rapidly, there is a relative paucity of human studies, particularly in older women. Here, we evaluate how the nine classic hallmarks of ageing: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication contribute to skeletal muscle ageing and the pathophysiology of sarcopenia. We also highlight five novel hallmarks of particular significance to skeletal muscle ageing: inflammation, neural dysfunction, extracellular matrix dysfunction, reduced vascular perfusion, and ionic dyshomeostasis, and discuss how the classic and novel hallmarks are interconnected. Their clinical relevance and translational potential are also considered.

DNA methylation of insulin signaling pathways is associated with HOMA2-IR in primary myoblasts from older adults

BACKGROUND

While ageing is associated with increased insulin resistance (IR), the molecular mechanisms underlying increased IR in the muscle, the primary organ for glucose clearance, have yet to be elucidated in older individuals. As epigenetic processes are suggested to contribute to the development of ageing-associated diseases, we investigated whether differential DNA methylation was associated with IR in human primary muscle stem cells (myoblasts) from community-dwelling older individuals.

METHODS

We measured DNA methylation (Infinium HumanMethylationEPIC BeadChip) in myoblast cultures from vastus lateralis biopsies (119 males/females, mean age 78.24 years) from the Hertfordshire Sarcopenia Study extension (HSSe) and examined differentially methylated cytosine phosphate guanine (CpG) sites (dmCpG), regions (DMRs) and gene pathways associated with HOMA2-IR, an index for the assessment of insulin resistance, and levels of glycated hemoglobin HbA1c.

RESULTS

Thirty-eight dmCpGs (false discovery rate (FDR) < 0.05) were associated with HOMA2-IR, with dmCpGs enriched in genes linked with JNK, AMPK and insulin signaling. The methylation signal associated with HOMA2-IR was attenuated after the addition of either BMI (6 dmCpGs), appendicular lean mass index (ALMi) (7 dmCpGs), grip strength (15 dmCpGs) or gait speed (23 dmCpGs) as covariates in the model. There were 8 DMRs (Stouffer < 0.05) associated with HOMA2-IR, including DMRs within T-box transcription factor (TBX1) and nuclear receptor subfamily-2 group F member-2 (NR2F2); the DMRs within TBX1 and NR2F2 remained associated with HOMA2-IR after adjustment for BMI, ALMi, grip strength or gait speed. Forty-nine dmCpGs and 21 DMRs were associated with HbA1c, with cg13451048, located within exoribonuclease family member 3 (ERI3) associated with both HOMA2-IR and HbA1c. HOMA2-IR and HbA1c were not associated with accelerated epigenetic ageing.

CONCLUSIONS

These findings suggest that insulin resistance is associated with differential DNA methylation in human primary myoblasts with both muscle mass and body composition making a significant contribution to the methylation changes associated with IR.

Methylome and proteome integration in human skeletal muscle uncover group and individual responses to high-intensity interval training

Exercise is a major beneficial contributor to muscle metabolism, and health benefits acquired by exercise are a result of molecular shifts occurring across multiple molecular layers (i.e., epigenome, transcriptome, and proteome). Identifying robust, across-molecular level targets associated with exercise response, at both group and individual levels, is paramount to develop health guidelines and targeted health interventions. Sixteen, apparently healthy, moderately trained (VO2 max = 51.0 ± 10.6 mL min-1  kg-1 ) males (age range = 18-45 years) from the Gene SMART (Skeletal Muscle Adaptive Responses to Training) study completed a longitudinal study composed of 12-week high-intensity interval training (HIIT) intervention. Vastus lateralis muscle biopsies were collected at baseline and after 4, 8, and 12 weeks of HIIT. DNA methylation (~850 CpG sites) and proteomic (~3000 proteins) analyses were conducted at all time points. Mixed models were applied to estimate group and individual changes, and methylome and proteome integration was conducted using a holistic multilevel approach with the mixOmics package. A total of 461 proteins significantly changed over time (at 4, 8, and 12 weeks), whilst methylome overall shifted with training only one differentially methylated position (DMP) was significant (adj.p-value < .05). K-means analysis revealed cumulative protein changes by clusters of proteins that presented similar changes over time. Individual responses to training were observed in 101 proteins. Seven proteins had large effect-sizes >0.5, among them are two novel exercise-related proteins, LYRM7 and EPN1. Integration analysis showed bidirectional relationships between the methylome and proteome. We showed a significant influence of HIIT on the epigenome and more so on the proteome in human muscle, and uncovered groups of proteins clustering according to similar patterns across the exercise intervention. Individual responses to exercise were observed in the proteome with novel mitochondrial and metabolic proteins consistently changed across individuals. Future work is required to elucidate the role of these proteins in response to exercise.

Sex differences in muscle protein expression and DNA methylation in response to exercise training

BACKGROUND

Exercise training elicits changes in muscle physiology, epigenomics, transcriptomics, and proteomics, with males and females exhibiting differing physiological responses to exercise training. However, the molecular mechanisms contributing to the differing adaptations between the sexes are poorly understood.

METHODS

We performed a meta-analysis for sex differences in skeletal muscle DNA methylation following an endurance training intervention (Gene SMART cohort and E-MTAB-11282 cohort). We investigated for sex differences in the skeletal muscle proteome following an endurance training intervention (Gene SMART cohort). Lastly, we investigated whether the methylome and proteome are associated with baseline cardiorespiratory fitness (maximal oxygen consumption; VO2max) in a sex-specific manner.

RESULTS

Here, we investigated for the first time, DNA methylome and proteome sex differences in response to exercise training in human skeletal muscle (n = 78; 50 males, 28 females). We identified 92 DNA methylation sites (CpGs) associated with exercise training; however, no CpGs changed in a sex-dependent manner. In contrast, we identified 189 proteins that are differentially expressed between the sexes following training, with 82 proteins differentially expressed between the sexes at baseline. Proteins showing the most robust sex-specific response to exercise include SIRT3, MRPL41, and MBP. Irrespective of sex, cardiorespiratory fitness was associated with robust methylome changes (19,257 CpGs) and no proteomic changes. We did not observe sex differences in the association between cardiorespiratory fitness and the DNA methylome. Integrative multi-omic analysis identified sex-specific mitochondrial metabolism pathways associated with exercise responses. Lastly, exercise training and cardiorespiratory fitness shifted the DNA methylomes to be more similar between the sexes.

CONCLUSIONS

We identified sex differences in protein expression changes, but not DNA methylation changes, following an endurance exercise training intervention; whereas we identified no sex differences in the DNA methylome or proteome response to lifelong training. Given the delicate interaction between sex and training as well as the limitations of the current study, more studies are required to elucidate whether there is a sex-specific training effect on the DNA methylome. We found that genes involved in mitochondrial metabolism pathways are differentially modulated between the sexes following endurance exercise training. These results shed light on sex differences in molecular adaptations to exercise training in skeletal muscle.

Adiposity is associated with widespread transcriptional changes and downregulation of longevity pathways in aged skeletal muscle

BACKGROUND

Amongst healthy older people, a number of correlates of impaired skeletal muscle mass and function have been defined. Although the prevalence of obesity is increasing markedly in this age group, information is sparse about the particular impacts of obesity on ageing skeletal muscle or the molecular mechanisms that underlie this and associated disease risk.

METHODS

Here, we examined genome-wide transcriptional changes using RNA sequencing in muscle biopsies from 40 older community-dwelling men from the Hertfordshire Sarcopenia Study with regard to obesity (body mass index [BMI] >30 kg/m2 , n = 7), overweight (BMI 25-30, n = 19), normal weight (BMI < 25, n = 14), and per cent and total fat mass. In addition, we used EPIC DNA methylation array data to investigate correlations between DNA methylation and gene expression in aged skeletal muscle tissue and investigated the relationship between genes within altered regulatory pathways and muscle histological parameters.

RESULTS

Individuals with obesity demonstrated a prominent modified transcriptional signature in muscle tissue, with a total of 542 differentially expressed genes associated with obesity (false discovery rate ≤0.05), of which 425 genes were upregulated when compared with normal weight. Upregulated genes were enriched in immune response (P = 3.18 × 10-41 ) and inflammation (leucocyte activation, P = 1.47 × 10-41 ; tumour necrosis factor, P = 2.75 × 10-15 ) signalling pathways and downregulated genes enriched in longevity (P = 1.5 × 10-3 ) and AMP-activated protein kinase (AMPK) (P = 4.5 × 10-3 ) signalling pathways. Furthermore, differentially expressed genes in both longevity and AMPK signalling pathways were associated with a change in DNA methylation, with a total of 256 and 360 significant cytosine-phosphate-guanine-gene correlations identified, respectively. Similar changes in the muscle transcriptome were observed with respect to per cent fat mass and total fat mass. Obesity was further associated with a significant increase in type II fast-fibre area (P = 0.026), of which key regulatory genes within both longevity and AMPK pathways were significantly associated.

CONCLUSIONS

We provide for the first time a global transcriptomic profile of skeletal muscle in older people with and without obesity, demonstrating modulation of key genes and pathways implicated in the regulation of muscle function, changes in DNA methylation associated with such pathways and associations between genes within the modified pathways implicated in muscle regulation and changes in muscle fibre type.

Aerobic exercise training resets the human skeletal muscle methylome 10 years after breast cancer treatment and survival

Cancer survivors suffer impairments in skeletal muscle in terms of reduced mass and function. Interestingly, human skeletal muscle possesses an epigenetic memory of earlier stimuli, such as exercise. Long-term retention of epigenetic changes in skeletal muscle following cancer survival and/or exercise training has not yet been studied. We, therefore, investigated genome-wide DNA methylation (methylome) in skeletal muscle following a 5-month, 3/week aerobic-training intervention in breast cancer survivors 10-14 years after diagnosis and treatment. These results were compared to breast cancer survivors who remained untrained and to age-matched controls with no history of cancer, who undertook the same training intervention. Skeletal muscle biopsies were obtained from 23 females before(pre) and after(post) the 5-month training period. InfiniumEPIC 850K DNA methylation arrays and RT-PCR for gene expression were performed. The breast cancer survivors displayed a significant retention of increased DNA methylation (i.e., hypermethylation) at a larger number of differentially methylated positions (DMPs) compared with healthy age-matched controls pre training. Training in cancer survivors led to an exaggerated number of DMPs with a hypermethylated signature occurring at non-regulatory regions compared with training in healthy age-matched controls. However, the opposite occurred in important gene regulatory regions, where training in cancer survivors elicited a considerable reduction in methylation (i.e., hypomethylation) in 99% of the DMPs located in CpG islands within promoter regions. Importantly, training was able to reverse the hypermethylation identified in cancer survivors back toward a hypomethylated signature that was observed pre training in healthy age-matched controls at 300 (out of 881) of these island/promoter-associated CpGs. Pathway enrichment analysis identified training in cancer survivors evoked a predominantly hypomethylated signature in pathways associated with cell cycle, DNA replication/repair, transcription, translation, mTOR signaling, and the proteosome. Differentially methylated region (DMR) analysis also identified genes: BAG1, BTG2, CHP1, KIFC1, MKL2, MTR, PEX11B, POLD2, S100A6, SNORD104, and SPG7 as hypermethylated in breast cancer survivors, with training reversing these CpG island/promoter-associated DMRs toward a hypomethylated signature. Training also elicited a largely different epigenetic response in healthy individuals than that observed in cancer survivors, with very few overlapping changes. Only one gene, SIRT2, was identified as having altered methylation in cancer survivors at baseline and after training in both the cancer survivors and healthy controls. Overall, human skeletal muscle may retain a hypermethylated signature as long as 10-14 years after breast cancer treatment/survival. Five months of aerobic training reset the skeletal muscle methylome toward signatures identified in healthy age-matched individuals in gene regulatory regions.

Ageing Skeletal Muscle: The Ubiquitous Muscle Stem Cell

In 1999, in a review by Beardsley, the potential of adult stem cells, in repair and regeneration was heralded (Beardsley Sci Am 281:30-31, 1999). Since then, the field of regenerative medicine has grown exponentially, with the capability of restoring or regenerating the function of damaged, diseased or aged human tissues being an underpinning motivation. If successful, stem cell therapies offer the potential to treat, for example degenerative diseases. In the subsequent 20 years, extensive progress has been made in the arena of adult stem cells (for a recent review see (Zakrzewski et al. Stem Cell Res Ther 10:68, 2019)). Prior to the growth of the adult stem cell research arena, much focus had been placed on the potential of embryonic stem cells (ESCs). The first research revealing the potential of these cells was published in 1981, when scientists reported the ability of cultured stem cells from murine embryos, to not only self-renew, but to also become all cells of the three germ layers of the developing embryo (Evans and Kaufman Nature 292:154-156, 1981), (Martin Proc Natl Acad Sci U S A 78:7634-7638, 1981). It took almost 20 years, following these discoveries, for this technology to translate to human ESCs, using donated human embryos. In 1998, Thomson et al. reported the creation of the first human embryonic cell line (Thomson et al. Science 282:1145-1147, 1998). However, research utilising human ESCs was hampered by ethical and religious constraints and indeed in 2001 George W. Bush restricted US research funding to human ESCs, which had already been banked. The contentious nature of this arena perhaps facilitated the use of and the research potential for adult stem cells. It is beyond the scope of this review to focus on ESCs, although their potential for enhancing our understanding of human development is huge (for a recent review see (Cyranoski Nature 555:428-430, 2018)). Rather, although ESCs and their epigenetic regulation will be introduced for background understanding, the focus will be on stem cells more generally, the role of epigenetics in stem cell fate, skeletal muscle, skeletal muscle stem cells, the impact of ageing on muscle wasting and the mechanisms underpinning loss, with a focus on epigenetic adaptation.

Within subject cross-tissue analyzes of epigenetic clocks in substance use disorder postmortem brain and blood

There is a possible accelerated biological aging in patients with substance use disorders (SUD). The evaluation of epigenetic clocks, which are accurate estimators of biological aging based on DNA methylation changes, has been limited to blood tissue in patients with SUD. Consequently, the impact of biological aging in the brain of individuals with SUD remains unknown. In this study, we evaluated multiple epigenetic clocks (DNAmAge, DNAmAgeHannum, DNAmAgeSkinBlood, DNAmPhenoAge, DNAmGrimAge, and DNAmTL) in individuals with SUD (n = 42), including alcohol (n = 10), opioid (n = 19), and stimulant use disorder (n = 13), and controls (n = 10) in postmortem brain (prefrontal cortex) and blood tissue obtained from the same individuals. We found a higher DNAmPhenoAge (β = 0.191, p-value = 0.0104) and a nominally lower DNAmTL (β = -0.149, p-value = 0.0603) in blood from individuals with SUD compared to controls. SUD subgroup analysis showed a nominally lower brain DNAmTL in subjects with alcohol use disorder, compared to stimulant use disorder and controls (β = 0.0150, p-value = 0.087). Cross-tissue analyzes indicated a lower blood DNAmTL and a higher blood DNAmAge compared to their respective brain values in the SUD group. This study highlights the relevance of tissue specificity in biological aging studies and suggests that peripheral measures of epigenetic clocks in SUD may depend on the specific type of drug used.

5-Aza-2'-Deoxycytidine Regulates White Adipocyte Browning by Modulating miRNA-133a/Prdm16

The conversion of white adipocytes into brown adipocytes improves their thermogenesis and promotes energy consumption. Epigenetic modifications affect related genes and interfere with energy metabolism, and these are the basis of new ideas for obesity treatment. Neonatal mice show high levels of DNA hypermethylation in white adipose tissue early in life and low levels in brown adipose tissue. Thus, we considered that the regulation of DNA methylation may play a role in the conversion of white adipose to brown. We observed growth indicators, lipid droplets of adipocytes, brown fat specific protein, and miRNA-133a after treatment with 5-Aza-2'-deoxycytidine. The expression of Prdm16 and Ucp-1 in adipocytes was detected after inhibiting miRNA-133a. The results showed a decrease in total lipid droplet formation and an increased expression of the brown fat specific proteins Prdm16 and Ucp-1. This study indicated that 5-Aza-2'-deoxycytidine promotes white adipocyte browning following DNA demethylation, possibly via the modulation of miR-133a and Prdm16.

A review of epigenetics and its association with ageing of muscle and bone

Ageing is defined as the 'increasing frailty of an organism with time that reduces the ability of that organism to deal with stress'. It has been suggested that epigenetics may underlie the observation that some individuals appear to age faster than others. Epigenetics is the study of changes which occur in an organism due to changes in expression of the genetic code rather than changes to the genetic code itself; that is, epigenetic mechanisms impact upon the function of DNA without changing the DNA sequence. It is important to recognise that epigenetic changes, in contrast to genetic changes, can vary according to different cell types and therefore can demonstrate significant tissue-specificity. There are different types of epigenetic mechanisms: histone modification, non-coding RNAs and DNA methylation. Epigenetic clocks have been developed using statistical techniques to identify the optimal combination of CpG sites (from methylation arrays) to correlate with chronological age. This review considers how epigenetic factors may affect rates of ageing of muscle and bone and provides an overview of current understanding in this area. We discuss studies using first-generation epigenetic clocks, as well as the second-generation iterations, which appear to show stronger associations with the ageing muscle phenotype. We also review epigenome-wide association studies that have been performed in various tissues examining relationships with osteoporosis and fracture. It is hoped that an understanding of this area will lead to interventions that might prevent or reduce rates of musculoskeletal ageing in later life.

Methylation status of VTRNA2-1/nc886 is stable across populations, monozygotic twin pairs and in majority of tissues

Aims & methods: The aim of this study was to characterize the methylation level of a polymorphically imprinted gene, VTRNA2-1/nc886, in human populations and somatic tissues.48 datasets, consisting of more than 30 tissues and >30,000 individuals, were used. Results: nc886 methylation status is associated with twin status and ethnic background, but the variation between populations is limited. Monozygotic twin pairs present concordant methylation, whereas ∼30% of dizygotic twin pairs present discordant methylation in the nc886 locus. The methylation levels of nc886 are uniform across somatic tissues, except in cerebellum and skeletal muscle. Conclusion: The nc886 imprint may be established in the oocyte, and, after implantation, the methylation status is stable, excluding a few specific tissues.

Brain age estimation reveals older adults' accelerated senescence after traumatic brain injury

Adults aged 60 and over are most vulnerable to mild traumatic brain injury (mTBI). Nevertheless, the extent to which chronological age (CA) at injury affects TBI-related brain aging is unknown. This study applies Gaussian process regression to T1-weighted magnetic resonance images (MRIs) acquired within [Formula: see text]7 days and again [Formula: see text]6 months after a single mTBI sustained by 133 participants aged 20-83 (CA [Formula: see text] = 42.6 ± 17 years; 51 females). Brain BAs are estimated, modeled, and compared as a function of sex and CA at injury using a statistical model selection procedure. On average, the brains of older adults age by 15.3 ± 6.9 years after mTBI, whereas those of younger adults age only by 1.8 ± 5.6 years, a significant difference (Welch's t32 =  - 9.17, p ≃ 9.47 × 10-11). For an adult aged [Formula: see text]30 to [Formula: see text]60, the expected amount of TBI-related brain aging is [Formula: see text]3 years greater than in an individual younger by a decade. For an individual over [Formula: see text]60, the respective amount is [Formula: see text]7 years. Despite no significant sex differences in brain aging (Welch's t108 = 0.78, p > 0.78), the statistical test is underpowered. BAs estimated at acute baseline versus chronic follow-up do not differ significantly (t264 = 0.41, p > 0.66, power = 80%), suggesting negligible TBI-related brain aging during the chronic stage of TBI despite accelerated aging during the acute stage. Our results indicate that a single mTBI sustained after age [Formula: see text]60 involves approximately [Formula: see text]10 years of premature and lasting brain aging, which is MRI detectable as early as [Formula: see text]7 days post-injury.

Evaluation of different computational methods for DNA methylation-based biological age

In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.

DNA Methylation, Aging, and Cancer Risk: A Mini-Review

Accumulation of somatic mutations and genomic instability are hallmarks of both aging and cancer. Epigenetic alterations occur across cell types and tissues with advancing age. DNA methylation-based estimates of biologic age can predict important age-related outcomes, including risk of frailty and mortality, and most recently have been shown to be associated with risk of developing cancer. In this mini-review, we examine pathways known to exhibit altered methylation in aging tissues, pre-malignant lesions, and tumors and review methodologies of epigenetic clocks that reliably predict cancer risk, including those derived from methylation studies of peripheral blood, as well as those methylation levels from within the tissues at high risk of cancer.

Making sense of the ageing methylome

Over time, the human DNA methylation landscape accrues substantial damage, which has been associated with a broad range of age-related diseases, including cardiovascular disease and cancer. Various age-related DNA methylation changes have been described, including at the level of individual CpGs, such as differential and variable methylation, and at the level of the whole methylome, including entropy and correlation networks. Here, we review these changes in the ageing methylome as well as the statistical tools that can be used to quantify them. We detail the evidence linking DNA methylation to ageing phenotypes and the longevity strategies aimed at altering both DNA methylation patterns and machinery to extend healthspan and lifespan. Lastly, we discuss theories on the mechanistic causes of epigenetic ageing.

Epigenetic Effects of Healthy Foods and Lifestyle Habits from the Southern European Atlantic Diet Pattern: A Narrative Review

Recent scientific evidence has shown the importance of diet and lifestyle habits for the proper functioning of the human body. A balanced and healthy diet, physical activity, and psychological well-being have a direct beneficial effect on health and can have a crucial role in the development and prognosis of certain diseases. The Southern European Atlantic diet, also named the Atlantic diet, is a unique dietary pattern that occurs in regions that present higher life expectancy, suggesting that this specific dietary pattern is associated with positive health effects. In fact, it is enriched with nutrients of high biological value, which, together with its cooking methods, physical activity promotion, reduction in carbon footprint, and promoting of family meals, promote these positive effects on health. The latest scientific advances in the field of nutri-epigenetics have revealed that epigenetic markers associated with food or nutrients and environmental factors modulate gene expression and, therefore, are involved with both health and disease. Thus, in this review, we evaluated the main aspects that define the Southern European Atlantic diet and the potential epigenetic changes associated with them based on recent studies regarding the main components of these dietary patterns. In conclusion, based on the information existing in the literature, we postulate that the Southern European Atlantic diet could promote healthy aging by means of epigenetic mechanisms. This review highlights the necessity of performing longitudinal studies to demonstrate this proposal.

Epigenome-wide association study of sarcopenia: findings from the Hertfordshire Sarcopenia Study (HSS)

BACKGROUND

Sarcopenia is the age-related loss of muscle mass, strength, and function. Epigenetic processes such as DNA methylation, which integrate both genetic and environmental exposures, have been suggested to contribute to the development of sarcopenia. This study aimed to determine whether differences in the muscle methylome are associated with sarcopenia and its component measures: grip strength, appendicular lean mass index (ALMi), and gait speed.

METHODS

Using the Infinium Human MethylationEPIC BeadChip, we measured DNA methylation in vastus lateralis muscle biopsies of 83 male participants (12 with sarcopenia) with a mean (standard deviation) age of 75.7 (3.6) years from the Hertfordshire Sarcopenia Study (HSS) and Hertfordshire Sarcopenia Study extension (HSSe) and examined associations with sarcopenia and its components. Pathway, histone mark, and transcription factor enrichment of the differentially methylated CpGs (dmCpGs) were determined, and sodium bisulfite pyrosequencing was used to validate the sarcopenia-associated dmCpGs. Human primary myoblasts (n = 6) isolated from vastus lateralis muscle biopsies from male individuals from HSSe were treated with the EZH2 inhibitor GSK343 to assess how perturbations in epigenetic processes may impact myoblast differentiation and fusion, measured by PAX7 and MYHC immunocytochemistry, and mitochondrial bioenergetics determined using the Seahorse XF96.

RESULTS

Sarcopenia was associated with differential methylation at 176 dmCpGs (false discovery rate ≤ 0.05) and 141 differentially methylated regions (Stouffer ≤ 0.05). The sarcopenia-associated dmCpGs were enriched in genes associated with myotube fusion (P = 1.40E-03), oxidative phosphorylation (P = 2.78E-02), and voltage-gated calcium channels (P = 1.59E-04). ALMi was associated with 71 dmCpGs, grip strength with 49 dmCpGs, and gait speed with 23 dmCpGs (false discovery rate ≤ 0.05). There was significant overlap between the dmCpGs associated with sarcopenia and ALMi (P = 3.4E-35), sarcopenia and gait speed (P = 4.78E-03), and sarcopenia and grip strength (P = 7.55E-06). There was also an over-representation of the sarcopenia, ALMi, grip strength, and gait speed-associated dmCpGs with sites of H3K27 trimethylation (all P ≤ 0.05) and amongst EZH2 target genes (all P ≤ 0.05). Furthermore, treatment of human primary myoblasts with the EZH2 inhibitor GSK343 inhibitor led to an increase in PAX7 expression (P ≤ 0.05), decreased myotube fusion (P = 0.043), and an increase in ATP production (P = 0.008), with alterations in the DNA methylation of genes involved in oxidative phosphorylation and myogenesis.

CONCLUSIONS

These findings show that differences in the muscle methylome are associated with sarcopenia and individual measures of muscle mass, strength, and function in older individuals. This suggests that changes in the epigenetic regulation of genes may contribute to impaired muscle function in later life.

Late-life exercise mitigates skeletal muscle epigenetic aging

There are functional benefits to exercise in muscle, even when performed late in life, but the contributions of epigenetic factors to late‐life exercise adaptation are poorly defined. Using reduced representation bisulfite sequencing (RRBS), ribosomal DNA (rDNA) and mitochondrial‐specific examination of methylation, targeted high‐resolution methylation analysis, and DNAge™ epigenetic aging clock analysis with a translatable model of voluntary murine endurance/resistance exercise training (progressive weighted wheel running, PoWeR), we provide evidence that exercise may mitigate epigenetic aging in skeletal muscle. Late‐life PoWeR from 22–24 months of age modestly but significantly attenuates an age‐associated shift toward promoter hypermethylation. The epigenetic age of muscle from old mice that PoWeR‐trained for eight weeks was approximately eight weeks younger than 24‐month‐old sedentary counterparts, which represents ~8% of the expected murine lifespan. These data provide a molecular basis for exercise as a therapy to attenuate skeletal muscle aging.

Epigenetic models developed for plains zebras predict age in domestic horses and endangered equids

Effective conservation and management of threatened wildlife populations require an accurate assessment of age structure to estimate demographic trends and population viability. Epigenetic aging models are promising developments because they estimate individual age with high accuracy, accurately predict age in related species, and do not require invasive sampling or intensive long-term studies. Using blood and biopsy samples from known age plains zebras (Equus quagga), we model epigenetic aging using two approaches: the epigenetic clock (EC) and the epigenetic pacemaker (EPM). The plains zebra EC has the potential for broad application within the genus Equus given that five of the seven extant wild species of the genus are threatened. We test the EC's ability to predict age in sister taxa, including two endangered species and the more distantly related domestic horse, demonstrating high accuracy in all cases. By comparing chronological and estimated age in plains zebras, we investigate age acceleration as a proxy of health status. An interaction between chronological age and inbreeding is associated with age acceleration estimated by the EPM, suggesting a cumulative effect of inbreeding on biological aging throughout life.

Epigenetic Age Acceleration Is Not Associated with Age-Related Macular Degeneration

DNA methylation age (DNAm age) estimation is a powerful biomarker of human ageing. To date, epigenetic clocks have not been evaluated in age-related macular degeneration (AMD). Here, we perform genome-wide DNA methylation analyses in blood of AMD patients with a documented smoking history (14 AMD, 16 Normal), identifying loci of differential methylation (DML) with a relaxed p-value criterion (p ≤ 10⁻⁴). We conduct DNAm age analyses using the Horvath-multi tissue, Hannum and Skin & Blood epigenetic clocks in both blood and retinal pigment epithelium (RPE). We perform Ingenuity Pathway Analysis Causal Network Analysis (IPA CNA) on the topmost significantly differentially methylated CpG probes in blood and RPE. Results show poor performance of epigenetic clocks in RPE. Epigenetic age acceleration (EAA) was not observed in AMD. However, we observe positive EAA in blood of smokers, and in smokers with AMD. DML analysis revealed hypomethylation at cg04953735 within RPTOR (p = 6.51 × 10⁻⁵; Δβ = −11.95%). IPA CNA in the RPE also identified RPTOR as the putative master regulator, predicted to be inhibited in AMD. In conclusion, this is the first study evaluating an association of epigenetic ageing in AMD. We posit a role for RPTOR as a common master regulator of methylation changes in the RPE in AMD.

Resistance training rejuvenates the mitochondrial methylome in aged human skeletal muscle

Resistance training (RT) dynamically alters the skeletal muscle nuclear DNA methylome. However, no study has examined if RT affects the mitochondrial DNA (mtDNA) methylome. Herein, ten older, Caucasian untrained males (65 ± 7 y.o.) performed six weeks of full-body RT (twice weekly). Body composition and knee extensor torque were assessed prior to and 72 h following the last RT session. Vastus lateralis (VL) biopsies were also obtained. VL DNA was subjected to reduced representation bisulfite sequencing providing excellent coverage across the ~16-kilobase mtDNA methylome (254 CpG sites). Biochemical assays were also performed, and older male data were compared to younger trained males (22 ± 2 y.o., n = 7, n = 6 Caucasian & n = 1 African American). RT increased whole-body lean tissue mass (p = .017), VL thickness (p = .012), and knee extensor torque (p = .029) in older males. RT also affected the mtDNA methylome, as 63% (159/254) of the CpG sites demonstrated reduced methylation (p < .05). Several mtDNA sites presented a more "youthful" signature in older males after RT in comparison to younger males. The 1.12 kilobase mtDNA D-loop/control region, which regulates replication and transcription, possessed enriched hypomethylation in older males following RT. Enhanced expression of mitochondrial H- and L-strand genes and complex III/IV protein levels were also observed (p < .05). While limited to a shorter-term intervention, this is the first evidence showing that RT alters the mtDNA methylome in skeletal muscle. Observed methylome alterations may enhance mitochondrial transcription, and RT evokes mitochondrial methylome profiles to mimic younger men. The significance of these findings relative to broader RT-induced epigenetic changes needs to be elucidated.

Meta-analysis of genome-wide DNA methylation and integrative omics of age in human skeletal muscle

BACKGROUND

Knowledge of age-related DNA methylation changes in skeletal muscle is limited, yet this tissue is severely affected by ageing in humans.

METHODS

We conducted a large-scale epigenome-wide association study meta-analysis of age in human skeletal muscle from 10 studies (total n = 908 muscle methylomes from men and women aged 18-89 years old). We explored the genomic context of age-related DNA methylation changes in chromatin states, CpG islands, and transcription factor binding sites and performed gene set enrichment analysis. We then integrated the DNA methylation data with known transcriptomic and proteomic age-related changes in skeletal muscle. Finally, we updated our recently developed muscle epigenetic clock (https://bioconductor.org/packages/release/bioc/html/MEAT.html).

RESULTS

We identified 6710 differentially methylated regions at a stringent false discovery rate <0.005, spanning 6367 unique genes, many of which related to skeletal muscle structure and development. We found a strong increase in DNA methylation at Polycomb target genes and bivalent chromatin domains and a concomitant decrease in DNA methylation at enhancers. Most differentially methylated genes were not altered at the mRNA or protein level, but they were nonetheless strongly enriched for genes showing age-related differential mRNA and protein expression. After adding a substantial number of samples from five datasets (+371), the updated version of the muscle clock (MEAT 2.0, total n = 1053 samples) performed similarly to the original version of the muscle clock (median of 4.4 vs. 4.6 years in age prediction error), suggesting that the original version of the muscle clock was very accurate.

CONCLUSIONS

We provide here the most comprehensive picture of DNA methylation ageing in human skeletal muscle and reveal widespread alterations of genes involved in skeletal muscle structure, development, and differentiation. We have made our results available as an open-access, user-friendly, web-based tool called MetaMeth (https://sarah-voisin.shinyapps.io/MetaMeth/).

Assessing the rate of aging to monitor aging itself

Healthy aging is the prime goal of aging research and interventions. Healthy aging or not can be quantified by biological aging rates estimated by aging clocks. Generation and accumulation of large scale high-dimensional biological data together with maturation of artificial intelligence among other machine learning techniques, have enabled and spurred the rapid development of various aging rate estimators (aging clocks). Here we review the data sources and compare the algorithms of recent human aging clocks, and the applications of these clocks in both researches and daily life. We envision that not only more and multiscale data on cross-sectional data will add momentum to the aging clock development, new longitudinal and interventional data will further raise the aging clock development to the next level to be trained by true biological age such as morbidity and mortality age.

Breast-Specific Molecular Clocks Comprised of ELF5 Expression and Promoter Methylation Identify Individuals Susceptible to Cancer Initiation

A robust breast cancer prevention strategy requires risk assessment biomarkers for early detection. We show that expression of ELF5, a transcription factor critical for normal mammary development, is downregulated in mammary luminal epithelia with age. DNA methylation of the ELF5 promoter is negatively correlated with expression in an age-dependent manner. Both ELF5 methylation and gene expression were used to build biological clocks to estimate chronological ages of mammary epithelia. ELF5 clock-based estimates of biological age in luminal epithelia from average-risk women were within three years of chronological age. Biological ages of breast epithelia from BRCA1 or BRCA2 mutation carriers, who were high risk for developing breast cancer, suggested they were accelerated by two decades relative to chronological age. The ELF5 DNA methylation clock had better performance at predicting biological age in luminal epithelial cells as compared with two other epigenetic clocks based on whole tissues. We propose that the changes in ELF5 expression or ELF5-proximal DNA methylation in luminal epithelia are emergent properties of at-risk breast tissue and constitute breast-specific biological clocks. PREVENTION RELEVANCE: ELF5 expression or DNA methylation level at the ELF5 promoter region can be used as breast-specific biological clocks to identify women at higher than average risk of breast cancer.

Estimage: a webserver hub for the computation of methylation age

Methylage is an epigenetic marker of biological age that exploits the correlation between the methylation state of specific CG dinucleotides (CpGs) and chronological age (in years), gestational age (in weeks), cellular age (in cell cycles or as telomere length, in kilobases). Using DNA methylation data, methylage is measurable via the so called epigenetic clocks. Importantly, alterations of the correlation between methylage and age (age acceleration or deceleration) have been stably associated with pathological states and occur long before clinical signs of diseases become overt, making epigenetic clocks a potentially disruptive tool in preventive, diagnostic and also in forensic applications. Nevertheless, methylage dependency from CpGs selection, mathematical modelling, tissue specificity and age range, still makes the potential of this biomarker limited. In order to enhance model comparisons, interchange, availability, robustness and standardization, we organized a selected set of clocks within a hub webservice, EstimAge (Estimate of methylation Age, http://estimage.iac.rm.cnr.it), which intuitively and informatively enables quick identification, computation and comparison of available clocks, with the support of standard statistics.

Blood and skeletal muscle ageing determined by epigenetic clocks and their associations with physical activity and functioning

The aim of this study was to investigate the correspondence of different biological ageing estimates (i.e. epigenetic age) in blood and muscle tissue and their associations with physical activity (PA), physical function and body composition. Two independent cohorts (N = 139 and N = 47) were included, whose age span covered adulthood (23–69 years). Whole blood and m. vastus lateralis samples were collected, and DNA methylation was analysed. Four different DNA methylation age (DNAmAge) estimates were calculated using genome-wide methylation data and publicly available online tools. A novel muscle-specific methylation age was estimated using the R-package ‘MEAT’. PA was measured with questionnaires and accelerometers. Several tests were conducted to estimate cardiorespiratory fitness and muscle strength. Body composition was estimated by dual-energy X-ray absorptiometry. DNAmAge estimates from blood and muscle were highly correlated with chronological age, but different age acceleration estimates were weakly associated with each other. The monozygotic twin within-pair similarity of ageing pace was higher in blood (r = 0.617–0.824) than in muscle (r = 0.523–0.585). Associations of age acceleration estimates with PA, physical function and body composition were weak in both tissues and mostly explained by smoking and sex. The muscle-specific epigenetic clock MEAT was developed to predict chronological age, which may explain why it did not associate with functional phenotypes. The Horvath’s clock and GrimAge were weakly associated with PA and related phenotypes, suggesting that higher PA would be linked to accelerated biological ageing in muscle. This may, however, be more reflective of the low capacity of epigenetic clock algorithms to measure functional muscle ageing than of actual age acceleration. Based on our results, the investigated epigenetic clocks have rather low value in estimating muscle ageing with respect to the physiological adaptations that typically occur due to ageing or PA. Thus, further development of methods is needed to gain insight into muscle tissue-specific ageing and the underlying biological pathways.

Mapping the transcriptomic changes of endothelial compartment in human hippocampus across aging and mild cognitive impairment

Compromise of the vascular system has important consequences on cognitive abilities and neurodegeneration. The identification of the main molecular signatures present in the blood vessels of human hippocampus could provide the basis to understand and tackle these pathologies. As direct vascular experimentation in hippocampus is problematic, we achieved this information by computationally disaggregating publicly available whole microarrays data of human hippocampal homogenates. Three conditions were analyzed: 'Young Adults', 'Aged', and 'aged with Mild Cognitive Impairment' (MCI). The genes identified were contrasted against two independent data-sets. Here we show that the endothelial cells from the Younger Group appeared in an 'activated stage'. In turn, in the Aged Group, the endothelial cells showed a significant loss of response to shear stress, changes in cell adhesion molecules, increased inflammation, brain-insulin resistance, lipidic alterations, and changes in the extracellular matrix. Some specific changes in the MCI group were also detected. Noticeably, in this study the features arisen from the Aged Group (high tortuosity, increased bifurcations, and smooth muscle proliferation), pose the need for further experimental verification to discern between the occurrence of arteriogenesis and/or vascular remodeling by capillary arterialization. This article has an associated First Person interview with the first author of the paper.

The biphasic and age-dependent impact of klotho on hallmarks of aging and skeletal muscle function

Aging is accompanied by disrupted information flow, resulting from accumulation of molecular mistakes. These mistakes ultimately give rise to debilitating disorders including skeletal muscle wasting, or sarcopenia. To derive a global metric of growing 'disorderliness' of aging muscle, we employed a statistical physics approach to estimate the state parameter, entropy, as a function of genes associated with hallmarks of aging. Escalating network entropy reached an inflection point at old age, while structural and functional alterations progressed into oldest-old age. To probe the potential for restoration of molecular 'order' and reversal of the sarcopenic phenotype, we systemically overexpressed the longevity protein, Klotho, via AAV. Klotho overexpression modulated genes representing all hallmarks of aging in old and oldest-old mice, but pathway enrichment revealed directions of changes were, for many genes, age-dependent. Functional improvements were also age-dependent. Klotho improved strength in old mice, but failed to induce benefits beyond the entropic tipping point.

Assessing the co-variability of DNA methylation across peripheral cells and tissues: Implications for the interpretation of findings in epigenetic epidemiology

Most epigenome-wide association studies (EWAS) quantify DNA methylation (DNAm) in peripheral tissues such as whole blood to identify positions in the genome where variation is statistically associated with a trait or exposure. As whole blood comprises a mix of cell types, it is unclear whether trait-associated DNAm variation is specific to an individual cellular population. We collected three peripheral tissues (whole blood, buccal epithelial and nasal epithelial cells) from thirty individuals. Whole blood samples were subsequently processed using fluorescence-activated cell sorting (FACS) to purify five constituent cell-types (monocytes, granulocytes, CD4⁺ T cells, CD8⁺ T cells, and B cells). DNAm was profiled in all eight sample-types from each individual using the Illumina EPIC array. We identified significant differences in both the level and variability of DNAm between different sample types, and DNAm data-derived estimates of age and smoking were found to differ dramatically across sample types from the same individual. We found that for the majority of loci variation in DNAm in individual blood cell types was only weakly predictive of variance in DNAm measured in whole blood, although the proportion of variance explained was greater than that explained by either buccal or nasal epithelial samples. Covariation across sample types was much higher for DNAm sites influenced by genetic factors. Overall, we observe that DNAm variation in whole blood is additively influenced by a combination of the major blood cell types. For a subset of sites, however, variable DNAm detected in whole blood can be attributed to variation in a single blood cell type providing potential mechanistic insight about EWAS findings. Our results suggest that associations between whole blood DNAm and traits or exposures reflect differences in multiple cell types and our data will facilitate the interpretation of findings in epigenetic epidemiology.

As epigenetic variation is cell-type specific, an ongoing challenge in epigenetic epidemiology is how to interpret studies performed using bulk tissue (for example, whole blood) which comprises a mix of different cell types. In this study, we identified major differences in DNA methylation (DNAm) across multiple peripheral tissues and different blood cell types, with each sample type being characterized by a unique signature across multiple genomic loci. We demonstrate how these differences influence commonly used prediction scores derived from DNAm data for age and tobacco smoking, with estimates for the same individual being highly variable across tissues and cell types. Our results enabled us to assess the extent to which variable DNAm in each individual blood cell type relates to variation measured in whole blood. We found that although individual blood cell types predict more of the variation in DNAm in whole blood compared to buccal and nasal epithelial cells, the actual proportion of variance explained is relatively small, except for at sites where DNAm is under genetic control. Our data indicate that for most sites variation in multiple blood cell types additively combines to drive variation in DNAm measured in whole blood. Of note, for a subset of sites, variation in DNAm detected in whole blood can be attributed to a specific blood cell type, potentially facilitating the interpretation of EWAS findings.

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex

Human DNA methylation data have been used to develop biomarkers of ageing, referred to as ‘epigenetic clocks’, which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

Leisure-Time and Occupational Physical Activity Associates Differently with Epigenetic Aging

PURPOSE

Greater leisure-time physical activity (LTPA) associates with healthier lives, but knowledge regarding occupational physical activity (OPA) is more inconsistent. DNA methylation (DNAm) patterns capture age-related changes in different tissues. We aimed to assess how LTPA and OPA are associated with three DNAm-based epigenetic age estimates, namely, DNAm age, PhenoAge, and GrimAge.

METHODS

The participants were young adult (21-25 yr, n = 285) and older (55-74 yr, n = 235) twin pairs, including 16 pairs with documented long-term LTPA discordance. Genome-wide DNAm from blood samples was used to compute DNAm age, PhenoAge, and GrimAge Age acceleration (Acc), which describes the difference between chronological and epigenetic ages. Physical activity was assessed with sport, leisure-time, and work indices based on the Baecke Questionnaire. Genetic and environmental variance components of epigenetic age Acc were estimated by quantitative genetic modeling.

RESULTS

Epigenetic age Acc was highly heritable in young adult and older twin pairs (~60%). Sport index was associated with slower and OPA with faster DNAm GrimAge Acc after adjusting the model for sex. Genetic factors and nonshared environmental factors in common with sport index explained 1.5%-2.7% and 1.9%-3.5%, respectively, of the variation in GrimAge Acc. The corresponding proportions considering OPA were 0.4%-1.8% and 0.7%-1.8%, respectively. However, these proportions were minor (<0.5%) after adjusting the model for smoking status.

CONCLUSIONS

LTPA associates with slower and OPA with faster epigenetic aging. However, adjusting the models for smoking status, which may reflect the accumulation of unhealthy lifestyle habits, attenuated the associations.

Modeling the human aging transcriptome across tissues, health status, and sex

Aging in humans is an incredibly complex biological process that leads to increased susceptibility to various diseases. Understanding which genes are associated with healthy aging can provide valuable insights into aging mechanisms and possible avenues for therapeutics to prolong healthy life. However, modeling this complex biological process requires an enormous collection of high‐quality data along with cutting‐edge computational methods. Here, we have compiled a large meta‐analysis of gene expression data from RNA‐Seq experiments available from the Sequence Read Archive. We began by reprocessing more than 6000 raw samples—including mapping, filtering, normalization, and batch correction—to generate 3060 high‐quality samples spanning a large age range and multiple different tissues. We then used standard differential expression analyses and machine learning approaches to model and predict aging across the dataset, achieving an R² value of 0.96 and a root‐mean‐square error of 3.22 years. These models allow us to explore aging across health status, sex, and tissue and provide novel insights into possible aging processes. We also explore how preprocessing parameters affect predictions and highlight the reproducibility limits of these machine learning models. Finally, we develop an online tool for predicting the ages of human transcriptomic samples given raw gene expression counts. Together, this study provides valuable resources and insights into the transcriptomics of human aging.

DNA methylation across the genome in aged human skeletal muscle tissue and muscle-derived cells: the role of HOX genes and physical activity

Skeletal muscle tissue demonstrates global hypermethylation with age. However, methylome changes across the time-course of differentiation in aged human muscle derived cells, and larger coverage arrays in aged muscle tissue have not been undertaken. Using 850K DNA methylation arrays we compared the methylomes of young (27 ± 4.4 years) and aged (83 ± 4 years) human skeletal muscle and that of young/aged heterogenous muscle-derived human primary cells (HDMCs) over several time points of differentiation (0, 72 h, 7, 10 days). Aged muscle tissue was hypermethylated compared with young tissue, enriched for; pathways-in-cancer (including; focal adhesion, MAPK signaling, PI3K-Akt-mTOR signaling, p53 signaling, Jak-STAT signaling, TGF-beta and notch signaling), rap1-signaling, axon-guidance and hippo-signalling. Aged cells also demonstrated a hypermethylated profile in pathways; axon-guidance, adherens-junction and calcium-signaling, particularly at later timepoints of myotube formation, corresponding with reduced morphological differentiation and reductions in MyoD/Myogenin gene expression compared with young cells. While young cells showed little alterations in DNA methylation during differentiation, aged cells demonstrated extensive and significantly altered DNA methylation, particularly at 7 days of differentiation and most notably in focal adhesion and PI3K-AKT signalling pathways. While the methylomes were vastly different between muscle tissue and HDMCs, we identified a small number of CpG sites showing a hypermethylated state with age, in both muscle tissue and cells on genes KIF15, DYRK2, FHL2, MRPS33, ABCA17P. Most notably, differential methylation analysis of chromosomal regions identified three locations containing enrichment of 6–8 CpGs in the HOX family of genes altered with age. With HOXD10, HOXD9, HOXD8, HOXA3, HOXC9, HOXB1, HOXB3, HOXC-AS2 and HOXC10 all hypermethylated in aged tissue. In aged cells the same HOX genes (and additionally HOXC-AS3) displayed the most variable methylation at 7 days of differentiation versus young cells, with HOXD8, HOXC9, HOXB1 and HOXC-AS3 hypermethylated and HOXC10 and HOXC-AS2 hypomethylated. We also determined that there was an inverse relationship between DNA methylation and gene expression for HOXB1, HOXA3 and HOXC-AS3. Finally, increased physical activity in young adults was associated with oppositely regulating HOXB1 and HOXA3 methylation compared with age. Overall, we demonstrate that a considerable number of HOX genes are differentially epigenetically regulated in aged human skeletal muscle and HDMCs and increased physical activity may help prevent age-related epigenetic changes in these HOX genes.