DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy

Epigenetic age prediction

Advanced age is the main common risk factor for cancer, cardiovascular disease and neurodegeneration. Yet, more is known about the molecular basis of any of these groups of diseases than the changes that accompany ageing itself. Progress in molecular ageing research was slow because the tools predicting whether someone aged slowly or fast (biological age) were unreliable. To understand ageing as a risk factor for disease and to develop interventions, the molecular ageing field needed a quantitative measure; a clock for biological age. Over the past decade, a number of age predictors utilising DNA methylation have been developed, referred to as epigenetic clocks. While they appear to estimate biological age, it remains unclear whether the methylation changes used to train the clocks are a reflection of other underlying cellular or molecular processes, or whether methylation itself is involved in the ageing process. The precise aspects of ageing that the epigenetic clocks capture remain hidden and seem to vary between predictors. Nonetheless, the use of epigenetic clocks has opened the door towards studying biological ageing quantitatively, and new clocks and applications, such as forensics, appear frequently. In this review, we will discuss the range of epigenetic clocks available, their strengths and weaknesses, and their applicability to various scientific queries.

Epigenetic Clocks Are Not Accelerated in COVID-19 Patients

Age is a major risk factor for severe outcome of the 2019 coronavirus disease (COVID-19). In this study, we followed the hypothesis that particularly patients with accelerated epigenetic age are affected by severe outcomes of COVID-19. We investigated various DNA methylation datasets of blood samples with epigenetic aging signatures and performed targeted bisulfite amplicon sequencing. Overall, epigenetic clocks closely correlated with the chronological age of patients, either with or without acute respiratory distress syndrome. Furthermore, lymphocytes did not reveal significantly accelerated telomere attrition. Thus, these biomarkers cannot reliably predict higher risk for severe COVID-19 infection in elderly patients.

Epigenetic age is associated with baseline and 3-year change in frailty in the Canadian Longitudinal Study on Aging

BACKGROUND

The trajectory of frailty in older adults is important to public health; therefore, markers that may help predict this and other important outcomes could be beneficial. Epigenetic clocks have been developed and are associated with various health-related outcomes and sociodemographic factors, but associations with frailty are poorly described. Further, it is uncertain whether newer generations of epigenetic clocks, trained on variables other than chronological age, would be more strongly associated with frailty than earlier developed clocks. Using data from the Canadian Longitudinal Study on Aging (CLSA), we tested the hypothesis that clocks trained on phenotypic markers of health or mortality (i.e., Dunedin PoAm, GrimAge, PhenoAge and Zhang in Nat Commun 8:14617, 2017) would best predict changes in a 76-item frailty index (FI) over a 3-year interval, as compared to clocks trained on chronological age (i.e., Hannum in Mol Cell 49:359-367, 2013, Horvath in Genome Biol 14:R115, 2013, Lin in Aging 8:394-401, 2016, and Yang Genome Biol 17:205, 2016).

RESULTS

We show that in 1446 participants, phenotype/mortality-trained clocks outperformed age-trained clocks with regard to the association with baseline frailty (mean = 0.141, SD = 0.075), the greatest of which is GrimAge, where a 1-SD increase in ΔGrimAge (i.e., the difference from chronological age) was associated with a 0.020 increase in frailty (95% CI 0.016, 0.024), or ~ 27% relative to the SD in frailty. Only GrimAge and Hannum (Mol Cell 49:359-367, 2013) were significantly associated with change in frailty over time, where a 1-SD increase in ΔGrimAge and ΔHannum 2013 was associated with a 0.0030 (95% CI 0.0007, 0.0050) and 0.0028 (95% CI 0.0007, 0.0050) increase over 3 years, respectively, or ~ 7% relative to the SD in frailty change.

CONCLUSION

Both prevalence and change in frailty are associated with increased epigenetic age. However, not all clocks are equally sensitive to these outcomes and depend on their underlying relationship with chronological age, healthspan and lifespan. Certain clocks were significantly associated with relatively short-term changes in frailty, thereby supporting their utility in initiatives and interventions to promote healthy aging.

Impact of excessive alcohol abuse on age prediction using the VISAGE enhanced tool for epigenetic age estimation in blood

DNA methylation-based clocks provide the most accurate age estimates with practical implications for clinical and forensic genetics. However, the effects of external factors that may influence the estimates are poorly studied. Here, we evaluated the effect of alcohol consumption on epigenetic age prediction in a cohort of extreme alcohol abusers. Blood samples from deceased alcohol abusers and age- and sex-matched controls were analyzed using the VISAGE enhanced tool for age prediction from somatic tissues that enables examination of 44 CpGs within eight age markers. Significantly altered DNA methylation was recorded for alcohol abusers in MIR29B2CHG. This resulted in a mean predicted age of 1.4 years higher compared to the controls and this trend increased in older individuals. The association of alcohol abuse with epigenetic age acceleration, as determined by the prediction analysis performed based on MIR29B2CHG, was small but significant (β = 0.190; P-value = 0.007). However, the observed alteration in DNA methylation of MIR29B2CHG had a non-significant effect on age estimation with the VISAGE age prediction model. The mean absolute error in the alcohol-abusing cohort was 3.1 years, compared to 3.3 years in the control group. At the same time, upregulation of MIR29B2CHG expression may have a biological function, which merits further studies.

Frailty and Biological Age

A reliable model of biological age is instrumental in the field of geriatrics and gerontology. This model should account for the heterogeneity and plasticity of aging and also accurately predict aging-related adverse outcomes. Epigenetic age models are based on DNA methylation levels at selected genomic sites and can be significant predictors of mortality and healthy/unhealthy aging. However, the biological function of DNA methylation at selected sites is yet to be determined. Frailty is a syndrome resulting from decreased physiological reserves and resilience. The frailty index is a probability-based extension of the concept of frailty. Defined as the proportion of health deficits, the frailty index quantifies the progression of unhealthy aging. The frailty index is currently the best predictor of mortality. It is associated with various biological factors and provides insight into the biological processes of aging. Investigation of the multi-omics factors associated with the frailty index will provide further insight.

DeepMAge: A Methylation Aging Clock Developed with Deep Learning

DNA methylation aging clocks have become an invaluable tool in biogerontology research since their inception in 2013. Today, a variety of machine learning approaches have been tested for the purpose of predicting human age based on molecular-level features. Among these, deep learning, or neural networks, is an especially promising approach that has been used to construct accurate clocks using blood biochemistry, transcriptomics, and microbiomics data-feats unachieved by other algorithms. In this article, we explore how deep learning performs in a DNA methylation setting and compare it to the current industry standard-the 353 CpG clock published in 2013. The aging clock we are presenting (DeepMAge) is a neural network regressor trained on 4,930 blood DNA methylation profiles from 17 studies. Its absolute median error was 2.77 years in an independent verification set of 1,293 samples from 15 studies. DeepMAge shows biological relevance by assigning a higher predicted age to people with various health-related conditions, such as ovarian cancer, irritable bowel diseases, and multiple sclerosis.

Feature Selection Algorithms Enhance the Accuracy of Frailty Indexes as Measures of Biological Age

Biological age captures some of the variance in life expectancy for which chronological age is not accountable, and it quantifies the heterogeneity in the presentation of the aging phenotype in various individuals. Among the many quantitative measures of biological age, the mathematically uncomplicated frailty/deficit index is simply the proportion of the total health deficits in various health items surveyed in different individuals. We used 3 different statistical methods that are popular in machine learning to select 17-28 health items that together are highly predictive of survival/mortality, from independent study cohorts. From the selected sets, we calculated frailty indexes and Klemera-Doubal's biological age estimates, and then compared their mortality prediction performance using Cox proportional hazards regression models. Our results indicate that the frailty index outperforms age and Klemera-Doubal's biological age estimates, especially among the oldest old who are most prone to biological aging-caused mortality. We also showed that a DNA methylation index, which was generated by applying the frailty/deficit index calculation method to 38 CpG sites that were selected using the same machine learning algorithms, can predict mortality even better than the best performing frailty index constructed from health, function, and blood chemistry.

DNA methylation-based age clocks: From age prediction to age reversion

Aging as an irretrievable occurrence throughout the entire life is characterized by a progressive decline in physiological functionality and enhanced disease vulnerability. Numerous studies have demonstrated that epigenetic modifications, particularly DNA methylation (DNAm), correlate with aging and age-related diseases. Several investigations have attempted to predict chronological age using the age-related alterations in the DNAm of certain CpG sites. Here we categorize different studies that tracked the aging process in the DNAm landscape to show how epigenetic age clocks evolved from a chronological age estimator to an indicator of lifespan and healthspan. We also describe the health and disease predictive potential of estimated epigenetic age acceleration regarding different clinical conditions and lifestyle factors. Considering the revealed age-related epigenetic changes, the recent age-reprogramming strategies are discussed which are promising methods for resetting the aging clocks.

Epigenetic clocks reveal a rejuvenation event during embryogenesis followed by aging

The notion that the germ line does not age goes back to the 19th-century ideas of August Weismann. However, being metabolically active, the germ line accumulates damage and other changes over time, i.e., it ages. For new life to begin in the same young state, the germ line must be rejuvenated in the offspring. Here, we developed a multi-tissue epigenetic clock and applied it, together with other aging clocks, to track changes in biological age during mouse and human prenatal development. This analysis revealed a significant decrease in biological age, i.e., rejuvenation, during early stages of embryogenesis, followed by an increase in later stages. We further found that pluripotent stem cells do not age even after extensive passaging and that the examined epigenetic age dynamics is conserved across species. Overall, this study uncovers a natural rejuvenation event during embryogenesis and suggests that the minimal biological age (ground zero) marks the beginning of organismal aging.

The pleiotropic neuroprotective effects of resveratrol in cognitive decline and Alzheimer's disease pathology: From antioxidant to epigenetic therapy

While the elderly segment of the population continues growing in importance, neurodegenerative diseases increase exponentially. Lifestyle factors such as nutrition, exercise, and education, among others, influence ageing progression, throughout life. Notably, the Central Nervous System (CNS) can benefit from nutritional strategies and dietary interventions that prevent signs of senescence, such as cognitive decline or neurodegenerative diseases such as Alzheimer's disease and Parkinson's Disease. The dietary polyphenol Resveratrol (RV) possesses antioxidant and cytoprotective effects, producing neuroprotection in several organisms. The oxidative stress (OS) occurs because of Reactive oxygen species (ROS) accumulation that has been proposed to explain the cause of the ageing. One of the most harmful effects of ROS in the cell is DNA damage. Nevertheless, there is also evidence demonstrating that OS can produce other molecular changes such as mitochondrial dysfunction, inflammation, apoptosis, and epigenetic modifications, among others. Interestingly, the dietary polyphenol RV is a potent antioxidant and possesses pleiotropic actions, exerting its activity through various molecular pathways. In addition, recent evidence has shown that RV mediates epigenetic changes involved in ageing and the function of the CNS that persists across generations. Furthermore, it has been demonstrated that RV interacts with gut microbiota, showing modifications in bacterial composition associated with beneficial effects. In this review, we give a comprehensive overview of the main mechanisms of action of RV in different experimental models, including clinical trials and discuss how the interconnection of these molecular events could explain the neuroprotective effects induced by RV.

An epigenetic clock to estimate the age of living beluga whales

DNA methylation data facilitate the development of accurate molecular estimators of chronological age or "epigenetic clocks." We present a robust epigenetic clock for the beluga whale, Delphinapterus leucas, developed for an endangered population in Cook Inlet, Alaska, USA. We used a custom methylation array to measure methylation levels at 37,491 cytosine-guanine sites (CpGs) from skin samples of dead whales (n = 67) whose chronological ages were estimated based on tooth growth layer groups. Using these calibration data, a penalized regression model selected 23 CpGs, providing an R 2 = 0.92 for the training data; and an R 2 = 0.74 and median absolute age error = 2.9 years for the leave one out cross-validation. We applied the epigenetic clock to an independent dataset of 38 skin samples collected with a biopsy dart from living whales between 2016 and 2018. Age estimates ranged from 11 to 27 years. We also report sex correlations in CpG data and describe an approach of identifying the sex of an animal using DNA methylation. The epigenetic estimators of age and sex presented here have broad applications for conservation and management of Cook Inlet beluga whales and potentially other cetaceans.

Biological aging in schizophrenia and psychosis severity: DNA methylation analysis

The physiological changes associated with normal aging are known to occur earlier in individuals with schizophrenia (SCZ). One of the phenomena linked with normal aging is the change in patterns of epigenetic modifications. We recruited 138 individuals with SCZ spectrum disorders and extracted DNA from white blood cells. The combinations of pre-selected DNA methylation sites were utilized to estimate the 'methylation age' (DNAm age) and evaluate evidence of epigenetic age acceleration. We investigated the correlation between the epigenetic age acceleration measures and psychosis severity; furthermore, we estimated blood cell counts based on DNA methylation levels. The extrinsic epigenetic age acceleration showed a significant correlation with the Brief Psychiatric Rating Scale (BPRS) disorganization subscale(r=0.222, p=0.039).Both Horvath age acceleration and Hannum age acceleration showed a significant correlation (r=0.221, p=0.029; r=0.242, p=0.017 respectively) with the Symptom Checklist 90 (SCL-90) psychotic domain. Overall, this study shows some evidence of epigenetic age acceleration associated with psychosis severity using two different algorithms for DNAm age analysis.

Specific Differentially Methylated and Expressed Genes in People with Longevity Family History

Background

We attempt to identify specific differentially methylated and expressed genes in people with longevity family history, it will contribute to discover significant features about human longevity.

Methods

A prevalence study was conducted during October 2017 to January 2019 in Bama County of Guangxi, China and individuals were recruited and grouped into longevity family (n=60) and non-longevity family (n=60) to identify differentially methylated genes (DMGs). The expression profile dataset GSE16717 was downloaded from the GEO database in which individuals were divided into 3 groups, namely longevity (n=50), longevity offspring (n=50) and control (n=50) for identifying differentially expressed genes (DEGs). It was considered significantly different when P or adjusted P≤0.05.

Results

In total, 117 longevity-related hypermethylated genes enriched in interleukin secretion/production regulation, chemokine signaling pathway and natural killer cell-mediated cytotoxicity. Another 296 significant key longevity-related DEGs primarily involved in protein binding, nucleus, cytoplasm, T cell receptor signaling pathway and Metabolic pathway, H19 and PFKFB4 were found to be both methylated and downregulated in people with longevity family history.

Conclusion

Human longevity-specific genes involve in many immunity regulations and cellular immunity pathways, H19 and PFKFB4 show hypermethylated and suppressed status in people with longevity family history and might serve as longevity candidate genes.

Promising biomarkers of human aging: In search of a multi-omics panel to understand the aging process from a multidimensional perspective

The aging process has been linked to the occurrence of chronic diseases and functional impairments, including cancer, sarcopenia, frailty, metabolic, cardiovascular, and neurodegenerative diseases. Nonetheless, aging is highly variable and heterogeneous and represents a challenge for its characterization. In this sense, intrinsic capacity (IC) stands as a novel perspective by the World Health Organization, which integrates the individual wellbeing, environment, and risk factors to understand aging. However, there is a lack of quantitative and qualitative attributes to define it objectively. Therefore, in this review we attempt to summarize the most relevant and promising biomarkers described in clinical studies at date over different molecular levels, including epigenomics, transcriptomics, proteomics, metabolomics, and the microbiome. To aid gerontologists, geriatricians, and biomedical researchers to understand the aging process through the IC. Aging biomarkers reflect the physiological state of individuals and the underlying mechanisms related to homeostatic changes throughout an individual lifespan; they demonstrated that aging could be measured independently of time (that may explain its heterogeneity) and to be helpful to predict age-related syndromes and mortality. In summary, we highlight the areas of opportunity and gaps of knowledge that must be addressed to fully integrate biomedical findings into clinically useful tools and interventions.

Epigenetic alterations in aging tooth and the reprogramming potential

Tooth compartments and associated supportive tissues exhibit significant alterations during aging, leading to their impaired functioning. Aging not only affects the structure and function of dental tissue but also reduces its capacity to maintain physiological homeostasis and the healing process. Decreased cementocyte viability; diminished regenerative potential of stem cells residing in the pulp, alveolar bone and periodontal ligament; and impaired osteogenic and odontogenic differentiation capacity of progenitor cells are among the cellular impacts associated with oral aging. Various physiological and pathological phenomena are regulated by the epigenome, and hence, changes in epigenetic markers due to external stimuli have been reported in aging oral tissues and are considered a possible molecular mechanism underlying dental aging. The role of nutri-epigenetics in aging has emerged as an attractive research area. Thus far, various nutrients and bioactive compounds have been identified to have a modulatory effect on the epigenetic machinery, showing a promising response in dental aging. The human microbiota is another key player in aging and can be a target for anti-aging interventions in dental tissue. Considering the reversible characteristics of epigenetic markers and the potential for environmental factors to manipulate the epigenome, to minimize the deteriorative effects of aging, it is important to evaluate the linkage between external stimuli and their effects in terms of age-related epigenetic modifications.

"Epigenetic clocks": Theory and applications in human biology

All humans age, but how we age-and how fast-differs considerably from person to person. This deviation between apparent age and chronological age is often referred to as "biological age" (BA) and until recently robust tools for studying BA have been scarce. "Epigenetic clocks" are starting to change this. Epigenetic clocks use predictable changes in the epigenome, usually DNA methylation, to estimate chronological age with unprecedented accuracy. More importantly, deviations between epigenetic age and chronological age predict a broad range of health outcomes and mortality risks better than chronological age alone. Thus, epigenetic clocks appear to capture fundamental molecular processes tied to BA and can serve as powerful tools for studying health, development, and aging across the lifespan. In this article, I review epigenetic clocks, especially as they relate to key theoretical and applied issues in human biology. I first provide an overview of how epigenetic clocks are constructed and what we know about them. I then discuss emerging applications of particular relevance to human biologists-those related to reproduction, life-history, stress, and the environment. I conclude with an overview of the methods necessary for implementing epigenetic clocks, including considerations of study design, sample collection, and technical considerations for processing and interpreting epigenetic clocks. The goal of this review is to highlight some of the ways that epigenetic clocks can inform questions in human biology, and vice versa, and to provide human biologists with the foundational knowledge necessary to successfully incorporate epigenetic clocks into their research.

Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities

The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations. More than a dozen aging clocks use molecular features to predict an organism's age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms. We also discuss promising future directions of research in biohorology - the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field.

New targeted approaches for epigenetic age predictions

Background

Age-associated DNA methylation changes provide a promising biomarker for the aging process. While genome-wide DNA methylation profiles enable robust age-predictors by integration of many age-associated CG dinucleotides (CpGs), there are various alternative approaches for targeted measurements at specific CpGs that better support standardized and cost-effective high-throughput analysis.

Results

In this study, we utilized 4647 Illumina BeadChip profiles of blood to select CpG sites that facilitate reliable age-predictions based on pyrosequencing. We demonstrate that the precision of DNA methylation measurements can be further increased with droplet digital PCR (ddPCR). In comparison, bisulfite barcoded amplicon sequencing (BBA-seq) gave slightly lower correlation between chronological age and DNA methylation at individual CpGs, while the age-predictions were overall relatively accurate. Furthermore, BBA-seq data revealed that the correlation of methylation levels with age at neighboring CpG sites follows a bell-shaped curve, often associated with a CTCF binding site. We demonstrate that within individual BBA-seq reads the DNA methylation at neighboring CpGs is not coherently modified, but reveals a stochastic pattern. Based on this, we have developed a new approach for epigenetic age predictions based on the binary sequel of methylated and non-methylated sites in individual reads, which reflects heterogeneity in epigenetic aging within a sample.

Conclusion

Targeted DNA methylation analysis at few age-associated CpGs by pyrosequencing, BBA-seq, and particularly ddPCR enables high precision of epigenetic age-predictions. Furthermore, we demonstrate that the stochastic evolution of age-associated DNA methylation patterns in BBA-seq data enables epigenetic clocks for individual DNA strands.

Development and optimization of the VISAGE basic prototype tool for forensic age estimation

The VISAGE (VISible Attributes through GEnomics) consortium aims to develop, optimize and validate prototype tools to broaden the use of DNA intelligence methods in forensic routine laboratories. This includes age estimation based on the quantification of DNA methylation at specific CpG sites. Here, we present the VISAGE basic prototype tool for age estimation targeting 32 CpGs from five genes ELOVL2, MIR29B2CHG (herein, MIR29B2C), FHL2, TRIM59 and KLF14. The assay interrogates these well described age markers by multiplex PCR for bisulfite converted DNA and massively parallel sequencing on a MiSeq FGx instrument. We describe protocol optimizations including tests on five bisulfite conversion kits and an evaluation of the assay's reproducibility and sensitivity with artificially methylated DNA standards. We observed robust quantification of methylation levels with a mean standard deviation of 1.4 % across ratios. Sensitivity tests showed no increase of variability down to 20 ng DNA input into bisulfite conversion with a median difference below 1.6 % between technical replicates.

Metformin: Sentinel of the Epigenetic Landscapes That Underlie Cell Fate and Identity

The biguanide metformin is the first drug to be tested as a gerotherapeutic in the clinical trial TAME (Targeting Aging with Metformin). The current consensus is that metformin exerts indirect pleiotropy on core metabolic hallmarks of aging, such as the insulin/insulin-like growth factor 1 and AMP-activated protein kinase/mammalian Target Of Rapamycin signaling pathways, downstream of its primary inhibitory effect on mitochondrial respiratory complex I. Alternatively, but not mutually exclusive, metformin can exert regulatory effects on components of the biologic machinery of aging itself such as chromatin-modifying enzymes. An integrative metabolo-epigenetic outlook supports a new model whereby metformin operates as a guardian of cell identity, capable of retarding cellular aging by preventing the loss of the information-theoretic nature of the epigenome. The ultimate anti-aging mechanism of metformin might involve the global preservation of the epigenome architecture, thereby ensuring cell fate commitment and phenotypic outcomes despite the challenging effects of aging noise. Metformin might therefore inspire the development of new gerotherapeutics capable of preserving the epigenome architecture for cell identity. Such gerotherapeutics should replicate the ability of metformin to halt the erosion of the epigenetic landscape, mitigate the loss of cell fate commitment, delay stochastic/environmental DNA methylation drifts, and alleviate cellular senescence. Yet, it remains a challenge to confirm if regulatory changes in higher-order genomic organizers can connect the capacity of metformin to dynamically regulate the three-dimensional nature of epigenetic landscapes with the 4th dimension, the aging time.

DNA methylation GrimAge strongly predicts lifespan and healthspan

It was unknown whether plasma protein levels can be estimated based on DNA methylation (DNAm) levels, and if so, how the resulting surrogates can be consolidated into a powerful predictor of lifespan. We present here, seven DNAm-based estimators of plasma proteins including those of plasminogen activator inhibitor 1 (PAI-1) and growth differentiation factor 15. The resulting predictor of lifespan, DNAm GrimAge (in units of years), is a composite biomarker based on the seven DNAm surrogates and a DNAm-based estimator of smoking pack-years. Adjusting DNAm GrimAge for chronological age generated novel measure of epigenetic age acceleration, AgeAccelGrim.Using large scale validation data from thousands of individuals, we demonstrate that DNAm GrimAge stands out among existing epigenetic clocks in terms of its predictive ability for time-to-death (Cox regression P=2.0E-75), time-to-coronary heart disease (Cox P=6.2E-24), time-to-cancer (P= 1.3E-12), its strong relationship with computed tomography data for fatty liver/excess visceral fat, and age-at-menopause (P=1.6E-12). AgeAccelGrim is strongly associated with a host of age-related conditions including comorbidity count (P=3.45E-17). Similarly, age-adjusted DNAm PAI-1 levels are associated with lifespan (P=5.4E-28), comorbidity count (P= 7.3E-56) and type 2 diabetes (P=2.0E-26). These DNAm-based biomarkers show the expected relationship with lifestyle factors including healthy diet and educational attainment.Overall, these epigenetic biomarkers are expected to find many applications including human anti-aging studies.

Exceptionally Long-Lived Individuals (ELLI) Demonstrate Slower Aging Rate Calculated by DNA Methylation Clocks as Possible Modulators for Healthy Longevity

Exceptionally long-lived individuals (ELLI) who are the focus of many healthy longevity studies around the globe are now being studied in Israel. The Israeli Multi-Ethnic Centenarian Study (IMECS) cohort is utilized here for assessment of various DNA methylation clocks. Thorough phenotypic characterization and whole blood samples were obtained from ELLI, offspring of ELLI, and controls aged 53–87 with no familial exceptional longevity. DNA methylation was assessed using Illumina MethylationEPIC Beadchip and applied to DNAm age online tool for age and telomere length predictions. Relative telomere length was assessed using qPCR T/S (Telomere/Single copy gene) ratios. ELLI demonstrated juvenile performance in DNAm age clocks and overall methylation measurement, with preserved cognition and relative telomere length. Our findings suggest a favorable DNA methylation profile in ELLI enabling a slower rate of aging in those individuals in comparison to controls. It is possible that DNA methylation is a key modulator of the rate of aging and thus the ELLI DNAm profile promotes healthy longevity.

Accelerated epigenetic age and cognitive decline among urban-dwelling adults

OBJECTIVES

Epigenetic modifications are closely linked with aging, but their relationship with cognition remains equivocal. Given known sex differences in epigenetic aging, we explored sex-specific associations of 3 DNA methylation (DNAm)-based measures of epigenetic age acceleration (EAA) with baseline and longitudinal change in cognitive performance among middle-aged urban adults.

METHODS

We used exploratory data from a subgroup of participants in the Healthy Aging in Neighborhoods of Diversity across the Life Span study with complete DNA samples and whose baseline ages were >50.0 years (2004-2009) to estimate 3 DNAm EAA measures: (1) universal EAA (AgeAccel); (2) intrinsic EAA (IEAA); and (3) extrinsic EAA (EEAA). Cognitive performance was measured at baseline visit (2004-2009) and first follow-up (2009-2013) with 11 test scores covering global mental status and specific domains such as learning/memory, attention, visuospatial, psychomotor speed, language/verbal, and executive function. A series of mixed-effects regression models were conducted adjusting for covariates and multiple testing (n = 147-156, ∼51% men, k = 1.7-1.9 observations/participant, mean follow-up time ∼4.7 years).

RESULTS

EEAA, a measure of both biological age and immunosenescence, was consistently associated with greater cognitive decline among men on tests of visual memory/visuoconstructive ability (Benton Visual Retention Test: γ₁₁ = 0.0512 ± 0.0176, p = 0.004) and attention/processing speed (Trail-Making Test, part A: γ₁₁ = 0.219 ± 0.080, p = 0.007). AgeAccel and IEAA were not associated with cognitive change in this sample.

CONCLUSIONS

EEAA capturing immune system cell aging was associated with faster decline among men in domains of attention and visual memory. Larger longitudinal studies are needed to replicate our findings.

Systematic underestimation of the epigenetic clock and age acceleration in older subjects

Background

The Horvath epigenetic clock is widely used. It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate “age acceleration” in various tissues and environments.

Results

The model systematically underestimates age in tissues from older people. This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets. Age acceleration is thus age-dependent, and this can lead to spurious associations. The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways.

Conclusions

The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration. Association tests of age acceleration should include age as a covariate.

A multi-tissue full lifespan epigenetic clock for mice

Human DNA-methylation data have been used to develop highly accurate biomarkers of aging ("epigenetic clocks"). Recent studies demonstrate that similar epigenetic clocks for mice (Mus Musculus) can be slowed by gold standard anti-aging interventions such as calorie restriction and growth hormone receptor knock-outs. Using DNA methylation data from previous publications with data collected in house for a total 1189 samples spanning 193,651 CpG sites, we developed 4 novel epigenetic clocks by choosing different regression models (elastic net- versus ridge regression) and by considering different sets of CpGs (all CpGs vs highly conserved CpGs). We demonstrate that accurate age estimators can be built on the basis of highly conserved CpGs. However, the most accurate clock results from applying elastic net regression to all CpGs. While the anti-aging effect of calorie restriction could be detected with all types of epigenetic clocks, only ridge regression based clocks replicated the finding of slow epigenetic aging effects in dwarf mice. Overall, this study demonstrates that there are trade-offs when it comes to epigenetic clocks in mice. Highly accurate clocks might not be optimal for detecting the beneficial effects of anti-aging interventions.

Dissecting Aging and Senescence-Current Concepts and Open Lessons

In contrast to the programmed nature of development, it is still a matter of debate whether aging is an adaptive and regulated process, or merely a consequence arising from a stochastic accumulation of harmful events that culminate in a global state of reduced fitness, risk for disease acquisition, and death. Similarly unanswered are the questions of whether aging is reversible and can be turned into rejuvenation as well as how aging is distinguishable from and influenced by cellular senescence. With the discovery of beneficial aspects of cellular senescence and evidence of senescence being not limited to replicative cellular states, a redefinition of our comprehension of aging and senescence appears scientifically overdue. Here, we provide a factor-based comparison of current knowledge on aging and senescence, which we converge on four suggested concepts, thereby implementing the newly emerging cellular and molecular aspects of geroconversion and amitosenescence, and the signatures of a genetic state termed genosenium. We also address the possibility of an aging-associated secretory phenotype in analogy to the well-characterized senescence-associated secretory phenotype and delineate the impact of epigenetic regulation in aging and senescence. Future advances will elucidate the biological and molecular fingerprints intrinsic to either process.

A pilot study of peripheral blood DNA methylation models as predictors of knee osteoarthritis radiographic progression: data from the Osteoarthritis Initiative (OAI)

Knee osteoarthritis (OA) is a leading cause of chronic disability worldwide, but no diagnostic or prognostic biomarkers are available. Increasing evidence supports epigenetic dysregulation as a contributor to OA pathogenesis. In this pilot study, we investigated epigenetic patterns in peripheral blood mononuclear cells (PBMCs) as models to predict future radiographic progression in OA patients enrolled in the longitudinal Osteoarthritis Initiative (OAI) study. PBMC DNA was analyzed from baseline OAI visits in 58 future radiographic progressors (joint space narrowing at 24 months, sustained at 48 months) compared to 58 non-progressors. DNA methylation was quantified via Illumina microarrays and beta- and M-values were used to generate linear classification models. Data were randomly split into a 60% development and 40% validation subsets, models developed and tested, and cross-validated in a total of 40 cycles. M-value based models outperformed beta-value based models (ROC-AUC 0.81 ± 0.01 vs. 0.73 ± 0.02, mean ± SEM, comparison p = 0.002), with a mean classification accuracy of 73 ± 1% (mean ± SEM) for M- and 69 ± 1% for beta-based models. Adjusting for covariates did not significantly alter model performance. Our findings suggest that PBMC DNA methylation-based models may be useful as biomarkers of OA progression and warrant additional evaluation in larger patient cohorts.

Early-life DNA methylation profiles are indicative of age-related transcriptome changes

Background

Alterations to cellular and molecular programs with brain aging result in cognitive impairment and susceptibility to neurodegenerative disease. Changes in DNA methylation patterns, an epigenetic modification required for various CNS functions are observed with brain aging and can be prevented by anti-aging interventions, but the relationship of altered methylation to gene expression is poorly understood.

Results

Paired analysis of the hippocampal methylome and transcriptome with aging of male and female mice demonstrates that age-related differences in methylation and gene expression are anti-correlated within gene bodies and enhancers. Altered promoter methylation with aging was found to be generally un-related to altered gene expression. A more striking relationship was found between methylation levels at young age and differential gene expression with aging. Highly methylated gene bodies and promoters in early life were associated with age-related increases in gene expression even in the absence of significant methylation changes with aging. As well, low levels of methylation in early life were correlated to decreased expression with aging. This relationship was also observed in genes altered in two mouse Alzheimer’s models.

Conclusion

DNA methylation patterns established in youth, in combination with other epigenetic marks, were able to accurately predict changes in transcript trajectories with aging. These findings are consistent with the developmental origins of disease hypothesis and indicate that epigenetic variability in early life may explain differences in aging trajectories and age-related disease.

Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies

DNA methylation (DNAm)-based biomarkers of aging have been developed for many tissues and organs. However, these biomarkers have sub-optimal accuracy in fibroblasts and other cell types used in ex vivo studies. To address this challenge, we developed a novel and highly robust DNAm age estimator (based on 391 CpGs) for human fibroblasts, keratinocytes, buccal cells, endothelial cells, lymphoblastoid cells, skin, blood, and saliva samples. High age correlations can also be observed in sorted neurons, glia, brain, liver, and even bone samples. Gestational age correlates with DNAm age in cord blood. When used on fibroblasts from Hutchinson Gilford Progeria Syndrome patients, this age estimator (referred to as the skin & blood clock) uncovered an epigenetic age acceleration with a magnitude that is below the sensitivity levels of other DNAm-based biomarkers. Furthermore, this highly sensitive age estimator accurately tracked the dynamic aging of cells cultured ex vivo and revealed that their proliferation is accompanied by a steady increase in epigenetic age. The skin & blood clock predicts lifespan and it relates to many age-related conditions. Overall, this biomarker is expected to become useful for forensic applications (e.g. blood or buccal swabs) and for a quantitative ex vivo human cell aging assay.

Modified aging of elite athletes revealed by analysis of epigenetic age markers

Recent progress in epigenomics has led to the development of prediction systems that enable accurate age estimation from DNA methylation data. Our objective was to track responses to intense physical exercise of individual age-correlated DNA methylation markers and to infer their potential impact on the aging processes. The study showed accelerated DNA hypermethylation for two CpG sites in TRIM59 and KLF14. Both markers predicted the investigated elite athletes to be several years older than controls and this effect was more substantial in subjects involved in power sports. Accordingly, the complete 5-CpG model revealed age acceleration of elite athletes (P=1.503x10^-7) and the result was more significant amongst power athletes (P=1.051x10^-9). The modified methylation of TRIM59 and KLF14 in top athletes may be accounted for by the biological roles played by these genes. Their known anti-tumour and anti-inflammatory activities suggests that intense physical training has a complex influence on aging and potentially launches signalling networks that contribute to the observed lower risk of elite athletes to develop cardiovascular disease and cancer.

Chicken or Egg: Is Clonal Hematopoiesis Primarily Caused by Genetic or Epigenetic Aberrations?

Hematopoietic malignancies, including multiple myeloma, are associated with characteristic mutations and genetic instabilities that drive malignant transformation. On the other hand, tumor formation is also associated with drastic epigenetic aberrations, which can impact the genetic sequence. Therefore, the question arises if malignant transformation is primarily caused by genetic or epigenetic events. The tight connection of these processes becomes obvious by the fact that in several malignancies, as well as in age-related clonal hematopoiesis, mutations are particularly observed in epigenetic writers such as DNMT3A and TET2. On the other hand, specific epigenetic aberrations, so-called “epimutations,” can mimic genomic mutations. In contrast to the genetic sequence, which remains relatively stable throughout life, the epigenome notoriously undergoes drastic changes in normal hematopoietic development and aging. It is conceivable that such epigenetic reorganization, e.g., in 3D chromatin conformation, paves the way for secondary chromosomal instabilities, which then result in tumor-specific genomic changes that further trigger disease progression. This scenario might explain the occurrence of tumor-specific mutations particularly in the elderly. Taken together, the causality dilemma is difficult to solve because genetic and epigenetic aberrations are interlinked during disease development. A better understanding of how the chromatin structure or 3D nuclear organization can evoke specific mutations might provide new perspectives for prevention, early diagnostics, and targeted therapy.

Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing

BACKGROUND

DNA methylation changes with age. Chronological age predictors built from DNA methylation are termed 'epigenetic clocks'. The deviation of predicted age from the actual age ('age acceleration residual', AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association.

METHODS

In this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues.

RESULTS

We found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor ) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91-1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79-1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor.

CONCLUSIONS

This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.

Novel age-associated DNA methylation changes and epigenetic age acceleration in middle-aged African Americans and whites

Background

African Americans (AAs) experience premature chronic health outcomes and longevity disparities consistent with an accelerated aging phenotype. DNA methylation (DNAm) levels at specific CpG positions are hallmarks of aging evidenced by the presence of age-associated differentially methylated CpG positions (aDMPs) that are the basis for the epigenetic clock for measuring biological age acceleration. Since DNAm has not been widely studied among non-European populations, we examined the association between DNAm and chronological age in AAs and whites, and the association between race, poverty, sex, and epigenetic age acceleration.

Results

We measured genome-wide DNA methylation (866,836 CpGs) using the Illumina MethylationEPIC BeadChip in blood DNA extracted from 487 middle-aged AA (N = 244) and white (N = 243), men (N = 248), and women (N = 239). The mean (sd) age was 48.4 (8.8) in AA and 49.0 (8.7) in whites (p = 0.48). We identified 4930 significantly associated aDMPs in AAs and 469 in whites. Of these, 75.6% and 53.1% were novel, largely driven by the increased number of measured CpGs in the EPIC array, in AA and whites, respectively. AAs had more age-associated DNAm changes than whites in genes implicated in age-related diseases and cellular pathways involved in growth and development. We assessed three epigenetic age acceleration measures (universal, intrinsic, and extrinsic). AAs had a significantly slower extrinsic aging compared to whites. Furthermore, compared to AA women, both AA and white men had faster aging in the universal age acceleration measure (+ 2.04 and + 1.24 years, respectively, p < 0.05).

Conclusions

AAs have more wide-spread methylation changes than whites. Race and sex interact to underlie biological age acceleration suggesting altered DNA methylation patterns may be important in age-associated health disparities.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0722-1) contains supplementary material, which is available to authorized users.

DNA methylation-based estimator of telomere length

Telomere length (TL) is associated with several aging-related diseases. Here, we present a DNA methylation estimator of TL (DNAmTL) based on 140 CpGs. Leukocyte DNAmTL is applicable across the entire age spectrum and is more strongly associated with age than measured leukocyte TL (LTL) (r ~-0.75 for DNAmTL versus r ~ -0.35 for LTL). Leukocyte DNAmTL outperforms LTL in predicting: i) time-to-death (p=2.5E-20), ii) time-to-coronary heart disease (p=6.6E-5), iii) time-to-congestive heart failure (p=3.5E-6), and iv) association with smoking history (p=1.21E-17). These associations are further validated in large scale methylation data (n=10k samples) from the Framingham Heart Study, Women's Health Initiative, Jackson Heart Study, InChianti, Lothian Birth Cohorts, Twins UK, and Bogalusa Heart Study. Leukocyte DNAmTL is also associated with measures of physical fitness/functioning (p=0.029), age-at-menopause (p=0.039), dietary variables (omega 3, fish, vegetable intake), educational attainment (p=3.3E-8) and income (p=3.1E-5). Experiments in cultured somatic cells show that DNAmTL dynamics reflect in part cell replication rather than TL per se. DNAmTL is not only an epigenetic biomarker of replicative history of cells, but a useful marker of age-related pathologies that are associated with it.

Screening for genes that accelerate the epigenetic aging clock in humans reveals a role for the H3K36 methyltransferase NSD1

BACKGROUND

Epigenetic clocks are mathematical models that predict the biological age of an individual using DNA methylation data and have emerged in the last few years as the most accurate biomarkers of the aging process. However, little is known about the molecular mechanisms that control the rate of such clocks. Here, we have examined the human epigenetic clock in patients with a variety of developmental disorders, harboring mutations in proteins of the epigenetic machinery.

RESULTS

Using the Horvath epigenetic clock, we perform an unbiased screen for epigenetic age acceleration in the blood of these patients. We demonstrate that loss-of-function mutations in the H3K36 histone methyltransferase NSD1, which cause Sotos syndrome, substantially accelerate epigenetic aging. Furthermore, we show that the normal aging process and Sotos syndrome share methylation changes and the genomic context in which they occur. Finally, we found that the Horvath clock CpG sites are characterized by a higher Shannon methylation entropy when compared with the rest of the genome, which is dramatically decreased in Sotos syndrome patients.

CONCLUSIONS

These results suggest that the H3K36 methylation machinery is a key component of the epigenetic maintenance system in humans, which controls the rate of epigenetic aging, and this role seems to be conserved in model organisms. Our observations provide novel insights into the mechanisms behind the epigenetic aging clock and we expect will shed light on the different processes that erode the human epigenetic landscape during aging.

DNA methylation-based biomarkers and the epigenetic clock theory of ageing

Identifying and validating molecular targets of interventions that extend the human health span and lifespan has been difficult, as most clinical biomarkers are not sufficiently representative of the fundamental mechanisms of ageing to serve as their indicators. In a recent breakthrough, biomarkers of ageing based on DNA methylation data have enabled accurate age estimates for any tissue across the entire life course. These 'epigenetic clocks' link developmental and maintenance processes to biological ageing, giving rise to a unified theory of life course. Epigenetic biomarkers may help to address long-standing questions in many fields, including the central question: why do we age?

Exploring the Relationship of Relative Telomere Length and the Epigenetic Clock in the LipidCardio Cohort

Telomere length has been accepted widely as a biomarker of aging. Recently, a novel candidate biomarker has been suggested to predict an individual's chronological age with high accuracy: The epigenetic clock is based on the weighted DNA methylation (DNAm) fraction of a number of cytosine-phosphate-guanine sites (CpGs) selected by penalized regression analysis. Here, an established methylation-sensitive single nucleotide primer extension method was adapted, to estimate the epigenetic age of the 1005 participants of the LipidCardio Study, a patient cohort characterised by high prevalence of cardiovascular disease, based on a seven CpGs epigenetic clock. Furthermore, we measured relative leukocyte telomere length (rLTL) to assess the relationship between the established and the promising new measure of biological age. Both rLTL (0.79 ± 0.14) and DNAm age (69.67 ± 7.27 years) were available for 773 subjects (31.6% female; mean chronological age= 69.68 ± 11.01 years; mean DNAm age acceleration = -0.01 ± 7.83 years). While we detected a significant correlation between chronological age and DNAm age (n = 779, R = 0.69), we found neither evidence of an association between rLTL and the DNAm age (β = 3.00, p = 0.18) nor rLTL and the DNAm age acceleration (β = 2.76, p = 0.22) in the studied cohort, suggesting that DNAm age and rLTL measure different aspects of biological age.

Patterns of DNA methylation as an indicator of biological aging: State of the science and future directions in precision health promotion

BACKGROUND

A rapidly expanding literature suggests that individuals of the same chronological age show significant variation in biological age.

PURPOSE

The purpose of this article is to review the literature surrounding epigenetic age as estimated by DNA methylation, involving the addition or removal of methyl groups to DNA that can alter gene expression without changing the DNA sequence.

METHODS

This state of the science literature review summarizes current approaches in epigenetic age determination and applications of aging algorithms.

FINDINGS

A number of algorithms estimate epigenetic age using DNA methylation markers, primarily among adults. Algorithm application has focused on determining predictive value for risk of disease and death and identifying antecedents to age acceleration. Several studies have incorporated epigenetic age to evaluate intervention effectiveness.

DISCUSSION

As the research community continues to refine aging algorithms, there may be opportunity to promote health from a precision health perspective.

The Link Between Epigenetic Clocks for Aging and Senescence

Replicative senescence of cells in vitro is often considered as counterpart for aging of the organism in vivo. In fact, both processes are associated with functional decay and similar molecular modifications. On epigenetic level, replicative senescence and aging evoke characteristic modifications in the DNA methylation (DNAm) pattern, but at different sites in the genome. Various epigenetic signatures, which are often referred to as epigenetic clocks, provide useful biomarkers: Senescence-associated epigenetic modifications can be used for quality control of cell preparations or to elucidate effects of culture conditions on the state of cellular aging. Age-associated epigenetic modifications hold high expectations to determine chronological age in forensics or to identify parameters that impact on biological aging. Despite these differences, there are some striking similarities between senescence- and age-associated DNAm, such as complete rejuvenation during reprogramming into induced pluripotent stem cells (iPSCs). It is yet unclear what makes epigenetic clocks tick, but there is evidence that the underlying mechanisms of both processes are related to similar modifications in the histone code or higher order chromatin. Replicative senescence therefore appears to be a suitable model system to gain better insight into how organismal aging might be governed epigenetically.

Dynamic DNA Methylation During Aging: A "Prophet" of Age-Related Outcomes

The biological markers of aging used to predict physical health status in older people are of great interest. Telomere shortening, which occurs during the process of cell replication, was initially considered a promising biomarker for the prediction of age and age-related outcomes (e.g., diseases, longevity). However, the high instability in detection and low correlation with age-related outcomes limit the extension of telomere length to the field of prediction. Currently, a growing number of studies have shown that dynamic DNA methylation throughout human lifetime exhibits strong correlation with age and age-related outcomes. Indeed, many researchers have built age prediction models with high accuracy based on age-dependent methylation changes in certain CpG loci. For now, DNA methylation based on epigenetic clocks, namely epigenetic or DNA methylation age, serves as a new standard to track chronological age and predict biological age. Measures of age acceleration (Δage, DNA methylation age – chronological age) have been developed to assess the health status of a person. In addition, there is evidence that an accelerated epigenetic age exists in patients with certain age-related diseases (e.g., Alzheimer’s disease, cardiovascular disease). In this review, we provide an overview of the dynamic signatures of DNA methylation during aging and emphasize its practical utility in the prediction of various age-related outcomes.

DNA methylation signatures as biomarkers of prior environmental exposures

Purpose of review

This review demonstrates the growing body of evidence connecting DNA methylation to prior exposure. It highlights the potential to use DNA methylation patterns as a feasible, stable, and accurate biomarker of past exposure, opening new opportunities for environmental and gene-environment interaction studies among existing banked samples.

Recent findings

We present the evidence for association between past exposure, including prenatal exposures, and DNA methylation measured at a later time in the life course. We demonstrate the potential utility of DNA methylation-based biomarkers of past exposure using results from multiple studies of smoking as an example. Multiple studies show the ability to accurately predict prenatal smoking exposure based on DNA methylation measured at birth, in childhood, and even adulthood. Separate sets of DNA methylation loci have been used to predict past personal smoking exposure (postnatal) as well. Further, it appears that these two types of exposures, prenatal and previous personal exposure, can be isolated from each other. There is also a suggestion that quantitative methylation scores may be useful for estimating dose. We highlight the remaining needs for rigor in methylation biomarker development including analytic challenges as well as the need for development across multiple developmental windows, multiple tissue types, and multiple ancestries.

Summary

If fully developed, DNA methylation-based biomarkers can dramatically shift our ability to carry out environmental and genetic-environmental epidemiology using existing biobanks, opening up unprecedented opportunities for environmental health.

Decelerated DNA methylation age predicts poor prognosis of breast cancer

BACKGROUND

DNA methylation (DNAm) age was found to be an indicator for all-cause mortality, cancer incidence, and longevity, but no study has involved in the associations of DNAm age with the prognosis of breast cancer.

METHODS

We retrieved information of 1076 breast cancer patients from Genomic Data Commons (GDC) data portal on March 30, 2017, including breast cancer DNAm profiling, demographic features, clinicopathological parameters, recurrence, and all-cause fatality. Horvath's method was applied to calculate the DNAm age. Cox proportional hazards regression models were used to test the associations between DNAm age of the cancerous tissues and the prognosis (recurrence of breast cancer and all-cause fatality) with or without adjusting for chronological age and clinicopathological parameters.

RESULTS

The DNAm age was markedly decelerated in the patients who were premenopausal, ER or PR negative, HER2-enriched or basal-like than their counterparts. In the first five-year follow-up dataset for survival, every ten-year increase in DNAm age was associated with a 15% decrease in fatality; subjects with DNAm age in the second (HR: 0.52; 95%CI: 0.29-0.92), the third (HR: 0.49; 95%CI: 0.27-0.87) and the fourth quartile (HR: 0.38; 95%CI: 0.20-0.72) had significant longer survival time than those in the first quartile. In the first five-year follow-up dataset for recurrence, every ten-year increase in DNAm age was associated with a 14% decrease of the recurrence; in the categorical analysis, a clear dose-response was shown (P for trend =0.02) and the fourth quartile was associated with a longer recurrence free survival (HR: 0.32; 95%CI: 0.14-0.74). In the full follow-up dataset, similar results were obtained.

CONCLUSIONS

DNAm age of breast cancer tissue, which associated with menopausal status and pathological features, was a strong independent predictor of the prognosis. It was suggested that the prognosis of breast cancer was related to intrinsic biological changes and specific molecular targets for treatment of breast cancer may be implicit.

Polyamine Metabolism and Gene Methylation in Conjunction with One-Carbon Metabolism

Recent investigations have revealed that changes in DNA methylation status play an important role in aging-associated pathologies and lifespan. The methylation of DNA is regulated by DNA methyltransferases (DNMT1, DNMT3a, and DNMT3b) in the presence of S-adenosylmethionine (SAM), which serves as a methyl group donor. Increased availability of SAM enhances DNMT activity, while its metabolites, S-adenosyl-l-homocysteine (SAH) and decarboxylated S-adenosylmethionine (dcSAM), act to inhibit DNMT activity. SAH, which is converted from SAM by adding a methyl group to cytosine residues in DNA, is an intermediate precursor of homocysteine. dcSAM, converted from SAM by the enzymatic activity of adenosylmethionine decarboxylase, provides an aminopropyl group to synthesize the polyamines spermine and spermidine. Increased homocysteine levels are a significant risk factor for the development of a wide range of conditions, including cardiovascular diseases. However, successful homocysteine-lowering treatment by vitamins (B6, B12, and folate) failed to improve these conditions. Long-term increased polyamine intake elevated blood spermine levels and inhibited aging-associated pathologies in mice and humans. Spermine reversed changes (increased dcSAM, decreased DNMT activity, aberrant DNA methylation, and proinflammatory status) induced by the inhibition of ornithine decarboxylase. The relation between polyamine metabolism, one-carbon metabolism, DNA methylation, and the biological mechanism of spermine-induced lifespan extension is discussed.

Plasma proteomic signature of age in healthy humans

To characterize the proteomic signature of chronological age, 1,301 proteins were measured in plasma using the SOMAscan assay (SomaLogic, Boulder, CO, USA) in a population of 240 healthy men and women, 22-93 years old, who were disease- and treatment-free and had no physical and cognitive impairment. Using a p ≤ 3.83 × 10-5 significance threshold, 197 proteins were positively associated, and 20 proteins were negatively associated with age. Growth differentiation factor 15 (GDF15) had the strongest, positive association with age (GDF15; 0.018 ± 0.001, p = 7.49 × 10-56 ). In our sample, GDF15 was not associated with other cardiovascular risk factors such as cholesterol or inflammatory markers. The functional pathways enriched in the 217 age-associated proteins included blood coagulation, chemokine and inflammatory pathways, axon guidance, peptidase activity, and apoptosis. Using elastic net regression models, we created a proteomic signature of age based on relative concentrations of 76 proteins that highly correlated with chronological age (r = 0.94). The generalizability of our findings needs replication in an independent cohort.

Epigenetic age-predictor for mice based on three CpG sites

Epigenetic clocks for mice were generated based on deep-sequencing analysis of the methylome. Here, we demonstrate that site-specific analysis of DNA methylation levels by pyrosequencing at only three CG dinucleotides (CpGs) in the genes Prima1, Hsf4, and Kcns1 facilitates precise estimation of chronological age in murine blood samples, too. DBA/2 mice revealed accelerated epigenetic aging as compared to C57BL6 mice, which is in line with their shorter life-expectancy. The three-CpG-predictor provides a simple and cost-effective biomarker to determine biological age in large intervention studies with mice.

Epigenetic marks are chemical modifications found throughout the genome – the DNA within cells. By influencing the activity of nearby genes, the marks govern developmental processes and help cells to adapt to changes in their surroundings. Some epigenetic marks can be gained or lost with age. A lot of aging research focuses on one type of mark, called “DNA methylation”. By measuring the presence or absence of specific methyl groups, scientists can estimate biological age – which may differ from calendar age.

Recent studies have developed computer models called epigenetic aging clocks to predict the biological age of mouse cells. These clocks use epigenetic data collected from the entire genomes of mice, and are useful for understanding how the aging process is affected by genetic parameters, diet, or other environmental factors. Yet, the genome sequencing methods used to construct most existing epigenetic clocks are expensive, labor-intensive, and cannot be easily applied to large groups of mice.

Han et al. have developed a new way to predict biological aging in mice that needs methylation information from just three particular sections of the genome. Even though this approach is much faster and less expensive than other epigenetic approaches to measuring aging, it has a similar level of accuracy to existing models. Han et al. use the new method to show that cells from different strains of laboratory mice age at different rates. Furthermore, in a strain that has a shorter life expectancy, aging seems to be accelerated.

The new approach developed by Han et al. will make it easier to study how aging in mice is affected by different interventions. Further studies will also be needed to better understand how epigenetic marks relate to biological aging.

Perinatally acquired HIV infection accelerates epigenetic aging in South African adolescents

OBJECTIVE

Recent studies demonstrate that infection with the HIV-1 is associated with accelerated aging effects in adults according to a highly accurate epigenetic biomarker of aging known as epigenetic clock. However, it is not yet known whether epigenetic age acceleration occurs as early as adolescence in perinatally HIV-infected (PHIV+) youth.

DESIGN

Observational study of PHIV and HIV-uninfected adolescents enrolled in the Cape Town Adolescent Antiretroviral Cohort Study.

METHODS

The Illumina EPIC array was used to generate blood DNA methylation data from 204 PHIV and 44 age-matched, uninfected (HIV-) adolescents aged 9-12 years old. The epigenetic clock software and method was used to estimate two measures of epigenetic age acceleration. Each participant completed a comprehensive neuropsychological test battery upon enrollment to Cape Town Adolescent Antiretroviral Cohort.

RESULTS

HIV is associated with biologically older blood in PHIV+ adolescents according to both measures of epigenetic age acceleration. One of the measures, extrinsic epigenetic age acceleration, is negatively correlated with measures of cognitive functioning (executive functioning, working memory, processing speed).

CONCLUSION

Overall, our results indicate that epigenetic age acceleration in blood can be observed in PHIV+ adolescents and that these epigenetic changes accompany poorer cognitive functioning.

Global DNA methylation changes spanning puberty are near predicted estrogen-responsive genes and enriched for genes involved in endocrine and immune processes

Background

The changes that occur during puberty have been implicated in susceptibility to a wide range of diseases later in life, many of which are characterized by sex-specific differences in prevalence. Both genetic and environmental factors have been associated with the onset or delay of puberty, and recent evidence has suggested a role for epigenetic changes in the initiation of puberty as well.

Objective

To identify global DNA methylation changes that arise across the window of puberty in girls and boys.

Methods

Genome-wide DNA methylation levels were measured using the Infinium 450K array. We focused our studies on peripheral blood mononuclear cells (PBMCs) from 30 girls and 25 boys pre- and post-puberty (8 and 14 years, respectively), in whom puberty status was confirmed by Tanner staging.

Results

Our study revealed 347 differentially methylated probes (DMPs) in females and 50 DMPs in males between the ages of 8 and 14 years (FDR 5%). The female DMPs were in or near 312 unique genes, which were over-represented for having high affinity estrogen response elements (permutation P < 2.0 × 10⁻⁶), suggesting that some of the effects of estrogen signaling in puberty are modified through epigenetic mechanisms. Ingenuity Pathway Analysis (IPA) of the 312 genes near female puberty DMPs revealed significant networks enriched for immune and inflammatory responses as well as reproductive hormone signaling. Finally, analysis of gene expression in the female PBMCs collected at 14 years revealed modules of correlated transcripts that were enriched for immune and reproductive system functions, and include genes that are responsive to estrogen and androgen receptor signaling. The male DMPs were in or near 48 unique genes, which were enriched for adrenaline and noradrenaline biosynthesis (Enrichr P = 0.021), with no significant networks identified. Additionally, no modules were identified using post-puberty gene expression levels in males.

Conclusion

Epigenetic changes spanning the window of puberty in females may be responsive to or modify hormonal changes that occur during this time and potentially contribute to sex-specific differences in immune-mediated and endocrine diseases later in life.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0491-2) contains supplementary material, which is available to authorized users.