DNA methylation age is associated with mortality in a longitudinal Danish twin study

An epigenetic profile defining the DNA methylation age (DNAm age) of an individual has been suggested to be a biomarker of aging, and thus possibly providing a tool for assessment of health and mortality. In this study, we estimated the DNAm age of 378 Danish twins, age 30–82 years, and furthermore included a 10‐year longitudinal study of the 86 oldest‐old twins (mean age of 86.1 at follow‐up), which subsequently were followed for mortality for 8 years. We found that the DNAm age is highly correlated with chronological age across all age groups (r = 0.97), but that the rate of change of DNAm age decreases with age. The results may in part be explained by selective mortality of those with a high DNAm age. This hypothesis was supported by a classical survival analysis showing a 35% (4–77%) increased mortality risk for each 5‐year increase in the DNAm age vs. chronological age. Furthermore, the intrapair twin analysis revealed a more‐than‐double mortality risk for the DNAm oldest twin compared to the co‐twin and a ‘dose–response pattern’ with the odds of dying first increasing 3.2 (1.05–10.1) times per 5‐year DNAm age difference within twin pairs, thus showing a stronger association of DNAm age with mortality in the oldest‐old when controlling for familial factors. In conclusion, our results support that DNAm age qualifies as a biomarker of aging.     

Increased age of transformed mouse neural progenitor/stem cells recapitulates age‐dependent clinical features of human glioma malignancy

Increasing age is the most robust predictor of greater malignancy and treatment resistance in human gliomas. However, the adverse association of clinical course with aging is rarely considered in animal glioma models, impeding delineation of the relative importance of organismal versus progenitor cell aging in the genesis of glioma malignancy. To address this limitation, we implanted transformed neural stem/progenitor cells (NSPCs), the presumed cells of glioma origin, from 3‐ and 18‐month‐old mice into 3‐ and 20‐month host animals. Transplantation with progenitors from older animals resulted in significantly shorter (P ≤ 0.0001) median survival in both 3‐month (37.5 vs. 83 days) and 20‐month (38 vs. 67 days) hosts, indicating that age‐dependent changes intrinsic to NSPCs rather than host animal age accounted for greater malignancy. Subsequent analyses revealed that increased invasiveness, genomic instability, resistance to therapeutic agents, and tolerance to hypoxic stress accompanied aging in transformed NSPCs. Greater tolerance to hypoxia in older progenitor cells, as evidenced by elevated HIF‐1 promoter reporter activity and hypoxia response gene (HRG) expression, mirrors the upregulation of HRGs in cohorts of older vs. younger glioma patients revealed by analysis of gene expression databases, suggesting that differential response to hypoxic stress may underlie age‐dependent differences in invasion, genomic instability, and treatment resistance. Our study provides strong evidence that progenitor cell aging is responsible for promoting the hallmarks of age‐dependent glioma malignancy and that consideration of progenitor aging will facilitate development of physiologically and clinically relevant animal models of human gliomas.     

Determinants of accelerated metabolomic and epigenetic aging in a UK cohort

Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry in urine and serum, within a large sample (N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration (N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.     

Molecular and physiological manifestations and measurement of aging in humans

Biological aging is associated with a reduction in the reparative and regenerative potential in tissues and organs. This reduction manifests as a decreased physiological reserve in response to stress (termed homeostenosis) and a time‐dependent failure of complex molecular mechanisms that cumulatively create disorder. Aging inevitably occurs with time in all organisms and emerges on a molecular, cellular, organ, and organismal level with genetic, epigenetic, and environmental modulators. Individuals with the same chronological age exhibit differential trajectories of age‐related decline, and it follows that we should assess biological age distinctly from chronological age. In this review, we outline mechanisms of aging with attention to well‐described molecular and cellular hallmarks and discuss physiological changes of aging at the organ‐system level. We suggest methods to measure aging with attention to both molecular biology (e.g., telomere length and epigenetic marks) and physiological function (e.g., lung function and echocardiographic measurements). Finally, we propose a framework to integrate these molecular and physiological data into a composite score that measures biological aging in humans. Understanding the molecular and physiological phenomena that drive the complex and multifactorial processes underlying the variable pace of biological aging in humans will inform how researchers assess and investigate health and disease over the life course. This composite biological age score could be of use to researchers seeking to characterize normal, accelerated, and exceptionally successful aging as well as to assess the effect of interventions aimed at modulating human aging.     

Analysis of epigenetic aging in vivo and in vitro: Factors controlling the speed and direction

The mechanism of aging is not yet fully understood. It has been recognized that there are age-dependent changes in the DNA methylation pattern of the whole genome. To date, there are several DNA methylation-based estimators of the chronological age. A majority of the estimators use the DNA methylation data from a single tissue type, such as blood. In 2013, for the first time, Steve Horvath reported the DNA methylation-based age estimator (353 CpGs were used) that could be applied to multiple tissues. A refined, more sensitive version that uses 391 CpGs was subsequently developed and validated in human cells, including fibroblasts. In this review, the age predicted by DNA methylation-based age estimator is referred to as DNAmAge, and the biological process controlling the progression of DNAmAge is referred to as the epigenetic aging in this minireview. The concepts of DNAmAge and epigenetic aging provide us opportunities to discover previously unrecognized biological events controlling aging. In this article, we discuss the frequently asked questions regarding DNAmAge and the epigenetic aging by introducing recent studies of ours and others. We focus on addressing the following questions: (1) Is there any synchronization of DNAmAge between cells in a human body?, (2) Can we use in vitro (cell culture) systems to study the epigenetic aging?, (3) Is there an age limit of DNAmAge?, and (4) Is it possible to change the speed and direction of the epigenetic aging? We describe our current understandings to these questions and outline potential future directions.Impact statementAging is associated with DNA methylation (DNAm) changes. Recent advancement of the whole-genome DNAm analysis technology allowed scientists to develop DNAm-based age estimators. A majority of these estimators use DNAm data from a single tissue type such as blood. In 2013, a multi-tissue age estimator using DNAm pattern of 353 CpGs was developed by Steve Horvath. This estimator was named “epigenetic clock”, and the improved version using DNAm pattern of 391 CpGs was developed in 2018. The estimated age by epigenetic clock is named DNAmAge. DNAmAge can be used as a biomarker of aging predicting the risk of age-associated diseases and mortality. Although the DNAm-based age estimators were developed, the mechanism of epigenetic aging is still enigmatic. The biological significance of epigenetic aging is not well understood, either. This minireview discusses the current understanding of the mechanism of epigenetic aging and the future direction of aging research.     

Mitochondrial DNA in extracellular vesicles declines with age

The mitochondrial free radical theory of aging suggests that accumulating oxidative damage to mitochondria and mitochondrial DNA (mtDNA) plays a central role in aging. Circulating cell‐free mtDNA (ccf‐mtDNA) isolated from blood may be a biomarker of disease. Extracellular vesicles (EVs) are small (30–400 nm), lipid‐bound vesicles capable of shuttling proteins, nucleic acids, and lipids as part of intercellular communication systems. Here, we report that a portion of ccf‐mtDNA in plasma is encapsulated in EVs. To address whether EV mtDNA levels change with human age, we analyzed mtDNA in EVs from individuals aged 30–64 years cross‐sectionally and longitudinally. EV mtDNA levels decreased with age. Furthermore, the maximal mitochondrial respiration of cultured cells was differentially affected by EVs from old and young donors. Our results suggest that plasma mtDNA is present in EVs, that the level of EV‐derived mtDNA is associated with age, and that EVs affect mitochondrial energetics in an EV age‐dependent manner.     

Epigenetic estimation of age in humpback whales

Age is a fundamental aspect of animal ecology, but is difficult to determine in many species. Humpback whales exemplify this as they have a lifespan comparable to humans, mature sexually as early as 4 years and have no reliable visual age indicators after their first year. Current methods for estimating humpback age cannot be applied to all individuals and populations. Assays for human age have recently been developed based on age‐induced changes in DNA methylation of specific genes. We used information on age‐associated DNA methylation in human and mouse genes to identify homologous gene regions in humpbacks. Humpback skin samples were obtained from individuals with a known year of birth and employed to calibrate relationships between cytosine methylation and age. Seven of 37 cytosines assayed for methylation level in humpback skin had significant age‐related profiles. The three most age‐informative cytosine markers were selected for a humpback epigenetic age assay. The assay has an R² of 0.787 (P = 3.04e?16) and predicts age from skin samples with a standard deviation of 2.991 years. The epigenetic method correctly determined which of parent–offspring pairs is the parent in more than 93% of cases. To demonstrate the potential of this technique, we constructed the first modern age profile of humpback whales off eastern Australia and compared the results to population structure 5 decades earlier. This is the first epigenetic age estimation method for a wild animal species and the approach we took for developing it can be applied to many other nonmodel organisms.     

Impact of early personal‐history characteristics on the Pace of Aging: implications for clinical trials of therapies to slow aging and extend healthspan

Therapies to extend healthspan are poised to move from laboratory animal models to human clinical trials. Translation from mouse to human will entail challenges, among them the multifactorial heterogeneity of human aging. To inform clinical trials about this heterogeneity, we report how humans’ pace of biological aging relates to personal‐history characteristics. Because geroprotective therapies must be delivered by midlife to prevent age‐related disease onset, we studied young‐adult members of the Dunedin Study 1972–73 birth cohort (n = 954). Cohort members’ Pace of Aging was measured as coordinated decline in the integrity of multiple organ systems, by quantifying rate of decline across repeated measurements of 18 biomarkers assayed when cohort members were ages 26, 32, and 38 years. The childhood personal‐history characteristics studied were known predictors of age‐related disease and mortality, and were measured prospectively during childhood. Personal‐history characteristics of familial longevity, childhood social class, adverse childhood experiences, and childhood health, intelligence, and self‐control all predicted differences in cohort members’ adulthood Pace of Aging. Accumulation of more personal‐history risks predicted faster Pace of Aging. Because trials of anti‐aging therapies will need to ascertain personal histories retrospectively, we replicated results using cohort members’ retrospective personal‐history reports made in adulthood. Because many trials recruit participants from clinical settings, we replicated results in the cohort subset who had recent health system contact according to electronic medical records. Quick, inexpensive measures of trial participants’ early personal histories can enable clinical trials to study who volunteers for trials, who adheres to treatment, and who responds to anti‐aging therapies.     

Life spanning murine gene expression profiles in relation to chronological and pathological aging in multiple organs

Aging and age‐related pathology is a result of a still incompletely understood intricate web of molecular and cellular processes. We present a C57BL/6J female mice in vivo aging study of five organs (liver, kidney, spleen, lung, and brain), in which we compare genome‐wide gene expression profiles during chronological aging with pathological changes throughout the entire murine life span (13, 26, 52, 78, 104, and 130 weeks). Relating gene expression changes to chronological aging revealed many differentially expressed genes (DEGs), and altered gene sets (AGSs) were found in most organs, indicative of intraorgan generic aging processes. However, only ≤ 1% of these DEGs are found in all organs. For each organ, at least one of 18 tested pathological parameters showed a good age‐predictive value, albeit with much inter‐ and intraindividual (organ) variation. Relating gene expression changes to pathology‐related aging revealed correlated genes and gene sets, which made it possible to characterize the difference between biological and chronological aging. In liver, kidney, and brain, a limited number of overlapping pathology‐related AGSs were found. Immune responses appeared to be common, yet the changes were specific in most organs. Furthermore, changes were observed in energy homeostasis, reactive oxygen species, cell cycle, cell motility, and DNA damage. Comparison of chronological and pathology‐related AGSs revealed substantial overlap and interesting differences. For example, the presence of immune processes in liver pathology‐related AGSs that were not detected in chronological aging. The many cellular processes that are only found employing aging‐related pathology could provide important new insights into the progress of aging.     

Epigenetic changes of mesenchymal stem cells in three‐dimensional (3D) spheroids

Mesenchymal stem cells (MSCs) hold profound promise in tissue repair/regeneration. However, MSCs undergo remarkable spontaneous differentiation and aging during monolayer culture expansion. In this study, we found that 2–3 days of three‐dimensional (3D) spheroid culture of human MSCs (hMSCs) that had been expanded in monolayer for six passages increased their clonogenicity and differentiation potency to neuronal cells. Moreover, in accordance with these changes, the expression levels of miRNA which were involved in stem cell potency were changed and levels of histone H3 acetylation in K9 in promoter regions of Oct4, Sox2 and Nanog were elevated. Our results indicate that spheroid culture increases their multi‐potency and changes the epigenetic status of pluripotent genes in hMSCs.     

Age‐associated microRNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits

Recent studies provide evidence of correlations of DNA methylation and expression of protein‐coding genes with human aging. The relations of microRNA expression with age and age‐related clinical outcomes have not been characterized thoroughly. We explored associations of age with whole‐blood microRNA expression in 5221 adults and identified 127 microRNAs that were differentially expressed by age at P < 3.3 × 10^?4 (Bonferroni‐corrected). Most microRNAs were underexpressed in older individuals. Integrative analysis of microRNA and mRNA expression revealed changes in age‐associated mRNA expression possibly driven by age‐associated microRNAs in pathways that involve RNA processing, translation, and immune function. We fitted a linear model to predict ‘microRNA age’ that incorporated expression levels of 80 microRNAs. MicroRNA age correlated modestly with predicted age from DNA methylation (r = 0.3) and mRNA expression (r = 0.2), suggesting that microRNA age may complement mRNA and epigenetic age prediction models. We used the difference between microRNA age and chronological age as a biomarker of accelerated aging (Δage) and found that Δage was associated with all‐cause mortality (hazards ratio 1.1 per year difference, P = 4.2 × 10^?5 adjusted for sex and chronological age). Additionally, Δage was associated with coronary heart disease, hypertension, blood pressure, and glucose levels. In conclusion, we constructed a microRNA age prediction model based on whole‐blood microRNA expression profiling. Age‐associated microRNAs and their targets have potential utility to detect accelerated aging and to predict risks for age‐related diseases.     

Altered proteome turnover and remodeling by short‐term caloric restriction or rapamycin rejuvenate the aging heart

Chronic caloric restriction (CR) and rapamycin inhibit the mechanistic target of rapamycin (mTOR) signaling, thereby regulating metabolism and suppressing protein synthesis. Caloric restriction or rapamycin extends murine lifespan and ameliorates many aging‐associated disorders; however, the beneficial effects of shorter treatment on cardiac aging are not as well understood. Using a recently developed deuterated‐leucine labeling method, we investigated the effect of short‐term (10 weeks) CR or rapamycin on the proteomics turnover and remodeling of the aging mouse heart. Functionally, we observed that short‐term CR and rapamycin both reversed the pre‐existing age‐dependent cardiac hypertrophy and diastolic dysfunction. There was no significant change in the cardiac global proteome (823 proteins) turnover with age, with a median half‐life 9.1 days in the 5‐month‐old hearts and 8.8 days in the 27‐month‐old hearts. However, proteome half‐lives of old hearts significantly increased after short‐term CR (30%) or rapamycin (12%). This was accompanied by attenuation of age‐dependent protein oxidative damage and ubiquitination. Quantitative proteomics and pathway analysis revealed an age‐dependent decreased abundance of proteins involved in mitochondrial function, electron transport chain, citric acid cycle, and fatty acid metabolism as well as increased abundance of proteins involved in glycolysis and oxidative stress response. This age‐dependent cardiac proteome remodeling was significantly reversed by short‐term CR or rapamycin, demonstrating a concordance with the beneficial effect on cardiac physiology. The metabolic shift induced by rapamycin was confirmed by metabolomic analysis.     

A metabolic signature predicts biological age in mice

Our understanding of the mechanisms by which aging is produced is still very limited. Here, we have determined the sera metabolite profile of 117 wild‐type mice of different genetic backgrounds ranging from 8 to 129 weeks of age. This has allowed us to define a robust metabolomic signature and a derived metabolomic score that reliably/accurately predicts the age of wild‐type mice. In the case of telomerase‐deficient mice, which have a shortened lifespan, their metabolomic score predicts older ages than expected. Conversely, in the case of mice that overexpress telomerase, their metabolic score corresponded to younger ages than expected. Importantly, telomerase reactivation late in life by using a TERT‐based gene therapy recently described by us significantly reverted the metabolic profile of old mice to that of younger mice, further confirming an anti‐aging role for telomerase. Thus, the metabolomic signature associated with natural mouse aging accurately predicts aging produced by telomere shortening, suggesting that natural mouse aging is in part produced by presence of short telomeres. These results indicate that the metabolomic signature is associated with the biological age rather than with the chronological age. This constitutes one of the first aging‐associated metabolomic signatures in a mammalian organism.     

Mitofusin 1 and optic atrophy 1 shift metabolism to mitochondrial respiration during aging

Replicative and chronological lifespan are two different modes of cellular aging. Chronological lifespan is defined as the duration during which quiescent normal cells retain their capacity to re‐enter the proliferative cycle. This study investigated whether changes in metabolism occur during aging of quiescent normal human fibroblasts (NHFs) and the mechanisms that regulate these changes. Bioenergetics measurements were taken in quiescent NHFs from younger (newborn, 3‐day, 5‐month, and 1‐year) and older (58‐, 61‐, 63‐, 68‐, and 70‐year) healthy donors as well as NHFs from the same individual at different ages (29, 36, and 46 years). Results show significant changes in cellular metabolism during aging of quiescent NHFs: Old NHFs exhibit a significant decrease in glycolytic flux and lactate levels, and increase in oxygen consumption rate (OCR) and ATP levels compared to young NHFs. Results from the Seahorse XF Cell Mito Stress Test show that old NHFs with a lower Bioenergetic Health Index (BHI) are more prone to oxidative stress compared to young NHFs with a higher BHI. The increase in OCR in old NHFs is associated with a shift in mitochondrial dynamics more toward fusion. Genetic knockdown of mitofusin 1 (MFN1) and optic atrophy 1 (OPA1) in old NHFs decreased OCR and shifted metabolism more toward glycolysis. Downregulation of MFN1 and OPA1 also suppressed the radiation‐induced increase in doubling time of NHFs. In summary, results show that a metabolic shift from glycolysis in young to mitochondrial respiration in old NHFs occurs during chronological lifespan, and MFN1 and OPA1 regulate this process.     

DNA methylation and healthy human aging

The process of aging results in a host of changes at the cellular and molecular levels, which include senescence, telomere shortening, and changes in gene expression. Epigenetic patterns also change over the lifespan, suggesting that epigenetic changes may constitute an important component of the aging process. The epigenetic mark that has been most highly studied is DNA methylation, the presence of methyl groups at CpG dinucleotides. These dinucleotides are often located near gene promoters and associate with gene expression levels. Early studies indicated that global levels of DNA methylation increase over the first few years of life and then decrease beginning in late adulthood. Recently, with the advent of microarray and next‐generation sequencing technologies, increases in variability of DNA methylation with age have been observed, and a number of site‐specific patterns have been identified. It has also been shown that certain CpG sites are highly associated with age, to the extent that prediction models using a small number of these sites can accurately predict the chronological age of the donor. Together, these observations point to the existence of two phenomena that both contribute to age‐related DNA methylation changes: epigenetic drift and the epigenetic clock. In this review, we focus on healthy human aging throughout the lifetime and discuss the dynamics of DNA methylation as well as how interactions between the genome, environment, and the epigenome influence aging rates. We also discuss the impact of determining ‘epigenetic age’ for human health and outline some important caveats to existing and future studies.