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. 2025 Jun 30;98(2):237–244. doi: 10.59249/BYOI5042

Investigating Aging and DNA Methylation: A Path to Improving Health Span?

Mark T Mc Auley 1,*, Amy E Morgan 1
PMCID: PMC12204039  PMID: 40589941

Abstract

Investigating aging has become a subject of intense biomedical focus. This has coincided with an unprecedented rise in epigenetic research. DNA methylation (DNAm) is the most comprehensively investigated epigenetic process. Epigenetic clocks are capable of statistically correlating DNAm changes with chronological age. DNAm changes are also proving to be a worthwhile biomarker of age-related disease, while emerging evidence suggests this epigenetic mechanism could be an effective diagnostic tool for disease detection. Such investigative progress has significant implications for health care. In this brief review we examine some recent findings in this area. The overarching aim and scope of the work is to address the relationship between aging, DNAm, and health. We commence by briefly introducing aging. Next, DNAm and age-related disease are discussed. Thirdly, we critically examine epigenetic clocks. We conclude by exploring recent advances in the use of biosensors for measuring DNAm and disease detection.

Keywords: Aging, DNA methylation, age-related disease, biomarker, epigenetics

Introduction - What is Aging and Why Do We Age?

Aging is a remarkably familiar yet incredibly difficult process to define. Attempts to define this captivating biological phenomenon have led to considerable debate within biogerontology [1]. Consequently, many definitions of aging exist. However, a simple definition of aging is that it is the changes which accumulate with age, that progressively render an organism increasingly vulnerable to mortality [2]. Despite the uncertainty in defining aging, the last few decades have witnessed considerable progress in unravelling both the proximate and ultimate causes of aging. The ultimate causes of aging center on why it evolved. Considerable debate surrounds this issue; however, subscribers to the classic evolutionary theories generally agree that aging is due to the diminishing capacity of natural selection with increasing age [3,4]. In other words, aging is mainly viewed as a non-adaptive by-product of evolution that escaped natural selection. This crucial evolutionary concept provides a template for understanding the proximate mechanisms of aging. Proximate mechanisms suggest how aging unfolds. Many processes have been empirically associated with aging in a wide variety of canonical organisms [5,6]. However, due to the complexity of aging, it is unlikely it can be reduced to a single process. Despite this, routine attempts are made at performing cartesian reduction on the aging process. Perhaps nowhere better exemplifies recent efforts to do this than the so called “hallmarks of aging” by López-Otín et al. (2023) [7]. In this work, aging is reduced to 12 biological processes. The hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Unquestionably each process is important, however as the authors would duly acknowledge other mechanisms, both discovered and yet to be elucidated, inevitably contribute to aging.

The recursive approach outlined above is understandable if it is acknowledged that biologists typically solve complex puzzles by breaking them down into more manageable problems; the solutions to the smaller problems are then assembled to provide an explanation of the larger problem [8,9]. Within this landscape it is apparent epigenetics has an increasing role to play in aging research. Epigenetics centers on the regulation of gene activity without altering the genetic sequence of DNA [10]. Several epigenetic processes exist; these include histone modification (acetylation and methylation), non-coding RNAs, chromatin remodeling, and DNA methylation (DNAm). DNAm is the most widely studied aspect of epigenetics. DNAm, is an epigenetic mark which occurs at cytosine phosphate-guanine (CpG) motifs; and is formed by the covalent bonding of methyl groups from S-adenosyl methionine to the fifth carbon of cytosines [11]. Persistent observational and empirical evidence over the last decade has revealed that age related changes to DNAm patterns correlate with the diseases of aging and chronological age in humans and other species [12-14]. There are numerous age-related diseases including dementia, cardiovascular disease (CVD), and cancer. The Surveillance, Epidemiology and End Results (SEER) Program (SEER 22 2017-2021) reported that the median age of a cancer diagnosis was 67 years of age. Notably, 30.2% of diagnoses occurred in people aged 65-74 years of age. This is much higher than in younger groups: 23.8% of cases were diagnosed in individuals aged 55-64 years, 11.0% in 45-54-year-olds, and 4.8% in 35-44 year-olds [15]. Similar trends are observed with heart disease and age. For example, the self-reported prevalence of heart disease in 2019 in adults between the ages of 18-44 years of age was 1.0%. This elevated to 9% in those aged 55-64 years, 14.3% in 65-74-year-olds and 24.2% in those over the age of 75 years [16]. Given that by 2050, the number of people over the age of 60 globally, will nearly double to 22%, (compared to 2015) [17], understanding the underpinning mechanisms of age-related disease is of vital importance. Thus, a continual drive exists to better understand how changes to DNAm are linked to both aging and age-related disease [18]. The aim and scope of this brief review is to examine the relationship between aging and DNAm.

DNA Methylation and Age-related Disease

As alluded to above, DNAm involves the covalent addition of a methyl group (-CH3) to a cytosine located within a CpG site. This process is tightly regulated by DNA methyltransferases (DNMT), which are responsible for maintenance methylation of hemimethylated DNA (DNMT1) during DNA replication, and de novo methylation (DNMT3a and DNMT3b) [19]. Demethylation is regulated by ten–eleven translocation (TET) enzymes [20]. The epigenetic mechanism of DNAm modulates gene expression; typically, transcriptional silencing is associated with hypermethylation of promotor sites, while gene expression is frequently upregulated when hypomethylation occurs in the gene body [21]. DNAm patterns change with age, in a process termed epigenetic drift. Specifically, regions exhibiting significant methylation in younger years often display reduced methylation levels, while hypomethylated sites gain methyl groups. This (accelerated) epigenetic drift has been associated with several age-related diseases, including diabetes [22], Alzheimer’s disease (AD) [23], dementia [24], CVD [25], and cancer [26].

Numerous modifiable risk factors are associated with aberrant DNAm patterns. One such risk factor is diet, which can encompass various aspects of nutrition including early postnatal overnutrition [27], vitamin intake [28,29] and alcohol intake [30,31]. Other risk factors include high BMI [32,33], physical inactivity [34,35], stress [36], exposure to pollutants [37,38], smoking [39,40], socioeconomic status [41,42], chemotherapy treatment [43] and virus exposure [44,45]. Significantly, these aberrant methylation patterns can be long lasting, persisting decades after exposure [46]. For example, research indicates that some CpG sites may remain differentially methylated up to 35 years after the cessation of smoking, whilst other revert to normal within decades [47]. Conversely, strategies to ameliorate aberrant methylation patterns have proved promising, with evidence suggesting that healthy diet and lifestyle treatment groups exhibit reduced epigenetic aging, which may have implications for age-related disease [48-50]. It is also important to recognize the significance of parental and grand-parental health on the epigenome of offspring [51]. For instance, there is evidence to indicate the parental exposure to pollution [52], prenatal stress [53], maternal undernutrition [54], maternal overnutrition [55], paternal under- [56] and over-nutrition [57] are also associated with perturbed offspring methylation in genes related to age-associated disease. Pivotal observations of the Dutch hunger winter birth cohort demonstrate the impact of famine on offspring in later life. Offspring were at a higher risk of developing several age-related diseases including type 2 diabetes, cancer, and CVD [58].

Typically affecting older people, CVD is the leading cause of death globally [59]. CVD is a general term for conditions affecting the heart or blood vessels, and typically affects older adults. CVD encompasses a range of conditions including coronary heart disease, cerebrovascular disease, and peripheral artery disease. A range of evidence exist which links perturbations in DNAm with CVD [60]. Recently we critically examined the role epigenetics has in vascular dementia (VaD), a condition related to CVD [61]. This work revealed the significant impact diet, aging, comorbidities, the gut microbiome, and pro-longevity molecules can have on vascular dementia pathobiology. More broadly, DNAm changes have been associated with the development and progression of CVD [60]. To this end, there have been several predictive models created in recent years which estimate CVD risk from DNAm [62].

In addition to vascular dementia, other forms of dementia such as AD [63], frontotemporal dementia [64], and Lewy Body dementia [65], have been associated with aberrant DNAm patterns. Over the past couple of decades, the mortality rate of dementia and AD has increased steadily. In 2016, these chronic and incurable neurodegenerative disorders were the leading cause of death in people over the age of 80 years in England [66]. In one epigenome-wide association study of the cortex, 334 differentially methylated positions (DMPs) were identified in AD [67]. Gene promoter DNAm has been observed to significantly differ with increased burden of amyloid beta (Aβ) plaques in neurons of the dentate gyrus. This includes the hypomethylation of the gene promoter for gamma-secretase which cleaves an amyloid precursor into Aβ peptides [68]. Other studies focused on extracranial tissues. One study found 64 DMPs associated with AD in white blood cells [69]. Significant alterations in DNAm patterns have also been observed in genes associated with lipid regulation and lysosomal transmembrane gene [63].

DNAm changes also play a significant role in cancer [21]. Importantly, the degree of methylation can be used as a prognostic marker, indicating the grade and sizing of a tumor [70,71]. Due to this association, the use of DNAm as a tool for detecting cancer in its early stages has emerged, particularly from liquid biopsy. This refers to a non-invasive collection of a sample such as blood, saliva, or urine to detect cancer from cfDNA, which has clear benefits when compared to the traditional collection of a biopsy [72]. As such techniques continue to progress, there is little doubt they will improve our understanding of the nexus between DNAm and aging.

DNA Methylation as a Measure of Aging

Many epigenetic clocks have been developed over the past decade to correlate biological age with chronological age [73,74]. Horvath’s clock is widely regarded as the first epigenetic clock [12]. It focuses on DNAm at 353 CpG sites and incorporates data from 51 healthy human tissues and cell types. This clock lacks specificity for disease. Moreover, it remains to be determined, how the 353 CpG’s which define the clock are linked to aging. In fact, they may have nothing to do with aging. Arguably the Hannum clock is simpler as it focuses on 71 CpG sites in blood [75]. However, this clock is limited to blood samples. The GrimAge clock, predicts mortality and age-related diseases using biomarkers including smoking and body mass index [76]. It is informed by clinical data from the Framingham study—this is a drawback as this is a very narrow demographic. PhenoAge combines DNAm data with a broad range of biomarkers to predict phenotypic age. It was also able to use DNAm biomarkers of aging to predict CVD and coronary heart disease. However, a limitation of PhenoAge is that its training data is built around a dataset that is not reflective of a diverse range of ethnic groups. This limitation of the clocks was brought into sharp focus recently when Yusipov and colleagues performed a comprehensive analysis using the largest compilation of publicly available DNAm data from healthy individuals—93 datasets encompassing 23 000 samples across 25 countries and 31 ethnicities [77]. The work investigated how geography, ethnicity, and health status influence epigenetic aging. The study evaluated PhenoAge and GrimAge. They identified significant inconsistencies in their outputs. PhenoAge often underestimated, while GrimAge tended to overestimate age in healthy individuals.

The criticism of the causality of epigenetic clocks outlined above is justified. However, emerging studies suggests that epigenetic clocks are improving. For instance, recent work was able to identify specific cytosines with methylation levels in genes implicated with longevity across different species [78]. Another clock was able to use DNAm levels to substantiate the age claims of centenarians [79]. Attempts to overcome clock limitations have also focused on using explainable AI (XAI) and deep learning (DL) tools. One study used a deep neural network-based epigenetic clock [80]. It was trained on a large dataset of 17 726 whole-blood samples across the human lifespan. Interestingly, it improved age prediction accuracy by integrating DNAm data and established CpG age markers. In another study DL and XAI were applied to large omic datasets [81]. A multi-view graph-level representation learning framework that incorporated biological networks was used to build molecular aging clocks at cell-type resolution. A ribosomal gene subnetwork linked to aging, independent of cell type was discovered. Using the same approach on DNAm data, they identified an age-related inflammatory pathway. This recent progress with richer AI methods suggests that the criticism of the original epigenetic clocks does not completely rule out the possibility that one day DNAm clocks will be embedded within a causal framework for aging. Thus, it is possible with further developments in AI and its applications to multi-omics data a more mechanistic understanding of aging could be achieved. This would constitute significant progress in how aging is measured.

Detecting DNA Methylation: A Diagnostic Tool for Future Healthcare?

Several methods exist which can quantify DNAm. Many analytical techniques require bisulphite treatment as a precursor to analysis. Bisulphite treatment results in the conversion of unmethylated cytosines to uracil, while methylated cytosine remain unchanged [21]. When followed with PCR, this results in methylated cytosines remaining as cytosines, while unmethylated cytosines appear as thymine. This modification can be detected at a single base level when followed by whole genome sequencing methods such as pyrosequencing [82]. In pyrosequencing, nucleotide incorporation is detected due to the release of pyrophosphate which transmits a light signal [82,83]. By performing a restriction digest prior to bisulphite treatment, targeted sequencing can be performed; reduced representation bisulphite treatment is a cost-effective alternative to genomic sequencing [84]. Microarray kits are a commonly used method of detecting DNAm due to the high throughput nature of the method and the ability to analyze up to almost one million CpG sites [85]. This method relies of the hybridization of test DNA to an immobilized oligonucleotide probe. Alternatively, nanopore technology enables the analysis of long sequence reads without the need for bisulphite treatment or PCR [86]. Other methods which don’t require bisulphite treatment include methylated DNA immunoprecipitation sequencing (MeDIP-seq), methylation-sensitive restriction enzyme sequencing (MRE-seq), and single-molecule real-time sequencing (SMRT-seq). In MEDIP-seq, 5mC antibodies bind to fragmented methylated DNA and undergo precipitation. Following DNA purification, sequencing can be conducted [87]. In MRE-seq, MREs can be exploited as an alternative way to study DNAm. These enzymes cleave only unmethylated sequences; the resulting fragments can be size-selected and sequenced (MRE-seq) [88]. SMRT-seq is a method which employs fluorescently labelled nucleotides and DNA polymerase. The addition of each nucleotide results in a fluorescence signal which indicates the nucleotide identity and/or modification such as 5mC [89].

Some electrochemical sensors rely on asymmetric PCR in place of traditional PCR methods to ascertain methylation. In this method, unequal concentrations of PCR primers result in ssDNA production. This complementary single stranded DNA can either be high in guanine (methylated, complementary to cytosine) or adenine (unmethylated, complementary to uracil). This outcome can be exploited by electrochemical techniques which utilize gold (working electrode). This is because adenines bind to gold with a higher affinity than guanines, resulting in greater resistance, which can be detected using techniques such as electrochemical impedance spectroscopy or differential pulse voltammetry [90]. DNA interactions with gold have also played an important role in the development of gold nanoparticle technology where DNAm can be detected via a color change [91,92].

Current research often centers on the exploration of DNAm data. Bioinformatics is a useful tool in epigenomic analysis, with numerous DNAm databases available [93-96] to aid in the understanding of the relationship between DNAm and disease [97], longevity [98], and environmental factors [99]. Following the advancement of machine learning, tools for diagnosis can be readily employed. For instance, one DL method achieved an overall accuracy of 95% for the diagnosis of CNS tumors, based on DNAm histopathology images and patient demographics [100]. Furthermore, in a clinical setting, it has been demonstrated that whole-genome bisulfite sequencing data for tumor samples could be utilized to predict long term survival in intrahepatic cholangiocarcinoma patients [101]. Moreover, cfDNA methylation data has been used to accurately predict patient response to neoadjuvant chemotherapy in 79% of bladder cancer cases [102].

Conclusions

Aging is an emergent process which is a by-product of the “simple” physical laws which have governed the evolution of life on this planet. In contrast to physical laws however, biological systems are far from simple. Natural selection has “generated” systems which are inherently complex. This complexity is all too evident when biological systems break down with age, and medical research strives to unravel the pathobiology of disease. This problem provokes the question, how can aging be better understood, so that diseases can be targeted earlier in life, and morbidity compressed. The challenge of elucidating aging becomes increasingly intractable if viewed solely from a physics perspective, and thus deemed due to increasing entropy with time. This is not scientific heresy. Solid arguments exist for interpreting aging entropically [103-105]. However, viewing aging through a thermodynamic lens does not mean the conundrum should be avoided. A significant body of evidence demonstrates that the trajectory of human aging is malleable [106]. Moreover, it has even been argued that aging is reversible. Indeed, DNAm is a strong candidate that could meditate the reversibility of aging because it has been shown that the partial reprogramming of induced pluripotent stem cells leads to rejuvenation in terms of epigenetic age [107].

The findings from this brief review also serve to emphasize the plasticity of aging and the contribution epigenetics makes to this. We revealed how epigenetics has presented significant therapeutic and diagnostic avenues for aging and health. It was illustrated how DNAm changes can be quantified and how these are associated with diseases including CVD, AD and cancer. An emphasis was also placed on how extrinsic factors such as diet and pollution can modulate the trajectory of epigenetic change. Measuring age using epigenetic clocks to determine health status was also critically evaluated. Importantly, we revealed DNAm clocks are imperfect measures of aging, which require further refinement if they are to be firmly embedded within a causality framework of aging. However, considerable effort is still required to understand the basic mechanisms of aging more broadly, and it is important not to lose sight of this, despite the remarkable progress which has been made in understanding aging and its relationship with health.

Acknowledgments

Some of this work was completed at a writing retreat at Missenden Abbey, Great Missenden, Buckinghamshire, UK, which was financially supported by the Food4Years network—a collaborative initiative funded by the Medical Research Council and the Biotechnology and Biological Sciences Research Council. In particular, the authors wish to thank Dr. Miriam Clegg from University College Cork and Dr. Chiara De Lucia of King’s College London, both members of Food4Years.

Glossary

CpG

5’-C-phosphate-G-3’

CVD

cardiovascular disease

DNAm

DNA methylation

DNMT

DNA methyltransferase

PCR

polymerase chain reaction

ssDNA

single stranded DNA

Author Contributions

MMcA: conceptualization, writing—original draft, writing—review and editing. AM (ORCID https://orcid.org/0000-0001-7557-6651): conceptualization, writing—original draft, writing—review and editing.

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