Epigenetic clock

An epigenetic clock is a biochemical test that can be used to measure age. The test is based on DNA methylation levels, measuring the accumulation of methyl groups to one's DNA molecules.

History

The strong effects of age on

The personal story behind the discovery was featured in Nature.[13] The age estimator was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. The major innovation of Horvath's epigenetic clock lies in its wide applicability: the same set of 353 CpGs and the same prediction algorithm is used irrespective of the DNA source within the organism, i.e. it does not require any adjustments or offsets.^[10] This property allows one to compare the ages of different areas of the human body using the same aging clock. Shortly afterwards, a derivation of Horvath's clock, the IEAA (Intrinsic Epigenetic Age Acceleration), an estimator based on the cellular composition of the blood, was developed.

A second generation of epigenetic clocks emerged a few years later and improved on the first in age estimation. This was thanks to the incorporation not only of epigenetic variants such as DNA methylation but also environmental variants such as smoking or chronological age. Among these clocks, the PhenoAge and GrimAge clocks stand out. PhenoAge is an epigenetic clock that takes chronological age into account, and GrimAge uses the mortality risks of age together with the smoking variant among others as a risk factor. Taking into account environmental variants allows GrimAge to outperform any other epigenetic clock in "predicting death".

Third-generation epigenetic clocks are designed to be applicable across multiple species simultaneously. Specifically, pan-mammalian epigenetic clocks determine the age of tissues from all mammalian species by analyzing cytosine methylation in DNA regions that are highly conserved.^[14]

New age estimation tools have been developed continuously, which also facilitate the prognosis of certain diseases.

Relationship to a cause of biological aging

It is not yet known what exactly is measured by DNA methylation age. Horvath hypothesized that DNA methylation age measures the cumulative effect of an epigenetic maintenance system but details are unknown. The fact that DNA methylation age of blood predicts all-cause mortality in later life^{[15][16][17][18]} has been used to argue that it relates to a process that causes aging.^[15] However, if a particular CpG played a direct causal role in the aging process, the mortality it created would make it less likely to be observed in older individuals, making the site less likely to have been chosen as a predictor; the 353 clock CpGs therefore likely have no causal effect whatsoever.^[19] Rather, the epigenetic clock captures an emergent property of the epigenome.

Epigenetic clock theory of aging

In 2010, a new unifying model of aging and the development of complex diseases was proposed, incorporating classical aging theories and epigenetics.^[20][21] Horvath and Raj^[22] extended this theory, proposing an epigenetic clock theory of aging with the following tenets:

• Biological aging results as an unintended consequence of both developmental programs and maintenance program, the molecular footprints of which give rise to DNA methylation age estimators.
• The precise mechanisms linking the innate molecular processes (underlying DNAm age) to the decline in tissue function probably relate to both intracellular changes (leading to a loss of cellular identity) and subtle changes in cell composition, for example, fully functioning somatic stem cells.
• At the molecular level, DNAm age is a proximal readout of a collection of innate aging processes that conspire with other, independent root causes of aging to the detriment of tissue function.

Motivation for biological clocks

In general, biological aging clocks and

• testing the validity of various
  theories of biological aging
  ,
• diagnosing various age related diseases and for defining cancer subtypes,
• predicting/prognosticating the onset of various diseases,
• serving as
  rejuvenation
  approaches,
• studying
  cell differentiation
  ,
• forensic
  applications, for example to estimate the age of a suspect based on blood left on a crime scene.

Overall, biological clocks are expected to be useful for studying what causes aging and what can be done against it. However, they can only capture the effects of interventions that affect the rate of future aging, i.e. the slope of the Gompertz curve by which mortality increases with age, and not that of interventions that act at one moment in time, e.g. to lower mortality across all ages, i.e. the intercept of the Gompertz curve.^[19]

Properties of Horvath's clock

The clock is defined as an age estimation method based on 353

• TRIM59: of the tripartite motif family, strongly associated with chronological age and whose expression has been observed in multiple cancers
• SMC4: inhibits cellular senescence, an established hallmark of ageing
• KPNA4
  : member of the importin family, nuclear transport receptors. Dysfunction of nuclear transport has been proposed as a marker of ageing
• CD46: encodes a regulator of T-cell function and the complement system, a key component of the innate immune system where it promotes inflammation
• ATP8B4: encodes for a lipid transporter protein and contains variants that have been reported in association with Alzheimer's disease
• CXXC4: encodes Idax, an inhibitor of Wnt signalling^[25]

Statistical approach

The basic approach is to form a weighted average of the 353 clock CpGs, which is then transformed to DNAm age using a calibration function. The calibration function reveals that the epigenetic clock has a high ticking rate until adulthood, after which it slows to a constant ticking rate. Using the training data sets, Horvath used a penalized regression model (Elastic net regularization) to regress a calibrated version of chronological age on 21,369 CpG probes that were present both on the Illumina 450K and 27K platform and had fewer than 10 missing values. DNAm age is defined as estimated ("predicted") age. The elastic net predictor automatically selected 353 CpGs. 193 of the 353 CpGs correlate positively with age while the remaining 160 CpGs correlate negatively with age. R software and a freely available web-based tool can be found at the following webpage.^[26]

Accuracy

The median error of estimated age is 3.6 years across a wide spectrum of tissues and cell types,^[10] although this increases for older individuals^[24] The epigenetic clock performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, kidney, liver, lung, saliva, uterine cervix, epidermis, muscle) as well as in individual cell types such as CD4 T cells, CD14 monocytes, glial cells, neurons, immortalized B cells, mesenchymal stromal cells.^[10] However, accuracy depends to some extent on the source of the DNA.

Comparison with other biological clocks

The epigenetic clock leads to a chronological age prediction that has a

In general, lifestyle factors have only weak associations with epigenetic age acceleration in blood.[35]^[36][37] Cross sectional studies of extrinsic epigenetic aging rates in blood show reduced epigenetic aging correlates with higher education, eating a high plant diet with lean meats, moderate alcohol consumption, and physical activity^[36] and the risks associated with metabolic syndrome. However, studies suggest that high levels of alcohol consumption are associated with accelerated aging of certain epigenetic clocks.^[37]

Obesity and metabolic syndrome

The epigenetic clock was used to study the relationship between high

DNAm age is higher than chronological age in female breast tissue that is adjacent to breast cancer tissue.[10] Since normal tissue which is adjacent to other cancer types does not exhibit a similar age acceleration effect, this finding suggests that normal female breast tissue ages faster than other parts of the body.^[10] Similarly, normal breast tissue samples from women without cancer have been found to be substantially older than blood samples collected from the same women at the same time.^[39]

Female breast cancer

In a study of three epigenetic clocks and breast cancer risk, DNAm age was found to be accelerated in blood samples of cancer-free women, years before diagnosis.^[40]

Cancer tissue

Cancer tissues show both positive and negative age acceleration effects. For most tumor types, no significant relationship can be observed between age acceleration and tumor morphology (grade/stage).

Down syndrome entails an increased risk of many chronic diseases that are typically associated with older age. The clinical manifestations of accelerated aging suggest that trisomy 21 increases the biological age of tissues, but molecular evidence for this hypothesis has been sparse. According to the epigenetic clock, trisomy 21 significantly increases the age of blood and brain tissue (on average by 6.6 years).^[43]

Alzheimer's disease related neuropathology

Epigenetic age acceleration of the human prefrontal cortex was found to be correlated with several neuropathological measurements that play a role in Alzheimer's disease^[31] Further, it was found to be associated with a decline in global cognitive functioning, and memory functioning among individuals with Alzheimer's disease.^[31] The epigenetic age of blood relates to cognitive functioning in the elderly.^[23] Overall, these results strongly suggest that the epigenetic clock lends itself for measuring the biological age of the brain.

Cerebellum ages slowly

It has been difficult to identify tissues that seem to evade aging due to the lack of biomarkers of tissue age that allow one to contrast compare the ages of different tissues. An application of epigenetic clock to 30 anatomic sites from six centenarians and younger subjects revealed that the cerebellum ages slowly: it is about 15 years younger than expected in a centenarian.^[44] This finding might explain why the cerebellum exhibits fewer neuropathological hallmarks of age related dementias compared to other brain regions. In younger subjects (e.g. younger than 70), brain regions and brain cells appear to have roughly the same age.^[10][44] Several SNPs and genes have been identified that relate to the epigenetic age of the cerebellum.^[32]

Huntington's disease

Huntington's disease has been found to increase the epigenetic aging rates of several human brain regions.^[45]

Centenarians age slowly

The offspring of semi-supercentenarians (subjects who reached an age of 105–109 years) have a lower epigenetic age than age-matched controls (age difference = 5.1 years in blood) and centenarians are younger (8.6 years) than expected based on their chronological age.^[18]

HIV infection

Infection with the Human Immunodeficiency Virus-1 (HIV) is associated with clinical symptoms of accelerated aging, as evidenced by increased incidence and diversity of age-related illnesses at relatively young ages. But it has been difficult to detect an accelerated aging effect on a molecular level. An epigenetic clock analysis of human DNA from HIV+ subjects and controls detected a significant age acceleration effect in brain (7.4 years) and blood (5.2 years) tissue due to HIV-1 infection.^[46] These results are consistent with an independent study that also found an age advancement of 5 years in blood of HIV patients and a strong effect of the HLA locus.^[47]

Parkinson's disease

A large-scale study suggests that the blood of Parkinson's disease subjects, in particular, their granulocyte ratio, exhibits (relatively weak) accelerated aging effects.^[48]

Developmental disorder: syndrome X

Children with a very rare disorder known as syndrome X maintain the façade of persistent toddler-like features while aging from birth to adulthood. Since the physical development of these children is dramatically delayed, these children appear to be a toddler or at best a preschooler. According to an epigenetic clock analysis, blood tissue from syndrome X cases is not younger than expected.^[49]

Menopause accelerates epigenetic aging

The following results strongly suggest that the loss of female hormones resulting from menopause accelerates the epigenetic aging rate of blood and possibly that of other tissues.

A confounding aspect of biological aging is the nature and role of senescent cells. It is unclear whether the three major types of cellular senescence, namely replicative senescence, oncogene-induced senescence and DNA damage-induced senescence are descriptions of the same phenomenon instigated by different sources, or if each of these is distinct, and how they are associated with epigenetic aging. Induction of replicative senescence (RS) and oncogene-induced senescence (OIS) were found to be accompanied by epigenetic aging of primary cells but senescence induced by DNA damage was not, even though RS and OIS activate the cellular DNA damage response pathway.[51] These results highlight the independence of cellular senescence from epigenetic aging. Consistent with this, telomerase-immortalised cells continued to age (according to the epigenetic clock) without having been treated with any senescence inducers or DNA-damaging agents, re-affirming the independence of the process of epigenetic ageing from telomeres, cellular senescence, and the DNA damage response pathway. Although the uncoupling of senescence from cellular aging appears at first sight to be inconsistent with the fact that senescent cells contribute to the physical manifestation of organism ageing, as demonstrated by Baker et al., where removal of senescent cells slowed down aging.^[52]

The epigenetic clock analysis of senescence, however, suggests that cellular senescence is a state that cells are forced into as a result of external pressures such as DNA damage, ectopic oncogene expression and exhaustive proliferation of cells to replenish those eliminated by external/environmental factors.^[51] These senescent cells, in sufficient numbers, will probably cause the deterioration of tissues, which is interpreted as organism ageing. However, at the cellular level, aging, as measured by the epigenetic clock, is distinct from senescence. It is an intrinsic mechanism that exists from the birth of the cell and continues. This implies that if cells are not shunted into senescence by the external pressures described above, they would still continue to age. This is consistent with the fact that mice with naturally long telomeres still age and eventually die even though their telomere lengths are far longer than the critical limit, and they age prematurely when their telomeres are forcibly shortened, due to replicative senescence. Therefore, cellular senescence is a route by which cells exit prematurely from the natural course of cellular aging.^[51]

Effect of sex and race/ethnicity

Men age faster than women according to epigenetic age acceleration in blood, brain, saliva, but it depends on the structure being researched and the lifestyle.^[53] The epigenetic clock method applies to all examined racial/ethnic groups in the sense that DNAm age is highly correlated with chronological age. But ethnicity can be associated with epigenetic age acceleration.

Hematopoietic stem cell transplantation, which transplants these cells from a young donor to an older recipient, rejuvenates the epigenetic age of blood to that of the donor. However, graft-versus-host disease is associated with increased DNA methylation age.^[54]

Progeria

Adult progeria also known as Werner syndrome is associated with epigenetic age acceleration in blood.^[55] Fibroblast samples from children with Hutchinson-Gilford Progeria exhibit accelerated epigenetic aging effects according to the "skin & blood" epigenetic clock but not according to the original pan tissue clock from Horvath.^[56]

Biological mechanism behind the epigenetic clock

Despite the fact that biomarkers of ageing based on DNA methylation data have enabled accurate age estimates for any tissue across the entire life course, the precise biological mechanism behind the epigenetic clock is currently unknown.^[22] However, epigenetic biomarkers may help to address long-standing questions in many fields, including the central question: why do we age? To understand the essence of mechanisms behind the epigenetic clock, it would be advisable to make a comparison and find the relationship between the readings of the epigenetic clock and the transcriptome aging clock.^[57] The following explanations have been proposed for now in the literature.

Possible explanation 1: Epigenomic maintenance system

Horvath hypothesized that his clock arises from a methylation footprint left by an epigenomic maintenance system.^[10]

Possible explanation 2: Unrepaired DNA damages

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Endogenous DNA damages occur frequently including about 50 double-strand DNA breaks per cell cycle^[58] and about 10,000 oxidative damages per day (see DNA damage (naturally occurring)). During repair of double-strand breaks many epigenetic alterations are introduced, and in a percentage of cases epigenetic alterations remain after repair is completed, including increased methylation of CpG island promoters.^[59][60][61] Similar, but usually transient epigenetic alterations were recently found during repair of oxidative damages caused by H₂O₂, and it was suggested that occasionally these epigenetic alterations may also remain after repair.^[62] These accumulated epigenetic alterations may contribute to the epigenetic clock. Accumulation of epigenetic alterations may parallel the accumulation of un-repaired DNA damages that are proposed to cause aging (see DNA damage theory of aging).

Other age estimators based on DNA methylation levels

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Several other age estimators have been described in the literature.

1) Weidner et al. (2014) describe an age estimator for DNA from blood that uses only three CpG sites of genes hardly affected by aging (cg25809905 in integrin, alpha 2b (ITGA2B); cg02228185 in aspartoacylase (ASPA) and cg17861230 in phosphodiesterase 4C, cAMP specific (PDE4C)).^[63] The age estimator by Weidener et al. (2014) applies only to blood. Even in blood this sparse estimator is far less accurate than Horvath's epigenetic clock (Horvath 2014) when applied to data generated by the Illumina 27K or 450K platforms.^[64] But the sparse estimator was developed for pyrosequencing data and is highly cost effective.^[65]

2) Hannum et al. (2013)^[9] report several age estimators: one for each tissue type. Each of these estimators requires covariate information (e.g. gender, body mass index, batch). The authors mention that each tissue led to a clear linear offset (intercept and slope). Therefore, the authors had to adjust the blood-based age estimator for each tissue type using a linear model. When the Hannum estimator is applied to other tissues, it leads to a high error (due to poor calibration) as can be seen from Figure 4A in Hannum et al. (2013). Hannum et al. adjusted their blood-based age estimator (by adjusting the slope and the intercept term) in order to apply it to other tissue types. Since this adjustment step removes differences between tissue, the blood-based estimator from Hannum et al. cannot be used to compare the ages of different tissues/organs. In contrast, a salient characteristic of the epigenetic clock is that one does not have to carry out such a calibration step:^[10] it always uses the same CpGs and the same coefficient values. Therefore, Horvath's epigenetic clock can be used to compare the ages of different tissues/cells/organs from the same individual. While the age estimators from Hannum et al. cannot be used to compare the ages of different normal tissues, they can be used to compare the age of a cancerous tissue with that of a corresponding normal (non-cancerous) tissue. Hannum et al. reported pronounced age acceleration effects in all cancers. In contrast, Horvath's epigenetic clock^[41][66] reveals that some cancer types (e.g. triple negative breast cancers or uterine corpus endometrial carcinoma) exhibit negative age acceleration, i.e. cancer tissue can be much younger than expected. An important difference relates to additional covariates. Hannum's age estimators make use of covariates such as gender, body mass index, diabetes status, ethnicity, and batch. Since new data involve different batches, one cannot apply it directly to new data. However, the authors present coefficient values for their CpGs in Supplementary Tables which can be used to define an aggregate measure that tends to be strongly correlated with chronological age but may be poorly calibrated (i.e. lead to high errors).

3) Giuliani et al. identify genomic regions whose DNA methylation level correlates with age in human teeth. They propose the evaluation of DNA methylation at ELOVL2, FHL2, and PENK genes in DNA recovered from both cementum and pulp of the same modern teeth.^[67] They wish to apply this method also to historical and relatively ancient human teeth.

4) Galkin et al. used deep neural networks to train an epigenetic aging clock of unprecedented accuracy using >6,000 blood samples.^[68] The clock uses information from 1000 CpG sites and predicts people with certain conditions older than healthy controls: IBD, frontotemporal dementia, ovarian cancer, obesity. The aging clock is planned to be released for public use in 2021 by an Insilico Medicine spinoff company Deep Longevity.

In a multicenter benchmarking study 18 research groups from three continents compared all promising methods for analyzing DNA methylation in the clinic and identified the most accurate methods, having concluded that epigenetic tests based on DNA methylation are a mature technology ready for broad clinical use.^[69]

5) McCartney et al. Plasminogen activator inhibitor 1 (PAI1) can also be used as an age estimator related to DNA methylation levels, as it has been shown to exhibit stronger associations with cardiometabolic disease than some epigenetic clocks.

6) de Lima Camillo et al. (2022)^[70] used an optimized deep neural network to create a highly accurate, robust pan-tissue epigenetic clock. The predictor, dubbed AltumAge, was trained on 142 datasets and uses 20,318 CpG sites to achieve one of the lowest reported median absolute errors for human epigenetic age prediction of 2.153 years. Partly, that increase in performance of Horvath's linear method is due to AltumAge's ability to detect CpG-CpG interactions. AltumAge predicts higher age for people with autism, HIV, multiple sclerosis, non-alcoholic fatty liver disease, type 2 diabetes, and atherosclerosis. In contrast to Horvath's clock, AltumAge predicts cancer to be older than normal tissues. The code for the clock is publicly available.^[71]

Other species

Wang et al., (in mice livers)^[72] and Petkovich et al. (based on mice blood DNA methylation profiles)^[73] examined whether mice and humans experience similar patterns of change in the methylome with age. They found that mice treated with lifespan-extending interventions (such as calorie restriction or dietary rapamycin) were significantly younger in epigenetic age than their untreated, wild-type age-matched controls. Mice age predictors also detects the longevity effects of gene knockouts, and rejuvenation of fibroblast-derived iPSCs.

Mice multi-tissue age predictor based on DNA methylation at 329 unique CpG sites reached a median absolute error of less than four weeks (~5 percent of lifespan). An attempt to use the human clock sites in mice for age predictions showed that the human clock is not fully conserved in mice.^[74] Differences between human and mouse clocks suggests that epigenetic clocks need to be trained specifically for different species.^[75]

A novel method of ageing lobsters was published in 2021 that used a ribosomal DNA methylation-based clock which may allow non-invasive sampling and ageing of wild European lobster populations (Homarus gammarus).^[76]

Changes to DNA methylation patterns have great potential for age estimation and biomarker search in domestic and wild animals.^[77]

The Mammalian Methylation Consortium generated DNA methylation data in highly conserved stretches of DNA.^[78] Using these data, epigenetic clocks have been published for hundreds of different mammalian species. In particular, pan-mammalian clocks apply to tissues from all mammalian species.^[14]

References