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.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.Horvath and Raj extended this theory, proposing an epigenetic clock theory of aging with the following tenets: In 2010, a new unifying model of aging and the development of complex diseases was proposed, incorporating classical aging theories and epigenetics. Rather, the epigenetic clock captures an emergent property of the epigenome.Įpigenetic clock theory of aging 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. The fact that DNA methylation age of blood predicts all-cause mortality in later life has been used to argue that it relates to a process that causes aging. Horvath hypothesized that DNA methylation age measures the cumulative effect of an epigenetic maintenance system but details are unknown. It is not yet known what exactly is measured by DNA methylation age. Relationship to a cause of biological aging New age estimation tools have been developed continuously, which also facilitate the prognosis of certain diseases. Taking into account environmental variants allows GrimAge to outperform any other epigenetic clock in "predicting death". 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. Among these clocks, the PhenoAge and GrimAge clocks stand out. 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. 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.Ī second generation of epigenetic clocks emerged a few years later and improved on the first in age estimation. This property allows one to compare the ages of different areas of the human body using the same aging clock. it does not require any adjustments or offsets. 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. The age estimator was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. The personal story behind the discovery was featured in Nature. Horvath spent over 4 years collecting publicly available Illumina DNA methylation data and identifying suitable statistical methods. The first multi-tissue epigenetic clock, Horvath's epigenetic clock, was developed by Steve Horvath, a professor of human genetics and biostatistics at UCLA (Horvath 2013). The laboratories of Trey Ideker and Kang Zhang at the University of California, San Diego published the Hannum epigenetic clock (Hannum 2013), which consisted of 71 markers that accurately estimate age based on blood methylation levels. The first robust demonstration that DNA methylation levels in saliva could generate age predictors with an average accuracy of 5.2 years was published by a UCLA team including Sven Bocklandt, Steve Horvath, and Eric Vilain in 2011 (Bocklandt et al. A vast literature describes sets of CpGs whose DNA methylation levels correlate with age. The strong effects of age on DNA methylation levels have been known since the late 1960s. 7 Other age estimators based on DNA methylation levels.6.2 Possible explanation 2: Unrepaired DNA damages.6.1 Possible explanation 1: Epigenomic maintenance system.6 Biological mechanism behind the epigenetic clock.5.18 Rejuvenation effect due to stem cell transplantation in blood.
5.16 Cellular senescence versus epigenetic aging.5.15 Menopause accelerates epigenetic aging.5.14 Developmental disorder: syndrome X.5.8 Alzheimer's disease related neuropathology.5.4 Female breast tissue is older than expected.5.1 Genetic studies of epigenetic age acceleration.4.4 Comparison with other biological clocks.4.1 Genetic estimators in the Horvath clock.2 Relationship to a cause of biological aging.
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