Epigenetic mutation load is weakly correlated with epigenetic age acceleration

Abstract

DNA methylation (DNAm) age estimators are widely used to study aging-related conditions. It is not yet known whether DNAm age is associated with the accumulation of stochastic epigenetic mutations (SEMs), which reflect dysfunctions of the epigenetic maintenance system. Here, we defined epigenetic mutation load (EML) as the total number of SEMs per individual. We assessed associations between EML and DNAm age acceleration estimators using biweight midcorrelations in four population-based studies (total n = 6,388). EML was not only positively associated with chronological age (meta r = 0.171), but also with four measures of epigenetic age acceleration: the Horvath pan tissue clock, intrinsic epigenetic age acceleration, the Hannum clock, and the GrimAge clock (meta-analysis correlation ranging from r = 0.109 to 0.179). We further conducted pathway enrichment analyses for each participant’s SEMs. The enrichment result demonstrated the stochasticity of epigenetic mutations, meanwhile implicated several pathways: signaling, neurogenesis, neurotransmitter, glucocorticoid, and circadian rhythm pathways may contribute to faster DNAm age acceleration. Finally, investigating genomic-region specific EML, we found that EMLs located within regions of transcriptional repression (TSS1500, TSS200, and 1stExon) were associated with faster age acceleration. Overall, our findings suggest a role for the accumulation of epigenetic mutations in the aging process.

Umbilical cord plasma concentrate has beneficial effects on DNA methylation GrimAge and human clinical biomarkers

“…Due to the failure of DNAm maintenance, the Shannon entropy measurement increases with age, indicating a less predictable methylome across a population of cells (Hannum et al, 2013). In addition, a previous study has shown a significant correlation between DNAm-based age acceleration and entropy (Yan et al, 2020). Here, the decreased entropy may implicate the rejuvenation of the epigenetic landscape after the plasma concentrate treatments.…”

DNA methylation‐based biomarkers of aging were slowed down in a two‐year diet and physical activity intervention trial: the DAMA study

“…Additionally, the total number of stochastic epigenetic mutations (SEMs) per individual has been proposed as an alternative biomarker of healthy aging based on whole‐genome DNAm data (Gentilini et al, 2015 ). The total number of SEMs per individual, also known as epigenetic mutation load (EML) (Yan et al, 2020 ), is defined as the sum of extreme (outliers) DNAm values per sample. Recently, (Gentilini et al, 2015 ) provided evidence of the exponential relationship between age and SEMs, which occurs naturally during aging as a consequence of the “epigenetic drift.” A higher EML has been associated with age‐related pathological conditions like X chromosome activation skewing (Gentilini et al, 2015 ) and risk factors for non‐communicable diseases like cigarette smoking, alcohol intake, exposure to toxicants, and low socioeconomic status (Curtis et al, 2019 ; Fiorito et al, 2019 ), and it is associated with increased risk of different types of cancer in prospective studies (Gagliardi et al, 2020 ; Wang et al, 2019 ).…”

“…Recently, (Gentilini et al, 2015 ) provided evidence of the exponential relationship between age and SEMs, which occurs naturally during aging as a consequence of the “epigenetic drift.” A higher EML has been associated with age‐related pathological conditions like X chromosome activation skewing (Gentilini et al, 2015 ) and risk factors for non‐communicable diseases like cigarette smoking, alcohol intake, exposure to toxicants, and low socioeconomic status (Curtis et al, 2019 ; Fiorito et al, 2019 ), and it is associated with increased risk of different types of cancer in prospective studies (Gagliardi et al, 2020 ; Wang et al, 2019 ). Interestingly, DNAm epigenetic clocks and EML are weakly correlated, suggesting they describe different aspects of epigenetic aging processes (Yan et al, 2020 ).…”

Bioinformatics-based construction of prognosis-related methylation prediction model for pancreatic cancer patients and its application value

“…Our study likewise demonstrated an expression imbalance between hypermethylation and hypomethylation in the TSS1500 region, and by using genes in the TSS1500 region, we were able to construct a classification model to distinguish PAAD from normal tissue, providing a useful tool to identify PAAD. tSS200 also belongs to the transcription factor repressor functional element, and methylation in the TSS200 region is not only related to tumor development, but also involved in the acceleration of epigenetic mutational load and epigenetic age, providing a new perspective for our understanding of the age of DNA methylation ( Yan et al, 2020 ).…”