Many chronological aging clocks can be found throughout the epigenome: Implications for quantifying biological aging

Abstract

Epigenetic alterations are a hallmark of aging and age‐related diseases. Computational models using DNA methylation data can create “epigenetic clocks” which are proposed to reflect “biological” aging. Thus, it is important to understand the relationship between predictive clock sites and aging biology. To do this, we examined over 450,000 methylation sites from 9,699 samples. We found ~20% of the measured genomic cytosines can be used to make many different epigenetic clocks whose age prediction performance surpasses that of telomere length. Of these predictive sites, the average methylation change over a lifetime was small (~1.5%) and these sites were under‐represented in canonical regions of epigenetic regulation. There was only a weak association between “accelerated” epigenetic aging and disease. We also compare tissue‐specific and pan‐tissue clock performance. This is critical to applying clocks both to new sample sets in basic research, as well as understanding if clinically available tissues will be feasible samples to evaluate “epigenetic aging” in unavailable tissues (e.g., brain). Despite the reproducible and accurate age predictions from DNA methylation data, these findings suggest they may have limited utility as currently designed in understanding the molecular biology of aging and may not be suitable as surrogate endpoints in studies of anti‐aging interventions. Purpose‐built clocks for specific tissues age ranges or phenotypes may perform better for their specific purpose. However, if purpose‐built clocks are necessary for meaningful predictions, then the utility of clocks and their application in the field needs to be considered in that context.

ZMAT3 hypomethylation contributes to early senescence of preadipocytes from healthy first‐degree relatives of type 2 diabetics

“…Nevertheless, we confirmed that FDR-associated DNA methylation changes at the ZMAT3 DMR have a functional impact on its expression. In addition, a recent meta-analysis of age-annotated methylation data available from GEO database has shown that DNA methylation changes at age-predictive loci, which are enriched in intergenic regions and gene enhancers, are small in magnitude (<5%) across the lifespan (Porter et al, 2021). This could be due to an increase in SNC, a relatively rare cell subtype in all tissues (Porter et al, 2021), which may also occur in FDR.…”

“…In addition, a recent meta-analysis of age-annotated methylation data available from GEO database has shown that DNA methylation changes at age-predictive loci, which are enriched in intergenic regions and gene enhancers, are small in magnitude (<5%) across the lifespan (Porter et al, 2021). This could be due to an increase in SNC, a relatively rare cell subtype in all tissues (Porter et al, 2021), which may also occur in FDR. These new findings prompted us to hypothesize that the methylation changes causing ZMAT3 upregulation also con- ZMAT3 regulates a wide range of transcripts which are implicated in several biological processes, including cell cycle, immune system function, and metabolic responses (Bersani et al, 2014(Bersani et al, , 2016Vilborg et al, 2009).…”

DNA methylation meta-analysis confirms the division of the genome into two functional groups

“…The specifics of the regulatory function of methylation continue to be studied, but there is no doubt that this process is related to ontogenetic development and aging [3,4]. The main direction of research in this direction focuses on the relationship between methylation and biological age in Mammalia [5,6,7,8]. The undoubted connection between methylation and aging processes provides an opportunity for experimental verification of various approaches to this problem.…”

Genetic loci and metabolic states associated with murine epigenetic aging

“…These DNA methylation (DNAm) age predictors are based on the methylation levels of select CpGs that are distributed across the genome. Each CpG that is used in a clock model is assigned a specific weight, typically derived from supervised training algorithms ( Bell et al, 2019 ; Thompson et al, 2018 ; Porter et al, 2021 ), and collectively, the methylation status across this ensemble of ‘clock CpGs’ is used to estimate the epigenetic age (DNAmAge). This estimate tracks closely, but not perfectly, with an individual’s chronological age.…”