Epigenetic age prediction

Abstract

Advanced age is the main common risk factor for cancer, cardiovascular disease and neurodegeneration. Yet, more is known about the molecular basis of any of these groups of diseases than the changes that accompany ageing itself. Progress in molecular ageing research was slow because the tools predicting whether someone aged slowly or fast (biological age) were unreliable. To understand ageing as a risk factor for disease and to develop interventions, the molecular ageing field needed a quantitative measure; a clock for biological age. Over the past decade, a number of age predictors utilising DNA methylation have been developed, referred to as epigenetic clocks. While they appear to estimate biological age, it remains unclear whether the methylation changes used to train the clocks are a reflection of other underlying cellular or molecular processes, or whether methylation itself is involved in the ageing process. The precise aspects of ageing that the epigenetic clocks capture remain hidden and seem to vary between predictors. Nonetheless, the use of epigenetic clocks has opened the door towards studying biological ageing quantitatively, and new clocks and applications, such as forensics, appear frequently. In this review, we will discuss the range of epigenetic clocks available, their strengths and weaknesses, and their applicability to various scientific queries.

Late‐life exercise mitigates skeletal muscle epigenetic aging

“…Several mammalian epigenetic aging clocks have been developed with the aim of expediting the discovery and validation of therapeutics and interventions to attenuate, prevent, or reverse biological aging (Simpson & Chandra, 2021 ). Zymo Research's validated DNAge™ algorithm, which expands upon the Horvath pan‐tissue clock built using elastic net regression (Horvath, 2013 ) and is accurate in murine muscle, was used to compare the chronological age of muscle from aged sedentary and PoWeR animals to that of young animals.…”

Metabolic predictors of phenotypic traits can replace and complement measured clinical variables in transcriptome-wide association studies

“…Differences between GSEA results of different outcome variable types could also arise from different biological information captured by the metabolic surrogates and by reported or measured values. This phenomenon is known from epigenetic clocks whose age predictions can differ from chronological age, and different clocks can reflect different aspects of biological age [38]). While many of the top-ranked pathways (S1 Appendix) for smoking were found by both outcome variable types, some of the pathways were solely found by using either reported smoking status ("smoking current") or metabolic surrogate("s current smoking").…”

Integrated analysis of expression, prognostic value and immune infiltration of GSDMs in hepatocellular carcinoma

“…As a key component of post-transcriptional modification, DNA methylation has been intensively reported to be involved in the regulation of cancer-associated genes [ 36 , 37 ]. In our study, we found that the methylation level of all the GSDMs members is significantly reduced in HCC tissues, which puts us in mind of the increased expression of GSDMs in HCC tissues.…”