image: Figure 1. Identification and characterization of aging-related skeletal muscle-specific CpGs. (A) Schematic overview of experimental workflow. The diagram, created using BioRender, illustrates the workflow starting with the collection of PM muscle tissue, indicated an arrow pointing to the anatomical site clearly demarcated through shaded surrounding areas. DNA was bisulfite-converted and analyzed using the Infinium Methylation EPIC array to identify age-associated CpGs. Cryptic and target CpGs were examined via NGS, while only target CpGs were used for SBE. (B) Two-way hierarchical clustering heatmap of methylation profiles from 23 male PM muscle samples profiled by the EPIC array. Samples are color-coded by age group. The heatmap displays 500 randomly selected CpGs from 91,899 significant age-related CpGs (p < 0.05), with beta values normalized to Z-scores (yellow for higher, blue for lower values). (C) Volcano plot showing regression coefficients (β1) from age-related linear regression. Each dot represents a CpG with p < 0.05. Yellow and blue dots indicate CpGs with methylation gain and loss with age, respectively. (D) Enrichment of significant CpGs by genomic context. The Venn diagram shows the distribution of methylation accelerated (yellow) and decelerated (blue) CpGs among 91,899 significant CpGs. Bar plots display CpG distributions relative to CpG islands (left bar graph) and chromatin regions (right bar graph) using raw counts (the number of CpG sites) and the Chi-square (χ2) test. Statistical significance is denoted as *p < 0.05, **p < 0.01 and **p < 0.001. (E) Filtering of CpGs through multi-step criteria. The Venn diagram displays the overlap of CpGs filtered by effect size (Δβ ≥ 0.2), model fit (R2 > 0.65), and prior muscle-specific clocks (MEAT v.1 and v.2). Twelve and eight final CpGs (underlined) were selected for NGS- and SBE-based modeling. (F) Heatmap of the final 20 CpGs selected for age prediction, grouped by age. Beta values are Z-score normalized; age groups are color-coded.
Credit: Copyright: © 2025 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
“This study introduces the skeletal muscle epigenetic clocks in an Asian population using postmortem skeletal muscle tissue.”
BUFFALO, NY — December 30, 2025 — A new research paper was published in Volume 17, Issue 11 of Aging-US on November 26, 2025, titled “Epigenetic aging signatures and age prediction in human skeletal muscle.”
In this study, first author Soo-Bin Yang and corresponding author Hwan Young Lee from Seoul National University College of Medicine investigated how DNA methylation patterns in skeletal muscle change with age. Their findings offer a new and highly accurate method for estimating a person’s age, with potential applications in forensic science and aging research.
Skeletal muscle is essential for movement, energy balance, and physical strength, functions that become more important to monitor as people age. This study improves our understanding of how muscle tissue changes over time at the molecular level. Unlike previous research, which mainly analyzed living individuals of European descent, this study used postmortem samples from an Asian population.
“We analyzed DNA methylation profiles from 103 pectoralis major muscle samples from autopsies of South Korean individuals (18–85 years) using the Infinium EPIC array.”
The researchers analyzed DNA from over 100 pectoralis major muscle samples taken from individuals aged 18 to 85. They identified 20 DNA methylation sites, called CpGs, that were strongly associated with age. These CpGs were found in genes involved in muscle function, stress response, metabolism, and age-related diseases. Using these markers, the team built two machine learning models to predict age: one using Next-Generation Sequencing (NGS) and another using Single Base Extension (SBE). Both models were highly accurate, with average prediction errors between 3.8 and 5.5 years.
The new “epigenetic clocks” outperformed existing age-prediction models designed for other tissue types. However, when applied to cardiac and uterine muscle, these models showed much lower accuracy, reinforcing the need for tissue-specific approaches in molecular age estimation.
Beyond predicting age, the study also provides insight into how DNA methylation may affect muscle aging. Several of the identified CpGs were located in regions that regulate gene expression, being associated with a reduction of it in older muscle samples. Some of the affected genes are associated with sarcopenia, an age-related loss of muscle mass and strength.
Overall, this study introduces two reliable and cost-effective methods to estimate age from skeletal muscle, even when the DNA is partially degraded, making it especially useful in forensic settings. It also offers a path forward for developing future therapies that may slow age-related muscle decline and highlights how skeletal muscle aging can differ depending on population, tissue type, and anatomical location.
Paper DOI: https://doi.org/10.18632/aging.206341
Corresponding author: Hwan Young Lee – hylee192@snu.ac.kr
Abstract video: https://www.youtube.com/watch?v=1i6Ua0cceMU
Keywords: aging, skeletal muscle, age, DNA methylation, next generation sequencing, single base extension
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Journal
Aging-US
Method of Research
News article
Subject of Research
Not applicable
Article Title
Epigenetic aging signatures and age prediction in human skeletal muscle
Article Publication Date
26-Nov-2025
COI Statement
The authors declare no conflicts of interest related to this study.