What do “epigenetic clocks” actually measure? These lab tests have become popular tools for studying biological aging, or how the body’s cellular function changes at a faster or slower rate than expected. However, the underlying biology behind each of these measurements has remained largely unknown until now.
A recent USC Leonard Davis School of Gerontology-led study published in npj Aging shows the variability in what five of the most widely used epigenetic clocks are actually measuring. The research team also developed new gene-expression based clocks to complement existing epigenetic clocks, which show even stronger predictive power for age-related disease and mortality.
Biology versus the calendar
Scientists have long known that people age at different rates biologically. Someone who is 70 years old on paper may have the health of a much younger person, while another 70-year-old may be much more frail than their same-age peers.
To measure that difference, researchers use epigenetic clocks to estimate biological age. These tools measure a chemical change called DNA methylation, which doesn’t change the genetic code itself but affects which genes are turned “off” or “on.” These chemical changes on top of the base DNA are collectively called the epigenome.
Examining the epigenome can predict health problems and even the risk of death better than chronological age alone. But until now, scientists did not know exactly what was happening inside cells that made the epigenetic clocks such powerful tools.
“Aging isn’t just about the number of candles on your birthday cake — it’s also about what’s happening inside your cells,” said lead author T. Em Arpawong , research associate professor of gerontology at the USC Leonard Davis School . “We found that different clocks capture different aspects of the biology of aging and developed new transcriptomic aging gene scores that complement existing clocks and, in some cases, better predict age-related disease and mortality.”
Surprising differences
In the study, Arpawong and the team studied blood samples from 3,227 participants in the Health and Retirement Study in the U.S. They compared DNA methylation patterns with gene expression, or how often genes are transcribed from DNA into RNA and used to make proteins. All of the transcription taking place within a cell at a given time is referred to as the transcriptome.
The researchers examined combined epigenetic and transcriptomic measurements to compare gene expression and identify the biological pathways highlighted by each of five epigenetic clocks.
They found that each aging clock was associated with different biological processes, from energy balance and cellular growth to immune cell activation and inflammatory signaling. Even though the clocks emphasized different biological pathways, they shared several common themes, including changes in the immune system, metabolism and cell communication — all well-known features of aging.
A new tool for prediction
The researchers further used the analysis to create new tools called transcriptomic aging gene scores (TAGS). The combined approach provided an even clearer picture of a person's biological health; in several cases, TAGS predicted health outcomes such as frailty, walking speed, heart disease, diabetes, lung disease and death better than the epigenetic clocks alone.
The findings could help researchers choose the best aging clock for different studies. For example, one clock may be better for studying treatments that target the immune system, while another may be better for testing therapies that improve metabolism or slow the decline in physiological resilience.
The work also helps explain the biology behind tools that are becoming increasingly common in aging research. By linking DNA methylation changes with gene expression activity, senior author Eileen Crimmins said the study has further illuminated the “black box” of biological aging clocks, bringing scientists one step closer to using these measures to better predict disease, track healthy aging and one day guide medical care.
“By uncovering the molecular programs behind these biomarkers, our findings improve their interpretability and help guide their use in geroscience, epidemiology, and future clinical applications,” she said.
About the study
Arpawong and Crimmins’ co-authors included Harshanna Badhesha, Jung Ki Kim, Christopher Beam, Kimberly Siegmund of USC; Steve Cole of UCLA; Eric T. Klopack of Indiana University; and Bharat Thyagarajan of the University of Minnesota. The study was supported by the National Institute on Aging (P30 AG017265).
npj Aging
Data/statistical analysis
People
How epigenetic clocks tick: unpacking the black box by deciphering biological pathways and transcriptomic signatures of accelerated aging
20-Jul-2026