Epigenetic clocks—tools that estimate biological aging through molecular changes in DNA—are widely used to study the aging process and its link to disease. However, a recent study led by researchers at the Spanish National Cancer Research Center (CNIO) has uncovered significant inaccuracies in these models.
The team has developed a new version of the epigenetic clock, designed to provide more precise readings by addressing limitations in current technologies. Their findings, published in Genome Medicine, highlight the need for more accurate tools to measure aging and its relationship to health outcomes.
The Promise and Challenges of Epigenetic Clocks
Epigenetic clocks are based on patterns of chemical modifications to DNA, known as epigenetic changes, that accumulate over time. These changes serve as markers of aging, with deviations from chronological age often linked to increased disease risk and mortality.
Despite their potential, current epigenetic clocks have significant limitations. CNIO researchers found that the results of commonly used clocks varied by an average of three years, with discrepancies reaching up to 25 years in some cases. This variability, they say, stems from mismatched technologies.
Existing epigenetic clocks use genomic data that aren’t always represented on the latest DNA analysis platforms.
Addressing Mismatched Technologies
Epigenetic clocks rely on mathematical models that interpret data from DNA chips. According to the study, many clocks assess regions of the genome that are not included in newer DNA chips, leading to inconsistencies.
To resolve this issue, the team developed a new epigenetic clock model tailored to modern DNA analysis platforms. They found that the variation in measurements is now less than a year, whether it’s from the same individual or between individuals. "This means that it's a robust and accurate model," explained Leonardo Garma, lead author of the study.
Insights into Cancer and Aging
The team applied their updated model to breast cancer research as part of CNIO’s "Digital Twins" project, which aims to create virtual patient models to improve personalized treatments. Their findings emphasize the relevance of epigenetic age in cancer biology.
"In cancer, accelerated epigenetic age [higher than chronological age] has been linked to an increased risk of developing breast and colon cancer," wrote Garma and Miguel Quintela, head of CNIO’s Breast Cancer Clinical Research Unit. Breast tissue from cancer patients often shows signs of epigenetic aging, with the rate of acceleration linked to treatment intensity.
Implications for Population-Level Health
The study also highlights the importance of accuracy in epigenetic clocks for population health research. Variability in measurements could distort conclusions about how lifestyle factors, like smoking, affect aging. "For an individual, it may not matter that the biological age is three years older, but for populations it does,” Garma noted. “For example, if the epigenetic age of a group of smokers is three years higher than that of the non-smoking population, this data could be relevant for drawing conclusions about the influence of tobacco on health."
Citation:
Garma, L.D., Quintela-Fandino, M. Applicability of epigenetic age models to next-generation methylation arrays. Genome Med 16, 116 (2024). https://doi.org/10.1186/s13073-024-01387-4
The team has developed a new version of the epigenetic clock, designed to provide more precise readings by addressing limitations in current technologies. Their findings, published in Genome Medicine, highlight the need for more accurate tools to measure aging and its relationship to health outcomes.
The Promise and Challenges of Epigenetic Clocks
Epigenetic clocks are based on patterns of chemical modifications to DNA, known as epigenetic changes, that accumulate over time. These changes serve as markers of aging, with deviations from chronological age often linked to increased disease risk and mortality.
Despite their potential, current epigenetic clocks have significant limitations. CNIO researchers found that the results of commonly used clocks varied by an average of three years, with discrepancies reaching up to 25 years in some cases. This variability, they say, stems from mismatched technologies.
Existing epigenetic clocks use genomic data that aren’t always represented on the latest DNA analysis platforms.
Addressing Mismatched Technologies
Epigenetic clocks rely on mathematical models that interpret data from DNA chips. According to the study, many clocks assess regions of the genome that are not included in newer DNA chips, leading to inconsistencies.
To resolve this issue, the team developed a new epigenetic clock model tailored to modern DNA analysis platforms. They found that the variation in measurements is now less than a year, whether it’s from the same individual or between individuals. "This means that it's a robust and accurate model," explained Leonardo Garma, lead author of the study.
Insights into Cancer and Aging
The team applied their updated model to breast cancer research as part of CNIO’s "Digital Twins" project, which aims to create virtual patient models to improve personalized treatments. Their findings emphasize the relevance of epigenetic age in cancer biology.
"In cancer, accelerated epigenetic age [higher than chronological age] has been linked to an increased risk of developing breast and colon cancer," wrote Garma and Miguel Quintela, head of CNIO’s Breast Cancer Clinical Research Unit. Breast tissue from cancer patients often shows signs of epigenetic aging, with the rate of acceleration linked to treatment intensity.
Implications for Population-Level Health
The study also highlights the importance of accuracy in epigenetic clocks for population health research. Variability in measurements could distort conclusions about how lifestyle factors, like smoking, affect aging. "For an individual, it may not matter that the biological age is three years older, but for populations it does,” Garma noted. “For example, if the epigenetic age of a group of smokers is three years higher than that of the non-smoking population, this data could be relevant for drawing conclusions about the influence of tobacco on health."
Citation:
Garma, L.D., Quintela-Fandino, M. Applicability of epigenetic age models to next-generation methylation arrays. Genome Med 16, 116 (2024). https://doi.org/10.1186/s13073-024-01387-4