New Method Uses DNA Methylation to Accurately Predict Chronological Age Within 1.36 Years

Researchers from the Hebrew University of Jerusalem have pioneered a highly precise technique to estimate an individual’s chronological age by analyzing specific regions of DNA. Leveraging advanced deep learning algorithms and single-molecule DNA methylation analysis, their method achieves a median error of just 1.36 years in individuals under 50. This breakthrough primarily relies on examining two short genomic regions that exhibit age-related methylation changes.

DNA methylation, a process where chemical groups are added to DNA molecules, acts as an internal biological clock that reflects aging. The team’s innovation was to focus on two key areas of the genome, enabling accurate age predictions from a simple blood sample. This approach was validated using data from over 300 healthy subjects and a longitudinal study from the Jerusalem Perinatal Study, demonstrating consistent accuracy regardless of variables such as sex, smoking status, or body weight.

The application of artificial intelligence, specifically through their tool named MAgeNet, enhances the ability to decode these methylation patterns. This technology not only benefits medical research and aging studies but also opens new avenues for forensic science, where estimating a suspect’s age from minimal DNA can be critical. Moreover, understanding DNA changes over time provides insights into cellular aging processes, revealing both random and coordinated patterns of methylation shifts, akin to biological clocks ticking within our cells.

This innovative method offers substantial potential for personalized medicine, enabling healthcare providers to assess biological age more accurately than traditional methods. It could also assist in forensic investigations, improving the accuracy of age estimations from DNA traces at crime scenes. Overall, this research marks a significant step closer to unraveling the mechanisms of aging and improving age-related diagnostics.

Source: https://medicalxpress.com/news/2025-07-chronological-age-years-dna-methylation.html