Epigenetic Clocks: The Gold Standard Enhanced by AI
Understanding biological age is crucial in aging research, and epigenetic clocks have emerged as a powerful tool in this field.
These clocks, particularly DNA methylation clocks, measure the biological age of individuals by analyzing specific patterns in their DNA.
The significance of biological age clocks lies in their ability to predict health risks and provide insights into the aging process.
While blood biomarker algorithms such as Phenotypic Age, which we talked about in our previous post, assess current physiological function and systemic health, making them more sensitive to short-term lifestyle or disease-related changes, Epigenetic tests estimate biological age by capturing stable, genome-wide aging patterns that reflect cumulative molecular changes over time.
By leveraging AI, researchers are enhancing the accuracy of epigenetic clocks, making them an even more reliable gold standard in aging research.
Key Topics covered in this post:
- What epigenetic clocks are and how DNA methylation works - The three generations of clock evolution (Horvath → GrimAge → DunedinPACE) - AI and machine learning in clock development - Commercial testing options and interpretation - Lifestyle factors affecting epigenetic age - The reversal question (realistic vs. marketing hype) - Limitations and ethical concerns - Future directions
Here is the list of references used to write this post. Some information in this post includes references to specific articles that you can find on the author's list.
Additional information can be found in my articles and reports:
- Epigenetic Age - Epigenetic Clocks and Epigenetic Age Determination: A Comprehensive Scientific Literature Review - "What are the latest advances in the science of epigenetic age and its determination?" - Epigenetic age and its measurement: recent advances - Interpretable Tailored Age Regression (ITARGET)
 Read full post here.
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