#1

This paper compares 14 epigenetic clocks to see how well they predict disease risk over 10 years.

Key Points

  • Purpose of the Study:
    The researchers wanted to find out how well different epigenetic clocks—tools that estimate biological age from DNA methylation—can predict the onset of 174 diseases and overall mortality over a 10-year period.
  • Study Design:
    • They analyzed data from 18,859 people in the Generation Scotland cohort.
    • Fourteen widely used epigenetic clocks were compared.
    • The study used statistical models to see how well each clock predicted disease, adjusting for common risk factors like age, sex, BMI, smoking, alcohol use, education, and socioeconomic status.
  • Types of Epigenetic Clocks:
    • First-generation clocks (e.g., Horvath, Hannum) were designed to predict chronological age.
    • Second-generation clocks (e.g., GrimAge, PhenoAge) and third-generation clocks (e.g., DunedinPACE) were designed to predict health outcomes and the pace of aging.
  • Main Findings:
    • Second- and third-generation clocks were much better at predicting disease risk than first-generation clocks.
    • Out of 176 significant associations, 27 diseases (like lung cancer and diabetes) had stronger links to the clocks than to all-cause mortality.
    • In 35 cases, adding an epigenetic clock to traditional risk models improved disease prediction accuracy by more than 1%, with some models reaching high clinical usefulness (AUC > 0.80).
    • The best results were seen for respiratory and liver diseases.
  • Implications:
    • Second- and third-generation clocks could be useful for predicting disease risk, especially for certain conditions.
    • No single clock was best for all diseases, but GrimAge v2 often performed well.
    • These clocks add value even after accounting for traditional risk factors.
  • Limitations:
    • The study used blood samples to predict diseases that may start in other tissues.
    • Some risk factors (like family history or medication use) were not included.
    • The findings need to be confirmed in other populations and with repeated samples over time.
  • Conclusion:
    • Epigenetic clocks, especially newer ones, show promise for predicting disease risk and could help improve clinical risk models in the future. However, more research is needed before they can be used in routine healthcare.
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#2

Brian Kennedy said in his recent interview with Peter Attia that he has a new clock based on like 50 parameters from a blood panel and a few other measurements, and that it predicts mortality better than methylation clocks.

In the following talk he gave (which may be about the same or related clock), he mentions something fairly interesting:

But one of the interesting things that we found is that there was one principal component that really just mapped distance from the green zone, not direction. So this was trying to tell you you’re diverging from health, but not toward any particular set of diseases. And we think these kinds of measures are probably the most proximal measures of aging that we can look at in this population. And when you look at what those [are] it’s mostly immune function. So, it’s coming back to this idea that chronic sterile inflammation is an event that’s happening increasingly as we get older and this is driving risk for a whole range of different kinds of diseases. So, the markers in this principal component are red cells, immune cells, inflammation, and kidney function. These things are all tightly linked together.

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