Causal Epigenetic Age Uncouples Damage and Adaptation

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Machine learning models that use DNA markers can estimate the age of biological samples. However, understanding why these markers change with age is challenging because it’s hard to prove that these changes cause aging-related traits.

In this week’s Everything Epigenetics podcast, I speak with Kejun Ying who uses large datasets to find specific DNA markers that directly influence aging traits.

We explore his recently published study which found casual CpGs that speed up aging and others that protect against it.

Kejun and colleagues created two new models, DamAge and AdaptAge, to measure harmful and beneficial changes related to aging. DamAge, which indicates negative aging effects, is linked to several health risks, including higher chances of dying. AdaptAge, on the other hand, shows positive aging adaptations.

Interestingly, only the negative changes seen in DamAge can be reversed by a process that makes aged cells young again.

The research findings provide a detailed understanding of the DNA markers that truly affect lifespan and overall health as we age. This helps us develop more accurate aging biomarkers and evaluate treatments aimed at reversing aging, improving longevity, and understanding events that speed up the aging process.

In this podcast you’ll learn about:

– Kejun’s unique journey into the aging field
– One of the biggest weaknesses of the epigenetic clocks (separating causation versus correlation)
– Mendelian randomization
– Casual inference
– Why causality matters for aging biomarkers
– Why it is important to separate deleterious and protective changes in aging
– DamAge (casual aging clock based on damaging sites)
– AdpateAge (casual aging clock based on protective sites)
– The applications of DamAge and what AdpateAge
– ClockBase: a comprehensive platform for biological age profiling in human and mouse
– The application of ClockBase
– Data privacy when using ClockBase

Where to find Kejun:
Twitter –
LinkedIn –
Google Scholar –

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