Weighted 2D-kernel density estimations provide a new probabilistic measure for epigenetic age.
Abstract
Epigenetic aging signatures provide insights into human aging, but traditional clocks rely on linear regression of DNA methylation levels, assuming linear trajectories. This study explores a non-parametric approach using 2D-kernel density estimation to determine epigenetic age. Our weighted model achieves similar predictive accuracy as conventional clocks and provides a variation score reflecting the inherent variability of age-related epigenetic changes within samples. This score is significantly increased in various diseases and associated with mortality risk in the Lothian Birth Cohort 1921. Thus, weighted 2D-kernel density estimation facilitates accurate epigenetic age predictions and offers an additional variable for biological age estimation.
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Research summary
The paper claims that a weighted 2D-kernel density estimation model can accurately predict epigenetic age and reflects variability in age-related epigenetic changes. This research is relevant as it explores a novel approach to understanding epigenetic aging, which is a fundamental aspect of the biological aging process and could contribute to insights into longevity and age-related diseases.
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