Understanding biological age has gained significant attention, especially through the lens of epigenetic clocks, which are biomarkers that estimate biological age based on DNA methylation patterns. This study investigates the role of blood cell composition in influencing these epigenetic clocks, particularly focusing on DNA methylation age (DNAmAge) and age acceleration (AgeAccel).
What this study does not show
While the study identifies the influence of blood cell composition on DNA methylation age and age acceleration, it does not establish a direct causal relationship between these factors and health outcomes.
The research involved a comprehensive analysis of 4,058 samples, utilizing a novel principal component analysis (PCA) method designed to address the collinearity of blood cell counts. The findings indicate that up to 53% of the variation in DNA methylation age can be attributed to blood cell counts, with a notable impact from the balance between naïve and memory T cells.
In contrast, the association between age acceleration and blood cell counts was found to be weaker, explaining up to 21% of the variance, primarily linked to neutrophil counts in the case of second-generation epigenetic clocks. The study also validated these findings through artificial cell mixtures.
Moreover, the analysis explored the relationship between age acceleration and 176 incident health outcomes, revealing that blood cell composition significantly attenuates this association. However, the effect was described as modest, suggesting that while cell composition plays a role, it does not fully account for the relationship between age acceleration and health outcomes.
The conclusions drawn from this research highlight that DNAmAge and AgeAccel reflect different biological processes, as evidenced by their distinct associations with blood cell composition. The study emphasizes the complexity of biological aging and the need for further investigation into the underlying mechanisms.
Evidence scorecard
- Analytic validity: established
- Clinical validity: not established
- Clinical utility: not established
The evidence is based on primary research involving human samples.