Epigenetic clocks, which are biomarkers of biological age, are influenced by various factors, including blood cell composition. This study, published in Genome Medicine, investigates the relationship between blood cell composition and DNA methylation age (DNAmAge) as well as age acceleration (AgeAccel). The authors developed a principal component analysis (PCA) method to address the complexities of collinearity among blood cell counts, providing insights into how these factors contribute to biological aging.
What this study does not show
While the study identifies significant contributions of blood cell composition to DNA methylation age and age acceleration, it does not establish a direct causal relationship between these factors and health outcomes.
The research analyzed a substantial dataset of 4,058 samples to quantify the influence of cell composition on DNAmAge and AgeAccel, utilizing six different epigenetic clocks. The findings indicate that up to 53% of the variation in DNAmAge can be attributed to cell counts, particularly the balance between naïve and memory T cells. In contrast, the association between AgeAccel and cell counts explained a maximum of 21% of the variance, with neutrophils playing a significant role in 2nd-generation clocks.
Furthermore, the study examined the impact of blood cell composition on the relationship between AgeAccel and 176 incident health outcomes, based on a larger cohort of 18,859 individuals. The results suggest that while cell composition does attenuate the association between AgeAccel and health outcomes, the effect is relatively modest.
In conclusion, the study highlights that DNAmAge and AgeAccel reflect different biological processes, with blood cell composition significantly influencing these measures. However, the contribution of cell composition to the association between AgeAccel and health outcomes appears limited, suggesting that other factors may also play a critical role in biological aging and its implications for health.
Evidence scorecard
- Analytic validity: Established
- Clinical validity: Partial
- Clinical utility: Not established
The evidence is based on primary research involving human samples.