In recent research published in Genome Medicine, the relationship between blood cell composition and DNA methylation age (DNAmAge) has been examined, providing new insights into biological aging. This study highlights the complexity of epigenetic clocks, which are biomarkers used to estimate biological age, and their connection to blood cell types.
What this study does not show
This study does not establish a direct causal relationship between blood cell composition and health outcomes, nor does it imply that modifications in cell composition can lead to changes in DNA methylation age or age acceleration.
The authors developed a principal component analysis (PCA) method to address the challenges posed by collinearity among blood cell counts. This innovative approach allowed them to quantify how much of the variation in DNA methylation age can be attributed to different blood cell types. The study analyzed a substantial sample size of 4,058 individuals to derive its conclusions.
Key findings indicate that up to 53% of the variation in DNA methylation age can be attributed to the proportions of various blood cells, particularly the balance between naïve and memory T cells. In contrast, the association between age acceleration and cell counts explained a smaller variance of up to 21%, with neutrophils being the primary contributors among the second-generation epigenetic clocks.
Furthermore, the study explored the influence of blood cell composition on the relationship between age acceleration and 176 different health outcomes across a larger cohort of 18,859 individuals. The results suggest that while blood cell composition does play a role in attenuating the association between age acceleration and health outcomes, this effect is relatively modest.
Interestingly, the findings reveal that DNAmAge and age acceleration reflect different biological processes, as evidenced by their distinct associations with blood cell composition. This distinction is crucial for understanding the underlying mechanisms of biological aging and how they may relate to health outcomes.
Evidence scorecard
- Analytic validity: Established
- Clinical validity: Not established
- Clinical utility: Not established
The evidence is based on primary research involving human participants.