The study published in Genome Medicine investigates the relationship between blood cell composition and DNA methylation age, a widely recognized biomarker of biological age. The authors, Jonkman et al., developed a principal component analysis (PCA) method to address the complexities arising from collinearity between different blood cell types. This innovative approach allows for a clearer understanding of how variations in blood cell counts can influence epigenetic clocks.
What this study does not show
While the study identifies significant contributions of blood cell composition to DNA methylation age, it does not establish a direct causal relationship between these factors and health outcomes.
The research analyzed a substantial dataset comprising 4,058 samples to quantify the impact of cell composition on DNA methylation age and age acceleration, as estimated by various epigenetic clocks. The findings suggest that up to 53% of the variation in DNA methylation age can be attributed to blood cell counts, particularly highlighting the roles of 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. Notably, for second-generation epigenetic clocks, neutrophils were primarily implicated in these associations.
The study further tested the influence of blood cell composition on the relationship between age acceleration and 176 incident health outcomes across a larger cohort of 18,859 individuals. The results indicated that while cell composition does attenuate the association of age acceleration with health outcomes, the effect was modest.
In conclusion, the findings reveal that DNA methylation age and age acceleration reflect different biological processes, with blood cell composition playing a significant role in these distinctions. However, the contribution of blood cell composition to the association of age acceleration with mortality and disease outcomes appears to be limited.
Evidence scorecard
- Analytic validity: Established
- Clinical validity: Not established
- Clinical utility: Not established
The evidence is based on primary research involving human samples.