Epigenetic clocks have become one of the most talked-about tools in modern aging science, promising to reveal whether a person’s body is aging faster or slower than the calendar suggests. Commercial kits now market these tests as a measure of a person’s “real age,” and thousands of studies have linked so-called epigenetic age acceleration to mortality, heart disease, cancer, and diabetes. But a major new analysis published in Genome Medicine delivers a sobering message: much of what these clocks measure may reflect the mixture of immune cells circulating in a person’s blood rather than a deep, cell-intrinsic aging process. The findings challenge the popular interpretation of epigenetic age acceleration as a straightforward readout of “biological age” and offer researchers a statistically rigorous way to disentangle the two.
The study, led by Thomas Jonkman, Erik van Zwet, and Bastiaan Heijmans of Leiden University Medical Center together with Anne Richmond and Riccardo Marioni of the University of Edinburgh, drew on DNA methylation data from 4,058 whole blood samples collected across six Dutch biobanks in the BIOS Consortium. Participants ranged from 18 to 87 years old. Using the EpiDISH deconvolution algorithm, the team estimated the proportions of twelve distinct blood cell types in each sample, including neutrophils, monocytes, natural killer cells, and naive and memory subsets of B cells and T cells. Neutrophils dominated the average blood profile, making up 56.8 percent of cells, while the lymphocyte fractions showed clear age-related shifts: naive CD4 and CD8 T cells declined with age, whereas memory T cell fractions increased.
A central methodological hurdle the researchers had to overcome is collinearity. Blood cell fractions are not independent measurements; they are biologically correlated and mathematically constrained to sum to 100 percent. As the authors demonstrate with a series of example regressions, this makes effect sizes for individual cell types deeply ambiguous. When the neutrophil fraction was modeled alone, its apparent effect on age was nearly zero, but adjusting for memory CD4 T cells flipped the estimate positive, and adjusting for naive CD8 T cells flipped it negative. In multivariable models, whichever cell fraction was omitted to satisfy the mathematical constraint had its effect absorbed into the intercept, systematically skewing the coefficients of the remaining fractions. The total variance explained stayed constant, but the interpretation of individual effects became meaningless.
To escape this trap, the team turned to principal component analysis. Because twelve cell fractions contain only eleven independent pieces of information, the researchers computed eleven principal components, each an uncorrelated linear combination of the original fractions. The leading components proved biologically interpretable: the first was almost perfectly correlated with the neutrophil fraction, while the second captured the balance between naive and memory T cells, one of the most striking signatures of immunosenescence. Crucially, because principal components are orthogonal to one another, the effect of each can be estimated independently and reliably, a property the authors verified by showing that removing a component left the remaining effect sizes unchanged.
Applying this framework, the researchers found that cell composition explains a remarkable share of the variation in DNA methylation age itself. Across the six clocks studied, including the first-generation Hannum, Horvath, and Zhang clocks and the second-generation PhenoAge, GrimAge, and DunedinPACE measures, cell counts accounted for 44 to 53 percent of the variance in predicted age, with the naive-to-memory T cell balance the dominant contributor. Calendar age itself was 43.5 percent explainable by cell composition. Yet when the team examined age acceleration, the residual difference between predicted and chronological age, the picture changed dramatically. Associations weakened sharply, explaining at most 21 percent of variance, and the specific cells involved shifted: for second-generation clocks, neutrophils took center stage while T cell contributions largely faded.
The divergence between DNA methylation age and age acceleration carries a profound implication. If age acceleration truly represented “biological age,” then a one-year advance in predicted age should reflect the same underlying biology as a one-year excess over chronological age. The data show this is not the case, at least with respect to blood cell composition. The CpG sites making up first-generation clocks responded almost identically to calendar age and to age acceleration, with correlations between the two effect estimates reaching 0.80 to 0.91. For second-generation clocks, those correlations collapsed to between 0.13 and 0.50, and the age-acceleration estimates carried standard errors nearly three times larger, indicating substantial noise layered on top of genuinely distinct biology.
To establish that cell composition actually drives clock readings rather than merely correlating with them, the researchers performed an elegant validation experiment. They downloaded DNA methylation profiles of 56 purified blood cell samples from a public repository and constructed artificial cell mixtures in silico, blending the average methylation profile of each cell type in proportions matching the population mean. They then generated twelve additional mixtures, each time raising one cell fraction by one standard deviation while proportionally shrinking the others. Running the clocks on these synthetic samples reproduced the observational findings: boosting naive CD4 or CD8 T cells lowered predicted age acceleration for the Hannum, PhenoAge, GrimAge, and DunedinPACE clocks, while increasing neutrophils raised it. The Horvath and Zhang clocks remained largely indifferent to these manipulations.
The final question was whether this cell-composition signal contaminates the clinically important associations between epigenetic age acceleration and disease. Using data from 18,859 participants in the Generation Scotland cohort, the team tested the clocks against 176 incident health outcomes, including all-cause mortality. Cell composition itself was significantly associated with twelve outcomes after Bonferroni correction, among them mortality, asthma, and non-Hodgkin lymphoma, with mortality linked to neutrophil-related and regulatory T cell components but notably not to the naive-to-memory T cell ratio. When the researchers adjusted the clock-outcome associations for cell composition, the hazard ratios were attenuated for four of the six clocks, but only modestly, by roughly 2 to 7 percent overall and between 1 and 6 percent for mortality.
That modest attenuation is perhaps the study’s most reassuring finding for the field. Even though blood cell composition drives a substantial fraction of clock variation, it explains less than 10 percent of the association between age acceleration and incident disease. The authors suggest that neutrophil abundance, which does not change with chronological age yet tracks mortality, frailty, and inflammation, may act as a health-relevant biomarker that clocks capture independently of the aging process itself. This raises a deeper conceptual question: if changes in cell composition are part of how aging manifests in blood, for example through chronic inflammation, then adjusting them away in epigenetic association studies may discard biologically meaningful signal rather than confounding.
For the growing consumer market in epigenetic age testing, the message is one of caution and nuance. A high epigenetic age acceleration reading on a second-generation clock may partly reflect an elevated neutrophil fraction rather than accelerated cellular aging, while a Horvath or Zhang reading may be largely insensitive to immune composition but noisier as a health predictor. The researchers provide their principal component loadings and analysis code publicly, allowing other teams to project the same decomposition onto new datasets. As epigenetic clocks continue to migrate from research laboratories into clinics and commercial services, this work makes clear that interpreting what a clock is actually measuring requires looking carefully at the blood it was drawn from, and that the biology of predicted age and the biology of age acceleration are, in important respects, two different stories.
Subject of Research: The contribution of blood cell composition to DNA methylation age and epigenetic age acceleration
Article Title: Blood cell composition reveals distinct biological interpretation of DNA methylation age and age acceleration
Article References: Jonkman, T. H., Richmond, A., BIOS Consortium, Marioni, R. E., van Zwet, E. W., & Heijmans, B. T. (2026). Blood cell composition reveals distinct biological interpretation of DNA methylation age and age acceleration. Genome Medicine, 18(1), Article 142. https://doi.org/10.1186/s13073-026-01751-6
Image Credits: AI Generated
DOI: 10.1186/s13073-026-01751-6
Keywords: epigenetic clocks, DNA methylation age, age acceleration, biological age, blood cell composition, T cells, neutrophils, immunosenescence, principal component analysis, collinearity, Genome Medicine, aging biomarkers
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Juliet Wilcox. (October 1, 2026). Blood Cell Mixtures Drive Epigenetic Age Clocks, Study Reveals. Scienmag. https://scienmag.com/blood-cell-mixtures-drive-epigenetic-age-clocks-study-reveals/
Juliet Wilcox. “Blood Cell Mixtures Drive Epigenetic Age Clocks, Study Reveals.” Scienmag, 1 October 2026, https://scienmag.com/blood-cell-mixtures-drive-epigenetic-age-clocks-study-reveals/. Accessed 1 October 2026.
Juliet Wilcox. “Blood Cell Mixtures Drive Epigenetic Age Clocks, Study Reveals.” Scienmag. October 1, 2026. https://scienmag.com/blood-cell-mixtures-drive-epigenetic-age-clocks-study-reveals/
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Tags: age accelerationaging biomarkersbiobank aging researchbiological agebiological age measurementblood cell compositionblood cell mixturesblood cell type influence on agingblood sample deconvolutioncell-intrinsic aging processescollinearityDNA methylation ageDNA methylation analysisepigenetic age accelerationepigenetic clocksepigenetic clocks validationGenome Medicinegenome-wide methylation studiesimmune cell compositionimmunosenescenceneutrophilsPrincipal Component AnalysisT Cells
