Deciphering the role of immune cell composition in epigenetic age acceleration: Insights from cell‐type deconvolution applied to human blood epigenetic clocks

Abstract

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health‐related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell‐type deconvolution with EAA predicted by six widely‐used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging‐related diseases, precision medicine, and therapeutic interventions.

Biological aging of different blood cell types

“…To put the 50-years-difference into perspective, the BA value difference is approximately 60 years between a 20- and 80-years-old person, but the cell type-specific difference is a few years between two persons with the same calendar age. Thus, in line with Zhang et al (2023) 24 , we conclude that calendar age and blood cell composition together explain the great majority of variation in BA values. As an exception among the BA indicators, the DunedinPACE values can have up to four-fold differences between the cell types, but the differences appear not to persist across human life course across all cell types studied here.…”

“…In Kananen et al (2016), Horvath values were higher with a higher FACS-analysis-based proportions of CD4+CD28-T cells as compared to CD4+CD28+ T cells when assessed from cells originating from individuals with the same calendar age. The other previous studies have reported up to twenty-years difference in Horvath values between different cell subtypes 23,24,48 .…”

“…The DunedinPACE 29 values, when measured in whole blood, have been shown to increase with a higher calendar age, even though this association is much weaker as compared to other epigenetic clocks 50 . The association between the donor’s calendar age and DunedinPACE 29 values within a separated blood cell type has been assessed previously in data with only a few individuals 24 . Our correlation statistics from dataset (GSE131989) with 49 blood donors indicate that the association between DunedinPACE and calendar age may not be the same in all cell types.…”

“…So far, reports on blood cell type-specificity in Horvath, DNAmPhenoAge and DunedinPACE values have been based on pairwise comparisons between cell subtypes originating from different individuals 24 , small datasets (number of individuals<10) 24 , on whole blood data, where cell proportions have been estimated using deconvolution methods 24,48 or for a single page 20 BA indicator at a time 23,43 . The strength of our approach was the inclusion of purified cell populations from four independent data sets, six BA indicators and the PC-clocks, and datasets consisting of the same sets of individuals for each cell type.…”

“…Importantly, previous such analyses have been made typically using separated cells originating from different individuals and datasets with less than 10 individuals each 24 . Further, it is unknown in what way cell subtype-specific epigenetic age values indicated by the 2 nd and 3 rd generation epigenetic clocks change with advancing calendar age across adulthood.…”