CheekAge, a next-generation epigenetic buccal clock, is predictive of mortality in human blood

Front Aging. 2024 Oct 1:5:1460360. doi: 10.3389/fragi.2024.1460360. eCollection 2024.

Abstract

While earlier first-generation epigenetic aging clocks were trained to estimate chronological age as accurately as possible, more recent next-generation clocks incorporate DNA methylation information more pertinent to health, lifestyle, and/or outcomes. Recently, we produced a non-invasive next-generation epigenetic clock trained using Infinium MethylationEPIC data from more than 8,000 diverse adult buccal samples. While this clock correlated with various health, lifestyle, and disease factors, we did not assess its ability to capture mortality. To address this gap, we applied CheekAge to the longitudinal Lothian Birth Cohorts of 1921 and 1936. Despite missing nearly half of its CpG inputs, CheekAge was significantly associated with mortality in this longitudinal blood dataset. Specifically, a change in one standard deviation corresponded to a hazard ratio (HR) of 1.21 (FDR q = 1.66e-6). CheekAge performed better than all first-generation clocks tested and displayed a comparable HR to the next-generation, blood-trained DNAm PhenoAge clock (HR = 1.23, q = 2.45e-9). To better understand the relative importance of each CheekAge input in blood, we iteratively removed each clock CpG and re-calculated the overall mortality association. The most significant effect came from omitting the CpG cg14386193, which is annotated to the gene ALPK2. Excluding this DNA methylation site increased the FDR value by nearly threefold (to 4.92e-06). We additionally performed enrichment analyses of the top annotated CpGs that impact mortality to better understand their associated biology. Taken together, we provide important validation for CheekAge and highlight novel CpGs that underlie a newly identified mortality association.

Keywords: DNA methylation; aging; biomarker; epigenetic aging clock; longitudinal; mortality.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The authors are thankful for internal funding from Tally Health (to MNS, DJK, TLC, and AAJ). This research was also funded in part by the Wellcome Trust [221890/Z/20/Z]. For the purpose of open access, a CC BY public copyright license has been applied to any Author Accepted Manuscript version arising from this submission. The LBC1936 was supported by joint funding from the Biotechnology and Biological Sciences Research Council and the Economic and Social Research Council [BB/W008793/1] and by Age UK [The Disconnected Mind], the Medical Research Council [G0701120, G1001245, MR/M01311/1, MR/R024065/1], the Milton Damerel Trust, and the University of Edinburgh. SRC is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society [221890/Z/20/Z].