DNA methylation-based age prediction from saliva: High age predictability by combination of 7 CpG markers

- We generated epigenome-wide DNA methylation profiles of 54 saliva samples using Illumina's HumanMethylation450 BeadChip array.
- DNA methylation at the SST, CNGA3, KLF14, TSSK6, TBR1, and SLC12A5 genes showed a high association with age.
- DNA methylation at 6 age-associated CpGs and a cell type-specific CpG were investigated in 226 saliva samples using methylation SNaPshot.
- A model composed of 7 CpGs exhibited a high correlation between predicted and chronological age with a MAD from chronological age of 3.1 years.
- The developed multiplex methylation SNaPshot assay produced reproducible results with a small amount of DNA (4 ng of bisulfite-converted DNA).

DNA methylation is currently one of the most promising age-predictive biomarkers. Many studies have reported DNA methylation-based age predictive models, but most of these are based on DNA methylation patterns from blood. Only a few studies have examined age-predictive DNA patterns in saliva, which is one of the most frequently-encountered body fluids at crime scenes. In this study, we generated genome-wide DNA methylation profiles of saliva from 54 individuals and identified CpG markers that showed a high correlation between methylation and age. Because the age-associated marker candidates from saliva differed from those of blood, we investigated DNA methylation patterns of 6 age-associated CpG marker candidates (cg00481951, cg19671120, cg14361627, cg08928145, cg12757011, and cg07547549 of the SST, CNGA3, KLF14, TSSK6, TBR1, and SLC12A5 genes, respectively) in addition to a cell type-specific CpG marker (cg18384097 of the PTPN7 gene) in an independent set of saliva samples obtained from 226 individuals aged 18 to 65 years. Multiplex methylation SNaPshot reactions were used to generate the data. We then generated a linear regression model with age information and the methylation profile from the 113 training samples. The model exhibited a 94.5% correlation between predicted and chronological age with a mean absolute deviation (MAD) from chronological age of 3.13 years. In subsequent validation using 113 test samples, we also observed a high correlation between predicted and chronological age (Spearman's rho = 0.952, MAD from chronological age = 3.15 years). The model composed of 7 selected CpG sites enabled age prediction in saliva with high accuracy, which will be useful in saliva analysis for investigative leads.