Epigenetic-based age prediction in blood samples: Model development

Abstract

Aging is a complex process influenced by genetic, epigenetic, and environmental factors that lead to tissue deterioration and frailty. Epigenetic mechanisms, such as DNA methylation, play a significant role in gene expression regulation and aging. This study presents a new age estimation model developed for the Turkish population using blood samples. Eight CpG sites in loci TOM1L1, ELOVL2, ASPA, FHL2, C1orf132, CCDC102B, cg07082267, and RASSF5 were selected based on their correlation with age. Methylation patterns of these sites were analyzed in blood samples from 100 volunteers, grouped into age categories (20–35, 36–55, and ≥56). Sensitivity analysis indicated a reliable performance with DNA inputs ≥1 ng. Statistical modeling, utilizing Multiple Linear Regression, underscores the reliability of the primary 6-CpG model, excluding cg07082267 and TOM1L1. This model demonstrates strong correlations with chronological age (r = 0.941) and explains 88% of the age variance with low error rates (MAE = 4.07, RMSE = 5.73 years). Validation procedures, including a training-test split and fivefold cross-validation, consistently confirm the model's accuracy and consistency. The study indicates minimal variation in error scores across age cohorts and no significant gender differences. The developed model showed strong predictive accuracy, with the ability to estimate age within certain prediction intervals. This study contributes to the age prediction by using DNA methylation patterns, which can have disparate applications, including forensic and clinical assessments.