Decoding disease–specific ageing mechanisms through pathway-level epigenetic clock: insights from multi-cohort validation

作者：Li Pan, Jijun Zhu, Shenghan Wang, Haowen Zhuang, Shunjie Zhang, Zhongting Huang, Fuqiang Cai, Zhijian Song, Yuxin Liu, Weixin Liu, Sebastian Freidel, Sijia Wang, Emanuel Schwarz, Junfang Chen · 发表于：EBioMedicine · 年份：2025 · DOI：10.1016/j.ebiom.2025.105829 · 被引用次数：12 · 研究领域：Epigenetics and DNA Methylation、Genetic Associations and Epidemiology、Ferroptosis and cancer prognosis

BACKGROUND: Ageing is a multifactorial process closely associated with increased risk of chronic diseases. While epigenetic clocks have advanced ageing research, most rely on isolated CpG sites, limiting biological interpretability. We developed PathwayAge, a biologically informed model that captures coordinated methylation changes at the pathway level, providing interpretable insights into ageing biology and disease mechanisms. METHODS: We conducted a cross-sectional study using genome-wide DNA methylation data from 10,615 individuals across 19 cohorts and 3413 Han Chinese participants, along with transcriptomic data from 3384 samples. A two-stage machine learning model aggregated CpG sites into GO or KEGG pathway-level features to predict chronological age. Model accuracy was assessed using mean absolute error (MAE) and Pearson correlation (Rho). Age acceleration residuals (AgeAcc) were computed and tested for associations with nine diseases using non-parametric statistics. FINDINGS: PathwayAge achieved high predictive accuracy (Rho = 0.977, MAE = 2.350) in cross-validation and across 15 independent blood-based validation cohorts (Rho = 0.677-0.979, MAE = 2.113-6.837), including a Chinese population (Rho = 0.972, MAE = 2.302). Compared to established clocks, PathwayAge showed improved performance in both age estimation and disease association analyses. Significant AgeAcc differences were observed across nine diseases, with disease-specific pathways confirmed by permutation ...