| 论文作者 |
Li, P; Zhu, JJ; Wang, SH; Zhuang, HW; Zhang, SJ; Huang, ZT; Cai, FQ; Song, ZJ; Liu, YX; Liu, WX; Freidel, S; Wang, SJ; Schwarz, E; Chen, JF |
| 摘要 |
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 tests (P < 0.02). Top pathways implicated in ageing included autophagy, cell adhesion, synaptic signalling, and metabolic regulation. GO-based clustering revealed consistent ageing signatures across disease categories, including neuropsychiatric, immune, metabolic, and cancer-related conditions. Cross-omics validation using transcriptomic data further supported the model's biological relevance (Rho = 0.70, MAE = 7.21). Interpretation PathwayAge represents an interpretable, biologically grounded framework for estimating epigenetic age. By integrating pathway-level methylation signals, it uncovers mechanistic links between ageing and disease, with potential applications in biomarker development and precision ageing medicine. Copyright (c) 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). |
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