Decoding disease-specific ageing mechanisms through pathway-level epigenetic clock: insights from multi-cohort validation
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 captur...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
August 2025
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| In: |
EBioMedicine
Year: 2025, Volume: 118, Pages: 1-18 |
| ISSN: | 2352-3964 |
| DOI: | 10.1016/j.ebiom.2025.105829 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.ebiom.2025.105829 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2352396425002737 
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| Author Notes: | Pan Li, Jijun Zhu, Shenghan Wang, Haowen Zhuang, Shunjie Zhang, Zhongting Huang, Fuqiang Cai, Zhijian Song, Yuxin Liu, Weixin Liu, Sebastian Freidel, Sijia Wang, Emanuel Schwarz, and Junfang Chen |
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| 245 | 1 | 0 | |a Decoding disease-specific ageing mechanisms through pathway-level epigenetic clock |b insights from multi-cohort validation |c Pan Li, Jijun Zhu, Shenghan Wang, Haowen Zhuang, Shunjie Zhang, Zhongting Huang, Fuqiang Cai, Zhijian Song, Yuxin Liu, Weixin Liu, Sebastian Freidel, Sijia Wang, Emanuel Schwarz, and Junfang Chen |
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| 520 | |a 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. - Funding - This research was supported by the Greater Bay Area Institute of Precision Medicine (Grant No. I0007), the National Social Science Foundation of China (Grant No. 32370639), and was further supported by the Shanghai Key Laboratory of Psychotic Disorders Open Grant (Grant No: 21-K01). ES received funding from the Hector II Foundation and the German Federal Ministry of Education and Research (BEST project, Grant No: 01EK2101B), and was endorsed by the German Center for Mental Health (DZPG). ES received speaker fees from bfd Buchholz-Fachinformationsdienst GmbH and editorial fees from the Lundbeck Foundation. SW received funding from the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant no. XDB38020400), CAS Young Team Program for Stable Support of Basic Research (YSBR-077), CAS Interdisciplinary Innovation Team, Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX01 to SW), the National Natural Science Foundation of China (32325013 and 92249302), the National Key Research and Development Project (2018YFC0910403), Shanghai Science and Technology Commission Excellent Academic Leaders Program (22XD1424700). | ||
| 650 | 4 | |a Disease association | |
| 650 | 4 | |a DNA methylation | |
| 650 | 4 | |a Epigenetic clock | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Pathway-level analysis | |
| 700 | 1 | |a Zhu, Jijun |e VerfasserIn |4 aut | |
| 700 | 1 | |a Wang, Shenghan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zhuang, Haowen |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zhang, Shunjie |e VerfasserIn |4 aut | |
| 700 | 1 | |a Huang, Zhongting |e VerfasserIn |4 aut | |
| 700 | 1 | |a Cai, Fuqiang |e VerfasserIn |4 aut | |
| 700 | 1 | |a Song, Zhijian |e VerfasserIn |4 aut | |
| 700 | 1 | |a Liu, Yuxin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Liu, Weixin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Freidel, Sebastian |e VerfasserIn |4 aut | |
| 700 | 1 | |a Wang, Sijia |e VerfasserIn |4 aut | |
| 700 | 1 | |a Schwarz, Emanuel |e VerfasserIn |0 (DE-588)1055051260 |0 (DE-627)792581040 |0 (DE-576)411121596 |4 aut | |
| 700 | 1 | |a Chen, Junfang |e VerfasserIn |4 aut | |
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