| Title: | Novel machine learning framework for multidimensional biological age estimation reveals heterogeneous aging of organ systems |
| Journal: | Computer Methods and Programs in Biomedicine |
| Published: | 20 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41349435/ |
| DOI: | https://doi.org/10.1016/j.cmpb.2025.109176 |
Publication 18864
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Abstract
OBJECTIVE: Existing biological age (BA) models often oversimplify aging's complexity, offering single-dimensional metrics. However, these fail to capture the critical heterogeneity of aging across organs.. This study aims to develop a machine learning-based unified framework to assess and interpret multi-organ biological aging comprehensively.</p>
METHOD: Using data from UK Biobank participants, we trained and integrated organ-specific BA estimates to assess multidimensional BA within an ensemble learning framework, and uncover distinct aging patterns.</p>
RESULT: Our Fusion BA (an overall estimation of BA) was significantly correlated with chronological age (CA) (mean absolute error (MAE): 4.473 years; Pearson correlation: 0.718, P < 0.01). Accelerated Fusion BA derived from the ensemble model (contrast between Fusion BA and CA) predicted 10-year mortality (HR=1.504, 95 % CI: 1.438-1.574). Organ-specific BA correlated with organ disease risk and effectively captured distinct aging patterns.</p>
CONCLUSION: This framework enables systemic and organ-specific aging assessment, provides actionable tools and insights for clinical risk.</p>
10 Authors
- Qi Yu
- Lijuan Da
- Qian Ma
- Yushu Huang
- Yue Dong
- Yuan Liu
- Xiaoyu Li
- Xifeng Wu
- Zilin Li
- Wenyuan Li
1 Application
| Application ID | Title |
|---|---|
| 91486 | Social, lifestyle, and biological risk factors of age-related diseases and mortality |
Enabling scientific discoveries that improve human health