Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915571027673088 |
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| author | Li, Huifa Tang, Feilong Xue, Haochen Li, Yulong Zhuang, Xinlin Zhang, Bin Segal, Eran Razzak, Imran |
| author_facet | Li, Huifa Tang, Feilong Xue, Haochen Li, Yulong Zhuang, Xinlin Zhang, Bin Segal, Eran Razzak, Imran |
| contents | Aging is a highly complex and heterogeneous process that progresses at different rates across individuals, making biological age (BA) a more accurate indicator of physiological decline than chronological age. While previous studies have built aging clocks using single-omics data, they often fail to capture the full molecular complexity of human aging. In this work, we leveraged the Human Phenotype Project, a large-scale cohort of 10,000 adults aged 40-70 years, with extensive longitudinal profiling that includes clinical, behavioral, environmental, and multi-omics datasets spanning transcriptomics, lipidomics, metabolomics, and the microbiome. By employing advanced machine learning frameworks capable of modeling nonlinear biological dynamics, we developed and rigorously validated a multi-omics aging clock that robustly predicts diverse health outcomes and future disease risk. Unsupervised clustering of the integrated molecular profiles from multi-omics uncovered distinct biological subtypes of aging, revealing striking heterogeneity in aging trajectories and pinpointing pathway-specific alterations associated with different aging patterns. These findings demonstrate the power of multi-omics integration to decode the molecular landscape of aging and lay the groundwork for personalized healthspan monitoring and precision strategies to prevent age-related diseases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12384 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging Li, Huifa Tang, Feilong Xue, Haochen Li, Yulong Zhuang, Xinlin Zhang, Bin Segal, Eran Razzak, Imran Genomics Artificial Intelligence Aging is a highly complex and heterogeneous process that progresses at different rates across individuals, making biological age (BA) a more accurate indicator of physiological decline than chronological age. While previous studies have built aging clocks using single-omics data, they often fail to capture the full molecular complexity of human aging. In this work, we leveraged the Human Phenotype Project, a large-scale cohort of 10,000 adults aged 40-70 years, with extensive longitudinal profiling that includes clinical, behavioral, environmental, and multi-omics datasets spanning transcriptomics, lipidomics, metabolomics, and the microbiome. By employing advanced machine learning frameworks capable of modeling nonlinear biological dynamics, we developed and rigorously validated a multi-omics aging clock that robustly predicts diverse health outcomes and future disease risk. Unsupervised clustering of the integrated molecular profiles from multi-omics uncovered distinct biological subtypes of aging, revealing striking heterogeneity in aging trajectories and pinpointing pathway-specific alterations associated with different aging patterns. These findings demonstrate the power of multi-omics integration to decode the molecular landscape of aging and lay the groundwork for personalized healthspan monitoring and precision strategies to prevent age-related diseases. |
| title | Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging |
| topic | Genomics Artificial Intelligence |
| url | https://arxiv.org/abs/2510.12384 |