{"ID":2855699,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12384","arxiv_id":"2510.12384","title":"Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging","abstract":"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.","short_abstract":"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 mol...","url_abs":"https://arxiv.org/abs/2510.12384","url_pdf":"https://arxiv.org/pdf/2510.12384v3","authors":"[\"Huifa Li\",\"Feilong Tang\",\"Haochen Xue\",\"Yulong Li\",\"Xinlin Zhuang\",\"Bin Zhang\",\"Eran Segal\",\"Imran Razzak\"]","published":"2025-10-14T11:00:51Z","proceeding":"q-bio.GN","tasks":"[\"q-bio.GN\",\"cs.AI\"]","methods":"[]","has_code":false}