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arXiv · 2510.12384

Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging

Huifa Li·Feilong Tang·Haochen Xue·Yulong Li·Xinlin Zhuang·Bin Zhang·Eran Segal·Imran Razzak
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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.

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Huifa Li, Feilong Tang, Haochen Xue, Yulong Li, Xinlin Zhuang, Bin Zhang, Eran Segal, Imran Razzak. 2025-10-14. Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging. https://arxiv.org/abs/2510.12384

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