标题： 全表型多组学整合揭示了人类衰老的不同类型 显示英文标题

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

摘要： 衰老是一个高度复杂且异质的过程，不同个体的进展速度各不相同，这使得生物年龄（BA）比出生年龄更能准确地反映生理衰退。 尽管之前的研究使用单一组学数据构建了衰老时钟，但它们往往无法捕捉人类衰老的完整分子复杂性。 在本研究中，我们利用了人类表型项目，这是一个大规模队列，包含12,000名30--70岁的成年人，具有广泛的纵向分析，包括临床、行为、环境和多组学数据集——涵盖转录组学、脂质组学、代谢组学和微生物组。 通过采用能够建模非线性生物动态的先进机器学习框架，我们开发并严格验证了一个多组学衰老时钟，该时钟能够稳健地预测多种健康结果和未来的疾病风险。 对多组学整合分子特征的无监督聚类揭示了衰老的不同生物学亚型，显示出衰老轨迹的显著异质性，并指明了与不同衰老模式相关的通路特异性改变。 这些发现展示了多组学整合在解码衰老分子景观方面的力量，并为个性化健康期监测和预防与年龄相关疾病的精准策略奠定了基础。 显示英文摘要

摘要： 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 12,000 adults aged 30--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.