基于多器官衰老表型组的数据集成和人工智能计算的衰老预警系统

增龄伴随的器官退行性变化表现出高度的异质性和复杂性，增加了对器官衰老状态评估的难度。表型组学通过对除基因型之外的众多微观分子表型及宏观生理表型的测量，可发现大量微观与宏观之间的新关联并解析决定宏观表型的微观机制，成为器官衰老研究的新范式。本项目在前期已测定近千人（20-60岁健康人）约四万个宏观及微观表型的基础上提出人工智能新策略并构建多器官衰老表型网络，发现增龄伴随网络恢复力下降且基于不同组学的生物学年龄反映不同功能衰老，后续拟进一步完善包括血细胞端粒、血液转录组、蛋白质组、代谢组、多器官影像表型、皮肤表型、认知表型等多模态、多层次器官衰老队列表型的测量（补充60-80岁200人），系统构建年龄跨度20-80岁的基因-通路-宏观表型网络，揭示多器官衰老的系统演变规律及网络调节机制，构建多模态生物标志物与人工智能算法相结合的器官衰老早期预警模型，发现并验证干预器官衰老的新因子和新途径。

The degenerative changes of organs with aging show a high degree of heterogeneity and complexity, which increases the difficulty of evaluating the aging state of organs. Phenomics, through the measurement of many micro molecular phenotypes and physiological phenotypes except genotypes, can find a large number of novel associations between micro and macro phenotype levels, and reveal the micro mechanism that determines the macro phenotype. Therefore, using the phenomics-centered strategy has become a new paradigm of organ aging research. Based on the previous determination of about 40000 macro and micro phenotypes of nearly 1000 people (healthy people aged from 20 to 60), this project proposes a new artificial intelligence (AI) strategy and constructs a multi organ aging phenotype network. It is found that aging is accompanied by a decline in network resilience, and biological age based on different omics reflects different functional aging. In this proposal, we plan to deeply phenotyping people aged from 20 to 80 by adding 200 people aged 60-80 to the existing cohort. The phenotypes include blood cell telomere, blood transcriptome, proteome, metabolome, multi organ imaging phenotype, skin phenotype and cognitive phenotype. By systematically analyzing these multi-modal and multi-level phenotypic data, we will build a gene-pathway-phenotype network with an age span of 20-80, reveal the dynamic changes of network during aging, discover the regulation mechanism of multi-organ aging, and build an early warning model of organ aging based on the combination of multimodal biomarkers and artificial intelligence algorithms. We will also discover and verify new factors and new targets to regulate and intervene organ aging.