建立大规模人群衰老的生物标记物、评价体系及数据平台

Reliable prediction of the aging process is not only important for quantitatively assessing the degree of the aging process and its reversal, but also important for assessing the risks of aging-associated diseases and for designing individualized treatment schemes. Here, using a metabolic and cardiovascular health cohort of about 9,000 human subjects in northern China (Jidong Community) established by Beijing Anzhen Hospital, Capital Medical University with annual examinations, we will select fast-aging, slow-aging outliers and normal-aging subjects based on the 3D facial aging evaluation, with 120 people in each group. Then we will generate DNA methylation, transcriptome data, analyze and compare features in the multi-omic and multilayer data, and establish accurate, multi-level, multi-variable cardiovascular health and aging model. Through transcriptome and epigenome analysis and regulatory network reverse engineering, we will identify important intervention nodes for human aging, establish multi-layer, multi-factor predictive computational models for early warning of cardiovascular disease based on multiple parameters, including aging markers. We will also establish the first comprehensive human aging omics database and computational platform in the world. This study will not only provide a valuable data resource and computational models for aging and cardiovascular health, but also provide system strategies for extending health life in a fast aging society.

精准预测衰老过程不仅对定量评估老龄化进程及其逆转程度很重要，对评估衰老相关性疾病发生风险及制定个性化治疗方案也非常重要。本项目计划利用首都医科大学附属北京安贞医院每年一次在中国北方（冀东社区）建立的约9,000人的代谢及心血管健康测试人群，根据人脸3D图像选择快老、慢老者及正常衰老者各120人产生DNA甲基化组、转录组数据，分析开发与比较基于多组学、多层次数据中的特征值，建立精准的、多层次、多变量的心血管健康及衰老计算模型。通过转录组、表观组及网络逆向工程分析发现人类衰老的调控网络与重要干预节点，针对心血管疾病开发一套基于包括衰老标志物在内的多层次、多因素疾病预警计算模型，并建立国际上第一个全面的人类衰老组学数据库和计算平台。该研究不仅为衰老和心血管健康的生物分子网络提供数据基础与理论模型，也可为现代快速老龄化的社会提供延长健康寿命的系统策略。

衰老是一个机体功能逐渐衰退，并逐渐诱发多种疾病直至死亡的过程。目前全球老龄化趋势加剧，给社会带来巨大的负担。因此如何精准地评估个体的生物年龄，并探究其背后的调控机制是一个至关重要的问题。同时当前学术界人群研究主要集中在高加索人群，鉴于人群的遗传差异和生活方式差异，东亚人群的衰老调控机制的意义对我们更加重要。相较于侵入性的生物标志物，我们利用三维人脸成像系统采集了5000人的图像，构建了第一个大规模三维人脸衰老时钟，误差仅有2.8岁。结合人脸衰老速率与外周血单核细胞转录组测序，我们发现衰老速率在分子水平上富集在炎症免疫通路，并且与细胞类型相关。此外我们利用因果推断检验，构建了生活方式-转录组-衰老速率的调控网络，系统阐述了不同的生活方式如何从分子水平影响衰老过程。利用舌苔宏基因组和血液代谢组数据，我们发现以Filifactor alocis和BOB-1为中心的菌群参与宿主的炎症和脂质代谢，并构建了一个生活方式-物种-健康参数的因果调控网络，以秀丽隐杆线虫为模型验证了BOB-1抗衰老的潜力。我们的研究对于健康衰老具有重大的指导意义，有助于中国人群选择合适的生活方式延缓衰老。