Metabolomic and omic assessment of biological ageing across the life-course (METAGE)

遗传和环境因素都会影响衰老过程，导致衰老速度的差异。因此，一个人的“生理年龄”，或整体生理状态，可能与他们实际（实足）年龄的预期不同。生物衰老率的差异意味着一些人死得早，晚年有更多的健康问题。在我项目的第一部分，我使用了代谢组学（全套代谢物或小分子）数据，这些数据来自英国、欧洲和美国的多个基于人群的队列研究，使用高级统计学开发生物年龄的血液测试。我们发现，基于代谢组学的生物年龄测试提供了比实际年龄本身更好的预测与年龄相关的疾病和死亡率。此外，我们发现，在FINGER研究中，与那些没有得到这种支持的人相比，支持遵循更健康生活方式的人在两年内的生物衰老速度要慢一些。在我项目的下一部分，我将在两项老年人队列研究（战车PRO和TILDA研究）中研究基于代谢组学的生物年龄测试是否可以预测认知和身体衰老。我将进一步完善基于代谢组学的生物年龄测试，使其更便宜，更容易在临床实验室测量。我还将测试蛋白质的使用，这是另一种重要的生物学分子，在EPIC和CHARIOT PRO研究中，在2万多人身上开发生物年龄模型。既然我们知道身体的哪些器官会产生特定的蛋白质，就有可能测试“器官特异性”衰老。这是有用的，因为体内不同的生物系统可能以不同的速度衰老，我将测试针对多种疾病的器官特异性衰老。此外，我将使用基因数据和一种称为中位数随机化的技术来测试与年龄相关的蛋白质和代谢物是否可能导致不健康的衰老。我将利用这些信息进一步改进生物年龄测试。最后，我将分析与衰老有关的蛋白质、代谢物和遗传因素之间的联系途径，以更深入地了解随年龄变化的生物过程。

Both genetic and environmental factors affect the ageing process, leading to differences in ageing rates. Therefore, a person's "biological age", or overall physiological state, may differ from what is expected given their actual (chronological) age. Differences in biological aging rates mean some people die earlier and have more health problems in later life. In the first part of my project, I used metabolomics (whole sets of metabolites or small molecules) data from multiple population-based cohort studies in the UK, Europe, and the USA, to develop blood tests of biological age using advanced statistics. We found that the metabolomic-based biological age tests provided improved prediction, over chronological age itself, of age-related ill health and mortality. Furthermore, We found that that biological ageing was slower over two years among people who were supported to follow a healthier lifestyle, compared to those who did not receive this support in the FINGER study. For the next part of my project, I will examine if the metabolomic-based biological age test can predict cognitive and physical aging among two cohort studies of older people (the CHARIOT PRO and TILDA studies). I will further refine the metabolomic-based biological age test, so it is less expensive and easier to measure in clinical laboratories. I will also test the use of proteins, another biologically important class of molecules, to develop biological age models in over 20,000 people in the EPIC and CHARIOT PRO studies. Since we know which organs in the body produce specific proteins, it is possible to test "organ-specific" ageing. This is useful as different biological systems in the body may age at different rates, and I will test organ-specific ageing against many types of disease. Furthermore, I will use genetic data and a technique called Medelian Randomization to test if proteins and metabolites that are correlated with age, may cause unhealthy ageing. I will use this information to further improve the biological age tests. Finally, I will analysis the pathways that connect proteins, metabolites and genetic factors involved in ageing to more deeply understand the biological processes that change with age.