A pilot study to advance translation of molecular signatures of biological aging

项目总结 这项提案的主要目的是确定几种拟议的生物衰老量化方法中是否有任何一种 在人类中被用于延长健康寿命的干预试验中是有希望的。生物过程 老龄化被认为是许多致残健康状况和死亡的风险。有证据表明 早在成年初期，衰老的轨迹就开始出现分化。如果这个过程是可以测量的，那么它 将加快制定预防疾病和残疾的干预措施，延长健康寿命。一 测量的方法是计算一个“生物年龄”。与一个人的实际年龄形成对比的是， 计算出生以来的时间，一个人的生物年龄反映了他们的身体和精神状况相对于他们 同伴们。例如，一个30岁的人，拥有50岁普通人的身心，就会有一个 生物学年龄50岁。因此，能够降低生物年龄或减缓其增长的干预措施将是强有力的。 延长健康寿命的候选对象。但为了确定这样的干预措施，生物学措施 年龄是需要的。已经提出了几种算法来根据面板计算一个人的生物年龄 关于血液DNA甲基化和RNA表达的临床生物标志物和全基因组数据。这些 算法代表了高度可扩展的方法，非常适合在干预试验中实施。但一位批评者 知识缺口是指算法是否真的测量了生物衰老的过程，如果进行修改， 会延长健康寿命。本申请中提出的研究旨在通过以下方式填补这一知识空白 在已经创建的数据库The Dunedin中实现和测试五种最有希望的算法 学习。达尼丁的这项研究是在一个具有人口代表性的出生队列中进行的，该队列现在已经进入了生命的第五个十年。这个 数据库包括基因组范围的DNA甲基化、RNA表达、SNP和954的临床生物标志物数据 以及广泛的身体和认知功能测试。研究目的将测试不同的 算法衡量了导致疾病和残疾的生物衰老的常见过程。学习所有的 随着时间的推移，在一个年轻的、仍然健康的队列中，这些算法将回答三个问题：1) 不同的算法相互关联，即它们测量的是同一事物吗？2)它们能测量 年轻人衰老轨迹开始分化时发生的变化--时间干预 可能会给他们带来最大的好处？以及3)他们是否衡量了健康下降的真实经历 衰老--身体和认知功能的缺陷以及对衰老的主观看法？结果将通知 如果有的话，建议的生物衰老算法中的哪一个显示出在干预中实施的前景 审判。这可能会立即导致它们在已完成试验的存档生物标本中实施。 研究结果还将为未来开发生物衰老测量方法提供信息，方法是确定 管用什么不管用。

PROJECT SUMMARY The broad aim of this proposal is to determine if any of several proposed methods to quantify biological aging in humans are promising for use in trials of interventions to increase healthy lifespan. The biological process of aging is thought to drive risk for many disabling health conditions and mortality. There is evidence that trajectories of aging begin to diverge as early in life as young adulthood. If this process can be measured, it will speed development of interventions to prevent disease and disability and prolong healthy life. One measurement approach is to calculate a “biological age.” In contrast to a person's chronological age, which counts time since birth, a person's biological age reflects the condition of their body and mind relative to their peers. For example a 30-year-old person with the body and mind of an average 50-year-old would have a biological age of 50. Interventions shown to reduce biological age or slow its increase would thus be strong candidates for increasing healthy lifespan. But in order to identify such interventions, measures of biological age are needed. Several algorithms have been proposed to calculate a person's biological age from panels of clinical biomarkers and whole-genome data on blood DNA methylation and RNA expression. These algorithms represent highly-scaleable methods ideal for implementation in intervention trials. But a critical knowledge gap is whether the algorithms actually measure the process of biological aging that, if modified, would extend healthy lifespan. The research proposed in this application aims to fill that knowledge gap by implementing and testing five of the most promising algorithms in an already-created database, the Dunedin Study. The Dunedin Study follows a population-representative birth cohort now in it's fifth decade of life. The database includes genome-wide DNA-methylation, RNA-expression, SNP, and clinical biomarker data on 954 individuals along with extensive physical and cognitive function testing. Research aims will test if the different algorithms measure a common process of biological aging that drives disease and disability. Studying all of the algorithms together in a young, still-healthy cohort followed over time will answer three questions: 1) Are the different algorithms related to one another, i.e. do they measure the same thing? 2) Can they measure changes occurring in young adults as their trajectories of aging begin to diverge – the time interventions would likely have their greatest benefit? and 3) Do they measure real-life experiences of health decline in aging – deficits in physical and cognitive functions and subjective perceptions of aging? Results will inform which, if any, of the proposed biological aging algorithms show promise for implementation in intervention trials. This could lead immediately to their implementation in archived biospecimens from completed trials. Results will also inform future approaches to developing measures of biological aging by identifying what works and what doesn't.