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

项目摘要 这项提案的主要目的是确定几种量化生物衰老的方法中是否有任何一种 有希望用于增加健康寿命的干预试验。所述生物过程 老龄化被认为是导致许多残疾健康状况和死亡的风险。有证据表明 衰老的轨迹早在青年时期就开始开始分化。如果这个过程可以测量， 将加快制定干预措施，以预防疾病和残疾，延长健康寿命。一 测量方法是计算“生物年龄”。与一个人的实际年龄相比， 从出生算起，一个人的生物年龄反映了他们的身体和精神状况， 同龄人例如，一个30岁的人，其身体和精神相当于一个50岁的人， 生物学年龄50因此，减少生物年龄或减缓其增长的干预措施将是强有力的 增加健康寿命的候选人。但为了确定此类干预措施，生物学措施 年龄是需要的。已经提出了几种算法来计算一个人的生物年龄从面板 临床生物标志物和血液DNA甲基化和RNA表达的全基因组数据。这些 算法代表了在干预试验中实施的理想的高度可缩放的方法。但一个关键的 知识差距是算法是否实际测量生物老化的过程，如果修改， 可以延长健康寿命本申请中提出的研究旨在填补这一知识空白， 实现和测试五个最有前途的算法在一个已经创建的数据库，达尼丁 Study.这项达尼丁研究追踪了一个具有代表性的出生队列，现在已经是50岁了。的 数据库包括954个基因组的全基因组DNA甲基化、RNA表达、SNP和临床生物标志物数据。 个人沿着广泛的身体和认知功能测试。研究目标将测试不同的 算法测量导致疾病和残疾的生物老化的共同过程。学习所有 在一个年轻的，仍然健康的队列中，随着时间的推移，这些算法将回答三个问题：1） 不同的算法相互关联，也就是说，它们测量的是相同的东西吗？2)他们能测量 随着年轻人的衰老轨迹开始出现分歧，他们发生的变化--时间干预 可能会得到最大的好处以及3）他们是否测量了健康下降的真实体验， 衰老-身体和认知功能的缺陷以及对衰老的主观看法？结果将告知 如果有的话，所提出的生物老化算法显示出在干预中实施的希望。 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的实施。 研究结果还将为未来的方法提供信息，通过确定哪些因素来制定生物衰老的措施。 什么有用什么没用

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.