Machine learning based frailty index for the genetically diverse mice

项目总结 衰老是一个影响所有生物系统的终极过程。生物老化--与时间顺序的对比 衰老--不同个体的衰老速度不同。在人类中，变老伴随着健康的增加 问题和死亡率，然而，一些人活得很长很健康，另一些人更早死于 疾病和紊乱。脆弱性的概念被用来量化这种异质性，并被称为状态(defiNed 更容易受到不利健康后果的影响。脆弱性指数(Fi)是一种价值无价、应用广泛的工具。 这比其他量化脆弱性的方法要好得多。FIS已经被改造成用于小鼠身上，使用了各种 行为指标和生理指标均为指标项目。然而，因为进行鼠标FI需要 对于训练有素的个人进行手动评分，往往会限制该工具的可伸缩性。因此，尽管FI是一个 非常有用的老化研究工具，通过以下方式提高其可扩展性、可靠性和重复性 自动化将增强其效用。我们使用应用于视频数据的机器学习来创建自动 视觉保真(VFI)它易于实现、无偏见且可伸缩。在这里，我们建议改进我们的工具和工具 进行一项干预性研究。我们将采用VFI来处理遗传多样性的小鼠(R61：Aim 1)。我们会 还可以创建来自长期监控的功能，以提高在 VFI(R61：AIM 2)。最后，我们将把VFI应用于饮食干预研究，以显示其在大规模研究中的有效性 (R33：目标3)。我们将测试高脂肪高糖饮食(增加的脆弱)和热量限制组(减少 脆弱)和正常饮食(对照)，在一个不同的近交系小鼠群体中。这个项目的结果将是一个 完全经过验证的自动化VFI，可用于高通量介入研究，实现 健康老龄化治疗学。

PROJECT SUMMARY Aging is a terminal process that affects all biological systems. Biological aging—in contrast to chronological aging—occurs at different rates for different individuals. In humans, growing old comes with increased health issues and mortality rates, yet some individuals live long and healthy lives, and others succumb earlier to diseases and disorders. The concept of frailty is used to quantify this heterogeneity and is defined as the state of increased vulnerability to adverse health outcomes. The frailty index (FI) is an invaluable and widely used tool which outperforms other methods to quantify frailty. FIs have been adapted for use in mice using a variety of both behavioral and physiological measures as index items. However, because conducting mouse FI requires trained individuals for manual scoring, it often limits the scalability of the tool. Thus, although the FI is an extremely useful tool for aging research, an increase in its scalability, reliability, and reproducibility through automation would enhance its utility. We used machine learning applied to video data to create an automated visual FI (vFI). The is easy to implement, unbiased, and scalable. Here we propose to improve our tool and carry out an interventional study. We will adopt the vFI to function with genetically diverse mice (R61: Aim 1). We will also create features from long-term monitoring to increase accuracy and breadth of systems measured in the vFI (R61: Aim 2). Finally, we will apply the vFI to a diet intervention study to show its utility for large scale studies (R33: Aim 3). We will test a high fat high sugar diet (increased frailty) and caloric restriction group (decreased frailty) with normal chow (control) in a Diversity Outbred population of mice. The result of this project will be a fully validated and automated vFI that can be used for high-throughput interventional studies, enabling therapeutics for healthy aging.