Machine learning based frailty index for the genetically diverse mice

基于机器学习的遗传多样性小鼠的衰弱指数

基本信息

批准号：
10513177
负责人：
VIVEK KUMAR
金额：
$ 33.6万
依托单位：
JACKSON LABORATORY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10513177
关键词：
Adopted Affect Age Aging Animal Model Animals Applied Research Automation Basic Science Behavior Behavioral Behavioral Model Biological Biological Aging Biological Assay Caloric Restriction Chronic Chronology Clinical Collaborations Computer Vision Systems Data Data Set Diet Dietary Intervention Disease Environmental Risk Factor Etiology Fatty acid glycerol esters Future Genetic Gerontology Health Health Status Heterogeneity High Fat Diet Hour Human Individual Intervention Intervention Studies Judgment Letters Longevity Machine Learning Manuals Measures Methods Monitor Mouse Strains Mus Nesting Behavior Organism Outcome Phase Phenotype Physiological Physiology Population Population Heterogeneity Process Public Health Reproducibility Research Shock Sleep Social Behavior System Testing The Jackson Laboratory Therapeutic Time Training Translational Research Visual age related aged base biological systems clinical heterogeneity dietary restriction diverse data drinking behavior feeding frailty hands-on learning healthy aging high reward high risk human model improved indexing machine learning model mortality mortality risk novel open field behavior pre-clinical preclinical study social sugar tool translational study

项目摘要

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.

项目概要衰老是影响所有生物系统的最终过程——与时间顺序相反。衰老——不同个体的衰老速度不同，随着年龄的增长，健康状况也会有所改善。问题和死亡率，但有些人健康长寿，而另一些人则更早死于疾病虚弱的概念用于量化这种异质性，并被定义为状态。脆弱指数（FI）是一种非常宝贵且广泛使用的工具。 FIs 优于其他量化衰弱的方法，已适用于使用多种方法的小鼠。行为和生理测量作为指标项但是，因为进行小鼠FI需要。训练有素的人员进行手动评分，但它通常会限制该工具的可扩展性，因此，尽管 FI 是一种工具。对于老化研究来说非常有用的工具，通过提高其可扩展性、可靠性和可重复性自动化将增强其实用性。我们使用应用于视频数据的机器学习来创建自动化。视觉 FI (vFI) 易于实现、公正且可扩展。我们将采用 vFI 对基因多样化的小鼠进行干预研究（R61：目标 1）。还创建长期监控的功能，以提高系统测量的准确性和广度 vFI（R61：目标 2）最后，我们将 vFI 应用于饮食干预研究，以展示其在大规模研究中的实用性。（R33：目标 3）我们将测试高脂肪高糖饮食（增加虚弱）和热量限制组（减少）。多样性远交小鼠群体中的正常饮食（对照）。完全验证和自动化的 vFI，可用于高通量介入研究，使健康老龄化疗法。