# Machine learning based frailty index for the genetically diverse mice

> **NIH NIH R33** · JACKSON LABORATORY · 2024 · $659,051

## Abstract

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.

## Key facts

- **NIH application ID:** 11143328
- **Project number:** 4R33AG078530-03
- **Recipient organization:** JACKSON LABORATORY
- **Principal Investigator:** VIVEK KUMAR
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $659,051
- **Award type:** 4N
- **Project period:** 2022-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11143328

## Citation

> US National Institutes of Health, RePORTER application 11143328, Machine learning based frailty index for the genetically diverse mice (4R33AG078530-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11143328. Licensed CC0.

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