# 3D body shape analysis for predicting sarcopenia and obesity in older adults

> **NIH NIH R56** · GEORGE WASHINGTON UNIVERSITY · 2024 · $333,125

## Abstract

Project Summary
If successful, the proposed study will provide accessible and scalable methods with measurable and validated
accuracy to assess key indicators of an older adult’s health related to body composition phenotypes and
associated physical function. The approach uses inexpensive and widely available commodity optical scanners
as a novel imaging modality with the potential to practically reduce healthcare costs and disparities. The study
meets NIA strategic priorities by developing improved approaches for the early detection and diagnosis of
disabling illnesses and age-related debilitating conditions (C1) and in providing a foundation for developing
interventions for treating, preventing, or mitigating the impact of age-related conditions (C3).
Early screening and diagnosis of body composition and physical function combined with timely, dietary, exercise,
and/or pharmacological interventions can mitigate the risk of functional decline and negative health outcomes in
individuals with sarcopenic obesity. Simple anthropometric measures are easy to perform but have poor
diagnostic accuracy and are inconsistently associated with morbidity and long-term physical function. While other
modalities (e.g., dual-energy X-ray absorptiometry (DEXA), CT, MRI) may have higher diagnostic accuracy, they
are impractical for widescale integration into clinical practice. There are relatively inexpensive systems and
mobile apps that use 3D body shape from optical scanners for predicting body composition. While their validity
is sufficient for consumer-oriented applications, these prediction algorithms may not be applicable for clinical use
on older adults ─ they were not trained on data from this population. These systems also do not predict both
muscle mass and physical function which are critical in the diagnosis of sarcopenia and obesity. To address
these limitations, our team has previously developed highly accurate prediction algorithms using optical body
scanning technology (R21HL124443, R01DK129809). These promising results merit us to further test and
validate our system to translate such technologies into routine clinical and home-based care.
We propose collecting data in an observational cross-sectional study of participants recruited from community-
dwelling older adults. Participants will undergo: (i) 3D optical body scans to determine body shape; (ii) DEXA to
assess body composition; (iii) D3-creatine dilution tests to determine total muscle mass; and (iv) validated
physical function assessments. This data will be used to train artificial intelligence algorithms to predict body
composition and physical function. We will investigate the usability of the approach for clinicians and for older
adults using mobile platforms.
We anticipate that the next step in this line of research is to conduct a cohort study that demonstrates the
predictive nature on adverse outcomes in participants with sarcopenic obesity, or in using this system as part of
a clinic...

## Key facts

- **NIH application ID:** 11170842
- **Project number:** 1R56AG089080-01
- **Recipient organization:** GEORGE WASHINGTON UNIVERSITY
- **Principal Investigator:** JAMES K HAHN
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $333,125
- **Award type:** 1
- **Project period:** 2024-09-17 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11170842

## Citation

> US National Institutes of Health, RePORTER application 11170842, 3D body shape analysis for predicting sarcopenia and obesity in older adults (1R56AG089080-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11170842. Licensed CC0.

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