# INVESTigations of Muscle and Bone Health: Developing Automated Approaches for CT-Based Analyses

> **NIH NIH F31** · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2024 · $48,974

## Abstract

PROJECT SUMMARY
Obesity is a common, serious, and costly condition among older adults. Weight loss (WL) is an effective strategy
to combat obesity-related comorbidities; however, the safety of WL interventions for older adults remains
controversial due to potential exacerbation of age-related muscle and bone loss that increases fracture risk.
Mechanical stimuli, such as resistance training (RT), can be effective in mitigating WL-associated bone loss, but
there are many barriers to success of RT programs for older adults, including low adherence and accessibility.
In response, our group has proposed that a weighted vest may serve as an alternative for maintaining mechanical
stress during intentional WL, but it is unclear whether this vest will have similar preservation effects on bone
when compared to RT. Therefore, our ongoing 12-month WL trial of 150 older adults living with obesity, INVEST
in Bone Health (NCT04076618) is exploring the effects of WL alone versus WL plus weighted vest use or WL
plus RT on indicators of bone health and fracture risk, with the primary outcome being 12-month change in total
hip volumetric (vBMD) as measured by computed tomography (CT). This F31 proposal enhances the parent
study by adding new analyses of the CT scans acquired for this study to investigate indicators of muscle health.
As WL-associated muscle loss often precedes bone loss, preserving muscle health may also have clinical
relevance to the reduction of fracture risk. Aim 1 will develop an automated image analysis platform to process
baseline, 6- and 12-month participant CT scans to measure muscle quantity and quality and assess intervention
effects. Aim 2 will utilize CT data for development of subject-specific finite element models to assess longitudinal
bone strength and muscle-bone associations in response to WL. Taken together, completion of these aims will
provide automated techniques to support future large-scale research projects and opportunistic CT assessments
of muscle in clinical care, while also furthering our understanding of the mechanistic relationship between WL-
associated changes in muscle and bone. In addition to augmenting the suite of musculoskeletal outcomes in the
INVEST trial, this fellowship will provide the predoctoral principal investigator (PI) with valuable training from an
experienced mentorship team in the areas of: 1) aging and clinical trials, 2) data management, interpretation,
and biostatistics, 3) muscle and bone epidemiology, 4) machine learning and imaging informatics, and 5)
computational biomechanics. This research will be conducted at Wake Forest University as an interdisciplinary
collaboration between the Departments of Biomedical Engineering, Health and Exercise Science, Radiology,
and Statistical Sciences. Together, this collaborative environment and an expert team of mentors will support
the PI’s training to achieve independency in her research career while successfully completing her doctoral
dissertation. Th...

## Key facts

- **NIH application ID:** 10825724
- **Project number:** 1F31AG086010-01
- **Recipient organization:** WAKE FOREST UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Delanie Lynch
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $48,974
- **Award type:** 1
- **Project period:** 2024-06-12 → 2027-06-11

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10825724

## Citation

> US National Institutes of Health, RePORTER application 10825724, INVESTigations of Muscle and Bone Health: Developing Automated Approaches for CT-Based Analyses (1F31AG086010-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10825724. Licensed CC0.

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