Accurate and rapid assessment of sarcopenia in older adults through electrical impedance myography - Project Summary
Forty-two percent of older adults (OAs) have one or more physical limitations that are essential for in-
dependence. Age-related muscle wasting and weakness (sarcopenia) are important contributors to these phy-
sical impairments, including most notably gait impairment. Moreover, sarcopenia is closely associated with an
increased risk of falls and other injuries leading to loss of independence and increased mortality. Healthcare
providers of OAs need improved means of evaluating lower extremity muscle condition for the development of
sarcopenia. MRI, CT, and DXA provide considerable information, but none are office-based and DXA only
provides information on lean body mass and fat content. In this application, we advance electrical
impedance myography (EIM) for assessment of lower extremity muscle condition in OAs. EIM relies
upon application of directionally focused, multi-frequency electrical current to specific muscles or muscle
groups. By applying the technology in such a localized fashion, it is virtually unaffected by hydration status or
other issues that commonly impact other bioimpedance-based methods. EIM has been studied for nearly two
decades in the field of neuromuscular disease and has been shown to be sensitive to a variety of alterations in
skeletal muscle including atrophy, degeneration, and simple deconditioning. Taking EIM one step further and
applying machine learning (ML) techniques to the complex multifrequency EIM data set, it even becomes
possible to predict histological features, including myofiber size, fat, and connective tissue content. Given this
demonstrated capability, EIM has the potential to serve as a convenient, office-based approach for assessing
muscle health in OAs. In this direct-to-Phase 2 SBIR application, Myolex proposes to establish EIM, via
its new device, the mScan, in conjunction with machine learning cloud-based platform, as a means of
obtaining MRI-like quantitative data in muscle of OAs. In Specific Aim 1, in conjunction with aging expert
researchers at 3 different clinical sites, we will collect MRI and EIM data on a cohort of healthy older adults,
along with standard functional measures as well as measuring specific force, via electrically stimulated muscle
contraction. Using this data, in Specific Aim 2, we will develop predictive algorithms, via the penalized
regression technique of Lasso (least absolute shrinkage and selection operator), leveraging EIM values to
predict muscle volume, muscle specific force, and muscle fatty infiltration. We will then incorporate these
algorithms into a cloud-based engine that will provide meaningful, easy-to-interpret values. At the conclusion of
this proposed work, we will have developed an accurate, powerful system that clinicians and researchers can
use for the rapid assessment of OA muscle health.
