Friday, April 26, 2024
4/26/2024
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.
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