Accurate and rapid assessment of sarcopenia in older adults through electrical impedance myography

通过电阻抗肌电图准确快速评估老年人肌少症

基本信息

批准号：
10484558
负责人：
William David Arnold
金额：
$ 88.94万
依托单位：
MYOLEX, INC.
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10484558
关键词：
Abdomen Adoption Adult Aging Algorithms American Assessment tool Atrophic Bilateral Caring Clinical Collaborations Complex Connective Tissue Data Data Science Data Set Dependence Development Devices Diagnosis Diagnostic Dual-Energy X-Ray Absorptiometry Elderly Electric Stimulation Extensor Fatty acid glycerol esters Frequencies Gait Grant Health Health Personnel Healthcare Histologic Hydration status Impairment Individual Infiltration Injury Institutes Knee Lasso Leg Libraries Lower Extremity Machine Learning Magnetic Resonance Imaging Measurement Measures Methods Modeling Modernization Monitor Muscle Muscle Contraction Muscle Weakness Muscle function Myography Neuromuscular Diseases Outcome Pathology Phase Production Research Research Personnel Skeletal Muscle Small Business Innovation Research Grant System Techniques Technology Testing Time Training Validation Work X-Ray Computed Tomography age related age-related muscle loss algorithm development base clinical research site cloud based cohort cost effective deconditioning electric impedance fall risk functional status improved lean body mass mortality muscle form muscle strength prediction algorithm quadriceps muscle sarcopenia screening tool virtual wasting

项目摘要

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.

项目概要 42% 的老年人 (OA) 存在一种或多种身体限制，这些限制对于老年人来说至关重要。依赖性。与年龄相关的肌肉萎缩和无力（肌肉减少症）是这些肌肉萎缩的重要原因。身体损伤，包括最明显的步态损伤。此外，肌肉减少症与以下疾病密切相关：跌倒和其他伤害的风险增加，导致丧失独立性和死亡率增加。卫生保健 OA 提供者需要改进评估下肢肌肉状况的方法，以促进肌肉减少症。 MRI、CT 和 DXA 提供了大量信息，但都不是基于办公室的且仅 DXA 提供有关去脂体重和脂肪含量的信息。在此应用中，我们推进了电气阻抗肌动描记 (EIM) 用于评估 OA 下肢肌肉状况。 EIM 依赖将定向聚焦的多频电流施加到特定的肌肉或肌肉上组。通过以这种局部方式应用该技术，它几乎不受水合状态或通常影响其他基于生物阻抗的方法的其他问题。 EIM已经研究了近两年在神经肌肉疾病领域研究了数十年，并已被证明对各种变化敏感骨骼肌，包括萎缩、退化和简单的失调。让 EIM 更进一步将机器学习 (ML) 技术应用于复杂的多频 EIM 数据集，它甚至变得可以预测组织学特征，包括肌纤维大小、脂肪和结缔组织含量。鉴于此 EIM 有潜力作为一种方便的、基于办公室的评估方法 OA 中的肌肉健康。在这个直接进入第 2 阶段 SBIR 的应用中，Myolex 建议建立 EIM，通过其新设备 mScan 与机器学习云平台相结合，作为一种手段获得 OA 肌肉中类似 MRI 的定量数据。在具体目标 1 中，与老龄化专家合作 3 个不同临床地点的研究人员，我们将收集一组健康老年人的 MRI 和 EIM 数据，以及标准功能测量以及通过电刺激肌肉测量比力收缩。使用这些数据，在具体目标 2 中，我们将开发预测算法，通过惩罚 Lasso（最小绝对收缩和选择算子）的回归技术，利用 EIM 值预测肌肉体积、肌肉比力和肌肉脂肪浸润。然后我们将把这些将算法集成到基于云的引擎中，该引擎将提供有意义的、易于解释的值。结束时这项拟议的工作，我们将开发出一个准确、强大的系统，临床医生和研究人员可以用于快速评估 OA 肌肉健康状况。