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

项目摘要 42%的老年人(OAS)有一个或多个身体缺陷，这些限制对老年人的健康至关重要。 依赖。与年龄相关的肌肉萎缩和虚弱(肌萎缩症)是导致这些肌肉萎缩的重要因素。 体征障碍，包括最明显的步态障碍。此外，骨质疏松症与 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。医疗保健 OAS的提供者需要改进的方法来评估下肢肌肉状况，以发展 石棺减少症。MRI、CT和DXA提供了大量信息，但没有一个是基于办公室和DXA的 提供有关瘦体重和脂肪含量的信息。在这个应用中，我们推进了电气 阻抗肌图(EIM)在评估OAS患者肢体肌肉状况中的应用EIM依赖 在向特定肌肉或肌肉施加定向聚焦的多频电流时 组。通过以这种本地化的方式应用该技术，它几乎不受水化状态或 通常影响其他基于生物阻抗的方法的其他问题。对EIM的研究已经有近两年的历史了 在神经肌肉疾病领域研究了几十年，并已被证明对多种变化敏感 骨骼肌包括萎缩、变性和简单的去条件反射。使EIM更进一步，并 将机器学习(ML)技术应用于复杂的多频率EIM数据集，它甚至成为 有可能预测组织学特征，包括肌纤维大小、脂肪和结缔组织含量。鉴于此， 经过验证的能力，EIM有可能成为一种方便的、基于办公室的评估方法 OAS中的肌肉健康。在这个直接到第二阶段SBIR的应用中，Myolex建议通过以下方式建立EIM 其新设备MSCAN与基于云的机器学习平台相结合，作为一种手段 获取OAS肌肉的MRI样定量数据。在具体目标1中，与老龄化专家合作 3个不同临床站点的研究人员，我们将收集一组健康老年人的MRI和EIM数据， 以及标准的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，在特定的目标2中，我们将开发预测算法，通过惩罚 套索回归技术(最小绝对收缩和选择算子)，利用EIM值来 预测肌肉体积、肌肉比力和肌肉脂肪渗透。然后我们将合并这些内容 算法集成到一个基于云的引擎中，提供有意义的、易于解释的价值。在……结束时 这项拟议的工作，我们将开发出一种准确、强大的系统，临床医生和研究人员可以 用于快速评估骨性关节炎肌肉健康状况。

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.