A precision medicine approach to identify walking phenotypes and rehabilitation targets after injury

走路的表型在人与人之间是不同的，以至于我们可以通过他们的方式来识别他们 步行。尽管存在这些明显的个体差异，研究人员仍将获得特定领域的平均值 人群，如健康人或因肌肉骨骼或其他原因导致行走障碍的人 神经损伤，以派生步行功能的组级描述符。这些平均值，消除了 重要的个体间差异，为制定相同处方的康复研究提供信息 对所有参与者进行干预。精准医学是美国国立卫生研究院提出的一种创新方法 患者个体的生物、环境和生活方式的差异，以告知治疗处方。 尽管精准医学在药物基因组学和肿瘤学方面拥有据点和成功，但它 尚未实施，以微调步行行为的干预措施。我们的目标是识别个人- 特定的行走表型及其潜在的关节和肌肉水平损伤有效地指导临床 决策的长期性。为了实现这一目标，我们将使用使用机器学习的数据分析管道 利用丰富的临床和实验室数据来表征受伤后的功能。这种方法 将使我们能够识别负责每种行走表型的特定肌肉和关节，这可以 作为康复干预目标。我们的KL2资助项目朝着这个方向迈出了第一步：使用 中风幸存者作为病态行走行为的模型，我们确定了四种不同的行走表型， 这表明他们在行走速度、平衡性、推进力或减震方面存在缺陷。剩下的是什么？ 确定的是负责这些独特行走表型的单个关节和肌肉(目标1)， 是否可以在受伤后早期发现这些损害，以便在早期制定治疗策略 阶段(目标2)，以及国际功能、残疾、 和健康，可以用来推断损害的机制(目标3)，允许容易 在临床环境中实施我们的发现。我们将通过以下二次分析来实现这些目标 作为我们KL2项目的一部分收集的纵向数据，以确定关节和肌肉损伤 描述每种行走表型的特征。我们将利用我们在R01提案中的结果来评估 使用干预目标的步行干预的通用处方与表型特定处方 在这里确认了身份。确定个体化干预目标可提高步行效果 干预和更普遍地改善流动性，以及相关的参与、健康和福祉，所有这些 与美国国立卫生研究院的任务保持一致。

Walking phenotypes are different between people, to the point that we can recognize people by the way they walk. Despite these evident individual differences, researchers will obtain averages across a specific population, such as healthy individuals or individuals with walking impairments due to musculoskeletal or neurological injury, to derive group-level descriptors of walking function. These averages, which eliminate important between-individual differences, inform rehabilitation research studies that prescribe the same intervention for all participants. Precision Medicine is an innovative approach put forth by the NIH to consider individual patients’ biological, environmental, and lifestyle differences to inform the prescription of treatment. Despite the stronghold and success that precision medicine has had in pharmaco-genomics and oncology, it has yet to be implemented to fine-tune interventions for walking behaviors. Our goal is to identify individual- specific walking phenotypes and their underlying joint and muscle level impairments to effectively guide clinical decision-making long-term. To achieve this goal, we will use data analysis pipelines that use machine learning to leverage the wealth of clinical and laboratory data used to characterize function after injury. This approach will allow us to identify the specific muscles and joints responsible for each walking phenotype, which can serve as rehabilitation intervention targets. Our KL2 funded project took the first step in this direction: using stroke survivors as a model of pathological walking behavior, we identified four distinct walking phenotypes, which point at deficits in either walking speed, balance, propulsion, or shock absorption. What remains to be determined are the individual joints and muscles responsible for these distinctive walking phenotypes (Aim 1), whether these impairments can be detected early after injury to develop treatment strategies in the early stages (Aim 2), and whether clinical measures within the International Classification of Functioning, Disability, and Health, can be used to draw inference on mechanisms of impairment (Aim 3), allowing easy implementation of our findings in clinical settings. We will achieve these aims via secondary analyses of longitudinal data collected as part of our KL2 project to identify the joint and muscle impairments that characterize each walking phenotype. We will harness our results in an R01 proposal to assess the effects of generalized vs. phenotype-specific prescription of walking interventions, using the intervention targets identified here. Identification of individualized intervention targets could improve the efficacy of walking interventions and more generally improve mobility, and associated participation, health, and well-being, all aligned with the NIH mission.