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

行走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，研究人员将获得特定 人群，例如健康个体或由于肌肉骨骼或肌肉损伤而具有行走障碍的个体。 神经损伤，以获得行走功能的组级描述符。这些平均值， 重要的个体间差异，告知康复研究，规定相同的 对所有参与者进行干预。精准医学是NIH提出的一种创新方法， 个体患者的生物学、环境和生活方式差异，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取得了巨大的成功， 还有待实施，以微调对步行行为的干预。我们的目标是找出- 特定的行走表型及其潜在的关节和肌肉水平损伤，以有效地指导临床 长期决策。为了实现这一目标，我们将使用利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损伤后的功能。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， 作为康复干预目标。我们的KL 2资助项目朝着这个方向迈出了第一步：使用 中风幸存者作为病理性步行行为的模型，我们鉴定了四种不同的步行表型， 这表明行走速度、平衡、推进力或减震方面的缺陷。还剩哪些尚待 确定负责这些独特的行走表型的个体关节和肌肉（目标1）， 这些损伤是否可以在损伤后早期发现，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，可用于推断损伤机制（目标3）， 在临床环境中实施我们的研究结果。我们将通过对以下方面的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，以识别关节和肌肉损伤， 描述每种行走表型。我们将在R01提案中利用我们的结果来评估 使用干预目标的步行干预的广义与表型特异性处方 在这里识别。确定个体化的干预目标可以提高步行的疗效 干预措施，更普遍地改善流动性以及相关的参与、健康和福祉，所有 符合NIH的使命。

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.