Mentoring in Patient Oriented Research in Osteoarthritis

项目总结 这项以患者为中心的研究(POR)的K24职业中期研究员奖将利用我现有的POR 计划，促进我和我的实习生的专业发展，并支持我不断努力 为风湿性和肌肉骨骼POR的早期临床研究人员提供必要的指导 疾病(RMD)，重点是骨关节炎(OA)和健康公平。尽管是一种非常普遍的和 严重疾病，OA缺乏有效的治疗方法，仍然是个人和医疗保健的巨大负担 系统。我们已经发表了大量关于不同性别、种族和社会经济差异的研究报告。 根据我们对两个不同的基于人口的队列的经验，约翰斯顿县OA项目 (JoCoOA)和约翰斯顿县健康研究(JoCoHS)。我的POR职业生涯专注于办公自动化的各个方面 流行病学、影像和生物标记物，最近重点是使用机器识别表型 学习和精准医学，以改善对这种慢性病的研究和管理。这是至关重要的 理解包括机器学习在内的人工智能在改进或永久化方面的作用 健康不平等，如已知的在开放式办公室存在的不平等。这种先进的方法，如果应用得当，将 可能提供关于骨关节炎表型的新见解，并为不同人群的治疗提供信息 改善卫生公平的目标。此外，POR、健康公平和机器学习的交集是 学员的极大兴趣，以及培训未来临床医生科学家在RMD方面的迫切需要的一个领域，即 K24机制的目标。我们将利用来自JoCoOA的现有数据，这是一项为期30年的纵向研究 超过4000名年龄在45岁及以上的黑人和白人男女，以及新的JoCoHS，一个积极的招生 队列(2019-，n~1500)，包括35-70岁并认为是西班牙裔的个人， 黑色或白色。我们将利用这些丰富的数据源和我们广泛的机构资源来解决两个问题 围绕疼痛和症状的亚组及其与结构和功能的关联的具体目标， 关注健康公平。在目标1中，我们将致力于验证和优化以下深度学习算法 不同的队列，以更好地确定膝关节的放射学特征和疼痛体验之间的联系 骨关节炎。在目标2中，我们将描述JoCoHS队列中的症状表型(具有更广泛的 可用症状数据)，并将这些与在目标1中收集的射线照相信息以及 经过验证的功能和体力活动评估，以确定最能从中受益的子组 有针对性的干预。此K24项目将提供关键的受保护时间和资源来推广我的 专业发展并增强我的指导能力，同时提供 为学员提供跳板：1)在个性化项目的背景下发展他们的研究技能；2)发展 掌握大数据分析和基于机器学习的方法的关键技能，以及3)回答关键的POR问题 通过多样性和健康公平的视角，促进可持续发展和可持续发展。

Project summary This K24 Midcareer Investigator Award in Patient Oriented Research (POR) will leverage my existing POR program, enhance my professional development and that of my trainees, and support my ongoing efforts to provide essential mentoring to early stage clinical investigators in POR in the rheumatic and musculoskeletal diseases (RMDs), with a focus on osteoarthritis (OA) and health equity. Despite being a highly prevalent and serious disease, OA lacks effective therapies and remains a large burden on individuals and the health care system. We have published extensively on sex, race, and socioeconomic differences in various aspects of OA based on our experience with two diverse population-based cohorts, the Johnston County OA Project (JoCoOA) and the Johnston County Health Study (JoCoHS). My POR career has focused on aspects of OA epidemiology, imaging, and biomarkers, with a more recent emphasis on identifying phenotypes using machine learning and precision medicine to improve studies and management of this chronic disease. It is essential to understand the role of artificial intelligence, including machine learning, in either improving or perpetuating health inequities such as those known to exist in OA. Such advanced approaches, if applied appropriately, will likely provide new insights on phenotypes of OA, and inform therapeutics in diverse populations with the objective of improving health equity. Further, the intersection of POR, health equity, and machine learning is of great interest to trainees and an area of critical need for training future clinician scientists in RMDs, which is the goal of the K24 mechanism. We will leverage existing data from the JoCoOA, a 30-year longitudinal study of over 4000 Black and White men and women aged 45 and older, and the new JoCoHS, an actively enrolling cohort (2019-, n~1500) that includes individuals who are 35-70 years of age and who identify as Hispanic, Black, or White. We will utilize these rich data sources and our extensive institutional resources to address two specific aims around subgroups of pain and symptoms and their associations with structure and function, with a focus on health equity. In Aim 1, we will work to validate and optimize a deep learning algorithm in these diverse cohorts to better define the association between radiographic features and the pain experience in knee OA. In Aim 2, we will characterize symptomatic phenotypes in the JoCoHS cohort (which has more extensive available symptomatic data), and associate these with the radiographic information gleaned in Aim 1 as well as validated assessments of function and physical activity, to identify subgroups that could most benefit from targeted interventions. This K24 project will provide crucial protected time and resources to promote my professional development and enhance my mentoring capabilities while simultaneously providing a springboard for trainees to 1) develop their research skills in the context of individualized projects, 2) develop critical skills in big data analytics and machine learning-based methodology, and 3) answer key POR questions in RMDs and OA through a diversity and health equity lens.