Mentoring in Patient Oriented Research in Osteoarthritis

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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.