Spencer Fox Eccles School of Medicine
50 Evaluating the Use of All of Us to Understand the Interaction of Exposures and Rheumatological Conditions
Divya Sundar; Caden Stewart; Naomi Schlesinger; Julio Facelli; and Ramkiran Gouripeddi
Faculty Mentor: Ramkiran Gouripeddi (Biomedical Informatics, University of Utah)
Introduction
Over 46 million Americans currently suffer from rheumatological conditions characterized by the destruction of joints and loss of muscle tissue (1, 2). Diseases affecting the musculoskeletal system are caused by genetic and environmental factors. First-degree relatives of patients with rheumatoid arthritis (RA) showed a 12fold increase of disease prevalence (3). Cigarette smoking along with the expression of HLA-DRB1 genes was found to increase the risk of RA by 23-fold (4). However, the interaction of genetic and environmental factors in rheumatological conditions still remain poorly understood, especially in underrepresented populations who already experience decreased access to healthcare and medical resources.
An observational study was carried out to assess the feasibility of using the All of Us research enclave for understanding the role of exposures in patients with rheumatologic conditions. Exposures were studied inclusively to determine the feasibility of All of Us in examining environmental exposures, social exposures, and prevalence of comorbid conditions. All of Us is a newly developed program that aims to gather health data from underrepresented populations in the United States to increase understanding of diverse populations in medical research. All of Us strives to achieve this by oversampling underrepresented groups such as Hispanic/Latino and Black or African American populations. This enclave has been used for studying various disease conditions (5).
Methods
Methods similar to those in Beese at al. were followed to evaluate the suitability of All of Us for rheumatological conditions (6). To study the interaction of exposures and rheumatological conditions, ten of the most common rheumatological diseases from the National Institute of Health (7) were chosen as disease cohorts within All of Us (Figure 1). Data on these disease cohorts included demographic information, health surveys, and physical activity levels of participants diagnosed with a rheumatological disorder. This data from each cohort was collectively compiled into a Jupyter notebook within the All of Us enclave for data extraction and analysis. Using the Python language and Pandas DataFrames, information about lifestyle such as cigarette smoking, alcohol consumption, physical activity, and sleep/energy levels were extracted. Additional health survey data was extracted to determine the percentage of participants with comorbid conditions, a marker for increased disease-related morbidity. Finally, these values were compared with the Centers for Disease Control national averages of adults with similar comorbid conditions and lifestyle patterns (8).
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