Health digital twin (HDT) for social risk management in type 2 diabetes (T2D)

Diabetes is the 7th leading cause of death in the United States (US), threatening >37 million Americans. Over 90% of diabetes cases are type 2 diabetes (T2D). Racial and ethnic minority groups and individuals with socioeconomic disadvantages bear a disproportionate burden of T2D. Social determinants of health (SDoH) are the root causes of T2D disparities and account for 80% of modifiable factors. T2D is heterogeneous and associated with various risk factors, including individuals’ biological and clinical characteristics, environmental exposures, and SDoH. Understanding the disease progression pathways, predicting disease progression, and identifying actionable risk factors are crucial for tailoring T2D management and offering personalized medicine. The proliferation of real-world data (RWD), such as electronic health records (EHRs) and administrative claims data, offers unique opportunities to generate real-world evidence (RWE) for T2D research. This proposal creates a T2D cohort based on a unique RWD source—the OneFlorida+ research network (~20 million patients from Florida, Georgia, and Alabama) integrated with both individual- (e.g., education, social cohesion) and contextual- (e.g., neighborhood characteristics) SDoH. Built upon this unique resource, this study has three specific objectives: (1) develop a novel health digital twins (HDT) framework considering both individual- and contextual-SDoH, (2) co-design with stakeholders and develop a fair HDT-based platform that can be implemented as a decision support tool for personalized social risk management (e.g., recognize “actionable” SDoH and link to appropriate social services).