MON-581 - Precision Diabetes Consortium (PREDICT): Creating a Data Commons for Monogenic Diabetes

Background: Monogenic diabetes (MD) is a group of disorders caused by pathogenic variants in key genes, most often affecting beta-cell function. Which each sub-type is rare, collectively MD represents 1-5% of diabetes cases diagnosed before age 35. With several subtypes having distinct treatment approaches, MD is in exemplar for precision medicine in diabetes. Unfortunately, most MD cases remain unidentified, resulting in suboptimal treatment and outcomes. Historically, progress in MD research has been limited by fragmented data, inconsistent data standards, and lack of collaboration across institutions. Increased collaboration and streamlined data sharing is necessary for further advances in MD research.

Objectives: To facilitate research and improve clinical care for MD, we aimed to develop a multicenter data commons by harmonizing data collection, establishing consensus standards, and fostering collaborative investigation among leading academic institutions.

Methods: Building on the foundation set by the UChicago Monogenic Diabetes Registry (UCMDR) and leveraging the expertise of UChicago Data for the Common Good (D4CG), we convened the Precision Diabetes Consortium (PREDICT). Representatives from 12 major U.S. academic centers participated in monthly meetings to design governance, prioritize research questions, and create a standardized data dictionary. Each institution harmonized and de-identified MD data using IT-facilitated EMR pulls, manual chart reviews, and REDCap forms, in accordance with local regulatory protocols. A technical infrastructure and project request process are being established to enable responsible data access.

Results: Twelve consortium institutions have adopted a Memorandum of Understanding and nominated Executive Committee representatives. PREDICT has established a unified data dictionary with over 75 clinical and genetic variables, enabling the pooling of approximately 2,500 MD cases, which is the first-of-its kind and largest harmonized dataset in the United States. A governance framework has been established to ensure secure, controlled data sharing and transparent project review. A publicly available web-based cohort discovery tool is being developed to explore data and assess study feasibility. Interest from additional national and international sites continues to grow.

Conclusions: The PREDICT consortium has demonstrated that structured collaboration, harmonized data standards, and consensus-based governance markedly reduce barriers in rare disease research. This multicenter data commons sets a precedent for advancing MD care and research, supporting efficient diagnosis, collaborative trials, and comprehensive understanding of MD. The model may prove beneficial for other rare diseases that would benefit from pooled, harmonized data.

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