Single Source for the Continuation of the Epidemiology of Diabetes Interventions and Complications (EDIC) Study Research Center (Collaborative U01 Clinical Trial Not Allowed)
This grant supports continued long-term follow-up of people with type 1 diabetes to study complications, new therapies, and quality of life using advanced technologies and data analysis methods.
Funder: National Institutes of Health
Due Dates (Anticipated): July 2026 (full application deadline, projected)
Funding Amounts: Estimated total program funding: $6,300,000; expected number of awards: 1; typical award duration aligns with multi-year collaborative research centers.
Summary: Supports the continuation of the EDIC Research Center to study long-term complications in type 1 diabetes, with a focus on comorbidities, advanced analytics, and modern technologies.
Key Information: Forecasted, single-source competition; only eligible institutions will be invited to apply.
Description
This funding opportunity supports the continuation of the Epidemiology of Diabetes Interventions and Complications (EDIC) Study Research Center. The program's primary aim is to enable long-term follow-up of the EDIC cohort to investigate the development and progression of complications in individuals with type 1 diabetes (T1D). Research areas include severe microvascular disease, cardiovascular and liver disease, sleep disorders, mortality, and other comorbidities. Additional objectives are to evaluate age-related morbidities such as cognition, frailty, and physical function, and to identify risk factors impacting quality of life, self-management, and caregiver burden.
The initiative encourages the use of advanced statistical methods, including machine learning and artificial intelligence, to identify phenotypes susceptible or resilient to diabetes complications. Modern technologies—such as continuous glucose monitoring, coronary calcification imaging, and vascular tonometry—will be integrated and compared with existing cohort data. The program also expects assessments of obesity-related outcomes, including metabolic-associated steatotic liver disease (MASLD) and obstructive sleep apnea (OSA), within the T1D population. Multi-omic approaches to identify biochemical signatures and leveraging external databases for cost-effectiveness and quality-of-life analyses are also anticipated.
This is a forecasted, single-source competition: only eligible organizations will be invited to apply, and applications will undergo NIH peer review.
