Multimodal AI to Accelerate Precision Medicine for Type 1 Diabetes (U01- Clinical Trials Not Allowed)
🏛 National Institutes of Health (HHS-NIH11)
✓ Free, no account · Source: Grants.gov · Last verified Oct 19, 2026
Can you apply?
This grant is for research teams developing AI-driven approaches to advance precision medicine for Type 1 diabetes using existing multimodal datasets. Eligible applicants typically include academic institutions, research hospitals, and nonprofit research organizations with capacity for advanced computational and biomedical research. The work must focus on integrating existing datasets from major T1D consortia and developing multimodal AI models. This is a cooperative agreement requiring coordination with the NIDDK and other funded projects.
Not the right fit? Find grants for your organization in 5 questions →
Key dates
- Aug 20, 2026 Applications open
- Feb 10, 2027 Application deadline in 113 days
- Nov 1, 2027 Award announced
- Dec 1, 2027 Project start
Program description
Type 1 diabetes (T1D) is a complex disease involving interactions among many different cell types, tissues, organs and biological processes. Genetics, the environment, and lifestyle all play a role in disease development and progression. Over the past two decades, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) has supported major T1D research consortia covering basic, translational, and clinical research. Each consortium has focused on different aspects of T1D, such as specific organs, stages of disease or clinical outcomes. Together, these consortia have collected large longitudinal and multimodal datasets using standardized protocols and coordinated studies. While individual consortia have improved our understanding of specific areas, combining their work offers a rare opportunity to see how different biological processes interact to drive T1D development and progression. This integrated view can answer fundamental questions about T1D biology that cannot be answered using individual datasets or studies alone.
Recent advances in artificial intelligence (AI) now make it possible to integrate these complementary datasets and study T1D across various tissues, disease stages, and biological levels. Building on these resources, NIDDK will support a coordinated, milestone-driven research project to answer fundamental questions about T1D biology and accelerate the development of personalized medicine.
The project will combine multimodal human pancreas and clinical datasets to better understand how biological processes interact as T1D develops and progresses. The research will generate new insights into T1D biology, help us understand the different forms and stages of T1D, identify biomarkers, and inform new approaches for disease prevention, intervention, and treatment. To support the research, the project will develop and test advanced multimodal AI models. It will also develop AI-enabled research workflows, new methods to integrate data, and privacy-preserving synthetic datasets.
Who can apply
Eligible applicants
How to apply
Application links
Key dates & requirements
Required documents
- SF-424 (R&R) Federal Application Cover Sheet
- Project Narrative (with specific aims, research design, methods)
- Data Management and Sharing Plan
- Budget and Budget Justification
- Institutional Biosafety Committee approval (if applicable)
- Human Subjects Protection documentation
- Letters of Support from consortium collaborators
Program contact
- 👤 Division of Diabetes, Endocrinology and Metabolic Diseases
- 📧 NIDDK_DEM@nih.gov
- 📞 NIDDK_DEM@nih.gov
Funding track record
Recent awards under CFDA 93.847 from the last 3 years — real organizations that won funding through this same program.
Top 10 Largest Recent Awards
-
$444,814,918
-
$200,221,259
-
$181,279,494
-
$112,529,392
-
$82,457,560
-
$79,214,478
-
$48,585,892
-
$45,186,589
-
$37,997,540
-
$34,242,949
Top States by Funding
- WA 4 awards $520.0M
- NC 4 awards $293.3M
- FL 3 awards $291.6M
- NY 5 awards $184.3M
- PA 6 awards $176.9M
Source: USAspending.gov — federal spending transparency. Data covers last 3 years.
Funding history
Annual funding for this program — Federal obligations (CFDA 93.847). How funding has trended year over year.
| 2024 | $1,971,472,000 | |
| 2025 | $2,043,166,000 | |
| 2026 est. | $111,289,000 |
FAQ
Who can apply for this grant?
Academic institutions, research hospitals, and nonprofit research organizations with advanced computational capabilities. Applicants should have experience with large datasets and AI/machine learning.
What is the deadline and timeline?
The deadline is February 10, 2027. Projects are typically 3–5 years in duration with milestone-driven funding.
What research activities are supported?
Development of multimodal AI models, integration of existing T1D datasets, creation of privacy-preserving synthetic datasets, and research workflows. Clinical trials are not allowed.
How competitive is this funding?
Very competitive. This is an NIH cooperative agreement with a $5M total pool funding coordinated, large-scale research. Strong computational and consortium management experience required.
What is the typical funding range?
Individual awards typically range from $500K–$2M per year depending on scope, though specific amounts vary by project.
💡 Tips for applicants
- Emphasize your team's expertise in AI/machine learning, data integration, and Type 1 diabetes biology. Show prior collaborative experience with consortia or large datasets.
- Clearly outline how your AI models will integrate multimodal datasets and address fundamental questions about T1D development and progression.
- Include a detailed project timeline with specific milestones, deliverables, and metrics for success aligned with NIDDK goals.
- Plan for data privacy, security, and ethical considerations. Address how you'll create and validate privacy-preserving synthetic datasets.
- Build a collaborative team with expertise spanning computational biology, pancreatic biology, clinical endocrinology, and AI/machine learning.
⚠️ Common mistakes
Applications fail when they propose novel data collection instead of integrating existing consortium datasets. Weak AI methodology sections or lack of clear biological questions undermine competitiveness. Poor coordination plans with NIDDK and existing consortia create fatal reviewer concerns.
Similar grants
- OPEN Continuation of the Cardiovascular Repository for-Type 1 Diabetes (CARE-T1D) Consortium U01 (Open Competition)- Research (U01, Clinical Trial Not Allowed) — National Institutes of Health
- CLOSED Model-to-Clinic (M2C) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional) — National Institutes of Health
- CLOSED PRIMED-AI: Data-to-Model Academic-Industrial Partnerships (D2M-AIP) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (UG3/UH3 Clinical Trial Optional) — National Institutes of Health
- OPEN Translational Bioinformatics and Experimental Approaches to Advance Drug Repositioning and Combination Therapy Development for Alzheimers Disease and Related Dementias (R01 Clinical Trial Not Allowed) — National Institutes of Health
- OPEN Advancing Bioinformatics, Translational Bioinformatics and Computational Biology Research (R01 Clinical Trial Optional) — National Institutes of Health