Smart tools to find common long-term diet patterns
Official title: iPAT:Intelligent Diet Quality Pattern Analysis for Harmonized MA-National Trials
Funded at University of Massachusetts Dartmouth · NIH-10876321 · Data updated 2026-11-12
This project uses artificial intelligence to find common long-term eating patterns in people from large U.S. diet and health studies to help link eating habits with chronic disease risk.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Massachusetts Dartmouth NIH-funded |
| Lab location | 1 site (North Dartmouth, United States) |
| Project start | July 15, 2021 |
| Project end | June 30, 2026 |
| Funding year | 2025 |
| Award (this year) | $652,466 |
| NIH project number | 5R01DK129432-04 |
| Project ID | NIH-10876321 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with long-term diet records or those who have taken part in long-term nutrition or cardiovascular health studies would be most relevant to this work.
✕Less likely to help
People seeking immediate treatment for an acute condition or those without any long-term dietary information are unlikely to benefit directly from this project.
What this research studies
Researchers are combining and harmonizing dietary records from many long-term U.S. studies, including several Massachusetts trials and national cohorts, covering up to 35 years. They will apply visualization-aided, AI-based trajectory pattern recognition to identify repeating diet-quality patterns over time. The team will validate those patterns across studies and connect them to health and chronic disease outcomes. The goal is to make it clearer which long-term eating patterns relate to better or worse health.
Why it matters
Potential benefit: If successful, this work could lead to clearer, evidence-based dietary guidance that better prevents or manages chronic diseases.
How similar studies have gone: Previous studies have linked dietary patterns to health, but applying AI across many harmonized long-term datasets is a new and less-tested approach.
Where this research is happening
-
University of Massachusetts Dartmouth
North Dartmouth, United States Active
Researchers
- Principal investigator: Fang, Hua — University of Massachusetts Dartmouth
- Study coordinator: Fang, Hua
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.