The AI-READI study has launched its first major dataset, which aims to explore the connections between environmental factors, biomarkers, and type 2 diabetes, with a goal of collecting data from 4,000 diverse participants.

A key focus of the research is on diversity, ensuring equal representation among white, Black, Hispanic, and Asian participants, as well as across various levels of diabetes severity.

This initiative sets itself apart from traditional diabetes research by utilizing environmental sensors, eye scans, depression scales, and biological markers to gain a comprehensive understanding of diabetes development.

The study investigates both the progression from health to disease and the factors that contribute to health improvement, aiming to provide valuable insights for prevention and treatment.

Dr. Cecilia Lee from the University of Washington emphasizes the heterogeneity among type 2 diabetes patients, highlighting the varied experiences they encounter in managing the disease.

Published in Nature Metabolism on November 8, 2024, the study includes data from 1,067 participants and reveals significant connections between air pollution and diabetes status.

The dataset is specifically designed for artificial intelligence analysis, focusing on both technical and ethical considerations, and is accessible through a custom online platform that ensures patient privacy.

The initial pilot data release has garnered significant interest, being downloaded by over 110 research organizations globally.