Flagship dataset released to better understand type 2 diabetes development
Study participants include people with no diabetes as well as those with various stages of the condition, creating a set of information distinct from previous research.
(Image credit: AdobeStock/ZeNDaY)
Researchers have released the flagship dataset from a study of biomarkers and environmental factors that might influence the development of type 2 diabetes.
Study participants include people with no diabetes as well as those with various stages of the condition, creating a set of information distinct from previous research. The collected data includes survey responses, depression scales, eye-imaging scans and traditional measures of glucose and other biologic variables. These data are intended to be used by artificial intelligence to provide novel insights about risks, preventive measures, and pathways between disease and health.1
An expert explains
Cecilia S. Lee, MD, a professor of ophthalmology at the University of Washington School of Medicine, spoke to the potential of this dataset, saying, “We see data supporting heterogeneity among type 2 diabetes patients — that people aren’t all dealing with the same thing. And because we’re getting such large, granular datasets, researchers will be able to explore this deeply.” The press release notes that she also expressed excitement at the quality of the collected data, which represent 1,067 people, just 25% of the study’s total expected enrollees.1
Lee is program director of AI-READI (Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights). The National Institutes of Health-supported initiative aims to collect and share AI-ready data for scientists worldwide to analyze for new clues about health and disease.1
Publication of data
The initial data release is highlighted in a paper published in the journal Nature Metabolism. In this paper, the authors restated their aim to gather health information from a more racially and ethnically diverse population than has been measured previously, and to make the resulting data ready, technically and ethically, for AI mining.1
Aaron Y. Lee, MD, University of Washington Medicine professor of ophthalmology and the project’s principal investigator, spoke about the process of this research in the press release.1 He said, “This process of discovery has been invigorating. We’re a consortium of 7 institutions and multidisciplinary teams that had not worked together before. But we have shared goals of drawing on unbiased data and protecting the security of that data as we make it accessible to colleagues everywhere.”
He continued, “We want our datasets to also be studied for salutogenesis, or factors that contribute to health. So, if your diabetes gets better, what factors might be contributing to that? We expect that the flagship dataset will lead to novel discoveries about type 2 diabetes in both of these ways.”1
At study sites in Seattle, San Diego, and Birmingham, Alabama, recruiters are collectively enrolling 4,000 participants, with inclusion criteria promoting balance:
- race/ethnicity (1,000 each – white, Black, Hispanic, and Asian)
- disease severity (1,000 each – no diabetes, prediabetes, medication/non-insulin-controlled, and insulin-controlled type 2 diabetes)
- sex (equal male/female split)1
Accessing data
Hosted on a custom online platform, the data are produced in 2 sets: a controlled-access set requiring a usage agreement, and a registered, publicly available version stripped of participants' HIPAA-protected information. The pilot data release (summer 2024) involving 204 participants has been downloaded by more than 110 research organizations worldwide. Researchers must verify their identity and agree to ethical-usage terms.1
This work was supported by the NIH (grants OT2OD032644 and P30 DK035816).
Learn more about accessing the data at aireadi.org.
