Researchers today are releasing the flagship dataset from an ambitious study of biomarkers and environmental factors that might influence the development of type 2 diabetes, which is associated with a range of eye diseases such as diabetic retinopathy. Because the study participants include people with no diabetes and others with various stages of the condition, the early findings hint at a tapestry of information distinct from previous research.
For instance, data from a customized environmental sensor in participants’ homes show a clear association between disease state and exposure to tiny particulates of pollution. The collected data also includes survey responses, depression scales, eye-imaging scans and traditional measures of glucose and other biologic variables.
All of these data are intended to be mined by artificial intelligence for novel insights about risks, preventive measures, and pathways between disease and health.
“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,” said Dr. Cecilia Lee, a professor of ophthalmology at the University of Washington School of Medicine.
The project is funded through a grant from Bridge2AI, an NIH Common Fund Program, supported by the National Eye Institute.
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