AI Ready and Exploratory Atlas for Diabetes Insights

NCT ID: NCT06002048

Last Updated: 2025-04-06

Basic Information

• Recruitment Status: ENROLLING_BY_INVITATION
• Total Enrollment: 4000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-07-19
• Study Completion Date: 2027-01-01

Brief Summary

The study will collect a cross-sectional dataset of 4000 people across the US from diverse racial/ethnic groups who are either 1) healthy, or 2) belong in one of the three stages of diabetes severity (pre-diabetes/diet controlled, oral medication and/or non-insulin-injectable medication controlled, or insulin dependent), forming a total of four groups of patients. Clinical data (social determinants of health surveys, continuous glucose monitoring data, biomarkers, genetic data, retinal imaging, cognitive testing, etc.) will be collected. The purpose of this project is data generation to allow future creation of artificial intelligence/machine learning (AI/ML) algorithms aimed at defining disease trajectories and underlying genetic links in different racial/ethnic cohorts. A smaller subgroup of participants will be invited to come for a follow-up visit in year 4 of the project (longitudinal arm of the study). Data will be placed in an open-source repository and samples will be sent to the study sample repository and used for future research.

Detailed Description

The Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights (AI-READI) project seeks to create a flagship ethically-sourced dataset to enable future generations of artificial intelligence/machine learning (AI/ML) research to provide critical insights into type 2 diabetes mellitus (T2DM), including salutogenic pathways to return to health. The ability to understand and affect the course of complex, multi-organ diseases such as T2DM has been limited by a lack of well-designed, high quality, large, and inclusive multimodal datasets. The AI-READI team of investigators will aim to collect a cross-sectional dataset of 4,000 people and longitudinal data from 10% of the study cohort across the US. The study cohort will be balanced for self-reported race/ethnicity, gender, and diabetes disease stage. Data collection will be specifically designed to permit downstream pseudo-time manifold analysis, an approach used to predict disease trajectories by collecting and learning from complex, multimodal data from participants with differing disease severity (normal to insulin-dependent T2DM). The long-term objective for this project is to develop a foundational dataset in T2DM, agnostic to existing classification criteria or biases, which can be used to reconstruct a temporal atlas of T2DM development and reversal towards health (i.e., salutogenesis). Six cross-disciplinary project modules involving teams located across eight institutions will work together to develop this flagship dataset. Data will be optimized for downstream AI/ML research and made publicly available. This project will also create a roadmap for ethical and equitable research that focuses on the diversity of the research participants and the workforce involved at all stages of the research process (study design and data collection, curation, analysis, and sharing and collaboration).

Conditions

Type 2 Diabetes

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: CROSS_SECTIONAL

Study Groups

Healthy

Participants who do not have Type 1 or Type 2 Diabetes

No interventions assigned to this group

Pre-diabetes/Diet Controlled

Participants with pre-Type 2 Diabetes and those with Type 2 Diabetes whose blood sugar is controlled by diet

No interventions assigned to this group

Oral Medication and/or Non-insulin-injectable Medication Controlled

Participants with Type 2 Diabetes whose blood sugar is controlled by oral or injectable medications other than insulin

No interventions assigned to this group

Insulin Dependent

Participants with Type 2 Diabetes whose blood sugar is controlled by insulin

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• Adults (≥ 40 years old)
• Patients with and without type 2 diabetes
• Able to provide consent
• Must be able to read and speak English

Exclusion Criteria

• Adults older than 85 years of age
• Pregnancy
• Gestational diabetes
• Type 1 diabetes

• Minimum Eligible Age: 40 Years
• Maximum Eligible Age: 85 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institutes of Health (NIH)

NIH

Sponsor Role: collaborator

University of Washington

OTHER

Sponsor Role: lead

Responsible Party

Aaron Y Lee

Associate Professor, Department of Ophthalmology

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Aaron Lee

Role: PRINCIPAL_INVESTIGATOR

University of Washington

Locations

University of Alabama, Birmingham

Birmingham, Alabama, United States

UC San Diego

San Diego, California, United States

University of Washington

Seattle, Washington, United States

Countries

United States

Other Identifiers

3OT2OD032644-01S3

Identifier Type: NIH

Identifier Source: secondary_id

View Link

STUDY00016228

Identifier Type: -

Identifier Source: org_study_id