AI Ready and Equitable Atlas for Diabetes Insights

Type 2 Diabetes Clinical Trial

— AI-READI  

Official title:

AI Ready and Equitable Atlas for Diabetes Insights

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT06002048
• Other study ID #: STUDY00016228
• Secondary ID: 3OT2OD032644-01S
• Status: Enrolling by invitation
• Start date: July 19, 2023
• Est. completion date: January 1, 2027

Study information

• Verified date: August 2023
• Source: University of Washington
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Description:

The Artificial Intelligence Ready and Equitable 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).

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 4000
• Est. completion date: January 1, 2027
• Est. primary completion date: January 1, 2027
• Accepts healthy volunteers: No
• Gender: All
• Age group: 40 Years to 85 Years

Eligibility

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

Related Conditions & MeSH terms

• Diabetes Mellitus
• Diabetes Mellitus, Type 2
• Type 2 Diabetes

Locations

Country	Name	City	State
United States	University of Alabama, Birmingham	Birmingham	Alabama
United States	UC San Diego	San Diego	California
United States	University of Washington	Seattle	Washington

Sponsors (2)

Lead Sponsor	Collaborator
University of Washington	National Institutes of Health (NIH)

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Best-corrected visual acuity	Both photopic and mesopic for right and left eyes individually	July 19, 2023-January 1, 2027
Primary	Contrast Sensitivity	Both photopic and mesopic for right and left eyes individually	July 19, 2023-January 1, 2027
Primary	Optical coherence tomography (OCT)		July 19, 2023-January 1, 2027
Primary	fundus photography		July 19, 2023-January 1, 2027
Primary	fluorescence lifetime imaging ophthalmoscopy (FLIO)		July 19, 2023-January 1, 2027
Primary	optical coherence tomography angiography (OCTA)		July 19, 2023-January 1, 2027
Primary	Continuous Glucose Monitoring	Participants wear the Dexcom G6 Pro for 10 days	July 19, 2023-January 1, 2027
Primary	Home humidity		July 19, 2023-January 1, 2027
Primary	Home temperature	measured in Fahrenheit	July 19, 2023-January 1, 2027
Primary	Volatile Organic Compounds (VOC) in home		July 19, 2023-January 1, 2027
Primary	Fine particulate matter that are 2.5 microns or less in diameter (PM2. 5) in home		July 19, 2023-January 1, 2027
Primary	Montreal Cognitive Assessment (MoCA)	Testing memory and cognitive function. Scores range from 0-30, with scores above 26 indicating normal functioning.	July 19, 2023-January 1, 2027