AI Ready and Exploratory Atlas for Diabetes Insights
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Type 2 Diabetes. Basic parameters: 40 years — 85 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Overview
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).
Primary outcome measures
- Best-corrected visual acuity [Time frame: July 19, 2023-January 1, 2027]
- Contrast Sensitivity [Time frame: July 19, 2023-January 1, 2027]
- Optical coherence tomography (OCT) [Time frame: July 19, 2023-January 1, 2027]
- fundus photography [Time frame: July 19, 2023-January 1, 2027]
- fluorescence lifetime imaging ophthalmoscopy (FLIO) [Time frame: July 19, 2023-January 1, 2027]
- optical coherence tomography angiography (OCTA) [Time frame: July 19, 2023-January 1, 2027]
- Continuous Glucose Monitoring [Time frame: July 19, 2023-January 1, 2027]
- Home humidity [Time frame: July 19, 2023-January 1, 2027]
- Home temperature [Time frame: July 19, 2023-January 1, 2027]
- Volatile Organic Compounds (VOC) in home [Time frame: July 19, 2023-January 1, 2027]
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
United States · 3 centers
- University of Alabama, Birmingham — Birmingham
- UC San Diego — San Diego
- University of Washington — Seattle
Identifiers
NCT: NCT06002048 · STUDY00016228 · 3OT2OD032644-01S3