AI Ready and Equitable Atlas for Diabetes Insights

Study Overview
=================
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 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).

Official Title
-----------------
AI Ready and Equitable Atlas for Diabetes Insights

Conditions
-----------------
Type 2 Diabetes

Participation Criteria
=================
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

Ages Eligible for Study
-----------------
Minimum Age: 40 Years
Maximum Age: 85 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Healthy<br>Participants who do not have Type 1 or Type 2 Diabetes | |
| Pre-diabetes/Diet Controlled<br>Participants with pre-Type 2 Diabetes and those with Type 2 Diabetes whose blood sugar is controlled by diet | |
| Oral Medication and/or Non-insulin-injectable Medication Controlled<br>Participants with Type 2 Diabetes whose blood sugar is controlled by oral or injectable medications other than insulin | |
| Insulin Dependent<br>Participants with Type 2 Diabetes whose blood sugar is controlled by insulin | |

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Best-corrected visual acuity | Both photopic and mesopic for right and left eyes individually | July 19, 2023-January 1, 2027 | 
| Contrast Sensitivity | Both photopic and mesopic for right and left eyes individually | July 19, 2023-January 1, 2027 | 
| Optical coherence tomography (OCT) |  | July 19, 2023-January 1, 2027 | 
| fundus photography |  | July 19, 2023-January 1, 2027 | 
| fluorescence lifetime imaging ophthalmoscopy (FLIO) |  | July 19, 2023-January 1, 2027 | 
| optical coherence tomography angiography (OCTA) |  | July 19, 2023-January 1, 2027 | 
| Continuous Glucose Monitoring | Participants wear the Dexcom G6 Pro for 10 days | July 19, 2023-January 1, 2027 | 
| Home humidity |  | July 19, 2023-January 1, 2027 | 
| Home temperature | measured in Fahrenheit | July 19, 2023-January 1, 2027 | 
| Volatile Organic Compounds (VOC) in home |  | July 19, 2023-January 1, 2027 | 
| Fine particulate matter that are 2.5 microns or less in diameter (PM2. 5) in home |  | July 19, 2023-January 1, 2027 | 
| 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 | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Data Sharing, Machine Learning, Type 2 Diabetes, Retinal Imaging, Equitable Data Collection