Clinical trial NCT06002048: AI Ready and Exploratory Atlas for Diabetes Insights
Official title: AI Ready and Exploratory Atlas for Diabetes Insights
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Type 2 Diabetes
Keywords: Machine Learning
Type 2 Diabetes
Retinal Imaging
Data Sharing
Equitable Data Collection
Lead sponsor: University of Washington
Sponsor class: OTHER
Collaborators: name: National Institutes of Health (NIH)
First posted: 2023-08-21
Enrollment: 4000
Sex: ALL
Minimum age (years): 40
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients will be recruited into one of four groups: 1) healthy/no diabetes, 2) pre-diabetes/borderline diabetes/diet-controlled diabetes, 3) oral medication and/or non-insulin injectable medication controlled type 2 diabetes, or 4) insulin dependent type 2 diabetes. The investigators aim to recruit approximately 1000 patients into each of the four groups. Patients will be recruited from University of Washington (UW), University of California at San Diego (UCSD), and University of Alabama at Birmingham (UAB). The study aims to recruit 1,000 subjects from each of the following racial and ethnic groups: white, Asian, Hispanic, and Black. Subjects will be age- and sex-matched within and between groups.

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).

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

Primary outcomes:
measure: Best-corrected visual acuity
timeFrame: July 19, 2023-January 1, 2027
measure: Contrast Sensitivity
timeFrame: July 19, 2023-January 1, 2027
measure: Optical coherence tomography (OCT)
timeFrame: July 19, 2023-January 1, 2027
measure: fundus photography
timeFrame: July 19, 2023-January 1, 2027
measure: fluorescence lifetime imaging ophthalmoscopy (FLIO)
timeFrame: July 19, 2023-January 1, 2027
measure: optical coherence tomography angiography (OCTA)
timeFrame: July 19, 2023-January 1, 2027
measure: Continuous Glucose Monitoring
timeFrame: July 19, 2023-January 1, 2027
measure: Home humidity
timeFrame: July 19, 2023-January 1, 2027
measure: Home temperature
timeFrame: July 19, 2023-January 1, 2027
measure: Volatile Organic Compounds (VOC) in home
timeFrame: July 19, 2023-January 1, 2027
measure: Fine particulate matter that are 2.5 microns or less in diameter (PM2. 5) in home
timeFrame: July 19, 2023-January 1, 2027
measure: Montreal Cognitive Assessment (MoCA)
timeFrame: July 19, 2023-January 1, 2027
Secondary outcomes:
Other outcomes:
Locations:
city: Birmingham
country: United States
facility: University of Alabama, Birmingham
state: Alabama
city: San Diego
country: United States
facility: UC San Diego
state: California
city: Seattle
country: United States
facility: University of Washington
state: Washington
Study officials:
affiliation: University of Washington
name: Aaron Lee
role: PRINCIPAL_INVESTIGATOR
References: