Artificial Intelligence-based Methods to Predict Disease Progression in Youth With Type 2 Diabetes

Part of paid clinical trials in Oakland, California.

Sponsor
University of California, San Francisco
Study ID
NCT07116902
Status
Not Yet Recruiting

Notify me when recruiting opens

Save your spot on the interest list for this study. We'll keep your details with this study so our team can follow up when recruiting opens.

Not yet recruiting

Add your contact details and location so we can keep your interest tied to this study.

Conditions

Eligibility Criteria

Sex
ALL
Age
10 Years - 21 Years
Healthy Volunteers
Not accepted

Interventions

  • phone application — DEVICE
    Participants in the digital twin arm will receive information on their disease progression which will be based on projected change in HbA1C in alternative realities and specific recommendations on medication dosing and lifestyle changes based on this data. The digital twin information will be presented on an iPad in a game- like manner. The alternate realities will include scenarios of change in medication adherence, physical activity metrics, dietary changes etc.
  • Standard of Care (SOC) — OTHER
    Participants in the control arm will receive standard of care which is medication change recommendations based on HbA1C and blood glucose values every 3 months and standard lifestyle education.

Study Details

Currently, clinicians are unable to predict a patient's risk of long-term disease progression and development of a long-term complication based on the data that is available to them. The first aim of this is to develop and validate an Artificial Intelligence (AI) powered prediction model for Type 2 Diabetes (T2D) disease progression using existing data from previously collected studies and real-world electronic health medical data. Investigators will use clinical, pharmacologic, and genomic factors to develop the prediction model based on the most relevant clinical outcomes of change in Hemoglobin A1c (HbA1c) and the development of a microvascular complication. Despite the availability of newer medication options, lifestyle intervention is not effective in most youth and current therapeutic options are ineffective at producing sustained glycemic control. Newer and innovative methods are needed to identify the youth at highest risk of progression in terms of increase in HbA1c and development of long-term complications and to motivate behavioral change in youth. The goal of this aim is to create an AI-powered digital twin model for 50 youth with T2D using their baseline clinical, genetic, pharmacologic and lifestyle data and utilize AI algorithms developed in Aim 1 to simulate disease progression and treatment response. Investigators will then evaluate the digital twin model in an randomized controlled trail and prospectively compare the generated digital twin data to observed values over one year. Investigators will also measure whether knowledge of the digital twin prediction with targeted healthcare recommendations influence medication and lifestyle change adherence in the digital twin arm (n= 25) compared to the control arm (n= 25).

Key Dates

Start date
Apr 30, 2026
Status verified
Dec 2025
Primary completion
Sep 30, 2026
Completion
Sep 30, 2026

Study Design

Enrollment
50 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC

Arms

  • Experimental: Digital twin arm
    Participants in the digital twin arm will receive information on their disease progression which will be based on projected change in HbA1C in alternative realities and specific recommendations on medication dosing and lifestyle changes based on this data. The digital twin information will be presented on an iPad in a game- like manner. The alternate realities will include scenarios of change in medication adherence, physical activity metrics, dietary changes etc.
  • Placebo Comparator: Control arm
    Participants in the control arm will receive standard of care which is medication change recommendations based on HbA1C and blood glucose values every 3 months and standard lifestyle education.

Primary Outcome Measure

Change in HbA1C [ Time Frame: From enrollment to the close out visit at the 1-year mark ]

Central Contacts

Locations (2)

FacilityCityStateZIPSite coordinators
UCSF Benioff Children's Hospital Oakland, Pediatric Diabetes ClinicOaklandCalifornia94609
Shylaja A Srinivasan, MD
[email protected]
415-353-9084
Laura A Dapkus Humphries, NCPT
[email protected]
628-224-8364
Shylaja A Srinivasan, MD (PRINCIPAL_INVESTIGATOR)
UCSF Benioff Children's Hospital San Francisco, Madison Clinic for Pediatric DiabetesSan FranciscoCalifornia94158
Shylaja A Srinivasan, MD
[email protected]
415-353-9084
Laura A Dapkus Humphries, NCPT
[email protected]
628-224-8364

Find similar trials in Oakland, CA

By condition
Type 2 diabetes
By specialty
EndocrinologyMetabolic healthWeight loss
By research site
UCSF Benioff Children's Hospital Oakland, Pediatric Diabetes Clinic· Oakland, CAUCSF Benioff Children's Hospital San Francisco, Madison Clinic for Pediatric Diabetes· San Francisco, CA

Related Studies