NCT07116902Not yet recruiting
Artificial Intelligence-based Methods to Predict Disease Progression in Youth With Type 2 Diabetes
Artificial Intelligence-based Methods to Predict Disease Progression in Youth With Type 2 Diabetes: A Digital Twin Study
Source: ClinicalTrials.gov · Last updated 4 Dec 2025
Brief summary (registry, in English)
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).
- Status (registry)
- Not yet recruiting
- Study type
- Interventional
- Enrollment
- 50 (estimated)
- First submitted
- 13 Jun 2025
- Start date
- Apr 2026
- Primary completion date
- Sept 2026
- Lead sponsor
- University of California, San Francisco
- Diseases
- Type 2 diabetes
- NCT number
- NCT07116902
What you commit to
- Study type: Interventional
- Planned participants: 50
- From Apr 2026 to the primary completion date, Sept 2026
- 2 sites in 1 country
- Visits and tests: ask the trial team, the registry does not list them
Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.
Who cannot take part
Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.
Prior treatment
- Plan for undergoing bariatric surgery during the study period
Other illness
- Presence of a condition or abnormality that in the opinion of the Investigator would compromise the safety of the patient or the quality of the data.
- Presence of a condition or abnormality that in the opinion of the Investigator would cause repeated hospitalizations or significant changes in medications.
Other
- Pancreatic autoantibody positivity (GAD-65, insulin, IA-2, ICA 512, ZnT8).
- Anticipated use of systemic glucocorticoids during the study period
- Unable to stop taking more than 500mg/day of Vitamin C during the study period as this may affect the sensor readings.
Who the study is looking for
- Age 10- 21 years
- Diagnosis of T2D based on clinical diagnosis or ICD 9 and 10 codes
- Duration of T2D ≥ 3 months
- HbA1C ≥ 7% which is the target HbA1C recommended by the American Diabetes Association
- Stable medication regimen (No medication changes and no change in basal insulin dose by more than 20% in the 2 weeks prior to enrollment)
- Ability to wear CGM for a total of 6 weeks while in the study.
- English or Spanish speakers.
- Willing to abide by recommendations and study procedures.
- Willing and able to sign the Informed Consent Form (ICF) and/or has a parent or guardian willing and able to sign the ICF.
Age in the registry: 10 years to 21 years · Sex: All · Does not accept healthy volunteers
Sites (2)
Sites in registry order. Pick your city on the disease page to sort them by distance.
| Site | City | Country | Site status |
|---|---|---|---|
| UCSF Benioff Children's Hospital Oakland, Pediatric Diabetes Clinic Coordinator of this site? Confirm its status | Oakland | United States | Unknown status |
| UCSF Benioff Children's Hospital San Francisco, Madison Clinic for Pediatric Diabetes Coordinator of this site? Confirm its status | San Francisco | United States | Unknown status |
This page is information, not medical advice. Discuss any trial with your doctor.