Artificial Intelligence-based Methods to Predict Disease Progression in Youth With Type 2 Diabetes
Public ClinicalTrials.gov record NCT07116902. Field values are reproduced from the official study page; the official ClinicalTrials.gov record remains the source of truth for eligibility, enrollment, and contact information.
Data is sourced from official ClinicalTrials.gov public API records. Always review the official ClinicalTrials.gov record for the latest information.
Official title
Artificial Intelligence-based Methods to Predict Disease Progression in Youth With Type 2 Diabetes: A Digital Twin Study
Brief summary
Reproduced verbatim from the official ClinicalTrials.gov record. Not medical advice.
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).
Study identification
- NCT ID
- NCT07116902
- Recruitment status
- Not yet recruiting
- Study type
- Interventional
- Phase
- Not applicable
- Enrollment
- 50 participants
Conditions and interventions
Conditions
Eligibility (public fields only)
- Age range
- 10 Years to 21 Years
- Sex
- All
- Healthy volunteers
- Healthy volunteers not accepted
This page does not interpret eligibility. Detailed inclusion and exclusion criteria are on the official ClinicalTrials.gov record.
Study timeline
- Start date
- Mar 31, 2026
- Primary completion
- Aug 31, 2026
- Completion
- Aug 31, 2026
- Last update posted
- Dec 3, 2025
2026
United States locations
- U.S. sites
- 2
- U.S. states
- 1
- U.S. cities
- 2
| Facility | City | State | ZIP | Site status |
|---|---|---|---|---|
| UCSF Benioff Children's Hospital Oakland, Pediatric Diabetes Clinic | Oakland | California | 94609 | — |
| UCSF Benioff Children's Hospital San Francisco, Madison Clinic for Pediatric Diabetes | San Francisco | California | 94158 | — |
Site contact phone numbers, emails, and investigator names are intentionally not displayed here. Open the official ClinicalTrials.gov record for site contact information.
About this trial record page
- What this page shows
- Public field values for ClinicalTrials.gov record NCT07116902, including study identification, conditions, interventions, eligibility (age, sex, healthy volunteer), timeline, and U.S. site list.
- What this page does not do
- No medical advice, eligibility judgments, treatment recommendations, study quality scoring, or AI-generated medical summaries. No site contact phone numbers, emails, or investigator names.
- Where the data comes from
- Sourced from the official ClinicalTrials.gov public API. The official record is the source of truth.
- Last refresh
- Last update posted Dec 3, 2025 · Synced Sep 2, 2026
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Open the official record
The complete protocol, eligibility criteria, and contact information for NCT07116902 live on ClinicalTrials.gov.