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

NCT07116902 | Not Yet Recruiting FDA Device

• 2 other identifiers: 24-42259#7-24-ICTST2DY-05
• Study Type: interventional
• Target: 50
• Locations: 1 country
• Sites: 2

Brief Summary

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).

Conditions

Type 2 Diabetes

Keywords

T2D; Type 2 Diabetes; AI; Artificial Intelligence; CGM

Outcome Measures

Primary Outcomes (1)

• Change in HbA1C

  The primary outcome will be the ability of the digital twin model to accurately predict longitudinal disease progression measured as the digital twin predicted HbA1C versus measured HbA1C and the difference in HbA1C between the digital twin arm and control arms.

  From enrollment to the close out visit at the 1-year mark

Secondary Outcomes (10)

• Sleep Quality

  From time of enrollment to the study close out visit at the 1-year mark

• Psycho-Social Outcome

  From time of enrollment to the study close out visit at the 1-year mark

• Sugar Intake

  From time of enrollment to the study close out visit at the 1-year mark

• Physical Activity

  From time of enrollment to the study close out visit at the 1-year mark

• BMI

  From time of enrollment to the study close out visit at the 1-year mark

Study Arms (2)

Digital twin arm

EXPERIMENTAL

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.

Device: phone application

Control arm

PLACEBO COMPARATOR

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.

Other: Standard of Care (SOC)

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.

Digital twin arm

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.

Control arm

Eligibility Criteria

• Age: 10 Years - 21 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)

You may qualify if:

• 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.

You may not qualify if:

• Pancreatic autoantibody positivity (GAD-65, insulin, IA-2, ICA 512, ZnT8).
• Plan for undergoing bariatric surgery during the study period
• 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.
• 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.

Sponsors & Collaborators

• University of California, San Francisco: lead
• Stanford University: collaborator
• American Diabetes Association: collaborator

Study Sites (2)

UCSF Benioff Children's Hospital Oakland, Pediatric Diabetes Clinic

Oakland, California, 94609, United States

Location

UCSF Benioff Children's Hospital San Francisco, Madison Clinic for Pediatric Diabetes

San Francisco, California, 94158, United States

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 2

Interventions

Standard of Care

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Intervention Hierarchy (Ancestors)

Quality Indicators, Health Care; Quality of Health Care; Health Services Administration; Health Care Quality, Access, and Evaluation

Study Officials

• Shylaja A Srinivasan, MD

  University of California, San Francisco

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Avani A Narayan, MS

CONTACT

415-530-8047; avani.narayan@ucsf.edu

Laura A Dapkus Humphries, NCPT

CONTACT

628-224-8364; laura.dapkus@ucsf.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 13, 2025
• First Posted: August 12, 2025
• Study Start: April 1, 2026
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: December 4, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

US (2)