This study is not yet accepting patients

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

a study on Diabetes Diabetes Type 2

Summary

Eligibility
for people ages 10-21 (full criteria)
Location
at Oakland, California and other locations
Dates
study started
study ends around
Principal Investigator
by Shylaja A Srinivasan, MD
Headshot of Shylaja A Srinivasan
Shylaja A Srinivasan

Description

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).

Official Title

Artificial Intelligence-based Methods to Predict Disease Progression in Youth With Type 2 Diabetes: A Digital Twin Study

Keywords

Type 2 Diabetes, T2D, AI, Artificial Intelligence, CGM, Diabetes Mellitus, Type 2 Diabetes Mellitus, Disease Progression, phone application

Eligibility

You can join if…

Open to people ages 10-21

  • 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 CAN'T join 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.

Locations

  • UCSF Benioff Children's Hospital Oakland, Pediatric Diabetes Clinic
    Oakland California 94609 United States
  • UCSF Benioff Children's Hospital San Francisco, Madison Clinic for Pediatric Diabetes
    San Francisco California 94158 United States

Lead Scientist at UCSF

  • Shylaja A Srinivasan, MD
    I am a clinican-scientist and Assistant Professor of Pediatrics in the Division of Pediatric Endocrinology at UCSF. My clinical and scientific focus is on prediabetes and type 2 diabetes in youth. Specifically, I use pharmacologic and genetic approaches to evaluate pathophysiology and therapeutic strategies for youth with prediabetes and type 2 diabetes.

Details

Status
not yet accepting patients
Start Date
Completion Date
(estimated)
Sponsor
University of California, San Francisco
ID
NCT07116902
Study Type
Interventional
Participants
Expecting 50 study participants
Last Updated