Type 2 Diabetes
Conditions
Keywords
T2D, Type 2 Diabetes, AI, Artificial Intelligence, CGM
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).
Interventions
DEVICEphone application
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.
OTHERStandard of Care (SOC)
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.
Sponsors
Stanford University
American Diabetes Association
University of California, San Francisco
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
10 Years to 21 Years
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in HbA1C | From enrollment to the close out visit at the 1-year mark | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Psycho-Social Outcome | From time of enrollment to the study close out visit at the 1-year mark | Change in measures related to psycho-social outcomes for Quality of Life. (PedsQL-Diabetes Module). The Pediatric Quality of Life (PedsQL) Diabetes Module scores quality of life in children and adolescents with diabetes using a 0-100 scale, where higher scores indicate better quality of life. The PedsQL Diabetes Module uses a 5-point response scale (0-4) for each item. It assesses various aspects of life affected by diabetes, including physical symptoms, treatment barriers, and emotional well-being. |
| Sugar Intake | From time of enrollment to the study close out visit at the 1-year mark | Change in measures related to sugar intake (g/day). |
| Physical Activity | From time of enrollment to the study close out visit at the 1-year mark | Change in measures related to physical activity (days/week) |
| Sleep Quality | From time of enrollment to the study close out visit at the 1-year mark | Change in measures related to sleep quality. (Actigraph measured duration, Insomnia Severity Index (ISI)). Respondents rate each element of the questionnaire using Likert-type scales. Responses can range from 0 to 4, where higher scores indicate more acute symptoms of insomnia. Scores are tallied and can be compared both to scores obtained at a different phase of treatment and to the scores of other individuals. A total score of: 0-7 indicates no clinically significant insomnia 8-14 means sub-threshold insomnia 15-21 means clinical insomnia (moderate severity) 22-28 means clinical insomnia (severe) |
| CGM Time In Range | From time of enrollment to the study close out visit at the 1-year mark | Change in CGM based measures including time in the target glucose range of 70 to 180 mg/dL. |
| CGM Time Above Range | From time of enrollment to the study close out visit at the 1-year mark | Change in CGM based measures including time above the target glucose range of greater than 250 mg/dL for glucose and co-efficient of variation of glucose. |
| BMI | From time of enrollment to the study close out visit at the 1-year mark | Change in BMI (BMI z-score). |
Countries
United States
Contacts
Primary ContactAvani A Narayan, MS
Backup ContactLaura A Dapkus Humphries, NCPT
Outcome results
None listed