Ignite Creation Date:
2025-12-25 @ 3:54 AM
Ignite Modification Date:
2025-12-26 @ 2:44 AM
Study NCT ID:
NCT07116902
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-04
First Post:
2025-06-13
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Artificial Intelligence-based Methods to Predict Disease Progression in Youth With Type 2 Diabetes
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003924', 'term': 'Diabetes Mellitus, Type 2'}], 'ancestors': [{'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D059039', 'term': 'Standard of Care'}], 'ancestors': [{'id': 'D019984', 'term': 'Quality Indicators, Health Care'}, {'id': 'D011787', 'term': 'Quality of Health Care'}, {'id': 'D006298', 'term': 'Health Services Administration'}, {'id': 'D017530', 'term': 'Health Care Quality, Access, and Evaluation'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 50}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-04', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-12', 'completionDateStruct': {'date': '2026-09', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-02', 'studyFirstSubmitDate': '2025-06-13', 'studyFirstSubmitQcDate': '2025-08-06', 'lastUpdatePostDateStruct': {'date': '2025-12-04', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-08-12', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-09', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Change in HbA1C', 'timeFrame': 'From enrollment to the close out visit at the 1-year mark', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Sleep Quality', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': 'Change in measures related to sleep quality. (Actigraph measured duration, Insomnia Severity Index (ISI)).\n\nRespondents 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.\n\nScores are tallied and can be compared both to scores obtained at a different phase of treatment and to the scores of other individuals.\n\nA total score of:\n\n0-7 indicates "no clinically significant insomnia" 8-14 means "sub-threshold insomnia" 15-21 means "clinical insomnia (moderate severity)" 22-28 means "clinical insomnia (severe)"'}, {'measure': 'Psycho-Social Outcome', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': 'Change in measures related to psycho-social outcomes for Quality of Life. (PedsQL-Diabetes Module).\n\nThe 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.\n\nIt assesses various aspects of life affected by diabetes, including physical symptoms, treatment barriers, and emotional well-being.'}, {'measure': 'Sugar Intake', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': 'Change in measures related to sugar intake (g/day).'}, {'measure': 'Physical Activity', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': 'Change in measures related to physical activity (days/week)'}, {'measure': 'BMI', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': 'Change in BMI (BMI z-score).'}, {'measure': 'CGM Time In Range', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': 'Change in CGM based measures including time in the target glucose range of 70 to 180 mg/dL.'}, {'measure': 'CGM Time Above Range', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': '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.'}, {'measure': 'Psycho-Social Outcome', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': 'Change in measures related to psycho-social outcomes for Diabetes Distress. (Diabetes Distress Scale).\n\nDiabetes Distress Scale (DDS) is used to assess the level of emotional distress related to managing diabetes.\n\n* Individual Item Score: Each item on the DDS is rated on a 6-point scale.\n* Minimum: 1 (not a problem)\n* Maximum: 6 (a very significant problem)\n* Total Score: The DDS yields an overall distress score calculated by averaging the responses to the individual items.\n* Minimum: 1\n* Maximum: 6\n* Interpretation of Scores:\n* \\< 2.0: Little or no distress\n* 2.0-2.9: Moderate distress\n* ≥ 3.0: High distress'}, {'measure': 'Psycho-Social Outcome', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': 'Change in measures related to psycho-social outcomes for Overall QOL and Wellbeing using the Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health questionnaire.\n\n* PROMIS Global Health is a broader measure of overall physical and mental health.\n* PROMIS Global Health scores are reported as T-scores, with a mean of 50 and a standard deviation of 10 in the US general population.\n* Higher T-scores indicate better physical and mental health. A score of 60 is one standard deviation above the mean, while 40 is one standard deviation below the mean.'}, {'measure': 'Psycho-Social Outcome', 'timeFrame': 'From time of enrollment to the study close out visit at the 1-year mark', 'description': 'Change in measures related to psycho-social outcomes for Overall QOL and Wellbeing using the World Health Organization- Five (WHO-5) questionnaire.\n\n* Measures well-being, specifically positive psychological states.\n* It consists of five statements relating to the past two weeks.\n* Aims to assess the subjective experience of well-being, capturing feelings of positive mood, energy, and interest. A short, 5-item questionnaire.\n* Typically uses a 6-point Likert scale for each item, with higher scores indicating better well-being.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isUnapprovedDevice': True, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'keywords': ['T2D', 'Type 2 Diabetes', 'AI', 'Artificial Intelligence', 'CGM'], 'conditions': ['Type 2 Diabetes']}, 'descriptionModule': {'briefSummary': "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.\n\nDespite 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)."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT'], 'maximumAge': '21 Years', 'minimumAge': '10 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age 10- 21 years\n* Diagnosis of T2D based on clinical diagnosis or ICD 9 and 10 codes\n* Duration of T2D ≥ 3 months\n* HbA1C ≥ 7% which is the target HbA1C recommended by the American Diabetes Association\n* Stable medication regimen (No medication changes and no change in basal insulin dose by more than 20% in the 2 weeks prior to enrollment)\n* Ability to wear CGM for a total of 6 weeks while in the study.\n* English or Spanish speakers.\n* Willing to abide by recommendations and study procedures.\n* Willing and able to sign the Informed Consent Form (ICF) and/or has a parent or guardian willing and able to sign the ICF.\n\nExclusion Criteria:\n\n* Pancreatic autoantibody positivity (GAD-65, insulin, IA-2, ICA 512, ZnT8).\n* Plan for undergoing bariatric surgery during the study period\n* Anticipated use of systemic glucocorticoids during the study period\n* Unable to stop taking more than 500mg/day of Vitamin C during the study period as this may affect the sensor readings.\n* 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.\n* Presence of a condition or abnormality that in the opinion of the Investigator would cause repeated hospitalizations or significant changes in medications.'}, 'identificationModule': {'nctId': 'NCT07116902', 'briefTitle': 'Artificial Intelligence-based Methods to Predict Disease Progression in Youth With Type 2 Diabetes', 'organization': {'class': 'OTHER', 'fullName': 'University of California, San Francisco'}, 'officialTitle': 'Artificial Intelligence-based Methods to Predict Disease Progression in Youth With Type 2 Diabetes: A Digital Twin Study', 'orgStudyIdInfo': {'id': '24-42259'}, 'secondaryIdInfos': [{'id': '#7-24-ICTST2DY-05', 'type': 'OTHER_GRANT', 'domain': "American Diabetes Association's (ADA)"}]}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Digital twin arm', 'description': '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.', 'interventionNames': ['Device: phone application']}, {'type': 'PLACEBO_COMPARATOR', 'label': 'Control arm', 'description': '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.', 'interventionNames': ['Other: Standard of Care (SOC)']}], 'interventions': [{'name': 'phone application', 'type': 'DEVICE', 'description': '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.', 'armGroupLabels': ['Digital twin arm']}, {'name': 'Standard of Care (SOC)', 'type': 'OTHER', 'description': '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.', 'armGroupLabels': ['Control arm']}]}, 'contactsLocationsModule': {'locations': [{'zip': '94609', 'city': 'Oakland', 'state': 'California', 'country': 'United States', 'contacts': [{'name': 'Shylaja A Srinivasan, MD', 'role': 'CONTACT', 'email': 'shylaja.srinivasan@ucsf.edu', 'phone': '415-353-9084'}, {'name': 'Laura A Dapkus Humphries, NCPT', 'role': 'CONTACT', 'email': 'laura.dapkus@ucsf.edu', 'phone': '628-224-8364'}, {'name': 'Shylaja A Srinivasan, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': "UCSF Benioff Children's Hospital Oakland, Pediatric Diabetes Clinic", 'geoPoint': {'lat': 37.80437, 'lon': -122.2708}}, {'zip': '94158', 'city': 'San Francisco', 'state': 'California', 'country': 'United States', 'contacts': [{'name': 'Shylaja A Srinivasan, MD', 'role': 'CONTACT', 'email': 'shylaja.srinivasan@ucsf.edu', 'phone': '415-353-9084'}, {'name': 'Laura A Dapkus Humphries, NCPT', 'role': 'CONTACT', 'email': 'laura.dapkus@ucsf.edu', 'phone': '628-224-8364'}], 'facility': "UCSF Benioff Children's Hospital San Francisco, Madison Clinic for Pediatric Diabetes", 'geoPoint': {'lat': 37.77493, 'lon': -122.41942}}], 'centralContacts': [{'name': 'Avani A Narayan, MS', 'role': 'CONTACT', 'email': 'avani.narayan@ucsf.edu', 'phone': '415-530-8047'}, {'name': 'Laura A Dapkus Humphries, NCPT', 'role': 'CONTACT', 'email': 'laura.dapkus@ucsf.edu', 'phone': '628-224-8364'}], 'overallOfficials': [{'name': 'Shylaja A Srinivasan, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University of California, San Francisco'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of California, San Francisco', 'class': 'OTHER'}, 'collaborators': [{'name': 'Stanford University', 'class': 'OTHER'}, {'name': 'American Diabetes Association', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}