An observational study in Diabetes Mellitus Type 2, Breast Neoplasms and Low Back Pain, sponsored by Jae Yong Jeon, MD. Recruiting at 1 site in South Korea. Open to participants aged 30 Years to 65 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-09-24.
Sponsored by Jae Yong Jeon, MD · Observational
Among 569 studies in Diabetes Mellitus, Type 2 with a site in Korea, Republic of — 68 open to participants now, 82 under way, 24 of them phase 3. Diabetes Mellitus, Type 2 · Korea, Republic of.
Among the 569 studies in Diabetes Mellitus, Type 2 with a site in Korea, Republic of, this one is the 45th largest by planned enrollment of the 78 observational ones, is one of 28 that started in 2026, is one of 78 observational ones, reaches primary completion later than 34 of the 81 other live ones, and was the 552nd registered.
10,371 studies on the registry are indexed under Diabetes Mellitus, Type 2; 1,406 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 300 across 1,741 observational studies indexed under Diabetes Mellitus, Type 2.
Browse Diabetes Mellitus, Type 2 studies →68 of the 569 studies in Diabetes Mellitus, Type 2 with a site in Korea, Republic of are open now, in 12 countries. Each count lists the studies.
Of the 883 Diabetes Mellitus, Type 2 studies recruiting now, 373 (42%) set no upper age limit and 384 (43%) stop at 75 or younger; 52 take children under 18; 203 take healthy volunteers.
This study: 30 Years to 65 Years · all sexes · healthy volunteers too.
The full table on the hub →569 studies; the count open now beside each. This study's row is marked.
First in 2001; this study’s year is marked. 28 started in 2026.
Median enrollment 173 across the 491 interventional studies, 1,470 across the 78 observational. Of the 432 that completed or were terminated, 159 (37%) have results posted.
433 of 569 have an industry lead sponsor. The 10 most frequent:
This is the only study on the registry with Jae Yong Jeon, MD as lead sponsor.
Jae Yong Jeon, MD has 1 study filed under Diabetes Mellitus, Type 2 — fewer than ten, so there is no comparison to draw.
24 of the studies in Diabetes Mellitus, Type 2 with a site in Korea, Republic of that are under way reach primary completion within a year, 8 of them phase 3. Phase 3 first, then by date:
| Study | Primary completion |
|---|---|
| NCT06647745Phase 3THPharm Corp.A Randomized, Double-blind, Multi-center, Active-controlled, Phase 3 Clinical Trial to Evaluate the Efficacy and Safety of Combination Therapy of THP-00101, THP-00102, and THP-00103 in Subjects With T2DM and Essential Hypertension | 2026-11-04 |
| NCT06772168Phase 3CMG Pharmaceutical Co. LtdEvaluate the Efficacy and Safety of CMG190303 in Patients With Type 2 Diabetes and Dyslipidemia | 2026-11-04 |
| NCT06220773Phase 3Boryung Pharmaceutical Co., LtdA Study to Evaluate the Efficacy and Safety of BR1019A and BR1019B Combination Therapy | 2026-12 |
| NCT07093476Phase 3CelltrionEfficacy and Safety of Add-On Therapy With Empagliflozin in Patients With Type 2 Diabetes on a Background of Alogliptin and Metformin | 2026-12 |
| NCT06858878Phase 3AmgenEfficacy and Safety of Maridebart Cafraglutide in Adult Participants With Type 2 Diabetes Mellitus Who Have Obesity or Are Overweight | 2027-01-21 |
Among the 569 studies in Diabetes Mellitus, Type 2 with a site in Korea, Republic of, from their registry updates:
This study aims to develop and validate an artificial intelligence (AI)-based predictive model to estimate the risk of incident onset of five major diseases or conditions: cardiovascular disease, type 2 diabetes mellitus, breast cancer, low back pain, and osteoarthritis, in adults aged 30 to 65 years.
For each participant, an index date will be defined as the date of a prior health screening or another protocol-defined baseline clinical date. Incident disease status for each target disease or condition will be ascertained by retrospective review of electronic medical records for up to 10 years after the index date.
The study integrates retrospective clinical, health screening, laboratory, imaging, and electronic medical record data with prospectively collected biospecimen, proteomic, genomic, questionnaire, lifestyle, and digital health data. Prospective study procedures will be completed over approximately 1 week, with up to 2 additional weeks if needed.
By combining multimodal data, this study seeks to improve disease risk prediction and to identify clinical and biological factors associated with disease onset, ultimately supporting personalized risk stratification and preventive healthcare strategies.
This observational study aims to develop and validate an artificial intelligence (AI)-based predictive model for assessing the risk of incident onset of five major diseases or conditions: cardiovascular disease, type 2 diabetes mellitus, breast cancer, low back pain, and osteoarthritis, in adults aged 30 to 65 years.
The study uses a hybrid retrospective and prospective data collection design. Retrospective clinical, health screening, laboratory, imaging, and electronic medical record data will be combined with prospectively collected biospecimen, proteomic, genomic, questionnaire, lifestyle, and digital health data.
For disease-onset analyses, an index date will be defined for each participant as the date of a prior health screening or another protocol-defined baseline clinical date. For each target disease or condition, participants without that target disease or condition at the index date will be classified as incident cases if a new diagnosis is identified in electronic medical records up to 10 years after the index date. Participants without a diagnosis of that target disease or condition through the available observation period will be classified as persistent controls. Disease occurrence will be ascertained through retrospective electronic medical record review rather than through new prospective long-term follow-up.
A total of approximately 1,000 participants will be enrolled. The disease group will include approximately 700 adults aged 30 to 65 years with a confirmed diagnosis of one or more of the five target diseases or conditions. The healthy control group will include approximately 300 adults aged 30 to 65 years without a prior diagnosis of any of the five target diseases or conditions.
Retrospective data collection will include medical records, health screening results, laboratory results, and imaging-related data. Prospective data collection will include blood samples for proteomic and genomic analyses, questionnaires, lifestyle and behavioral data, and digital health assessments. App-based questionnaires and digital assessments will be performed at home over approximately 7 days. If app-based sleep assessment or other digital assessments are not completed within this period, up to 2 additional weeks may be provided.
These multimodal data will be integrated to create a high-dimensional phenomic and omics dataset for AI model development. Machine learning and deep learning approaches will be applied to predict disease risk for each target disease or condition. Model performance will be evaluated using discrimination, diagnostic performance, and calibration metrics. Reclassification metrics will be evaluated only if a prespecified comparator risk score is available for the relevant target disease or condition.
The study aims to improve prediction of disease onset and to enhance understanding of biological and clinical factors associated with disease risk. The resulting model is expected to support personalized risk stratification and preventive healthcare strategies.
This study will involve two distinct groups of participants: a disease group and a healthy control group.
Disease Group:
The disease group will consist of adults aged 30 to 65 years who have been diagnosed with at least one of the following conditions:
Type 2 diabetes mellitus Breast cancer Cardiovascular disease Osteoarthritis Low back pain These participants will undergo questionnaires, digital assessments, physical examinations, and blood tests as part of the study.
The healthy control group will consist of adults aged 30 to 65 years with no prior diagnosis of any of the conditions listed above (type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain).
This group will also undergo similar assessments including questionnaires, physical examinations, and blood tests, but they will not have the aforementioned conditions.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Adults aged 30 to 65 years with one or more of the five major diseases. Five major diseases are Cardiovascular Diseases, Diabetes Mellitus, Type 2, Breast Neoplasms, Low Back Pain and Osteoarthritis.
Adults aged 30 to 65 years without five major diseases.
Number of Participants With Incident Target Disease or Condition Identified Up to 10 Years After the Index Date
Incident target disease or condition will be assessed for five prespecified target diseases or conditions: cardiovascular disease, type 2 diabetes mellitus, breast cancer, low back pain, and osteoarthritis. For each target disease or condition, incident occurrence will be defined as a new diagnosis recorded in electronic medical records after the index date among participants without that target disease or condition at the index date. Results will be summarized separately for each target disease or condition as the number and percentage of participants with incident disease or condition.
Time frame: Up to 10 years after the index date
Discriminative performance of the artificial intelligence model in distinguishing between disease and control groups using baseline data from health screenings and clinical records (AUROC, PR-AUC)
Discriminative performance will be assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PR-AUC). These metrics will evaluate the ability of the artificial intelligence model to distinguish participants with incident target disease or condition from persistent controls. Incident disease status will be ascertained by retrospective electronic medical record review up to 10 years after the index date.
Time frame: Through study completion, approximately 9 months
Diagnostic Performance of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Diagnostic performance will be assessed using sensitivity, specificity, positive predictive value, and negative predictive value at a prespecified risk threshold. These metrics will evaluate the ability of the artificial intelligence model to classify participants with incident target disease or condition and persistent controls. Incident disease status will be ascertained by retrospective electronic medical record review up to 10 years after the index date.
Time frame: Through study completion, approximately 9 months
Brier Score of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
The Brier score will be calculated as the mean squared difference between predicted risk and observed incident disease status for the five target diseases or conditions. Incident disease status will be ascertained by retrospective electronic medical record review up to 10 years after the index date. Lower values indicate better prediction accuracy.
Time frame: Through study completion, approximately 9 months
Calibration Slope of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Calibration slope will be estimated by comparing predicted risk with observed incident disease status for the five target diseases or conditions. Incident disease status will be ascertained by retrospective electronic medical record review up to 10 years after the index date. A value close to 1 indicates better calibration.
Time frame: Through study completion, approximately 9 months
Calibration Intercept of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Calibration intercept will be estimated by comparing predicted risk with observed incident disease status for the five target diseases or conditions. Incident disease status will be ascertained by retrospective electronic medical record review up to 10 years after the index date. A value close to 0 indicates better calibration-in-the-large.
Time frame: Through study completion, approximately 9 months
Plan to share: No
No publications or documents are linked to this record.
Started Apr 2026 (actual). Primary completion is estimated for Oct 2026, 6 months after the start.
Of 432 non-industry observational studies under Diabetes Mellitus, Type 2, half reached primary completion within 35 months of starting (the median), counting those still running; a quarter within 17 and three quarters within 96. This study's planned 6 months is below that.
Counting every sponsor, half of 602 studies reached it within 32 months; a quarter within 17 and three quarters within 62.
How these figures are worked out, and which studies are left out of them.
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