Diabetes Mellitus Type 2NCT07595718
AI-Based Phenome Data Analysis for Predicting the Onset of Major Diseases
An observational study of Diabetes Mellitus Type 2 and Breast Neoplasms, run by Jae Yong Jeon, MD at 1 site in South Korea.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 30 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Diabetes Mellitus Type 2, Breast Neoplasms, Low Back Pain
- How many people
- 1,000 participants
- Where
- 1 siteSouth Korea
- Study dates
- 2 Apr 2026to 31 Dec 2026
- How long will I take part?
- 1 Week
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 30 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: This study will involve two distinct groups of participants: a disease group and a healthy control group. 1. 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. 2. Healthy Control Group: 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.
Who may be able to join
- Adults aged 30 to 65 years.
- Disease group: Participants with a confirmed diagnosis of at least one of the following conditions: type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain.
- Healthy control group: Participants with no prior diagnosis of type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain.
- No history or current diagnosis of major medical conditions that may affect study outcomes, including but not limited to chronic kidney disease or liver cirrhosis.
- Ability to understand the study procedures and provision of written informed consent prior to participation.
Who cannot join
- Participants with incomplete or insufficient clinical or health screening data.
- Participants considered inappropriate for study participation by the investigator.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
South Korea1 site, 1 recruiting
- Recruiting
Seoul Asan Medical Center
Seoul, Seoul Special City, 05505
Jaeyong Jeon, +82-10-9970-3791, jyjeon71@gmail.com
About this study
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.
Read more
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.
Read the detailed description
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.
- Conditions
- Diabetes Mellitus Type 2, Breast Neoplasms, Low Back Pain, Osteoarthritis, Cardiovascular Diseases
- Medical subject headings
- Diabetes Mellitus, Type 2, Breast Neoplasms, Low Back Pain, Osteoarthritis, Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Disease Group | 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. | |
| Healthy Control Group | Adults aged 30 to 65 years without five major diseases. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Up to 10 years after the index date
Secondary outcomes (5)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Through study completion, approximately 9 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 May 2026
- Study start2 Apr 2026
- Primary completion31 Oct 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetes Mellitus Type 2Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
- Target duration
- 1 Week
Sponsor and investigators
- Lead sponsor
- Jae Yong Jeon, MD
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Jae Yong Jeon, MD, Asan Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07595718
- Sponsor's protocol ID
- 2026-0205
- Status last verified
- Sep 2026
- First submitted
- 18 Apr 2026
- Last updated
- 24 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Based Phenome Data Analysis for Predicting the Onset of Major Diseases
- ClinicalTrials.gov ID
- NCT07595718
- Acronym / sponsor ID
- 2026-0205
- Conditions
- Diabetes Mellitus Type 2, Breast Neoplasms, Low Back Pain, Osteoarthritis, Cardiovascular Diseases
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Jae Yong Jeon, MDUniversity, hospital or other
- Study start
- 2 Apr 2026 (actual)
- Primary completion
- 31 Oct 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 19 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Week
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Disease Group | – | –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. |
| Healthy Control Group | – | –Adults aged 30 to 65 years without five major diseases. |
Who may qualify
- Ages
- 30 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- This study will involve two distinct groups of participants: a disease group and a healthy control group. 1. 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. 2. Healthy Control Group: 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.
Key inclusion criteria (5 listed)
- Adults aged 30 to 65 years.
- Disease group: Participants with a confirmed diagnosis of at least one of the following conditions: type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain.
- Healthy control group: Participants with no prior diagnosis of type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain.
- No history or current diagnosis of major medical conditions that may affect study outcomes, including but not limited to chronic kidney disease or liver cirrhosis.
- Ability to understand the study procedures and provision of written informed consent prior to participation.
Key exclusion criteria (2 listed)
- Participants with incomplete or insufficient clinical or health screening data.
- Participants considered inappropriate for study participation by the investigator.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Number of Participants With Incident Target Disease or Condition Identified Up to 10 Years After the Index Date
Time frame: 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.
5 secondary outcomes
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)
Time frame: Through study completion, approximately 9 months
Diagnostic Performance of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Time frame: Through study completion, approximately 9 months
Brier Score of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Time frame: Through study completion, approximately 9 months
Calibration Slope of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Time frame: Through study completion, approximately 9 months
Calibration Intercept of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Time frame: Through study completion, approximately 9 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Sep 2026.
- Individual participant dataSharing planned: No
Related trials for Diabetes Mellitus Type 2
Active, not recruitingPhase 2
A Study on the Safety and Clinical Efficacy of Guominkang in the Treatment of Diabetic Kidney Disease
Diabetic Kidney Disease (DKD), Type 2 Diabetes
Recruiting
Culturally Tailored Program for Food-Insecure Adults: SPICE-D
Type 2 Diabetes Mellitus (T2D)
Not yet recruiting
Evaluation of the Effectiveness of Implementing a Screening Strategy for the Diagnosis of Prediabetes and Type 2 Diabetes Mellitus in Primary Health Care in a Brazilian Municipality
Diabetes Mellitus (Type 2), Prediabetes
Active, not recruitingPhase 1/2
A First-In-Human Study of ARO-INHBE in Adults With Obesity With and Without Type 2 Diabetes Mellitus
Obesity
Not yet recruiting
Role of Endothelial Dysfunction on Exercise Pressor Reflex in Type 2 Diabetes
Type 2 Diabetes (T2DM), Endothelial Dysfunction
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetes Mellitus Type 2 and Breast Neoplasms, run by Jae Yong Jeon, MD at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 24 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jaeyong Jeon (+82-2-3010-3791, jyjeon71@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of Participants With Incident Target Disease or Condition Identified Up to 10 Years After the Index Date (time frame: Up to 10 years after the index date). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Sep 2026.