MedTrace Logo

AI-Based Phenome Data Analysis for Predicting the Onset of Major Diseases

NCT07595718 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2026-05-19

No results posted yet for this study

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 60 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.

Conditions

Sponsors & Collaborators

  • Jae Yong Jeon, MD

    lead OTHER

Eligibility

Min Age
30 Years
Max Age
60 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-04-02
Primary Completion
2026-10-31
Completion
2026-12-31

Countries

  • South Korea

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07595718 on ClinicalTrials.gov