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

12 maja 2026 zaktualizowane przez: Jae Yong Jeon, MD

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.

Przegląd badań

Status

Rekrutacyjny

Warunki

Szczegółowy opis

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 60 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 880 adults aged 30 to 60 years with a confirmed diagnosis of one or more of the five target diseases or conditions. The healthy control group will include approximately 120 adults aged 30 to 60 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.

Typ studiów

Obserwacyjny

Zapisy (Szacowany)

1000

Kontakty i lokalizacje

Ta sekcja zawiera dane kontaktowe osób prowadzących badanie oraz informacje o tym, gdzie badanie jest przeprowadzane.

Kontakt w sprawie studiów

Lokalizacje studiów

  • Korea Południowa
    • Seoul Special City
      • Seoul, Seoul Special City, Korea Południowa, 05505
        • Rekrutacyjny
        • Seoul ASAN Medical Center
        • Kontakt:

Kryteria uczestnictwa

Badacze szukają osób, które pasują do określonego opisu, zwanego kryteriami kwalifikacyjnymi. Niektóre przykłady tych kryteriów to ogólny stan zdrowia danej osoby lub wcześniejsze leczenie.

Kryteria kwalifikacji

Wiek uprawniający do nauki

  • Dorosły

Akceptuje zdrowych ochotników

Tak

Metoda próbkowania

Próbka bez prawdopodobieństwa

Badana populacja

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 60 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 60 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.

Opis

Inclusion Criteria:

  1. Adults aged 30 to 60 years.
  2. 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.
  3. Healthy control group: Participants with no prior diagnosis of type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain.
  4. 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.
  5. Ability to understand the study procedures and provision of written informed consent prior to participation.

Exclusion Criteria:

  1. Participants with incomplete or insufficient clinical or health screening data.
  2. Participants considered inappropriate for study participation by the investigator.

Plan studiów

Ta sekcja zawiera szczegółowe informacje na temat planu badania, w tym sposób zaprojektowania badania i jego pomiary.

Jak projektuje się badanie?

Szczegóły projektu

Kohorty i interwencje

Grupa / Kohorta
Disease Group

Adults aged 30 to 60 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 60 years without five major diseases.

Co mierzy badanie?

Podstawowe miary wyniku

Miara wyniku
Opis środka
Ramy czasowe
Number of Participants With Incident Target Disease or Condition Identified Up to 10 Years After the Index Date
Ramy czasowe: 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.
Up to 10 years after the index date

Miary wyników drugorzędnych

Miara wyniku
Opis środka
Ramy czasowe
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)
Ramy czasowe: Through study completion, approximately 9 months
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.
Through study completion, approximately 9 months
Diagnostic Performance of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Ramy czasowe: Through study completion, approximately 9 months
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.
Through study completion, approximately 9 months
Brier Score of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Ramy czasowe: Through study completion, approximately 9 months
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.
Through study completion, approximately 9 months
Calibration Slope of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Ramy czasowe: Through study completion, approximately 9 months
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.
Through study completion, approximately 9 months
Calibration Intercept of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Ramy czasowe: Through study completion, approximately 9 months
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.
Through study completion, approximately 9 months

Współpracownicy i badacze

Tutaj znajdziesz osoby i organizacje zaangażowane w to badanie.

Sponsor

Jae Yong Jeon, MD

Daty zapisu na studia

Daty te śledzą postęp w przesyłaniu rekordów badań i podsumowań wyników do ClinicalTrials.gov. Zapisy badań i zgłoszone wyniki są przeglądane przez National Library of Medicine (NLM), aby upewnić się, że spełniają określone standardy kontroli jakości, zanim zostaną opublikowane na publicznej stronie internetowej.

Główne daty studiów

Rozpoczęcie studiów (Rzeczywisty)

2 kwietnia 2026

Zakończenie podstawowe (Szacowany)

31 października 2026

Ukończenie studiów (Szacowany)

31 grudnia 2026

Daty rejestracji na studia

Pierwszy przesłany

18 kwietnia 2026

Pierwszy przesłany, który spełnia kryteria kontroli jakości

12 maja 2026

Pierwszy wysłany (Rzeczywisty)

19 maja 2026

Aktualizacje rekordów badań

Ostatnia wysłana aktualizacja (Rzeczywisty)

19 maja 2026

Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości

12 maja 2026

Ostatnia weryfikacja

1 maja 2026

Więcej informacji

Terminy związane z tym badaniem

Dodatkowe istotne warunki MeSH

Inne numery identyfikacyjne badania

  • 2026-0205

Plan dla danych uczestnika indywidualnego (IPD)

Planujesz udostępniać dane poszczególnych uczestników (IPD)?

NIE

Informacje o lekach i urządzeniach, dokumenty badawcze

Bada produkt leczniczy regulowany przez amerykańską FDA

Nie

Bada produkt urządzenia regulowany przez amerykańską FDA

Nie

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