Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study
This study developed a noninvasive predictive model for metabolic syndrome with strong performance across diverse validation cohorts. By enabling early risk identification without laboratory tests, the model enhances accessibility in primary care and large-scale screenings. Its ability to predict long-term CVD risk supports proactive intervention strategies, potentially reducing the burden of cardiometabolic diseases. Further research should refine the model with additional clinical factors and broader population validation to maximize its clinical impact.
Authors
- Seogsong Jeong (ORCID: https://orcid.org/0000-0003-4646-8998)
- Jin-Hyun Park (ORCID: https://orcid.org/0009-0007-8062-7696)
- HwaMin Lee� (ORCID: https://orcid.org/0000-0002-6482-3511)
- Gang-Jee Ko (ORCID: https://orcid.org/0000-0001-7927-8651)
- Inyong Jeong (ORCID: https://orcid.org/0009-0009-1504-0463)
Institutions
- Korea University (KR)
- Korea University Medical Center (KR)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-05-02
- DOI
- https://doi.org/10.2196/67525
- Citations
- 5
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 9.39