Novel metabolic syndrome subgroups and their association with cardiovascular outcomes: A data‐driven cluster analysis of the <scp>UK</scp> Biobank
This clustering reveals clinical heterogeneity within the MS population. Identifying these subgroups provides a more nuanced approach to risk assessment than traditional criteria counting. This framework may help clinicians to better identify high-risk individuals who could benefit from more intensive monitoring and targeted management strategies.
Authors
- YONGIN CHO
- So Hun Kim (ORCID: https://orcid.org/0000-0002-2554-3664)
- Dae Hyung Lee
- Jong Ho Jhee
- Hye‐Sun Park
- Young Ju Suh
- Jong Hyun Jhee
Institutions
- Inserm (FR)
- Inha University (KR)
- Yonsei University (KR)
- Université Paris Cité (FR)
- Sorbonne Paris Cité (FR)
- Gangnam Severance Hospital (KR)
- Inha University Hospital (KR)
Publication Details
- Journal
- Diabetes Obesity and Metabolism
- Published
- 2026-01-12
- DOI
- https://doi.org/10.1111/dom.70424
- Primary Topic
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Type
- article
- Field-Weighted Citation Impact
- 0.00