Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0–3 and the development of a machine learning prediction model: a nation
The eGDR index outperforms six other IR indices in predicting CVD, heart disease, and stroke in individuals with CKM syndrome stages 0-3. Its incorporation into predictive models enhances risk stratification and may aid in the early identification of high-risk individuals in this population. Further studies are needed to validate these findings in external cohorts.
Authors
- Enfa Zhao (ORCID: https://orcid.org/0000-0002-8028-2997)
- Dong Chen (ORCID: https://orcid.org/0000-0001-6495-678X)
- Bingtian Dong (ORCID: https://orcid.org/0000-0003-0268-4445)
- Xiaocen Yang
- Yuan Gao (ORCID: https://orcid.org/0009-0003-1683-6638)
- Hua Zhang (ORCID: https://orcid.org/0000-0003-3501-8385)
- Yuping Chen (ORCID: https://orcid.org/0000-0001-7127-8323)
- Chaoxue Zhang
Institutions
- Dalian Medical University (CN)
- Anhui Medical University (CN)
- Westlake University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Zhongda Hospital Southeast University (CN)
- Zhongshan Hospital of Xiamen University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN)
- State Key Laboratory of Digital Medical Engineering (CN)
- Southeast University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1186/s12933-025-02729-1
- Citations
- 32
- Primary Topic
- Blood Pressure and Hypertension Studies
- Type
- article
- Field-Weighted Citation Impact
- 44.42