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 nationwide prospective cohort study
Authors' conclusion
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.
Study context
- Population
- Individuals with CKM syndrome stages 0–3, China Health and Retirement Longitudinal Study (CHARLS); mean age 73.46 ± 9.93 years, 50.4% females; categorized by estimated glucose disposal rate (eGDR) quartiles
- Participants
- 4,950 individuals
- Exposure
- eGDR vs six other insulin resistance indices for predicting incident cardiovascular diseases (CVD)
- Outcome
- Incident CVD (heart disease and stroke)
- Follow-up
- 2011–2018
Methodological detailsnationwide, machine learning prediction model
- Nationwide
- Machine learning prediction model
Main finding
As reported in the abstract
Incident CVD, heart disease and stroke · 2011–2018
- Developed CVD
- 697 (14.1%)
- Heart disease
- 486 (9.8%)
- Stroke
- 263 (5.3%)
- Lower risk of CVD per 1-unit increase in eGDR (fully adjusted)
- 14%
- Lower risk of heart disease per 1-unit increase in eGDR (fully adjusted)
- 14%
- Lower risk of stroke per 1-unit increase in eGDR (fully adjusted)
- 19%
Observational data: associations, not effects.
Further findings
Prediction performance of machine learning models incorporating eGDR
- Incorporating eGDR significantly improved prediction of CVD events; AUC of ROC and precision-recall (PR) curves in training and testing sets
- ≥0.90
- Seven machine learning models
Original publication
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 nationwide prospective cohort study. Cardiovasc Diabetol. Published 16 April 2025. doi:10.
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