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December 4, 2025Cardiorenal Medicine1 citationsOpen Access

Predicting stroke incidence using estimated glucose disposal rate index in the population with cardiovascular-kidney-metabolic syndrome stage 0-3 in the Chinese middle-aged and elderly population

QLQian LüXuzhou Medical College ZCZhiwei CaoXiamen University CCChristopher ChenRutgers, The State University of New Jersey

Key Points

Lower baseline estimated glucose disposal rate correlates with higher stroke risk, indicating insulin resistance's role.
In total, 383 stroke incidents were observed during follow-up, emphasizing significant health implications.
Using K-means clustering and Cox regression, the study identified risk factors and thresholds for effective stroke prediction.
Enhancing risk stratification can support interventions for metabolically at-risk populations, stressing tailored surveillance strategies.

Abstract

Stroke remains a leading cause of death and disability worldwide. Although individuals in cardiovascular-kidney-metabolic (CKM) syndrome stages 0-3 have not yet experienced overt clinical events such as stroke, mounting evidence suggests that these stages often involve subclinical metabolic and vascular injury. Insulin resistance (IR), a hallmark of CKM syndrome, contributes to cerebrovascular injury via mechanisms such as inflammation, oxidative stress, and endothelial dysfunction. The estimated glucose disposal rate (eGDR), a noninvasive index of IR, has shown cardiovascular prognostic value, yet its utility in predicting stroke across early CKM stages remains unclear. Methods: This study analyzed 4,065 participants from the China Health and Retirement Longitudinal Study (CHARLS) classified as CKM stages 0-3. The eGDR was computed using waist circumference, HbA1c, and hypertension status. K-means clustering identified four longitudinal eGDR changes. Restricted cubic spline (RCS) models were used to determine stage-specific eGDR thresholds for stroke prediction. Cox regression assessed associations between baseline, cumulative, and dynamic eGDR with stroke incidence, while subgroup analyses also employed. Results: During follow-up, 383 incident stroke cases were observed. Lower baseline and cumulative eGDR levels were independently associated with higher stroke risk. Participants with persistently low or sharply declining eGDR group exhibited more than double the stroke risk compared to those with stable high eGDR. The RCS analysis confirmed a linear inverse association between cumulative eGDR and stroke in CKM stages 2-3, and identified optimal thresholds that maximized sensitivity and specificity. Notably, Stage 2 presented the lowest threshold (7.41), potentially reflecting a critical tipping point where metabolic burden exceeds vascular compensation. Conclusion: eGDR is a clinically relevant predictor of stroke in asymptomatic individuals with CKM syndrome. The application of stage-specific thresholds enhances the precision of early risk stratification and highlights the need for tailored surveillance strategies. These findings suggest the potential value of eGDR monitoring as a practical tool for early risk stratification in metabolically at-risk populations.

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