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Background <p>Stroke continues to be a predominant contributor to morbidity and mortality, with insulin resistance (IR) recognized as a significant risk factor. The predictive utility of cumulative IR-related indices for assessing stroke risk across varying glucose regulation statuses remains ambiguous. The present study investigated the associations between six cumulative IR-related indices—triglyceride-glucose (TyG) index, triglyceride-glucose-body mass index (TyG-BMI), Chinese visceral adiposity index (CVAI), metabolic score for insulin resistance (METS-IR), atherogenic index of plasma (AIP), and estimated glucose disposal rate (eGDR)—and stroke risk in individuals with abnormal glucose regulation (AGR) compared to those with normal glucose regulation (NGR).</p> Methods <p>Data from the China Health and Retirement Longitudinal Study (CHARLS), comprising 5,129 individuals aged over 45 years, were analyzed. Six insulin resistance (IR) surrogate indices and their cumulative values were calculated, and their associations with incident stroke risk were examined via Cox proportional hazards models and RCS modeling. The predictive&#xa0;accuracy of these indices was evaluated with receiver-operating characteristic (ROC) curves, NRI, IDI, and relative importance.</p> Results <p>In the overall cohort, elevated AIP, CVAI, METS-IR, TyG, and TyG-BMI were correlated with an increased risk of stroke, whereas higher eGDR values were associated with a decreased risk of stroke. The statistically significant associations of some indices with stroke risk were lost within both the AGR subgroup and the NGR subgroup. Compared with the other indices, the cumulative eGDR showed the strongest and most consistent association with stroke risk in all the subgroups, with the highest predictive power (overall AUC = 0.663; AGR subgroup AUC = 0.647; and NGR subgroup AUC = 0.673). Adding the cumulative eGDR to the conventional risk factor model enhanced the accuracy of stroke prediction.</p> Conclusions <p>Cumulative IR-related indices, particularly the eGDR, are strong predictors of stroke risk in individuals with both AGR and NGR. Owing to the superior predictive performance of the cumulative eGDR, its use may improve stroke risk assessment, enabling more targeted interventions for stroke prevention among middle-aged and elderly individuals.</p>

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Associations between six cumulative insulin resistance-related indices and incident stroke in individuals with different glucose regulation statuses: a national cohort study in China

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  • 发表日期：

    2025年11月17日

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  • Weicheng Huang,
  • Yubin Chen,
  • Gang Peng,
  • Qun Xiao,
  • Yueran Li

摘要

Background

Stroke continues to be a predominant contributor to morbidity and mortality, with insulin resistance (IR) recognized as a significant risk factor. The predictive utility of cumulative IR-related indices for assessing stroke risk across varying glucose regulation statuses remains ambiguous. The present study investigated the associations between six cumulative IR-related indices—triglyceride-glucose (TyG) index, triglyceride-glucose-body mass index (TyG-BMI), Chinese visceral adiposity index (CVAI), metabolic score for insulin resistance (METS-IR), atherogenic index of plasma (AIP), and estimated glucose disposal rate (eGDR)—and stroke risk in individuals with abnormal glucose regulation (AGR) compared to those with normal glucose regulation (NGR).

Methods

Data from the China Health and Retirement Longitudinal Study (CHARLS), comprising 5,129 individuals aged over 45 years, were analyzed. Six insulin resistance (IR) surrogate indices and their cumulative values were calculated, and their associations with incident stroke risk were examined via Cox proportional hazards models and RCS modeling. The predictive accuracy of these indices was evaluated with receiver-operating characteristic (ROC) curves, NRI, IDI, and relative importance.

Results

In the overall cohort, elevated AIP, CVAI, METS-IR, TyG, and TyG-BMI were correlated with an increased risk of stroke, whereas higher eGDR values were associated with a decreased risk of stroke. The statistically significant associations of some indices with stroke risk were lost within both the AGR subgroup and the NGR subgroup. Compared with the other indices, the cumulative eGDR showed the strongest and most consistent association with stroke risk in all the subgroups, with the highest predictive power (overall AUC = 0.663; AGR subgroup AUC = 0.647; and NGR subgroup AUC = 0.673). Adding the cumulative eGDR to the conventional risk factor model enhanced the accuracy of stroke prediction.

Conclusions

Cumulative IR-related indices, particularly the eGDR, are strong predictors of stroke risk in individuals with both AGR and NGR. Owing to the superior predictive performance of the cumulative eGDR, its use may improve stroke risk assessment, enabling more targeted interventions for stroke prevention among middle-aged and elderly individuals.