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

Huang et al. Lipids in Health and Disease (2025) 24:368 https://doi.org/10.1186/s12944-025-02775-4 Lipids in Health and Disease Open Access RESEARCH Associations between six cumulative insulin resistance-related indices and incident stroke in individuals with different glucose regulation statuses: a national cohort study in China Weicheng Huang1,2, Yubin Chen3, Gang Peng2, Qun Xiao1,2* and Yueran Li1* Abstract 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. *Correspondence: Qun Xiao Yueran Li Full list of author information is available at the end of the article © The Author(s) 2025. 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To view a copy of this licence, visit http://creati vecommons.org/licenses/by-nc-nd/4.0/. Huang et al. Lipids in Health and Disease (2025) 24:368 Page 2 of 12 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. Keywords Stroke, Insulin Resistance, Glucose Metabolic Disorders, Cohort Analysis Background Stroke remains a predominant contributor to morbidity and mortality globally, especially among middle-aged and older individuals in China, indicating a pressing need for tailored risk prediction strategies [1, 2]. Insulin resistance (IR) is widely acknowledged to be involved in metabolic disorders, such as obesity and diabetes, and IR is an independent risk factor for cardiovascular diseases, such as stroke [3, 4]. Recognized as the benchmark for assessing insulin resistance, the hyperinsulinemic-euglycemic clamp (HIEC) test is widely used. Because the HIEC test is time-consuming, expensive, and invasive, it may not be practical for large-scale epidemiological studies. As a result, IR-related indices, such as the triglyceride‒glucose (TyG) index and the metabolic score for insulin resistance (METS-IR), have emerged as more accessible alternatives for assessing cardiovascular risk [5, 6]. Individuals with abnormal glucose regulation (AGR) are at greater risk of experiencing stroke than those with normal glucose regulation (NGR) [7, 8]. Moreover, previous research has indicated that the sensitivity of cardiovascular disease risk prediction varies among IR-related indices—such as the plasma atherogenic index (AIP) and the glucose disposal rate estimate (eGDR)—across different glucose regulation states [9, 10]. Given the influence of adverse behavioral, lifestyle, and environmental factors on individuals with impaired glycemic regulation, which may confound results, the participants in the present study were stratified on the basis of their glucose regulation status. In most recent studies, only the IR-related indices at baseline are considered; therefore, cumulative exposure over time is neglected. However, IR-related indices fluctuate over time, and a solitary baseline measurement is insufficient to capture cumulative exposure over a defined period. Concurrently, several studies have demonstrated that cumulative exposure more accurately predicts the future likelihood of adverse cardiovascular outcomes and mortality than single baseline assessments [11, 12]. Few investigations have investigated the associations between cumulative insulin resistance-related metrics and stroke occurrence under various glucose regulation categories. It is hypothesized that the predictive effectiveness of these cumulative indices will vary based on whether glucose regulation is abnormal or normal. Data from a nationwide Chinese survey were utilized to explore how six cumulative IR-related indices—the TyG, TyG-body mass index (TyG-BMI), METS-IR, Chinese visceral adiposity index (CVAI), AIP, and eGDR—relate to incident stroke and to examine the capacity of the indices to predict stroke risk in individuals aged 45 and above with varying levels of glucose regulation. Methods Study participants The data used in this analysis were sourced from the China Health and Retirement Longitudinal Study (CHARLS), wh (...truncated)