AUTHOR=Miao Ying , Wang Yu , He Yuting , Yan Pijun , Wan Qin 

TITLE=TyG-BMI as a predictor of ischemic stroke over 10 years in middle-aged and older adults: findings from the China cardiometabolic disease and cancer cohort (4C) study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1609853

DOI=10.3389/fneur.2025.1609853

ISSN=1664-2295

ABSTRACT=BackgroundIschemic stroke (IS) is a leading cause of death and disability, imposing a significant economic burden globally. Research has demonstrated that insulin resistance (IR) plays a key role in the development of atherosclerosis, platelet dysfunction, and a hypercoagulable state, all of which contribute to the pathogenesis and progression of IS. The triglyceride-glucose (TyG) index serves as a practical tool for assessing insulin sensitivity, with previous studies exploring its correlation with IS. However, the relationship between the novel TyG-body mass index (TyG-BMI), which combines TyG with body mass index (BMI) as a measure of general obesity, and IS remains unclear. Therefore, this study employs a prospective design to assess the predictive value of TyG-BMI for the 10-year risk of IS in individuals without intervention.MethodsThe study population was derived from the China Cardiometabolic Disease and Cancer Cohort (4C) Study, predominantly comprising participants from Luzhou City, Sichuan Province, and primarily targeting individuals aged 40 and above. Comprehensive data collection was conducted using both questionnaires and specialized medical equipment, covering physical measurements, blood pressure, and relevant biochemical markers. Participants with a history of stroke were excluded from the study. Based on the initial data, participants were divided into four groups according to the TyG-BMI quartiles. Spearman correlation analysis was used to examine the relationship between TyG-BMI and clinical and laboratory parameters. The Log-rank test was applied to analyze differences in the cumulative incidence of IS among the four groups. The Cox proportional hazards model was used to analyze the relationship between TyG-BMI and the 10-year incidence of new IS. Additionally, the ROC curve was employed to assess the predictive value of TyG-BMI for the 10-year incidence of new IS in the middle-aged and elderly population.ResultsThis study included 9,406 participants, consisting of 3,139 males (33.4%) and 6,267 females (66.6%). During the non-interventional follow-up period of 10 years, 483 deaths were recorded, resulting in a mortality rate of 5.1%. In addition, 527 new cases of IS were reported, yielding an incidence rate of 5.6%. The Log-rank test revealed a significant increase in the cumulative incidence of IS across increasing TyG-BMI quartiles (p < 0.01). Furthermore, Cox regression analysis identified a significant correlation between TyG-BMI levels, as a risk factor, and the occurrence of IS. After adjusting for other risk factors, the risk of developing new IS in the Q2 group was 1.449 times that of the Q1 group (p = 0.012), while the risk in the Q3 group was 1.438 times that of the Q1 group (p = 0.014), and the risk in the Q4 group was 1.434 times that of the Q1 group (p = 0.020). ROC curve analysis showed that, in the overall study population, TyG-BMI demonstrated a predictive value for new IS over 10 years (AUC = 0.566, 95% CI = 0.542–0.590, p < 0.001), with a cutoff value of 204.1307, sensitivity of 64.3%, and specificity of 47.8%. In male participants, TyG-BMI showed a predictive value for new IS over 10 years (AUC = 0.537, 95% CI = 0.501–0.574, p = 0.067), with a cutoff value of 195.1996, sensitivity of 73.8%, and specificity of 37.0%. In female participants, TyG-BMI demonstrated a predictive value for new IS over 10 years (AUC = 0.583, 95% CI = 0.551–0.615, p < 0.001), with a cutoff value of 204.295, sensitivity of 65.8%, and specificity of 48.7%.ConclusionThis study revealed a significant association between TyG-BMI and the 10-year incidence of new-onset IS among middle-aged and elderly individuals, indicating that TyG-BMI may serve as a valuable predictive marker for assessing IS risk in this population.