Cardiovascular diabetology · 2025
Elevated triglyceride-glucose index and its combination with anthropometric indices as indicators of the risk of new-onset transient ischemic attack: a prospective cohort study.
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Xiao Zhehao, Luo Ling, Weng Weipin, Yu Erhan, Zheng Jiahao, Pan Xiaodong, Wang Yanping, Wang Rui
Аннотация
BACKGROUND: The association between the triglyceride-glucose (TyG) and combined anthropometric indices with transient ischemic attack (TIA) has not previously been explored in detail. This study investigated the relationship of TyG and anthropometric indices with incident TIA in healthy populations.
METHODS: Data from a prospective UK Biobank cohort (361,336 participants) were analyzed using Cox regression, population attributable fraction (PAF), restricted cubic splines (RCS), and receiver operating characteristic (ROC) curves. Robustness was evaluated using subgroup and interaction analyses and sensitivity tests.
RESULTS: During a median follow-up of 13.7 years, TyG, glucose triglyceride-waist circumference (TyG-WC), glucose triglyceride-body mass index (TyG-BMI), glucose triglyceride-waist height ratio (TyG-WHtR), visceral adiposity index (VAI), and lipid accumulation product (LAP) were significantly associated with increased TIA risk (all P < 0.001). Cox regression analysis revealed that participants in the highest quartile (Q4) had higher hazard ratios (HRs) than those in the lowest quartile (Q1): TyG (HR: 1.22, 95% confidence interval [CI]: 1.12-1.33); TyG-WC (HR: 1.29, 95% CI: 1.17-1.42); TyG-BMI (HR: 1.21, 95% CI: 1.11-1.33); TyG-WHtR (HR: 1.29, 95% CI: 1.18-1.42); VAI (HR: 1.35, 95% CI: 1.24-1.47); and LAP (HR: 1.29, 95% CI: 1.18-1.42). PAF analysis indicated that maintaining Q1 levels of indices for TyG, TyG-WC, TyG-BMI, TyG-WHtR, VAI, and LAP could reduce TIA incidence by 9.07%, 14.56%, 11.48%, 15.75%, 17.18%, and 15.83%, respectively. RCS analysis revealed that TIA risk had linear associations with TyG, TyG-WC, and TyG-BMI, and nonlinear associations with TyG-WHtR, VAI, and LAP (P for nonlinear < 0.05). ROC analysis revealed that the indicators with the highest diagnostic accuracy were TyG-WHtR (area under the curve [AUC] 0.678, 95% CI: 0.676-0.679) and TyG-WC (AUC: 0.673, 95% CI: 0.672-0.675). Subgroup analysis identified age (P < 0.001) and diabetes (P = 0.021) as significant effect modifiers.
CONCLUSIONS: Elevated levels of TyG and combined anthropometric indices are associated with an increased risk of incident TIA. TyG-WHtR and TyG-WC showed a higher accuracy than the TyG index alone as indicators of TIA risk. This study demonstrates the importance of maintaining optimal TyG and anthropometric-related index levels for TIA prevention.
METHODS: Data from a prospective UK Biobank cohort (361,336 participants) were analyzed using Cox regression, population attributable fraction (PAF), restricted cubic splines (RCS), and receiver operating characteristic (ROC) curves. Robustness was evaluated using subgroup and interaction analyses and sensitivity tests.
RESULTS: During a median follow-up of 13.7 years, TyG, glucose triglyceride-waist circumference (TyG-WC), glucose triglyceride-body mass index (TyG-BMI), glucose triglyceride-waist height ratio (TyG-WHtR), visceral adiposity index (VAI), and lipid accumulation product (LAP) were significantly associated with increased TIA risk (all P < 0.001). Cox regression analysis revealed that participants in the highest quartile (Q4) had higher hazard ratios (HRs) than those in the lowest quartile (Q1): TyG (HR: 1.22, 95% confidence interval [CI]: 1.12-1.33); TyG-WC (HR: 1.29, 95% CI: 1.17-1.42); TyG-BMI (HR: 1.21, 95% CI: 1.11-1.33); TyG-WHtR (HR: 1.29, 95% CI: 1.18-1.42); VAI (HR: 1.35, 95% CI: 1.24-1.47); and LAP (HR: 1.29, 95% CI: 1.18-1.42). PAF analysis indicated that maintaining Q1 levels of indices for TyG, TyG-WC, TyG-BMI, TyG-WHtR, VAI, and LAP could reduce TIA incidence by 9.07%, 14.56%, 11.48%, 15.75%, 17.18%, and 15.83%, respectively. RCS analysis revealed that TIA risk had linear associations with TyG, TyG-WC, and TyG-BMI, and nonlinear associations with TyG-WHtR, VAI, and LAP (P for nonlinear < 0.05). ROC analysis revealed that the indicators with the highest diagnostic accuracy were TyG-WHtR (area under the curve [AUC] 0.678, 95% CI: 0.676-0.679) and TyG-WC (AUC: 0.673, 95% CI: 0.672-0.675). Subgroup analysis identified age (P < 0.001) and diabetes (P = 0.021) as significant effect modifiers.
CONCLUSIONS: Elevated levels of TyG and combined anthropometric indices are associated with an increased risk of incident TIA. TyG-WHtR and TyG-WC showed a higher accuracy than the TyG index alone as indicators of TIA risk. This study demonstrates the importance of maintaining optimal TyG and anthropometric-related index levels for TIA prevention.