Application of TyG index and carotid ultrasound parameters in the prediction of ischemic stroke

Objective: The triglyceride - glucose (TyG) index has been confirmed as an independent risk factor for ischemic stroke (IS) in numerous studies. In terms of the role of carotid ultrasound in the risk assessment of IS, the focus has shifted from merely concentrating on the degree of stenosis to paying more attention to the status of carotid plaques. However, there are limited studies on combining clinical indicators such as the TyG index with carotid ultrasound parameters to assess the risk of IS. Through a retrospective study, we aim to explore the role of combining these two types of indicators in the risk assessment of IS. Methods: This study included 145 patients with IS and 99 no ischemic stroke (NIS) patients diagnosed by magnetic resonance imaging (MRI) from January 2020 to June 2024. The TyG index was calculated as ln fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2. The carotid ultrasound parameters integrated were as follows: the presence or absence of carotid plaques, the location of the largest carotid plaque, carotid intima - media thickness (CIMT), the lengthness and thickness diameters of the largest carotid plaque, and the degree of carotid stenosis. Univariate (multivariate) logistic regression analysis, ROC curve analysis, etc. were conducted on the data using SPSS 26 and MATLAB Online. These were aimed at assessing the effectiveness of integrating clinical indicators with carotid ultrasound parameters in predicting the risk of IS. Results: The univariate logistic regression analysis (ULR) demonstrated that age, gender, TyG index, history of diabetes, history of hypertension, fasting blood glucose (FBG), systolic blood pressure(SBP), diastolic blood pressure(DBP), low-density lipoprotein cholesterol(LDL-C), cystatin C(Cys C), the presence or absence of carotid plaques, plaque location, carotid intima-media thickness(CIMT), the length and thickness of the largest plaque were significantly associated with IS (P < 0.05), while the P-values of triglycerides(TG), total cholesterol(TC), uric acid(UA) and carotid stenosis rate were greater than 0.05. The area under the ROC curve (AUC) of the TyG index for predicting IS was 0.645 (P < 0.001), indicating a certain predictive ability but relatively limited. The optimal cut-off value was 8.28, with a sensitivity of 0.83 and a specificity of 0.63 at this cut-off value. The stratified analysis based on quartiles of the TyG index revealed that as the TyG index increased, the prevalence of hypertension and diabetes, as well as multiple lipid and metabolic indicators, increased, and the characteristics of carotid plaques also changed. Multiple risk prediction models were constructed and analyzed by ROC curves. Model 1, which integrated traditional clinical indicators, TyG index and carotid ultrasound parameters, performed best (AUC = 0.932) (P < 0.001), while Model 16, which only included some carotid ultrasound indicators, had relatively low predictive efficacy (AUC = 0.750) (P < 0.001). Conclusion: This study confirms that the combination of TyG index and carotid ultrasound parameters is of great significance in predicting the risk of IS. The predictive ability of TyG index alone is limited, and Model 1 integrating multiple indicators has the best predictive effect and can provide a reference for clinical practice. However, due to the retrospective nature of this study and the limitations such as selection bias, small sample size and single-center, there are some discrepancies between some results and those of previous studies. Future studies need to conduct multi-center, large-sample studies and incorporate more factors to improve the model.