医学
四分位数
比例危险模型
入射(几何)
危险系数
内科学
队列研究
疾病
人口
纵向研究
接收机工作特性
糖尿病
回归分析
预测值
队列
子群分析
人口学
肾脏疾病
试验预测值
线性回归
前瞻性队列研究
全国健康与营养检查调查
回归
多级模型
人口研究
作者
Xiaoxiao Chen,Luni Guo,Zongliang Yu
标识
DOI:10.1186/s13098-026-02209-w
摘要
BACKGROUND: Cardiovascular disease (CVD) remains a major global health challenge. The triglyceride-glucose (TyG) index and its derived indices serve as indicators of insulin resistance (IR) and are closely associated with CVD incidence. This study aimed to compare the predictive value of different TyG-related indices for CVD incidence and assess the impact of the TyG-BMI across various subgroups, including potential interaction effects. METHODS: This study conducted a secondary analysis using data from the China Health and Retirement Longitudinal Study (CHARLS), which included 5,382 participants aged 45 years and older. Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and Cox proportional hazards regression models were employed to evaluate the associations between TyG-related indices and CVD incidence. Subgroup analyses were performed to evaluate the predictive performance of the TyG-BMI across different population categories and assess potential interactions. RESULTS: Cox proportional hazards regression indicated a significantly increased risk of CVD among participants in the highest quartile of the TyG-BMI, with a hazard ratio (HR) of 1.60 (95% CI: 1.33-1.99). Subgroup analyses confirmed this association across multiple demographic and clinical subgroups, including sex, residence, education level, alcohol consumption, smoking history, and history of hypertension, diabetes, stroke, liver disease, and kidney disease. Restricted cubic spline (RCS) analysis revealed a nonlinear relationship between the TyG-BMI and CVD incidence. Interaction analysis revealed a significant positive interaction between kidney disease and the TyG-BMI. CONCLUSIONS: The TyG-BMI demonstrated a modestly higher predictive value than other TyG-related indices in predicting CVD risk, establishing it as a valuable tool for clinicians assessing the incidence of CVD.
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