Li Chenyang, Zhang Zixi, Luo Xiaoqin, Xiao Yichao, Tu Tao, Liu Chan, Liu Qiming, Wang Cancan, Dai Yongguo, Zhang Zeying, Zheng Cheng, Lin Jiafeng
Department of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, People's Republic of China.
Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China.
Cardiovasc Diabetol. 2025 Jan 29;24(1):47. doi: 10.1186/s12933-025-02591-1.
Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resistance (IR) plays a crucial role in HTN-related metabolic dysfunction, but its assessment remains challenging. The triglyceride-glucose (TyG) index and its derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) have emerged as reliable IR markers. In this study, we evaluated their associations with all-cause and cardiovascular mortality in hypertensive patients using machine learning techniques.
Data from 9432 hypertensive participants in the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were analysed. Cox proportional hazards models and restricted cubic splines were employed to explore mortality risk and potential nonlinear relationships. Machine learning models were utilized to assess the predictive value of the TyG index and its derivatives for mortality outcomes.
The TyG index and its derivatives were independent predictors of both all-cause and cardiovascular mortality in hypertensive patients. The TyG-WHtR exhibited the strongest association, with each 1-unit increase linked to a 41.7% and 48.1% higher risk of all-cause and cardiovascular mortality, respectively. L-shaped relationships were observed between TyG-related indices and mortality. The incorporation of the TyG index or its derivatives into predictive models modestly improved the prediction performance for mortality outcomes.
The TyG index and its derivatives are significant predictors of mortality in hypertensive patients. Their inclusion in predictive models enhances risk stratification and may aid in the early identification of high-risk individuals in this population. Further studies are needed to validate these findings in external hypertensive cohorts.
高血压是一个全球公共卫生问题，也是心血管疾病（CVD）和死亡的主要危险因素。胰岛素抵抗（IR）在高血压相关的代谢功能障碍中起关键作用，但其评估仍然具有挑战性。甘油三酯-葡萄糖（TyG）指数及其衍生指标（TyG-BMI、TyG-WC和TyG-WHtR）已成为可靠的IR标志物。在本研究中，我们使用机器学习技术评估了它们与高血压患者全因死亡率和心血管死亡率的关联。
分析了1999 - 2018年美国国家健康与营养检查调查（NHANES）中9432名高血压参与者的数据。采用Cox比例风险模型和受限立方样条来探讨死亡风险和潜在的非线性关系。利用机器学习模型评估TyG指数及其衍生指标对死亡结局的预测价值。
TyG指数及其衍生指标是高血压患者全因死亡率和心血管死亡率的独立预测因子。TyG-WHtR表现出最强的关联，每增加1个单位，全因死亡率和心血管死亡率的风险分别升高41.7%和48.1%。观察到TyG相关指数与死亡率之间呈L形关系。将TyG指数或其衍生指标纳入预测模型可适度提高对死亡结局的预测性能。
TyG指数及其衍生指标是高血压患者死亡率的重要预测因子。将它们纳入预测模型可增强风险分层，并可能有助于早期识别该人群中的高危个体。需要进一步研究在外部高血压队列中验证这些发现。
