Cardiovascular diabetology · 2025
The triglyceride-glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis.
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Li Chenyang, Zhang Zixi, Luo Xiaoqin, Xiao Yichao, Tu Tao, Liu Chan, Liu Qiming, Wang Cancan, Dai Yongguo, Zhang Zeying, Zheng Cheng, Lin Jiafeng
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.