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
Abstract 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) i...
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BMC
2025-01-01
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| Series: | Cardiovascular Diabetology |
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| Online Access: | https://doi.org/10.1186/s12933-025-02591-1 |
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| author | Chenyang Li Zixi Zhang Xiaoqin Luo Yichao Xiao Tao Tu Chan Liu Qiming Liu Cancan Wang Yongguo Dai Zeying Zhang Cheng Zheng Jiafeng Lin |
| author_facet | Chenyang Li Zixi Zhang Xiaoqin Luo Yichao Xiao Tao Tu Chan Liu Qiming Liu Cancan Wang Yongguo Dai Zeying Zhang Cheng Zheng Jiafeng Lin |
| author_sort | Chenyang Li |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-57bf924ff3e441ebbe5da3bf860c475f |
| institution | Kabale University |
| issn | 1475-2840 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Cardiovascular Diabetology |
| spelling | doaj-art-57bf924ff3e441ebbe5da3bf860c475f2025-02-02T12:07:29ZengBMCCardiovascular Diabetology1475-28402025-01-0124111310.1186/s12933-025-02591-1The 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 analysisChenyang Li0Zixi Zhang1Xiaoqin Luo2Yichao Xiao3Tao Tu4Chan Liu5Qiming Liu6Cancan Wang7Yongguo Dai8Zeying Zhang9Cheng Zheng10Jiafeng Lin11Department of Cardiology, The Second Affiliated Hospital, Wenzhou Medical UniversityDepartment of Cardiology, The Second Xiangya Hospital, Central South UniversityDepartment of Geriatrics, The Second Xiangya Hospital, Central South UniversityDepartment of Cardiology, The Second Xiangya Hospital, Central South UniversityDepartment of Cardiology, The Second Xiangya Hospital, Central South UniversityDepartment of International MedicineThe Second Xiangya Hospital, Central South UniversityDepartment of Cardiology, The Second Xiangya Hospital, Central South UniversityFirst Clinical College, Changsha Medical UniversityDepartment of Pharmacy, Xiangya Hospital, Central South UniversityDepartment of Cardiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Cardiology, The Second Affiliated Hospital, Wenzhou Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital, Wenzhou Medical UniversityAbstract 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.https://doi.org/10.1186/s12933-025-02591-1Triglyceride–glucose (TyG) indexHypertensionMortalityMachine learningNational Health and Nutrition Examination Survey (NHANES) |
| spellingShingle | Chenyang Li Zixi Zhang Xiaoqin Luo Yichao Xiao Tao Tu Chan Liu Qiming Liu Cancan Wang Yongguo Dai Zeying Zhang Cheng Zheng Jiafeng Lin 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 Cardiovascular Diabetology Triglyceride–glucose (TyG) index Hypertension Mortality Machine learning National Health and Nutrition Examination Survey (NHANES) |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Triglyceride–glucose (TyG) index Hypertension Mortality Machine learning National Health and Nutrition Examination Survey (NHANES) |
| url | https://doi.org/10.1186/s12933-025-02591-1 |
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