Evidence map›Paper›PMID 39881352›Full record

ArticleCardiovascular diabetology2025

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.

Chenyang Li, Zixi Zhang, Xiaoqin Luo, Yichao Xiao, Tao Tu, Chan Liu, Qiming Liu, Cancan Wang, Yongguo Dai, Zeying Zhang and 2 more

Abstract read
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Article in Cardiovascular diabetology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed, 2 pooled it
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

93 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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33 more citing papers are in PubMed but not listed here.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Chenyang Li *Department of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, People's Republic of China.
Zixi Zhang *Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China.
Xiaoqin LuoDepartment of Geriatrics, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China.
Yichao XiaoDepartment of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China.
Tao TuDepartment of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China.
Chan LiuDepartment of International MedicineThe Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China.
Qiming LiuDepartment of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China.
Cancan WangFirst Clinical College, Changsha Medical University, Changsha, 410219, Hunan, People's Republic of China.
Yongguo DaiDepartment of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Zeying ZhangDepartment of Cardiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361004, Fujian, People's Republic of China.
Cheng ZhengDepartment of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, People's Republic of China. zhengcheng_wzmufey@163.com.
Jiafeng LinDepartment of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, People's Republic of China. linjiafeng_wzmcfey@163.com.

Funding

Clinical Medical Technology Innovation Guidance Project of Hunan Science and Technology Agency 2021SK53519Hunan Provincial Natural Science Foundation of China 2021JJ30033Hunan Provincial Natural Science Foundation of China 2023JJ30791Key Project of Hunan Provincial Science and Technology Innovation 2024JK2119National Natural Science Foundation of China 82070333National Natural Science Foundation of China 82070356National Natural Science Foundation of China 82300357National Natural Science Foundation of China 82470333Wenzhou Municipal Science and Technology Commission Y20220472
6 · The paper itself

Abstract

backgroundHypertension (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.

methodsData 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

Blood GlucoseHypertensionMachine LearningObesityTriglyceridesAdultAgedBiomarkersCause of DeathCross-Sectional StudiesFemaleHumansInsulin ResistanceMaleMiddle AgedNutrition SurveysBiomarkersBlood GlucoseTriglyceridesHypertensionMachine learningMortalityNational Health and Nutrition Examination Survey (NHANES)Triglyceride–glucose (TyG) index

Identifiers

PMID39881352
PMCPMC11780913

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.