The triglyceride–glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from National Health and Nutrition Examination Survey (NHANES) data with machine learning analysis
Authors' conclusion
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.
Study context
- Population
- Hypertensive participants in NHANES 1999–2018
- Participants
- 9,432 participants
- Exposure
- Triglyceride-glucose (TyG) index and derivatives as predictors of mortality
- Outcome
- All-cause and cardiovascular mortality
Methodological detailsNHANES 1999-2018, Cox proportional hazards models, restricted cubic splines, machine learning models
- NHANES 1999-2018
- Cox proportional hazards models
- Restricted cubic splines
- Machine learning models
Main finding
As reported in the abstract
TyG-WHtR, strongest association: higher all-cause mortality risk per 1-unit increase
41.7%
TyG-WHtR: higher CV mortality risk per 1-unit increase
48.1%
Observational data: associations, not effects.
Further findings
Prediction performance of machine learning models
- Adding TyG index or derivatives modestly improved prediction performance for mortality
Original publication
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. Cardiovasc Diabetol. Published 29 January 2025. doi:10.
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