Abstract
Objective
Our purpose was to explore the relationship between triglyceride glucose (TyG) index and the risk of new-onset hypertension in Chinese individuals aged ≥45 years.
Methods
From 2011 to 2018, data from the China Health and Retirement Longitudinal Survey (CHARLS) were analyzed. The relationship between TyG index and hypertension was assessed utilizing Cox regression and restricted cubic spline (RCS) plot, and the importance of the TyG index in hypertension development was demonstrated by a random forest machine learning model. Finally, subgroup analysis was conducted to test for potential interactions on hypertension development between the TyG index and subgroups.
Results
19.7% of the 4755 individuals who were involved in this survey developed hypertension over an average follow-up period of 5.22 years. Compared with the first quartile of albumin, the multivariate HR (95% CI) for the risk of new-onset hypertension across the TyG index quartiles was 1.09 (0.89, 1.33), 1.09 (0.89, 1.33), and 1.29 (1.06, 1.58), respectively (P for trend <0.001). The RCS plot revealed a linear relationship (P for nonlinear = 0.322), and the random forest machine learning model illustrated that the TyG index was a significant hazard factor on hypertension development. There was no interaction between subgroups and the relationships of the TyG index with the prevalence of hypertension (all P-value >0.05).
Conclusion
TyG index was an independent hazard indicator for new-onset hypertension, and routine measurement and control of TyG index level might be great for preventing hypertension development.
Ethical Approval
The data for this research was obtained from CHARLS database and conform to the Declaration of Helsinki, and this survey had been approved to be conducted by the Peking University’s Ethical Review Committee (Ethical Number: IRB00001052-11015). In addition, this study was also approved by the Medical Research Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (Ethical Number: XYFY2022-KL375-01).
Acknowledgments
Thanks to the National Development Research Institute of Peking University and the Chinese Social Science Research Center of Peking University for providing CHARLS data.
Disclosure
All authors declare no conflicting interests in this work.