C-reactive protein-triglyceride glucose index predicts stroke incidence in a hypertensive population: a national cohort study

Songyuan Tang; Han Wang; Kunwei Li; Yaqing Chen; Qiaoqi Zheng; Jingjing Meng; Xin Chen

doi:10.1186/s13098-024-01529-z

C-reactive protein-triglyceride glucose index predicts stroke incidence in a hypertensive population: a national cohort study

Diabetol Metab Syndr. 2024 Nov 21;16(1):277. doi: 10.1186/s13098-024-01529-z.

Authors

Songyuan Tang^#¹, Han Wang^#², Kunwei Li¹, Yaqing Chen¹, Qiaoqi Zheng¹, Jingjing Meng¹, Xin Chen³

Affiliations

¹ School of Life Science and Engineering, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.
² Department of Cardiology, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.
³ School of Life Science and Engineering, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China. xinchencd@yeah.net.

^# Contributed equally.

PMID: 39574139
DOI: 10.1186/s13098-024-01529-z

Abstract

Background: Both the triglyceride-glucose (TyG) index, a predictor of insulin resistance (IR), and inflammation are risk factors for stroke in hypertensive patients. However, only a handful of studies have coupled the TyG index and inflammation indices to predict stroke risk in hypertensive patients. The C-reactive protein-triglyceride-glucose index (CTI) is a novel marker that comprehensively assesses the severity of IR and inflammation. The present study explored the association between CTI and the risk of stroke in patients with hypertension.

Methods: A total of 3,834 hypertensive patients without a history of stroke at baseline were recruited from the China Health and Retirement Longitudinal Study (CHARLS). Multivariate Cox regression and restricted cubic spline (RCS) analyses were employed to assess the relationship between CTI and stroke risk in hypertensive patients. Furthermore, the Boruta algorithm was applied to evaluate the importance of CTI and construct prediction models to forecast the incidence of stroke in the study cohort.

Results: After 7 years of follow-up, the incidence of stroke in hypertensive patients was 9.6% (368 cases). Multivariate Cox regression analysis revealed a 21% increase in stroke risk with an increase in each CTI unit (hazard ratio (HR) = 1.21, 95% confidence interval (CI) = 1.08-1.37). The top quartile group was 66% more likely to have a stroke than the bottom quartile group (HR = 1.66, 95% CI = 1.23-2.25). RCS analysis confirmed a linear relationship between CTI and stroke risk. The Boruta algorithm validated CTI as a crucial indicator of stroke risk. The Support Vector Machine (SVM) survival model exhibited the best predictive performance for stroke risk in hypertensive patients, with an area under the curve (AUC) of 0.956.

Conclusions: An increase in CTI levels is associated with a higher risk of stroke in hypertensive patients. This study suggests that CTI may emerge as a unique predictive marker for stroke risk.

Keywords: C-reactive protein-triglyceride-glucose index; Hypertension; Inflammation; Insulin resistance; Stroke.