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Updated: May 2, 2026

A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology
Published on: February 7, 2015
Association between triglyceride-glucose index and carotid atherosclerosis in Chinese steelworkers: a cross-sectional
Haoyue Cao1, Qinglin Li2, Juxiang Yuan3
1School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New City, Tangshan City, 063210, Hebei Province, China.
Abstract:
This study aims to explore the association between the triglyceride-glucose (TyG) index and the risk of carotid atherosclerosis (CAS) among Chinese steelworkers. This is a cross-sectional study involving a total of 4,203 Chinese steelworkers. The TyG index was calculated using the formula: TyG = Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2]. The association between the TyG index and CAS was analyzed using multivariable logistic regression and restricted cubic spline (RCS) models. The association between TyG and CAS was evaluated across different subgroups, including sex, age, lifestyle, medical history, and the state of occupational exposure. In addition, the TyG index was used to perform calibration and risk reclassification analyses on the fully adjusted model. Feature importance and predictive models were generated using LASSO regression and machine learning methods. In the fully adjusted model, compared to the lowest quartile group, the odds ratios (OR, 95% CI) for CAS, increased CIMT, and plaque in the highest quartile group of the TyG index were 3.199 (2.423 ~ 4.223), 2.877 (2.149 ~ 3.851), and 3.738 (2.700 ~ 5.177), respectively. Steelworkers exposed to occupational hazards had a higher risk of CAS compared to those not exposed. There was a nonlinear positive correlation between the TyG index and CAS (P < 0.05), and the risk of CAS increased when TyG > 8.72. Additionally, the TyG index demonstrated additional predictive capability beyond existing risk factors, significantly improving discriminatory performance (P < 0.05). In LASSO regression, TyG index and other covariables are screened as important feature variables to be incorporated into the development of machine learning models. The TyG index is associated with an increased risk of CAS among steelworkers, underscoring its potential as a reliable and practical predictive tool for assessing CAS risk in this population. Steelworkers with a TyG index>8.72 should receive additional CAS screening and health interventions. In addition, attention should be paid to those exposed to occupational hazards. The TyG index may serve as a simple, rapid, and reliable assessment tool for identifying high-risk individuals with CAS among steelworkers.
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