ArticleScientific reports2025
Association between triglyceride-glucose index and carotid atherosclerosis in Chinese steelworkers: a cross-sectional study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Carotid Atherosclerosis and Metabolic Characteristics Among High-Risk Cardiovascular Populations of Four Ethnic Groups - Yunnan Province, China, 2024.China CDC weekly · 2026Article
- A Link Between the Triglyceride-Glucose Index and the Risk of Hypertension Among Employees in the Titanium Dioxide Industry.Vascular health and risk management · 2026Article
- Non-linear relationship between the triglyceride glucose-a body shape index and stroke risk: a prospective cohort study from the China health and retirement longitudinal study.Diabetology & metabolic syndrome · 2025Article
- The triglyceride-glucose index: updating evidence from clinical settings to molecular mechanisms in ageing-related cerebrovascular diseases.Cardiovascular diabetology · 2025Review
- Longitudinal TyG-BMI trajectories predict carotid atherosclerosis progression in a Chinese retrospective cohort.Frontiers in cardiovascular medicine · 2025Article
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3 authors.
Funding
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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