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Related Concept Videos

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Atherosclerosis II: Clinical Manifestations and Diagnostic Tests

Atherosclerosis is a progressive disorder that leads to the thickening and narrowing of arterial walls due to plaque buildup. This condition can cause various symptoms depending on the arteries affected:Coronary Artery Disease (CAD): This condition affects the coronary arteries and may lead to chest pain (angina), shortness of breath (dyspnea), heart attacks, and other heart disease symptoms.Cerebrovascular Disease: This affects blood flow to the brain, causing transient ischemic attacks (TIAs)...
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Related Experiment Video

Updated: May 23, 2026

Quantification of Atherosclerosis in Mice
06:59

Quantification of Atherosclerosis in Mice

Published on: June 12, 2019

Machine learning evaluation of TyG-based metrics for arteriosclerosis progression.

Yihui Li1,2,3, Kui Chen1,2,3, Lei Yan1,2,3

  • 1Department of Health Management, The Third Xiangya Hospital, Central South University, Changsha, Hunan Province, China.

Scientific Reports
|May 21, 2026
PubMed
Summary

Cumulative exposure to the triglyceride-glucose (TyG) index and TyG-WHtR predicts arteriosclerosis progression in non-diabetics. These markers aid early identification of high-risk individuals, even before diabetes develops.

Keywords:
ArteriosclerosisCumulative exposureMachine learningNon-diabetic populationTriglyceride glucose (TyG)Triglyceride glucose-body mass index (TyG-BMI)Triglyceride glucose-waist circumference (TyG-WC)Triglyceride glucose-waist height ratio (TyG-WHtR)

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Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
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Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

Area of Science:

  • Cardiovascular Disease Epidemiology
  • Metabolic Syndrome Research
  • Biomarker Discovery in Atherosclerosis

Background:

  • Insulin resistance (IR) is a key risk factor for arteriosclerosis.
  • Triglyceride-glucose (TyG) index and its obesity-related derivatives (TyG-BMI, TyG-WC, TyG-WHtR) are established IR markers.
  • Arteriosclerosis can precede diabetes diagnosis, necessitating early detection in non-diabetic individuals.

Purpose of the Study:

  • To evaluate the association between cumulative exposure to IR indicators and arteriosclerosis progression in a non-diabetic cohort.
  • To assess the predictive value of the TyG index and its derivatives for arteriosclerosis development using machine learning.
  • To explore potential nonlinear relationships between IR markers and arteriosclerosis progression.

Main Methods:

  • A cohort study of 4,160 participants in southern China (2017-2024) with regular follow-up.
  • Multivariable linear regression and restricted cubic splines to analyze associations.
  • Machine learning models to predict arteriosclerosis, evaluated using AUC and PR-AUC.

Main Results:

  • Cumulative average exposure to the TyG index and TyG-WHtR strongly correlated with arteriosclerosis progression (p < 0.05).
  • A potential U-shaped relationship was observed between cumulative TyG-WHtR and pulse wave velocity (baPWV) changes.
  • Machine learning models incorporating TyG index or derivatives showed moderate improvement in predicting arteriosclerosis progression (AUC increased to 0.737-0.744).

Conclusions:

  • Cumulative exposure to the TyG index and its derivatives are significant predictors of arteriosclerosis progression in non-diabetic populations.
  • These readily available, low-cost metrics can aid in the early identification of individuals at high risk for arteriosclerosis.
  • Integrating these markers into predictive models enhances early risk stratification for cardiovascular events.