Triglyceride-glucose Index as a Valuable Marker to Predict Severity of Coronary Artery Disease: A Retrospective

Xu Geng1, Xi Zhang1,2, XiaoWei Li3

  • 1Department of Clinical Laboratory, Chest Hospital, Tianjin University, Tianjin, China.

Insights

The triglyceride-glucose (TyG) index, along with fasting blood glucose (FBG) and high-sensitivity C-reactive protein (hs-CRP), can help predict the severity of coronary artery disease (CAD). Higher TyG index values indicate more severe CAD, offering a potential non-invasive diagnostic marker.

Area of Science:

  • Cardiology
  • Biomarker Discovery
  • Medical Diagnostics

Background:

  • Coronary angiography (CAG) is the gold standard for diagnosing coronary artery disease (CAD) but is invasive.
  • There is a need for non-invasive, effective biomarkers to assess CAD severity.
  • Hematological markers are being investigated as potential predictors of CAD.

Purpose of the Study:

  • To retrospectively analyze hematological markers for predicting CAD severity.
  • To evaluate the triglyceride-glucose (TyG) index as a non-invasive biomarker for CAD.
  • To assess the diagnostic efficacy of the TyG index, alone and in combination with other markers.

Main Methods:

  • Retrospective analysis of data from 195 CAD patients undergoing CAG.
  • Patients were stratified into mild, moderate, and severe CAD groups based on the Gensini score.
  • Analysis of blood indexes including the TyG index, fasting blood glucose (FBG), and high-sensitivity C-reactive protein (hs-CRP).

Main Results:

  • The TyG index was significantly higher in patients with moderate and severe CAD compared to mild CAD.
  • The TyG index showed an AUC of 0.615 for predicting CAD severity.
  • A combination of TyG index, FBG, and hs-CRP demonstrated improved diagnostic efficiency.

Conclusions:

  • The severity of CAD is positively correlated with elevated TyG index values.
  • The TyG index shows potential as a non-invasive indicator for CAD progression.
  • Combining TyG index, FBG, and hs-CRP enhances diagnostic capabilities for CAD.
Abstract