The Role of the Triglyceride–Glucose Index and Other Prognostic Factors in Predicting Coronary Slow Flow

TL;DR

This study explores how the triglyceride–glucose index (TGI) and other factors might help predict coronary slow flow, a condition linked to insulin resistance.

Contribution

The study evaluates TGI as a potential predictor of coronary slow flow and identifies its limitations and possible future use.

Findings

TGI was significantly higher in patients with coronary slow flow compared to those with normal flow.

A TGI cut-off of ≥8.93 showed 67.6% sensitivity and 66.7% specificity for predicting coronary slow flow.

Abstract

Background: Insulin resistance (IR) has been implicated in cardiovascular diseases, and a correlation between IR and the slow flow phenomenon (CSF)has been identified. The triglyceride–glucose index (TGI), a simple surrogate marker for IR, has recently emerged as a potential predictor of CSF, though data are limited. The aim of this study was to evaluate the association of TGI and other prognostic parameters in patients with CSF. Methods: This retrospective study included 693 patients who underwent diagnostic coronary angiography between January 2022 and December 2024. A total of 132 patients were diagnosed with CSF based on the corrected TIMI frame count (cTFC > 27 in at least one epicardial coronary artery), while 561 patients had normal coronary flow (NCF). Patients with confounding cardiovascular or systemic conditions were excluded. Clinical, demographic, and laboratory data were gathered, and TGI was calculated as ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2].Results: Statistically significant distinctions were found between the CSF and NCF groups regarding TGI, age, glucose, HbA1c, creatinine, sodium, CRP, platelet count, heart rate, PR interval, and cQT interval (p < 0.05). Age, hypertension, diabetes mellitus, HbA1c, glucose, sodium, and cQT were identified as potential clinical and laboratory factors associated with CSF in univariate logistic regression analysis; however, no independent predictor was found in multivariate analysis. ROC analysis showed that a TGI cut-off value of ≥8.93 predicted CSF with 67.6% sensitivity and 66.7% specificity. Conclusions: Our study demonstrated that TGI was significantly greater in patients with CSF compared to those with NCF. Although TGI showed limited sensitivity and specificity in discriminating CSF, its possible value as a prognostic indicator warrants further validation in prospective, large-scale studies.

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