Association between triglyceride glucose index and adverse cardiovascular prognosis in patients with atrial fibrillation without diabetes: a retrospective cohort study.

Abstract

Background: Atrial fibrillation (AF) is the most prevalent arrhythmia encountered in clinical practice. Triglyceride glucose index (Tyg), a convenient evaluation variable for insulin resistance, has shown associations with adverse cardiovascular outcomes. However, studies on the Tyg index's predictive value for adverse prognosis in patients with AF without diabetes are lacking.

Methods: This retrospective study utilized electronic medical records to collect data on patients with AF hospitalized at West China Hospital from January to June 2020. Participants were categorized into three groups based on their Tyg index levels. The primary outcome, major adverse cardiovascular events, included cardiac death, stroke, and myocardial infarction. Kaplan-Meier curve, Cox proportional hazards regression model, and restricted cubic spline were employed to explore the relationship between the Tyg index and outcomes. The predictive performance of the CHA2DS2-VASc model was evaluated after incorporating the Tyg index.

Results: The study comprised 864 participants (mean age 67.69 years, 55.32% male, 57.52% paroxysmal AF). Patients with high Tyg index had a significantly higher risk of developing major adverse cardiovascular events (MACE) (P < 0.001, hazard ratio: 2.05, 95% confidence interval:1.65-2.56). The MACE risk in the middle Tyg group was similar to that in the low Tyg group (P = 0.1) during the 48-month follow-up period. However, focusing on the last 24 months revealed a higher MACE risk (P = 0.015) in the middle Tyg group. The restricted cubic spline analysis revealed an S-shaped correlation between Tyg and MACE. The CHA2DS2-VASc model combined with the Tyg index showed improved predictive performance and net benefit.

Conclusions: A high Tyg index is associated with poorer prognosis in patients with AF without diabetes. Integrating the Tyg index into the CHA2DS2-VASc model may enhance its predictive performance, offering clinical utility.