BACKGROUND

Elevated serum uric acid (SUA) is regarded as a risk factor for the development of cardiovascular diseases. Triglyceride-glucose (TyG) index, a novel surrogate for insulin resistance (IR), has been proven to be an independent predictor for adverse cardiac events. However, no study has specifically focused on the interaction between the two metabolic risk factors. Whether combining the TyG index and SUA could achieve more accurate prognostic prediction in patients undergoing coronary artery bypass grafting (CABG) remains unknown.

METHODS

This was a multicenter, retrospective cohort study. A total of 1225 patients who underwent CABG were included in the final analysis. The patients were grouped based on the cut-off value of the TyG index and the sex-specific criteria of hyperuricemia (HUA). Cox regression analysis was conducted. The interaction between the TyG index and SUA was estimated using relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). The improvement of model performance yielded by the inclusion of the TyG index and SUA was examined by C-statistics, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The goodness-of-fit of models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC) and χ likelihood ratio test.

RESULTS

During follow-up, 263 patients developed major adverse cardiovascular events (MACE). The independent and joint associations of the TyG index and SUA with adverse events were significant. Patients with higher TyG index and HUA were at higher risk of MACE (Kaplan-Meier analysis: log-rank P < 0.001; Cox regression: HR = 4.10; 95% CI 2.80-6.00, P < 0.001). A significant synergistic interaction was found between the TyG index and SUA [RERI (95% CI): 1.83 (0.32-3.34), P = 0.017; AP (95% CI): 0.41 (0.17-0.66), P = 0.001; SI (95% CI): 2.13 (1.13-4.00), P = 0.019]. The addition of the TyG index and SUA yielded a significant improvement in prognostic prediction and model fit [change in C-statistic: 0.038, P < 0.001; continuous NRI (95% CI): 0.336 (0.201-0.471), P < 0.001; IDI (95% CI): 0.031 (0.019-0.044), P < 0.001; AIC: 3534.29; BIC: 3616.45; likelihood ratio test: P < 0.001).

CONCLUSIONS

The TyG index interacts synergistically with SUA to increase the risk of MACE in patients undergoing CABG, which emphasizes the need to use both measures concurrently when assessing cardiovascular risk.