A Synergistic Effect of Remnant Cholesterol and C-Reactive Protein on Predicting the Severity of Coronary Artery Disease.

BACKGROUND

Increased levels of remnant cholesterol (RC) and inflammation are linked to higher risks of atherosclerotic cardiovascular disease. Whether a combination of C-reactive protein (CRP) and RC improves the predictive ability for evaluating the severity of coronary artery lesions remains unknown.

METHODS

A total of 1675 patients with coronary artery disease were stratified according to the Synergy Between Percutaneous Coronary Intervention (SYNTAX) score (SYNTAX score ≤22 versus SYNTAX score >22). Logistic regression and restricted cubic spline models were used to evaluate the relationship between RC, CRP and the severity of coronary artery lesions. Multivariate logistic regression was used to identify predictors of a mid/high SYNTAX score (SYNTAX score >22). The predictive value of RC combined with CRP was estimated by the ROC curve, continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

The multivariable-adjusted odds ratios (95% CIs) for the highest versus lowest quartile of RC were 2.143 (1.450-3.166) for a mid/high SYNTAX score (SYNTAX score >22). The association of RC with severity of coronary artery lesions was maintained significant in the subsample of patients, regardless of traditional cardiovascular risk factors like LDL-C levels and glycemic metabolism status. Moreover, the addition of CRP and RC to the baseline risk model had an incremental effect on the predictive value for a mid/high SYNTAX score (increase in C‑statistic value from 0.650 to 0.698; IDI 0.03; NRI 0.306; all P < 0.01).

CONCLUSION

Elevated RC levels were significantly associated with the severity of coronary artery lesions even in patients with optimal low-density lipoprotein cholesterol levels. Adjustment of the RC by CRP further improved the predictive ability for the severity of coronary artery lesions. The combination of RC and CRP might serve as a noninvasive predictor of CAD complexity and could potentially influence the management and therapeutic approach.