Ceramides and pro-inflammatory cytokines for the prediction of acute coronary syndrome: a multi-marker approach

Abstract

Abstract Background There is a growing body of evidence supporting the significant involvement of both ceramides and pro-inflammatory cytokines in the occurrence and progression of acute coronary syndrome (ACS). Methods This study encompassed 216 participants whose laboratory variables were analysed using standardised procedures. Parameters included baseline serum lipid markers, comprising total cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, triglycerides (TGs), lipoprotein(a) (LPa), fasting blood glucose, B-natriuretic peptide and hypersensitive C-reactive protein. Liquid chromatography-tandem mass spectrometry measured the concentrations of plasma ceramides. Enzyme-linked immunosorbent assay quantified tumour necrosis factor-α (TNF-α), interleukin 6 (IL6) and IL8. The correlation between ceramides and inflammatory factors was determined through Pearson’s correlation coefficient. Receiver operating characteristic (ROC) curve analysis and multivariate logistic regression evaluated the diagnostic potential of models incorporating traditional risk factors, ceramides and pro-inflammatory cytokines in ACS detection. Results Among the 216 participants, 138 (63.89%) were diagnosed with ACS. Univariate logistic regression analysis identified significant independent predictors of ACS, including age, gender, history of diabetes, smoking history, TGs, TNF-α, IL-6, ceramide (d18:1/16:0), ceramide (d18:1/18:0), ceramide (d18:1/24:0), ceramide (d18:1/20:0) and ceramide (d18:1/22:0). Multivariate logistic regression analysis revealed significant associations between gender, diabetes mellitus history, smoking history, LPa, IL-6, ceramide (d18:1/16:0) and ACS. Receiver operating characteristic analysis indicated that model 4, which integrated traditional risk factors, IL-6 and ceramide (d18:1/16:0), achieved the highest area under the curve (AUC) of 0.827 (95% CI 0.770–0.884), compared with model 3 (traditional risk factors and ceramide [d18:1/16:0]) with an AUC of 0.782 (95% CI 0.720–0.845) and model 2 (traditional risk factors and IL-6), with an AUC of 0.785 (95% CI 0.723–0.846) in ACS detection. Conclusions In summary, incorporating the simultaneous measurement of traditional risk factors, pro-inflammatory cytokine IL-6 and ceramide (d18:1/16:0) can improve the diagnostic accuracy of ACS.