Cer(d18:1/16:0) as Predictive Biomarkers for Acute Coronary Syndrome: Insights from a Chinese Cohort

Background : Ceramides play a crucial role in atherosclerosis progression and have been linked to cardiovascular events. The objective of this study was to investigate the association between serum ceramide levels and Acute coronary syndrome, as well as evaluate their potential for predicting ACS in Chinese population. Methods: Data of 1327 patients with suspected or known coronary artery disease from Beijing anzhen Hospital and Handan First hospital were collected. Plasma ceramide were measured using the LC-MS/MS system. The area under the ROC curve was used to screen the most valuable predictor. Machine learning algorithms were used to identify critical ACS-related variables. Subgroup analysis and interaction test were performed to control for confounding factors. Multivariate Logistic models and restricted cubic spline analysis were conducted to examine the associations between Ceramide and ACS. Results: Cer (d18:1/14:0), Cer (d18:1/16:0), Cer (d18:1/18:0), Cer (d18:1/20:0), Cer (d18:1/22:0), and Cer (d18:1/24:0) were significantly elevated in the ACS group. Diagnostic performance assessments showed that Cer(d18:1/16:0) had superior accuracy in detecting ACS compared to other ceramides tested. The Boruta algorithm identified 15 significant variables related to ACS. Cer(d18:1/16:0) associated with ACS were discovered using the LASSO logistic regression technique. Subgroup analyses and logistic regression models further supported the relationship between Cer(d18:1/16:0) and ACS. Additionally, a significant nonlinear relationship was observed between Cer(d18:1/16:0) and ACS, with a threshold of 150umol/L. Conclusion: The study found that ceramides, particularly Cer(d18:1/16:0), were significantly associated with ACS and could be a potential biomarker for predicting and diagnosing ACS in Chinese populations experiencing chest pain.