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

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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. ceramide acute coronary syndrome Risk factors Biomarkers lipids Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Acute coronary syndrome (ACS) is a leading cause of morbidity and mortality worldwide, significantly contributing to the global burden of cardiovascular disease. Early detection and treatment of patients at risk for acute coronary events are essential for effectively mitigating this burden. The core pathogenesis of ACS lies in the unstable state of atherosclerotic plaques and the associated pathophysiological processes[ 1 – 2 ]. Ceramides, known for their role in accelerating the uptake of low-density lipoprotein (LDL) particles and their penetration into the arterial wall, are involved in several key aspects of atherosclerosis progression, including inflammation and apoptosis[ 3 ]. The concentration of ceramide in these plaques is significantly higher than in the blood: histological studies have revealed that ceramide concentrations in atherosclerotic plaques are over 50 times higher than those in the blood[ 4 ]. Three ceramides—Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1)—were identified as significant predictors of sudden cardiac death and major adverse cardiovascular events (MACE)[ 5 ]. These associations have been confirmed in both secondary prevention cohorts with known CVD and primary prevention general population cohorts. Therefore, as a potential biomarker, ceramide can provide valuable information for early identification, risk stratification, and treatment decision-making in patients with angina. In our recent study, Cer(d18:1/16:0) was found to be significantly associated with residual inflammatory risk among CAD patients[ 6 ]. Despite many studies linking ceramides to cardiovascular disease severity and risk, no clinical studies have specifically investigated the diagnostic value of ceramides in ACS within the Chinese population. Hence, we performed a clinical trial (Evaluating the Role of Serum Ceramide Testing in the Diagnosis of Acute Coronary Syndrome, Chinese Clinical Trial Registry, ChiCTR-2200056697) that aims to evaluate the role of seven ceramides in the adjunctive diagnosis of acute coronary syndrome. The objective of this study was to investigate the association between serum ceramide levels and ACS, as well as evaluate their potential for predicting ACS in patients who have experienced chest pain. Methods Study design and participants We conducted a Multicenter Clinical Study to assess the value of plasma ceramide in the diagnosis of ACS (Chinese Clinical Trial Registry, ChiCTR-2200056697). This study protocol was approved by the Institutional Review Board at Beijing Anzhen Hospital under the Declaration of Helsinki. All participants provided written informed consent for participation in this study. The recruited patients with suspected CAD were from Beijing Anzhen Hospital and Handan First Hospital, and the recruitment took place between April 2021 and May 2023. The inclusion criteria were: 1) Patients aged 18 years or older who are undergoing coronary angiography for chest pain, and 2) patients who had a complete clinical data record. The exclusion criteria were: 1) Pregnant women, 2) Patients with familial hypercholesterolemia, 3) Patients suffering from bleeding disorders, 4) Patients with neoplasms with a life expectancy < 1 year. 5) Patients with mental illness. 6) Patients with a history of drug abuse or alcohol dependence. 7) Patients with chronic kidney disease (eGFR < 60 mL/min/1.73 m2). A total of 1327 patients were included finally. Ceramide measurement For laboratory analysis, blood samples taken from participants were first treated with EDTA for anticoagulation. Specifically, a volume of 500 microliters of blood was collected from each individual. The plasma was swiftly separated within an hour of collection and then preserved at -80° until the time of analysis. The ABSciex TripleQuad™ 4500MD LC-MS/MS system, manufactured by Sciex in Framingham, MA, United States, was employed for the quantification of circulating plasma ceramides. This sophisticated system facilitated the simultaneous quantification of various ceramides, including 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), Cer(d18:1/24:0), and Cer(d18:1/24:1). The detailed methodology for measuring ceramides has been previously published and is readily available in our recent publication[ 7 ]. Based on the measured ceramide values, we further calculated the ratios of different ceramides to Cer(d18:1/24:0), abbreviated as CerXR. For example: Cer(d18:1/14:0)R [Cer(d18:1/14:0)/Cer(d18:1/24:0)], Cer(d18:1/16:0)R [Cer(d18:1/16:0)/ Cer(d18:1/24:0)],etc. Diagnostic protocol and Data collection Each center will form a clinical panel of four senior cardiologists to assess participants' diagnoses. The evaluation will follow guidelines from the fourth edition of the Unified Global definition of myocardial infarction, the 2017 ESC guidelines for Acute ST-segment elevation myocardial infarction, the 2015 ESC guidelines for Acute non-ST-segment elevation acute coronary syndrome, and the 2019 ESC guidelines for chronic conditions. Patients' demographic and clinical characteristics were obtained through a review of their medical records. The recorded clinical characteristics of the patients included age, sex, presence of diabetes, hypertension, and smoking status. Statistical analysis Sample Size : Using the PASS 15 (NCSS, LLC. Kaysville, Utah, USA), we estimated the area under the ROC curve of a diagnostic index compared to clinical diagnostic criteria to determine the required sample size. We conducted a two-sided test at a significance level (α) of 0.05 and a confidence level (1-β) of 0.8. The AUC of the ROC curve was 0.75, with the lowest acceptable AUC set at 0.7. The ratio of patients in the clinical diagnosis negative group to the positive group was 2:5. To ensure adequate statistical power, we aimed to enroll a minimum of 741 patients with acute coronary syndrome and 296 patients without acute coronary syndrome, totaling at least 1037 participants. Accounting for a 10% dropout rate, we decided to enroll 824 patients with acute coronary syndrome and 329 patients without the syndrome. The target effective sample size for the clinical trial was set at 1200 or higher, with the acute coronary syndrome group comprising no less than 824 participants. Study endpoints ༚If the area under the ROC curve for any ceramide parameter reached 0.7, ceramide was considered to have an auxiliary diagnostic value for clinical acute coronary syndrome. We started with 19 variables, including demographic characteristics, medical history and ceramide, as predictors. Continuous variables with a normal distribution, as determined by the Kolmogorov–Smirnov test, were presented as means ± standard deviation. Variables without a normal distribution were expressed as median (range). Categorical variables were presented as n (%) and analyzed using the chi-square test. Spearman's correlation test was used to assess the correlation between the ceramide variables. The differences in serum ceramide levels between the two groups (ACS group and non-ACS group) were compared using Mann–Whitney U tests. The performance of each independent ceramide in predicting ACS was determined by the area under curve (AUC). We employed the Boruta algorithm with 5-fold cross-validation for feature selection. Furthermore, lasso screening was performed to address multicollinearity. The intersection variables between the Lasso regression and Boruta analysis were selected as the independent risk factors for ACS. The receiver operating characteristic (ROC) curve analysis was performed to assess the Cer(d18:1/16:0), and Cer(d18:1/16:0)R in predicting ACS. For sensitivity analysis, Cer(d18:1/16:0) was analyzed as a continuous variable and a categories variable, respectively. The factors entered into the logistic regression models were sex and Cer(d18:1/16:0)R); Results were reported as odds ratios (ORs) with associated 95% confidence intervals (CIs). All statistical analyses were performed in SPSS 26.0 (IBM, Inc., Chicago United States) and R studio with the R version ( R version 4.2.2.). Statistical significance was defined as a P-value of less than 0.05 for a two-tailed test. Results Characteristics of the Study Population 1327 patients with suspected or known CAD were evaluated with invasive coronary angiography. Patient demographics data and ceramide parameters were summarized in Table 1 . The levels of Ceramide under different groupings were demonstrated in Fig. 1 . Compared with the non-ACS group, patients with ACS had higher Cer(d18:1/14:0) (P < 0.01), Cer(d18:1/16:0) (P < 0.001), Cer(d18:1/18:0) (P < 0.001), Cer(d18:1/20:0) (P < 0.05), Cer(d18:1/22:0) (P < 0.05) and Cer(d18:1/24:0) (P < 0.05), and Cer(d18:1/24:1) (P < 0.05) did not differ significantly between the groups. Table 1 Patient demographics and baseline characteristics Characteristic N = 1,327 SEX Female 444 (33.5%) Male 883 (66.5%) AGE 61 ± 10 AGE_60 No 590 (44.5%) Yes 737 (55.5%) Hypertension No 324 (24.4%) Yes 1,003 (75.6%) Diabete melltius No 901 (67.9%) Yes 426 (32.1%) Smoking Never 385 (29.0%) Former 403 (30.4%) Current 539 (40.6%) Coronary artery disease Non obstructive coronary artery disease 213 (16.1%) Stable coronary artery disease 113 (8.5%) Unstable angina 856 (64.5%) Acute myocardial infarction 145 (10.9%) Ceramide and Ratio Cer(d18:1/14:0) 2.44 (1.76, 3.28) Cer(d18:1/16:0) 150 (123, 183) Cer(d18:1/18:0) 40.7 (30.4, 55.2) Cer(d18:1/20:0) 56.3 (43.3, 71.9) Cer(d18:1/22:0) 404 (318, 519) Cer(d18:1/24:0) 1760 (1380, 2230) Cer(d18:1/24:1) 460 (356, 575) Cer(d18:1/14:0)R 0.0013(0.0010, 0.0018) Cer(d18:1/16:0)R 0.08 (0.06, 0.10) Cer(d18:1/18:0)R 0.022 (0.017, 0.031) Cer(d18:1/20:0)R 0.031 (0.024, 0.041) Cer(d18:1/22:0)R 0.22 (0.20, 0.25) Cer(d18:1/24:1)R 0.25 (0.19, 0.33) For categorical variables, absolute numbers (n) and relative proportions (%) are presented. For continuous variables, the median along with the interquartile range is presented. Variables selection Spearman test was conducted to assess the correlation between ceramides, revealing a significant positive correlation among them, as depicted in the Fig. 2 . Collinearity among the variables was assessed before modeling. The results of the correlation analysis indicated a high degree of collinearity among the independent variables(STable 1). LASSO regression and Boruta feature selection algorithms were employed to identify essential variables in the DataSet, and distinctive ACS related variables were screened out using these methods(STable 2, STable3). The Boruta algorithm identified 15 significant variables related to ACS. Three critical variables (sex, Cer(d18:1/16:0), Cer(d18:1/16:0)R ) associated with ACS were discovered using the LASSO logistic regression technique (Fig. 3 ). Diagnostic performance of Ceramides for ACS The diagnostic performance of Cer(d18:1/16:0)(cut-off value 150umol/L) for detecting ACS, measured by sensitivity, accuracy, PPV and AUC, was significantly better than those of Ceramide(Table 2 ). Moreover, Cer(d18:1/20:0) had the highest specificity (83.1%), but lower sensitivity(25.6%) and NPV(26.6%). Applied ROC curve to assess the values of Cer(d18:1/16:0) and Cer(d18:1/16:0)R in predicting ACS. The ROC curve showed that the AUC values of Cer(d18:1/16:0) were significantly higher compared to those of Cer(d18:1/16:0)R(AUC: 0.678 vs 0.608, P = 0.001) (Fig. 4 ). Table 2 Diagnosis accuracy assessment of ceramides in predicting ACS AUC Accuracy Sensitivity Specificity PPV NPV Cer(d18:1/14:0) 0.551 (0.516–0.586) 48.9% 44.0% 66.6% 80.1% 27.8% Cer(d18:1/16:0) 0.678 (0.645–0.710) 61.3% 58.7% 69.3% 85.4% 35.3% Cer(d18:1/18:0) 0.565 (0.530–0.600) 46.5% 38.4% 71.5% 80.5% 27.4% Cer(d18:1/20:0) 0.545 (0.509–0.580) 39.7% 25.6% 83.1% 82.3% 26.6% Cer(d18:1/22:0) 0.542 (0.506–0.577) 44.5% 32.8% 75.5% 80.3% 26.7% Cer(d18:1/24:0) 0.547 (0.512–0.583) 51.8% 49.3% 59.8% 79.0% 27.7% Cer(d18:1/24:1) 0.524 (0.489–0.559) 51.4% 49.3% 58.0% 78.2% 27.1% Data are expressed as n (95% confidence interval). Association between Cer(d18:1/16:0) and ACS As indicated in Table 3 , three different models (logistic regression models) were constructed to evaluate the correlation between Cer(d18:1/16:0) and ACS. Adjustment was made for demographic variables in Model 1 and Model 2, while different ceramide indicators were considered in Model 3. Despite these adjustments, Cer(d18:1/16:0) maintained a positive correlation with ACS. This correlation persisted even when Cer(d18:1/16:0) was converted into a categorical variable. Results from subgroup analyses validated the relationship between the Cer(d18:1/16:0)(per 1SD) and ACS across age, sex, hypertension, diabetes, and smoking status subgroups. Hypertension was found to interact with the relationship between the Cer(d18:1/16:0) and ACS ( Table 4 ) . Table 3 Association between Cer(d18:1/16:0) and ACS Characteristic Model 1 Model 2 Model3 OR(95% CI) p-value OR(95% CI) p-value OR(95% CI) p-value Cer(d18:1/16:0) as a continuous variable Cer(d18:1/16:0) 1.02(1.01, 1.02) 2.15(1.82, 2.55) < 0.001 1.01(1.01, 1.02) 2.06(1.73, 2.45) < 0.001 1.03(1.01, 1.04) 3.01(1.62, 5.58) < 0.001 Cer(d18:1/16:0) (per 1SD) < 0.001 < 0.001 < 0.001 Cer(d18:1/16:0) as a categories variable (Quartiles) Cer(d18:1/16:0) Q1 Reference Q2 1.69(1.21, 2.35) 0.002 1.62(1.16, 2.26) 2.74(1.91, 3.92) 6.61(4.23, 10.32) 0.005 1.69(1.14, 2.50) 2.97(1.72, 5.11) 7.38(3.02, 18.00) < 0.001 Q3 2.87(2.00, 4.10) 7.40(4.77, 11.47) < 0.001 < 0.001 < 0.001 Q4 < 0.001 < 0.001 < 0.001 P for trend < 0.001 < 0.001 < 0.001 OR = Odds Ratio, CI = Confidence Interval Model 1 : adjusted for SEX and Age; Model 2 : adjusted for SEX, Age and Cer(d18:1/16:0)R; Model 3 : adjusted for 12 Ceramide indices (Cer(d18:1/14:0), Cer(d18:1/18:0), Cer(d18:1/20:0), Cer(d18:1/22:0), Cer(d18:1/24:0), Cer(d18:1/24:1), Cer(d18:1/14:0)R, Cer(d18:1/16:0)R Cer(d18:1/18:0)R, Cer(d18:1/20:0)R, Cer(d18:1/22:0)R, Cer(d18:1/24:1)R) Table 4 Association between Cer(d18:1/16:0) (per 1SD) and ACS in different subgroups Subgroup N OR (95% CI) P value P for interaction Overall 1327 2.04 (1.73–2.41) < 0.001 SEX 0.772 Female 444 2.22 (1.70–2.89) < 0.001 Male 883 2.11 (1.69–2.63) < 0.001 AGE_60 0.539 No 590 1.94 (1.53–2.45) < 0.001 Yes 737 2.15 (1.70–2.71) < 0.001 Hypertension 0.021 No 324 3.03 (2.06–4.45) < 0.001 Yes 1003 1.83 (1.53–2.20) < 0.001 Diabete melltius 0.741 No 901 2.04 (1.67–2.48) < 0.001 Yes 426 1.92 (1.41–2.61) < 0.001 Smoking 0.162 Never 385 2.60 (1.91–3.54) < 0.001 Former 403 1.90 (1.40–2.57) < 0.001 Current 539 1.79 (1.37–2.35) < 0.001 The nonlinearity is addressed by the Logistic regression model with a Restricted Cubic Spline The RCS analysis revealed a non-linear association between Cer(d18:1/16:0) and ACS, with an inflection point detected at Cer(d18:1/16:0) = 150umol/L ( Fig. 5 A ) . Using the inflection point, the data was stratified into two groups, and segmented regression was then performed on each group separately: Cer(d18:1/16:0) < 150 umol/L [OR (per 1SD) = 1.29, 95%CI:1.10–1.51, P = 0.002 ] and Cer(d18:1/16:0) ≥ 150 umol/L [OR (per 1SD) = 1.46, 95%CI:1.11–1.93, P = 0.007 ]. Similar results were also observed in the odds ratio analysis of Cer(d18:1/16:0) after adjusting for sex, age, and Cer(d18:1/16:0)R in relation to ACS ( Fig. 5 B ) . Discussion The findings of this study demonstrate that Cer(d18:1/16:0) is an independent biomarker significantly associated with ACS. Moreover, a nonlinear relationship with a positive correlation between Cer(d18:1/16:0) and ACS was observed using RCS models. The first study, to the best of our knowledge, also presented alternative cut-off points for Cer(d18:1/16:0) levels for diagnosing ACS in the Chinese population. This is conducive to optimizing clinical management strategies. Some previous studies have shown consistent results with ours investigating the correlation between Cer(d18:1/16:0) and ACS. Previous clinical research found that elevated ceramide plasma concentrations are associated with coronary plaque vulnerability evaluated by endovascular imaging[ 8 – 9 ]. Laaksonen found higher expression levels of certain ceramides in ACS patients compared to stable coronary heart disease patients[ 10 ]. Advances in lipidomics have identified circulating ceramides as significant predictors of atherosclerotic cardiovascular events[ 11 ]. In comparison to traditional cardiovascular disease risk factors, ceramide—a bioactive sphingolipid—has demonstrated enhanced predictive abilities for cardiovascular disease events[ 12 ]. Ceramide risk scores, derived from high-risk ceramide subtypes (Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:1)) identified in previous studies, have been developed and adapted for routine clinical practice[ 13 – 14 ]. Research in rodents shows that ceramides contribute to cardiovascular diseases by causing metabolic issues. Interventions that either reduce ceramide production or increase their breakdown improve conditions like atherosclerosis, insulin resistance, fatty liver disease, and cardiomyopathy[ 15 ]. Study in mouse models shows that ASM and ceramide signaling critically mediate hypocholesterolemia-induced NLRP3 inflammasome activation in endothelial cells, leading to endothelial dysfunction, vascular inflammation, and atherosclerosis[ 16 ]. Hammerschmidt et al. revealed that the CerS6/Cer16:0/Mff(mitochondrial fission factor) pathway regulates mitochondrial dynamics and insulin resistance in obesity, proposing the interaction between CerS6-derived sphingolipids and Mff as a therapeutic target for metabolic diseases[ 17 ]. They represent a promising class of molecules with considerable therapeutic potential and clinical applicability. Previous data indicate that various ceramides may increase in cases of hypertension, type 2 diabetes, and insulin resistance[ 18 – 20 ]. Our subgroup analyses show that Cer(d18:1/16:0) is independently linked to ACS. This study also applied RCS models to assess a nonlinear relationship, allowing for an in-depth analysis of the correlations between Cer(d18:1/16:0) and ACS. Combined with previous research, we believe that Cer(d18:1/16:0) may play a significant role in the pathophysiology of coronary atheroma plaque rupture and could potentially serve as a novel biomarker for ACS. Strength and Limitation Our study possesses inherent strengths which are listed below. Firstly, to mitigate the inherent limitations of a cross-sectional study design, we substantially enlarged our study population and employed machine learning techniques to identify key variables. Secondly, we verified the strength of the results through sensitivity analysis, which involved transforming variable forms, such as normalization or reclassification, and conducting subgroup analyses. Lastly, our study was the first to focus on the Chinese population to determine the relationship between ceramide and ACS, ours study highlights a nonlinear relationship and identifies an inflection point in the correlation between ceramide levels and ACS. However, it had to be acknowledged that there were some limitations. First of all, we did not examine the causal relationship between ceramide and ACS due to the cross-sectional study design in this paper. Second, our study focused solely on ceramides and did not take into account other clinical markers, such as cholesterol et.al. In addition, fewer patients with acute myocardial infarction were included in the study population. Additional studies are required to investigate the diagnostic and prognostic value of ceramides in ACS by conducting follow-up assessments in various clinical models among diverse populations. Additionally, it is important to note that female patients in our study were predominantly postmenopausal; therefore, the results' external generalizability should not make a substantial difference. Conclusion In the present study, the clinical features of ceramide were reviewed in the Chinese population suffered chest pain, and a machine learning algorithm was employed to screen risk factors. Our study demonstrated a significant positive association between Cer(d18:1/16:0) and ACS, and the establishment of a clear ceramide threshold will enhance our ability to identify ACS. Declarations Ethics approval and consent to participate This study complied with the Declaration of Helsinki and received approval from the Ethics Committee of Beijing Anzhen Hospital, affiliated with Capital Medical University. Consent for publication All authors have reviewed and approved the final version of the manuscript. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Availability of data and materials Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher Competing interests The authors declare no competing interests. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 82270345) and the Capital’s Funds for Health Improvement and Research (Grant No. CFH2024-1-2061). Authors' contributions Liang Zhang: Writing – review & editing, Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Yang Zhang and YaoDong Ding: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Funding acquisition, Data curation. Tong Jin and Yi Song: Visualization, Data curation. Lin Li: Project administration, Methodology, Funding acquisition, Formal analysis. Yong Zeng and XiaoFang Wang: Resources, Project administration, Methodology, Investigation, Funding acquisition. Acknowledgements We would like to thank the Beijing Health Biotechnology Co., Ltd. for their help in biomarker detection. References Roy P, Orecchioni M, Ley K. How the immune system shapes atherosclerosis: roles of innate and adaptive immunity. Nat Rev Immunol 2021. Nicholls SJ, Ballantyne CM, Barter PJ, et al. 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Sokolowska E, Blachnio-Zabielska A. The role of ceramides in insulin resistance. Front Endocrinol. 2019;10:577. 10.3389/fendo.2019.00577 . Brittain EL, Talati M, Fessel JP, et al. Fatty Acid Metabolic Defects and Right Ventricular Lipotoxicity in Human Pulmonary Arterial Hypertension. Circulation. 2016;133(20):1936–44. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5453741","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":384822880,"identity":"7e763aa6-78ef-40a0-a8ae-4dbd9caed790","order_by":0,"name":"Liang Zhang","email":"","orcid":"","institution":"Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Disease","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Zhang","suffix":""},{"id":384822881,"identity":"c51041e2-8f53-43c5-b8b6-8d5a48c76f3b","order_by":1,"name":"Yang Zhang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhang","suffix":""},{"id":384822882,"identity":"b2f446ac-b859-4eec-9aa1-f53692c7dc29","order_by":2,"name":"YaoDong Ding","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"YaoDong","middleName":"","lastName":"Ding","suffix":""},{"id":384822883,"identity":"b2f487b4-b42b-4c8c-af2a-c2033bb2eb4b","order_by":3,"name":"Tong Jin","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Jin","suffix":""},{"id":384822884,"identity":"80fbf1f3-af1f-4307-8a07-704131e56fc5","order_by":4,"name":"Yi Song","email":"","orcid":"","institution":"Beijing Dong zhi men Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Song","suffix":""},{"id":384822885,"identity":"a5c5d216-e972-4501-a1b6-6e2c5b5bf0ba","order_by":5,"name":"Lin Li","email":"","orcid":"","institution":"Beijing Health Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Li","suffix":""},{"id":384822886,"identity":"29ee0ef6-da92-4c38-a4ee-761ecd16f777","order_by":6,"name":"XiaoFang Wang","email":"","orcid":"","institution":"Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Disease","correspondingAuthor":false,"prefix":"","firstName":"XiaoFang","middleName":"","lastName":"Wang","suffix":""},{"id":384822887,"identity":"4fc92086-c151-4d1a-ba59-d22d6589d2f2","order_by":7,"name":"Yong Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYPCCAwwM7I2NDz+QpoXncLOxBGlaJNLbBHiIUSs/I4Hxc8GfO3Lmkg/bGCQY7OR0GwhoYZyRwCw9g+eZseXsxLYHBQzJxmYHCGhhlkhgkOaROJy44XZiu4EEw4HEbYS0sEkkMP/mMThcv+HmwTYJHmK08EgksEnzJBxOMLjBSKQWCZ4HbNY8Bw4bbjiTCAxkAyL8It+ewHyb589heYPjxx8+/FBhJ0dQC4NAPnKUGxBSDgL8BA0dBaNgFIyCEQ8AwWBAonp8Q98AAAAASUVORK5CYII=","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2024-11-14 12:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5453741/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5453741/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71741677,"identity":"d6e3560e-8cd0-4f10-92ff-933c954557ca","added_by":"auto","created_at":"2024-12-18 08:16:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":642580,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum Ceramide levels under different groupings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparison of Cer(d18:1/14:0) (A), Cer(d18:1/16:0) (B), Cer(d18:1/18:0) (C), Cer(d18:1/20:0) (D), Cer(d18:1/22:0) (E), (d18:1/24:0) (F), Cer(d18:1/24:1) (G) between ACS patients and nonACS patients.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5453741/v1/742e8911ec391c85f4492ab2.png"},{"id":71742733,"identity":"77e6acb7-417a-4b24-9545-83d604de53cd","added_by":"auto","created_at":"2024-12-18 08:24:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":541807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlation analysis of correlation between ceramides\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe area and color of the circle represent the value of the correlation coefficient. The larger the area of the circle, the larger the absolute value of the correlation coefficient; the color of the circle corresponds to the value of the correlation coefficient on the color scale. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5453741/v1/6699c2ecc2c74c48159236ae.png"},{"id":71741682,"identity":"bda4d3f2-de19-45a6-9ead-f0533286a768","added_by":"auto","created_at":"2024-12-18 08:16:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":676351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of characteristic variable-related diagnositic signature in ACS using Lasso.cv and Boruta.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Relevant features (highlighted in green) to the interest selected with Boruta algorithm. (B) Lasso regression analysis to eliminate collinearity，LASSO coefficient profile are plotted against the log (λ) sequence. (C) Partial likelihood deviance for different numbers of variables, there are 3 non-zero coefficients (sex, Cer(d18:1/16:0), Cer(d18:1/16:0)R ) obtained according to the λ value.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5453741/v1/b2d299216fc44f0c4ee34da0.png"},{"id":71741681,"identity":"cf65a05e-bdc4-483a-b604-234e5fcd2aa8","added_by":"auto","created_at":"2024-12-18 08:16:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":621421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis of Cer(d18:1/16:0) and Cer(d18:1/16:0)R in discriminating ACS.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5453741/v1/62d90576a6e8e2d8e02b470c.png"},{"id":71741689,"identity":"3ffb46c4-ed38-4863-85c1-baf69c596012","added_by":"auto","created_at":"2024-12-18 08:16:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":733115,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNonlinear relationship between Cer(d18:1/16:0) and ACS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjustment for age, male, hypertension, diabetes mellitus, and smoking. The solid lines represent OR, and blue areas indicate the 95% CIs.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5453741/v1/ffdf49efb3a78f9157c85392.png"},{"id":71744675,"identity":"86d5027d-b0c6-4f1c-980b-41b0a34f5833","added_by":"auto","created_at":"2024-12-18 08:40:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4030617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5453741/v1/8123ca78-c4d1-4163-8bc6-1db91ad978f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cer(d18:1/16:0) as Predictive Biomarkers for Acute Coronary Syndrome: Insights from a Chinese Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute coronary syndrome (ACS) is a leading cause of morbidity and mortality worldwide, significantly contributing to the global burden of cardiovascular disease. Early detection and treatment of patients at risk for acute coronary events are essential for effectively mitigating this burden.\u003c/p\u003e \u003cp\u003eThe core pathogenesis of ACS lies in the unstable state of atherosclerotic plaques and the associated pathophysiological processes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Ceramides, known for their role in accelerating the uptake of low-density lipoprotein (LDL) particles and their penetration into the arterial wall, are involved in several key aspects of atherosclerosis progression, including inflammation and apoptosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The concentration of ceramide in these plaques is significantly higher than in the blood: histological studies have revealed that ceramide concentrations in atherosclerotic plaques are over 50 times higher than those in the blood[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Three ceramides\u0026mdash;Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1)\u0026mdash;were identified as significant predictors of sudden cardiac death and major adverse cardiovascular events (MACE)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These associations have been confirmed in both secondary prevention cohorts with known CVD and primary prevention general population cohorts. Therefore, as a potential biomarker, ceramide can provide valuable information for early identification, risk stratification, and treatment decision-making in patients with angina. In our recent study, Cer(d18:1/16:0) was found to be significantly associated with residual inflammatory risk among CAD patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite many studies linking ceramides to cardiovascular disease severity and risk, no clinical studies have specifically investigated the diagnostic value of ceramides in ACS within the Chinese population.\u003c/p\u003e \u003cp\u003eHence, we performed a clinical trial (Evaluating the Role of Serum Ceramide Testing in the Diagnosis of Acute Coronary Syndrome, Chinese Clinical Trial Registry, ChiCTR-2200056697) that aims to evaluate the role of seven ceramides in the adjunctive diagnosis of acute coronary syndrome. The objective of this study was to investigate the association between serum ceramide levels and ACS, as well as evaluate their potential for predicting ACS in patients who have experienced chest pain.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eWe conducted a Multicenter Clinical Study to assess the value of plasma ceramide in the diagnosis of ACS (Chinese Clinical Trial Registry, ChiCTR-2200056697). This study protocol was approved by the Institutional Review Board at Beijing Anzhen Hospital under the Declaration of Helsinki. All participants provided written informed consent for participation in this study.\u003c/p\u003e \u003cp\u003eThe recruited patients with suspected CAD were from Beijing Anzhen Hospital and Handan First Hospital, and the recruitment took place between April 2021 and May 2023. The inclusion criteria were: 1) Patients aged 18 years or older who are undergoing coronary angiography for chest pain, and 2) patients who had a complete clinical data record. The exclusion criteria were: 1) Pregnant women, 2) Patients with familial hypercholesterolemia, 3) Patients suffering from bleeding disorders, 4) Patients with neoplasms with a life expectancy\u0026thinsp;\u0026lt;\u0026thinsp;1 year. 5) Patients with mental illness. 6) Patients with a history of drug abuse or alcohol dependence. 7) Patients with chronic kidney disease (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m2). A total of 1327 patients were included finally.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCeramide measurement\u003c/h3\u003e\n\u003cp\u003eFor laboratory analysis, blood samples taken from participants were first treated with EDTA for anticoagulation. Specifically, a volume of 500 microliters of blood was collected from each individual. The plasma was swiftly separated within an hour of collection and then preserved at -80\u0026deg; until the time of analysis. The ABSciex TripleQuad\u0026trade; 4500MD LC-MS/MS system, manufactured by Sciex in Framingham, MA, United States, was employed for the quantification of circulating plasma ceramides. This sophisticated system facilitated the simultaneous quantification of various ceramides, including 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), Cer(d18:1/24:0), and Cer(d18:1/24:1). The detailed methodology for measuring ceramides has been previously published and is readily available in our recent publication[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Based on the measured ceramide values, we further calculated the ratios of different ceramides to Cer(d18:1/24:0), abbreviated as CerXR. For example: Cer(d18:1/14:0)R [Cer(d18:1/14:0)/Cer(d18:1/24:0)], Cer(d18:1/16:0)R [Cer(d18:1/16:0)/ Cer(d18:1/24:0)],etc.\u003c/p\u003e\n\u003ch3\u003eDiagnostic protocol and Data collection\u003c/h3\u003e\n\u003cp\u003eEach center will form a clinical panel of four senior cardiologists to assess participants' diagnoses. The evaluation will follow guidelines from the fourth edition of the Unified Global definition of myocardial infarction, the 2017 ESC guidelines for Acute ST-segment elevation myocardial infarction, the 2015 ESC guidelines for Acute non-ST-segment elevation acute coronary syndrome, and the 2019 ESC guidelines for chronic conditions. Patients' demographic and clinical characteristics were obtained through a review of their medical records. The recorded clinical characteristics of the patients included age, sex, presence of diabetes, hypertension, and smoking status.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e\u003cb\u003eSample Size\u003c/b\u003e: Using the PASS 15 (NCSS, LLC. Kaysville, Utah, USA), we estimated the area under the ROC curve of a diagnostic index compared to clinical diagnostic criteria to determine the required sample size. We conducted a two-sided test at a significance level (α) of 0.05 and a confidence level (1-β) of 0.8. The AUC of the ROC curve was 0.75, with the lowest acceptable AUC set at 0.7. The ratio of patients in the clinical diagnosis negative group to the positive group was 2:5. To ensure adequate statistical power, we aimed to enroll a minimum of 741 patients with acute coronary syndrome and 296 patients without acute coronary syndrome, totaling at least 1037 participants. Accounting for a 10% dropout rate, we decided to enroll 824 patients with acute coronary syndrome and 329 patients without the syndrome. The target effective sample size for the clinical trial was set at 1200 or higher, with the acute coronary syndrome group comprising no less than 824 participants. \u003cb\u003eStudy endpoints\u003c/b\u003e༚If the area under the ROC curve for any ceramide parameter reached 0.7, ceramide was considered to have an auxiliary diagnostic value for clinical acute coronary syndrome.\u003c/p\u003e \u003cp\u003eWe started with 19 variables, including demographic characteristics, medical history and ceramide, as predictors. Continuous variables with a normal distribution, as determined by the Kolmogorov\u0026ndash;Smirnov test, were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Variables without a normal distribution were expressed as median (range). Categorical variables were presented as n (%) and analyzed using the chi-square test. Spearman's correlation test was used to assess the correlation between the ceramide variables. The differences in serum ceramide levels between the two groups (ACS group and non-ACS group) were compared using Mann\u0026ndash;Whitney U tests. The performance of each independent ceramide in predicting ACS was determined by the area under curve (AUC). We employed the Boruta algorithm with 5-fold cross-validation for feature selection. Furthermore, lasso screening was performed to address multicollinearity. The intersection variables between the Lasso regression and Boruta analysis were selected as the independent risk factors for ACS. The receiver operating characteristic (ROC) curve analysis was performed to assess the Cer(d18:1/16:0), and Cer(d18:1/16:0)R in predicting ACS.\u003c/p\u003e \u003cp\u003eFor sensitivity analysis, Cer(d18:1/16:0) was analyzed as a continuous variable and a categories variable, respectively. The factors entered into the logistic regression models were sex and Cer(d18:1/16:0)R); Results were reported as odds ratios (ORs) with associated 95% confidence intervals (CIs). All statistical analyses were performed in SPSS 26.0 (IBM, Inc., Chicago United States) and \u003cem\u003eR\u003c/em\u003e studio with the \u003cem\u003eR version\u003c/em\u003e (\u003cem\u003eR version\u003c/em\u003e 4.2.2.). Statistical significance was defined as a P-value of less than 0.05 for a two-tailed test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the Study Population\u003c/h2\u003e \u003cp\u003e1327 patients with suspected or known CAD were evaluated with invasive coronary angiography. Patient demographics data and ceramide parameters were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The levels of Ceramide under different groupings were demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared with the non-ACS group, patients with ACS had higher Cer(d18:1/14:0) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Cer(d18:1/16:0) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Cer(d18:1/18:0) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Cer(d18:1/20:0) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Cer(d18:1/22:0) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Cer(d18:1/24:0) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Cer(d18:1/24:1) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) did not differ significantly between the groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographics and baseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,327\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e444 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e883 (66.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGE_60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e590 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e737 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,003 (75.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabete melltius\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e901 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e403 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e539 (40.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoronary artery disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon obstructive coronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable coronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnstable angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e856 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCeramide and Ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/14:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.44 (1.76, 3.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/16:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (123, 183)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/18:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.7 (30.4, 55.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/20:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.3 (43.3, 71.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/22:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e404 (318, 519)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/24:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1760 (1380, 2230)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/24:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e460 (356, 575)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/14:0)R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0013(0.0010, 0.0018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/16:0)R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08 (0.06, 0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/18:0)R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022 (0.017, 0.031)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/20:0)R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031 (0.024, 0.041)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/22:0)R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22 (0.20, 0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/24:1)R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25 (0.19, 0.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eFor categorical variables, absolute numbers (n) and relative proportions (%) are presented. For continuous variables, the median along with the interquartile range is presented.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables selection\u003c/h3\u003e\n\u003cp\u003eSpearman test was conducted to assess the correlation between ceramides, revealing a significant positive correlation among them, as depicted in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Collinearity among the variables was assessed before modeling. The results of the correlation analysis indicated a high degree of collinearity among the independent variables(STable 1). LASSO regression and Boruta feature selection algorithms were employed to identify essential variables in the DataSet, and distinctive ACS related variables were screened out using these methods(STable 2, STable3). The Boruta algorithm identified 15 significant variables related to ACS. Three critical variables (sex, Cer(d18:1/16:0), Cer(d18:1/16:0)R ) associated with ACS were discovered using the LASSO logistic regression technique (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDiagnostic performance of Ceramides for ACS\u003c/h3\u003e\n\u003cp\u003eThe diagnostic performance of Cer(d18:1/16:0)(cut-off value 150umol/L) for detecting ACS, measured by sensitivity, accuracy, PPV and AUC, was significantly better than those of Ceramide(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, Cer(d18:1/20:0) had the highest specificity (83.1%), but lower sensitivity(25.6%) and NPV(26.6%). Applied ROC curve to assess the values of Cer(d18:1/16:0) and Cer(d18:1/16:0)R in predicting ACS. The ROC curve showed that the AUC values of Cer(d18:1/16:0) were significantly higher compared to those of Cer(d18:1/16:0)R(AUC: 0.678 vs 0.608, P\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnosis accuracy assessment of ceramides in predicting ACS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/14:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003cp\u003e(0.516\u0026ndash;0.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/16:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003cp\u003e(0.645\u0026ndash;0.710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/18:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003cp\u003e(0.530\u0026ndash;0.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/20:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003cp\u003e(0.509\u0026ndash;0.580)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/22:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003cp\u003e(0.506\u0026ndash;0.577)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/24:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003cp\u003e(0.512\u0026ndash;0.583)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/24:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003cp\u003e(0.489\u0026ndash;0.559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are expressed as \u003cem\u003en\u003c/em\u003e (95% confidence interval).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Cer(d18:1/16:0) and ACS\u003c/h2\u003e \u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, three different models (logistic regression models) were constructed to evaluate the correlation between Cer(d18:1/16:0) and ACS. Adjustment was made for demographic variables in Model 1 and Model 2, while different ceramide indicators were considered in Model 3. Despite these adjustments, Cer(d18:1/16:0) maintained a positive correlation with ACS. This correlation persisted even when Cer(d18:1/16:0) was converted into a categorical variable. Results from subgroup analyses validated the relationship between the Cer(d18:1/16:0)(per 1SD) and ACS across age, sex, hypertension, diabetes, and smoking status subgroups. Hypertension was found to interact with the relationship between the Cer(d18:1/16:0) and ACS\u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between Cer(d18:1/16:0) and ACS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eCer(d18:1/16:0) as a continuous variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/16:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003e1.02(1.01, 1.02)\u003c/p\u003e \u003cp\u003e2.15(1.82, 2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c6\" namest=\"c5\" rowspan=\"2\"\u003e \u003cp\u003e1.01(1.01, 1.02)\u003c/p\u003e \u003cp\u003e2.06(1.73, 2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c10\" namest=\"c8\" rowspan=\"2\"\u003e \u003cp\u003e1.03(1.01, 1.04)\u003c/p\u003e \u003cp\u003e3.01(1.62, 5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/16:0) (per 1SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCer(d18:1/16:0) as a categories variable (Quartiles)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/16:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.69(1.21, 2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c6\" namest=\"c5\" rowspan=\"3\"\u003e \u003cp\u003e1.62(1.16, 2.26)\u003c/p\u003e \u003cp\u003e2.74(1.91, 3.92)\u003c/p\u003e \u003cp\u003e6.61(4.23, 10.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"2\" nameend=\"c10\" namest=\"c8\" rowspan=\"3\"\u003e \u003cp\u003e1.69(1.14, 2.50)\u003c/p\u003e \u003cp\u003e2.97(1.72, 5.11)\u003c/p\u003e \u003cp\u003e7.38(3.02, 18.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003e2.87(2.00, 4.10)\u003c/p\u003e \u003cp\u003e7.40(4.77, 11.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eOR\u0026thinsp;=\u0026thinsp;Odds Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eModel 1 : adjusted for SEX and Age;\u003c/p\u003e \u003cp\u003eModel 2 : adjusted for SEX, Age and Cer(d18:1/16:0)R;\u003c/p\u003e \u003cp\u003eModel 3 : adjusted for 12 Ceramide indices (Cer(d18:1/14:0), Cer(d18:1/18:0), Cer(d18:1/20:0), Cer(d18:1/22:0), Cer(d18:1/24:0), Cer(d18:1/24:1), Cer(d18:1/14:0)R, Cer(d18:1/16:0)R Cer(d18:1/18:0)R, Cer(d18:1/20:0)R, Cer(d18:1/22:0)R, Cer(d18:1/24:1)R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between Cer(d18:1/16:0) (per 1SD) and ACS in different subgroups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04 (1.73\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22 (1.70\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11 (1.69\u0026ndash;2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGE_60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94 (1.53\u0026ndash;2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.15 (1.70\u0026ndash;2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.03 (2.06\u0026ndash;4.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83 (1.53\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabete melltius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04 (1.67\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92 (1.41\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60 (1.91\u0026ndash;3.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90 (1.40\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79 (1.37\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe nonlinearity is addressed by the Logistic regression model with a Restricted Cubic Spline\u003c/h2\u003e \u003cp\u003eThe RCS analysis revealed a non-linear association between Cer(d18:1/16:0) and ACS, with an inflection point detected at Cer(d18:1/16:0)\u0026thinsp;=\u0026thinsp;150umol/L\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Using the inflection point, the data was stratified into two groups, and segmented regression was then performed on each group separately: Cer(d18:1/16:0)\u0026thinsp;\u0026lt;\u0026thinsp;150 umol/L [OR (per 1SD)\u0026thinsp;=\u0026thinsp;1.29, 95%CI:1.10\u0026ndash;1.51, P\u0026thinsp;=\u0026thinsp;0.002 ] and Cer(d18:1/16:0)\u0026thinsp;\u0026ge;\u0026thinsp;150 umol/L [OR (per 1SD)\u0026thinsp;=\u0026thinsp;1.46, 95%CI:1.11\u0026ndash;1.93, P\u0026thinsp;=\u0026thinsp;0.007 ]. Similar results were also observed in the odds ratio analysis of Cer(d18:1/16:0) after adjusting for sex, age, and Cer(d18:1/16:0)R in relation to ACS\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study demonstrate that Cer(d18:1/16:0) is an independent biomarker significantly associated with ACS. Moreover, a nonlinear relationship with a positive correlation between Cer(d18:1/16:0) and ACS was observed using RCS models. The first study, to the best of our knowledge, also presented alternative cut-off points for Cer(d18:1/16:0) levels for diagnosing ACS in the Chinese population. This is conducive to optimizing clinical management strategies.\u003c/p\u003e \u003cp\u003eSome previous studies have shown consistent results with ours investigating the correlation between Cer(d18:1/16:0) and ACS. Previous clinical research found that elevated ceramide plasma concentrations are associated with coronary plaque vulnerability evaluated by endovascular imaging[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Laaksonen found higher expression levels of certain ceramides in ACS patients compared to stable coronary heart disease patients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Advances in lipidomics have identified circulating ceramides as significant predictors of atherosclerotic cardiovascular events[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In comparison to traditional cardiovascular disease risk factors, ceramide\u0026mdash;a bioactive sphingolipid\u0026mdash;has demonstrated enhanced predictive abilities for cardiovascular disease events[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Ceramide risk scores, derived from high-risk ceramide subtypes (Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:1)) identified in previous studies, have been developed and adapted for routine clinical practice[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Research in rodents shows that ceramides contribute to cardiovascular diseases by causing metabolic issues. Interventions that either reduce ceramide production or increase their breakdown improve conditions like atherosclerosis, insulin resistance, fatty liver disease, and cardiomyopathy[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Study in mouse models shows that ASM and ceramide signaling critically mediate hypocholesterolemia-induced NLRP3 inflammasome activation in endothelial cells, leading to endothelial dysfunction, vascular inflammation, and atherosclerosis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Hammerschmidt et al. revealed that the CerS6/Cer16:0/Mff(mitochondrial fission factor) pathway regulates mitochondrial dynamics and insulin resistance in obesity, proposing the interaction between CerS6-derived sphingolipids and Mff as a therapeutic target for metabolic diseases[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. They represent a promising class of molecules with considerable therapeutic potential and clinical applicability.\u003c/p\u003e \u003cp\u003ePrevious data indicate that various ceramides may increase in cases of hypertension, type 2 diabetes, and insulin resistance[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our subgroup analyses show that Cer(d18:1/16:0) is independently linked to ACS. This study also applied RCS models to assess a nonlinear relationship, allowing for an in-depth analysis of the correlations between Cer(d18:1/16:0) and ACS. Combined with previous research, we believe that Cer(d18:1/16:0) may play a significant role in the pathophysiology of coronary atheroma plaque rupture and could potentially serve as a novel biomarker for ACS.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrength and Limitation\u003c/h2\u003e \u003cp\u003eOur study possesses inherent strengths which are listed below. Firstly, to mitigate the inherent limitations of a cross-sectional study design, we substantially enlarged our study population and employed machine learning techniques to identify key variables. Secondly, we verified the strength of the results through sensitivity analysis, which involved transforming variable forms, such as normalization or reclassification, and conducting subgroup analyses. Lastly, our study was the first to focus on the Chinese population to determine the relationship between ceramide and ACS, ours study highlights a nonlinear relationship and identifies an inflection point in the correlation between ceramide levels and ACS. However, it had to be acknowledged that there were some limitations. First of all, we did not examine the causal relationship between ceramide and ACS due to the cross-sectional study design in this paper. Second, our study focused solely on ceramides and did not take into account other clinical markers, such as cholesterol et.al. In addition, fewer patients with acute myocardial infarction were included in the study population. Additional studies are required to investigate the diagnostic and prognostic value of ceramides in ACS by conducting follow-up assessments in various clinical models among diverse populations. Additionally, it is important to note that female patients in our study were predominantly postmenopausal; therefore, the results' external generalizability should not make a substantial difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present study, the clinical features of ceramide were reviewed in the Chinese population suffered chest pain, and a machine learning algorithm was employed to screen risk factors. Our study demonstrated a significant positive association between Cer(d18:1/16:0) and ACS, and the establishment of a clear ceramide threshold will enhance our ability to identify ACS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complied with the Declaration of Helsinki and received approval from the Ethics Committee of Beijing Anzhen Hospital, affiliated with Capital Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed and approved the final version of the manuscript. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAny product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant No. 82270345) and the Capital\u0026rsquo;s Funds for Health Improvement and Research (Grant No. CFH2024-1-2061).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiang Zhang:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Validation, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. \u003cstrong\u003eYang Zhang and YaoDong Ding:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Validation, Methodology, Investigation, Funding acquisition, Data curation. \u003cstrong\u003eTong Jin and Yi Song:\u003c/strong\u003e Visualization, Data curation. \u003cstrong\u003e\u0026nbsp;Lin Li:\u003c/strong\u003e Project administration, Methodology, Funding acquisition, Formal analysis. \u003cstrong\u003eYong Zeng and XiaoFang Wang:\u003c/strong\u003e Resources, Project administration, Methodology, Investigation, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Beijing Health Biotechnology Co., Ltd. for their help in biomarker detection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoy P, Orecchioni M, Ley K. How the immune system shapes atherosclerosis: roles of innate and adaptive immunity. Nat Rev Immunol 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicholls SJ, Ballantyne CM, Barter PJ, et al. Effect of two intensive Statin regimens on progression of coronary disease. N Engl J Med. 2011;365:2078\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi RH, Tatum SM, Symons JD, et al. Ceramides and other sphingolipids as drivers of cardiovascular disease. Nat Rev Cardiol. 2021;18:701\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeeusen JW, Donato LJ, Kopecky SL, et al. Ceramides improve atherosclerotic cardiovascular disease risk assessment beyond standard risk factors. Clin Chim Acta. 2020;511:138\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHilvo M, Vasile VC, Donato LJ, et al. Ceramides and ceramide scores: clinical applications for cardiometabolic risk stratification. Front Endocrinol. 2020;11:570628.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Ding Y, Chen M et al. The relationship between ceramide profile and residual inflammatory risk in patients with coronary artery disease: Insights from an prospective study. J Clin Lipidol. 2024:S1933-2874(24)00224-1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Tan D, Zhang Y, et al. Ceramides and metabolic profiles of patients with acute coronary disease: a cross-sectional study. Front Physiol. 2023;14:1177765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan W, Sun M, Wu J, et al. Relationship between elevated plasma ceramides and plaque rupture in patients with ST-segment elevation myocardial infarction. Atherosclerosis. 2020;302:8\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan W, Dong H, Sun R, et al. Plasma Ceramides in Relation to Coronary Plaque Characterization Determined by Optical Coherence Tomography. J Cardiovasc Transl Res. 2021;14(1):140\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaaksonen R, Ekroos K, Sysi-Aho M, et al. Plasma ceramides predict cardiovascular death in patients with stable coronary artery disease and acute coronary syndromes beyond LDL-cholesterol. Eur Heart J. 2016;37(25):1967\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFretts AM, Jensen PN, Sitlani CM, et al. Circulating Sphingolipids and All-Cause Mortality: The Strong Heart Family Study. J Am Heart Assoc. 2024;13(13):e032536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Q, Scherer PE. Ceramides and Atherosclerotic Cardiovascular Disease: A Current Perspective. Circulation. 2024;149(21):1624\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapazoglou AS, Stalikas N, Moysidis DV, et al. CERT2 ceramide- and phospholipid-based risk score and major adverse cardiovascular events: A systematic review and meta-analysis. J Clin Lipidol. 2022;16(3):272\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantovani A, Dugo C. Ceramides and risk of major adverse cardiovascular events: A meta-analysis of longitudinal studies. J Clin Lipidol. 2020;14(2):176\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaurasia B, Summers SA. Ceramides - Lipotoxic Inducers of Metabolic Disorders. Trends Endocrinol Metab. 2018;29(1):66\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoka S, Xia M, Chen Y, et al. Endothelial NLRP3 inflammasome activation and arterial neointima formation associated with acid sphingomyelinase during hypercholesterolemia. Redox Biol. 2017;13:336\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammerschmidt P, Ostkotte D, Nolte H, et al. CerS6-Derived Sphingolipids Interact with Mff and Promote Mitochondrial Fragmentation in Obesity. Cell. 2019;177(6):1536\u0026ndash;e155223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaribeygi H, Bo S, Ruscica M, Sahebkar A. Ceramides and diabetes mellitus: an update on the potential molecular relationships. Diabet Med. 2020;37:11\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/dme.13943\u003c/span\u003e\u003cspan address=\"10.1111/dme.13943\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSokolowska E, Blachnio-Zabielska A. The role of ceramides in insulin resistance. Front Endocrinol. 2019;10:577. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2019.00577\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2019.00577\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrittain EL, Talati M, Fessel JP, et al. Fatty Acid Metabolic Defects and Right Ventricular Lipotoxicity in Human Pulmonary Arterial Hypertension. Circulation. 2016;133(20):1936\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ceramide, acute coronary syndrome, Risk factors, Biomarkers, lipids","lastPublishedDoi":"10.21203/rs.3.rs-5453741/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5453741/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 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. \u0026nbsp;Additionally, a significant nonlinear relationship was observed \u0026nbsp;between Cer(d18:1/16:0) and ACS, with a threshold of 150umol/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e 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.\u003c/p\u003e","manuscriptTitle":"Cer(d18:1/16:0) as Predictive Biomarkers for Acute Coronary Syndrome: Insights from a Chinese Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 08:16:06","doi":"10.21203/rs.3.rs-5453741/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96942e12-3f5c-40ce-a2f1-ef7519165b69","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-19T06:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 08:16:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5453741","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5453741","identity":"rs-5453741","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}