Plasma concentrations of molecular lipid species predict long-term clinical outcome in coronary artery disease patients

We investigated the associations of ten previously identified high risk molecular lipid species and three ceramide ratios with the occurrence of major adverse cardiac events (MACEs) during a median follow-up of 4.7 years in patients with coronary artery disease (CAD). Between 2008 and 2011, 581 patients underwent diagnostic coronary angiography or percutaneous coronary intervention for stable angina pectoris (SAP) or acute coronary syndrome (ACS). Blood was drawn prior to the index procedure and lipid species were determined. The primary endpoint was the occurrence of a MACE, comprising all-cause mortality, nonfatal ACS, or unplanned coronary revascularization. The secondary endpoint comprised all-cause mortality or nonfatal ACS. During a median follow-up of 4.7 [IQR: 4.2–5.6] years, 155 patients (27%) had MACEs. In multivariable analyses, Cer(d18:1/16:0) concentration was associated with MACEs {hazard ratio 2.32; 95% CI [1.09–4.96] per natural logarithm (ln) (pmol/ml) P = 0.030} after adjustment for cardiac risk factors, clinical presentation, statin use at baseline, and admission nonHDL cholesterol level. Furthermore, after multivariable adjustment, concentrations of Cer(d18:1/16:0), Cer(d18:1/20:0), Cer(d18:1/24:1), and their ratios to Cer(d18:1/24:0) were associated with the composite endpoint death or nonfatal ACS. The data together show the circulating ceramide lipids we investigated here are associated with adverse cardiac outcome during long-term follow-up independent of clinical risk factors. We investigated the associations of ten previously identified high risk molecular lipid species and three ceramide ratios with the occurrence of major adverse cardiac events (MACEs) during a median follow-up of 4.7 years in patients with coronary artery disease (CAD). Between 2008 and 2011, 581 patients underwent diagnostic coronary angiography or percutaneous coronary intervention for stable angina pectoris (SAP) or acute coronary syndrome (ACS). Blood was drawn prior to the index procedure and lipid species were determined. The primary endpoint was the occurrence of a MACE, comprising all-cause mortality, nonfatal ACS, or unplanned coronary revascularization. The secondary endpoint comprised all-cause mortality or nonfatal ACS. During a median follow-up of 4.7 [IQR: 4.2–5.6] years, 155 patients (27%) had MACEs. In multivariable analyses, Cer(d18:1/16:0) concentration was associated with MACEs {hazard ratio 2.32; 95% CI [1.09–4.96] per natural logarithm (ln) (pmol/ml) P = 0.030} after adjustment for cardiac risk factors, clinical presentation, statin use at baseline, and admission nonHDL cholesterol level. Furthermore, after multivariable adjustment, concentrations of Cer(d18:1/16:0), Cer(d18:1/20:0), Cer(d18:1/24:1), and their ratios to Cer(d18:1/24:0) were associated with the composite endpoint death or nonfatal ACS. The data together show the circulating ceramide lipids we investigated here are associated with adverse cardiac outcome during long-term follow-up independent of clinical risk factors. Established lipid markers such as total cholesterol, LDL cholesterol, triglycerides (TGs), and HDL cholesterol have long formed the cornerstone of lipid-based risk stratification in coronary artery disease (CAD) (1.Tarasov K. Ekroos K. Suoniemi M. Kauhanen D. Sylvanne T. Hurme R. Gouni-Berthold I. Berthold H.K. Kleber M.E. Laaksonen R. et al.Molecular lipids identify cardiovascular risk and are efficiently lowered by simvastatin and PCSK9 deficiency.J. Clin. Endocrinol. Metab. 2014; 99: E45-E52Crossref PubMed Scopus (149) Google Scholar, 2.Alshehry Z.H. Mundra P.A. Barlow C.K. Mellett N.A. Wong G. McConville M.J. Simes J. Tonkin A.M. Sullivan D.R. Barnes E.H. et al.Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus.Circulation. 2016; 134: 1637-1650Crossref PubMed Scopus (153) Google Scholar, 3.Havulinna A.S. Sysi-Aho M. Hilvo M. Kauhanen D. Hurme R. Ekroos K. Salomaa V. Laaksonen R. Circulating ceramides predict cardiovascular outcomes in the population-based FINRISK 2002 cohort.Arterioscler. Thromb. Vasc. Biol. 2016; 36: 2424-2430Crossref PubMed Scopus (182) Google Scholar, 4.Ekroos K. Janis M. Tarasov K. Hurme R. Laaksonen R. Lipidomics: a tool for studies of atherosclerosis.Curr. Atheroscler. Rep. 2010; 12: 273-281Crossref PubMed Scopus (82) Google Scholar). However these measures alone do not fully capture the complexity of the altered lipid metabolism in cardiovascular disease (2.Alshehry Z.H. Mundra P.A. Barlow C.K. Mellett N.A. Wong G. McConville M.J. Simes J. Tonkin A.M. Sullivan D.R. Barnes E.H. et al.Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus.Circulation. 2016; 134: 1637-1650Crossref PubMed Scopus (153) Google Scholar), and this may be the reason that they fail to identify a substantial proportion of patients at high risk for coronary events (1.Tarasov K. Ekroos K. Suoniemi M. Kauhanen D. Sylvanne T. Hurme R. Gouni-Berthold I. Berthold H.K. Kleber M.E. Laaksonen R. et al.Molecular lipids identify cardiovascular risk and are efficiently lowered by simvastatin and PCSK9 deficiency.J. Clin. Endocrinol. Metab. 2014; 99: E45-E52Crossref PubMed Scopus (149) Google Scholar). Lipidomics is a systems-based study of all lipids (5.Watson A.D. Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Lipidomics: a global approach to lipid analysis in biological systems.J. Lipid Res. 2006; 47: 2101-2111Abstract Full Text Full Text PDF PubMed Scopus (366) Google Scholar) that has been defined as the full characterization of lipid molecular species and their biological roles (6.Roberts L.D. McCombie G. Titman C.M. Griffin J.L. A matter of fat: an introduction to lipidomic profiling methods.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2008; 871: 174-181Crossref PubMed Scopus (102) Google Scholar). In its most advanced form, lipidomics is able to quantify hundreds of diverse molecular lipid species across multiple lipid classes such as sphingolipids, phospholipids, sterol esters, and acylglycerols (7.Jänis M.T. Laaksonen R. Oresic M. Metabolomic strategies to identify tissue-specific effects of cardiovascular drugs.Expert Opin. Drug Metab. Toxicol. 2008; 4: 665-680Crossref PubMed Scopus (13) Google Scholar), many of which play an integral role in modulation of biological function such as formation of cellular membranes, energy storage, and cell signaling (8.Stock J. The emerging role of lipidomics.Atherosclerosis. 2012; 221: 38-40Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar, 9.Hu C. van der Heijden R. Wang M. van der Greef J. Hankemeier T. Xu G. Analytical strategies in lipidomics and applications in disease biomarker discovery.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2009; 877: 2836-2846Crossref PubMed Scopus (168) Google Scholar). Because lipidomics provides such detailed lipid profiles, it may further improve risk stratification of CAD patients and provide novel mechanistic insights into CAD (4.Ekroos K. Janis M. Tarasov K. Hurme R. Laaksonen R. Lipidomics: a tool for studies of atherosclerosis.Curr. Atheroscler. Rep. 2010; 12: 273-281Crossref PubMed Scopus (82) Google Scholar). In line with this hypothesis, we have recently performed lipidomics in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study and identified several molecular lipid species that are associated with fatal events in patients with CAD (1.Tarasov K. Ekroos K. Suoniemi M. Kauhanen D. Sylvanne T. Hurme R. Gouni-Berthold I. Berthold H.K. Kleber M.E. Laaksonen R. et al.Molecular lipids identify cardiovascular risk and are efficiently lowered by simvastatin and PCSK9 deficiency.J. Clin. Endocrinol. Metab. 2014; 99: E45-E52Crossref PubMed Scopus (149) Google Scholar). In the current study, we hypothesized that these ten previously identified high risk molecular lipid species and three ceramide ratios are associated with occurrence of major adverse cardiac events (MACEs) during long-term follow-up. The design of the European Collaborative Project on Inflammation and Vascular Wall Remodeling in Atherosclerosis (ATHEROREMO) has been described elsewhere in detail (10.de Boer S.P. Cheng J.M. Garcia-Garcia H.M. Oemrawsingh R.M. van Geuns R.J. Regar E. Zijlstra F. Laaksonen R. Halperin E. Kleber M.E. et al.Relation of genetic profile and novel circulating biomarkers with coronary plaque phenotype as determined by intravascular ultrasound: rationale and design of the ATHEROREMO-IVUS study.EuroIntervention. 2014; 10: 953-960Crossref PubMed Scopus (23) Google Scholar). In brief, from 2008 until 2011, 581 patients with an indication for diagnostic coronary angiography (CAG) and/or percutaneous coronary intervention (PCI) due to stable angina pectoris (SAP) or acute coronary syndrome (ACS) at the Erasmus MC, Rotterdam, The Netherlands, were included. Prior to the CAG or PCI procedure, blood samples were collected from the arterial sheath and were transported to the clinical laboratory of Erasmus MC within 2 h after blood collection for storage at −80°C. All included patients were 18 years or older. The ATHEROREMO study was approved by the medical ethics committee of Erasmus MC and was performed in accordance with the criteria described in the Declaration of Helsinki. Written informed consent was obtained from all included patients. Levels of total cholesterol, LDL cholesterol, HDL cholesterol, and TGs were measured in the clinical laboratory of the Erasmus MC in serum samples using a Roche/Hitachi cobas c 701/702 analyzer (Roche Diagnostics, Indianapolis, IN) on the Cobas 8000 modular analyzer platform (Roche Diagnostics). Molecular lipids and lipid ratios that were previously found to be associated with fatal cardiovascular outcome at a P < 0.05 level in the LURIC study were selected for evaluation in the current study (1.Tarasov K. Ekroos K. Suoniemi M. Kauhanen D. Sylvanne T. Hurme R. Gouni-Berthold I. Berthold H.K. Kleber M.E. Laaksonen R. et al.Molecular lipids identify cardiovascular risk and are efficiently lowered by simvastatin and PCSK9 deficiency.J. Clin. Endocrinol. Metab. 2014; 99: E45-E52Crossref PubMed Scopus (149) Google Scholar). These included cholesteryl esters (CEs): CE 14:0, CE 18:3, CE 20:4, CE 20:5, and CE 22:5; ceramides (Cer): Cer(d18:1/16:0), Cer(d18:1/20:0), Cer(d18:1/24:0), and Cer(d18:1/24:1); ceramide ratios: Cer(d18:1/16:0)/Cer(d18:1/24:0), Cer(d18:1/20:0)/Cer(d18:1/24:0), and Cer(d18:1/24:1)/Cer(d18:1/24:0)), and lactosylceramide (LacCer): LacCer(d18:1/18:0). Plasma samples for measurement of lipid concentrations were available for 574 patients. Stored plasma samples were subjected to lipid extraction at Zora Biosciences, Finland. Briefly, samples (10 μl) were spiked with known amounts of lipid-class specific, nonendogenous synthetic internal standards, D6-CE 18:0 (C/D/N Isotopes Inc.,Pointe-Claire, Quebec, Canada), Cer(d18:1/17:0) (Avanti Polar Lipids Inc., Alabaster, AL) and D3-LacCer(d18:1/16:0) (Matreya LLC, State College, PA). Lipid extraction was performed using chloroform (HPLC grade) (Rathburn Chemicals Ltd., Walkerburn, Scotland), methanol, and acetic acid (both LC-MS grade) (Sigma-Aldrich GmbH, Steinheim, Germany) (11.Heiskanen L.A. Suoniemi M. Ta H.X. Tarasov K. Ekroos K. Long-term performance and stability of molecular shotgun lipidomic analysis of human plasma samples.Anal. Chem. 2013; 85: 8757-8763Crossref PubMed Scopus (61) Google Scholar). After lipid extraction, samples were reconstituted in chloroform-methanol (1:2, v/v) for sphingolipids analysis, and for molecular shotgun lipidomic analysis, the extracts were further diluted with chloroform-methanol (1:2, v/v) containing 5 mM ammonium acetate. Quality control samples were prepared along with the actual samples for lipidomic analyses to monitor the extraction and MS performance. The intra-day (n = 3) average coefficient of variation of sphingolipids and CEs was less than or equal to 6% and inter-day [n = 24 for Cer and LacCer; n = 23 for CE except for CE(22:5) n = 22] coefficient of variation was less than 21% for both sphingolipids and CE. Sphingolipids were analyzed on a QTRAP® 5500 mass spectrometer (AB SCIEX, Concord, Canada) equipped with an ultra-high pressure liquid chromatography (UHPLC) system CTC PAL autosampler (Leap Technologies) and Accela 1250 Pump (Thermo Fisher Scientific, Agawam, MA). Chromatographic separation was performed on an Acquity BEH C18, 2.1 × 50 mm column with a particle size of 1.7 μm (Waters, Milford, MA). Mobile phases were 10 mM ammonium acetate in water with 0.1% formic acid (solvent A) and 10 mM ammonium acetate in acetonitrile-isopropanol (4:3, v/v) containing 0.1% formic acid (solvent B). Lipids were separated with linear gradient from 75% B to 100% B in 15 min. Flow rate was 500 µl/min and column temperature was 60°C. Data was collected using multiple reaction monitoring in positive ion mode (12.Merrill Jr., A.H. Sullards M.C. Allegood J.C. Kelly S. Wang E. Sphingolipidomics: high-throughput, structure-specific, and quantitative analysis of sphingolipids by liquid chromatography tandem mass spectrometry.Methods. 2005; 36: 207-224Crossref PubMed Scopus (468) Google Scholar). Curtain gas was set at 25, ion spray voltage was set at 5000, and ion source was heated to 400°C. Collision energy was optimized for each lipid class. Collision energy for Cer and LacCer was set to 40 and 45, respectively. Shotgun lipidomics was performed to monitor CEs on a QTRAP® 5500 mass spectrometer (AB SCIEX) equipped with a robotic nanoflow ion source NanoMate HD (Advion, Ithaca, NY) as described (11.Heiskanen L.A. Suoniemi M. Ta H.X. Tarasov K. Ekroos K. Long-term performance and stability of molecular shotgun lipidomic analysis of human plasma samples.Anal. Chem. 2013; 85: 8757-8763Crossref PubMed Scopus (61) Google Scholar). CEs were analyzed in positive ion mode using precursor ion scanning of 369.35 with collision energy 30 (13.Ejsing C.S. Duchoslav E. Sampaio J. Simons K. Bonner R. Thiele C. Ekroos K. Shevchenko A. Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning.Anal. Chem. 2006; 78: 6202-6214Crossref PubMed Scopus (329) Google Scholar). Mass spectrometry data files were processed using MultiQuant™ 2.0.1 or LipidView™ 1.0 (AB SCIEX) (13.Ejsing C.S. Duchoslav E. Sampaio J. Simons K. Bonner R. Thiele C. Ekroos K. Shevchenko A. Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning.Anal. Chem. 2006; 78: 6202-6214Crossref PubMed Scopus (329) Google Scholar). Identified lipids were quantified by normalizing against their respective internal standard and volume of plasma used for the extraction. The limit of quantification (LOQ) for Cer, LacCer, and CE in extract was 0.0004 µM, 0.0016 µM, and 0.012 µM, respectively. All lipids monitored were within the LOQ. The LOQ was defined as the lowest point in the calibration curve with a signal-to-noise ratio greater than or equal to 10. Clinical and vital status of patients were collected from medical charts, civil registries, or by written or telephone contacts with the patients or relatives. All living patients participating in this study received a questionnaire consisting of queries regarding the occurrence of MACEs and readmissions. For patients with adverse events, hospital discharge letters were obtained and treating physicians or institutions were contacted if necessary for additional information. The primary endpoint was the occurrence of a MACE comprising all-cause mortality, nonfatal ACS, or unplanned coronary revascularization. The secondary endpoint comprised all-cause mortality and nonfatal ACS. ACS was defined as the clinical diagnosis of ST-segment elevation myocardial infarction (STEMI), nonSTEMI, or unstable angina pectoris in accordance with the guidelines of the European Society of Cardiology (14.Roffi M. Patrono C. Collet J.P. Mueller C. Valgimigli M. Andreotti F. Bax J.J. Borger M.A. Brotons C. Chew D.P. et al.ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: Task Force for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European Society of Cardiology (ESC).Eur. Heart J. 2016; 37: 267-315Crossref PubMed Scopus (4341) Google Scholar, 15.Ibanez B. James S. Agewall S. Antunes M.J. Bucciarelli-Ducci C. Bueno H. Caforio A.L.P. Crea F. Goudevenos J.A. Halvorsen S. et al.ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the task force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC).Eur. Heart J. 2018; 39: 119-177Crossref PubMed Scopus (5409) Google Scholar). Unplanned coronary revascularization was defined as unplanned repeated PCI or unplanned coronary artery bypass grafting (CABG). The endpoints were adjudicated according to their definitions by a clinical events committee that was blinded to the lipid data. Categorical variables are presented as numbers and percentages. The distributions of continuous variables, including lipid concentrations and lipid ratios, were examined for normality by visual inspection of the histogram. Normally distributed continuous variables are presented as mean ± SD. Nonnormally distributed continuous variables (which included molecular lipid concentrations and lipid ratios) are presented as median (interquartile range [IQR]) and their natural logarithm (ln) was used for further analyses. Patients lost during follow-up were considered at risk until the date of last contact, at which time-point they were censored. Cox proportional hazards models were used to evaluate the associations between molecular lipids and clinical study endpoints. For patients who experienced more than one event, the first was considered. The results are presented as hazard ratios (HRs) per unit increase in (ln-transformed) molecular lipid concentrations or lipid ratios, with 95% CIs. First, all analyses were performed univariably. In the multivariable analyses, gender, age, hypertension, hypercholesterolemia, diabetes mellitus, and statin use were considered as potential confounders and were entered as covariates. These covariates were chosen for etiologic reasons and were based on existing literature (16.Stone G.W. Maehara A. Lansky A.J. de Bruyne B. Cristea E. Mintz G.S. Mehran R. McPherson J. Farhat N. Marso S.P. et al.A prospective natural-history study of coronary atherosclerosis.N. Engl. J. Med. 2011; 364: 226-235Crossref PubMed Scopus (2291) Google Scholar). To evaluate whether the associations between molecular lipids and the clinical endpoints are independent of serum LDL cholesterol levels or serum nonHDL cholesterol levels, baseline serum LDL cholesterol level and baseline serum nonHDL cholesterol level were additionally (and consecutively) added into the multivariable models. Serum nonHDL level was calculated by subtracting HDL cholesterol level from total cholesterol level. In the full cohort, indication for CAG (ACS versus SAP) was also entered as a covariate. Interaction terms were added to the model to account for possible effect modification by indication for baseline CAG. Subsequently, analyses were stratified on indication for CAG. All data were analyzed with SPSS software (SPSS 23.0 IBM Corp., Armonk, NY). All statistical tests were two-tailed and P-values < 0.05 were considered statistically significant. The baseline clinical characteristics and the lipid concentrations of the ATHEROREMO study are summarized in Table 1 and Table 2. In total, 574 patients were included. The mean age of the patients was 61.5 years and 75% were men. A total of 55% patients were diagnosed with ACS (28% STEMI and 26% nonSTEMI) and 46% patients with SAP. PCI was performed in 88% of the patients during the index procedure. Prior to the index procedure, median serum LDL cholesterol level was 2.71 [IQR: 2.12–3.54] mmol/l, median serum HDL cholesterol level was 1.04 [IQR: 0.87–1.29] mmol/l, median serum nonHDL cholesterol level was 3.23 [IQR: 2.54–4.00] mmol/l, and median serum TG level was 1.27 [IQR: 0.88–1.83] mmol/l in the full cohort. ACS patients had significantly higher serum LDL cholesterol level {(median: 3.10 [IQR: 2.32–3.87] mmol/l) P = < 0.001}, higher serum nonHDL cholesterol level {(median: 3.56 [2.81–4.36]mmol/l) P = < 0.001} and lower serum TG level {(median = 1.15 [IQR: 0.77–1.77] mmol/l) P = < 0.001)} compared with SAP patients (median: 2.37 [IQR: 1.94–2.99] mmol/l, 2.83 [2.35–3.56] mmol/l and 1.41 [IQR: 1.05–1.94] mmol/l, respectively). In addition, several other clinical characteristics were significantly different between the ACS patients and the SAP patients (Table 1). At the time of hospital admission, 89% of the patients in the full cohort used statins.TABLE 1Clinical characteristicsClinical characteristicsTotal (n = 574)ACS patients (n = 313)SAP patients (n = 261)PAge, years, mean ± SD61.5 ± 11.359.7 ± 11.963.6 ± 10.3<0.001Male, n (%)432 (75)230 (74)202 (77)0.279Diabetes mellitus, n (%)97 (17)40 (13)57 (22)0.004Hypertension, n (%)298 (52)137 (44)161 (62)<0.001Hypercholesterolemia, n (%)318 (55)138 (44)180 (69)<0.001Smoking, n (%)166 (29)116 (37)50 (19)<0.001Positive family history of CAD, n (%)298 (52)145 (46)153 (59)0.004Previous MI, n (%)184 (32)80 (26)104 (40)<0.001Previous PCI, n (%)184 (32)57 (18)127 (49)<0.001Previous CABG, n (%)18 (3)7 (2)11 (4)0.176Previous stroke, n (%)26 (5)11 (4)15 (6)0.200Peripheral artery disease, n (%)35 (6)11 (4)24 (9)0.005History of heart failure, n (%)19 (3)6 (2)13 (5)0.041Serum LDL cholesterol, mmol/L2.71 [2.12–3.54]3.10 [2.32–3.87]2.37 [1.94–2.99]<0.001Serum HDL cholesterol, mmol/L1.04 [0.87–1.29]1.05 [0.87–1.27]1.03 [0.86–1.30]0.80Serum nonHDL levels, mmol/L3.23 [2.54–4.00]3.56 [2.81–4.36]2.83 [2.35–3.56]<0.001Serum TG, mmol/L1.27 [0.88-1.83]1.15 [0.77–1.77]1.41 [1.05–1.94]<0.001Statin use at baseline, n (%)508 (89%)308 (98%)235 (90%)0.499Procedural characteristicsIndication for CAGACS, n (%)313 (55)313 (100)0 (0)STEMI, n (%)162 (28)162 (52)0 (0)Non-ST-elevation, n (%)151 (26)151 (48)0 (0)Stable angina pectoris, n (%)261 (46)0 (0)261 (100)PCI performed, n (%)505 (88)291 (93)214 (82)aA significant stenosis was defined as a stenosis ≥ 50% of the vessel diameter by visual assessment of the coronary angiogram.No significant stenosis, n (%)42 (7)18 (6)24 (9)1-vessel disease, n (%)304 (53)172 (55)132 (51)2-vessel disease, n (%)167 (29)88 (28)79 (30)3-vessel disease, n (%)61 (11)35 (11)26 (10)Continuous variables are presented as mean ± (SD) or median [IQR]. Categorical variables are presented in numbers (n) and percentages (%). P-value was obtained from Student's t-test or Chi square test. ACS, acute coronary syndrome; CABG, coronary artery bypass grafting; CAD, coronary artery disease; CAG, coronary angiography; IQR, interquartile range; MI, myocardial infarction; PCI, percutaneous coronary intervention; SAP, stable angina pectoris; TG, triglyceride.a A significant stenosis was defined as a stenosis ≥ 50% of the vessel diameter by visual assessment of the coronary angiogram. Open table in a new tab TABLE 2Lipid concentrations in the full cohort, patients with MACEs during follow-up, and patients without MACEs during follow-upLipid concentrations in the full cohortTotal (n = 574)ACS patients (n = 313)SAP patients (n = 261)PCE 14:0, pmol/µl21.7 [15.9–28.1]22.9 [16.5–30.5]21.2 [15.4–26.8]0.008CE 18:3, pmol/µl70.3 [51.8–90.7]72.3 [53.6–99.5]66.1 [50.3–85.2]0.003CE 20:4, pmol/µl386 [317–457]394 [324–453]374 [307–471]0.31CE 20:5, pmol/µl49.1 [36.3–72.6]49.2 [36.4–72.1]49.0 [35.9–74.7]0.69CE 22:5, pmol/µl2.65 [2.00–3.62]2.81 [2.12–3.77]2.53 [1.90–3.40]0.037Cer(d18:1/16:0) pmol/µl0.12 [0.10–0.15]0.13 [0.11–0.17]0.11 [0.09–0.13]<0.001Cer(d18:1/20:0) pmol/µl0.11 [0.09–0.15]0.12 [0.10–0.16]0.11 [0.08–0.13]<0.001Cer(d18:1/24:0) pmol/µl5.98 [4.72–7.49]6.43 [5.00–8.07]5.65 [4.49–6.61]<0.001Cer(d18:1/24:1) pmol/µl1.79 [1.42–2.25]1.89 [1.52–2.44]1.67 [1.35–2.05]<0.001LacCer(d18:1/18:0) pmol/µl0.13 [0.10–0.16]0.13 [0.11–0.16]0.12 [0.10–0.15]0.001Cer(d18:1/16:0)/Cer(d18:1/24:0) pmol/µl0.020 [0.018–0.024]0.021 [0.018–0.025]0.020 [0.017–0.023]0.001Cer(d18:1/20:0)/Cer(d18:1/24:0) pmol/µl0.019 [0.016–0.024]0.019 [0.015–0.024]0.019 [0.016–0.023]0.62Cer(d18:1/24:1)/Cer(d18:1/24:0) pmol/µl0.31 [0.26–0.36]0.31 [0.26–0.36]0.31 [0.26–0.36]0.65Lipid concentrations in those with MACEsaInformation on MACEs was available in n = 566.Total (n = 155)ACS patients (n = 65)SAP patients (n = 90)PCE 14:0, pmol/µl22.6 [15.7–27.1]21.7 [15.5–30.3]22.7 [15.7–26.6]0.67CE 18:3, pmol/µl67.9 [51.2–90.3]70.3 [52.8–103]66.9 [50–84.1]0.17CE 20:4, pmol/µl381 [310–445]381 [310–432]379 [310–447]0.95CE 20:5, pmol/µl52.4 [37.4–74.5]52.6 [38.4–76.3]50.9 [36.6–74.4]0.73CE 22:5, pmol/µl2.57 [1.86–3.71]2.61 [2.04–3.97]2.51 [1.80–3.45]0.065Cer(d18:1/16:0) pmol/µl0.12 [0.10–0.16]0.15 [0.11–0.17]0.11 [0.09–0.13]<0.001Cer(d18:1/20:0) pmol/µl0.11 [0.09–0.16]0.13 [0.10–0.17]0.11 [0.08–0.14]0.011Cer(d18:1/24:0) pmol/µl5.86 [4.65–7.48]6.44 [5.02–8.10]5.66 [4.54–6.58]0.063Cer(d18:1/24:1) pmol/µl1.78 [1.35–2.36]2.10 [1.66–2.84]1.62 [1.33–2.13]0.008LacCer(d18:1/18:0) pmol/µl0.13 [0.10–0.16]0.14 [0.10–0.18]0.13 [0.10–0.16]0.137Cer(d18:1/16:0)/Cer(d18:1/24:0) pmol/µl0.021 [0.018–0.025]0.022 [0.019–0.027]0.019 [0.017–0.024]0.006Cer(d18:1/20:0)/Cer(d18:1/24:0) pmol/µl0.020 [0.016–0.025]0.020 [0.016–0.024]0.020 [0.015–0.025]0.504Cer(d18:1/24:1)/Cer(d18:1/24:0) pmol/µl0.31 [0.27–0.37]0.33 [0.27–0.36]0.31 [0.26–0.37]0.222Lipid concentrations in those without MACEsaInformation on MACEs was available in n = 566.Total (n = 411)ACS patients (n = 242)SAP patients (n = 169)PCE 14:0, pmol/µl21.5 [15.8–28.7]23 [16.5–30.5]20.7 [15.3–27]0.007CE 18:3, pmol/µl70.5 [51.7–91.3]73.8 [53.5–99]66 [50.3–86.4]0.011CE 20:4, pmol/µl391 [321–467]397 [310–432]374 [304–476]0.272CE 20:5, pmol/µl49.1 [35.6–70.5]49.2 [35.5–70.7]47.5 [35.6–70]0.575CE 22:5, pmol/µl2.69 [2.04–3.58]2.79 [2.12–3.70]2.54 [1.92–3.40]0.041Cer(d18:1/16:0) pmol/µl0.12 [0.10–0.15]0.13 [0.11–0.16]0.11 [0.09–0.13]<0.001Cer(d18:1/20:0) pmol/µl0.12 [0.09–0.14]0.12 [0.09–0.15]0.11 [0.08–0.13]<0.001Cer(d18:1/24:0) pmol/µl6 [4.75–7.47]6.39 [4.97–8.07]5.64 [4.49–6.64]<0.001Cer(d18:1/24:1) pmol/µl1.78 [1.35–2.36]1.86 [1.51–2.33]1.68 [1.35–2.04]<0.001LacCer(d18:1/18:0) pmol/µl0.13 [0.10–0.16]0.13 [0.11–0.16]0.12 [0.10–0.15]0.001Cer(d18:1/16:0)/Cer(d18:1/24:0) pmol/µl0.021 [0.018–0.025]0.021 [0.018–0.024]0.020 [0.017–0.023]0.017Cer(d18:1/20:0)/Cer(d18:1/24:0) pmol/µl0.020 [0.016–0.025]0.019 [0.015–0.024]0.019 [0.016–0.023]0.60Cer(d18:1/24:1)/Cer(d18:1/24:0) pmol/µl0.31 [0.27–0.37]0.30 [0.25–0.36]0.31 [0.26–0.36]0.77Concentrations are presented in μM as median [IQR]. P-value was obtained from Student's t-test for difference in ln-transformed mean lipid concentration. ACS, acute coronary syndrome; CE, cholesteryl ester; Cer, ceramide; LacCer, lactosylceramide; MACE, major adverse cardiac event; SAP, stable angina pectoris.a Information on MACEs was available in n = 566. Open table in a new tab Continuous variables are presented as mean ± (SD) or median [IQR]. Categorical variables are presented in numbers (n) and percentages (%). P-value was obtained from Student's t-test or Chi square test. ACS, acute coronary syndrome; CABG, coronary artery bypass grafting; CAD, coronary artery disease; CAG, coronary angiography; IQR, interquartile range; MI, myocardial infarction; PCI, percutaneous coronary intervention; SAP, stable angina pectoris; TG, triglyceride. Concentrations are presented in μM as median [IQR]. P-value was obtained from Student's t-test for difference in ln-transformed mean lipid concentration. ACS, acute coronary syndrome; CE, cholesteryl ester; Cer, ceramide; LacCer, lactosylceramide; MACE, major adverse cardiac event; SAP, stable angina pectoris. As shown in Table 2, ACS patients had significantly higher plasma concentrations of CE 14:0, CE 18:3, CE 22:5; Cer(d18:1/16:0), Cer(d18:1/20:0), Cer(d18:1/24:0), Cer(d18:1/24:1), LacCer(d18:1/18:0) and Cer(d18:1/16:0)/Cer(d18:1/24:0) as compared with SAP patients, both in the full cohort and in patients who remained free of MACEs. In patients with MACEs during follow-up, except plasma concentration of CE 14:0, CE 18:3, and LacCer(d18:1/18:0), all of the above-mentioned lipid species plasma concentrations were significantly higher in the ACS patients as compared with the SAP patients. In addition, in ACS patients, concentration of Cer(d18:1/16:0) tended to be higher (P = 0.054) in those with MACEs as compared with those without MACEs during follow-up. In the full cohort (n = 574), vital status was acquired for 572 patients (99.7%). The follow-up questionnaire assessing the occurrence of MACEs was completed by 99% of the 574 patients. During a median follow-up time of 4.7 years (IQR: [4.2–5.6]) years, a total of 155 patients (27%) experienced at least one MACE (primary endpoint). In the ACS group, 65 patients (21%) experienced MACEs during long-term follow-up; in the SAP group, this was 90 patients (34%). The results for the associations between the molecular lipids concentrations and MACEs are depicted in Fig. 1 and supplemental Table S1a. In multivariable analyses, after adjustment for cardiac risk factors, clinical presentation, and statin use at baseline, Cer(d18:1/16:0) concentration {HR: 2.14; 95% CI [1.22–3.76] per ln(pmol/ml) P = 0.008} and Cer(d18:1/24:1) concentration {HR: 1.64; 95% CI [1.00–2.68] per ln(pmol/ml) P = 0.049} were significantly associ