Metabolic Disturbances Identified in Plasma Samples from ST-Segment Elevation Myocardial Infarction Patients

Sphingolipid Metabolism Is Associated with Cardiac Dyssynchrony in Patients with Acute Myocardial Infarction

AIM

Sphingolipids are a class of complex and bioactive lipids that are involved in the pathological processes of cardiovascular disease. Fabry disease is an X-linked storage disorder that results in the pathological accumulation of glycosphingolipids in body fluids and the heart. Cardiac dyssynchrony is observed in patients with Fabry disease and left ventricular (LV) hypertrophy. However, little information is available on the relationship between plasma sphingolipid metabolites and LV remodelling after acute myocardial infarction (AMI). The purpose of this study was to assess whether the baseline plasma sphingomyelin/acid ceramidase (aCD) ratio predicts LV dyssynchrony at 6M after AMI.

METHODS

A total of 62 patients with AMI undergoing primary angioplasty were recruited. Plasma aCD and sphingomyelin were measured prior to primary angioplasty. Three-dimensional echocardiographic measurements of the systolic dyssynchrony index (SDI) were performed at baseline and 6 months of follow-up. The patients were divided into three groups according to the level of aCD and sphingomyelin above or below the median. Group 1 denotes lower aCD and lower sphingomyelin; Group 3 denotes higher aCD and higher sphingomyelin. Group 2 represents different categories of patients with aCD and sphingomyelin. Trend analysis showed a significant increase in the SDI from Group 1 to Group 3. Logistic regression analysis showed that the sphingomyelin/aCD ratio was a significant predictor of a worsening SDI at 6 months.

CONCLUSIONS

AMI patients with high baseline plasma sphingomyelin/aCD ratios had a significantly increased SDI at six months. The sphingomyelin/aCD ratio can be considered as a surrogate marker of plasma ceramide load or inefficient ceramide metabolism. Plasma sphingolipid pathway metabolism may be a new biomarker for therapeutic intervention to prevent adverse remodelling after MI.

Lipidomic-Based Algorithms Can Enhance Prediction of Obstructive Coronary Artery Disease

Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statistical analyses have shown significant discrimination between the serum lipidomes of patients with obstructive CAD. Finally, the XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, a triacylglycerol, galectin-3, glucose, LDL, and LDH) as totally sensitive (100% sensitivity, 62.1% specificity, 100% negative predictive value) for the prediction of obstructive CAD. Our findings shed light on dysregulated lipid metabolism's role in CAD, validating existing evidence and suggesting promise for novel therapies and improved risk stratification.

Crosstalk among Reactive Oxygen Species, Autophagy and Metabolism in Myocardial Ischemia and Reperfusion Stages

Myocardial ischemia is the most common cardiovascular disease. Reperfusion, an important myocardial ischemia tool, causes unexpected and irreversible damage to cardiomyocytes, resulting in myocardial ischemia/reperfusion (MI/R) injury. Upon stress, especially oxidative stress induced by reactive oxygen species (ROS), autophagy, which degrades the intracellular energy storage to produce metabolites that are recycled into metabolic pathways to buffer metabolic stress, is initiated during myocardial ischemia and MI/R injury. Excellent cardioprotective effects of autophagy regulators against MI and MI/R have been reported. Reversing disordered cardiac metabolism induced by ROS also exhibits cardioprotective action in patients with myocardial ischemia. Herein, we review current knowledge on the crosstalk between ROS, cardiac autophagy, and metabolism in myocardial ischemia and MI/R. Finally, we discuss the possible regulators of autophagy and metabolism that can be exploited to harness the therapeutic potential of cardiac metabolism and autophagy in the diagnosis and treatment of myocardial ischemia and MI/R.

Myocardial infarction complexity: A multi-omics approach

Myocardial infarction (MI), a prevalent cardiovascular disease, is fundamentally precipitated by thrombus formation in the coronary arteries, which subsequently decreases myocardial perfusion and leads to cellular necrosis. The intricacy of MI pathogenesis necessitates extensive research to elucidate the disease's root cause, thereby addressing the limitations present in its diagnosis and prognosis. With the continuous advancement of genomics technology, genomics, proteomics, metabolomics and transcriptomics are widely used in the study of MI, which provides an excellent way to identify new biomarkers that elucidate the complex mechanisms of MI. This paper provides a detailed review of various genomics studies of MI, including genomics, proteomics, transcriptomics, metabolomics and multi-omics studies. The metabolites and proteins involved in the pathogenesis of MI are investigated through integrated protein-protein interactions and multi-omics analysis by STRING and Metascape platforms. In conclusion, the future of omics research in myocardial infarction offers significant promise.

Untargeted metabolomics identified kynurenine as a predictive prognostic biomarker in acute myocardial infarction

OBJECTIVE

The occurrence of cardiovascular adverse events in the first year after ST-acute myocardial infarction (STEMI) remains high; therefore, identification of patients with poor prognosis is essential for early intervention. This study aimed to evaluate the prognostic value of metabolomics-based biomarkers in STEMI patients and explore their functional mechanisms.

METHODS

Metabolite profiling was performed using nuclear magnetic resonance. The plasma concentration of Kynurenine (Kyn) was measured using ultraperformance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry. Major adverse cardiac and cerebral events were assessed for 1 year. A functional metabolomics strategy was proposed for investigating the role of Kyn in both vitro and vivo models.

RESULTS

The adjusted hazard ratios in STEMI patients for Kyn in the 4^th quartile 7.12(5.71-10.82) was significantly higher than that in the 3^rd quartile 3.03(2.62-3.74), 2^nd quartile 1.86(1.70-2.03), and 1^st quartile 1.20(0.93-1.39).The incidence of MACCE was significantly different among Kyn quartiles and the highest incidence of MACCE was observed in the 4th quartile when compared with the 1st quartile (9.84% vs.2.85%, P<0.001).Immunofluorescence staining indicated that indoleamine-pyrrole 2,3-dioxygenase (IDO1) was located in the CD68 positive staining area of thrombi from STEMI patients and Kyn was induced in the early phase after myocardial infarction. Kyn could trigger inflammation and oxidative stress of macrophage cells by activation of the Sirt3-acSOD2/IL-1β signaling pathway in vitro.

CONCLUSIONS

Plasma Kyn levels were positively associated with the occurrence of STEMI. Kyn could induce macrophage cells inflammation and oxidative stress by activating the Sirt3-acSOD2/IL-1β pathway following myocardial ischemia injury. Kyn could be a robust biomarker for STEMI prognosis and reduction of Kyn could be beneficial in STEMI patients.

Plasma Quantitative Lipid Profiles: Identification of CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1) as Novel Biomarkers for Pre-warning and Prognosis in Acute Myocardial Infarction

This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.

Metabolism disorder promotes isoproterenol-induced myocardial injury in mice with high temperature and high humidity and high-fat diet

Background

Isoproterenol (ISO), a synthetic on selective β-adrenergic agonist, provides a simple and non-invasive method for inducing myocardial injury with lower mortality and higher reproducibility. Phlegm-damp syndrome, as known as “Tanshi” in Chinese, is one of Traditional Chinese Medicine (TCM) syndrome differentiation, which plays an important role in the development of cardiovascular diseases. However, the underlying mechanism remains unknown.

Methods

In our present study, a myocardial injury mouse model was introduced by ISO administration combined with high temperature and high humidity and high-fat diet to simulate phlegm-damp syndrome. Nontargeted metabolomics with LC–MS/MS was adopted to reveal serum metabolism profile for elucidating the possible molecular mechanism.

Results

The results of our study showed that phlegm-damp syndrome promoted ISO-induced myocardial injury by aggravating left ventricular hypertrophy and fibrosis, and increasing cardiac index. Our study also confirmed the presence of specific metabolites and disturbed metabolic pathways by comparing ISO mice and Tanshi mice, mainly including glycerophospholipid metabolism, arginine–proline metabolism, and sphingolipid signaling pathway. The lysoPCs, PCs, SMs, Sphingosine, and L-Arginine were the main metabolites that showed a difference between ISO and Tanshi mice, which might be the result of the underlying mechanism in the promotion of ISO-induced myocardial injury in mice with high temperature and high humidity and high-fat diet.

Conclusion

Our current study provides new insights into contribution of metabolism disorder in promotion of ISO-induced myocardial injury in mice with high temperature and high humidity and high-fat diet, and new targets for clinical diagnosis and pharmacologic treatment of cardiovascular disease with phlegm-damp syndrome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02583-z.

Defining Acute Coronary Syndrome through Metabolomics

As an emerging platform technology, metabolomics offers new insights into the pathomechanisms associated with complex disease conditions, including cardiovascular diseases. It also facilitates assessing the risk of developing the disease before its clinical manifestation. For this reason, metabolomics is of growing interest for understanding the pathogenesis of acute coronary syndromes (ACS), finding new biomarkers of ACS, and its associated risk management. Metabolomics-based studies in ACS have already demonstrated immense potential for biomarker discovery and mechanistic insights by identifying metabolomic signatures (e.g., branched-chain amino acids, acylcarnitines, lysophosphatidylcholines) associated with disease progression. Herein, we discuss the various metabolomics approaches and the challenges involved in metabolic profiling, focusing on ACS. Special attention has been paid to the clinical studies of metabolomics and lipidomics in ACS, with an emphasis on ischemia/reperfusion injury.

From Prevention to Disease Perturbations: A Multi-Omic Assessment of Exercise and Myocardial Infarctions

While a molecular assessment of the perturbations and injury arising from diseases is essential in their diagnosis and treatment, understanding changes due to preventative strategies is also imperative. Currently, complex diseases such as cardiovascular disease (CVD), the leading cause of death worldwide, suffer from a limited understanding of how the molecular mechanisms taking place following preventive measures (e.g., exercise) differ from changes occurring due to the injuries caused from the disease (e.g., myocardial infarction (MI)). Therefore, this manuscript assesses lipidomic changes before and one hour after exercise treadmill testing (ETT) and before and one hour after a planned myocardial infarction (PMI) in two separate patient cohorts. Strikingly, unique lipidomic perturbations were observed for these events, as could be expected from their vastly different stresses on the body. The lipidomic results were then combined with previously published metabolomic characterizations of the same patients. This integration provides complementary insights into the exercise and PMI events, thereby giving a more holistic understanding of the molecular changes associated with each.

Sphingolipid composition of circulating extracellular vesicles after myocardial ischemia

Sphingolipids are structural components of cell membrane, displaying several functions in cell signalling. Extracellular vesicles (EV) are lipid bilayer membrane nanoparticle and their lipid composition may be different from parental cells, with a significant enrichment in sphingolipid species, especially in pathological conditions. We aimed at optimizing EV isolation from plasma and describing the differential lipid content of EV, as compared to whole plasma. As pilot study, we evaluated the diagnostic potential of lipidomic signature of circulating EV in patients with a diagnosis of ST-segment-elevation myocardial infarction (STEMI). STEMI patients were evaluated before reperfusion and 24-h after primary percutaneous coronary intervention. Twenty sphingolipid species were quantified by liquid-chromatography tandem-mass-spectrometry. EV-ceramides, -dihydroceramides, and -sphingomyelins increased in STEMI vs. matched controls and decreased after reperfusion. Their levels correlated to hs-troponin, leucocyte count, and ejection fraction. Plasma sphingolipids levels were 500-to-700-fold higher as compared to EV content; nevertheless, only sphingomyelins differed in STEMI vs. control patients. Different sphingolipid species were enriched in EV and their linear combination by machine learning algorithms accurately classified STEMI patients at pre-PCI evaluation. In conclusion, EV lipid signature discriminates STEMI patients. These findings may contribute to the identification of novel biomarkers and signaling mechanisms related to cardiac ischemia.