Plasma Metabolic Profile Determination in Young ST-segment Elevation Myocardial Infarction Patients with Ischemia and Reperfusion: Ultra-performance Liquid Chromatography and Mass Spectrometry for Pathway Analysis

Chemometrics assisted untargeted metabolomic analysis to explore metabolic alterations in chronic urticaria via LC/Q-TOF/MS/MS

Chronic urticaria (CU) is a common disease characterized by the development of recurrent itchy blisters and/or angioedema lasting longer than 6 weeks. The evidence-based diagnosis of CU is described in the most recent urticaria guideline. Metabolomics has the potential to offer diagnostic biomarkers for the detection and prognosis of diseases and predict the efficacy and safety of pharmaceutical interventions. Determining the variation in metabolites found in the plasma of CU patients (n = 20) and 20 controls has therefore been the goal of this investigation. Samples were analyzed using liquid chromatography quadrupole time-of-flight mass spectrometry after applying acetonitrile precipitation. For the purpose of identifying and characterizing metabolites, the METLIN database was utilized. According to results, 21 metabolites were found to be significantly (VIP score > 0.7, p < .05 and fold analysis >1.5) altered. Differentiations between each group were successful via both OPLS-DA and ROC analysis. While plasma allantoate, homogentisate, indole acetate, proline, phenylalanine levels decreased in CU patients compared to healthy subjects, tryptophan, spermidine, phenyl pyruvate, oleic acid, lysine, valine, ornithine, histidine, glutamate, leucine, kynurenine, hypoxanthine, tyrosine, glucose, creatine and cortisol levels were significantly increased. Diagnosis of CU could be achieved by evaluating the metabolic profile of patients.

Sphingolipid metabolism and signaling in cardiovascular diseases

Sphingolipids are a structural component of the cell membrane, derived from sphingosine, an amino alcohol. Its sphingoid base undergoes various types of enzymatic transformations that lead to the formation of biologically active compounds, which play a crucial role in the essential pathways of cellular signaling, proliferation, maturation, and death. The constantly growing number of experimental and clinical studies emphasizes the pivotal role of sphingolipids in the pathophysiology of cardiovascular diseases, including, in particular, ischemic heart disease, hypertension, heart failure, and stroke. It has also been proven that altering the sphingolipid metabolism has cardioprotective properties in cardiac pathologies, including myocardial infarction. Recent studies suggest that selected sphingolipids may serve as valuable biomarkers useful in the prognosis of cardiovascular disorders in clinical practice. This review aims to provide an overview of the current knowledge of sphingolipid metabolism and signaling in cardiovascular diseases.

Clinical Parameters and Metabolomic Biomarkers That Predict Inhospital Outcomes in Patients With ST-Segment Elevated Myocardial Infarctions

Background

Postoperative risk stratification is challenging in patients with ST-segment elevation myocardial infarction (STEMI) who undergo percutaneous coronary intervention. This study aimed to characterize the metabolic fingerprints of patients with STEMI with different inhospital outcomes in the early stage of morbidity and to integrate the clinical baseline characteristics to develop a prognostic prediction model.

Methods

Plasma samples were collected retrospectively from two propensity score-matched STEMI cohorts from May 6, 2020 to April 20, 2021. Cohort 1 consisted of 48 survivors and 48 non-survivors. Cohort 2 included 48 patients with unstable angina pectoris, 48 patients with STEMI, and 48 age- and sex-matched healthy controls. Metabolic profiling was generated based on ultra-performance liquid chromatography and a mass spectrometry platform. The comprehensive metabolomic data analysis was performed using MetaboAnalyst version 5.0. The hub metabolite biomarkers integrated into the model were tested using multivariate linear support vector machine (SVM) algorithms and a generalized estimating equation (GEE) model. Their predictive capabilities were evaluated using areas under the curve (AUCs) of receiver operating characteristic curves.

Results

Metabonomic analysis from the two cohorts showed that patients with STEMI with different outcomes had significantly different clusters. Seven differentially expressed metabolites were identified as potential candidates for predicting inhospital outcomes based on the two cohorts, and their joint discriminative capabilities were robust using SVM (AUC = 0.998, 95% CI 0.983–1) and the univariate GEE model (AUC = 0.981, 95% CI 0.969–0.994). After integrating another six clinical variants, the predictive performance of the updated model improved further (AUC = 0.99, 95% CI 0.981–0.998).

Conclusion

A survival prediction model integrating seven metabolites from non-targeted metabonomics and six clinical indicators may generate a powerful early survival prediction model for patients with STEMI. The validation of internal and external cohorts is required.

Emerging Roles of Ceramide in Cardiovascular Diseases

Ceramide is a core molecule of sphingolipid metabolism that causes selective insulin resistance and dyslipidemia. Research on its involvement in cardiovascular diseases has grown rapidly. In resting cells, ceramide levels are extremely low, while they rapidly accumulate upon encountering external stimuli. Recently, the regulation of ceramide levels under pathological conditions, including myocardial infarction, hypertension, and atherosclerosis, has drawn great attention. Increased ceramide levels are strongly associated with adverse cardiovascular risks and events while inhibiting the synthesis of ceramide or accelerating its degradation improves a variety of cardiovascular diseases. In this article, we summarize the role of ceramide in cardiovascular disease, investigate the possible application of ceramide as a new diagnostic biomarker and a therapeutic target for cardiovascular disorders, and highlight the remaining problems.

Human Plasma Metabolomics Identify 9-cis-retinoic Acid and Dehydrophytosphingosine Levels as Novel biomarkers for Early Ventricular Fibrillation after ST-elevated Myocardial Infarction

The relevant metabolite biomarkers for risk prediction of early onset of ventricular fibrillation (VF) after ST-segment elevation myocardial infarction (STEMI) remain unstudied. Here, we aimed to identify these imetabolites and the important metabolic pathways involved, and explore whether these metabolites could be used as predictors for the phenotype. Plasma samples were obtained retrospectively from a propensity-score matched cohort including 42 STEMI patients (21 consecutive VF and 21 non-VF). Ultra-performance liquid chromatography and mass spectrometry in combination with a comprehensive analysis of metabolomic data using Metaboanalyst 5.0 version were performed. As a result, the retinal metabolism pathway proved to be the most discriminative for the VF phenotype. Furthermore, 9-cis-Retinoic acid (9cRA) and dehydrophytosphingosine proved to be the most discriminative biomarkers. Biomarker analysis through receiver operating characteristic (ROC) curve showed the 2-metabolite biomarker panel yielding an area under the curve (AUC) of 0.836. The model based on Monte Carlo cross-validation found that 9cRA had the greatest probability of appearing in the predictive panel of biomarkers in the model. Validation of model efficiency based on an ROC curve showed that the combination model constructed by 9cRA and dehydrophytosphingosine had a good predictive value for early-onset VF after STEMI, and the AUC was 0.884 (95% CI 0.714–1). Conclusively, the retinol metabolism pathway was the most powerful pathway for differentiating the post-STEMI VF phenotype. 9cRA was the most important predictive biomarker of VF, and a plasma biomarker panel made up of two metabolites, may help to build a potent predictive model for VF.

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.

Investigation of the metabolic difference between ST-elevated myocardial infarction and non-ST-elevated myocardial infarction via LC/Q-TOF/MS/MS

Acute coronary syndrome (ACS) is a clinical condition caused by a disturbance in myocardial blood flow. ACS can be basically divided into two forms: ST elevation myocardial infarction (STEMI) due to complete occlusion of the coronary artery and non-ST elevation myocardial infarction (NSTEMI) due to partial occlusion of the coronary artery. In this study, we aimed to monitor the metabolite profile of STEMI and NSTEMI patients and compare the results via untargeted metabolomics approach. Serum samples were collected from STEMI and NSTEMI patients, and each group consists of 20 participants. Extraction was achieved by acetonitrile, and chromatographic separation was performed by LC/Q-TOF/MS/MS accompanied with dual AJS ESI positive ion mode. METLIN, MATLAB 2017a-PLS Toolbox7.2, and Human Metabolome Database were utilized for bioinformatics evaluation of obtained findings. In our results, 203 m/z ratio was detected and 163 m/z ratio passed the significance criteria (fold analysis > 1.5 and p < 0.05). Twenty-five metabolites including BCAAs, LysoPC species, lactic acid, succinate, malonic acid, maleic acid, butyric acid, carnitine, and betaine were identified. In conclusion, new biomarker candidates were identified to differentiate the diagnosis of STEMI and NSTEMI. Identified metabolites are indicative of alterations in oxidative stress, hypoxia, TCA cycle, and amino acid metabolism.

The role of dihydrosphingolipids in disease

Dihydrosphingolipids refer to sphingolipids early in the biosynthetic pathway that do not contain a C4-trans-double bond in the sphingoid backbone: 3-ketosphinganine (3-ketoSph), dihydrosphingosine (dhSph), dihydrosphingosine-1-phosphate (dhS1P) and dihydroceramide (dhCer). Recent advances in research related to sphingolipid biochemistry have shed light on the importance of sphingolipids in terms of cellular signalling in health and disease. However, dihydrosphingolipids have received less attention and research is lacking especially in terms of their molecular mechanisms of action. This is despite studies implicating them in the pathophysiology of disease, for example dhCer in predicting type 2 diabetes in obese individuals, dhS1P in cardiovascular diseases and dhSph in hepato-renal toxicity. This review gives a comprehensive summary of research in the last 10-15 years on the dihydrosphingolipids, 3-ketoSph, dhSph, dhS1P and dhCer, and their relevant roles in different diseases. It also highlights gaps in research that could be of future interest.

UPLC-QTOF/MS-Based Lipidomic Profiling of Liver Qi-Stagnation and Spleen-Deficiency Syndrome in Patients with Hyperlipidemia

Hyperlipidemia is a common disease caused by abnormal plasma lipid metabolism. Lipidomics is a powerful and efficient technology to study the integration of disease and syndrome of Chinese medicine. This study investigated specific changes in lipid metabolites from hyperlipidemia patients with syndrome of liver qi-stagnation and spleen-deficiency (SLQSD). Lipid profiles in plasma samples from 29 hyperlipidemia patients including 10 SLQSD and 19 non-SLQSD and 26 healthy volunteers (NC) were tested by UPLC-QTOF/MS. PLS-DA analysis and database searching were performed to discover differentiating metabolites. Differences in lipid metabolites between hyperlipidemia and healthy people mainly include phosphatidylcholines, phosphatidylethanolamines, phosphatidylglycerols, and ceramides. Hyperlipidemia patients with SLQSD and non-SLQSD could be differentiated by using identified lipid metabolites including phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, triglycerides, diacylglycerols, lysophosphatidylethanolamines, sphingomyelins, lysophosphatidylcholines, and lactosylceramides. There were significant differences of lipid metabolism between between different syndromes of the same disease such as hyperlipidemia which showed significant differences between SLQSD and non-SLQSD.

Human Plasma Metabolomics Implicates Modified 9-cis-Retinoic Acid in the Phenotype of Left Main Artery Lesions in Acute ST-Segment Elevated Myocardial Infarction

The detection of left main coronary artery disease (LMCAD) is crucial before ST-segment elevated myocardial infarction (STEMI) or sudden cardiac death. The aim of this study was to identify characteristic metabolite modifications in the LMCAD phenotype, using the metabolomics technique. Metabolic profiles were generated based on ultra-performance liquid chromatography and mass spectrometry, combined with multivariate statistical analysis. Plasma samples were collected prospectively from a propensity-score matched cohort including 44 STEMI patients (22 consecutive LMCAD and 22 non-LMCAD), and 22 healthy controls. A comprehensive metabolomics data analysis was performed with Metaboanalyst 3.0 version. The retinol metabolism pathway was shown to have the strongest discriminative power for the LMCAD phenotype. According to biomarker analysis through receiver-operating characteristic curves, 9-cis-retinoic acid (9cRA) dominated the first page of biomarkers, with area under the curve (AUC) value 0.888. Next highest were a biomarker panel consisting of 9cRA, dehydrophytosphingosine, 1H-Indole-3-carboxaldehyde, and another seven variants of lysophosphatidylcholines, exhibiting the highest AUC (0.933). These novel data propose that the retinol metabolism pathway was the strongest differential pathway for the LMCAD phenotype. 9cRA was the most critical biomarker of LMCAD, and a ten-metabolite plasma biomarker panel, in which 9cRA remained the weightiest, may help develop a potent predictive model for LMCAD in clinic.

Transfusion of Plasma Collected at Late Phase after Preconditioning Reduces Myocardial Infarct Size Induced by Ischemia-reperfusion in Rats In vivo

BACKGROUND

Plasma transfusion is a common clinical practice. Remote ischemic preconditioning (RIPC) protects organs against ischemia-reperfusion (IR) injury. Whether preconditioned plasma (PP), collected at late phase after RIPC, could protect organs against IR injury in vivo is unknown. This study explored whether transfusion of PP could reduce myocardial infarct size (IS) after IR in rat in vivo.

METHODS

Eighty Lewis rats were randomized to eight groups (n = 10 for each group). Two groups of plasma donor rats donated plasma at 48 h after transient limb ischemia (PP) or control protocol (nonpreconditioned plasma [NPP]). Six groups of recipient rats received normal saline (NS; NS-IR 1, and NS-IR 24 groups), NPP (NPP-IR 1 and NPP-IR 24 groups), or PP (PP-IR 1 and PP-IR 24 groups) at one or 24 h before myocardial IR. Myocardial IR consisted of 30-min left anterior descending (LAD) coronary artery occlusion and 180-min reperfusion. The area at risk (AAR) and infarct area were determined by double-staining with Evans blue and triphenyltetrazolium chloride. IS was calculated by infarct area divided by AAR. This was a 3 × 2 factorial design study, and factorial analysis was used to evaluate the data. If an interaction between the fluid and transfusion time existed, one-way analysis of variance with Bonferroni correction for multiple comparisons was used to analyze the single effects of fluid type when the transfusion time was fixed.

RESULTS

IS in the NPP-IR 1 and PP-IR 1 groups was smaller than in the NS-IR 1 group (F = 6.838, P = 0.005; NPP-IR 1: 57 ± 8% vs. NS-IR1: 68 ± 6%, t = 2.843, P = 0.020; PP-IR 1: 56 ± 8% vs. NS-IR 1: 68 ± 6%, t = 3.102, P = 0.009), but no significant difference was detected between the NPP-IR 1 and PP-IR 1 groups (57 ± 8% vs. 56 ± 8%, t = 0.069, P = 1.000). IS in the NPP-IR 24 and PP-IR 24 groups was smaller than in the NS-IR 24 group (F = 24.796, P< 0.001; NPP-IR 24: 56% ± 7% vs. NS-IR 24: 68 ± 7%, t = 3.102, P = 0.026; PP-IR 24: 40 ± 9% vs. NS-IR 24: 68 ± 7%, t = 7.237, P< 0.001); IS in the PP-IR 24 group was smaller than in the NPP-IR 24 group (40 ± 9% vs. 56 ± 7%, t = 4.135, P = 0.002).

CONCLUSION

Transfusion of PP collected at late phase after remote ischemic preconditioning could reduce IS, suggesting that late-phase cardioprotection was transferable in vivo.

Analysis of Serum Metabolites to Diagnose Bicuspid Aortic Valve

Bicuspid aortic valve (BAV) is the most common congenital heart disease. The current study aims to construct a diagnostic model based on metabolic profiling as a non-invasive tool for BAV screening. Blood serum samples were prepared from an estimation group and a validation group, each consisting of 30 BAV patients and 20 healthy individuals, and analyzed by liquid chromatography-mass spectrometry (LC-MS). In total, 2213 metabolites were detected and 41 were considered different. A model for predicting BAV in the estimation group was constructed using the concentration levels of monoglyceride (MG) (18:2) and glycerophospho-N-oleoyl ethanolamine (GNOE). A novel model named Zhongshan (ZS) was developed to amplify the association between BAV and the two metabolites. The area under curve (AUC) of ZS for BAV prediction was 0.900 (0.782–0.967) and was superior to all single-metabolite models when applied to the estimation group. Using optimized cutoff (−0.1634), ZS model had a sensitivity score of 76.7%, specificity score of 90.0%, positive predictive value of 80% and negative predictive value of 85.0% for the validation group. These results support the use of serum-based metabolomics profiling method as a complementary tool for BAV screening in large populations.

Systems Medicine as an Emerging Tool for Cardiovascular Genetics

Cardiovascular disease (CVD) is a major contributor to morbidity and mortality worldwide. However, the pathogenesis of CVD is complex and remains elusive. Within the last years, systems medicine has emerged as a novel tool to study the complex genetic, molecular, and physiological interactions leading to diseases. In this review, we provide an overview about the current approaches for systems medicine in CVD. They include bioinformatical and experimental tools such as cell and animal models, omics technologies, network, and pathway analyses. Additionally, we discuss challenges and current literature examples where systems medicine has been successfully applied for the study of CVD.