Defining Acute Coronary Syndrome through Metabolomics

Lipidomics and cardiovascular disease

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating innovative approaches for early detection and personalized interventions. Lipidomics, leveraging advanced mass spectrometry techniques, has become instrumental in deciphering lipid-mediated mechanisms in CVDs. This review explores the application of lipidomics in identifying biomarkers for myocardial infarction, heart failure, stroke, and calcific aortic valve stenosis (CAVS). This review examines the technological advancements in shotgun lipidomics and LC/MS, which provide unparalleled insights into lipid composition and function. Key lipid biomarkers, including ceramides and lysophospholipids, have been linked to disease progression and therapeutic outcomes. Integrating lipidomics with genomic and proteomic data reveals the molecular underpinnings of CVDs, enhancing risk prediction and intervention strategies. This review positions lipidomics as a transformative tool in reshaping cardiovascular research and clinical practice.

Urinary metabolomics analysis based on LC-MS for the diagnosis and monitoring of acute coronary syndrome

Background

Acute coronary syndrome (ACS) is a cardiovascular disease caused by acute myocardial ischemia. The aim of this study was to use urine metabolomics to explore potential biomarkers for the diagnosis of ACS and the changes in metabolites during the development of this disease.

Methods

Urine samples were collected from 81 healthy controls and 130 ACS patients (103 UA and 27 AMI). Metabolomics based on liquid chromatography-mass spectrometry (LC-MS) was used to analyze urine samples. Statistical analysis and functional annotation were applied to identify potential metabolite panels and altered metabolic pathways between ACS patients and healthy controls, unstable angina (UA), and acute myocardial infarction (AMI) patients.

Results

There were significant differences in metabolic profiles among the UA, AMI and control groups. A total of 512 differential metabolites were identified in this study. Functional annotation revealed that changes in arginine biosynthesis, cysteine and methionine metabolism, galactose metabolism, sulfur metabolism and steroid hormone biosynthesis pathways occur in ACS. In addition, a panel composed of guanidineacetic acid, S-adenosylmethionine, oxindole was able to distinguish ACS patients from healthy controls. The AUC values were 0.8339 (UA VS HCs) and 0.8617 (AMI VS HCs). Moreover, DL-homocystine has the ability to distinguish between UA and AMI, and the area under the ROC curve is 0.8789. The metabolites whose levels increased with disease severity the disease were involved mainly in cysteine and methionine metabolism and the galactose metabolism pathway. Metabolites that decrease with disease severity are related mainly to tryptophan metabolism.

Conclusion

The results of this study suggest that urinary metabolomics studies can reveal differences between ACS patients and healthy controls, which may help in understanding its mechanisms and the discovery of related biomarkers.

Identification of potential biomarkers for coronary slow flow using untargeted metabolomics

BACKGROUND

Coronary slow flow (CSF) is associated with poor cardiovascular prognosis. However, its pathogenesis is unclear. This study aimed to identify potential characteristic biomarkers in patients with CSF using untargeted metabolomics.

METHODS

We prospectively enrolled 30 patients with CSF, 30 with coronary artery disease (CAD), and 30 with normal coronary arteries (NCA), all of whom were age-matched, according to the results of coronary angiography. Serum metabolomics were analyzed using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Differentially expressed metabolites were identified through orthogonal partial least squares-discriminant analysis (OPLS-DA) combined with univariate fold-change and VIP value analysis. Pathway enrichment of these metabolites was performed using the KEGG database, and ROC curves were plotted to assess the diagnostic value of the metabolites in CSF patients.

RESULTS

Compared to the CAD and NCA groups, 256 metabolites showed specific expression in CSF, with 18 meeting stringent screening criteria (VIP > 1, FC ≥ 2, or FC ≤ 0.5, and P < 0.05). Seven metabolites demonstrated high diagnostic value for CSF: inositol 1,3,4-trisphosphate (AUC: 1.0), Cer (d24:1/18:0 (2OH)) (AUC: 0.984), Creosol (AUC: 0.976), Chaps (AUC: 0.904), Arg-Thr-Lys-Arg (AUC: 0.929), Ser-Tyr-Arg (AUC: 0.912), and Methyl Indole-3-Acetate (AUC: 0.909). Pathway analysis highlighted the HIF-1 signaling pathway as the most significant metabolic pathway.

CONCLUSIONS

We identified seven metabolites that may serve as serum biomarkers for predicting and diagnosing CSF through untargeted metabolomics. The HIF-1 signaling pathway appears to be crucial in the development of CSF.

GC-MS validation and analysis of targeted plasma metabolites related to carbonyl stress in type 2 diabetes mellitus patients with and without acute coronary syndrome

Methylglyoxal (MG) is responsible for advanced glycation end-product formation, the mechanisms leading to diabetes pathogenesis and complications like acute coronary syndrome (ACS). Sugar metabolites, amino acids and fatty acids are possible substrates for MG. The study aimed to measure plasma MG substrate levels using a validated gas chromatography-mass spectrometry (GC-MS) method and explore their association with ACS risk in type 2 diabetes mellitus (T2DM). The study included 150 T2DM patients with ACS as cases and 150 T2DM without ACS as controls for the analysis of glucose, fructose, ribulose, sorbitol, glycerol, pyruvate, lactate, glycine, serine, threonine, C16:0, C16:1, C18:0, C18:1, C18:2, C18:3, C20:0 and C22:6 by GC-MS. Validated GC-MS methods were accurate, precise and sensitive. Cases significantly differed in plasma MG and metabolite levels except for lactate, C16:0, C18:0, C18:2, and C18:3 levels compared with controls. On multivariable logistic regression, plasma C20:0, C18:1, glycine and glycerol levels had increased odds of ACS risk. On multivariate receiver operating characteristic analysis, a model containing plasma C20:0, C16:1, C18:1, C18:2, serine, glycerol, lactate and threonine levels had the highest area under the curve value (0.932) for ACS diagnosis. In conclusion, plasma C20:0, C16:1, C18:1, glycine, glycerol and sorbitol levels were associated with ACS risk in T2DM.

Lactate and lactylation in cardiovascular diseases: current progress and future perspectives

Cardiovascular diseases (CVDs) are often linked to structural and functional impairments, such as heart defects and circulatory dysfunction, leading to compromised peripheral perfusion and heightened morbidity risks. Metabolic remodeling, particularly in the context of cardiac fibrosis and inflammation, is increasingly recognized as a pivotal factor in the pathogenesis of CVDs. Metabolic syndromes further predispose individuals to these conditions, underscoring the need to elucidate the metabolic underpinnings of CVDs. Lactate, a byproduct of glycolysis, is now recognized as a key molecule that connects cellular metabolism with the regulation of cellular activity. The transport of lactate between different cells is essential for metabolic homeostasis and signal transduction. Disruptions to lactate dynamics are implicated in various CVDs. Furthermore, lactylation, a novel post-translational modification, has been identified in cardiac cells, where it influences protein function and gene expression, thereby playing a significant role in CVD pathogenesis. In this review, we summarized recent advancements in understanding the role of lactate and lactylation in CVDs, offering fresh insights that could guide future research directions and therapeutic interventions. The potential of lactate metabolism and lactylation as innovative therapeutic targets for CVD is a promising avenue for exploration.

Omics Science and Social Aspects in Detecting Biomarkers for Diagnosis, Risk Prediction, and Outcomes of Carotid Stenosis

Carotid stenosis is characterized by the progressive narrowing of the carotid arteries due to the formation of atherosclerotic plaque, which can lead to stroke and death as major complications. Numerous biomarkers allow for its study and characterization, particularly those related to "omics" sciences. Through the most common research databases, we report representative studies about carotid stenosis biomarkers based on genomics, transcriptomics, proteomics, and metabolomics in a narrative review. To establish a priority among studies based on their internal validity, we used a quality assessment tool, the Scale for the Assessment of Narrative Review Articles (SANRA). Genes, transcriptomes, proteins, and metabolites can diagnose the disease, define plaque connotations, predict consequences after revascularization interventions, and associate carotid stenosis with other patient comorbidities. It also emerged that many aspects determining the patient's psychological and social sphere are implicated in carotid disease. In conclusion, when taking the multidisciplinary approach that combines human sciences with biological sciences, it is possible to comprehensively define a patient's health and thus improve their clinical management through precision medicine.

Exploring Novel Biomarkers for an Acute Coronary Syndrome Diagnosis Utilizing Plasma Metabolomics

Acute coronary syndrome (ACS) is a life-threatening condition that requires a prompt diagnosis and therapeutic intervention. Although serum troponin I and creatinine kinase-MB (CK-MB) are established biomarkers for ACS, reaching diagnostic values for ACS may take several hours. In this study, we attempted to explore novel biomarkers for ACS with higher sensitivity than that of troponin I and CK-MB. The metabolomic profiles of 18 patients with ACS upon hospital arrival and those of the age-matched control (HC) group of 24 healthy volunteers were analyzed using liquid chromatography time-of-flight mass spectrometry. Volcano plots showed 24 metabolites whose concentrations differed significantly between the ACS and HC groups. Using these data, we developed a multiple logistic regression model for the ACS diagnosis, in which lysine, isocitrate, and tryptophan were selected as minimum-independent metabolites. The area under the receiver operating characteristic curve value for discriminating ACS from HC was 1.00 (95% confidence interval [CI]: 1.00-1.00). In contrast, those for troponin I and CK-MB were 0.917 (95% confidence interval [CI]: 0.812-1.00) and 0.988 (95% CI: 0.966-1.00), respectively. This study showed the potential for combining three plasma metabolites to discriminate ACS from HC with a higher sensitivity than troponin I and CK-MB.

Metabolomics Analysis Identifies Differential Metabolites as Biomarkers for Acute Myocardial Infarction

Myocardial infarction (MI), including ST-segment elevation MI (STEMI) and non-ST-segment elevation MI (NSTEMI), is still a leading cause of death worldwide. Metabolomics technology was used to explore differential metabolites (DMs) as potential biomarkers for early diagnosis of STEMI and NSTEMI. In the study, 2531 metabolites, including 1925 DMs, were discovered. In the selected 27 DMs, 14 were successfully verified in a new cohort, and the AUC values were all above 0.8. There were 10 in STEMI group, namely L-aspartic acid, L-acetylcarnitine, acetylglycine, decanoylcarnitine, hydroxyphenyllactic acid, ferulic acid, itaconic acid, lauroylcarnitine, myristoylcarnitine, and cis-4-hydroxy-D-proline, and 5 in NSTEMI group, namely L-aspartic acid, arachidonic acid, palmitoleic acid, D-aspartic acid, and palmitelaidic acid. These 14 DMs may be developed as biomarkers for the early diagnosis of MI with high sensitivity and specificity. These findings have particularly important clinical significance for NSTEMI patients because these patients have no typical ECG changes.

Bile Acids and Risk of Adverse Cardiovascular Events and All-Cause Mortality in Patients with Acute Coronary Syndrome

The relationship between bile acids (BAs) and adverse cardiovascular events following acute coronary syndrome (ACS) have been little investigated. We aimed to examine the associations of BAs with the risk of cardiovascular events and all-cause mortality in ACS. We conducted a prospective study on 309 ACS patients who were followed for 10 years. Plasma BAs were quantified by liquid chromatography coupled to tandem mass spectrometry. Cox regression analyses with elastic net penalties were performed to associate BAs with MACE and all-cause mortality. Weighted scores were computed using the 100 iterated coefficients corresponding to each selected BA, and the associations of these scores with these adverse outcomes were assessed using multivariable Cox regression models. A panel of 10 BAs was significantly associated with the increased risk of MACE. The hazard ratio of MACE per SD increase in the estimated BA score was 1.35 (95% CI 1.12-1.63). Furthermore, four BAs were selected from the elastic net model for all-cause mortality, although their weighted score was not independently associated with mortality. Our findings indicate that primary and secondary BAs may play a significant role in the development of MACE. This insight holds potential for developing strategies to manage ACS and prevent adverse outcomes.

Acute venous thromboembolism plasma and red blood cell metabolomic profiling reveals potential new early diagnostic biomarkers: observational clinical study

BACKGROUND

Venous thromboembolism (VTE) is a leading cause of cardiovascular mortality. The diagnosis of acute VTE is based on complex imaging exams due to the lack of biomarkers. Recent multi-omics based research has contributed to the development of novel biomarkers in cardiovascular diseases. Our aim was to determine whether patients with acute VTE have differences in the metabolomic profile compared to non-acute VTE.

METHODS

This observational trial included 62 patients with clinical suspicion of acute deep vein thrombosis or pulmonary embolism, admitted to the emergency room. There were 50 patients diagnosed with acute VTE and 12 with non-acute VTE conditions and no significant differences were found between the two groups for clinical and demographic characteristics. Metabolomics assays identified and quantified a final number of 91 metabolites in plasma and 55 metabolites in red blood cells (RBCs). Plasma from acute VTE patients expressed tendency to a specific metabolomic signature, with univariate analyses revealing 23 significantly different molecules between acute VTE patients and controls (p < 0.05). The most relevant metabolic pathway with the strongest impact on the acute VTE phenotype was D-glutamine and D-glutamate (p = 0.001, false discovery rate = 0.06). RBCs revealed a specific metabolomic signature in patients with a confirmed diagnosis of DVT or PE that distinguished them from other acutely diseased patients, represented by 20 significantly higher metabolites and four lower metabolites. Three of those metabolites revealed high performant ROC curves, including adenosine 3',5'-diphosphate (AUC 0.983), glutathione (AUC 0.923), and adenine (AUC 0.91). Overall, the metabolic pathway most impacting to the differences observed in the RBCs was the purine metabolism (p = 0.000354, false discovery rate = 0.68).

CONCLUSIONS

Our findings show that metabolite differences exist between acute VTE and nonacute VTE patients admitted to the ER in the early phases. Three potential biomarkers obtained from RBCs showed high performance for acute VTE diagnosis. Further studies should investigate accessible laboratory methods for the future daily practice usefulness of these metabolites for the early diagnosis of acute VTE in the ER.

Gut microbiota and metabolomics profiles in patients with chronic stable angina and acute coronary syndrome

Cardiovascular disease (CVD) is the leading cause of death worldwide. The gut microbiota and its associated metabolites may be involved in the development and progression of CVD, although the mechanisms and impact on clinical outcomes are not fully understood. This study investigated the gut microbiome profile and associated metabolites in patients with chronic stable angina (CSA) and acute coronary syndrome (ACS) compared with healthy controls. Bacterial alpha diversity in stool from patients with ACS or CSA was comparable to healthy controls at both baseline and follow-up visits. Differential abundance analysis identified operational taxonomic units (OTUs) assigned to commensal taxa differentiating patients with ACS from healthy controls at both baseline and follow-up. Patients with CSA and ACS had significantly higher levels of trimethylamine N-oxide compared with healthy controls (CSA: 0.032 ± 0.023 mmol/L, P < 0.01 vs. healthy, and ACS: 0.032 ± 0.023 mmol/L, P = 0.02 vs. healthy, respectively). Patients with ACS had reduced levels of propionate and butyrate (119 ± 4 vs. 139 ± 5.1 µM, P = 0.001, and 14 ± 4.3 vs. 23.5 ± 8.1 µM, P < 0.001, respectively), as well as elevated serum sCD14 (2245 ± 75.1 vs. 1834 ± 45.8 ng/mL, P < 0.0001) and sCD163 levels (457.3 ± 31.8 vs. 326.8 ± 20.7 ng/mL, P = 0.001), compared with healthy controls at baseline. Furthermore, a modified small molecule metabolomic and lipidomic signature was observed in patients with CSA and ACS compared with healthy controls. These findings provide evidence of a link between gut microbiome composition and gut bacterial metabolites with CVD. Future time course studies in patients to observe temporal changes and subsequent associations with gut microbiome composition are required to provide insight into how these are affected by transient changes following an acute coronary event.NEW & NOTEWORTHY The study found discriminative microorganisms differentiating patients with acute coronary syndrome (ACS) from healthy controls. In addition, reduced levels of certain bacterial metabolites and elevated sCD14 and sCD163 were observed in patients with ACS compared with healthy controls. Furthermore, modified small molecule metabolomic and lipidomic signatures were found in both patient groups. Although it is not known whether these differences in profiles are associated with disease development and/or progression, the findings provide exciting options for potential new disease-related mechanism(s) and associated therapeutic target(s).

Postmortem Alteration of Purine Metabolism in Coronary Artery Disease

A new approach for assisting in the diagnosis of coronary artery disease (CAD) as a cause of death is essential in cases where complete autopsy examinations are not feasible. The purine pathway has been associated with CAD patients, but the understanding of this pathway in postmortem changes needs to be explored. This study investigated the levels of blood purine metabolites in CAD after death. Heart blood samples (n = 60) were collected and divided into CAD (n = 23) and control groups (n = 37). Purine metabolites were measured via proton nuclear magnetic resonance. Guanosine triphosphate (GTP), nicotinamide adenine dinucleotide (NAD), and xanthine levels significantly decreased (p < 0.05); conversely, adenine and deoxyribose 5-phosphate levels significantly increased (p < 0.05) in the CAD group compared to the control group. Decreasing xanthine levels may serve as a marker for predicting the cause of death in CAD (AUC = 0.7). Our findings suggest that the purine pathway was interrupted by physiological processes after death, causing the metabolism of the deceased to differ from that of the living. Additionally, xanthine levels should be studied further to better understand their relationship with CAD and used as a biomarker for CAD diagnosis under decomposition and skeletonization settings.

Progress in the Metabolomics of Acute Coronary Syndrome

Acute coronary syndrome (ACS) is a severe type of coronary heart disease (CHD) with increasing prevalence and significant challenges for prevention and treatment. Metabolomics is an emerging technology with intrinsic dynamics and flexibility to better delineate the phenotypic and metabolic alterations in organisms at the time of altered pathological states. It provides new insights into the complex pathological mechanisms of cardiovascular disease and contributes to the early detection, monitoring and evaluation of ACS. In this review, we analyze and summarize the literature related to ACS metabolomics which has contributed to the diagnosis and prevention of ACS.

TCA cycle metabolites associated with adverse outcomes after acute coronary syndrome: mediating effect of renal function

Aims

To examine relationships of tricarboxylic acid (TCA) cycle metabolites with risk of cardiovascular events and mortality after acute coronary syndrome (ACS), and evaluate the mediating role of renal function in these associations.

Methods

This is a prospective study performed among 309 ACS patients who were followed for a mean of 6.7 years. During this period 131 patients developed major adverse cardiovascular events (MACE), defined as the composite of myocardial infarction, hospitalization for heart failure, and all-cause mortality, and 90 deaths were recorded. Plasma concentrations of citrate, aconitate, isocitrate, succinate, malate, fumarate, α-ketoglutarate and d/l-2-hydroxyglutarate were quantified using LC-tandem MS. Multivariable Cox regression models were used to estimate hazard ratios, and a counterfactual-based mediation analysis was performed to test the mediating role of estimated glomerular filtration rate (eGFR).

Results

After adjustment for traditional cardiovascular risk factors and medications, positive associations were found between isocitrate and MACE (HR per 1 SD, 1.25; 95% CI: 1.03, 1.50), and between aconitate, isocitrate, d/l-2-hydroxyglutarate and all-cause mortality (HR per 1 SD, 1.41; 95% CI: 1.07, 1.84; 1.58; 95% CI: 1.23, 2.02; 1.38; 95% CI: 1.14, 1.68). However, these associations were no longer significant after additional adjustment for eGFR. Mediation analyses demonstrated that eGFR is a strong mediator of these associations.

Conclusion

These findings underscore the importance of TCA metabolites and renal function as conjunctive targets in the prevention of ACS complications.

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

Defining atherosclerotic plaque biology by mass spectrometry-based omics approaches

Atherosclerosis is the principal cause of vascular diseases and one of the leading causes of worldwide death. Even though several insights into its natural course, risk factors and interventions have been identified, it is still an ongoing global pandemic. Since the structure and biochemical composition of the plaques show high heterogeneity, a comprehensive understanding of the intraplaque composition, its microenvironment, and the mechanisms of the progression and instability across different vascular beds at their progression stages is crucial for better risk stratification and treatment modalities. Even though several cell-based studies, animal studies, and extensive multicentric population studies have been conducted concerning cardiovascular diseases for assessing the risk factors and plaque biology, the studies on human clinical samples are very limited. New novel approaches utilize samples from percutaneous coronary interventions, which could possibly gain more access to clinical samples at different stages of the diseases without complex invasive resections. As an emerging technological platform in disease discovery research, mass spectrometry-based omics technologies offer capabilities for a comprehensive understanding of the mechanisms linked to several vascular diseases. Here, we discuss the cellular and molecular processes of atherosclerosis, different mass spectrometry-based omics approaches, and the studies mostly done on clinical samples of atheroma plaque using mass spectrometry-based proteomics, metabolomics and lipidomics approaches.

Molecular basis of acute coronary syndrome

Cardiovascular diseases (CVD) comprise of various heart and blood vessels-related diseases. Acute coronary syndrome (ACS) is one of them. Basic researchers and cardiologists have witnessed landmark developments related to ACS and despite rapid refinement in our understanding; scientists are seeking answers for more questions. Scientists have mapped wide ranging proteins and intricate protein networks which play central role in the pathogenesis in ACS. In this review, we have attempted to summarize underlying causes of ACS. Better understanding of the disease pathology will enable us to get a step closer to an effective clinical management.

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.