Lipidomics Revealed Alteration of Sphingolipid Metabolism During the Reparative Phase After Myocardial Infarction Injury

Plasma Ceramides and Sphingomyelins and Sudden Cardiac Death in the Cardiovascular Health Study

Importance

Sphingolipids, including ceramides and sphingomyelins, may influence the pathophysiology and risk of sudden cardiac death (SCD) through multiple biological activities. Whether the length of the fatty acid acylated to plasma sphingolipid species is associated with SCD risk is not known.

Objective

To determine whether the saturated fatty acid length of plasma ceramides and sphingomyelins influences the association with SCD risk.

Design, Setting, and Participants

In this cohort study, multivariable Cox proportional hazards regression models were used to examine the association of sphingolipid species with SCD risk. The study population included 4612 participants in the Cardiovascular Health Study followed up prospectively for a median of 10.2 (IQR, 5.5-11.6) years. Baseline data were collected from January 1992 to December 1995 during annual examinations. Data were analyzed from February 11, 2020, to September 9, 2023.

Exposures

Eight plasma sphingolipid species (4 ceramides and 4 sphingomyelins) with saturated fatty acids of 16, 20, 22, and 24 carbons.

Main Outcome and Measure

Association of plasma ceramides and sphingomyelins with saturated fatty acids of different lengths with SCD risk.

Results

Among the 4612 CHS participants included in the analysis (mean [SD] age, 77 [5] years; 2724 [59.1%] women; 6 [0.1%] American Indian; 4 [0.1%] Asian; 718 [15.6%] Black; 3869 [83.9%] White, and 15 [0.3%] Other), 215 SCD cases were identified. In adjusted Cox proportional hazards regression analyses, plasma ceramides and sphingomyelins with palmitic acid (Cer-16 and SM-16) were associated with higher SCD risk per higher SD of log sphingolipid levels (hazard ratio [HR] for Cer-16, 1.34 [95% CI, 1.12-1.59]; HR for SM-16, 1.37 [95% CI, 1.12-1.67]). Associations did not differ by baseline age, sex, race, or body mass index. No significant association of SCD with sphingolipids with very-long-chain saturated fatty acids was observed after correction for multiple testing (HR for ceramide with arachidic acid, 1.06 [95% CI, 0.90-1.24]; HR for ceramide with behenic acid, 0.92 [95% CI, 0.77-1.10]; HR for ceramide with lignoceric acid, 0.92 [95% CI, 0.77-1.09]; HR for sphingomyelin with arachidic acid, 0.83 [95% CI, 0.71-0.98]; HR for sphingomyelin with behenic acid, 0.84 [95% CI, 0.70-1.00]; HR for sphingomyelin with lignoceric acid, 0.86 [95% CI, 0.72-1.03]).

Conclusions and Relevance

The findings of this large, population-based cohort study of SCD identified that higher plasma levels of Cer-16 and SM-16 were associated with higher risk of SCD. Future studies are needed to examine the underlying mechanism of these associations.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Metabolomic Profiling in Patients with Different Hemodynamic Subtypes of Severe Aortic Valve Stenosis

Severe aortic stenosis (AS) is a common pathological condition in an ageing population imposing significant morbidity and mortality. Based on distinct hemodynamic features, i.e., ejection fraction (EF), transvalvular gradient and stroke volume, four different AS subtypes can be distinguished: (i) normal EF and high gradient, (ii) reduced EF and high gradient, (iii) reduced EF and low gradient, and (iv) normal EF and low gradient. These subtypes differ with respect to pathophysiological mechanisms, cardiac remodeling, and prognosis. However, little is known about metabolic changes in these different hemodynamic conditions of AS. Thus, we carried out metabolomic analyses in serum samples of 40 AS patients (n = 10 per subtype) and 10 healthy blood donors (controls) using ultrahigh-performance liquid chromatography-tandem mass spectroscopy. A total of 1293 biochemicals could be identified. Principal component analysis revealed different metabolic profiles in all of the subgroups of AS (All-AS) vs. controls. Out of the determined biochemicals, 48% (n = 620) were altered in All-AS vs. controls (p < 0.05). In this regard, levels of various acylcarnitines (e.g., myristoylcarnitine, fold-change 1.85, p < 0.05), ketone bodies (e.g., 3-hydroxybutyrate, fold-change 11.14, p < 0.05) as well as sugar metabolites (e.g., glucose, fold-change 1.22, p < 0.05) were predominantly increased, whereas amino acids (e.g., leucine, fold-change 0.8, p < 0.05) were mainly reduced in All-AS. Interestingly, these changes appeared to be consistent amongst all AS subtypes. Distinct differences between AS subtypes were found for metabolites belonging to hemoglobin metabolism, diacylglycerols, and dihydrosphingomyelins. These findings indicate that relevant changes in substrate utilization appear to be consistent for different hemodynamic subtypes of AS and may therefore reflect common mechanisms during AS-induced heart failure. Additionally, distinct metabolites could be identified to significantly differ between certain AS subtypes. Future studies need to define their pathophysiological implications.

Temporal lipid profiling in the progression from acute to chronic heart failure in mice and ischemic human hearts

BACKGROUND AND AIMS

Myocardial infarction (MI) is a leading cause of heart failure (HF). After MI, lipids undergo several phasic changes implicated in cardiac repair if inflammation resolves on time. However, if inflammation continues, that leads to end stage HF progression and development. Numerous studies have analyzed the traditional risk factors; however, temporal lipidomics data for human and animal models are limited. Thus, we aimed to obtain sequential lipid profiling from acute to chronic HF.

METHODS

Here, we report the comprehensive lipidome of the hearts from diseased and healthy subjects. To induce heart failure in mice, we used a non-reperfused model of coronary ligation, and MI was confirmed by echocardiography and histology, then temporal kinetics of lipids in different tissues (heart, spleen, kidney), and plasma was quantitated from heart failure mice and compared with naïve controls. For lipid analysis in mouse and human samples, untargeted liquid chromatography-linear trap quadrupole orbitrap mass spectrometry (LC-LTQ-Orbitrap MS) was performed.

RESULTS

In humans, multivariate analysis revealed distinct cardiac lipid profiles between healthy and ischemic subjects, with 16 lipid species significantly downregulated by 5-fold, mainly phosphatidylethanolamines (PE), in the ischemic heart. In contrast, PE levels were markedly increased in mouse tissues and plasma in chronic MI, indicating possible cardiac remodeling. Further, fold change analysis revealed site-specific lipid biomarkers for acute and chronic HF. A significant decrease in sulfatides (SHexCer (34:1; 2O)) and sphingomyelins (SM (d18:1/16:0)) was observed in mouse tissues and plasma in chronic HF.

CONCLUSIONS

Overall, a significant decreased lipidome in human ischemic LV and differential lipid metabolites in the transition of acute to chronic HF with inter-organ communication could provide novel insights into targeting integrative pathways for the early diagnosis or development of novel therapeutics to delay/prevent HF.

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.

Lipidomics in Understanding Pathophysiology and Pharmacologic Effects in Inflammatory Diseases: Considerations for Drug Development

The lipidome has a broad range of biological and signaling functions, including serving as a structural scaffold for membranes and initiating and resolving inflammation. To investigate the biological activity of phospholipids and their bioactive metabolites, precise analytical techniques are necessary to identify specific lipids and quantify their levels. Simultaneous quantification of a set of lipids can be achieved using high sensitivity mass spectrometry (MS) techniques, whose technological advancements have significantly improved over the last decade. This has unlocked the power of metabolomics/lipidomics allowing the dynamic characterization of metabolic systems. Lipidomics is a subset of metabolomics for multianalyte identification and quantification of endogenous lipids and their metabolites. Lipidomics-based technology has the potential to drive novel biomarker discovery and therapeutic development programs; however, appropriate standards have not been established for the field. Standardization would improve lipidomic analyses and accelerate the development of innovative therapies. This review aims to summarize considerations for lipidomic study designs including instrumentation, sample stabilization, data validation, and data analysis. In addition, this review highlights how lipidomics can be applied to biomarker discovery and drug mechanism dissection in various inflammatory diseases including cardiovascular disease, neurodegeneration, lung disease, and autoimmune disease.

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.