Systems characterization of differential plasma metabolome perturbations following thrombotic and non-thrombotic myocardial infarction

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Large-Scale Metabolomics and the Incidence of Cardiovascular Disease

Background The study aimed to show the relationship between a large number of circulating metabolites and subsequent cardiovascular disease (CVD) and subclinical markers of CVD in the general population. Methods and Results In 2278 individuals free from CVD in the EpiHealth study (aged 45-75 years, mean age 61 years, 50% women), 790 annotated nonxenobiotic metabolites were measured by mass spectroscopy (Metabolon). The same metabolites were measured in the PIVUS (Prospective Investigation of Vasculature in Uppsala Seniors) study (n=603, all aged 80 years, 50% women), in which cardiac and carotid artery pathologies were evaluated by ultrasound. During a median follow-up of 8.6 years, 107 individuals experienced a CVD (fatal or nonfatal myocardial infarction, stroke, or heart failure) in EpiHealth. Using a false discovery rate of 0.05 for age- and sex-adjusted analyses and P<0.05 for adjustment for traditional CVD risk factors, 37 metabolites were significantly related to incident CVD. These metabolites belonged to multiple biochemical classes, such as amino acids, lipids, and nucleotides. Top findings were dimethylglycine and N-acetylmethionine. A lasso selection of 5 metabolites improved discrimination when added on top of traditional CVD risk factors (+4.0%, P=0.0054). Thirty-five of the 37 metabolites were related to subclinical markers of CVD evaluated in the PIVUS study. The metabolite 1-carboxyethyltyrosine was associated with left atrial diameter as well as inversely related to both ejection fraction and the echogenicity of the carotid artery. Conclusions Several metabolites were discovered to be associated with future CVD, as well as with subclinical markers of CVD. A selection of metabolites improved discrimination when added on top of CVD risk factors.

Evaluating the association between amino acid and acylcarnitine profiles and different levels of coronary artery disease risk in postmenopausal women using targeted metabolomics technique

OBJECTIVES

Postmenopausal women are at increased risk of developing coronary artery disease (CAD). Metabolomic approaches aim at discovering more helpful biomarkers of CAD to reduce the disease burden in the future. Here, we intend to find potential blood biomarkers, amino acids, and acylcarnitines in postmenopausal women with different severity of CAD by using high-throughput methods.

METHOD

This cross-sectional study was performed on postmenopausal women ( n = 183) who underwent coronary CT scans. Coronary artery calcium scoring (CACS) was assessed to detect plaque burden and degree of coronary artery obstruction. The participants were divided into three groups based on the score as follows (i) "low CACS" ( n = 96); a score of 0 to 10, (ii) "medium CACS" ( n = 35); a score between 11 and 100 and (iii) "high CACS" ( n = 52); a score greater than 100. Metabolites, including amino acids and acylcarnitines, were quantified using a targeted mass spectrometry method in serum samples. The association between metabolites and disease status was evaluated using univariate and multivariate regression analyses with adjustment for confounding factors. Factor analysis was used to deal with multiple comparisons.

RESULTS

Metabolites, including proline, glutamic acid, and phenylalanine, were significantly lower in the high CACS group than the low CACS one. Also, a lower level of lysine and phenylalanine in high CACS compared with medium one was observed. Concerning acylcarnitines, it was found that C4 and C8:1 significantly were higher in women with high CACS. The logistic regression analysis revealed that the circulating levels of these metabolites (except C4) were associated with the presence of coronary artery calcification independently of age, body mass index, and time of menopause. Also, the amino acids were associated independently of medication and diabetes.

CONCLUSIONS

The present study indicated that circulating levels of amino acids and acylcarnitines profile in postmenopausal women are partly associated with the severity of CAD in these participants.

Type 2 Myocardial Infarction: Evolving Approaches to Diagnosis and Risk-Stratification

BACKGROUND

Type 2 myocardial infarction (T2MI) is frequently encountered in clinical practice and associated with adverse outcomes.

CONTENT

T2MI occurs most frequently due to noncoronary etiologies that alter myocardial oxygen supply and/or demand. The diagnosis of T2MI is often confused with acute nonischemic myocardial injury, in part because of difficulties in delineating the nature of symptoms and misunderstandings about disease categorization. The use of objective features of myocardial ischemia using electrocardiographic (ECG) or imaging abnormalities may facilitate more precise T2MI diagnosis. High-sensitivity cardiac troponin (hs-cTn) assays allow rapid MI diagnosis and risk stratification, yet neither maximum nor delta values facilitate differentiation of T2MI from T1MI. Several investigational biomarkers have been evaluated for T2MI, but none have robust data. There is interest in evaluating risk profiles among patients with T2MI. Clinically, the magnitude of maximum and delta cTn values as well as the presence and magnitude of ischemia on ECG or imaging is used to indicate disease severity. Scoring systems such as GRACE, TIMI, and TARRACO have been evaluated, but all have limited to modest performance, with substantial variation in time intervals used for risk-assessment and endpoints used.

SUMMARY

The diagnosis of T2MI requires biomarker evidence of acute myocardial injury and clear clinical evidence of acute myocardial ischemia without atherothrombosis. T2MIs are most often caused by noncoronary etiologies that alter myocardial oxygen supply and/or demand. They are increasingly encountered in clinical practice and associated with poor short- and long-term outcomes. Clinicians require novel biomarker or imaging approaches to facilitate diagnosis and risk-stratification.

Differential Network Analysis Reveals Metabolic Determinants Associated with Mortality in Acute Myocardial Infarction Patients and Suggests Potential Mechanisms Underlying Different Clinical Scores Used To Predict Death

We present here the differential analysis of metabolite–metabolite association networks constructed from an array of 24 serum metabolites identified and quantified via nuclear magnetic resonance spectroscopy in a cohort of 825 patients of which 123 died within 2 years from acute myocardial infarction (AMI). We investigated differences in metabolite connectivity of patients who survived, at 2 years, the AMI event, and we characterized metabolite–metabolite association networks specific to high and low risks of death according to four different risk parameters, namely, acute coronary syndrome classification, Killip, Global Registry of Acute Coronary Events risk score, and metabolomics NOESY RF risk score. We show significant differences in the connectivity patterns of several low-molecular-weight molecules, implying variations in the regulation of several metabolic pathways regarding branched-chain amino acids, alanine, creatinine, mannose, ketone bodies, and energetic metabolism. Our results demonstrate that the characterization of metabolite–metabolite association networks is a promising and powerful tool to investigate AMI patients according to their outcomes at a molecular level.

Quantitative analysis of metabolites in glucose metabolism in the aqueous humor of patients with central retinal vein occlusion

Quantitative analysis of aqueous humor (AH) was performed to investigate glucose metabolism in patients with central retinal vein occlusion (CRVO), and to explore metabolic changes after anti-vascular endothelial growth factor (VEGF) treatment. AH samples were collected from 35 patients. Participants diagnosed with CRVO (n = 15) were compared to participants who underwent cataract surgery (n = 20). Thirteen of the participants with CRVO received second-round anti-VEGF treatments. Ultra-high performance liquid chromatography tandem-mass spectrometry (UHPLC-MS/MS) was used to quantify metabolites of the AH. Central macular thickness (CMT) and retinal ganglion cell layer (RGC) thickness were measured using spectral-domain optical coherence tomography. Thirteen metabolites involved in glucose metabolism were identified. Among these metabolites, succinate, glutamate, and glutamine were significantly decreased for the CRVO group (p = 0.028, 0.009, and 0.017, respectively). The α-ketoglutarate/citrate (K/C) ratio had a significant positive correlation with glutamine levels for both control (r = 0.922, p < 0.001) and CRVO groups (r = 0.674, p = 0.006). A significant increase in lactate was observed after intravitreal anti-VEGF administration (t = 2.273, p = 0.045); the change in CMT was negatively correlated with this increase (r = -0.745, p = 0.003). The alteration of RGC thickness was negatively correlated with increases in both glutamine (r = -0.619, p = 0.024) and glucose (r = -0.754, p = 0.003). These results indicate that, compared to glucose metabolism, glutamine was significantly decreased in the AH of patients with CRVO, and may therefore serve as a potential target for CRVO therapy. The glycolytic pathway might be enhanced after intravitreal anti-VEGF injection, which is an important insight into CRVO pathophysiology.

UPLC-QTOF/MS-based metabolomics analysis of plasma reveals an effect of Xue-Fu-Zhu-Yu capsules on blood-stasis syndrome in CHD rats

ETHNOPHARMACOLOGICAL RELEVANCE

Blood-stasis syndrome (BSS) is a specific ZHENG type of coronary heart disease (CHD) in traditional Chinese medicine (TCM). The Xue-Fu-Zhu-Yu (XFZY) decoction is a common herbal formula that has been used for several centuries to treat BSS, but its mechanism has not been thoroughly elucidated to date.

AIM OF THE STUDY

In this study, serum lipid, blood haemorheology and metabolomics analyses were performed to depict a complete profile of XFZY capsules for the treatment of CHD with BSS and to reveal the potential mechanism of the XFZY capsules.

MATERIALS AND METHODS

A rat model of CHD with BSS was generated by combining a high-fat diet (HFD) with a left anterior descending coronary artery (LAD) ligation. After four weeks of treatment with XFZY capsules or simvastatin pills, an echocardiography was performed for a therapeutic evaluation. Blood samples and heart tissues were then collected for further analyses. A UPLC-QTOF/MS-based metabolomics analysis of the plasma was performed, and all metabolic features were fit by PCA and OPLS-DA pattern for the biomarker screen. The identified biomarkers were later implemented into a metabolic pathway analysis. Furthermore, we used qRT-PCR and Western blot analyses to verify the treatment effects of the XFZY capsules.

RESULTS

A total of 49 metabolites (VIP>1.0, p < 0.05, RSD%<20%) were identified in the Model rats, and 27 metabolites (VIP>1.0, p < 0.05, RSD%<20%) were identified in the XFZY-H rats. The results of the pathway analysis indicated that the XFZY capsules treated CHD primarily by regulating cardiac energy, phospholipid, polyunsaturated fatty acid (PUFA) and amino acid metabolism. In addition, blood viscosity and serum lipid assays suggested that XFZY capsules could decrease serum triglycerides, total cholesterol, low-density lipoprotein cholesterol and whole blood viscosity at a low shear rate.

CONCLUSION

This study demonstrated that the XFZY capsule effectively decreases serum lipids and whole blood viscosity in CHD with BSS. The underlying metabolic mechanism mainly included improving cardiac energy supply, reducing phospholipid peroxide, maintaining the PUFA metabolic balance and regulating amino acid metabolism.

Assessment and Treatment of Patients With Type 2 Myocardial Infarction and Acute Nonischemic Myocardial Injury

Although coronary thrombus overlying a disrupted atherosclerotic plaque has long been considered the hallmark and the primary therapeutic target for acute myocardial infarction (MI), multiple other mechanisms are now known to cause or contribute to MI. It is further recognized that an MI is just one of many types of acute myocardial injury. The Fourth Universal Definition of Myocardial Infarction provides a taxonomy for acute myocardial injury, including 5 subtypes of MI and nonischemic myocardial injury. The diagnosis of MI is reserved for patients with myocardial ischemia as the cause of myocardial injury, whether attributable to acute atherothrombosis (type 1 MI) or supply/demand mismatch without acute atherothrombosis (type 2 MI). Myocardial injury in the absence of ischemia is categorized as acute or chronic nonischemic myocardial injury. However, optimal evaluation and treatment strategies for these etiologically distinct diagnoses have yet to be defined. Herein, we review the epidemiology, risk factor associations, and diagnostic tools that may assist in differentiating between nonischemic myocardial injury, type 1 MI, and type 2 MI. We identify limitations, review new research, and propose a framework for the diagnostic and therapeutic approach for patients who have suspected MI or other causes of myocardial injury.

Metabolomic fingerprint of coronary blood in STEMI patients depends on the ischemic time and inflammatory state

In this study we investigated whether the metabolomic analysis could identify a specific fingerprint of coronary blood collected during primary PCI in STEMI patients. Fifteen samples was subjected to metabolomic analysis. Subsequently, the study population was divided into two groups according to the peripheral blood neutrophil-to-lymphocyte ratio (NLR), a marker of the systemic inflammatory response. Regression analysis was then applied separately to the two NLR groups. A partial least square (PLS) regression identified the most significant involved metabolites and the PLS-class analysis revealed a significant correlation between the metabolic profile and the total ischemic time only in patients with an NLR > 5.77.

Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach

Coronary artery disease (CAD) and myocardial infarction (MI) remain among the leading causes of mortality worldwide, urgently demanding a better understanding of disease etiology, and more efficient therapeutic strategies. Genetic predisposition as well as the environment and lifestyle are thought to contribute to disease risk. It is likely that non-linear and complex interactions occur between these multiple factors, involving simultaneous pathological changes in diverse cell types, tissues, and organs, at multiple molecular levels. Recent technological advances have exponentially expanded the breadth of available -omics data, from genome, epigenome, transcriptome, proteome, metabolome to even the microbiome. Integration of multiple layers of information across several -omics domains, i.e., the so-called multi-omics approach, currently holds the promise as a path toward precision medicine. Indeed, a more meaningful interpretation of genotype-phenotype relationships and the development of successful therapeutics tailored to individual patients are urgently needed. In this review, we will summarize recent findings and applications of integrative multi-omics in elucidating the etiology of CAD/MI; with a special focus on established disease susceptibility loci sequentially identified in genome-wide association studies (GWAS) over the last 10 years. Moreover, in addition to the autosomal genome, we will also consider the genetic variation in our “second genome”—the mitochondrial genome. Finally, we will summarize the current challenges in the field and point to future research directions required in order to successfully and effectively apply these approaches for precision medicine.

Targeted Metabolomic Profiling of Plasma and Survival in Heart Failure Patients

OBJECTIVES

This study sought to derive and validate plasma metabolite associations with survival in heart failure (HF) patients.

BACKGROUND

Profiling of plasma metabolites to predict the course of HF appears promising, but validation and incremental value of these profiles are less established.

METHODS

Patients (n = 1,032) who met Framingham HF criteria with a history of reduced ejection fraction were randomly divided into derivation and validation cohorts (n = 516 each). Amino acids, organic acids, and acylcarnitines were quantified using mass spectrometry in fasting plasma samples. We derived a prognostic metabolite profile (PMP) in the derivation cohort using Lasso-penalized Cox regression. Validity was assessed by 10-fold cross validation in the derivation cohort and by standard testing in the validation cohort. The PMP was analyzed as both a continuous variable (PMPscore) and dichotomized at the median (PMPcat), in univariate and multivariate models adjusted for clinical risk score and N-terminal pro-B-type natriuretic peptide.

RESULTS

Overall, 48% of patients were African American, 35% were women, and the average age was 69 years. After a median follow-up of 34 months, there were 256 deaths (127 and 129 in derivation and validation cohorts, respectively). Optimized modeling defined the 13 metabolite PMPs, which was cross validated as both the PMPscore (hazard ratio [HR]: 3.27; p < 2 × 10^-16) and PMPcat (HR: 3.04; p = 2.93 × 10^-8). The validation cohort showed similar results (PMPscore HR: 3.9; p < 2 × 10^-16 and PMPcat HR: 3.99; p = 3.47 × 10^-9). In adjusted models, PMP remained associated with mortality in the cross-validated derivation cohort (PMPscore HR: 1.63; p = 0.0029; PMPcat HR: 1.47; p = 0.081) and the validation cohort (PMPscore HR: 1.54; p = 0.037; PMPcat HR: 1.69; p = 0.043).

CONCLUSIONS

Plasma metabolite profiles varied across HF subgroups and were associated with survival incremental to conventional predictors. Additional investigation is warranted to define mechanisms and clinical applications.

Wisdom of artificial crowds feature selection in untargeted metabolomics: An application to the development of a blood-based diagnostic test for thrombotic myocardial infarction

INTRODUCTION

Heart disease remains a leading cause of global mortality. While acute myocardial infarction (colloquially: heart attack), has multiple proximate causes, proximate etiology cannot be determined by a blood-based diagnostic test. We enrolled a suitable patient cohort and conducted a non-targeted quantification of plasma metabolites by mass spectrometry for developing a test that can differentiate between thrombotic MI, non-thrombotic MI, and stable disease. A significant challenge in developing such a diagnostic test is solving the NP-hard problem of feature selection for constructing an optimal statistical classifier.

OBJECTIVE

We employed a Wisdom of Artificial Crowds (WoAC) strategy for solving the feature selection problem and evaluated the accuracy and parsimony of downstream classifiers in comparison with traditional feature selection techniques including the Lasso and selection using Random Forest variable importance criteria.

MATERIALS AND METHODS

Artificial Crowd Wisdom was generated via aggregation of the best solutions from independent and diverse genetic algorithm populations that were initialized with bootstrapping and a random subspaces constraint.

RESULTS/CONCLUSIONS

Strong evidence was observed that a statistical classifier utilizing WoAC feature selection can discriminate between human subjects presenting with thrombotic MI, non-thrombotic MI, and stable Coronary Artery Disease given abundances of selected plasma metabolites. Utilizing the abundances of twenty selected metabolites, a leave-one-out cross-validation estimated misclassification rate of 2.6% was observed. However, the WoAC feature selection strategy did not perform better than the Lasso over the current study.