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.

Cardiovascular risk of metabolically healthy obesity in two european populations: Prevention potential from a metabolomic study

BACKGROUND

A new definition of metabolically healthy obesity (MHO) has recently been proposed to stratify the heterogeneous mortality risk of obesity. Metabolomic profiling provides clues to metabolic alterations beyond clinical definition. We aimed to evaluate the association between MHO and cardiovascular events and assess its metabolomic pattern.

METHODS

This prospective study included Europeans from two population-based studies, the FLEMENGHO and the Hortega study. A total of 2339 participants with follow-up were analyzed, including 2218 with metabolomic profiling. Metabolic health was developed from the third National Health and Nutrition Examination Survey and the UK biobank cohorts and defined as systolic blood pressure < 130 mmHg, no antihypertensive drugs, waist-to-hip ratio < 0.95 for women or 1.03 for men, and the absence of diabetes. BMI categories included normal weight, overweight, and obesity (BMI < 25, 25-30, ≥ 30 kg/m²). Participants were classified into six subgroups according to BMI category and metabolic healthy status. Outcomes were fatal and nonfatal composited cardiovascular events.

RESULTS

Of 2339 participants, the mean age was 51 years, 1161 (49.6%) were women, 434 (18.6%) had obesity, 117 (5.0%) were classified as MHO, and both cohorts had similar characteristics. Over a median of 9.2-year (3.7-13.0) follow-up, 245 cardiovascular events occurred. Compared to those with metabolically healthy normal weight, individuals with metabolic unhealthy status had a higher risk of cardiovascular events, regardless of BMI category (adjusted HR: 3.30 [95% CI: 1.73-6.28] for normal weight, 2.50 [95% CI: 1.34-4.66] for overweight, and 3.42 [95% CI: 1.81-6.44] for obesity), whereas those with MHO were not at increased risk of cardiovascular events (HR: 1.11 [95% CI: 0.36-3.45]). Factor analysis identified a metabolomic factor mainly associated with glucose regulation, which was associated with cardiovascular events (HR: 1.22 [95% CI: 1.10-1.36]). Individuals with MHO tended to present a higher metabolomic factor score than those with metabolically healthy normal weight (0.175 vs. -0.057, P = 0.019), and the score was comparable to metabolically unhealthy obesity (0.175 vs. -0.080, P = 0.91).

CONCLUSIONS

Individuals with MHO may not present higher short-term cardiovascular risk but tend to have a metabolomic pattern associated with higher cardiovascular risk, emphasizing a need for early intervention.

Blood-Based Fingerprint of Cardiorespiratory Fitness and Long-Term Health Outcomes in Young Adulthood

Background Cardiorespiratory fitness is a powerful predictor of health outcomes that is currently underused in primary prevention, especially in young adults. We sought to develop a blood-based biomarker of cardiorespiratory fitness that is easily translatable across populations. Methods and Results Maximal effort cardiopulmonary exercise testing for quantification of cardiorespiratory fitness (by peak oxygen uptake) and profiling of >200 metabolites at rest were performed in the FHS (Framingham Heart Study; 2016-2019). A metabolomic fitness score was derived/validated in the FHS and was associated with long-term outcomes in the younger CARDIA (Coronary Artery Risk Development in Young Adults) study. In the FHS (derivation, N=451; validation, N=914; age 54±8 years, 53% women, body mass index 27.7±5.3 kg/m2), we used LASSO (least absolute shrinkage and selection operator) regression to develop a multimetabolite score to predict peak oxygen uptake (correlation with peak oxygen uptake r=0.77 in derivation, 0.61 in validation; both P<0.0001). In a linear model including clinical risk factors, a ≈1-SD higher metabolomic fitness score had equivalent magnitude of association with peak oxygen uptake as a 9.2-year age increment. In the CARDIA study (N=2300, median follow-up 26.9 years, age 32±4 years, 44% women, 44% Black individuals), a 1-SD higher metabolomic fitness score was associated with a 44% lower risk for mortality (hazard ratio [HR], 0.56 [95% CI, 0.47-0.68]; P<0.0001) and 32% lower risk for cardiovascular disease (HR, 0.68 [95% CI, 0.55-0.84]; P=0.0003) in models adjusted for age, sex, and race, which remained robust with adjustment for clinical risk factors. Conclusions A blood-based biomarker of cardiorespiratory fitness largely independent of traditional risk factors is associated with long-term risk of cardiovascular disease and mortality in young adults.

Association of Serum β-Hydroxybutyrate and Coronary Artery Disease in an Urban Chinese Population

Ketone bodies, including β-hydroxybutyrate (BHB), acetoacetate (AA), and acetone, can substitute and alternate with glucose under conditions of fuel/food deficiency. Ketone-body metabolism is increased in a myriad of tissue-metabolism disorders. Perturbations in metabolism are major contributors to coronary artery disease (CAD). We investigated the association of BHB with CAD. A total of 2,970 people of Chinese Han ethnicity were enrolled. The Gensini score was calculated for all patients who had positive findings. The serum level of BHB and other laboratory parameters were measured. The association of serum levels of metabolites with traditionally risk factors and CAD severity was analyzed. The BHB was found to be associated with some traditional risk factors of CAD and CAD severity, as determined by the Gensini score or the number of diseased regions. Moreover, BHB was associated with the T3/T1 tertiles of the Gensini score after the adjustment for traditional risk factors by multivariable logistic regression analysis. The association of BHB with CAD severity was more obvious in women. Taken together, these data suggest that the circulating BHB level is independently associated with CAD severity, and that this association is more pronounced in women.

Metabolomics Signatures and Subsequent Maternal Health among Mothers with a Congenital Heart Defect-Affected Pregnancy

Congenital heart defects (CHDs) are the most prevalent and serious of all birth defects in the United States. However, little is known about the impact of CHD-affected pregnancies on subsequent maternal health. Thus, there is a need to characterize the metabolic alterations associated with CHD-affected pregnancies. Fifty-six plasma samples were identified from post-partum women who participated in the National Birth Defects Prevention Study between 1997 and 2011 and had (1) unaffected control offspring (n = 18), (2) offspring with tetralogy of Fallot (ToF, n = 22), or (3) hypoplastic left heart syndrome (HLHS, n = 16) in this pilot study. Absolute concentrations of 408 metabolites using the AbsoluteIDQ^® p400 HR Kit (Biocrates) were evaluated among case and control mothers. Twenty-six samples were randomly selected from above as technical repeats. Analysis of covariance (ANCOVA) and logistic regression models were used to identify significant metabolites after controlling for the maternal age at delivery and body mass index. The receiver operating characteristic (ROC) curve and area-under-the-curve (AUC) are reported to evaluate the performance of significant metabolites. Overall, there were nine significant metabolites (p < 0.05) identified in HLHS case mothers and 30 significant metabolites in ToF case mothers. Statistically significant metabolites were further evaluated using ROC curve analyses with PC (34:1), two sphingolipids SM (31:1), SM (42:2), and PC-O (40:4) elevated in HLHS cases; while LPC (18:2), two triglycerides: TG (44:1), TG (46:2), and LPC (20:3) decreased in ToF; and cholesterol esters CE (22:6) were elevated among ToF case mothers. The metabolites identified in the study may have profound structural and functional implications involved in cellular signaling and suggest the need for postpartum dietary supplementation among women who gave birth to CHD offspring.

Dyslipidemia and associated cardiovascular risk factors in HIV-positive and HIV-negative patients visiting ambulatory clinics: A hospital-based study

Background

Dyslipidemia is a well-known risk factor for cardiovascular disease (CVD), accounting for more than half of all instances of coronary artery disease globally (CAD).

Purpose

The purpose of this study was to determine lipid-related cardiovascular risks in HIV-positive and HIV-negative individuals by evaluating lipid profiles, ratios, and other related parameters.

Methods

A hospital-based study was carried out from January 2019 to February 2021 in both HIV + and HIV- ambulatory patients.

Results

High TG (p = .003), high TC (p = .025), and low HDL (p < .001) were all associated with a two-fold increased risk of CVD in people aged 45 and up. Due to higher TG (p < .001) and lower HDL (p < .001), males were found to have a higher risk of atherogenic dyslipidemia. A twofold increase in the likelihood of higher TG levels has been associated with smoking (p = .032) and alcohol intake (p = .022). A twofold increase in a high TC/HDL ratio and an elevated TG/HDL ratio was observed with an increase in waist-to-height ratio (p = .030) and a high level of FBS (126 mg/dl) and/or validated diabetes (p = .017), respectively. In HIV + participants, central obesity (p < .001), diabetes (p < .001), and high blood pressure (p < .001) were all less common than in HIV- participants.

Conclusions

Dyslipidemia is linked to advanced age, male gender, diabetes, smoking, alcohol consumption, and increased waist circumference, all of which could lead to an increased risk of CVD, according to the study. The study also revealed that the risks are less common in HIV + people than in HIV-negative ambulatory patients.

Metabolomics as a potential tool for the diagnosis of growth hormone deficiency (GHD): a review

Metabolomics uses several analytical tools to identify the chemical diversity of metabolites present in organisms. These metabolites are low molecular weight molecules (<1500 Da) classified as a final or intermediary product of metabolic processes. The application of this omics technology has become prominent in inferring physiological conditions through reporting on the phenotypic state; therefore, the introduction of metabolomics into clinical studies has been growing in recent years due to its efficiency in discriminating pathophysiological states. Regarding endocrine diseases, there is a great interest in verifying comprehensive and individualized physiological scenarios, in particular for growth hormone deficiency (GHD). The current GHD diagnostic tests are laborious and invasive and there is no exam with ideal reproducibility and sensitivity for diagnosis neither standard GH cut-off point. Therefore, this review was focussed on articles that applied metabolomics in the search for new biomarkers for GHD. The present work shows that the applications of metabolomics in GHD are still limited, since the little complementarily of analytical techniques, a low number of samples, GHD combined to other deficiencies, and idiopathic diagnosis shows a lack of progress. The results of the research are relevant and similar; however, their results do not provide an application for clinical practice due to the lack of multidisciplinary actions that would be needed to mediate the translation of the knowledge produced in the laboratory, if transferred to the medical setting.

Metabolomics to understand placental biology: Where are we now?

Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.

Metabolomics and health: from nutritional crops and plant-based pharmaceuticals to profiling of human biofluids

During the past decade metabolomics has emerged as one of the fastest developing branches of “-omics” technologies. Metabolomics involves documentation, identification, and quantification of metabolites through modern analytical platforms in various biological systems. Advanced analytical tools, such as gas chromatography–mass spectrometry (GC/MS), liquid chromatography–mass spectroscopy (LC/MS), and non-destructive nuclear magnetic resonance (NMR) spectroscopy, have facilitated metabolite profiling of complex biological matrices. Metabolomics, along with transcriptomics, has an influential role in discovering connections between genetic regulation, metabolite phenotyping and biomarkers identification. Comprehensive metabolite profiling allows integration of the summarized data towards manipulation of biosynthetic pathways, determination of nutritional quality markers, improvement in crop yield, selection of desired metabolites/genes, and their heritability in modern breeding. Along with that, metabolomics is invaluable in predicting the biological activity of medicinal plants, assisting the bioactivity-guided fractionation process and bioactive leads discovery, as well as serving as a tool for quality control and authentication of commercial plant-derived natural products. Metabolomic analysis of human biofluids is implemented in clinical practice to discriminate between physiological and pathological state in humans, to aid early disease biomarker discovery and predict individual response to drug therapy. Thus, metabolomics could be utilized to preserve human health by improving the nutritional quality of crops and accelerating plant-derived bioactive leads discovery through disease diagnostics, or through increasing the therapeutic efficacy of drugs via more personalized approach. Here, we attempt to explore the potential value of metabolite profiling comprising the above-mentioned applications of metabolomics in crop improvement, medicinal plants utilization, and, in the prognosis, diagnosis and management of complex diseases.

Cardiac function dependence on carbon monoxide

Nitric oxide, studied to evaluate its role in cardiovascular physiology, has cardioprotective and therapeutic effects in cellular signaling, mitochondrial function, and in regulating inflammatory processes. Heme oxygenase (major role in catabolism of heme into biliverdin, carbon monoxide (CO), and iron) has similar effects as well. CO has been suggested as the molecule that is responsible for many of the above mentioned cytoprotective and therapeutic pathways as CO is a signaling molecule in the control of physiological functions. This is counterintuitive as toxic effects are related to its binding to hemoglobin. However, CO is normally produced in the body. Experimental evidence indicates that this toxic gas, CO, exerts cytoprotective properties related to cellular stress including the heart and is being assessed for its cytoprotective and cytotherapeutic properties. While survival of adult cardiomyocytes depends on oxidative phosphorylation (survival and resulting cardiac function is impaired by mitochondrial damage), mitochondrial biogenesis is modified by the heme oxygenase-1/CO system and can result in promotion of mitochondrial biogenesis by associating mitochondrial redox status to the redox-active transcription factors. It has been suggested that the heme oxygenase-1/CO system is important in differentiation of embryonic stem cells and maturation of cardiomyocytes which is thought to mitigate progression of degenerative cardiovascular diseases. Effects on other cardiac cells are being studied. Acute exposure to air pollution (and, therefore, CO) is associated with cardiovascular mortality, myocardial infarction, and heart failure, but changes in the endogenous heme oxygenase-1 system (and, thereby, CO) positively affect cardiovascular health. We will review the effect of CO on heart health and function in this article.

Purine metabolite-based machine learning models for risk prediction, prognosis, and diagnosis of coronary artery disease

Alterations in xanthine oxidase activity are known to be pathologically influential on coronary artery disease (CAD), but the association between purine-related blood metabolites and CAD has only been partially elucidated. We performed global metabolomics profiling and network analysis on blood samples from the Wonju and Pyeongchang (WP) cohort study (n = 2055) to elucidate the importance of purine related metabolites associated with potential CAD risk. Then, 5 selected serum metabolites were quantified from the WP cohort, Shinchon cohort (n = 259), and Shinchon case control (n = 424) groups to develop machine learning models for 10-year risk prediction, relapse within 10 years and diagnosis of the disease via 100 repeated 5-fold cross-validations of logistic models. The combination of purine metabolite levels or only xanthine levels in blood could be applied for machine learning model development for major adverse cardiac and cerebrovascular event (MACCE, cerebrovascular death, nonfatal myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass graft, and stroke) risk prediction, relapse of MACCEs among patients with myocardial infarction history and diagnosis of stable CAD. In particular, our research provided initial evidence that blood xanthine and uric acid levels play different roles in the development of machine learning models for primary/secondary prevention or diagnosis of CAD. In this research, we determined that purine-related metabolites in blood are applicable to machine learning model development for CAD risk prediction and diagnosis. Also, our work advances current CAD biomarker discovery strategies mainly relying on clinical features; emphasizes the differential biomarkers in first/secondary prevention or diagnosis studies.

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.

Identification of novel cardiovascular disease associated metabolites using untargeted metabolomics

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality around the world. Early diagnosis of CVD could provide the opportunity for sensible management and better clinical outcome along with the prevention of further progression of the disease. In the current study, we used an untargeted metabolomic approach to identify possible metabolite(s) that associate well with the CVD and could serve either as therapeutic target or disease-associated metabolite. We identified 26 rationally adjusted unique metabolites that were differentially present in the serum of CVD patients compared with healthy individuals, among them 15 were found to be statistically significant. Out of these metabolites, we identified some novel metabolites like UDP-l-rhamnose and N1-acetylspermidine that have not been reported to be linked with CVD directly. Further, we also found that some metabolites like ethanolamide, solanidine, dimethylarginine, N-acetyl-l-tyrosine, can act as a discriminator of CVD. Metabolites integrating pathway enrichment analysis showed enrichment of various important metabolic pathways like histidine metabolism, methyl histidine metabolism, carnitine synthesis, along with arginine and proline metabolism in CVD patients. Our study provides a great opportunity to understand the pathophysiological role and impact of the identified unique metabolites and can be extrapolated as specific CVD specific metabolites.

Analysis of genes and underlying mechanisms involved in foam cells formation and atherosclerosis development

Background

Foam cells (FCs) play crucial roles in the process of all stages of atherosclerosis. Smooth muscle cells (SMCs) and macrophages are the major sources of FCs. This study aimed to identify the common molecular mechanism in these two types of FCs.

Methods

GSE28829, GSE43292, GSE68021, and GSE54666 were included to identify the differentially expressed genes (DEGs) associated with FCs derived from SMCs and macrophages. Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by using the DAVID database. The co-regulated genes associated with the two origins of FCs were validated (GSE9874), and their expression in vulnerable atherosclerosis plaques (GSE120521 and GSE41571) was assessed.

Results

A total of 432 genes associated with FCs derived from SMCs (SMC-FCs) and 81 genes associated with FCs derived from macrophages (M-FCs) were identified, and they were mainly involved in lipid metabolism, inflammation, cell cycle/apoptosis. Furthermore, three co-regulated genes associated with FCs were identified: GLRX, RNF13, and ABCA1. These three common genes showed an increased tendency in unstable or ruptured plaques, although in some cases, no statistically significant difference was found.

Conclusions

DEGs related to FCs derived from SMCs and macrophages have contributed to the understanding of the molecular mechanism underlying the formation of FCs and atherosclerosis. GLRX, RNF13, and ABCA1 might be potential targets for atherosclerosis treatment.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Investigation of Crystal Structures in Structure-Based Virtual Screening for Protein Kinase Inhibitors

Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.

First exploratory study on the metabolome from plasma exosomes in patients with paroxysmal nocturnal hemoglobinuria

INTRODUCTION

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare disease in which patients are at increased risk of thrombosis. The mechanisms underlying the associated thrombosis risk are still poorly understood, although it is known that Eculizumab, the drug of choice for symptomatic patients, prevents thrombotic events. Exosomes are extracellular vesicles that can carry and disseminate genetic material, tumor biomarkers and inflammatory mediators. To date, the metabolite cargo of plasma exosomes from PNH patients has not yet been explored. In this pilot trial, we compared the metabolome of plasma exosomes from PNH patients with that of healthy subjects in order to provide further insights into this rare disease.

RESULTS

We used a non-targeted metabolomics approach with UPLC-ESI-QTOF-MS/MS and GC-MS platforms. Multivariate analyses revealed the differential occurrence (p < .001) of 78 metabolites in plasma exosomes from PNH patients vs healthy control subjects. Remarkably, prostaglandin F2-alpha (6.1-fold), stearoyl arginine (5.3-fold) and 26-hydroxycholesterol-3-sulfate (11.2-fold) were higher in PNH patients vs healthy controls (p < .001).

CONCLUSIONS

This is the first description on the differential metabolite cargo occurring in plasma exosomes from PNH patients. Our results could contribute to the search for possible prognostic biomarkers of thrombotic risk in patients with PNH. Further research in a larger cohort to validate these results is warranted.

Composite score analysis for unsupervised comparison and network visualization of metabolomics data

Metabolomics-based approaches are becoming increasingly popular to interrogate the chemical basis for phenotypic differences in biological systems. Successful metabolomics studies employ multivariate data analysis to compare large and highly complex datasets. A primary tool for unsupervised statistical analyses, principal component analysis (PCA), relies on the selection of a subsection of a maximum of three components from a larger model to visually represent similarity. The use of only three principal components limits the comprehensiveness of the model and can mask discrimination between samples. We have developed a new statistical metric, the composite score (CS), as a univariate statistic that incorporates multiple principal components to calculate a correlation matrix that enables quantitative comparisons of sample similarity between samples within one dataset based upon measured metabolome profiles. Composite score values were tabulated using profiles of complex extracts of dietary supplements from the plant Hydrastis canadensis (goldenseal) as a case study. Several outliers were unambiguously identified, and a PCA composite score network was developed to provide a graphical representation of the composite score matrix. Comparison with visualization using PCA score plots or dendrograms from hierarchical clustering analysis (HCA) demonstrates the utility of the composite score to as a tool for metabolomics studies that seek to quantify similarity among samples. An R-script for the calculation of composite score has been made available.

Altered Metabolomic Profile in Patients with Peripheral Artery Disease

Peripheral artery disease (PAD) is a common atherosclerotic disease characterized by narrowed or blocked arteries in the lower extremities. Circulating serum biomarkers can provide significant insight regarding the disease progression. Here, we explore the metabolomics signatures associated with different stages of PAD and investigate potential mechanisms of the disease. We compared the serum metabolites of a cohort of 26 PAD patients presenting with claudication and 26 PAD patients presenting with critical limb ischemia (CLI) to those of 26 non-PAD controls. A difference between the metabolite profiles of PAD patients from non-PAD controls was observed for several amino acids, acylcarnitines, ceramides, and cholesteryl esters. Furthermore, our data demonstrate that patients with CLI possess an altered metabolomic signature different from that of both claudicants and non-PAD controls. These findings provide new insight into the pathophysiology of PAD and may help develop future diagnostic procedures and therapies for PAD patients.

Metabolic modulation predicts heart failure tests performance

The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO₂), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO₂ slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction < 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO₂ and Peak VO₂ were the most fitted models, and the highest predictors’ coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio.

Diastolic left ventricular function in relation to circulating metabolic biomarkers in a population study

Aims

We studied the association of circulating metabolic biomarkers with asymptomatic left ventricular diastolic dysfunction, a risk-carrying condition that affects 25% of the population.

Methods and results

In 570 randomly recruited people, we assessed in 2005–2010 and in 2009–2013 the multivariable-adjusted correlations of e’ (early left ventricular relaxation) and E/e’ (left ventricular filling pressure) measured by Doppler echocardiography with 43 serum metabolites, quantified by magnetic resonance spectroscopy. In 2009–2013, e’ cross-sectionally increased (Bonferroni corrected p ≤ 0.016) with the branched-chain amino acid valine (per one standard deviation increment, +0.274 cm/s (95% confidence interval, 0.057–0.491)) and glucose+the amino acid (AA) taurine (+0.258 cm/s (0.067–0.481)), while E/e’ decreased ( p ≤ 0.017) with valine (–0.264 (–0.496– –0.031)). The risk of developing left ventricular diastolic dysfunction over follow-up (9.4%) was inversely associated ( p ≤ 0.0059) with baseline glucose+amino acid taurine (odds ratio, 0.64 (0.44–0.94). In partial least squares analyses of all the baseline and follow-up data, markers consistently associated with better diastolic left ventricular function included the amino acids 2-aminobutyrate and 4-hydroxybutyrate and the branched-chain amino acids leucine and valine, and those consistently associated with worse diastolic left ventricular function glucose+amino acid glutamine and fatty acid pentanoate. Branched-chain amino acid metabolism (–log10 p = 12.6) and aminoacyl-tRNA biosynthesis (9.9) were among the top metabolic pathways associated with left ventricular diastolic dysfunction.

Conclusion

The associations of left ventricular diastolic dysfunction with circulating amino acids and branched-chain amino acids were consistent over a five-year interval and suggested a key role of branched-chain amino acid metabolism and aminoacyl-tRNA biosynthesis in maintaining diastolic left ventricular function.

Ldlr-/- and ApoE-/- mice better mimic the human metabolite signature of increased carotid intima media thickness compared to other animal models of cardiovascular disease

BACKGROUND AND AIMS

Preclinical experiments on animal models are essential to understand the mechanisms of cardiovascular disease (CVD). Metabolomics allows access to the metabolic perturbations associated with CVD in heart and vessels. Here we assessed which potential animal CVD model most closely mimics the serum metabolite signature of increased carotid intima-media thickness (cIMT) in humans, a clinical parameter widely accepted as a surrogate of CVD.

METHODS

A targeted mass spectrometry assay was used to quantify and compare a series of blood metabolites between 1362 individuals (KORA F4 cohort) and 5 animal CVD models: ApoE^-/-, Ldlr^-/-, and klotho-hypomorphic mice (kl/kl) and SHRSP rats with or without salt feeding. The metabolite signatures were obtained using linear regressions adjusted for various co-variates.

RESULTS

In human, increased cIMT [quartile Q4 vs. Q1] was associated with 26 metabolites (9 acylcarnitines, 2 lysophosphatidylcholines, 9 phosphatidylcholines and 6 sphingomyelins). Acylcarnitines correlated preferentially with serum glucose and creatinine. Phospholipids correlated preferentially with cholesterol (total and LDL). The human signature correlated positively and significantly with Ldlr^-/- and ApoE^-/- mice, while correlation with kl/kl mice and SHRP rats was either negative and non-significant. Human and Ldlr^-/- mice shared 11 significant metabolites displaying the same direction of regulation: 5 phosphatidylcholines, 1 lysophosphatidylcholines, 5 sphingomyelins; ApoE^-/- mice shared 10.

CONCLUSIONS

The human cIMT signature was partially mimicked by Ldlr^-/- and ApoE^-/- mice. These animal models might help better understand the biochemical and molecular mechanisms involved in the vessel metabolic perturbations associated with, and contributing to metabolic disorders in CVD.

Metabolomic profile of arterial stiffness in aged adults

BACKGROUND

Increasing arterial stiffness is an important contributor to declining cardiovascular health in ageing. Changes in whole-body fuel metabolism could be related to alterations in arterial stiffness in ageing adults.

METHODS

Targeted high-performance liquid and gas chromatography mass spectrometry were used to measure 84 circulating metabolites in a group of community elderly adults ( n = 141, 58% men; mean age = 70.6 ± 11.2 years) without cardiovascular disease. In basic and adjusted models, we correlated the measured metabolites to carotid-femoral pulse wave velocity assessed by applanation tonometry.

RESULTS

Age ( β = 0.10, p < 0.0001), smoking status ( β = 1.32, p = 0.02), dyslipidemia ( β = 1.22, p = 0.01), central systolic blood pressure ( β = 0.05, p < 0.0001), central mean arterial pressure ( β = 0.04, p = 0.03) and central pulse pressure ( β = 0.05, p < 0.0001) were significantly associated with pulse wave velocity. Amino acids such as histidine, methionine and valine correlated with pulse wave velocity. In multivariable models adjusted for clinical covariates, only Factor 5, comprising the medium- and long-chain dicarboxyl and hydroxyl acylcarnitines was independently associated with pulse wave velocity ( β = 0.24, p = 0.015).

CONCLUSION

An upstream metabolic perturbation comprising medium- and long-chain dicarboxyl and hydroxyl acylcarnitines, likely reflecting changes in cellular fatty acid oxidation, was associated with arterial stiffness among aged adults. This advances mechanistic understanding of arterial stiffness among aged adults before clinical disease.

Personalized medicine-a modern approach for the diagnosis and management of hypertension

The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension.

Integration of Ion Mobility MSE after Fully Automated, Online, High-Resolution Liquid Extraction Surface Analysis Micro-Liquid Chromatography

Direct analysis by mass spectrometry (imaging) has become increasingly deployed in preclinical and clinical research due to its rapid and accurate readouts. However, when it comes to biomarker discovery or histopathological diagnostics, more sensitive and in-depth profiling from localized areas is required. We developed a comprehensive, fully automated online platform for high-resolution liquid extraction surface analysis (HR-LESA) followed by micro–liquid chromatography (LC) separation and a data-independent acquisition strategy for untargeted and low abundant analyte identification directly from tissue sections. Applied to tissue sections of rat pituitary, the platform demonstrated improved spatial resolution, allowing sample areas as small as 400 μm to be studied, a major advantage over conventional LESA. The platform integrates an online buffer exchange and washing step for removal of salts and other endogenous contamination that originates from local tissue extraction. Our carry over–free platform showed high reproducibility, with an interextraction variability below 30%. Another strength of the platform is the additional selectivity provided by a postsampling gas-phase ion mobility separation. This allowed distinguishing coeluted isobaric compounds without requiring additional separation time. Furthermore, we identified untargeted and low-abundance analytes, including neuropeptides deriving from the pro-opiomelanocortin precursor protein and localized a specific area of the pituitary gland (i.e., adenohypophysis) known to secrete neuropeptides and other small metabolites related to development, growth, and metabolism. This platform can thus be applied for the in-depth study of small samples of complex tissues with histologic features of ∼400 μm or more, including potential neuropeptide markers involved in many diseases such as neurodegenerative diseases, obesity, bulimia, and anorexia nervosa.

Technical Challenges in Mass Spectrometry-Based Metabolomics

Metabolomics is a strategy for analysis, and quantification of the complete collection of metabolites present in biological samples. Metabolomics is an emerging area of scientific research because there are many application areas including clinical, agricultural, and medical researches for the biomarker discovery and the metabolic system analysis by employing widely targeted analysis of a few hundred preselected metabolites from 10-100 biological samples. Further improvement in technologies of mass spectrometry in terms of experimental design for larger scale analysis, computational methods for tandem mass spectrometry-based elucidation of metabolites, and specific instrumentation for advanced bioanalysis will enable more comprehensive metabolome analysis for exploring the hidden secrets of metabolism.

Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention

Intervention with Mediterranean diet (MedDiet) has provided a high level of evidence in primary prevention of cardiovascular events. Besides enhancing protection from classical risk factors, an improvement has also been described in a number of non-classical ones. Benefits have been reported on biomarkers of oxidation, inflammation, cellular adhesion, adipokine production, and pro-thrombotic state. Although the benefits of the MedDiet have been attributed to its richness in antioxidants, the mechanisms by which it exercises its beneficial effects are not well known. It is thought that the integration of omics including genomics, transcriptomics, epigenomics, and metabolomics, into studies analyzing nutrition and cardiovascular diseases will provide new clues regarding these mechanisms. However, omics integration is still in its infancy. Currently, some single-omics analyses have provided valuable data, mostly in the field of genomics. Thus, several gene-diet interactions in determining both intermediate (plasma lipids, etc.) and final cardiovascular phenotypes (stroke, myocardial infarction, etc.) have been reported. However, few studies have analyzed changes in gene expression and, moreover very few have focused on epigenomic or metabolomic biomarkers related to the MedDiet. Nevertheless, these preliminary results can help to better understand the inter-individual differences in cardiovascular risk and dietary response for further applications in personalized nutrition.

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.

Analytical pitfalls and challenges in clinical metabolomics

Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.

Genomics and Precision Medicine to Direct Statin Use in the Young

The delivery of precision medicine to pediatric cardiology remains complex with a number of challenges ahead. With recent advances in whole genome sequencing, rapid acquisition of a patient's genomic data is possible. However, the challenge remains how we best implement this new data into clinical practice. Predicting drug disposition and response of the individual patient requires a thorough knowledge of the entire dose-exposure-response relationship of each individual drug and knowledge of the factors that make each individual unique. This goal of precision medicine is even more complex in the developing child where drug disposition and response pathways may still be maturing. Herein, we will illustrate the challenges and pitfalls that may occur when trying to deliver pediatric precision medicine using the statins as a prototype.