Narrative review of metabolomics in cardiovascular disease

Effect of traditional Chinese medicine on metabolism disturbance in ischemic heart diseases

ETHNOPHARMACOLOGICAL RELEVANCE

Ischemic heart diseases (IHD), characterized by metabolic dysregulation, contributes majorly to the global morbidity and mortality. Glucose, lipid and amino acid metabolism are critical energy production for cardiomyocytes, and disturbances of these metabolism lead to the cardiac injury. Traditional Chinese medicine (TCM), widely used for treating IHD, have been demonstrated to effectively and safely regulate the cardiac metabolism reprogramming.

AIM OF THE REVIEW

This study discussed and analyzed the disturbed cardiac metabolism induced by IHD and development of formulas, extracts, single herb, bioactive compounds of TCM ameliorating IHD injury via metabolism regulation, with the aim of providing a basis for the development of clinical application of therapeutic strategies for TCM in IHD.

MATERIALS AND METHODS

With "ischemic heart disease", "myocardial infarction", "myocardial ischemia", "metabolomics", "Chinese medicine", "herb", "extracts" "medicinal plants", "glucose", "lipid metabolism", "amino acid" as the main keywords, PubMed, Web of Science, and other online search engines were used for literature retrieval.

RESULTS

IHD exhibits a close association with metabolism disorders, including but not limited to glycolysis, the TCA cycle, oxidative phosphorylation, branched-chain amino acids, fatty acid β-oxidation, ketone body metabolism, sphingolipid and glycerol-phospholipid metabolism. The therapeutic potential of TCM lies in its ability to regulate these disturbed cardiac metabolisms. Additionally, the active ingredients of TCM have depicted wonderful effects in cardiac metabolism reprogramming in IHD.

CONCLUSION

Drawing from the principles of TCM, we have pinpointed specific herbal remedies for the treatment of IHD, and leveraged advanced metabolomics technologies to uncover the effect of these TCMs on metabolomics alteration. In the future, further clinical experimental studies should be included to explore whether more TCM medicines can play a therapeutic role in IHD by reversing cardiac metabolism disorders; multi-omics would be conducted to explore more pathways and genes targeting such metabolism reprogramming by TCMs, and to seek more TCM therapies for IHD.

Effect of genetically determined BCAA levels on cardiovascular diseases and their risk factors: A Mendelian randomization study

BACKGROUND AND AIMS

Observational studies have demonstrated that serum branched-chain amino acids (BCAAs) are associated with the risk of various cardiovascular diseases (CVDs) and their risk factors. However, the causal effect is unclear. The aim of this study was to investigate the effect of genetically determined BCAA levels on CVDs and their risk factors using Mendelian randomization (MR).

METHODS AND RESULTS

We performed univariable and multivariable MR analyses using summary-level data from multiple GWASs and the FinnGen consortium to investigate the association between BCAA levels and the risk of CVDs (myocardial infarction, ischemic stroke, and intracerebral hemorrhage) and their risk factors (atrial fibrillation, hypertension, heart failure, and valvular heart disease). We used the random-effects IVW approach as the primary statistical method and incorporated MR estimates from different data sources using the fixed-effects model. We found genetically determined total and individual BCAA levels and a high risk of hypertension. However, there is no evidence of a causal relationship between BCAA levels and 3 cardiovascular diseases and other their risk factors. The odds of hypertension increased per 1-SD increase in BCAA levels (OR = 1.02 95% CI: 1.01, 1.04; P = 0.005), valine (OR = 1.02 95% CI: 1.01, 1.03; P<0.0001), leucine (OR = 1.02 95% CI: 1.01, 1.04; P＜0.01), and isoleucine (OR = 1.02 95% CI: 1.01, 1.03; P < 0.0001). This result was also significant in the multivariable MR.

CONCLUSIONS

This MR study suggests that total and individual BCAA levels could be associated with a high risk of hypertension.

Deficiency of Pdk1 drives heart failure by impairing taurine homeostasis through Slc6a6

3-Phosphoinositide-dependent protein kinase-1 (Pdk1) as a serine/threonine protein kinase plays a critical role in multiple signaling pathways. Analysis of the gene expression omnibus database showed that Pdk1 was significantly downregulated in patients with heart diseases. Gene set enrichment analysis of the proteomics dataset identified apoptotic- and metabolism-related signaling pathways directly targeted by Pdk1. Previously, our research indicated that Pdk1 deletion-induced metabolic changes might be involved in the pathogenesis of heart failure; however, the underlying mechanism remains elusive. Here, we demonstrated that deficiency of Pdk1 resulted in apoptosis, oxidative damage, and disturbed metabolism, both in vivo and in vitro. Furthermore, profiling of metabonomics by 1 H-NMR demonstrated that taurine was the major differential metabolite in the heart of Pdk1-knockout mice. Taurine treatment significantly reduced the reactive oxygen species production and apoptosis, improved cardiac function, and prolonged the survival time in Pdk1 deficient mice. Proteomic screening identified solute carrier family 6 member 6 (Slc6a6) as the downstream that altered taurine levels in Pdk1-expression cells. Consistently, cellular apoptosis and oxidative damage were rescued by Slc6a6 in abnormal Pdk1 expression cells. These findings collectively suggest that Pdk1 deficiency induces heart failure via disturbances in taurine homeostasis, triggered by Slc6a6.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

Comparative Metabolomics in Single Ventricle Patients after Fontan Palliation: A Strong Case for a Targeted Metabolic Therapy

Most studies on single ventricle (SV) circulation take a physiological or anatomical approach. Although there is a tight coupling between cardiac contractility and metabolism, the metabolic perspective on this patient population is very recent. Early findings point to major metabolic disturbances, with both impaired glucose and fatty acid oxidation in the cardiomyocytes. Additionally, Fontan patients have systemic metabolic derangements such as abnormal glucose metabolism and hypocholesterolemia. Our literature review compares the metabolism of patients with a SV circulation after Fontan palliation with that of patients with a healthy biventricular (BV) heart, or different subtypes of a failing BV heart, by Pubmed review of the literature on cardiac metabolism, Fontan failure, heart failure (HF), ketosis, metabolism published in English from 1939 to 2023. Early evidence demonstrates that SV circulation is not only a hemodynamic burden requiring staged palliation, but also a metabolic issue with alterations similar to what is known for HF in a BV circulation. Alterations of fatty acid and glucose oxidation were found, resulting in metabolic instability and impaired energy production. As reported for patients with BV HF, stimulating ketone oxidation may be an effective treatment strategy for HF in these patients. Few but promising clinical trials have been conducted thus far to evaluate therapeutic ketosis with HF using a variety of instruments, including ketogenic diet, ketone esters, and sodium-glucose co-transporter-2 (SGLT2) inhibitors. An initial trial on a small cohort demonstrated favorable outcomes for Fontan patients treated with SGLT2 inhibitors. Therapeutic ketosis is worth considering in the treatment of Fontan patients, as ketones positively affect not only the myocardial energy metabolism, but also the global Fontan physiopathology. Induced ketosis seems promising as a concerted therapeutic strategy.

A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches?

Cardiovascular diseases (CVDs) present a significant global health challenge and remain a primary cause of death. Early detection and intervention are crucial for improved outcomes in acute coronary syndrome (ACS), particularly acute myocardial infarction (AMI) cases. Artificial intelligence (AI) can detect heart disease early by analyzing patient information and electrocardiogram (ECG) data, providing invaluable insights into this critical health issue. However, the imbalanced nature of ECG and patient data presents challenges for traditional machine learning (ML) algorithms in performing unbiasedly. Investigators have proposed various data-level and algorithm-level solutions to overcome these challenges. In this study, we used a systematic literature review (SLR) approach to give an overview of the current literature and to highlight the difficulties of utilizing ML, deep learning (DL), and AI algorithms in predicting, diagnosing, and prognosis of heart diseases. We reviewed 181 articles from reputable journals published between 2013 and June 15, 2023, focusing on eight selected papers for in-depth analysis. The analysis considered factors such as heart disease type, algorithms used, applications, and proposed solutions and compared the benefits of algorithms combined with clinicians versus clinicians alone. This systematic review revealed that the current ML-based diagnostic approaches face several open problems and issues when implementing ML, DL, and AI in real-life settings. Although these algorithms show higher sensitivities, specificities, and accuracies in detecting heart disease, we must address the ethical concerns while implementing these models into clinical practice. The transparency of how these algorithms operate remains a challenge. Nevertheless, further exploration and research in ML, DL, and AI are necessary to overcome these challenges and fully harness their potential to improve health outcomes for patients with AMI.

Elevated temperature decreases cardiovascular toxicity of nanoplastics but adds to their lethality: A case study during zebrafish (Danio rerio) development

To highlight the key role of global warming on the toxicity of contaminants, the cardiovascular toxicity of nanoparticles (NPs) was estimated in developing zebrafish (Danio rerio) at different exposure temperatures, and the toxicity mechanisms were explored via multi-omic analyses. Polystyrene NPs (50 nm) at 0.1 mg·L^-1 entered zebrafish embryos at 24 h post-fertilization and caused cardiovascular toxicity in the developing zebrafish at 27 ℃. This was explained by the down-regulation of the branched-chain amino acid and insulin signaling pathways owing to induced oxidative stress. Elevated exposure temperatures promoted the accumulation of NPs in developing zebrafish, increased the levels of oxidative stress and enhanced the oxidative phosphorylation rate in mitochondria, thus resulting in an additive effect on the mortality of zebrafish larvae. Notably, elevated exposure temperatures reduced the cardiovascular toxicity of NPs, as the effective concentration of NPs for inhibiting embryonic heartbeat rate increased from 0.1 mg·L^-1 at 27 ℃ to 1.0 mg·L^-1 at 30 ℃. Experiments of transgenic zebrafish Tg(myl7:GFP) and multi-omic analyses revealed that elevated temperatures enhanced the myocardial contractility of larvae, thus reducing the cardiovascular toxicity of NPs. However, the health risks of enhanced myocardial contraction caused by NP exposure at elevated temperatures requires further consideration.

Association of Serum Metabolites and Salt Sensitivity of Blood Pressure in Chinese Population: The EpiSS Study

BACKGROUND

To identify novel metabolites associated with salt sensitivity of blood pressure (SSBP) in Chinese Han population.

METHODS

A case-control study was conducted with 25 salt sensitive (SS) and 26 salt resistant (SR) participants, which was selected from the Systems Epidemiology Study on Salt Sensitivity of Blood Pressure (EpiSS) study. The modified Sullivan's acute oral saline load and diuresis shrinkage test (MSAOSL-DST) was conducted to identify SS. Untargeted, ultra-high performance liquid chromatograph-high resolution mass spectrometer (UPLC-HRMS) was conducted and orthogonal partial least squares-discriminate analysis (OPLS-DA) and multivariable logistic regression model were used to screen the metabolites related to SS, mixed linear regressions models were used to examined the association of SSBP with metabolites during saline load period and diuresis shrinkage period. Receiver operating characteristic (ROC) curve analysis was performed. The area under the curve's (AUC) sensitivity and specificity were calculated to identified metabolites biomarkers for SS.

RESULTS

There were 39 differentially expressed metabolites (DE-metabolites) between SS and SR. Thirty-five and four of DE-metabolites were inversely or positively associated with SS, respectively. Four biochemical pathways demonstrated significant enrichment for identified metabolites. In single-metabolite analyses, L-Glutamine displayed the best diagnostic performance (AUC = 0.88, 95% CI: 0.78-0.97). In multi-metabolites analyses, L-Glutamine + Cholesterol ester 22:5n6 combination showed the best diagnostic performance (AUC = 0.96, 95% CI: 0.91-1.00). Adjusted for traditional risk factors, L-Glutamine and Cholesterol ester 22:5n6 explained an additional 38.3% of SS susceptibility.

CONCLUSIONS

This study provide potential evidence for clarifying the mechanism of SS and provide novel biological insights into salt sensitive hypertension.

Thiram induces myocardial oxidative damage and apoptosis in broilers via interfering their cardiac metabolism

Thiram is a dithiocarbamate pesticide extensively used as a fungicide to preserve crops and seeds. Long-term exposure to thiram causes potential harm to the health of human beings and animals. So far, most of the researches on thiram focused on erythrocyte toxicity, immune system, kidney damage, and tibial dyschondroplasia; however, there is less data on cardiac toxicity. In this study, we examined cardiac histopathology, inflammatory factors, oxidative stress indicators, and apoptosis markers in the heart of broilers that were exposed to thiram. According to our findings, the continuous exposure to thiram caused pathological changes and abnormal function of myocardial tissues with increased level of inducible nitric oxide synthase (iNOS), inflammatory factors (IL-6, IL-8, TNF-α and NF-κB), and decreased level of anti-inflammatory factor (IL-10). In addition, thiram significantly upregulated the protein expression of cleaved-caspase 3, cleaved-PARP, and caused cardiomyocyte apoptosis. Meanwhile, the expression of heat shock proteins (HSP60, HSP70, HSP90) markedly decreased in the thiram-treated groups. An excessive accumulation of peroxidation products (MDA, H₂O₂), a decrease in T-AOC, and antioxidant activity enzymes (T-SOD, GST and GPX) were also noticed, all of which led to oxidative stress and activation of Nrf2 signal pathway by up-regulating key target genes (HO-1 and SODs). Thiram-induced metabolites were further identified via non-targeted metabonomic analysis. Correlation analysis revealed eighteen differentially expressed metabolites, closely related to cardiac injury. Importantly, thiram primarily affected the taurine and hypotaurine metabolism, pyrimidine metabolism as well as glycerol metabolism. Collectively, our study suggests that thiram could cause cardiotoxicity by interfering with taurine and hypotaurine metabolism, pyrimidine metabolism, and glycerolipid metabolism, which further induce oxidative stress via triggering Nrf2 signal pathway. This study may provide new evidence for the molecular mechanism of cardiotoxicity caused by thiram and resonate the alarm for animals and workers who have been exposed to thiram for a long time.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Clinical features and metabolic reprogramming of atherosclerotic lesions in patients with chronic thromboembolic pulmonary hypertension

Background

Chronic thromboembolic pulmonary hypertension (CTEPH) patients may present with atherosclerotic lesions in their pulmonary arteries, but their clinical characteristics remain unclear. The metabolic pathways associated with the atherosclerotic lesions may explain their occurrence and have implications for interventions, but they have not been investigated.

Methods

We collected pulmonary endarterectomy (PEA) samples of CTEPH patients from December 2016 to August 2021. Following a detailed pathological examination of the PEA specimen, the patients were divided into those with and without lesions, and age- and sex matching were performed subsequently using propensity score matching (n = 25 each). Metabolomic profiling was used to investigate the metabolites of the proximal lesions in the PEA specimens.

Results

In our study population, 27.2% of all PEA specimens were found to contain atherosclerotic lesions. CTEPH patients with atherosclerotic lesions were more likely to have a history of symptomatic embolism and had a longer timespan between embolism and surgery, whereas the classic risk factors of systemic and coronary circulation could not distinguish CTEPH patients with or without atherosclerotic lesions. Metabolomic profiling revealed that the formation of atherosclerotic lesions in CTEPH was closely related to altered glycine, serine, and threonine metabolic axes, possibly involved in cellular senescence, energy metabolism, and a proinflammatory microenvironment.

Conclusion

The occurrence of atherosclerotic lesions in the pulmonary arteries of CTEPH was associated with symptomatic thromboembolic history and prolonged disease duration. The results revealed a new link between atherosclerotic lesions and aberrant amino acid metabolism in the context of CTEPH for the first time. This study has characterized the clinical and metabolic profiles of this distinct group of CTEPH patients, providing new insights into disease pathogenesis and potential interventions.

Amino acid profiling to predict prognosis in patients with heart failure: an expert review

Heart failure is a complex disease with a poor prognosis. A number of widely used prognostic tools have limitations, so efforts to identify novel predictive markers and measures are important. As a metabolomics tool, amino acid profiling has shown promise in predicting heart failure prognosis; however, the evidence has not yet been sufficiently evaluated. We describe the utilization of amino acids in the healthy heart and in heart failure before reviewing the literature on amino acid profiling for prognostic prediction. We expertly interpret the findings and provide suggestions for future research to advance the understanding of the prognostic potential of amino acid profiling in heart failure. Our analysis revealed correlations between amino acid biomarkers and traditional prognostic factors, the additional prognostic value of amino acid biomarkers over traditional prognostic factors, and the successful use of amino acid biomarkers to distinguish heart failure aetiology. Although certain amino acid biomarkers have demonstrated additional prognostic value over traditional measures, such as New York Heart Association functional class, these measures are deeply rooted in clinical practice; thus, amino acid biomarkers may be best placed as additional prognostic tools to improve current risk stratification rather than as surrogate tools. Once the metabolic profiles of different heart failure aetiologies have been clearly delineated, the amino acid biomarkers with the most value in prognostic prediction should be determined. Amino acid profiling could be useful to evaluate the pathophysiology and metabolic status of different heart failure cohorts, distinguish heart failure aetiologies, and improve risk stratification and prognostic prediction.

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations.

METHODS

We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty.

RESULTS

Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk.

CONCLUSIONS

These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

Disturbed Cardiac Metabolism Triggers Atrial Arrhythmogenesis in Diabetes Mellitus: Energy Substrate Alternate as a Potential Therapeutic Intervention

Atrial fibrillation (AF) is the most common type of sustained arrhythmia in diabetes mellitus (DM). Its morbidity and mortality rates are high, and its prevalence will increase as the population ages. Despite expanding knowledge on the pathophysiological mechanisms of AF, current pharmacological interventions remain unsatisfactory; therefore, novel findings on the underlying mechanism are required. A growing body of evidence suggests that an altered energy metabolism is closely related to atrial arrhythmogenesis, and this finding engenders novel insights into the pathogenesis of the pathophysiology of AF. In this review, we provide comprehensive information on the mechanistic insights into the cardiac energy metabolic changes, altered substrate oxidation rates, and mitochondrial dysfunctions involved in atrial arrhythmogenesis, and suggest a promising advanced new therapeutic approach to treat patients with AF.

Metabolomic Profiling of End-Stage Heart Failure Secondary to Chronic Chagas Cardiomyopathy

Chronic Chagas cardiomyopathy (CCC) is the most frequent and severe clinical form of chronic Chagas disease, representing one of the leading causes of morbidity and mortality in Latin America, and a growing global public health problem. There is currently no approved treatment for CCC; however, omics technologies have enabled significant progress to be made in the search for new therapeutic targets. The metabolic alterations associated with pathogenic mechanisms of CCC and their relationship to cellular and immunopathogenic processes in cardiac tissue remain largely unknown. This exploratory study aimed to evaluate the potential underlying pathogenic mechanisms in the failing myocardium of patients with end-stage heart failure (ESHF) secondary to CCC by applying an untargeted metabolomic profiling approach. Cardiac tissue samples from the left ventricle of patients with ESHF of CCC etiology (n = 7) and healthy donors (n = 7) were analyzed using liquid chromatography-mass spectrometry. Metabolite profiles showed altered branched-chain amino acid and acylcarnitine levels, decreased fatty acid uptake and oxidation, increased activity of the pentose phosphate pathway, dysregulation of the TCA cycle, and alterations in critical cellular antioxidant systems. These findings suggest processes of energy deficit, alterations in substrate availability, and enhanced production of reactive oxygen species in the affected myocardium. This profile potentially contributes to the development and maintenance of a chronic inflammatory state that leads to progression and severity of CCC. Further studies involving larger sample sizes and comparisons with heart failure patients without CCC are needed to validate these results, opening an avenue to investigate new therapeutic approaches for the treatment and prevention of progression of this unique and severe cardiomyopathy.

Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691−0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.

Impact of the Gastrointestinal Tract Microbiota on Cardiovascular Health and Pathophysiology

ABSTRACT

The microbiota of the gastrointestinal tract (GIT) is an extremely diverse community of microorganisms, and their collective genomes (microbiome) provide a vast arsenal of biological activities, in particular enzymatic ones, which are far from being fully elucidated. The study of the microbiota (and the microbiome) is receiving great interest from the biomedical community as it carries the potential to improve risk-prediction models, refine primary and secondary prevention efforts, and also design more appropriate and personalized therapies, including pharmacological ones. A growing body of evidence, though sometimes impaired by the limited number of subjects involved in the studies, suggests that GIT dysbiosis, i.e. the altered microbial composition, has an important role in causing and/or worsening cardiovascular disease (CVD). Bacterial translocation as well as the alteration of levels of microbe-derived metabolites can thus be important to monitor and modulate, because they may lead to initiation and progression of CVD, as well as to its establishment as chronic state. We hereby aim to provide readers with details on available resources and experimental approaches that are used in this fascinating field of biomedical research, and on some novelties on the impact of GIT microbiota on CVD.

A Pilot Study on the 1H-NMR Serum Metabolic Profile of Takotsubo Patients Reveals Systemic Response to Oxidative Stress

Takotsubo syndrome (TTS) presents as an acute coronary syndrome characterized by severe left ventricular (LV) dysfunction and non-obstructive coronary artery disease that typically shows spontaneous recovery within days or weeks. The mechanisms behind TTS are mainly related to beta-adrenergic overstimulation and acute endogenous catecholamine surge, both of which could increase oxidative status that may induce further deterioration of cardiac function. Although several studies reported evidence of inflammation and oxidative stress overload in myocardial tissue of TTS models, systemic biochemical evidence of augmented oxidant activity in patients with TTS is lacking. In this study, serum samples of ten TTS patients and ten controls have been analyzed using ¹H-NMR spectroscopy. The results of this pilot study show a marked alteration in the systemic metabolic profile of TTS patients, mainly characterized by significant elevation of ketone bodies, 2-hydroxybutyrate, acetyl-L-carnitine, and glutamate levels, in contrast with a decrease of several amino acid levels. The overall metabolic fingerprint reflects a systemic response to oxidative stress caused by the stressor that triggered the syndrome’s onset.