Real-world performance and accuracy of stress echocardiography: The EVAREST observational multi-centre study

Aims - Stress echocardiography is widely used to identify obstructive coronary artery disease. High accuracy is reported in expert hands but is dependent on operator training and image quality. The EVAREST study provides UK-wide data to evaluate real-world performance and accuracy of stress echocardiography. Methods and Results - Participants undergoing stress echocardiography for coronary artery disease were recruited from 31 hospitals. Participants were followed up through health records which underwent expert adjudication. Cardiac outcome was defined as anatomically or functionally-significant stenosis on angiography, revascularisation, medical management of ischaemia, acute coronary syndrome or cardiac-related death within six months. 5131 patients (55% male) participated with a median age of 65 years (IQR 57 – 74). 72.9% of studies used dobutamine and 68.5% were contrast studies. Inducible ischaemia was present in 19.3% of scans. Sensitivity and specificity for prediction of a cardiac outcome were 95.4% and 96.0%, respectively, with an accuracy of 95.9%. Sub-group analysis revealed high levels of predictive accuracy across a wide range of patient and protocol sub-groups, with the presence of a resting regional wall motion abnormalitiy significantly reducing the performance of both dobutamine (p<0.01) and exercise (p<0.05) stress echocardiography (p<0.05). Overall accuracy remained consistently high across all participating hospitals. Conclusion – Stress echocardiography has high accuracy across UK-based hospitals and thus indicates stress echocardiography is being delivered effectively in real-world practice, reinforcing its role as a first-line investigation in the assessment of patients with stable chest pain

Untargeted UPLC-MS Profiling Pipeline to Expand Tissue Metabolome Coverage: Application to Cardiovascular Disease.

Metabolic profiling studies aim to achieve broad metabolome coverage in specific biological samples. However, wide metabolome coverage has proven difficult to achieve, mostly because of the diverse physicochemical properties of small molecules, obligating analysts to seek multiplatform and multimethod approaches. Challenges are even greater when it comes to applications to tissue samples, where tissue lysis and metabolite extraction can induce significant systematic variation in composition. We have developed a pipeline for obtaining the aqueous and organic compounds from diseased arterial tissue using two consecutive extractions, followed by a different untargeted UPLC-MS analysis method for each extract. Methods were rationally chosen and optimized to address the different physicochemical properties of each extract: hydrophilic interaction liquid chromatography (HILIC) for the aqueous extract and reversed-phase chromatography for the organic. This pipeline can be generic for tissue analysis as demonstrated by applications to different tissue types. The experimental setup and fast turnaround time of the two methods contributed toward obtaining highly reproducible features with exceptional chromatographic performance (CV % < 0.5%), making this pipeline suitable for metabolic profiling applications. We structurally assigned 226 metabolites from a range of chemical classes (e.g., carnitines, α-amino acids, purines, pyrimidines, phospholipids, sphingolipids, free fatty acids, and glycerolipids) which were mapped to their corresponding pathways, biological functions and known disease mechanisms. The combination of the two untargeted UPLC-MS methods showed high metabolite complementarity. We demonstrate the application of this pipeline to cardiovascular disease, where we show that the analyzed diseased groups (<i>n </i>= 120) of arterial tissue could be distinguished based on their metabolic profiles

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided

Metabolic profiling reveals changes in serum predictive of venous ulcer healing

Objective: The aim of this study was to identify potential biomarkers predictive of healing or failure to heal in a population with venous leg ulceration. Summary Background Data: Venous leg ulceration presents important physical, psychological, social and financial burdens. Compression therapy is the main treatment, but it can be painful and time-consuming, with significant recurrence rates. The identification of a reliable biochemical signature with the ability to identify nonhealing ulcers has important translational applications for disease prognostication, personalized health care and the development of novel therapies. Methods: Twenty-eight patients were assessed at baseline and at 20 weeks. Untargeted metabolic profiling was performed on urine, serum, and ulcer fluid, using mass spectrometry and nuclear magnetic resonance spectroscopy. Results: A differential metabolic phenotype was identified in healing (n = 15) compared to nonhealing (n = 13) venous leg ulcer patients. Analysis of the assigned metabolites found ceramide and carnitine metabolism to be relevant pathways. In this pilot study, only serum biofluids could differentiate between healing and nonhealing patients. The ratio of carnitine to ceramide was able to differentiate between healing phenotypes with 100% sensitivity, 79% specificity, and 91% accuracy. Conclusions: This study reports a metabolic signature predictive of healing in venous leg ulceration and presents potential translational applications for disease prognostication and development of targeted therapies

Microbial−mammalian cometabolites dominate the age-associated urinary metabolic phenotype in Taiwanese and American populations

Understanding the metabolic processes associated with aging is key to developing effective management and treatment strategies for age-related diseases. We investigated the metabolic profiles associated with age in a Taiwanese and an American population. 1H NMR spectral profiles were generated for urine specimens collected from the Taiwanese Social Environment and Biomarkers of Aging Study (SEBAS; n = 857; age 54–91 years) and the Mid-Life in the USA study (MIDUS II; n = 1148; age 35–86 years). Multivariate and univariate linear projection methods revealed some common age-related characteristics in urinary metabolite profiles in the American and Taiwanese populations, as well as some distinctive features. In both cases, two metabolites—4-cresyl sulfate (4CS) and phenylacetylglutamine (PAG)—were positively associated with age. In addition, creatine and β-hydroxy-β-methylbutyrate (HMB) were negatively correlated with age in both populations (p < 4 × 10–6). These age-associated gradients in creatine and HMB reflect decreasing muscle mass with age. The systematic increase in PAG and 4CS was confirmed using ultraperformance liquid chromatography–mass spectrometry (UPLC–MS). Both are products of concerted microbial–mammalian host cometabolism and indicate an age-related association with the balance of host–microbiome metabolism

Systematic Review of Clinical Prediction Scores for Deep Vein Thrombosis

Objective: Diagnosis of Deep Vein Thrombosis (DVT) remains a challenging problem. Various clinical prediction rules have been developed in order to improve diagnosis and decision-making in relation to DVT. The purpose of this review is to summarise the available clinical scores and describe their applicability and limitations. Methods: A systematic search of PubMed, MEDLINE and EMBASE databases was conducted in accordance with PRISMA guidance using the keywords: clinical score, clinical prediction rule, risk assessment, clinical probability, pretest probability, diagnostic score and MeSH terms: “Venous Thromboembolism/diagnosis” OR “Venous Thrombosis/diagnosis”. Both development and validation studies were eligible for inclusion. Results: The search strategy returned a total of 2036 articles, of which 102 articles met a priori criteria for inclusion. Eight different diagnostic scores were identified. The development of these scores differs in respect of the population included (hospital inpatients, hospital outpatients or primary care patients), the exclusion criteria, the inclusion of distal DVT and the use of D-dimer. The reliability and applicability of the scores in the context of specific subgroups (inpatients, cancer patients, elderly patients, and those with recurrent DVT) remains controversial. Conclusion: Detailed knowledge of the development of the various clinical prediction scores for DVT is essential in understanding the power, generalisability and limitations of these clinical tools

A comprehensive characterisation of the metabolic profile of varicose veins; implications in elaborating plausible cellular pathways for disease pathogenesis

Metabolic phenotypes reflect both the genetic and environmental factors which contribute to the development of varicose veins (VV). This study utilises analytical techniques to provide a comprehensive metabolic picture of VV disease, with the aim of identifying putative cellular pathways of disease pathogenesis. VV (n = 80) and non-VV (n = 35) aqueous and lipid metabolite extracts were analysed using 600 MHz 1H Nuclear Magnetic Resonance spectroscopy and Ultra-Performance Liquid Chromatography Mass Spectrometry. A subset of tissue samples (8 subjects and 8 controls) were analysed for microRNA expression and the data analysed with mirBase (www.mirbase.org). Using Multivariate statistical analysis, Ingenuity pathway analysis software, DIANALAB database and published literature, the association of significant metabolites with relevant cellular pathways were understood. Higher concentrations of glutamate, taurine, myo-inositol, creatine and inosine were present in aqueous extracts and phosphatidylcholine, phosphatidylethanolamine and sphingomyelin in lipid extracts in the VV group compared with non-VV group. Out of 7 differentially expressed miRNAs, spearman correlation testing highlighted correlation of hsa-miR-642a-3p, hsa-miR-4459 and hsa-miR-135a-3p expression with inosine in the vein tissue, while miR-216a-5p, conversely, was correlated with phosphatidylcholine and phosphatidylethanolamine. Pathway analysis revealed an association of phosphatidylcholine and sphingomyelin with inflammation and myo-inositol with cellular proliferation

Bariatric surgery modulates circulating and cardiac metabolites

Bariatric procedures such as the Roux-en-Y gastric bypass (RYGB) operation offer profound metabolic enhancement in addition to their well-recognized weight loss effects. They are associated with significant reduction in cardiovascular disease risk and mortality, which suggests a surgical modification on cardiac metabolism. Metabolic phenotyping of the cardiac tissue and plasma postsurgery may give insight into cardioprotective mechanisms. The aim of the study was to compare the metabolic profiles of plasma and heart tissue extracts from RYGB- and sham-operated Wistar rats to identify the systemic and cardiac signature of metabolic surgery. A total of 27 male Wistar rats were housed individually for a week and subsequently underwent RYGB (n = 13) or sham (n = 14) operation. At week 8 postoperation, a total of 27 plasma samples and 16 heart tissue samples (8 RYGB; 8 Sham) were collected from animals and analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy and ultra performance liquid chromatography (UPLC-MS) to characterize the global metabolite perturbation induced by RYGB operation. Plasma bile acids, phosphocholines, amino acids, energy-related metabolites, nucleosides and amine metabolites, and cardiac glycogen and amino acids were found to be altered in the RYGB operated group. Correlation networks were used to identify metabolite association. The metabolic phenotype of this bariatric surgical model inferred systematic change in both myocardial and systemic activity post surgery. The altered metabolic profile following bariatric surgery reflects an enhancement of cardiac energy metabolism through TCA cycle intermediates, cardiorenal protective activity, and biochemical caloric restriction. These surgically induced metabolic shifts identify some of the potential mechanisms that contribute toward bariatric cardioprotection through gut microbiota ecological fluxes and an enterocardiac axis to shield against metabolic syndrome of cardiac dysfunction

Deep vein thrombosis exhibits characteristic serum and vein wall metabolic phenotypes in the inferior vena cava ligation mouse model

Deep vein thrombosis (DVT) is a major health problem, responsible for significant morbidity and mortality, and imposes a heavy economic burden to healthcare systems (1). Although most events resolve without complication through spontaneous lysis and recanalization, DVT can be complicated with life-threatening pulmonary embolism (2), while approximately one third of DVT patients develop post-thrombotic syndrome with swelling, pain, skin changes and/or venous ulceration (3).Treatment with anticoagulation prevents further thrombus extension, protects from pulmonary embolism and reduces the risk of chronic lower limb complications. Importantly, unnecessary treatment can result in bleeding. Therefore, accurate and reliable DVT diagnosis is essential. Currently, diagnosis relies on subjective clinical examination and ultrasound imaging (4). A number of biological markers have been investigated with variable results. D-dimer, the most widely used biomarker, is sensitive but lacks specificity (5, 6). Ongoing research efforts target the utility of alternative blood diagnostic biomarkers able to accurately diagnose DVT, guide length and type of treatment, and potentially identify patients who may benefit from more aggressive therapies than standard anticoagulation. New molecular technologies and methods have entered the scientific arena, offering the opportunity to revisit this important clinical need. Metabolic profiling has emerged as a new approach to investigate complex metabolic disease and enable precision medicine. Metabolomics is the comprehensive and systematic identification of the small molecules present in differential abundance in biofluids and are affected by various factors such as diet, lifestyle, genetics, disease, environmental factors and medications. Metabolic profiling approaches to characterizing the metabolome can be either targeted or untargeted. In targeted approaches specific metabolites, representative of suspected biological pathways, are analysed in each sample. Non-targeted analysis simultaneously screens multiple small molecules for alterations in their levels within biofluids. This latter approach can identify novel, unrecognised metabolites and pathways, elucidating the pathophysiology of the disease, and highlighting potential biomarkers, biomarker signatures and/or therapeutic targets. Omics studies, specifically the non-targeted approaches have allowed us to go from hypothesis led to hypothesis generating studies. Metabolomics employs high-throughput analytical platforms (both nuclear magnetic resonance [NMR] spectroscopy and ultra-performance liquid chromatography-mass spectrometry [UPLC-MS]) coupled with statistical modelling to identify metabolites, which are differentially present in the context of health and disease. This provides a platform to screen for candidate biomarkers for DVT diagnosis (7). NMR is a non-destructive, robust and quantitative method that provides information on molecular structure, but lacks sensitivity. MS is more sensitive, but requires pre-separation with a chromatographic method and ionisation of the sample. The combination of NMR and MS provides complimentary and accurate information on metabolites. Metabolomics has been shown to have utility in vascular disease and metabolic changes have previously been identified, at tissue level, between varicose and non-varicose vein, carotid and femoral atherosclerosis, and stable and unstable carotid plaques (8, 9). Recently, NMR spectrometry was used to identify metabolic changes of DVT in animals of different ages (10). This study aims to elucidate the DVT-specific metabolite profile in a murine experimental model