Publications
746 results found
Antcliffe D, Jimenez B, Veselkov K, et al., 2017, Metabolic profiling in patients with pneumonia on intensive care, EBioMedicine, Vol: 18, Pages: 244-253, ISSN: 2352-3964
Clinical features and investigations lack predictive value when diagnosing pneumonia, especially when patients are ventilated and when patients develop ventilator associated pneumonia (VAP). New tools to aid diagnosis are important to improve outcomes. This pilot study examines the potential for metabolic profiling to aid the diagnosis in critical care.In this prospective observational study ventilated patients with brain injuries or pneumonia were recruited in the intensive care unit and serum samples were collected soon after the start of ventilation. Metabolic profiles were produced using 1D 1H NMR spectra. Metabolic data were compared using multivariate statistical techniques including Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).We recruited 15 patients with pneumonia and 26 with brain injuries, seven of whom went on to develop VAP. Comparison of metabolic profiles using OPLS-DA differentiated those with pneumonia from those with brain injuries (R2Y = 0.91, Q2Y = 0.28, p = 0.02) and those with VAP from those without (R2Y = 0.94, Q2Y = 0.27, p = 0.05). Metabolites that differentiated patients with pneumonia included lipid species, amino acids and glycoproteins.Metabolic profiling shows promise to aid in the diagnosis of pneumonia in ventilated patients and may allow a more timely diagnosis and better use of antibiotics.
Shariff MIF, Kim JU, Ladep NG, et al., 2017, The plasma and serum metabotyping of hepatocellular carcinoma in a Nigerian and Egyptian cohort using proton nuclear magnetic resonance spectroscopy, Journal of Clinical and Experimental Hepatology, Vol: 7, Pages: 83-92, ISSN: 0973-6883
BACKGROUND/AIMS: Previous studies have observed disturbances in the (1)H nuclear magnetic resonance (NMR) blood spectral profiles in malignancy. No study has metabotyped serum or plasma of hepatocellular carcinoma (HCC) patients from two diverse populations. We aimed to delineate the HCC patient metabotype from Nigeria (mostly hepatitis B virus infected) and Egypt (mostly hepatitis C virus infected) to explore lipid and energy metabolite alterations that may be independent of disease aetiology, diet and environment. METHODS: Patients with HCC (53) and cirrhosis (26) and healthy volunteers (19) were recruited from Nigeria and Egypt. Participants provided serum or plasma samples, which were analysed using 600 MHz (1)H NMR spectroscopy with nuclear Overhauser enhancement spectroscopy pulse sequences. Median group spectra comparison and multivariate analysis were performed to identify regions of difference. RESULTS: Significant differences between HCC patients and healthy volunteers were detected in levels of low density lipoprotein (P = 0.002), very low density lipoprotein (P < 0.001) and lactate (P = 0.03). N-acetylglycoproteins levels in HCC patients were significantly different from both healthy controls and cirrhosis patients (P < 0.001 and 0.001). CONCLUSION: Metabotype differences were present, pointing to disturbed lipid metabolism and a switch from glycolysis to alternative energy metabolites with malignancy, which supports the Warburg hypothesis of tumour metabolism.
Qureshi MI, Greco M, Vorkas PA, et al., 2017, Application of metabolic profiling to abdominal aortic aneurysm research, Journal of Proteome Research, Vol: 16, Pages: 2325-2332, ISSN: 1535-3893
Abdominal aortic aneurysm (AAA) is a complex disease posing diagnostic and therapeutic challenges. Metabonomics may aid in the diagnosis of AAA, determination of individualized risk, discovery of therapeutic targets, and improve understanding of pathogenesis. A systematic review of the diversity and outcomes of existing AAA metabonomic research has been performed. Original research studies applying metabonomics to human aneurysmal disease are included. Seven relevant articles were identified: four studies were based on plasma/serum metabolite profiling, and three studies examined aneurysmal tissue. Aminomalonic acid, guanidinosuccinic acid, and glycerol emerge as potential plasma biomarkers of large aneurysm. Lipid profiling improves predictive models of aneurysm presence. Patterns of metabolite variation associated with AAA relate to carbohydrate and lipid metabolism. Perioperative perturbations in metabolites suggest differential systemic inflammatory responses to surgery, generating hypotheses for adjunctive perioperative therapy. Significant limitations include small study sizes, lack of correction for multiple testing false discovery rates, and single time-point sampling. Metabolic profiling carries the potential to identify biomarkers of AAA and elucidate pathways underlying aneurysmal disease. Statistically and methodologically robust studies are required for validation, addressing the hiatus in understanding mechanisms of aneurysm growth and developing effective treatment strategies.
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, Gut Microbiota for Health World Summit 2017
Objectives: To integrate metagenomic (faecal microbiome), transcriptomic, metabonomic and clinical data to evaluate the contribution of the gut microbiome to the molecular phenome (hepatic transcriptome, plasma and urine metabonome) of non-alcoholic fatty liver disease (NAFLD) independent of clinical confounders in morbidly obese women recruited to the FLORINASH study.Methods: Faecal, liver biopsy, blood and urine samples and data for 28 clinical variables were collected for 56 obese [body mass index (BMI) >35] women from Italy (n = 31) and Spain (n = 25) who elected for bariatric surgery. Confounder analyses of clinical data were done using linear modeling. Histological examination of liver biopsies was used to grade NAFLD (NAFLD activity score: 0, 1, 2, 3). Faecal metagenomes were generated and analysed using the Imperial Metagenomics Pipeline. Differentially expressed genes were identified in hepatic transcriptomes, and analysed using Enrichr, network analyses and Signaling Pathway Impact Analysis. 1H-NMR data were generated for plasma and urinary metabonomes. Clinical, metagenomic, transcriptomic and metabonomic data were integrated using partial Spearman’s correlation, taking confounders (age, body mass index and cohort) into account.Results: NAFLD activity score was anti-correlated with microbial gene richness, and correlated with abundance of Proteobacteria. KEGG analyses of metagenomic data suggested increased microbial processing of dietary lipids and amino acids, as well as endotoxin-related processes related to Proteobacteria. Metabonomic profiles highlighted imbalances in choline metabolism, branched-chain amino acid metabolism and gut-derived microbial metabolites resulting from metabolism of amino acids. NAFLD-associated hepatic transcriptomes were associated with branched-chain amino acid metabolism, endoplasmic reticulum/phagosome, and immune responses associated with microbial infections. Molecular phenomic signatures were stable and predic
Garcia Perez I, Posma JM, Gibson R, et al., 2017, Objective assessment of dietary patterns using metabolic phenotyping: a randomized, controlled, crossover trial, The Lancet Diabetes & Endocrinology, Vol: 5, Pages: 184-195, ISSN: 2213-8587
Background: The burden of non-communicable diseases, such as obesity, diabetes, coronary heart disease and cancer, can be reduced by the consumption of healthy diets. Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, many of them influenced by food intake. We aim to classify people according to dietary behaviour and enhance dietary reporting using metabolic profiling of urine.Methods: To develop metabolite models from 19 healthy volunteers who attended a clinical research unit for four day periods on four occasions. We used the World Health Organisation’s healthy eating guidelines (increase fruits, vegetables, wholegrains, dietary fibre and decrease fats, sugars, and salt) to develop four dietary interventions lasting for four days each that ranged from a diet associated with a low to high risk of developing non-communicable disease. Urine samples were measured by 1H-NMR spectroscopy. This study is registered as an International Standard Randomized Controlled Trial, number ISRCTN 43087333. INTERMAP U.K. (n=225) and a healthy-eating Danish cohort (n=66) were used as free-living validation datasets.Findings: There was clear separation between the urinary metabolite profiles of the four diets. We also demonstrated significant stepwise differences in metabolite levels between the lowest and highest metabolic risk diets and developed metabolite models for each diet. Application of the derived metabolite models to independent cohorts confirmed the association between urinary metabolic and dietary profiles in INTERMAP (P<0•001) and the Danish cohort (P<0•001).Interpretation: Urinary metabolite models, developed in a highly controlled environment, can classify groups of free-living people into consumers of dietary profiles associated with lower or higher non-communicable disease risk based on multivariate m
Chekmeneva E, Correia GDS, Chan Q, et al., 2017, Optimization and Application of Direct Infusion Nanoelectrospray HRMS Method for Large-Scale Urinary Metabolic Phenotyping in Molecular Epidemiology, JOURNAL OF PROTEOME RESEARCH, Vol: 16, Pages: 1646-1658, ISSN: 1535-3893
Large-scale metabolic profiling requires the development of novel economical high-throughput analytical methods to facilitate characterization of systemic metabolic variation in population phenotypes. We report a fit-for-purpose direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) method with time-of-flight detection for rapid targeted parallel analysis of over 40 urinary metabolites. The newly developed 2 min infusion method requires <10 μL of urine sample and generates high-resolution MS profiles in both positive and negative polarities, enabling further data mining and relative quantification of hundreds of metabolites. Here we present optimization of the DI-nESI-HRMS method in a detailed step-by-step guide and provide a workflow with rigorous quality assessment for large-scale studies. We demonstrate for the first time the application of the method for urinary metabolic profiling in human epidemiological investigations. Implementation of the presented DI-nESI-HRMS method enabled cost-efficient analysis of >10 000 24 h urine samples from the INTERMAP study in 12 weeks and >2200 spot urine samples from the ARIC study in <3 weeks with the required sensitivity and accuracy. We illustrate the application of the technique by characterizing the differences in metabolic phenotypes of the USA and Japanese population from the INTERMAP study.
Posma JM, Garcia Perez I, Heaton JC, et al., 2017, An integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers, Analytical Chemistry, Vol: 89, Pages: 3300-3309, ISSN: 1086-4377
A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol) and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved ¹H-NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g. 2D-NMR spectroscopy, Solid-Phase Extraction, Liquid Chromatography prefaced Mass Spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D ¹H-NMR as part of routine metabolic profiling in large datasets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resource.
Inglese P, McKenzie JS, Mroz A, et al., 2017, Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer, Chemical Science, Vol: 8, Pages: 3500-3511, ISSN: 2041-6539
Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
Hicks L, Powles S, Swann J, et al., 2017, Assessing the effect of ethnicity on urinary metabolic profiles in inflammatory bowel disease, JOURNAL OF CROHNS & COLITIS, Vol: 11, Pages: S168-S168, ISSN: 1873-9946
Ding NS, Perdones-Montero A, Sarafian M, et al., 2017, The microbiota and it's role in anti-TNF therapy non-response, Publisher: OXFORD UNIV PRESS, Pages: S482-S483, ISSN: 1873-9946
Ding NS, Perdones-Montero A, Sarafian M, et al., 2017, The microbiota and it's role in anti-TNF therapy non-response, Publisher: OXFORD UNIV PRESS, Pages: S482-S483, ISSN: 1873-9946
Ding NS, Sarafian M, Perdones-Montero A, et al., 2017, Complete metabonomic and microbiota profiling identifies biomarkers for anti-TNF therapy response, Publisher: OXFORD UNIV PRESS, Pages: S88-S89, ISSN: 1873-9946
Hicks L, Powles S, Swann J, et al., 2017, Effects of time on urinary metabolic signatures in inflammatory bowel disease, JOURNAL OF CROHNS & COLITIS, Vol: 11, Pages: S207-S207, ISSN: 1873-9946
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, MRC-PHE Centre for Environment & Health - Centre Training Programme Annual Meeting
Brignardello J, Holmes E, Garcia-Perez I, 2017, Metabolic phenotyping of diet and dietary Intake, Advances in Food and Nutrition Research, Pages: 231-270, ISBN: 978-0-12-811916-7
Nutrition provides the building blocks for growth, repair, and maintenance of the body and is key to maintaining health. Exposure to fast foods, mass production of dietary components, and wider importation of goods have challenged the balance between diet and health in recent decades, and both scientists and clinicians struggle to characterize the relationship between this changing dietary landscape and human metabolism with its consequent impact on health. Metabolic phenotyping of foods, using high-density data-generating technologies to profile the biochemical composition of foods, meals, and human samples (pre- and postfood intake), can be used to map the complex interaction between the diet and human metabolism and also to assess food quality and safety. Here, we outline some of the techniques currently used for metabolic phenotyping and describe key applications in the food sciences, ending with a broad outlook at some of the newer technologies in the field with a view to exploring their potential to address some of the critical challenges in nutritional science.
Kindinger LM, Bennett PR, Lee YS, et al., 2017, The interaction between vaginal microbiota, cervical length and vaginal progesterone treatment for preterm birth risk, Microbiome, Vol: 5, Pages: 1-14, ISSN: 2049-2618
Background:Preterm birth is the primary cause of infant death worldwide. A short cervix in the second trimester of pregnancy is a risk factor for preterm birth. In specific patient cohorts, vaginal progesterone reduces this risk. Using 16S rRNA gene sequencing we undertook a prospective study in women at risk of preterm birth(n=161) to assess 1) the relationship between vaginal microbiotaand cervical length in the second trimester and preterm birth-risk, and 2) the impact of vaginal progesterone on vaginal bacterial communities in women with a short cervix.Results:Lactobacillus iners dominance at 16 weeks gestation was significantly associated with both a short cervix <25mm (n=15, P<0.05), andpreterm birth <34+0 weeks (n=18, 38P<0.01; 69% PPV).In contrast, L. crispatus dominance was highly predictive of term birth (n=127, 98% PPV). Cervical shortening and preterm birthwere not associated with vaginal dysbiosis. A longitudinal characterization of vaginal microbiota (<18, 22, 28 and 34 weeks)was then undertaken in women receiving vaginal progesterone (400mg/OD, n=25) versus controls (n=42).Progesterone did not alter vaginal bacterial community structurenor reduce L. iners-associated preterm birth (<34 weeks). Conclusions:L. iners dominance of the vaginal microbiota at 16 weeks gestation is a risk factor for preterm birth, whereas L. crispatus dominance is protective against preterm birth. Vaginal progesterone does not appear to impact the pregnancy vaginal microbiota. Patients and clinicians who may be concerned about ‘infection risk’ associated with use of a vaginal pessary during high-risk pregnancy can be reassured.
Qureshi M, Vorkas P, Kaluarachchi M, et al., 2017, Biomarker research in thromboembolic stroke (BRUITS), Publisher: KARGER, ISSN: 1015-9770
Millar B, Richardson C, Mckay K, et al., 2017, Obeticholic acid therapy improves cognitive decline in cholestatic liver disease, International Liver Congress / 52nd Annual Meeting of the European-Association-for-the-Study-of-the-Liver, Publisher: ELSEVIER SCIENCE BV, Pages: S364-S365, ISSN: 0168-8278
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Sarafian MH, Ding NS, Holmes E, et al., 2017, Effect on the Host Metabolism, MICROBIOTA IN GASTROINTESTINAL PATHOPHYSIOLOGY: IMPLICATIONS FOR HUMAN HEALTH, PREBIOTICS, PROBIOTICS, AND DYSBIOSIS, Editors: Floch, Ringel, Walker, Publisher: ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD, Pages: 249-253, ISBN: 978-0-12-804024-9
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Pruski P, MacIntyre DA, Lewis HV, et al., 2016, Medical swab analysis using desorption electrospray ionization mass spectrometry (DESI-MS) – a non-invasive approach for mucosal diagnostics, Analytical Chemistry, Vol: 89, Pages: 1540-1550, ISSN: 0003-2700
Medical swabs are routinely used worldwide to sample human mucosa for microbiological screening with culture methods. These are usually time-consuming and have a narrow focus on screening for particular microorganism species. As an alternative, direct mass spectrometric profiling of the mucosal metabolome provides a broader window into the mucosal ecosystem. We present for the first time a minimal effort/minimal-disruption technique for augmenting the information obtained from clinical swab analysis with mucosal metabolome profiling using desorption electrospray ionization mass spectrometry (DESI-MS) analysis. Ionization of mucosal biomass occurs directly from a standard rayon swab mounted on a rotating device and analyzed by DESI MS using an optimized protocol considering swab–inlet geometry, tip–sample angles and distances, rotation speeds, and reproducibility. Multivariate modeling of mass spectral fingerprints obtained in this way readily discriminate between different mucosal surfaces and display the ability to characterize biochemical alterations induced by pregnancy and bacterial vaginosis (BV). The method was also applied directly to bacterial biomass to confirm the ability to detect intact bacterial species from a swab. These results highlight the potential of direct swab analysis by DESI-MS for a wide range of clinical applications including rapid mucosal diagnostics for microbiology, immune responses, and biochemistry.
Chan Q, Loo RL, Ebbels TMD, et al., 2016, Metabolic phenotyping for discovery of urinary biomarkers of diet, xenobiotics and blood pressure in the INTERMAP Study: An overview, Hypertension Research, Vol: 40, Pages: 336-345, ISSN: 1348-4214
The aetiopathogenesis of cardiovascular diseases (CVD) is multifactorial. Adverse bloodpressure (BP) is a major independent risk factor for epidemic CVD affecting about 40% of theadult population worldwide and resulting in significant morbidity and mortality. Metabolicphenotyping of biological fluids has proven its application in characterising low moleculeweight metabolites providing novel insights into gene-environmental-gut microbiomeinteraction in relations to a disease state. In this review, we synthesise key results from theInternational Study of Macro/Micronutrients and Blood Pressure (INTERMAP) Study, a cross-sectional epidemiological study of 4,680 men and women aged 40-59 years from Japan, thePeople’s Republic of China, the United Kingdom, and the United States. We describe theadvancements we have made on: 1) analytical techniques for high throughput metabolicphenotyping; 2) statistical analyses for biomarker identification; 3) discovery of unique food-specific biomarkers; and 4) application of metabolome-wide association (MWA) studies togain a better understanding into the molecular mechanisms of cross cultural and regional BPdifferences.
AHMAD MS, Alsaleh M, Kimhofer T, et al., 2016, The Metabolic Phenotype of Obesity in a Saudi Population, Journal of Proteome Research
Oude Griep LM, Chekmeneva E, Stamler J, et al., 2016, Urinary hippurate and proline betaine relative to fruit intake, blood pressure, and body mass index, Summer meeting 2016: New technology in nutrition research and practice, Publisher: Cambridge University Press (CUP), Pages: E178-E178, ISSN: 0029-6651
Lloyd AJ, Zubair H, Willis ND, et al., 2016, Quantification of dietary biomarkers in spot urine samples reflects the intake of foods of UK high public health importance, Publisher: Cambridge University Press (CUP), Pages: E248-E248, ISSN: 0029-6651
An understanding of causal relations between diet and health is hindered by the lack of robust biological markers of food exposure (1).The rapid development of metabolomics technology offers opportunity for the identification of urine biomarkers for the intake of arange of foods of high public health importance (2), (3). Using high mass resolution mass spectrometry and machine learning data analysis,we have discovered potential urinary biomarkers in controlled clinical studies with a range of analytical techniques (2). To haveutility for population monitoring, we aim to validate biomarker performance in free-living individuals using urine samples collected inthe home with a minimal impact on normal daily activities.Two complementary multiple reaction monitoring (MRM) routines using triple quadrupole mass spectrometry (QQQ-MS) havebeen developed to quantify concurrently dietary exposure biomarkers of more than 20 foods of high public health importance inthe UK. MRM quantification of metabolite levels in spot urines collected either before bed time or a first morning void identifieda sub-set of potential biomarkers that demonstrated robust linkage with reported dietary intake (examples in Table 1). Figure 1demonstrates the ability of selected biomarkers to report exposure in relation to muscle meat intake from lunch time to bedtime(Beefburger; 106gm, Chicken breast; 130gm; Processed Ham; 40·5 gm) in 6 free-living individuals. Anserine was strongly, and specifically,associated with poultry intake, whilst the urinary outputs of 3-methyl histidine and carnosine reflect striated muscle intake,with levels substantially reduced when meals contain lower quality, and processed, meats with reduced levels of striated musclecontent.
Heinzmann SS, Holmes E, Kochhar S, et al., 2016, 2-Furoylglycine as a Candidate Biomarker of Coffee Consumption (vol 63, pg 8615, 2015), JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, Vol: 64, Pages: 8958-8958, ISSN: 0021-8561
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Petropoulou K, Chambers ES, Morrison DJ, et al., 2016, Identifying crop variants with high resistant starch content to maintain healthy glucose homeostasis, Nutrition Bulletin, Vol: 41, Pages: 372-377, ISSN: 1467-3010
Identifying dietary tools that prevent disordered insulin secretion from pancreatic β-cells is an attractive strategy to combat the increasing prevalence of type 2 diabetes. Dietary resistant starch has been linked to improvements in the function of β-cells, possibly via increased colonic fermentation and production of short-chain fatty acids (SCFAs). Increasing the resistant starch content of commonly consumed foods could therefore maintain glucose homeostasis at the population level. As part of Biotechnology and Biological Sciences Research Council (BBSRC) Diet and Health Research Industry Club (DRINC) initiative, variants of Pisum sativum L. (pea) are being investigated to identify the features of pea starch that make it resistant to digestion and available for colonic fermentation and SCFA production. Parallel in vitro and in vivo studies are being conducted using both whole pea seeds and pea flour to facilitate a better understanding of how cells in the pea cotyledons are affected by processing and, in turn, how this influences starch digestibility. Trials in human volunteers are being used to monitor a full spectrum of short- and long-term physiological responses relevant to pancreatic β-cell function and glucose homeostasis. This project is providing new insights into variants of crops that are associated with the specific types of resistant starch that provide the best protection against defects in insulin secretion and function.
Qureshi M, Vorkas P, Jenkins I, et al., 2016, Biomarker research in thromboembolic stroke, Publisher: SAGE PUBLICATIONS LTD, Pages: S32-S32, ISSN: 1747-4930
Qureshi M, Coupland A, Vorkas P, et al., 2016, Metabolic profiling of ischaemic stroke, Publisher: SAGE PUBLICATIONS LTD, Pages: S32-S32, ISSN: 1747-4930
Qureshi MI, Vorkas PA, Coupland AP, et al., 2016, Lessons from metabonomics on the neurobiology of stroke, Neuroscientist, Vol: 23, Pages: 374-382, ISSN: 1073-8584
The application of metabonomic science to interrogate stroke permits the study of metabolite entities, small enough to cross the blood-brain barrier, that provide insight into neuronal dysfunction, and may serve as reservoirs of biomarker discovery. This systematic review examines the applicability of metabolic profiling in ischemic stroke research. Six human studies utilizing metabolic profiling to analyze biofluids from ischemic stroke patients have been included, employing 1H-NMR and/or mass spectrometry to analyze plasma, serum, and/or urine in a targeted or untargeted fashion. Three are diagnostic studies, and one investigates prognostic biomarkers of stroke recurrence following transient ischemic attack. Two studies focus on metabolic distinguishers of depression or cognitive impairment following stroke. Identified biomarkers from blood and urine predominantly relate to homocysteine and folate, branched chain amino acid, and lipid metabolism. Statistical models are well fitted and reproducible, with excellent validation outcomes, demonstrating the feasibility of metabolic profiling to study a complex disorder with multicausal pathology, such as stroke.
Mitra A, MacIntyre D, Lee Y, et al., 2016, Cervical intraepithelial neoplasia disease progression is associated with increased vaginal microbiome diversity, Blair Bell Research Society Annual Academic Meeting, Publisher: Wiley, Pages: E11-E12, ISSN: 1470-0328
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