1019 results found
Hoyles L, Abbott JC, Holmes E, et al., 2015, IMP: Imperial Metagenomics Pipeline for high-throughput sequence data, Exploring Human Host-Microbiome Interactions in Health and Disease
Merrifield CA, Lewis MC, Berger B, et al., 2015, Neonatal environment exerts a sustained influence on the development of the intestinal microbiota and metabolic phenotype, ISME Journal, Vol: 10, Pages: 145-157, ISSN: 1751-7362
The postnatal environment, including factors such as weaning and acquisition of the gut microbiota, has been causally linked to the development of later immunological diseases such as allergy and autoimmunity, and has also been associated with a predisposition to metabolic disorders. We show that the very early-life environment influences the development of both the gut microbiota and host metabolic phenotype in a porcine model of human infants. Farm piglets were nursed by their mothers for 1 day, before removal to highly controlled, individual isolators where they received formula milk until weaning at 21 days. The experiment was repeated, to create two batches, which differed only in minor environmental fluctuations during the first day. At day 1 after birth, metabolic profiling of serum by 1H nuclear magnetic resonance spectroscopy demonstrated significant, systemic, inter-batch variation which persisted until weaning. However, the urinary metabolic profiles demonstrated that significant inter-batch effects on 3-hydroxyisovalerate, trimethylamine-N-oxide and mannitol persisted beyond weaning to at least 35 days. Batch effects were linked to significant differences in the composition of colonic microbiota at 35 days, determined by 16 S pyrosequencing. Different weaning diets modulated both the microbiota and metabolic phenotype independently of the persistent batch effects. We demonstrate that the environment during the first day of life influences development of the microbiota and metabolic phenotype and thus should be taken into account when interrogating experimental outcomes. In addition, we suggest that intervention at this early time could provide ‘metabolic rescue’ for at-risk infants who have undergone aberrant patterns of initial intestinal colonisation.
Gray N, Lewis MR, Plumb RS, et al., 2015, High-Throughput Microbore UPLC-MS Metabolic Phenotyping of Urine for Large-Scale Epidemiology Studies, JOURNAL OF PROTEOME RESEARCH, Vol: 14, Pages: 2714-2721, ISSN: 1535-3893
Li J, Kinross JM, Posma JP, et al., 2015, COLONIC MICROBIOME-METABONOME NETWORK INTERACTIONS IN AFRICAN AMERICANS AND NATIVE AFRICANS: A PROSPECTIVE 2-WEEK FOOD EXCHANGE STUDY, 2nd Digestive-Disorders-Federation Conference, Publisher: BMJ PUBLISHING GROUP, Pages: A369-A369, ISSN: 0017-5749
Afzal AM, Mussa HY, Turner RE, et al., 2015, A multi-label approach to target prediction taking ligand promiscuity into account, Journal of Cheminformatics, Vol: 7, ISSN: 1758-2946
BackgroundAccording to Cobanoglu et al., it is now widely acknowledged that the single target paradigm (one protein/target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous – it can interact with more than one target protein.In recent years, in in silico target prediction methods the promiscuity issue has generally been approached computationally in three main ways: ligand-based methods; target-protein-based methods; and integrative schemes. In this study we confine attention to ligand-based target prediction machine learning approaches, commonly referred to as target-fishing.The target-fishing approaches that are currently ubiquitous in cheminformatics literature can be essentially viewed as single-label multi-classification schemes; these approaches inherently bank on the single target paradigm assumption that a ligand can zero in on one single target. In order to address the ligand promiscuity issue, one might be able to cast target-fishing as a multi-label multi-class classification problem. For illustrative and comparison purposes, single-label and multi-label Naïve Bayes classification models (denoted here by SMM and MMM, respectively) for target-fishing were implemented. The models were constructed and tested on 65,587 compounds/ligands and 308 targets retrieved from the ChEMBL17 database.ResultsOn classifying 3,332 test multi-label (promiscuous) compounds, SMM and MMM performed differently. At the 0.05 significance level, a Wilcoxon signed rank test performed on the paired target predictions yielded by SMM and MMM for the test ligands gave a p-value < 5.1 × 10−94 and test statistics value of 6.8 × 105, in favour of MMM. The two models performed differently when tested on four datasets comprising single-label (non-promiscuous) compounds; McNemar’s tes
Chekmeneva E, Correia G, Denes J, et al., 2015, Development of nanoelectrospray high resolution isotope dilution mass spectrometry for targeted quantitative analysis of urinary metabolites: application to population profiling and clinical studies, Analytical Methods, Vol: 7, Pages: 5122-5133, ISSN: 1759-9679
An automated chip-based electrospray platform was used to develop a high-throughput nanoelectrospray high resolution mass spectrometry (nESI-HRMS) method for multiplexed parallel untargeted and targeted quantitative metabolic analysis of urine samples. The method was demonstrated to be suitable for metabolic analysis of large sample numbers and can be applied to large-scale epidemiological and stratified medicine studies. The method requires a small amount of sample (5 μL of injectable volume containing 250 nL of original sample), and the analysis time for each sample is three minutes per sample to acquire data in both negative and positive ion modes. Identification of metabolites was based on the high resolution accurate mass and tandem mass spectrometry using authentic standards. The method was validated for 8 targeted metabolites and was shown to be precise and accurate. The mean accuracy of individual measurements being 106% and the intra- and inter-day precision (expressed as relative standard deviations) were 9% and 14%, respectively. Selected metabolites were quantified by standard addition calibration using the stable isotope labelled internal standards in a pooled urine sample, to account for any matrix effect. The multiple point standard addition calibration curves yielded correlation coefficients greater than 0.99, and the linear dynamic range was more than three orders of magnitude. As a proof-of-concept the developed method was applied for targeted quantitative analysis of a set of 101 urine samples obtained from female participants with different pregnancy outcomes. In addition to the specifically targeted metabolites, several other metabolites were quantified relative to the internal standards. Based on the calculated concentrations, some metabolites showed significant differences according to different pregnancy outcomes. The acquired high resolution full-scan data were used for further untargeted fingerprinting and improved the differentiation of
Guenther S, Muirhead LJ, Speller AVM, et al., 2015, Spatially resolved metabolic phenotyping of breast cancer by desorption electrospray ionization mass spectrometry, Cancer Research, Vol: 75, Pages: 1828-1837, ISSN: 0008-5472
Breast cancer is a heterogeneous disease characterized by varying responses to therapeutic agents and significant differences in long-term survival. Thus, there remains an unmet need for early diagnostic and prognostic tools and improved histologic characterization for more accurate disease stratification and personalized therapeutic intervention. This study evaluated a comprehensive metabolic phenotyping method in breast cancer tissue that uses desorption electrospray ionization mass spectrometry imaging (DESI MSI), both as a novel diagnostic tool and as a method to further characterize metabolic changes in breast cancer tissue and the tumor microenvironment. In this prospective single-center study, 126 intraoperative tissue biopsies from tumor and tumor bed from 50 patients undergoing surgical resections were subject to DESI MSI. Global DESI MSI models were able to distinguish adipose, stromal, and glandular tissue based on their metabolomic fingerprint. Tumor tissue and tumor-associated stroma showed evident changes in their fatty acid and phospholipid composition compared with normal glandular and stromal tissue. Diagnosis of breast cancer was achieved with an accuracy of 98.2% based on DESI MSI data (PPV 0.96, NVP 1, specificity 0.96, sensitivity 1). In the tumor group, correlation between metabolomic profile and tumor grade/hormone receptor status was found. Overall classification accuracy was 87.7% (PPV 0.92, NPV 0.9, specificity 0.9, sensitivity 0.92). These results demonstrate that DESI MSI may be a valuable tool in the improved diagnosis of breast cancer in the future. The identified tumor-associated metabolic changes support theories of de novo lipogenesis in tumor tissue and the role of stroma tissue in tumor growth and development and overall disease prognosis. Cancer Res; 75(9); 1828–37. ©2015 AACR.
Obesity is a major public health problem worldwide. We used 24-hour urinary metabolic profiling by proton (1H) nuclear magnetic resonance (NMR) spectroscopy and ion exchange chromatography to characterize the metabolic signatures of adiposity in the U.S. (n = 1880) and UK (n = 444) cohorts of the INTERMAP (International Study of Macro- and Micronutrients and Blood Pressure) epidemiologic study. Metabolic profiling of urine samples collected over two 24-hour time periods 3 weeks apart showed reproducible patterns of metabolite excretion associated with adiposity. Exploratory analysis of the urinary metabolome using 1H NMR spectroscopy of the U.S. samples identified 29 molecular species, clustered in interconnecting metabolic pathways, that were significantly associated (P = 1.5 × 10−5 to 2.0 × 10−36) with body mass index (BMI); 25 of these species were also found in the UK validation cohort. We found multiple associations between urinary metabolites and BMI including urinary glycoproteins and N-acetyl neuraminate (related to renal function), trimethylamine, dimethylamine, 4-cresyl sulfate, phenylacetylglutamine and 2-hydroxyisobutyrate (gut microbial co-metabolites), succinate and citrate (tricarboxylic acid cycle intermediates), ketoleucine and the ketoleucine/leucine ratio (linked to skeletal muscle mitochondria and branched-chain amino acid metabolism), ethanolamine (skeletal muscle turnover), and 3-methylhistidine (skeletal muscle turnover and meat intake). We mapped the multiple BMI-metabolite relationships as part of an integrated systems network that describes the connectivities between the complex pathway and compartmental signatures of human adiposity.
O'Keefe SJ, Li JV, Lahti L, et al., 2015, Fat, fibre and cancer risk in African Americans and rural Africans., Nat Commun, Vol: 6
Rates of colon cancer are much higher in African Americans (65:100,000) than in rural South Africans (<5:100,000). The higher rates are associated with higher animal protein and fat, and lower fibre consumption, higher colonic secondary bile acids, lower colonic short-chain fatty acid quantities and higher mucosal proliferative biomarkers of cancer risk in otherwise healthy middle-aged volunteers. Here we investigate further the role of fat and fibre in this association. We performed 2-week food exchanges in subjects from the same populations, where African Americans were fed a high-fibre, low-fat African-style diet and rural Africans a high-fat, low-fibre western-style diet, under close supervision. In comparison with their usual diets, the food changes resulted in remarkable reciprocal changes in mucosal biomarkers of cancer risk and in aspects of the microbiota and metabolome known to affect cancer risk, best illustrated by increased saccharolytic fermentation and butyrogenesis, and suppressed secondary bile acid synthesis in the African Americans.
Hedjazi L, Gauguier D, Zalloua PA, et al., 2015, MQTL.NMR: An integrated suite for genetic mapping of quantitative variations of <sup>1</sup>H NMR-based metabolic profiles, Analytical Chemistry, Vol: 87, Pages: 4377-4384, ISSN: 0003-2700
High-throughput <sup>1</sup>H nuclear magnetic resonance (NMR) is an increasingly popular robust approach for qualitative and quantitative metabolic profiling, which can be used in conjunction with genomic techniques to discover novel genetic associations through metabotype quantitative trait locus (mQTL) mapping. There is therefore a crucial necessity to develop specialized tools for an accurate detection and unbiased interpretability of the genetically determined metabolic signals. Here we introduce and implement a combined chemoinformatic approach for objective and systematic analysis of untargeted <sup>1</sup>H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate mQTL mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems. The mQTL.NMR package is freely available with its source code through the Comprehensive R/Bioconductor repository and its own website (http://www.ican-institute.org/tools/). It represents a significant advance to facilitate untargeted metabolomic data processing and quantitative analysis and their genetic mapping.
Wu Q, Li JV, Seyfried F, et al., 2015, Metabolic phenotype-microRNA data fusion analysis of the systemic consequences of Roux-en-Y gastric bypass surgery., International Journal of Obesity, Vol: 2015, Pages: 1126-1134, ISSN: 1476-5497
Background/Objectives: Bariatric surgery offers sustained dramatic weight loss and often remission of type 2 diabetes, yet the mechanisms of establishment of these health benefits are not clear.Subjects/MethodsWe mapped the co-ordinated systemic responses of gut hormones, the circulating miRNAome and the metabolome in a rat model of Roux-en-Y gastric bypass (RYGB) surgery. Results: The response of circulating miRNAs to RYGB was striking and selective. Analysis of 14 significantly altered circulating miRNAs within a pathway context was suggestive of modulation of signalling pathways including G protein signalling, neurodegeneration, inflammation, and growth and apoptosis responses. Concomitant alterations in the metabolome indicated increased glucose transport, accelerated glycolysis and inhibited gluconeogenesis in the liver. Of particular significance, we show significantly decreased circulating miRNA-122 levels and a more modest decline in hepatic levels, following surgery. In mechanistic studies, manipulation of miRNA-122 levels in a cell model induced changes in the activity of key enzymes involved in hepatic energy metabolism, glucose transport, glycolysis, TCA cycle, pentose phosphate shunt, fatty acid oxidation and gluconeogenesis, consistent with the findings of the in vivo surgery-mediated responses, indicating the powerful homeostatic activity of the miRNAs. Conclusions: The close association between energy metabolism, neuronal signalling and gut microbial metabolites derived from the circulating miRNA, plasma, urine and liver metabolite and gut hormone correlations further supports an enhanced gut-brain signaling, which we suggest is hormonally mediated by both traditional gut hormones and miRNAs. This transomic approach to map the crosstalk between the circulating miRNAome and metabolome offers opportunities to understand complex systems biology within a disease and interventional treatment setting.International Journal of Obesity accepted article previe
MacIntyre DA, Chandiramani M, Lee YS, et al., 2015, The vaginal microbiome during pregnancy and the postpartum period in a European population, Scientific Reports, Vol: 5, ISSN: 2045-2322
The composition and structure of the pregnancy vaginal microbiome may influence susceptibility to adverse pregnancy outcomes. Studies on the pregnant vaginal microbiome have largely been limited to Northern American populations. Using MiSeq sequencing of 16S rRNA gene amplicons, we characterised the vaginal microbiota of a mixed British cohort of women (n = 42) who experienced uncomplicated term delivery and who were sampled longitudinally throughout pregnancy (8–12, 20–22, 28–30 and 34–36 weeks gestation) and 6 weeks postpartum. We show that vaginal microbiome composition dramatically changes postpartum to become less Lactobacillus spp. dominant with increased alpha-diversity irrespective of the community structure during pregnancy and independent of ethnicity. While the pregnancy vaginal microbiome was characteristically dominated by Lactobacillus spp. and low alpha-diversity, unlike Northern American populations, a significant number of pregnant women this British population had a L. jensenii-dominated microbiome characterised by low alpha-diversity. L. jensenii was predominantly observed in women of Asian and Caucasian ethnicity whereas L. gasseri was absent in samples from Black women. This study reveals new insights into biogeographical and ethnic effects upon the pregnancy and postpartum vaginal microbiome and has important implications for future studies exploring relationships between the vaginal microbiome, host health and pregnancy outcomes.
Georgakopoulou N, MacIntyre DA, Jimenez B, et al., 2015, H-1-NMR-Based Characterisation of Maternal Plasma Metabolome During the Early 2nd Trimester Pregnancy Window, Publisher: SAGE PUBLICATIONS INC, Pages: 177A-178A, ISSN: 1933-7191
Vorkas PA, Shalhoub J, Isaac G, et al., 2015, Metabolic Phenotyping of Atherosclerotic Plaques Reveals Latent Associations between Free Cholesterol and Ceramide Metabolism in Atherogenesis., Journal of Proteome Research, Vol: 14, Pages: 1389-1399, ISSN: 1535-3907
Current optimum medical treatments have had limited success in the primary prevention of cardiovascular events, underscoring the need for new pharmaceutical targets and enhanced understanding of mechanistic metabolic dysregulation. Here, we use a combination of novel metabolic profiling methodologies, based on ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) followed by chemometric modeling, data integration, and pathway mapping, to create a systems-level metabolic atlas of atherogenesis. We apply this workflow to compare arterial tissue incorporating plaque lesions to intimal thickening tissue (immediate preplaque stage). We find changes in several metabolite species consistent with well-established pathways in atherosclerosis, such as the cholesterol, purine, pyrimidine, and ceramide pathways. We then illustrate differential levels of previously unassociated lipids to atherogenesis, namely, phosphatidylethanolamine-ceramides (t-test p-values: 3.8 × 10(-6) to 9.8 × 10(-12)). Most importantly, these molecules appear to be interfacing two pathways recognized for their involvement in atherosclerosis: ceramide and cholesterol. Furthermore, we show that β-oxidation intermediates (i.e., acylcarnitines) manifest a pattern indicating truncation of the process and overall dysregulation of fatty acid metabolism and mitochondrial dysfunction. We develop a metabolic framework that offers the ability to map significant statistical associations between detected biomarkers. These dysregulated molecules and consequent pathway modulations may provide novel targets for pharmacotherapeutic intervention.
Vorkas PA, Isaac G, Anwar MA, et al., 2015, Untargeted UPLC-MS Profiling Pipeline to Expand Tissue Metabolome Coverage: Application to Cardiovascular Disease., Analytical Chemistry, Vol: 87, Pages: 4184-4193, ISSN: 1086-4377
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 (n = 120) of arterial tissue could be distinguished based on their metabolic profiles.
Chang KL, Pee HN, Tan WP, et al., 2015, Metabolic Profiling of CHO-A beta PP695 Cells Revealed Mitochondrial Dysfunction Prior to Amyloid-beta Pathology and Potential Therapeutic Effects of Both PPAR gamma and PPAR alpha Agonisms for Alzheimer's Disease, JOURNAL OF ALZHEIMERS DISEASE, Vol: 44, Pages: 215-231, ISSN: 1387-2877
Rainville PD, Murphy JP, Tomany M, et al., 2015, An integrated ceramic, micro-fluidic device for the LC/MS/MS analysis of pharmaceuticals in plasma, ANALYST, Vol: 140, Pages: 5546-5556, ISSN: 0003-2654
Villasenor A, Kinross JM, Li JV, et al., 2014, H-1 NMR Global Metabolic Phenotyping of Acute Pancreatitis in the Emergency Unit, JOURNAL OF PROTEOME RESEARCH, Vol: 13, Pages: 5362-5375, ISSN: 1535-3893
Dona AC, Jimenez B, Schaefer H, et al., 2014, Precision High-Throughput Proton NMR Spectroscopy of Human Urine, Serum, and Plasma for Large-Scale Metabolic Phenotyping, ANALYTICAL CHEMISTRY, Vol: 86, Pages: 9887-9894, ISSN: 0003-2700
Ladep NG, Dona AC, Lewis MR, et al., 2014, Discovery and Validation of Urinary Metabotypes for the Diagnosis of Hepatocellular Carcinoma in West Africans, HEPATOLOGY, Vol: 60, Pages: 1291-1301, ISSN: 0270-9139
Lees H, Swann J, Poucher SM, et al., 2014, Age and Microenvironment Outweigh Genetic Influence on the Zucker Rat Microbiome, PLOS One, Vol: 9, ISSN: 1932-6203
ArticleAuthorsMetricsCommentsRelated ContentAbstractIntroductionMethodsResultsDiscussionConclusionsSupporting InformationAuthor ContributionsReferencesReader Comments (0)Media Coverage (0)FiguresAbstractAnimal models are invaluable tools which allow us to investigate the microbiome-host dialogue. However, experimental design introduces biases in the data that we collect, also potentially leading to biased conclusions. With obesity at pandemic levels animal models of this disease have been developed; we investigated the role of experimental design on one such rodent model. We used 454 pyrosequencing to profile the faecal bacteria of obese (n = 6) and lean (homozygous n = 6; heterozygous n = 6) Zucker rats over a 10 week period, maintained in mixed-genotype cages, to further understand the relationships between the composition of the intestinal bacteria and age, obesity progression, genetic background and cage environment. Phylogenetic and taxon-based univariate and multivariate analyses (non-metric multidimensional scaling, principal component analysis) showed that age was the most significant source of variation in the composition of the faecal microbiota. Second to this, cage environment was found to clearly impact the composition of the faecal microbiota, with samples from animals from within the same cage showing high community structure concordance, but large differences seen between cages. Importantly, the genetically induced obese phenotype was not found to impact the faecal bacterial profiles. These findings demonstrate that the age and local environmental cage variables were driving the composition of the faecal bacteria and were more deterministically important than the host genotype. These findings have major implications for understanding the significance of functional metagenomic data in experimental studies and beg the question; what is being measured in animal experiments in which different strains are housed separately, nature or nurture?
Xie G, Ma X, Zhao A, et al., 2014, The Metabolite Profiles of the Obese Population Are Gender-Dependent, JOURNAL OF PROTEOME RESEARCH, Vol: 13, Pages: 4062-4073, ISSN: 1535-3893
Venkatesh M, Mukherjee S, Wang H, et al., 2014, Symbiotic Bacterial Metabolites Regulate Gastrointestinal Barrier Function via the Xenobiotic Sensor PXR and Toll-like Receptor 4, IMMUNITY, Vol: 41, Pages: 296-310, ISSN: 1074-7613
Lindon JC, Nicholson JK, 2014, The emergent role of metabolic phenotyping in dynamic patient stratification, EXPERT OPINION ON DRUG METABOLISM & TOXICOLOGY, Vol: 10, Pages: 915-919, ISSN: 1742-5255
Sarafian MH, Gaudin M, Lewis MR, et al., 2014, Objective Set of Criteria for Optimization of Sample Preparation Procedures for Ultra-High Throughput Untargeted Blood Plasma Lipid Profiling by Ultra Performance Liquid Chromatography-Mass Spectrometry, ANALYTICAL CHEMISTRY, Vol: 86, Pages: 5766-5774, ISSN: 0003-2700
MARTIN FP, Boulange CL, Montoliu Roura I, et al., 2014, Hexanoylglycine as biomarker for the predisposition for weight gain and obesity, WO 2014/086603
The present invention relates generally to the field of nutrition and health. In particular, the present invention relates to a new biomarker, its use and a method that allows it to diagnose the likelihood to resist diet induced weight gain, and/or to be susceptible to a diet induced weight gain. For example, the biomarker may be hexanoylglycine.
Martin FP, Boulange CL, Montoliu Roura I, et al., 2014, Trimethylamine-N-oxide as biomarker for the predisposition for weight gain and obesity, WO2014086604
The present invention relates generally to the field of nutrition and health. In particular, the present invention relates to a new biomarker, its use and a nnethod that allows it to diagnose the likelihood to resist diet induced weight gain, and/or to be susceptible to a diet induced weight gain. For example, the biomarker may be trimethylamine-N-oxide.
Martin FP, BOULANGE CL, MONTOLIU ROURA I, et al., 2014, Isovalerylglycine as biomarker for the predisposition for weight gain and obesity, WO2014086605
The present invention relates generally to the field of nutrition and health. In particular, the present invention relates to a new biomarker, its use and a method that allows it to diagnose the likelihood to resist diet induced weight gain, and/or to be susceptible to a diet induced weight gain. For example, the biomarker may be isovalerylglycine.
Zou X, Holmes E, Nicholson JK, et al., 2014, Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): An Optimized Statistical Approach for Clustering of H-1 NMR Spectral Data to Reduce Interference and Enhance Robust Biomarkers Selection, ANALYTICAL CHEMISTRY, Vol: 86, Pages: 5308-5315, ISSN: 0003-2700
Dumas ME, Hoyles L, Chilloux J, et al., 2014, Gut Microbial Metabolomic Predictors of Dietary-induced Obesity and Diabetes, Publisher: AMER DIABETES ASSOC, Pages: A497-A497, ISSN: 0012-1797
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