Ontology type: schema:ScholarlyArticle Open Access: True
2018-10
AUTHORSParastoo Fazelzadeh, Huub C. J. Hoefsloot, Thomas Hankemeier, Jasper Most, Sander Kersten, Ellen E. Blaak, Mark Boekschoten, John van Duynhoven
ABSTRACTINTRODUCTION: Current metabolomics approaches to unravel impact of diet- or lifestyle induced phenotype variation and shifts predominantly deploy univariate or multivariate approaches, with a posteriori interpretation at pathway level. This however often provides only a fragmented view on the involved metabolic pathways. OBJECTIVES: To demonstrate the feasibility of using Goeman's global test (GGT) for assessment of variation and shifts in metabolic phenotype at the level of a priori defined pathways. METHODS: Two intervention studies with identified phenotype variations and shifts were examined. In a weight loss (WL) intervention study obese subjects received a mixed meal challenge before and after WL. In a polyphenol (PP) intervention study obese subjects received a high fat mixed meal challenge (61E% fat) before and after a PP intervention. Plasma samples were obtained at fasting and during the postprandial response. Besides WL- and PP-induced phenotype shifts, also correlation of plasma metabolome with phenotype descriptors was assessed at pathway level. The plasma metabolome covered organic acids, amino acids, biogenic amines, acylcarnitines and oxylipins. RESULTS: For the population of the WL study, GGT revealed that HOMA correlated with the fasting levels of the TCA cycle, BCAA catabolism, the lactate, arginine-proline and phenylalanine-tyrosine pathways. For the population of the PP study, HOMA correlated with fasting metabolite levels of TCA cycle, fatty acid oxidation and phenylalanine-tyrosine pathways. These correlations were more pronounced for metabolic pathways in the fasting state, than during the postprandial response. The effect of the WL and PP intervention on a priori defined metabolic pathways, and correlation of pathways with insulin sensitivity as described by HOMA was in line with previous studies. CONCLUSION: GGT confirmed earlier biological findings in a hypothesis led approach. A main advantage of GGT is that it provides a direct view on involvement of a priori defined pathways in phenotype shifts. More... »
PAGES139
http://scigraph.springernature.com/pub.10.1007/s11306-018-1435-8
DOIhttp://dx.doi.org/10.1007/s11306-018-1435-8
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1107401122
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30830386
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Clinical Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical and Health Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "TiFN",
"id": "https://www.grid.ac/institutes/grid.420129.c",
"name": [
"Nutrition, Metabolism and Genomics Group, Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands",
"Top Institute Food and Nutrition, Wageningen, The Netherlands"
],
"type": "Organization"
},
"familyName": "Fazelzadeh",
"givenName": "Parastoo",
"id": "sg:person.0631124423.97",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0631124423.97"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Amsterdam",
"id": "https://www.grid.ac/institutes/grid.7177.6",
"name": [
"Swammerdam Institute of Life Sciences, University of Amsterdam, P.O. Box 94215, 1090 GE, Amsterdam, The Netherlands"
],
"type": "Organization"
},
"familyName": "Hoefsloot",
"givenName": "Huub C. J.",
"id": "sg:person.012737547773.76",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012737547773.76"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Netherlands Metabolomics Centre",
"id": "https://www.grid.ac/institutes/grid.450196.f",
"name": [
"Division for Analytical Biosciences, Leiden University, Leiden, The Netherlands",
"Netherlands Metabolomics Centre, Leiden, The Netherlands"
],
"type": "Organization"
},
"familyName": "Hankemeier",
"givenName": "Thomas",
"id": "sg:person.01042162625.66",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01042162625.66"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Maastricht University",
"id": "https://www.grid.ac/institutes/grid.5012.6",
"name": [
"Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands"
],
"type": "Organization"
},
"familyName": "Most",
"givenName": "Jasper",
"id": "sg:person.0722452224.46",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0722452224.46"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "TiFN",
"id": "https://www.grid.ac/institutes/grid.420129.c",
"name": [
"Nutrition, Metabolism and Genomics Group, Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands",
"Top Institute Food and Nutrition, Wageningen, The Netherlands"
],
"type": "Organization"
},
"familyName": "Kersten",
"givenName": "Sander",
"id": "sg:person.01254672457.43",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01254672457.43"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Maastricht University",
"id": "https://www.grid.ac/institutes/grid.5012.6",
"name": [
"Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands"
],
"type": "Organization"
},
"familyName": "Blaak",
"givenName": "Ellen E.",
"id": "sg:person.01207575720.40",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01207575720.40"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "TiFN",
"id": "https://www.grid.ac/institutes/grid.420129.c",
"name": [
"Nutrition, Metabolism and Genomics Group, Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands",
"Top Institute Food and Nutrition, Wageningen, The Netherlands"
],
"type": "Organization"
},
"familyName": "Boekschoten",
"givenName": "Mark",
"id": "sg:person.01004605632.34",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01004605632.34"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Unilever (Netherlands)",
"id": "https://www.grid.ac/institutes/grid.10761.31",
"name": [
"Laboratory of Biophysics, Wageningen University, Wageningen, The Netherlands",
"Netherlands Metabolomics Centre, Leiden, The Netherlands",
"Unilever R&D, Vlaardingen, The Netherlands"
],
"type": "Organization"
},
"familyName": "van Duynhoven",
"givenName": "John",
"id": "sg:person.011736172661.83",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011736172661.83"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s11306-014-0673-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001675467",
"https://doi.org/10.1007/s11306-014-0673-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12263-014-0423-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008045525",
"https://doi.org/10.1007/s12263-014-0423-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12263-014-0423-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008045525",
"https://doi.org/10.1007/s12263-014-0423-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11306-011-0306-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010999574",
"https://doi.org/10.1007/s11306-011-0306-3"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cmet.2009.02.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013089119"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/gkp896",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013384337"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0026-0495(03)00155-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015038306"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0026-0495(03)00155-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015038306"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/28.1.27",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017305614"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00216-014-7752-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018222706",
"https://doi.org/10.1007/s00216-014-7752-5"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btg382",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019958062"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.metabol.2011.06.008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021913806"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0007114507803400",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022311606"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0007114507803400",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022311606"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1136/bmj.300.6725.680",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023642592"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1136/bmj.d4163",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025723877"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/gkj102",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028128110"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btn209",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031967674"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1467-789x.2008.00544.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035141919"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.aca.2011.12.051",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040776114"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-9-303",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041933952",
"https://doi.org/10.1186/1471-2105-9-303"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-9-303",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041933952",
"https://doi.org/10.1186/1471-2105-9-303"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11306-011-0320-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046922882",
"https://doi.org/10.1007/s11306-011-0320-5"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1096/fj.11-198093",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047473398"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cmet.2012.01.024",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053615992"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1021/pr900499r",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1056295009"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3945/ajcn.115.122937",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1071753711"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3945/ajcn.116.143552",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1071754001"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11306-018-1328-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101340187",
"https://doi.org/10.1007/s11306-018-1328-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11306-018-1328-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101340187",
"https://doi.org/10.1007/s11306-018-1328-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11306-018-1328-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101340187",
"https://doi.org/10.1007/s11306-018-1328-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11306-018-1328-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101340187",
"https://doi.org/10.1007/s11306-018-1328-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11306-018-1328-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101340187",
"https://doi.org/10.1007/s11306-018-1328-x"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1096/fj.201800330r",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1103761210"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1096/fj.201800330r",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1103761210"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1096/fj.201800330r",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1103761210"
],
"type": "CreativeWork"
}
],
"datePublished": "2018-10",
"datePublishedReg": "2018-10-01",
"description": "INTRODUCTION: Current metabolomics approaches to unravel impact of diet- or lifestyle induced phenotype variation and shifts predominantly deploy univariate or multivariate approaches, with a posteriori interpretation at pathway level. This however often provides only a fragmented view on the involved metabolic pathways.\nOBJECTIVES: To demonstrate the feasibility of using Goeman's global test (GGT) for assessment of variation and shifts in metabolic phenotype at the level of a priori defined pathways.\nMETHODS: Two intervention studies with identified phenotype variations and shifts were examined. In a weight loss (WL) intervention study obese subjects received a mixed meal challenge before and after WL. In a polyphenol (PP) intervention study obese subjects received a high fat mixed meal challenge (61E% fat) before and after a PP intervention. Plasma samples were obtained at fasting and during the postprandial response. Besides WL- and PP-induced phenotype shifts, also correlation of plasma metabolome with phenotype descriptors was assessed at pathway level. The plasma metabolome covered organic acids, amino acids, biogenic amines, acylcarnitines and oxylipins.\nRESULTS: For the population of the WL study, GGT revealed that HOMA correlated with the fasting levels of the TCA cycle, BCAA catabolism, the lactate, arginine-proline and phenylalanine-tyrosine pathways. For the population of the PP study, HOMA correlated with fasting metabolite levels of TCA cycle, fatty acid oxidation and phenylalanine-tyrosine pathways. These correlations were more pronounced for metabolic pathways in the fasting state, than during the postprandial response. The effect of the WL and PP intervention on a priori defined metabolic pathways, and correlation of pathways with insulin sensitivity as described by HOMA was in line with previous studies.\nCONCLUSION: GGT confirmed earlier biological findings in a hypothesis led approach. A main advantage of GGT is that it provides a direct view on involvement of a priori defined pathways in phenotype shifts.",
"genre": "research_article",
"id": "sg:pub.10.1007/s11306-018-1435-8",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isPartOf": [
{
"id": "sg:journal.1036887",
"issn": [
"1573-3882",
"1573-3890"
],
"name": "Metabolomics",
"type": "Periodical"
},
{
"issueNumber": "10",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "14"
}
],
"name": "Global testing of shifts in metabolic phenotype",
"pagination": "139",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"56f299fb3260449539f13cf329ab12812a210702f00ec81183354c1f70eb1986"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"30830386"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"101274889"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s11306-018-1435-8"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1107401122"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s11306-018-1435-8",
"https://app.dimensions.ai/details/publication/pub.1107401122"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T11:21",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000354_0000000354/records_11728_00000002.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1007%2Fs11306-018-1435-8"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11306-018-1435-8'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11306-018-1435-8'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11306-018-1435-8'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11306-018-1435-8'
This table displays all metadata directly associated to this object as RDF triples.
219 TRIPLES
21 PREDICATES
55 URIs
21 LITERALS
9 BLANK NODES