000282465 001__ 282465
000282465 005__ 20240229155042.0
000282465 0247_ $$2doi$$a10.1172/jci.insight.171225
000282465 0247_ $$2pmid$$apmid:37651185
000282465 0247_ $$2altmetric$$aaltmetric:153954742
000282465 037__ $$aDKFZ-2023-01780
000282465 041__ $$aEnglish
000282465 082__ $$a610
000282465 1001_ $$aLöding, Sebastian$$b0
000282465 245__ $$aAltered plasma metabolite levels can be detected years before a glioma diagnosis.
000282465 260__ $$aAnn Arbor, Michigan$$bJCI Insight$$c2023
000282465 3367_ $$2DRIVER$$aarticle
000282465 3367_ $$2DataCite$$aOutput Types/Journal article
000282465 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1698324069_31145
000282465 3367_ $$2BibTeX$$aARTICLE
000282465 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000282465 3367_ $$00$$2EndNote$$aJournal Article
000282465 500__ $$a2023 Oct 9;8(19):e171225
000282465 520__ $$aGenetic and metabolic changes in tissue and blood are reported to occur several years before glioma diagnosis. As gliomas are currently detected late, a liquid biopsy for early detection could impact the quality of life and prognosis of patients. Here, we present a nested case-control study of 550 pre-diagnostic glioma cases and 550 healthy controls, from the Northern Sweden Health and Disease study (NSHDS) and the European Prospective Investigation into Cancer and Nutrition (EPIC) study. We identified 93 significantly altered metabolites related to glioma development up to eight years before diagnosis. Out of these metabolites, a panel of 20 selected metabolites showed strong disease correlation and consistent progression pattern towards diagnosis in both the NSHDS and EPIC cohorts, and separated favorably future cases from controls independently of biological sex. The blood metabolite panel also successfully separated both lower grade glioma and glioblastoma cases from controls, up to eight years before diagnosis in NSHDS (glioma AUC=0.85, P=3.1e-12; glioblastoma AUC=0.85, P=6.3e-8), and up to two years before diagnosis in EPIC (glioma AUC=0.81, P=0.005; glioblastoma AUC=0.89, P=0.04). Pathway enrichment analysis detected metabolites related to the TCA-cycle, Warburg effect, gluconeogenesis, cysteine-, pyruvate- and tyrosine metabolism as the most affected.
000282465 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0
000282465 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
000282465 650_7 $$2Other$$aBrain cancer
000282465 650_7 $$2Other$$aMetabolism
000282465 650_7 $$2Other$$aOncology
000282465 7001_ $$aAndersson, Ulrika$$b1
000282465 7001_ $$0P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a$$aKaaks, Rudolf$$b2$$udkfz
000282465 7001_ $$aSchulze, Matthias B$$b3
000282465 7001_ $$aPala, Valeria$$b4
000282465 7001_ $$aUrbarova, Ilona$$b5
000282465 7001_ $$aAmiano, Pilar$$b6
000282465 7001_ $$aColorado-Yohar, Sandra M$$b7
000282465 7001_ $$aGuevara, Marcela$$b8
000282465 7001_ $$aHeath, Alicia K$$b9
000282465 7001_ $$aChatziioannou, Anastasia Chrysovalantou$$b10
000282465 7001_ $$aJohansson, Mattias$$b11
000282465 7001_ $$aNyberg, Lars$$b12
000282465 7001_ $$aAntti, Henrik$$b13
000282465 7001_ $$aBjörkblom, Benny$$b14
000282465 7001_ $$aMelin, Beatrice$$b15
000282465 773__ $$0PERI:(DE-600)2874757-4$$a10.1172/jci.insight.171225$$n19$$pe171225$$tJCI insight$$v8$$x2379-3708$$y2023
000282465 909CO $$ooai:inrepo02.dkfz.de:282465$$pVDB
000282465 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000282465 9131_ $$0G:(DE-HGF)POF4-313$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vKrebsrisikofaktoren und Prävention$$x0
000282465 9141_ $$y2023
000282465 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2022-05-18T13:47:27Z
000282465 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2022-05-18T13:47:27Z
000282465 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2022-05-18T13:47:27Z
000282465 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-03-31
000282465 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-03-31
000282465 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2023-03-31
000282465 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2023-03-31
000282465 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJCI INSIGHT : 2022$$d2023-08-23
000282465 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-08-23
000282465 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-08-23
000282465 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2023-08-23
000282465 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2022-05-18T13:47:27Z
000282465 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-08-23
000282465 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-08-23
000282465 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2023-08-23
000282465 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bJCI INSIGHT : 2022$$d2023-08-23
000282465 9201_ $$0I:(DE-He78)C020-20160331$$kC020$$lC020 Epidemiologie von Krebs$$x0
000282465 980__ $$ajournal
000282465 980__ $$aVDB
000282465 980__ $$aI:(DE-He78)C020-20160331
000282465 980__ $$aUNRESTRICTED