000157074 001__ 157074
000157074 005__ 20240229123128.0
000157074 0247_ $$2doi$$a10.3390/metabo10050213
000157074 0247_ $$2pmid$$apmid:32455751
000157074 0247_ $$2pmc$$apmc:PMC7281389
000157074 0247_ $$2altmetric$$aaltmetric:82559875
000157074 037__ $$aDKFZ-2020-01365
000157074 041__ $$aeng
000157074 082__ $$a540
000157074 1001_ $$aHardikar, Sheetal$$b0
000157074 245__ $$aImpact of Pre-blood Collection Factors on Plasma Metabolomic Profiles.
000157074 260__ $$aBasel$$bMDPI$$c2020
000157074 3367_ $$2DRIVER$$aarticle
000157074 3367_ $$2DataCite$$aOutput Types/Journal article
000157074 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1594720649_24603
000157074 3367_ $$2BibTeX$$aARTICLE
000157074 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000157074 3367_ $$00$$2EndNote$$aJournal Article
000157074 520__ $$aDemographic, lifestyle and biospecimen-related factors at the time of blood collection can influence metabolite levels in epidemiological studies. Identifying the major influences on metabolite concentrations is critical to designing appropriate sample collection protocols and considering covariate adjustment in metabolomics analyses. We examined the association of age, sex, and other short-term pre-blood collection factors (time of day, season, fasting duration, physical activity, NSAID use, smoking and alcohol consumption in the days prior to collection) with 133 targeted plasma metabolites (acylcarnitines, amino acids, biogenic amines, sphingolipids, glycerophospholipids, and hexoses) among 108 individuals that reported exposures within 48 h before collection. The differences in mean metabolite concentrations were assessed between groups based on pre-collection factors using two-sided t-tests and ANOVA with FDR correction. Percent differences in metabolite concentrations were negligible across season, time of day of collection, fasting status or lifestyle behaviors at the time of collection, including physical activity or the use of tobacco, alcohol or NSAIDs. The metabolites differed in concentration between the age and sex categories for 21.8% and 14.3% metabolites, respectively. In conclusion, extrinsic factors in the short period prior to collection were not meaningfully associated with concentrations of selected endogenous metabolites in a cross-sectional sample, though metabolite concentrations differed by age and sex. Larger studies with more coverage of the human metabolome are warranted.
000157074 536__ $$0G:(DE-HGF)POF3-313$$a313 - Cancer risk factors and prevention (POF3-313)$$cPOF3-313$$fPOF III$$x0
000157074 588__ $$aDataset connected to CrossRef, PubMed,
000157074 7001_ $$aAlbrechtsen, Richard D$$b1
000157074 7001_ $$aAchaintre, David$$b2
000157074 7001_ $$aLin, Tengda$$b3
000157074 7001_ $$aPauleck, Svenja$$b4
000157074 7001_ $$aPlaydon, Mary$$b5
000157074 7001_ $$aHolowatyj, Andreana N$$b6
000157074 7001_ $$aGigic, Biljana$$b7
000157074 7001_ $$0P:(DE-He78)01ef71f71b01a3ec3b698653fd43fe86$$aSchrotz-King, Petra$$b8$$udkfz
000157074 7001_ $$aBoehm, Juergen$$b9
000157074 7001_ $$aHabermann, Nina$$b10
000157074 7001_ $$aBrezina, Stefanie$$b11
000157074 7001_ $$aGsur, Andrea$$b12
000157074 7001_ $$avan Roekel, Eline H$$b13
000157074 7001_ $$aWeijenberg, Matty P$$b14
000157074 7001_ $$aKeski-Rahkonen, Pekka$$b15
000157074 7001_ $$aScalbert, Augustin$$b16
000157074 7001_ $$aOse, Jennifer$$b17
000157074 7001_ $$aUlrich, Cornelia M$$b18
000157074 773__ $$0PERI:(DE-600)2662251-8$$a10.3390/metabo10050213$$gVol. 10, no. 5, p. 213 -$$n5$$p213 $$tMetabolites$$v10$$x2218-1989$$y2020
000157074 909CO $$ooai:inrepo02.dkfz.de:157074$$pVDB
000157074 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)01ef71f71b01a3ec3b698653fd43fe86$$aDeutsches Krebsforschungszentrum$$b8$$kDKFZ
000157074 9131_ $$0G:(DE-HGF)POF3-313$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vCancer risk factors and prevention$$x0
000157074 9141_ $$y2020
000157074 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bMETABOLITES : 2018$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review$$d2020-01-10
000157074 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$f2020-01-10
000157074 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2020-01-10
000157074 9201_ $$0I:(DE-He78)C120-20160331$$kC120$$lPräventive Onkologie$$x0
000157074 980__ $$ajournal
000157074 980__ $$aVDB
000157074 980__ $$aI:(DE-He78)C120-20160331
000157074 980__ $$aUNRESTRICTED