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dc.contributor.authorSkogvold, Hanne Bendiksen
dc.contributor.authorWilson, Steven Ray Haakon
dc.contributor.authorRønning, Per Ola
dc.contributor.authorFerrante, Linda
dc.contributor.authorOpdal, Siri Hauge
dc.contributor.authorRognum, Torleiv Ole
dc.contributor.authorRootwelt, Helge
dc.contributor.authorElgstøen, Katja B. Prestø
dc.date.accessioned2023-11-23T06:57:54Z
dc.date.available2023-11-23T06:57:54Z
dc.date.created2023-09-19T16:43:29Z
dc.date.issued2023
dc.identifier.citationAnalytical Science Advances (ASA). 2023, 4 (7-8), 255-266.en_US
dc.identifier.issn2628-5452
dc.identifier.urihttps://hdl.handle.net/11250/3104204
dc.description.abstractA common challenge when studying rare diseases or medical conditions is the limited number of patients, usually resulting in long inclusion periods as well as unequal sam- pling and storage conditions. The main purpose of this study was to demonstrate the challenges when comparing samples subject to different preanalytical conditions. We performed a global (commonly referred to as “untargeted”) liquid chromatography- high resolution mass spectrometry metabolomics analysis of blood samples from cases of sudden infant death syndrome and controls stored as dried blood spots on a chemical-free filter card for 15 years at room temperature compared with the same blood samples stored as whole blood at −80◦C before preparing new dried blood spots using a chemically treated filter card. Principal component analysis plots dis- tinctly separated the samples based on the type of filter card and storage, but not sudden infant death syndrome versus controls. Note that, 1263 out of 5161 and 642 out of 1587 metabolite features detected in positive and negative ionization mode, respectively, were found to have significant 2-fold changes in amounts correspond- ing to different preanalytical conditions. The study demonstrates that the dried blood spot metabolome is largely affected by preanalytical factors. This emphasizes the importance of thoroughly addressing preanalytical factors during study design and interpretation, enabling identification of real, biological differences between sample groups whilst preventing other factors or random variation to be falsely interpreted as positive results.en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA global metabolomics minefield: Confounding effects of preanalytical factors when studying rare disordersen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1002/ansa.202300010
dc.identifier.cristin2176701
dc.source.journalAnalytical Science Advances (ASA)en_US
dc.source.volume4en_US
dc.source.issue7-8en_US
dc.source.pagenumber255-266en_US


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