ArticleAnalytical science advances2023
A global metabolomics minefield: Confounding effects of preanalytical factors when studying rare disorders.
Article in Analytical science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Global Metabolomics Using LC-MS for Clinical Applications.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A global metabolomics minefield: Confounding effects of preanalytical factors when studying rare disorders.Analytical science advances · 2023Article
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8 authors.
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Abstract
A 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 sampling 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 distinctly 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 corresponding 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.
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