학술논문

A global metabolomics minefield: Confounding effects of preanalytical factors when studying rare disorders
Document Type
Report
Source
Analytical Science Advances. August 2023, Vol. 4 Issue 7-8, p255, 12 p.
Subject
Analysis
Infants -- Analysis
Infant mortality -- Analysis
Metabolites -- Analysis
Liquid chromatography -- Analysis
Medical research -- Analysis
Mass spectrometry -- Analysis
EDTA -- Analysis
Medicine, Experimental -- Analysis
Infants -- Patient outcomes -- Analysis
Ethylenediaminetetraacetic acid -- Analysis
Language
English
Abstract
Abbreviations INTRODUCTION Metabolomics is the study of all metabolites in a sample, i.e., small molecules typically having a molecular weight < 1500 Da.[sup.1,2] The total composition of metabolites in a [...]
: 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.