The influence of citrate, EDTA, and heparin anticoagulants to human plasma LC–MS lipidomic profiling

González-Covarrubias, Vanessa; Dane, Adrie; Hankemeier, Thomas; Vreeken, Rob J.

doi:10.1007/s11306-012-0450-4

Cited by 61 publications

(47 citation statements)

References 21 publications

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“…The anticoagulant used for blood plasma preparation may also affect the resulting metabolic profile, due to the different mechanisms involved in reducing coagulation by various agents (e.g., heparin, EDTA, citrate). For instance, the presence of cations can cause problems in metabolomic and lipidomic analysis by binding to negatively charged phospholipids thereby causing ion enhancement . In this line, it has been observed that lithium ions from heparin may increase the ionization efficiency of many metabolites including phospholipids and triglycerides, but lithium also exacerbates matrix effects by increasing the signals of plastic polymers .…”

Section: Sample Characteristics and Preanalytical Processingmentioning

confidence: 99%

“…For instance, the presence of cations can cause problems in metabolomic and lipidomic analysis by binding to negatively charged phospholipids thereby causing ion enhancement. [87] In this line, it has been observed that lithium ions from heparin may increase the ionization efficiency of many metabolites including phospholipids and triglycerides, but lithium also exacerbates matrix effects by increasing the signals of plastic polymers. [66,88] Different anticoagulants can also have an effect on various aspects of sample preparation, such as the extraction procedure or the derivatization process (for GC-MS metabolomics).…”

Section: Serum/plasmamentioning

confidence: 99%

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Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

Ulaszewska

Weinert

Trimigno

et al. 2018

Molecular Nutrition Food Res

185

153

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The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

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Section: Sample Characteristics and Preanalytical Processingmentioning

confidence: 99%

Section: Serum/plasmamentioning

confidence: 99%

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

Ulaszewska

Weinert

Trimigno

et al. 2018

Molecular Nutrition Food Res

185

153

View full text Add to dashboard Cite

show abstract

“…Decreased citrate to blood ratios have been shown significantly affecting assessments of platelet function and will add another factor of variation [25,26]. Additionally, the ionic strength and pH of sodium citrate may not be ideal for classical lipid extraction or LC-MS analysis [26] and moreover, citrate cannot be measured as a metabolite. Barri et al do not recommend either citrate or EDTA because those compounds showed formation of sodium and potassium formate clusters and may additionally cause ion suppression or enhancement of metabolites coeluting with anticoagulant peaks [11].…”

Section: The Choice Of Anticoagulantmentioning

confidence: 99%

A review of blood sample handling and pre‐processing for metabolomics studies

2017

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Metabolomics has been found to be applicable to a wide range of clinical studies, bringing a new era for improving clinical diagnostics, early disease detection, therapy prediction and treatment efficiency monitoring. A major challenge in metabolomics, particularly untargeted studies, is the extremely diverse and complex nature of biological specimens. Despite great advances in the field there still exist fundamental needs for considering pre-analytical variability that can introduce bias to the subsequent analytical process and decrease the reliability of the results and moreover confound final research outcomes. Many researchers are mainly focused on the instrumental aspects of the biomarker discovery process, and sample related variables sometimes seem to be overlooked. To bridge the gap, critical information and standardized protocols regarding experimental design and sample handling and pre-processing are highly desired. Characterization of a range variation among sample collection methods is necessary to prevent results misinterpretation and to ensure that observed differences are not due to an experimental bias caused by inconsistencies in sample processing. Herein, a systematic discussion of pre-analytical variables affecting metabolomics studies based on blood derived samples is performed. Furthermore, we provide a set of recommendations concerning experimental design, collection, pre-processing procedures and storage conditions as a practical review that can guide and serve for the standardization of protocols and reduction of undesirable variation.

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“…However, there is no clear conclusion for which matrix is optimal for lipidomics [3][4][5]. Some studies suggest that several PCs, PEs, SMs, and TGs are found at lower levels in citrate plasma than in EDTA plasma [5,6]. A recent study showed that the reliability of metabolite measurements was higher in serum samples than in plasma samples [3], whereas another study suggested that plasma was a preferable matrix for lipidomic analyses [4].…”

Section: Analytical Factors Affecting Lipidomic Analysismentioning

confidence: 99%

Optimizing the lipidomics workflow for clinical studies—practical considerations

Hyötyläinen

Orešič

2015

Anal Bioanal Chem

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Lipidomics is increasingly being used in clinical research, offering new opportunities for disease prediction and detection. One of the key challenges of clinical applications of lipidomics is the high sensitivity of measured lipid levels to many analytical, physiological, and environmental factors, which therefore must be taken into account when designing the studies. Here we critically discuss the complete clinical lipidomics workflow, including selection of the subjects, the sample type, the sample preprocessing conditions, and the analytical method and methods for data processing. We also review the lipidomics applications which investigate the confounding factors such as age, gender, fasting time, and handling procedures for measuring blood lipid metabolites.

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The influence of citrate, EDTA, and heparin anticoagulants to human plasma LC–MS lipidomic profiling

Cited by 61 publications

References 21 publications

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

A review of blood sample handling and pre‐processing for metabolomics studies

Optimizing the lipidomics workflow for clinical studies—practical considerations

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