Characterization of Differences between Blood Sample Matrices in Untargeted Metabolomics

Highly sensitive and accurate measurement of underivatized phosphoenolpyruvate in plasma and serum via EDTA-facilitated hydrophilic interaction liquid chromatography-tandem mass spectrometry

Phosphoenolpyruvate (PEP) is an essential intermediate metabolite that is involved in various vital biochemical reactions. However, achieving the direct and accurate quantification of PEP in plasma or serum poses a significant challenge owing to its strong polarity and metal affinity. In this study, a sensitive method for the direct determination of PEP in plasma and serum based on ethylenediaminetetraacetic acid (EDTA)-facilitated hydrophilic interaction liquid chromatography-tandem mass spectrometry was developed. Superior chromatographic retention and peak shapes were achieved using a zwitterionic stationary-phase HILIC column with a metal-inert inner surface. Efficient dechelation of PEP-metal complexes in serum/plasma samples was achieved through the introduction of EDTA, resulting in a significant enhancement of the PEP signal. A PEP isotopically labelled standard was employed as a surrogate analyte for the determination of endogenous PEP, and validation assessments proved the sensitivity, selectivity, and reproducibility of this method. The method was applied to the comparative quantification of PEP in plasma and serum samples from mice and rats, as well as in HepG2 cells, HEK293T cells, and erythrocytes; the results confirmed its applicability in PEP-related biomedical research. The developed method can quantify PEP in diverse biological matrices, providing a feasible opportunity to investigate the role of PEP in relevant biomedical research.

Short-Term Stability of Serum and Liver Extracts for Untargeted Metabolomics and Lipidomics

Thermal reactions can significantly alter the metabolomic and lipidomic content of biofluids and tissues during storage. In this study, we investigated the stability of polar metabolites and complex lipids in dry human serum and mouse liver extracts over a three-day period under various temperature conditions. Specifically, we tested temperatures of -80 °C (freezer), -24 °C (freezer), -0.5 °C (polystyrene box with gel-based ice packs), +5 °C (refrigerator), +23 °C (laboratory, room temperature), and +30 °C (thermostat) to simulate the time between sample extraction and analysis, shipping dry extracts to different labs as an alternative to dry ice, and document the impact of higher temperatures on sample integrity. The extracts were analyzed using five fast liquid chromatography-mass spectrometry (LC-MS) methods to screen polar metabolites and complex lipids, and over 600 metabolites were annotated in serum and liver extracts. We found that storing dry extracts at -24 °C and partially at -0.5 °C provided comparable results to -80 °C (reference condition). However, increasing the storage temperatures led to significant changes in oxidized triacylglycerols, phospholipids, and fatty acids within three days. Polar metabolites were mainly affected at storage temperatures of +23 °C and +30 °C.

Pre-analytical aspects in metabolomics of human biofluids - sample collection, handling, transport, and storage

Metabolomics is the field of omics research that offers valuable insights into the complex composition of biological samples. It has found wide application in clinical diagnostics, disease investigation, therapy prediction, monitoring of treatment efficiency, drug discovery, or in-depth analysis of sample composition. A suitable study design constitutes the fundamental requirements to ensure robust and reliable results from the study data. The study design process should include a careful selection of conditions for each experimental step, from sample collection to data analysis. The pre-analytical variability that can introduce bias to the subsequent analytical process, decrease the outcome reliability, and confuse the final results of the metabolomics research, should also be considered. Herein, we provide key information regarding the pre-analytical variables affecting the metabolomics studies of biological fluids that are the most desirable type of biological samples. Our work offers a practical review that can serve and guide metabolomics pre-analytical design. It indicates pre-analytical factors, which can introduce artificial data variation and should be identified and understood during experimental design (through literature overview or analytical experiments).

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Point-of-care COPD diagnostics: biomarkers, sampling, paper-based analytical devices, and perspectives

Chronic obstructive pulmonary disease (COPD) has become the third leading cause of global death. Insufficiency in early diagnosis and treatment of COPD, especially COPD exacerbations, leads to a tremendous economic burden and medical costs. A cost-effective and timely prevention requires decentralized point-of-care diagnostics at patients' residences at affordable prices. Advances in point-of-care (POC) diagnostics may offer new solutions to reduce medical expenditures by measuring salivary and blood biomarkers. Among them, paper-based analytical devices have been the most promising candidates due to their advantages of being affordable, biocompatible, disposable, scalable, and easy to modify. In this review, we present salivary and blood biomarkers related to COPD endotypes and exacerbations, summarize current technologies to collect human whole saliva and whole blood samples, evaluate state-of-the-art paper-based analytical devices that detect COPD biomarkers in saliva and blood, and discuss existing challenges with outlooks on future paper-based POC systems for COPD diagnosis and management.

Serum or Plasma (and Which Plasma), That Is the Question

Blood derivatives are the biofluids of choice for metabolomic clinical studies since blood can be collected with low invasiveness and is rich in biological information. However, the choice of the blood collection tubes has an undeniable impact on the plasma and serum metabolic content. Here, we compared the metabolomic and lipoprotein profiles of blood samples collected at the same time and place from six healthy volunteers but using different collection tubes (each enrolled volunteer provided multiple blood samples at a distance of a few weeks/months): citrate plasma, EDTA plasma, and serum tubes. All samples were analyzed via nuclear magnetic resonance spectroscopy. Several metabolites showed statistically significant alterations among the three blood matrices, and also metabolites’ correlations were shown to be affected. The effects of blood collection tubes on the lipoproteins’ profiles are relevant too, but less marked. Overcoming the issue associated with different blood collection tubes is pivotal to scale metabolomics and lipoprotein analysis at the level of epidemiological studies based on samples from multicenter cohorts. We propose a statistical solution, based on regression, that is shown to be efficient in reducing the alterations induced by the different collection tubes for both the metabolomic and lipoprotein profiles.

Pre-analytical challenges for the quantification of endocannabinoids in human serum

Endocannabinoids (ECs) are potent lipid mediators with high physiological relevance. They are involved in a wide variety of diseases like depression or multiple sclerosis and are closely connected to metabolic parameters in humans. Therefore, their suitability as a biomarker in different (patho-)physiological conditions is discussed intensively and predominantly investigated by analyzing systemic concentrations in easily accessible matrices like blood. Carefully designed pre-analytical sample handling is of major importance for high-quality data, but harmonization is not achieved yet. Whole blood is either processed to serum or plasma before the onset of analytical workflows and while knowledge about pre-analytical challenges in plasma handling is thorough they were not systematically investigated for serum. Therefore, the ECs AEA and 2-AG, and closely related EC-like substances 1-AG, DHEA, and PEA were examined by LC-MS/MS in serum samples of nine healthy volunteers employing different pre-analytical sample handling protocols, including prolonged coagulation, and storage after centrifugation at room temperature (RT) or on ice. Furthermore, all analytes were also assessed in plasma samples obtained from the same individuals at the same time points to investigate the comparability between those two blood-based matrices regarding obtained concentrations and their 2-AG/1-AG ratio. This study shows that ECs and EC-like substances in serum samples were significantly higher than in plasma and are especially prone to ex vivo changes during initial and prolonged storage for coagulation at RT. Storage on ice after centrifugation is less critical. However, storage at RT further increases 1-AG and 2-AG concentrations, while also lowering the already reduced 2-AG/1-AG ratio due to isomerization. Thus, avoidance of prolonged processing at RT can increase data quality if serum as the matrix of choice is unavoidable. However, serum preparation in itself is expected to initiate changes of physiological concentrations as standard precautionary measures like fast and cooled processing can only be utilized by using plasma, which should be the preferred matrix for analyses of ECs and EC-like substances.

Endocannabinoids as potential biomarkers: It's all about pre-analytics

Introduction

Arachidonoyl ethanolamide (AEA) and 2-arachidonoyl glycerol (2-AG) are central lipid mediators of the endocannabinoid system. They are highly relevant due to their involvement in a wide variety of inflammatory, metabolic or malign diseases. Further elucidation of their modes of action and use as biomarkers in an easily accessible matrix, like blood, is restricted by their susceptibility to deviations during blood sampling and physiological co-dependences, which results in high variability of reported concentrations in low ng/mL ranges.

Objectives

The objective of this review is the identification of critical parameters during the pre-analytical phase and proposal of minimum requirements for reliable determination of endocannabinoids (ECs) in blood samples.

Methods

Reported physiological processes influencing the EC concentrations were put into context with published pre-analytical research and stability data from bioanalytical method validation.

Results

The cause for variability in EC concentrations is versatile. In part, they are caused by inter-individual factors like sex, metabolic status and/or diurnal changes. Nevertheless, enzymatic activity in freshly drawn blood samples is the main reason for changing concentrations of AEA and 2-AG, besides additional non-enzymatic isomerization of the latter.

Conclusion

Blood samples for EC analyses require immediate processing at low temperatures (>0 °C) to maintain sample integrity. Standardization of the respective blood tube or anti-coagulant, sampling time point, applied centrifugal force and complete processing time can further decrease variability caused by sample handling. Nevertheless, extensive characterization of study participants is needed to reduce distortion of clinical data caused by co-variables and facilitate research on the endocannabinoid system.

Plasma Metabolomic Markers of Insulin Resistance and Diabetes and Rate of Incident Parkinson's Disease

BACKGROUND

Although there is evidence of shared dysregulated pathways between diabetes and Parkinson's disease, epidemiologic research on an association between the two diseases has produced inconsistent results.

OBJECTIVE

We aimed to assess whether known metabolomic markers of insulin resistance and diabetes are also associated with Parkinson's disease development.

METHODS

We conducted a nested case-control study among Nurses' Health Study and Health Professionals Follow-up Study participants who had provided blood samples up to twenty years prior to Parkinson's diagnosis. Cases were matched to risk-set sampled controls by age, sex, fasting status, and time of blood collection. Participants provided covariate information via regularly collected cohort questionnaires. We used conditional logistic regression models to assess whether plasma levels of branched chain amino acids, acylcarnitines, glutamate, or glutamine were associated with incident development of Parkinson's disease.

RESULTS

A total of 349 case-control pairs were included in this analysis. In the primary analyses, none of the metabolites of interest were associated with Parkinson's disease development. In investigations of the association between each metabolite and Parkinson's disease at different time intervals prior to diagnosis, some metabolites showed marginally significant association but, after correction for multiple testing, only C18 : 2 acylcarnitine was significantly associated with Parkinson's disease among subjects for whom blood was collected less than 60 months prior to case diagnosis.

CONCLUSIONS

Plasma levels of diabetes-related metabolites did not contribute to predict risk of Parkinson's disease. Further investigation of the relationship between pre-diagnostic levels of diabetes-related metabolites and Parkinson's disease in other populations is needed to confirm these findings.

Free Amino Acid Alterations in Patients with Gynecological and Breast Cancer: A Review

Gynecological and breast cancers still remain a significant health problem worldwide. Diagnostic methods are not sensitive and specific enough to detect the disease at an early stage. During carcinogenesis and tumor progression, the cellular need for DNA and protein synthesis increases leading to changes in the levels of amino acids. An important role of amino acids in many biological pathways, including biosynthesis of proteins, nucleic acids, enzymes, etc., which serve as an energy source and maintain redox balance, has been highlighted in many research articles. The aim of this review is a detailed analysis of the literature on metabolomic studies of gynecology and breast cancers with particular emphasis on alterations in free amino acid profiles. The work includes a brief overview of the metabolomic methodology and types of biological samples used in the studies. Special attention was paid to the possible role of selected amino acids in the carcinogenesis, especially proline and amino acids related to its metabolism. There is a clear need for further research and multiple external validation studies to establish the role of amino acid profiling in diagnosing gynecological and breast cancers.

A Comparison of Serum and Plasma Blood Collection Tubes for the Integration of Epidemiological and Metabolomics Data

Blood is a rich biological sample routinely collected in clinical and epidemiological studies. With advancements in high throughput -omics technology, such as metabolomics, epidemiology can now delve more deeply and comprehensively into biological mechanisms involved in the etiology of diseases. However, the impact of the blood collection tube matrix of samples collected needs to be carefully considered to obtain meaningful biological interpretations and understand how the metabolite signatures are affected by different tube types. In the present study, we investigated whether the metabolic profile of blood collected as serum differed from samples collected as ACD plasma, citrate plasma, EDTA plasma, fluoride plasma, or heparin plasma. We identified and quantified 50 metabolites present in all samples utilizing nuclear magnetic resonance (NMR) spectroscopy. The heparin plasma tubes performed the closest to serum, with only three metabolites showing significant differences, followed by EDTA which significantly differed for five metabolites, and fluoride tubes which differed in eleven of the fifty metabolites. Most of these metabolite differences were due to higher levels of amino acids in serum compared to heparin plasma, EDTA plasma, and fluoride plasma. In contrast, metabolite measurements from ACD and citrate plasma differed significantly for approximately half of the metabolites assessed. These metabolite differences in ACD and citrate plasma were largely due to significant interfering peaks from the anticoagulants themselves. Blood is one of the most banked samples and thus mining and comparing samples between studies requires understanding how the metabolite signature is affected by the different media and different tube types.

The evolving landscape of untargeted metabolomics

AIMS

Untargeted Metabolomics is a "hypothesis-generating discovery strategy" that compares groups of samples (e.g., cases vs controls); identifies the metabolome and establishes (early signs of) perturbations. Targeted Metabolomics helped gather key information in life sciences and disclosed novel strategies for the treatment of major clinical entities (e.g., malignancy, cardiovascular diabetes mellitus, drug toxicity). Because of its relevance in biomarker discovery, attention is now devoted to improving the translational potential of untargeted Metabolomics.

DATA SYNTHESIS

Expertise in laboratory medicine and in bioinformatics helps solve challenges/pitfalls that may bias metabolite profiling in untargeted Metabolomics. Clinical validation (availability/reliability of analytical instruments) and profitability (how many people will use the test) are mandatory steps for potential biomarkers. Biomarkers to predict individual patient response, patient populations that will best respond to specific strategies and/or approaches for an optimal response to treatment are now being developed. Additional help is expected from professional, and regulatory Agencies as to guidelines for study design and data acquisition and analysis, to be applied from the very beginning of a project. Evidence from food, plant, human, environmental, and animal research argues for the need of miniaturized approaches that employ low-cost, easy to use, mobile devices. ELISA kits with such characteristics that employ targeted metabolites are already available.

CONCLUSIONS

Improving knowledge of the mechanisms behind the disease status (pathophysiology) will help untargeted Metabolomics gather a direct positive impact on welfare and industrial advancements, and fade uncertainties perceived by regulators/payers and patients concerning variables related to miniaturised instruments and user-friendly software and databases.

Effects of Bisphenol A and Bisphenol S Exposure at Low Doses on the Metabolome of Adolescent Male Sprague-Dawley Rats

Toxic effects induced upon exposure to low-dose bisphenol A (BPA) or bisphenol S (BPS) remains controversial. In this study, metabolomics was used to examine the metabolomic perturbation arising from 28 days of exposure to BPA or BPS at 50 μg/kg bw/day in Sprague-Dawley (SD) rats. Endogenous metabolite profiling revealed a clear discrimination of metabolome in the rat plasma among BPA-treatment, BPS-treatment, and control groups. BPA exposure induced the up-regulation of 19 metabolites and down-regulation of 32 metabolites in plasma of SD rats, compared with the control. BPS exposure induced the up-regulation of 15 metabolites and the down-regulation of 33 metabolites in the plasma of SD rats, compared with the control. Joint pathway analysis suggested marked perturbations in the citrate cycle, butanoate metabolism, and alanine, aspartate, and glutamate metabolism for BPA-exposed rats as well as glycerophospholipid metabolism for BPS-exposed rats. These findings provide novel insights into associations between the metabolomic perturbation and phenotypic changes arising from BPA and BPS exposure.

Serum or plasma? An old question looking for new answers

Serum or plasma? An old question looking for new answers. There is a continual debate on what type of sample a clinical laboratory should use. While serum is still considered the gold standard and remains the required sample for some assays, laboratories must consider turn-around time, which is an important metric for laboratory performance and, more importantly, plays a critical role in patient care. In addition, a body of evidence emphasise the choice of plasma in order to prevent modifications of some analytes due to the coagulation process and related interferences. Advantages and disadvantages of serum and plasma are discussed on the basis of current literature and evidence. In addition, data are provided on the current utilisation of the samples (serum or plasma) in Italy and in other countries. Finally, a rationale for a possible switch from serum to plasma is provided.

Enhanced Metabolome Coverage and Evaluation of Matrix Effects by the Use of Experimental-Condition-Matched 13C-Labeled Biological Samples in Isotope-Assisted LC-HRMS Metabolomics

Stable isotope-assisted approaches can improve untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally ¹³C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve the detection of treatment-relevant metabolites and can aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly ¹³C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched ¹³C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.

Metabolomics in acute myeloid leukemia

Acute myeloid leukemia (AML) is a complex, heterogenous hematological malignancy caused by mutations in myeloid differentiation and proliferation. Response to therapy and long-term outcomes vary widely based on chromosomal and molecular aberrations. Many platforms have been used to characterize and stratify AML. Metabolomics, the global profiling of small molecules in a biological sample, has emerged in the last decade as an important tool for studying the metabolic dependency of cancer cells. Metabolic reprogramming is not only an important manifestation of AML but clinically relevant for diagnosis, risk stratification and targeted drug development. In this review, we discuss notable metabolic studies of the last decade and their application to novel therapies.

Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics

Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these technical issues, as well as for maintaining the metabolic integrity of biological samples to ensure that metabolomic profiles are a direct expression of the in vivo biochemical status. This review article provides an updated literature revision of the most important factors related to sample handling and pre-processing that may affect metabolomics results, particularly focusing on the most commonly investigated biofluids in metabolomics, namely blood plasma/serum and urine. Finally, we also provide some general recommendations and best practices aimed to standardize and accurately report all these pre-analytical aspects in metabolomics research.

The key points in the pre-analytical procedures of blood and urine samples in metabolomics studies

BACKGROUND

Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results.

AIM OF REVIEW

In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze-thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.

Phocea, Pseudoflavonifractor and Lactobacillus intestinalis: Three Potential Biomarkers of Gut Microbiota That Affect Progression and Complications of Obesity-Induced Type 2 Diabetes Mellitus

PURPOSE

The purpose of this study was to explore the difference and association between intestinal microbiota and plasma metabolomics between type 2 diabetes mellitus (T2DM) and normal group and to identify potential microbiota biomarkers that contribute the most to the difference in metabolites.

METHODS

Six male ZDF model (fa/fa) rats were fed by a Purina #5008 Lab Diet (crude protein 23.5%, crude fat 6.5%) for 3 weeks and their age-matched 6 ZDF control (fa/+) rats were fed by normal rodent diet. Their stool and blood samples were collected at 12 weeks. To analyze the microbial populations in these samples, we used a 16S rRNA gene sequencing approach. Liquid chromatography-mass spectrometry (LC-MS) followed by multivariate statistical analysis was applied to the plasma metabolites profiling. Correlation analysis of them was calculated by Pearson statistical method.

RESULTS

Twelve potential biomarkers of intestinal microbial flora and 357 differential metabolites were found in ZDF fa/fa rats, among which there are three flora that contributed the most to the perturbation of metabolites, including genus Phocea, Pseudoflavonifractor and species Lactobacillus intestinalis.

CONCLUSION

Our study demonstrates the alterations of the abundance and diversity of the intestinal microbiota and the perturbation of metabolites in ZDF rats (fa/fa). We found three potential biomarkers of intestinal microbiota that may lead to perturbation in plasma metabolites. This may prompt new pathogenesis of obesity-related T2DM, but we also need to study further about the causal relationship between intestinal microbe and T2DM, so as to find the target of T2DM treatment or preventive measures.

Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions

Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells).

Preparation of uniformly labelled 13C- and 15N-plants using customised growth chambers

BACKGROUND

Stable isotopically labelled organisms have found wide application in life science research including plant physiology, plant stress and defense as well as metabolism related sciences. Therefore, the reproducible production of plant material enriched with stable isotopes such as ¹³C and ¹⁵N is of considerable interest. A high degree of enrichment (> 96 atom %) with a uniformly distributed isotope (global labelling) is accomplished by a continuous substrate supply during plant growth/cultivation. In the case of plants, ¹³C-labelling can be achieved by growth in ¹³CO_2(g) atmosphere while global ¹⁵N-labelling needs ¹⁵N- containing salts in the watering/nutrient solution. Here, we present a method for the preparation of ¹³C and ¹⁵N-labelled plants by the use of closed growth chambers and hydroponic nutrient supply. The method is exemplified with durum wheat.

RESULTS

In total, 330 g of globally ¹³C- and 295 g of ¹⁵N-labelled Triticum durum wheat was produced during 87 cultivation days. For this, a total of 3.88 mol of ¹³CO_2(g) and 58 mmol of ¹⁵N were consumed. The degree of enrichment was determined by LC-HRMS and ranged between 96 and 98 atom % for ¹³C and 95-99 atom % for ¹⁵N, respectively. Additionally, the isotopically labelled plant extracts were successfully used for metabolome-wide internal standardisation of native T.durum plants. Application of an isotope-assisted LC-HRMS workflow enabled the detection of 652 truly wheat-derived metabolites out of which 143 contain N.

CONCLUSION

A reproducible cultivation which makes use of climate chambers and hydroponics was successfully adapted to produce highly enriched, uniformly ¹³C- and ¹⁵N-labelled wheat. The obtained plant material is suitable to be used in all kinds of isotope-assisted research. The described technical equipment and protocol can easily be applied to other plants to produce ¹³C-enriched biological samples when the necessary specific adaptations e.g. temperature and light regime, as well as nutrient supply are considered. Additionally, the ¹⁵N-labelling method can also be carried out under regular glasshouse conditions without the need for customised atmosphere.

A Critical Evaluation of Wet Biomarkers for Huntington's Disease: Current Status and Ways Forward

There is an unmet clinical need for objective biomarkers to monitor disease progression and treatment response in Huntington's disease (HD). The aim of this review is, therefore, to provide practical advice for biomarker discovery and to summarise studies on biofluid markers for HD. A PubMed search was performed to review literature with regard to candidate saliva, urine, blood and cerebrospinal fluid biomarkers for HD. Information has been organised into tables to allow a pragmatic approach to the discussion of the evidence and generation of practical recommendations for future studies. Many of the markers published converge on metabolic and inflammatory pathways, although changes in other analytes representing antioxidant and growth factor pathways have also been found. The most promising markers reflect neuronal and glial degeneration, particularly neurofilament light chain. International collaboration to standardise assays and study protocols, as well as to recruit sufficiently large cohorts, will facilitate future biomarker discovery and development.

Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review

Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.

The Effect of Anticoagulants, Temperature, and Time on the Human Plasma Metabolome and Lipidome from Healthy Donors as Determined by Liquid Chromatography-Mass Spectrometry

Liquid-chromatography mass spectrometry is commonly used to identify and quantify metabolites from biological samples to gain insight into human physiology and pathology. Metabolites and their abundance in biological samples are labile and sensitive to variations in collection conditions, handling and processing. Variations in sample handling could influence metabolite levels in ways not related to biology, ultimately leading to the misinterpretation of results. For example, anticoagulants and preservatives modulate enzyme activity and metabolite oxidization. Temperature may alter both enzymatic and non-enzymatic chemistry. The potential for variation induced by collection conditions is particularly important when samples are collected in remote locations without immediate access to specimen processing. Data are needed regarding the variation introduced by clinical sample collection processes to avoid introducing artifact biases. In this study, we used metabolomics and lipidomics approaches paired with univariate and multivariate statistical analyses to assess the effects of anticoagulant, temperature, and time on healthy human plasma samples collected to provide guidelines on sample collection, handling, and processing for vaccinology. Principal component analyses demonstrated clustering by sample collection procedure and that anticoagulant type had the greatest effect on sample metabolite variation. Lipids such as glycerophospholipids, acylcarnitines, sphingolipids, diacylglycerols, triacylglycerols, and cholesteryl esters are significantly affected by anticoagulant type as are amino acids such as aspartate, histidine, and glutamine. Most plasma metabolites and lipids were unaffected by storage time and temperature. Based on this study, we recommend samples be collected using a single anticoagulant (preferably EDTA) with sample processing at <24 h at 4 °C.

An NMR-based lipidomic approach to identify Parkinson's disease-stage specific lipoprotein-lipid signatures in plasma

Disturbances in lipid composition and lipoproteins metabolism can play a crucial role in the pathogenesis of Parkinson's disease (PD) and other neurodegenerative diseases. The lipidomic strategy proposed here involves lipoprotein profiling using NMR spectroscopy and multivariate data pre-processing and analysis tools on 94 plasma samples (belonging to 38 early-stage PD patients, 10 PD-related dementia patients, 23 persons with Alzheimer's dementia, and 23 healthy control subjects) to firstly differentiate PD patients (irrespective of the stage of the disease) from persons with Alzheimer's disease (AD) as well as from controls, and then to discriminate among PD patients according to disease severity. The whole data set was subdivided into 86 training and 8 external test samples for validation purposes. A two-step classification scheme, based on linear discriminant analysis with variable selection accomplished by a stepwise orthogonalisation procedure, was proposed to optimise classification performance. Careful pre-processing of NMR signals was crucial to ensure data set quality. A total of 30 chemical shift buckets enabled differentiation between PD patients (regardless of disease severity), AD and control subjects, providing classification, cross-validation and external prediction rates of 100% in all cases. Only 15 variables were required to further discriminate between early-stage PD and PD-related dementia, again with 100% correct classifications, and internal/external predictions. The simplicity and effectiveness of the classification methodology proposed support the use of NMR spectroscopy, in combination with chemometrics, as a viable alternative diagnostic tool to conventional PD clinical diagnosis.

Exploiting High-Resolution Mass Spectrometry for Targeted Metabolite Quantification and 13C-Labeling Metabolism Analysis

Quantification of targeted metabolites, especially trace metabolites and structural isomers, in complex biological materials is an ongoing challenge for metabolomics. In this chapter, we summarize high-resolution mass spectrometry-based approaches mainly used for targeted metabolite and metabolomics analysis, and then introduce an MS1/MS2-combined PRM workflow for quantification of central carbon metabolism intermediates, amino acids, and shikimate pathway-related metabolites. Major steps in the workflow, including cell culture, metabolite extraction, LC-MS analysis and data processing, are described. Furthermore, we adapt this new approach to a dynamic ¹³C-labeling experiment and demonstrate its unique advantage in capturing and correcting isotopomer labeling curves to facilitate nonstationary ¹³C-labeling metabolism analysis.

Obtaining High-Quality Blood Specimens for Downstream Applications: A Review of Current Knowledge and Best Practices

Blood is a biological fluid that contains multiple blood fraction and cellular components. High-quality blood specimens are essential prerequisites for various downstream applications such as molecular epidemiology studies, genomics, and proteomics studies. Currently, protocols and research publications concerning the collection, handling, preservation, and stability of blood or blood fractions are constantly emerging. Moreover, standardized guidelines are a requirement for biorepositories to tightly control preanalytical variables originating from these procedures and obtain high-quality blood specimen for downstream analyses. In this review article, we summarize the best practices and fit-for-purpose protocols regarding blood collection, processing, storage, and stability. In addition, we present some typical quality biomarkers, which could be used to evaluate the integrity of blood specimens.

Sample management for clinical biochemistry assays: Are serum and plasma interchangeable specimens?

The constrained economic context leads laboratories to centralize their routine analyses on high-throughput platforms, to which blood collection tubes are sent from peripheral sampling sites that are sometimes distantly located. Providing biochemistry results as quickly as possible implies to consolidate the maximum number of tests on a minimum number of blood collection tubes, mainly serum tubes and/or tubes with anticoagulants. However, depending on the parameters and their pre-analytical conditions, the type of matrix - serum or plasma - may have a significant impact on results, which is often unknown or underestimated in clinical practice. Importantly, the matrix-related effects may be a limit to the consolidation of analyses on a single tube, and thus must be known by laboratory professionals. The purpose of the present critical review is to put forward the main differences between using serum and plasma samples on clinical biochemistry analyses, in order to sensitize laboratory managers to the need for standardization. To enrich the debate, we also provide an additional comparison of serum and plasma concentrations for approximately 30 biochemistry parameters. Properties, advantages, and disadvantages of serum and plasma are discussed from a pre-analytical standpoint - before, during, and after centrifugation - with an emphasis on the importance of temperature, delay, and transport conditions. Then, differences in results between these matrices are addressed for many classes of biochemistry markers, particularly proteins, enzymes, electrolytes, lipids, circulating nucleic acids, metabolomics markers, and therapeutic drugs. Finally, important key-points are proposed to help others choose the best sample matrix and guarantee quality of clinical biochemistry assays. Moreover, awareness of the implications of using serum and plasma samples on various parameters assayed in the laboratory is an important requirement to ensure reliable results and improve patient care.

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

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.

Metabolomics Approach for Validation of Self-Reported Ibuprofen and Acetaminophen Use

Over-the-counter analgesic use is common and is typically assessed through self-report; therefore, it is subject to misclassification. Detection of drug metabolites in biofluids offers a viable tool for validating self-reported analgesic use. Thus, the aim of this study was to determine the utility of a metabolomics approach for the validation of acetaminophen and ibuprofen use in blood samples. Untargeted mass spectrometry-based metabolomics analysis was conducted in serum samples from 1547 women and plasma samples from 556 men. The presence of two metabolites each for acetaminophen and ibuprofen at levels at or above a defined cutoff value was used to determine concordance with self-reported use. For acetaminophen use based on the presence of both acetaminophen and acetamidophenylglucuronide, concordance was 98.5–100% among individuals reporting use today, and 79.8–91.4% for those reporting never or rare use. Ibuprofen use based on the presence of both carboxyibuprofen and hydroxyibuprofen resulted in concordance of 51.3–52.5% for individuals reporting use today and 99.4–100% for those reporting never or rare use. Our findings suggest that an untargeted metabolomics approach in blood samples may be useful for validating self-reported acetaminophen use. However, this approach appears unlikely to be suitable for validating ibuprofen use.

Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative)

BACKGROUND

The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data.

CONTENT

This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices.

SUMMARY

Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, "time to freezer," sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.

Promises and pitfalls of untargeted metabolomics

Metabolomics is one of the newer omics fields, and has enabled researchers to complement genomic and protein level analysis of disease with both semi-quantitative and quantitative metabolite levels, which are the chemical mediators that constitute a given phenotype. Over more than a decade, methodologies have advanced for both targeted (quantification of specific analytes) as well as untargeted metabolomics (biomarker discovery and global metabolite profiling). Untargeted metabolomics is especially useful when there is no a priori metabolic hypothesis. Liquid chromatography coupled to mass spectrometry (LC-MS) has been the preferred choice for untargeted metabolomics, given the versatility in metabolite coverage and sensitivity of these instruments. Resolving and profiling many hundreds to thousands of metabolites with varying chemical properties in a biological sample presents unique challenges, or pitfalls. In this review, we address the various obstacles and corrective measures available in four major aspects associated with an untargeted metabolomics experiment: (1) experimental design, (2) pre-analytical (sample collection and preparation), (3) analytical (chromatography and detection), and (4) post-analytical (data processing).

Lipidomics biomarker studies: Errors, limitations, and the future

Lipidomics is an ever-expanding subfield of metabolomics that surveys 3000 to 5000 individual lipids across more than 56 lipid subclasses, including lipid peroxidation products. Unfortunately, there exists a large number of publications with poor quality data obtained with unit mass resolution leading to many lipid misidentifications. This is further complicated by poor scientific oversight with regard to recognition of isobar issues, sample collection, and sample storage issues that inexplicably requires more detailed attention. Inadvertent or intentional obfuscation of relative quantification data represented as absolute quantification is a subtle but profound difference that may readers outside of the field may not realize, therefore, instigating disservice and unnecessary distrust in the scientific community. These issues need to be addressed aggressively as high quality data are essential for the translation of biomarker research to clinical practice.

A comparison of human serum and plasma metabolites using untargeted 1H NMR spectroscopy and UPLC-MS

INTRODUCTION

Differences in the metabolite profiles between serum and plasma are incompletely understood.

OBJECTIVES

To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes.

METHODS

We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG ¹H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis.

RESULTS

Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples.

CONCLUSIONS

Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

Sphingolipid Analysis in Clinical Research

Sphingolipids are the most diverse class of lipids due to the numerous variations in their structural components. This diversity is also reflected in their extremely different functions. Sphingolipids are not only constituents of cell membranes but have also emerged as key signaling molecules involved in a variety of cellular functions, such as cell growth and differentiation, proliferation, and apoptotic cell death. Lipidomic analyses in clinical research have identified pathways and products of sphingolipid metabolism that are altered in several human pathologies. In this article, we describe how to properly design a lipidomic experiment in clinical research, how to handle plasma and serum samples for this purpose, and how to measure sphingolipids using liquid chromatography-mass spectrometry.

Serum and plasma amino acids as markers of prediabetes, insulin resistance, and incident diabetes

Presently, routine screening misses many cases of prediabetes and early type 2 diabetes (T2D). Therefore, better biomarkers are needed for a simple and early detection of abnormalities of glucose metabolism and prediction of future T2D. Possible candidates for this include plasma or serum amino acids because glucose and amino acid metabolism are closely connected. This review presents the available evidence of this connectivity and discusses its clinical implications. First, we examine the underlying physiological, pre-analytical, and analytical issues. Then, we summarize results of human studies that evaluate amino acid levels as markers for insulin resistance, prediabetes, and future incident T2D. Finally, we illustrate the interconnection of amino acid levels and metabolic syndrome with our own data from a deeply phenotyped human cohort. We also discuss how amino acids may contribute to the pathophysiology of T2D. We conclude that elevated branched-chain amino acids and reduced glycine are currently the most robust and consistent amino acid markers for prediabetes, insulin resistance, and future T2D. Yet, we are cautious regarding the clinical potential even of these parameters because their discriminatory power is insufficient and their levels depend not only on glycemia, but also on other components of the metabolic syndrome. The identification of more precise intermediates of amino acid metabolism or combinations with other biomarkers will, therefore, be necessary to obtain in order to develop laboratory tests that can improve T2D screening.

Overcoming Sample Matrix Effect in Quantitative Blood Metabolomics Using Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry

Blood is widely used for discovery metabolomics to search for disease biomarkers. However, blood sample matrix can have a profound effect on metabolome analysis, which can impose an undesirable restriction on the type of blood collection tubes that can be used for blood metabolomics. We investigated the effect of blood sample matrix on metabolome analysis using a high-coverage and quantitative metabolome profiling technique based on differential chemical isotope labeling (CIL) LC-MS. We used ¹²C-/¹³C-dansylation LC-MS to perform relative quantification of the amine/phenol submetabolomes of four types of samples (i.e., serum, EDTA plasma, heparin plasma, and citrate plasma) collected from healthy individuals and compare their metabolomic results. From the analysis of 80 plasma and serum samples in experimental triplicate, we detected a total of 3651 metabolites with an average of 1818 metabolites per run (n = 240). The number of metabolites detected and the precision and accuracy of relative quantification were found to be independent of the sample type. Within each sample type, the metabolome data set could reveal biological variation (e.g., sex separation). Although the relative concentrations of some individual metabolites might be different in the four types of samples, for sex separation, all 66 significant metabolites with larger fold-changes (FC ≥ 2 and p < 0.05) found in at least one sample type could be found in the other types of samples with similar or somewhat reduced, but still significant, fold-changes. Our results indicate that CIL LC-MS could overcome the sample matrix effect, thereby greatly broadening the scope of blood metabolomics; any blood samples properly collected in routine clinical settings, including those in biobanks originally used for other purposes, can potentially be used for discovery metabolomics.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

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

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.

Plasma Lipidomics of Healthy Japanese Adults Reveals Gender- and Age-Related Differences

Lipid metabolites in the blood are expected to be biomarker candidates to reflect disease states and responses to therapeutic drugs. However, their profiles are influenced by subject background, which may lead to confounding results. This study aimed to evaluate whether age and gender affect lipid metabolite levels in the plasma of healthy Japanese adults. Comprehensive lipidomic analysis was performed using liquid chromatography-mass spectrometry for overnight fasted volunteers consisting of 4 groups of 15 subjects each: young males (25-35 years), elderly males (55-64 years), young females (25-35 years), and elderly females (55-65 years). Of 326 detected lipids, none showed significant gender-associated differences in the young groups and 3 metabolites showed significant gender-associated differences in the elderly groups, suggesting that age has little impact on plasma lipid levels in Japanese subjects. We found age-associated differences in 111 (34%) and 115 (35%) metabolites in males and females, respectively, indicating that the subjects' age should be considered an important confounding factor for lipid biomarker exploration and validation studies in Japanese populations. These findings provide fundamental information on biomarker discovery, validation, and qualification processes in Japanese populations, and facilitate the evaluation of biomarker candidates found in other populations.

Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available.

OBJECTIVES

The aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS).

METHODS

A discovery cohort comprising PDAC (n = 44) and CP (n = 23) samples was analyzed by LC-MS followed by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n = 20) and CP (n = 31) samples.

RESULTS

Glycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.

CONCLUSION

Using LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.

The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.