Effects of pre-analytical processes on blood samples used in metabolomics studies

Elemental metabolomics

Elemental metabolomics is quantification and characterization of total concentration of chemical elements in biological samples and monitoring of their changes. Recent advances in inductively coupled plasma mass spectrometry have enabled simultaneous measurement of concentrations of > 70 elements in biological samples. In living organisms, elements interact and compete with each other for absorption and molecular interactions. They also interact with proteins and nucleotide sequences. These interactions modulate enzymatic activities and are critical for many molecular and cellular functions. Testing for concentration of > 40 elements in blood, other bodily fluids and tissues is now in routine use in advanced medical laboratories. In this article, we define the basic concepts of elemental metabolomics, summarize standards and workflows, and propose minimum information for reporting the results of an elemental metabolomics experiment. Major statistical and informatics tools for elemental metabolomics are reviewed, and examples of applications are discussed. Elemental metabolomics is emerging as an important new technology with applications in medical diagnostics, nutrition, agriculture, food science, environmental science and multiplicity of other areas.

Standard Operating Procedures for Biospecimen Collection, Processing, and Storage: From the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer

High-quality and well-annotated biorepositories are needed to better understand the pathophysiology and biologic mechanisms of chronic pancreatitis (CP) and its consequences. We report a methodology for the development of a robust standard operating procedure (SOP) for a biorepository based on the experience of the clinical centers within the consortium to study Chronic Pancreatitis, Diabetes and Pancreas Cancer Clinical Centers (CPDPC), supported by the National Cancer Institute and the National Institute for Diabetes and Digestive and Kidney Diseases as a unique multidisciplinary model to study CP, diabetes, and pancreatic cancer in both children and adults. Standard operating procedures from the CPDPC centers were evaluated and consolidated. The literature was reviewed for standard biorepository operating procedures that facilitated downstream molecular analysis. The existing literature on biobanking practices was harmonized with the SOPs from the clinical centers to produce a biorepository for pancreatic research. This article reports the methods and basic principles behind the creation of SOPs to develop a biorepository for the CPDPC. These will serve as a guide for investigators developing biorepositories in pancreas research. Rigorous and meticulous adherence to standardized biospecimen collection will facilitate investigations to better understand the pathophysiology and biologic mechanisms of CP, diabetes, and pancreatic cancer.

The application of metabolomics toward pulmonary tuberculosis research

In the quest to identify novel biomarkers for pulmonary tuberculosis (TB), high-throughput systems biology approaches such as metabolomics has become increasingly widespread. Such biomarkers have not only successfully been used for better disease characterization, but have also provided new insights toward the future development of improved diagnostic and therapeutic approaches. In this review, we give a summary of the metabolomics studies done to date, with a specific focus on those investigating various aspects of pulmonary TB, and the infectious agent responsible, Mycobacterium tuberculosis. These studies, done on a variety of sample matrices, including bacteriological culture, sputum, blood, urine, tissue, and breath, are discussed in terms of their intended research outcomes or future clinical applications. Additionally, a summary of the research model, sample cohort, analytical apparatus and statistical methods used for biomarker identification in each of these studies, is provided.

Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases

Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidney disease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidney disease and a control. These metabolites are likely to be robust indicators of kidney disease processes, and therefore potential biomarkers, warranting further investigation.

Preanalytical challenges – time for solutions

The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for the Preanalytical Phase (WG-PRE) was originally established in 2013, with the main aims of (i) promoting the importance of quality in the preanalytical phase of the testing process, (ii) establishing best practices and providing guidance for critical activities in the preanalytical phase, (iii) developing and disseminating European surveys for exploring practices concerning preanalytical issues, (iv) organizing meetings, workshops, webinars or specific training courses on preanalytical issues. As education is a core activity of the WG-PRE, a series of European conferences have been organized every second year across Europe. This collective article summarizes the leading concepts expressed during the lectures of the fifth EFLM Preanalytical Conference “Preanalytical Challenges – Time for solutions”, held in Zagreb, 22–23 March, 2019. The topics covered include sample stability, preanalytical challenges in hematology testing, feces analysis, bio-banking, liquid profiling, mass spectrometry, next generation sequencing, laboratory automation, the importance of knowing and measuring the exact sampling time, technology aids in managing inappropriate utilization of laboratory resources, management of hemolyzed samples and preanalytical quality indicators.

A pilot study of the effect of phospholipid curcumin on serum metabolomic profile in patients with non-alcoholic fatty liver disease: a randomized, double-blind, placebo-controlled trial

BACKGROUND/OBJECTIVES

Curcumin, a natural polyphenol compound in the spice turmeric, has been found to have potent anti-oxidative and anti-inflammatory activity. Curcumin may treat non-alcoholic fatty liver disease (NAFLD) through its beneficial effects on biomarkers of oxidative stress (OS) and inflammation, which are considered as two feature of this disease. However, the effects of curcumin on NAFLD have been remained poorly understood. This investigation evaluated the effects of administrating curcumin on metabolic status in NAFLD patients.

SUBJECTS/METHODS

Fifty-eight NAFLD patients participated in a randomized, double-blind, placebo-controlled parallel design of study. The subjects were allocated randomly into two groups, which either received 250 mg phospholipid curcumin or placebo, one capsule per day for a period of 8 weeks. Fasting blood samples were taken from each subject at the start and end of the study period. Subsequently, metabolomics analysis was performed for serum samples using NMR.

RESULTS

Compared with the placebo, supplementing phospholipid curcumin resulted in significant decreases in serum including 3- methyl-2-oxovaleric acid, 3-hydroxyisobutyrate, kynurenine, succinate, citrate, α-ketoglutarate, methylamine, trimethylamine, hippurate, indoxyl sulfate, chenodeoxycholic acid, taurocholic acid, and lithocholic acid. This profile of metabolic biomarkers could distinguish effectively NAFLD subjects who were treated with curcumin and placebo groups, achieving value of 0.99 for an area under receiver operating characteristic curve (AUC).

CONCLUSIONS

Characterizing the serum metabolic profile of the patients with NAFLD at the end of the intervention using NMR-based metabolomics method indicated that the targets of curcumin treatment included some amino acids, TCA cycle, bile acids, and gut microbiota.

Pre- and Post-analytical Factors in Biomarker Discovery

The translation of promising biomarkers, which were identified in biomarker discovery experiments, to clinical assays is one of the key challenges in present-day proteomics research. Many so-called "biomarker candidates" fail to progress beyond the discovery phase, and much emphasis is placed on pre- and post-analytical variability in an attempt to provide explanations for this bottleneck in the biomarker development pipeline. With respect to such variability, there is a large number of pre- and post-analytical factors which may impact the outcomes of proteomics experiments and thus necessitate tight control. This chapter highlights some of these factors and provides guidance for addressing them on the basis of examples from previously published proteomics studies.

Personalized Metabolomics

The human metabolome is the cumulative product of ingested metabolites and those produced by the body and its microbiota. Together these metabolites can dynamically report on the health and disease state of an individual, as well as their response to drug treatments and other external perturbations. Profiling metabolites in human body fluids provides an opportunity to identify biomarkers and stratify patients for personalized treatments but requires the development of high-throughput approaches compatible with large cohort and longitudinal studies. Here we review in detail sample preparation and analytical liquid chromatography-mass spectrometry (LC-MS) methods to measure the broad chemical diversity of metabolites found in human plasma and urine.

Low-Field, Benchtop NMR Spectroscopy as a Potential Tool for Point-of-Care Diagnostics of Metabolic Conditions: Validation, Protocols and Computational Models

Novel sensing technologies for liquid biopsies offer promising prospects for the early detection of metabolic conditions through omics techniques. Indeed, high-field nuclear magnetic resonance (NMR) facilities are routinely used for metabolomics investigations on a range of biofluids in order to rapidly recognise unusual metabolic patterns in patients suffering from a range of diseases. However, these techniques are restricted by the prohibitively large size and cost of such facilities, suggesting a possible role for smaller, low-field NMR instruments in biofluid analysis. Herein we describe selected biomolecule validation on a low-field benchtop NMR spectrometer (60 MHz), and present an associated protocol for the analysis of biofluids on compact NMR instruments. We successfully detect common markers of diabetic control at low-to-medium concentrations through optimised experiments, including α-glucose (≤2.8 mmol/L) and acetone (25 µmol/L), and additionally in readily accessible biofluids, particularly human urine. We present a combined protocol for the analysis of these biofluids with low-field NMR spectrometers for metabolomics applications, and offer a perspective on the future of this technique appealing to ‘point-of-care’ applications.

Quantification of protein markers monitoring the pre-analytical effect of blood storage time before plasma isolation using 15 N metabolically labeled recombinant proteins

In the hospital, blood samples are collected to monitor patients' health states, and thus various protein-based clinical methods have been developed. However, some proteins are found to change in abundances during the process of blood collection and storage. In order to account such pre-analytical effects, we performed liquid chromatography multiple reaction monitoring mass spectrometry (LC-MRM-MS) on 15 selected proteins in plasma samples prepared by varying storage time and temperature of whole blood prior to plasma isolation. Two cytosolic proteins, profilin-1 (PFN1) and thymosin beta-4 (TMSB4X), were absolutely quantified using ¹⁵ N-labeled recombinant proteins spiked externally. The other 13 proteins were quantified in a relative way compared with the two reference proteins. Triplicated LC-MRM-MS measurements showed that the median CV of MRM peak areas was 5.7%. The amounts of PFN1 and TMSB4X increased rapidly depending on the storage time between blood collection and plasma preparation. It indicates the leakage of cellular components into the plasma fraction. Relative quantification further revealed that five proteins including PFN1, S10A8, S10A9, S10A11, and TMSB4X showed significant difference (P < 0.05). We further monitored PFN1 and TMSB4X on 40 samples collected for protein diagnostics under a typical clinical study condition. Compared with the plasma samples prepared within a day, the level of both PFN1 and TMSB4X increased in the plasma samples prepared from the blood collected the day before and kept overnight at 4°C (0.51 to 3.11 μg/mL for PFN1 and 0.98 to 5.36 μg/mL for TMSB4X in average). Our result suggests an effort of assuring plasma quality for accurate protein-based diagnosis or biomarker discovery and validation.

Impact of post-collection freezing delay on the reliability of serum metabolomics in samples reflecting the California mid-term pregnancy biobank

BACKGROUND

Population-based biorepositories are important resources, but sample handling can affect data quality.

OBJECTIVE

Identify metabolites of value for clinical investigations despite extended postcollection freezing delays, using protocols representing a California mid-term pregnancy biobank.

METHODS

Blood collected from non-pregnant healthy female volunteers (n = 20) underwent three handling protocols after 30 min clotting at room temperature: (1) ideal-samples frozen (- 80 °C) within 2 h of collection; (2) delayed freezing-samples held at room temperature for 3 days, then 4 °C for 9 days, the median times for biobank samples, and then frozen; (3) delayed freezing with freeze-thaw-the delayed freezing protocol with a freeze-thaw cycle simulating retrieved sample sub-aliquoting. Mass spectrometry-based untargeted metabolomic analyses of primary metabolism and complex lipids and targeted profiling of oxylipins, endocannabinoids, ceramides/sphingoid-bases, and bile acids were performed. Metabolite concentrations and intraclass correlation coefficients (ICC) were compared, with the ideal protocol as the reference.

RESULTS

Sixty-two percent of 428 identified compounds had good to excellent ICCs, a metric of concordance between measurements of samples handled with the different protocols. Sphingomyelins, phosphatidylcholines, cholesteryl esters, triacylglycerols, bile acids and fatty acid diols were the least affected by non-ideal handling, while sugars, organic acids, amino acids, monoacylglycerols, lysophospholipids, N-acylethanolamides, polyunsaturated fatty acids, and numerous oxylipins were altered by delayed freezing. Freeze-thaw effects were assay-specific with lipids being most stable.

CONCLUSIONS

Despite extended post-collection freezing delays characteristic of some biobanks of opportunistically collected clinical samples, numerous metabolomic compounds had both stable levels and good concordance.

Phlebotomy tube interference with nuclear magnetic resonance (NMR) lipoprotein subclass analysis

BACKGROUND

Lipoprotein subclass analysis by nuclear magnetic resonance (NMR) can be used in risk assessment of atherosclerotic cardiovascular disease (ASCVD). There is little information in the literature regarding phlebotomy tube interferences with NMR testing.

METHODS

Pooled human serum was exposed to phlebotomy tubes manufactured by Becton Dickinson (BD), Greiner Bio-One, or Sarstedt. Serum was analyzed on the Axinon lipoFIT by NMR assay and by conventional lipid assays performed on a Roche Cobas 8000 system. The effect of incomplete fill volume was also assessed.

RESULTS

Analytical interference in NMR lipoprotein subclass results was observed across many different tube types. The 5 mL Greiner Bio-One Z Serum Sep Clot Activator tube correlated the best with non-gel containing serum tubes from BD and Greiner Bio-One. BD Serum Separator Tubes (SSTs) displayed strong interferences across several NMR analytes that were enhanced with decreased tube fill volumes. Interferences were also observed with different sizes of Greiner Bio-One Z Serum Sep Clot Activator tubes. Interference was generally not observed with conventional lipid testing, although minor interference was found for some tubes with lipoprotein(a) [Lp(a)].

CONCLUSIONS

NMR lipoprotein subclass analysis should be standardized by both tube type and tube size to prevent risk of analytical interference.

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.

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.

MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines

Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent.

The metabolomic quest for a biomarker in chronic kidney disease

Chronic kidney disease (CKD) is a growing burden on people and on healthcare for which the diagnostics are niether disease-specific nor indicative of progression. Biomarkers are sought to enable clinicians to offer more appropriate patient-centred treatments, which could come to fruition by using a metabolomics approach. This mini-review highlights the current literature of metabolomics and CKD, and suggests additional factors that need to be considered in this quest for a biomarker, namely the diet and the gut microbiome, for more meaningful advances to be made.

Metabolomics profiling of xenobiotics in elite athletes: relevance to supplement consumption

Background

Supplements are widely used among elite athletes to maintain health and improve performance. Despite multiple studies investigating use of dietary supplements by athletes, a comprehensive profiling of serum supplement metabolites in elite athletes is still lacking. This study aims to analyze the presence of various xenobiotics in serum samples from elite athletes of different sports, focusing on metabolites that potentially originate from nutritional supplements.

Methods

Profiling of xenobiotics in serum samples from 478 elite athletes from different sports (football, athletics, cycling, rugby, swimming, boxing and rowing) was performed using non-targeted metabolomics-based mass spectroscopy combined with ultrahigh-performance liquid chromatography. Multivariate analysis was performed using orthogonal partial least squares discriminant analysis. Differences in metabolic levels among different sport groups were identified by univariate linear models.

Results

Out of the 102 detected xenobiotics, 21 were significantly different among sport groups including metabolites that potentially prolong exercise tolerance (caffeic acid), carry a nootropic effect (2-pyrrolidinone), exert a potent anti-oxidant effect (eugenol, ferulic acid 4 sulfate, thioproline, retinol), or originate from drugs for different types of injuries (ectoine, quinate). Using Gaussian graphical modelling, a metabolic network that links various sport group-associated xenobiotics was constructed to further understand their metabolic pathways.

Conclusions

This pilot data provides evidence that athletes from different sports exhibit a distinct xenobiotic profile that may reflect their drug/supplement use, diet and exposure to various chemicals. Because of limitation in the study design, replication studies are warranted to confirm results in independent data sets, aiming ultimately for better assessment of dietary supplement use by athletes.

Reference Intervals for Plasma Biochemical Variables by Point-of-Care Testing in Captive Black-tailed Prairie Dogs (Cynomys ludovicianus)

Black-tailed prairie dogs (Cynomys ludovicianus) are kept in zoological collections, maintained as companion pets, and aretested in field and laboratory settings. Biochemical analysis for routine health and research purposes can be performed byusing point-of-care (POC) testing; however, analyzer- and species-specific reference intervals need to be determined. In this prospective study, 50 captive-raised sexually intact prairie dogs (16 females, 34 males) underwent plasma biochemical analysisby using a veterinary POC biochemical analyzer. We used a manufacturer-predetermined profile of 14 analytes: albumin, ALP,ALT, amylase, BUN, calcium, creatinine, glucose, potassium, sodium, phosphorus, total bilirubin, total protein and globulin.A subset of 17 samples was tested concurrently for the same 14 analytes by using a reference laboratory analyzer, and wedetermined RI for the POC analyzer for these 14 biochemical analytes. Sex had a significant effect on albumin and creatininevalues, which were higher in females than males, and on ALT, which was lower in females. In addition, age had an effect on9 plasma analytes: juvenile animals had higher plasma concentrations of albumin, ALP, ALT, BUN, and glucose than adultanimals, whereas adults had higher concentrations of creatinine, sodium, total protein, and globulins. Only calcium and BUNhad acceptable analytical agreement between the POC and reference analyzers. The reference intervals determined in this study can aid clinicians and researchers performing POC plasma biochemical analysis in prairie dogs, providing that they consider potential analyzer-, sex-, and age-related effects.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.

A feasibility study prior to an international multicentre paediatric study to assess pharmacokinetic/pharmacodynamic sampling and sample preparation procedures, logistics and bioanalysis

Background

Variability in pre-analytical procedures such as blood sampling, sample preparation and transport can substantially influence bioanalytical results and subsequently impair reliability of data gathered during clinical trials. Especially in vulnerable populations, all efforts should be made to facilitate high-quality data extraction excluding unnecessary or repeated intervention.

Methods

The EU-funded LENA project (Labeling of Enalapril from Neonates up to Adolescents) included a feasibility study in its preparatory procedures prior to first-in-child studies. Derived from a regular study visit, it encompassed all procedures, from sampling of two study-specific drugs and four sensitive humoral parameters to bioanalysis, to evaluate the quality of obtained samples and applicability of logistical and bioanalytical procedures. Drug administration to healthy adults was circumvented by pre-spiking the blood collection tubes with a drug solution. Five clinical sites were evaluated.

Results

Clinical teams' preparedness and applicability of required sampling procedures was investigated in 18 volunteers, on-site. 97% of collected pharmacokinetic (PK) samples and 93% of samples for humoral parameters were obtained eligibly. Results met expectations, though one team had to be re-trained and performed a re-run. Planned procedures for sampling, sample preparation, transport and analysis were found to be suitable for being applied within paediatric trials.

Conclusion

The concept of the presented feasibility study that simultaneously assesses PK/PD sampling, sample preparation, logistics and bioanalysis proved to be a promising tool for trial preparation. It revealed improperly installed processes and bottlenecks that required adjustments prior to start of recruitment. It facilitated high-quality conduct from the first moment of paediatric pivotal studies.

Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data

Motivation

Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples.

Results

This study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1–36 h pre-centrifugation delay time at 4 °C and 22 °C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 °C. Moreover, pre-centrifugation delay at 4 °C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 °C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS.

Availability and implementation

The sampleDrift R package is available at https://gitlab.com/CarlBrunius/sampleDrift.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of Metabolites Associated with Onset of CAD in Diabetic Patients Using CE-MS Analysis: A Pilot Study

Aim: Coronary artery disease (CAD) is the result of a complex metabolic disorder caused by various environmental and genetic factors. Metabolomics is a potential tool for identifying biomarkers for better risk classification and for understanding the pathophysiological mechanisms of CAD. With this background, we performed a pilot study to identify metabolites associated with the future onset of CAD in patients with type 2 diabetes.

Methods: Sixteen subjects who suffered from CAD event during the observation period and 39 non-CAD subjects who were matched to the CAD subjects for Framingham Coronary Heart Disease Risk Score, diabetes duration, and HbA1c were selected. Capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) was used to perform non-targeted metabolome analysis of serum samples collected in 2005.

Results: A total of 104 metabolites were identified. Unsupervised principal component analysis (PCA) did not to reveal two distinct clusters of individuals. However, a significant association with CAD was found for 7 metabolites (pelargonic acid, glucosamine:galactosamine, thymine, 3-hydroxybutyric acid, creatine, 2-aminoisobutyric acid, hypoxanthine) and the levels of all these metabolites were significantly lower in the CAD group compared with the non-CAD group.

Conclusions: We identified 7 metabolites related to long-term future onset of CAD in Japanese patients with diabetes. Further studies with large sample size would be necessary to confirm our findings, and future studies using in vivo or in vitro models would be necessary to elucidate whether direct relationships exist between the detected metabolites and CAD pathophysiology.

Quantitative metabolomics comparison of traditional blood draws and TAP capillary blood collection

INTRODUCTION

Mass spectrometry and computational biology have advanced significantly in the past ten years, bringing the field of metabolomics a step closer to personalized medicine applications. Despite these analytical advancements, collection of blood samples for routine clinical analysis is still performed through traditional blood draws.

OBJECTIVE

TAP capillary blood collection has been recently introduced for the rapid, painless draw of small volumes of blood (~ 100 μL), though little is known about the comparability of metabolic phenotypes of blood drawn via traditional venipuncture and TAP devices.

METHODS

UHPLC-MS-targeted metabolomics analyses were performed on blood drawn traditionally or through TAP devices from 5 healthy volunteers. Absolute quantitation of 45 clinically-relevant metabolites was calculated against stable heavy isotope-labeled internal standards.

RESULTS

Ranges for 39 out of 45 quantified metabolites overlapped between drawing methods. Pyruvate and succinate were over threefold higher in the TAP samples than in traditional blood draws. No significant changes were observed for other carboxylates, glucose or lactate. TAP samples were characterized by increases in reduced glutathione and decreases in urate and cystine, markers of oxidation of purines and cysteine-overall suggesting decreased oxidation during draws. The absolute levels of bile acids and acyl-carnitines, as well as almost all amino acids, perfectly correlated among groups (Spearman r ≥ 0.95).

CONCLUSION

Though further more extensive studies will be mandatory, this pilot suggests that TAP-derived blood may be a logistically-friendly source of blood for large scale metabolomics studies-especially those addressing amino acids, glycemia and lactatemia as well as bile acids, acyl-carnitine levels.

Quantification of run order effect on chromatography - mass spectrometry profiling data

Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery

This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.

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.

Preanalytical Biases in the Measurement of Human Blood Sphingolipids

Dysregulation of blood sphingolipids is an emerging topic in clinical science. The objective of this study was to determine preanalytical biases that typically occur in clinical and translational studies and that influence measured blood sphingolipid levels. Therefore, we collected blood samples from four healthy male volunteers to investigate the effect of storage conditions (time, temperature, long-term storage, freeze–thaw cycles), blood drawing (venous or arterial sampling, prolonged venous compression), and sample preparation (centrifugation, freezing) on sphingolipid levels measured by LC-MS/MS. Our data show that sphingosine 1-phosphate (S1P) and sphinganine 1-phosphate (SA1P) were upregulated in whole blood samples in a time- and temperature-dependent manner. Increased centrifugation at higher speeds led to lower amounts of S1P and SA1P. All other preanalytical biases did not significantly alter the amounts of S1P and SA1P. Further, in almost all settings, we did not detect differences in (dihydro)ceramide levels. In summary, besides time-, temperature-, and centrifugation-dependent changes in S1P and SA1P levels, sphingolipids in blood remained stable under practically relevant preanalytical conditions.

Multiomics tools for the diagnosis and treatment of rare neurological disease

Conventional workup of rare neurological disease is frequently hampered by diagnostic delay or lack of diagnosis. While biomarkers have been established for many neurometabolic disorders, improved methods are required for diagnosis of previously unidentified or underreported causes of rare neurological disease. This would result in a higher diagnostic yield and increased patient numbers required for interventional studies. Recent studies using next-generation sequencing and metabolomics have led to identification of novel disease-causing genes and biomarkers. This combined approach can assist in overcoming challenges associated with analyzing and interpreting the large amount of data obtained from each technique. In particular, metabolomics can support the pathogenicity of sequence variants in genes encoding enzymes or transporters involved in metabolic pathways. Moreover, metabolomics can show the broader perturbation caused by inborn errors of metabolism and identify a metabolic fingerprint of metabolic disorders. As such, using "omics" has great potential to meet the current needs for improved diagnosis and elucidation of rare neurological disease.

Advances in metabolome information retrieval: turning chemistry into biology. Part I: analytical chemistry of the metabolome

Metabolites are small molecules produced by enzymatic reactions in a given organism. Metabolomics or metabolic phenotyping is a well-established omics aimed at comprehensively assessing metabolites in biological systems. These comprehensive analyses use analytical platforms, mainly nuclear magnetic resonance spectroscopy and mass spectrometry, along with associated separation methods to gather qualitative and quantitative data. Metabolomics holistically evaluates biological systems in an unbiased, data-driven approach that may ultimately support generation of hypotheses. The approach inherently allows the molecular characterization of a biological sample with regard to both internal (genetics) and environmental (exosome, microbiome) influences. Metabolomics workflows are based on whether the investigator knows a priori what kind of metabolites to assess. Thus, a targeted metabolomics approach is defined as a quantitative analysis (absolute concentrations are determined) or a semiquantitative analysis (relative intensities are determined) of a set of metabolites that are possibly linked to common chemical classes or a selected metabolic pathway. An untargeted metabolomics approach is a semiquantitative analysis of the largest possible number of metabolites contained in a biological sample. This is part I of a review intending to give an overview of the state of the art of major metabolic phenotyping technologies. Furthermore, their inherent analytical advantages and limits regarding experimental design, sample handling, standardization and workflow challenges are discussed.

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).

A review of metabolomics approaches and their application in identifying causal pathways of childhood asthma

Because asthma is a disease that results from host-environment interactions, an approach that allows assessment of the effect of the environment on the host is needed to understand the disease. Metabolomics has appealing potential as an application to study pathways to childhood asthma development. The objective of this review is to provide an overview of metabolomics methods and their application to understanding host-environment pathways in asthma development. We reviewed recent literature on advances in metabolomics and their application to study pathways to childhood asthma development. We highlight the (1) potential of metabolomics in understanding the pathogenesis of disease and the discovery of biomarkers; (2) choice of metabolomics techniques, biospecimen handling, and data analysis; (3) application to studying the role of the environment on asthma development; (4) review of metabolomics applied to the outcome of asthma; (5) recommendations for application of metabolomics-based -omics data integration in understanding disease pathogenesis; and (6) limitations. In conclusion, metabolomics allows use of biospecimens to identify useful biomarkers and pathways involved in disease development and subsequently to inform a greater understanding of disease pathogenesis and endotypes and prediction of the clinical course of childhood asthma phenotypes.

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.

Comparison of serum serotonin and serum 5-HIAA LC-MS/MS assays in the diagnosis of serotonin producing neuroendocrine neoplasms: A pilot study

BACKGROUND

Serotonin (5-hydroxytyramine) is a mediator of gastrointestinal smooth muscle contraction, and is secreted by neuroendocrine neoplasms (NENs). We developed a liquid chromatography tandem mass spectrometry (LC-MS/MS) assay for serum serotonin to be used in NEN diagnostics and follow-up.

METHODS

We used serum samples from healthy volunteers (n = 31) and patients suspected or monitored for NEN (n = 98). Serotonin-D₄ internal standard was added to samples before solid phase extraction (SPE) and quantification by LC-MS/MS. The effects of sample handling and preparation on serotonin stability were studied. Finally, we established a provisional reference range for serum serotonin and compared our assay with serum 5-hydroxyindoleacetic acid (5-HIAA) for detection of NENs.

RESULTS

Our assay is sensitive and has a wide linear range (10-10,000 nmol/l). Serum serotonin is stable for 7 days at room temperature and for 3 months at -20 °C. Sampling temperature is not critical. Normal range for serum serotonin was 270-1490 nmol/l. We found that serum serotonin and 5-HIAA performed equally well as diagnostic tests for NENs.

CONCLUSIONS

Our LC-MS/MS assay for serum serotonin is well suited for clinical research and patient diagnostics. Our results confirm that it can complement 5-HIAA in diagnosis of NENs.

Genome Database of the Latvian Population (LGDB): Design, Goals, and Primary Results

BACKGROUND

The Genome Database of the Latvian Population (LGDB) is a national biobank that collects, maintains, and processes health information, data, and biospecimens collected from representatives of the Latvian population. These specimens serve as a foundation for epidemiological research and prophylactic and therapeutic purposes.

METHODS

Participant recruitment and biomaterial and data processing were performed according to specifically designed standard protocols, taking into consideration international quality requirements. Legal and ethical aspects, including broad informed consent and personal data protection, were applied according to legal norms of the Republic of Latvia.

RESULTS

Since its start in 2006, the LGDB is comprised of biosamples and associated phenotypic and clinical information from over 31,504 participants, constituting approximately 1.5% of the Latvian population. The LGDB represents a mixed-design biobank and includes participants from the general population as well as disease-based cohorts. The standard set of biosamples stored in the LGDB consists of DNA, plasma, serum, and white blood cells; in some cohorts, these samples are complemented by cancer biopsies and microbiome and urine samples. The LGDB acts as a core structure for the Latvian Biomedical Research and Study Centre (BMC), representing the national node of Latvia in Biobanking and BioMolecular resources Research Infrastructure - European Research Infrastructure Consortium (BBMRI-ERIC).

CONCLUSIONS

The development of the LGDB has enabled resources for biomedical research and promoted genetic testing in Latvia. Further challenges of the LGDB are the enrichment and harmonization of collected biosamples and data, the follow-up of selected participant groups, and continued networking and participation in collaboration projects.

Metabolomics and Lipidomics of Ischemic Stroke

Ischemic stroke is a sudden loss of brain function due to the reduction of blood flow. Brain tissues cease to function with subsequent activation of the ischemic cascade. Metabolomics and lipidomics are modern disciplines that characterize the metabolites and lipid components of a biological system, respectively. Because the pathogenesis of ischemic stroke is heterogeneous and multifactorial, it is crucial to establish comprehensive metabolomic and lipidomic approaches to elucidate these alterations in this disease. Fortunately, metabolomic and lipidomic studies have the distinct advantages of identifying tissue/mechanism-specific biomarkers, predicting treatment and clinical outcome, and improving our understanding of the pathophysiologic basis of disease states. Therefore, recent applications of these analytical approaches in the early diagnosis of ischemic stroke were discussed. In addition, the emerging roles of metabolomics and lipidomics on ischemic stroke were summarized, in order to gain new insights into the mechanisms underlying ischemic stroke and in the search for novel metabolite biomarkers and their related pathways.

Examining heat treatment for stabilization of the lipidome

AIM

To confidently determine lipid-based biomarkers, it is important to minimize variation introduced during preanalytical steps. We evaluated reducing variation associated with lipid measurements in invertebrate sentinel species using a state-of-the-art heat treatment technique.

MATERIALS AND METHODS

Earthworms (Eisenia fetida), house crickets (Acheta domestica) and ghost shrimp (Palaemonetes paludosus) were euthanized either by flash freezing or heat treatment. For both experiments, samples were either immediately extracted after removal from -80°C storage or incubated on ice for one hour prior to sample weighing and extraction. Lipidomics was performed on resulting extracts using liquid chromatography high resolution tandem mass spectrometry. LipidMatch and LipidSearch were used for lipid identification.

RESULTS

Lipid enzymatic products (e.g., phosphatidylmethanols, diglycerides, lysoglycerophospholipids and ether-linked/oxidized lysoglycerophospholipids), were in higher concentrations in flash-frozen samples, when compared with heat-treated samples. Results suggest that heat treatment reduces phospholipase A and phospholipase D activity.

CONCLUSION

Heat treatment reduced enzymatic products and increased precursors of these enzymatic products. We believe heat treatment warrants a closer interrogation for improving the robustness of lipid biomarker research, especially in tissue samples, where enzyme stabilizers are difficult to apply, and for use in field studies, where the stabilization of the collected sample is critical.

An efficient biomarker panel for diagnosis of breast cancer using surface-enhanced laser desorption ionization time-of-flight mass spectrometry

Breast cancer (BC) is the most frequently diagnosed cancer that affects women worldwide. Early detection of BC is important to improve survival rates and decrease mortality. The aim of the present study was to investigate serum biomarkers using surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to distinguish patients with BC from the healthy population and patients with benign breast diseases (BBDs). A total of 62 patients with invasive ductal carcinoma, as confirmed by histopathology, and 47 non-cancerous individuals (NCIs) [16 healthy controls (HCs) and 31 patients with BBD] were enrolled in the present study. Serum protein profiles were determined by SELDI-TOF-MS using an immobilized metal affinity capture array. Serum from patients with BC were compared with that from the HC group using univariate and multivariate statistical analyses. A total of 118 clusters were generated from the individual serum. Univariate analysis revealed that 5 peaks were significantly downregulated (m/z 1,452, 2,670, 3,972, 5,354 and 5,523; P<0.001) and 4 were upregulated (m/z 6,850, 7,926, 8,115 and 8,143; P<0.001) in patients with BC compared with the HC group. A comparison of patients with BC and patients with BBD revealed an additional 9 protein peaks. Among these, 3 peaks (m/z 3,972, 5,336 and 11,185) were significantly downregulated and 6 peaks (m/z 4,062, 4,071, 4,609, 6,850, 8,115 and 8,133) were significantly upregulated. A total of 3 peaks [mass-to-change ratio (m/z) 3,972, 6,850 and 8,115 (BC2)] were common in both sets. The results of the present study suggest that a 4 protein peak set [m/z 3,972, 6,850 and 8,115 (BC2) and 8,949 (BC3)] could be used to distinguish patients with BC from NCI.

Impact of Prolonged Blood Incubation and Extended Serum Storage at Room Temperature on the Human Serum Metabolome

Metabolomics is a powerful technology with broad applications in life science that, like other -omics approaches, requires high-quality samples to achieve reliable results and ensure reproducibility. Therefore, along with quality assurance, methods to assess sample quality regarding pre-analytical confounders are urgently needed. In this study, we analyzed the response of the human serum metabolome to pre-analytical variations comprising prolonged blood incubation and extended serum storage at room temperature by using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) -based metabolomics. We found that the prolonged incubation of blood results in a statistically significant 20% increase and 4% decrease of 225 tested serum metabolites. Extended serum storage affected 21% of the analyzed metabolites (14% increased, 7% decreased). Amino acids and nucleobases showed the highest percentage of changed metabolites in both confounding conditions, whereas lipids were remarkably stable. Interestingly, the amounts of taurine and O-phosphoethanolamine, which have both been discussed as biomarkers for various diseases, were 1.8- and 2.9-fold increased after 6 h of blood incubation. Since we found that both are more stable in ethylenediaminetetraacetic acid (EDTA) blood, EDTA plasma should be the preferred metabolomics matrix.

A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines

Background

The outstanding performance of an elite athlete might be associated with changes in their blood metabolic profile. The aims of this study were to compare the blood metabolic profiles between moderate- and high-power and endurance elite athletes and to identify the potential metabolic pathways underlying these differences.

Methods

Metabolic profiling of serum samples from 191 elite athletes from different sports disciplines (121 high- and 70 moderate-endurance athletes, including 44 high- and 144 moderate-power athletes), who participated in national or international sports events and tested negative for doping abuse at anti-doping laboratories, was performed using non-targeted metabolomics-based mass spectroscopy combined with ultrahigh-performance liquid chromatography. Multivariate analysis was conducted using orthogonal partial least squares discriminant analysis. Differences in metabolic levels between high- and moderate-power and endurance sports were assessed by univariate linear models.

Results

Out of 743 analyzed metabolites, gamma-glutamyl amino acids were significantly reduced in both high-power and high-endurance athletes compared to moderate counterparts, indicating active glutathione cycle. High-endurance athletes exhibited significant increases in the levels of several sex hormone steroids involved in testosterone and progesterone synthesis, but decreases in diacylglycerols and ecosanoids. High-power athletes had increased levels of phospholipids and xanthine metabolites compared to moderate-power counterparts.

Conclusions

This pilot data provides evidence that high-power and high-endurance athletes exhibit a distinct metabolic profile that reflects steroid biosynthesis, fatty acid metabolism, oxidative stress, and energy-related metabolites. Replication studies are warranted to confirm differences in the metabolic profiles associated with athletes’ elite performance in independent data sets, aiming ultimately for deeper understanding of the underlying biochemical processes that could be utilized as biomarkers with potential therapeutic implications.

Electronic supplementary material

The online version of this article (10.1186/s40798-017-0114-z) contains supplementary material, which is available to authorized users.

Differences in metabolite profiles caused by pre-analytical blood processing procedures

Recently, the use of metabolomic analysis of human serum and plasma for biomarker discovery and disease diagnosis in clinical studies has been increasing. The feasibility of using a metabolite biomarker for disease diagnosis is strongly dependent on the metabolite's stability during pre-analytical blood processing procedures, such as serum or plasma sampling and sample storage prior to centrifugation. However, the influence of blood processing procedures on the stability of metabolites has not been fully characterized. In the present study, we compared the levels of metabolites in matched human serum and plasma samples using gas chromatography coupled with mass spectrometry and liquid chromatography coupled with mass spectrometry. In addition, we evaluated the changes in plasma metabolite levels induced by storage at room temperature or at a cold temperature prior to centrifugation. As a result, it was found that 76 metabolites exhibited significant differences between their serum and plasma levels. Furthermore, the pre-centrifugation storage conditions significantly affected the plasma levels of 45 metabolites. These results highlight the importance of blood processing procedures during metabolome analysis, which should be considered during biomarker discovery and the subsequent use of biomarkers for disease diagnosis.

Long-Term Stability of Human Plasma Metabolites during Storage at -80 °C

Prolonged storage of biospecimen can lead to artificially altered metabolite concentrations and thus bias data analysis in metabolomics experiments. To elucidate the potential impact of long-term storage on the metabolite profile, a pooled human plasma sample was aliquoted and stored at -80 °C. During a time period of five years, 1012 of the aliquots were measured with the Biocrates AbsoluteIDQ p180 targeted-metabolomics assay at 193 time points. Modeling the concentration courses over time revealed that 55 out of 111 metabolites remained stable. The statistically significantly changed metabolites showed on average an increase or decrease of +13.7% or -14.5%, respectively. In detail, increased concentration levels were observed for amino acids (mean: + 15.4%), the sum of hexoses (+7.9%), butyrylcarnitine (+9.4%), and some phospholipids mostly with chain lengths exceeding 40 carbon atoms (mean: +18.0%). Lipids tended to exhibit decreased concentration levels with the following mean concentration changes: acylcarnitines, -12.1%; lysophosphatidylcholines, -15.1%; diacyl-phosphatidylcholines, -17.0%; acyl-alkyl-phosphatidylcholines, -13.3%; sphingomyelins, -14.8%. We conclude that storage of plasma samples at -80 °C for up to five years can lead to altered concentration levels of amino acids, acylcarnitines, glycerophospholipids, sphingomyelins, and the sum of hexoses. These alterations must be considered when analyzing metabolomics data from long-term epidemiological studies.

Multiple-stressor effects in an apex predator: combined influence of pollutants and sea ice decline on lipid metabolism in polar bears

There is growing evidence from experimental and human epidemiological studies that many pollutants can disrupt lipid metabolism. In Arctic wildlife, the occurrence of such compounds could have serious consequences for seasonal feeders. We set out to study whether organohalogenated compounds (OHCs) could cause disruption of energy metabolism in female polar bears (Ursus maritimus) from Svalbard, Norway (n = 112). We analyzed biomarkers of energy metabolism including the abundance profiles of nine lipid-related genes, fatty acid (FA) synthesis and elongation indices in adipose tissue, and concentrations of lipid-related variables in plasma (cholesterol, high-density lipoprotein, triglycerides). Furthermore, the plasma metabolome and lipidome were characterized by low molecular weight metabolites and lipid fingerprinting, respectively. Polychlorinated biphenyls, chlordanes, brominated diphenyl ethers and perfluoroalkyl substances were significantly related to biomarkers involved in lipid accumulation, FA metabolism, insulin utilization, and cholesterol homeostasis. Moreover, the effects of pollutants were measurable at the metabolome and lipidome levels. Our results indicate that several OHCs affect lipid biosynthesis and catabolism in female polar bears. Furthermore, these effects were more pronounced when combined with reduced sea ice extent and thickness, suggesting that climate-driven sea ice decline and OHCs have synergistic negative effects on polar bears.

Urinary Metabolic Phenotyping of Women with Lower Urinary Tract Symptoms

Lower urinary tract symptoms (LUTS), including urinary incontinence, urgency and nocturia, affect approximately half of women worldwide. Current diagnostic methods for LUTS are invasive and costly, while available treatments are limited by side effects leading to poor patient compliance. In this study, we aimed to identify urine metabolic signatures associated with LUTS using proton nuclear magnetic resonance (¹H NMR) spectroscopy. A total of 214 urine samples were collected from women attending tertiary urogynecology clinics (cases; n = 176) and healthy control women attending general gynecology clinics (n = 36). Despite high variation in the urine metabolome across the cohort, associations between urine metabolic profiles and BMI, parity, overactive bladder syndrome, frequency, straining, and bladder storage were identified using KODAMA (knowledge discovery by accuracy maximization). Four distinct urinary metabotypes were identified, one of which was associated with increased urinary frequency and low BMI. Urine from these patients was characterized by increased levels of isoleucine and decreased levels of hippurate. Our study suggests that metabolic profiling of urine samples from LUTS patients offers the potential to identify differences in underlying etiology, which may permit stratification of patient populations and the design of more personalized treatment strategies.