Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome

Influence of plasma collection tubes on N-glycome in human blood samples

Background and aims

Quantitative analysis of plasma N-glycome is a promising method for identifying disease biomarkers. This study aimed to investigate the impact of using blood collection tubes with different anticoagulants on plasma N-glycome.

Materials and methods

We used a robust mass spectrometry method to profile plasma N-glycomes in two cohorts of healthy volunteers (cohort 1, n = 16; cohort 2, n = 53). The influence of three commonly used blood collection tubes on fully characterized N-glycomic profiles were explored.

Results

Principal component analysis revealed distinct clustering of blood samples based on the collection tubes. Pairwise comparisons demonstrated significant differences between EDTA and heparin plasma in 55 out of 82 quantified N-glycan traits, and between EDTA and citrate plasma in 62 out of 82 traits. These differences encompassed various N-glycan features, including glycan type, sialylation, galactosylation, fucosylation, and bisection. Trends in N-glycan variations in citrate and heparin plasma were largely consistent compared to EDTA plasma. In correlation analysis (EDTA vs. heparin; EDTA vs. citrate), Pearson's correlation coefficients were consistently higher than 0.7 for the majority of N-glycan traits.

Conclusion

Sample matrix variations impact plasma N-glycome measurements. Caution is crucial when comparing samples from different plasma collection tubes in glycomics projects.

Sodium fluoride preserves blood metabolite integrity for biomarker discovery in large-scale, multi-site metabolomics investigations

Background: Metabolite profiling of blood by nuclear magnetic resonance (NMR) is invaluable to clinical biomarker discovery. To ensure robustness, biomarkers require validation in large cohorts and across multiple centres. However, collection procedures are known to impact on the stability of biofluids that may, in turn, degrade biomarker signals. We trialled three blood collection tubes with the aim of solving technical challenges due to preanalytical variation in blood metabolite levels that are common in cohort studies. Methods: We first investigated global NMR-based metabolite variability between biobanks, including the large-scale UK Biobank and TwinsUK biobank of the general UK population, and more targeted biobanks derived from multicentre clinical trials relating to inflammatory bowel disease. We then compared the blood metabolome of 12 healthy adult volunteers when collected into either sodium fluoride/potassium oxalate, lithium heparin, or serum blood tubes using different pre-processing parameters. Results: Preanalytical variation in the method of blood collection strongly influences metabolite composition within and between biobanks. This variability can largely be attributed to glucose and lactate. In the healthy control cohort, the fluoride oxalate collection tube prevented fluctuation in glucose and lactate levels for 24 hours at either 4 °C or room temperature (20 °C). Conclusions: Blood collection into a fluoride oxalate collection tube appears to preserve the blood metabolome with delayed processing up to 24 hours at 4 °C. This method may be considered as an alternative when rapid processing is not feasible.

Multi-omics analysis reveals overactive inflammation and dysregulated metabolism in severe community-acquired pneumonia patients

BACKGROUND

Severe community-acquired pneumonia (S-CAP) is a public health threat, making it essential to identify novel biomarkers and investigate the underlying mechanisms of disease severity.

METHODS

Here, we profiled host responses to S-CAP through proteomics analysis of plasma samples from a cohort of S-CAP patients, non-severe (NS)-CAP patients, diseases controls (DCs), and healthy controls (HCs). Then, typical differentially expressed proteins were then validated by ELISA in an independent cohort. Metabolomics analysis was further performed on both the cohort 1 and cohort 2. Then, the proteomic and metabolomic signatures were compared between the adult and child cohorts to explore the characteristics of severe pneumonia patients.

RESULTS

There were clear differences between CAP patients and controls, as well as substantial differences between the S-CAP and NS-CAP. Pathway analysis of changes revealed excessive inflammation, suppressed immunity, and lipid metabolic disorders in S-CAP cases. Interestingly, comparing these signatures between the adult and child cohorts confirmed that overactive inflammation and dysregulated lipid metabolism were common features of S-CAP patients, independent of age. The change proportion of glycerophospholipids, glycerolipids, and sphingolipids were obviously different in the adult and child S-CAP cases.

CONCLUSION

The plasma multi-omics profiling revealed that excessive inflammation, suppressed humoral immunity, and disordered metabolism are involved in S-CAP pathogenesis.

Comparative targeted metabolomic profiles of porcine plasma and serum

Metabolomics has been used to characterise many biological matrices and obtain detailed pictures of biological systems based on many metabolites. Plasma and serum are two blood-derived biofluids commonly used to assess and monitor the organismal metabolism and obtain information on the physiological and health conditions of an animal. Plasma is the supernatant that is separated from the cellular components after centrifugation of the blood that is first added with an anticoagulant. Serum is obtained after centrifugation of the blood that has been coagulated. The choice of one or the other biofluid for metabolomic analyses is related to specific analytical needs and technical issues, to problems derived by the collection and preparation steps, in particular when specimens are sampled from animals involved in field studies. Thus far, most of the metabolomic studies that compared plasma and serum have been carried out in humans and very little is known on the pigs. In this study, we used a targeted metabolomic platform that can detect about 180 metabolites of five biochemical classes to compare plasma and serum profiles of samples collected from 24 pigs. To also obtain a cross-species comparative metabolomic analysis, information for human plasma and serum derived from the same platform was retrieved from previous studies. Statistical analyses included univariate and multivariate approaches aimed at identifying stable and/or differentially abundant metabolites between the two porcine biofluids. A total of 154 (∼83%) metabolites passed the initial quality control, indicating a good repeatability of the analytical platform in pigs. Discarded metabolites included aspartate and biogenic amines that were already reported to be unstable in human studies. More than 80% of the metabolites had similar profiles in both porcine biofluids (average correlation was 0.75). Concentrations were usually higher in serum than in plasma, in agreement with what was already reported in humans. The univariate analysis identified 44 metabolites that had statistically different concentrations between porcine plasma and serum, of which 28 metabolites were also confirmed by the multivariate analysis. The obtained picture described similarities and differences between these two biofluids in pigs and the related human-pig comparisons. The obtained information can be useful for the choice of one or the other matrix for the implementation of metabolomic studies in this livestock species. The results can also provide useful hints to valuing the pig as animal model, in particular when metabolite-derived physiological states are relevant.

Insufficient sleep and weekend recovery sleep: classification by a metabolomics-based machine learning ensemble

Although weekend recovery sleep is common, the physiological responses to weekend recovery sleep are not fully elucidated. Identifying molecular biomarkers that represent adequate versus insufficient sleep could help advance our understanding of weekend recovery sleep. Here, we identified potential molecular biomarkers of insufficient sleep and defined the impact of weekend recovery sleep on these biomarkers using metabolomics in a randomized controlled trial. Healthy adults (n = 34) were randomized into three groups: control (CON: 9-h sleep opportunities); sleep restriction (SR: 5-h sleep opportunities); or weekend recovery (WR: simulated workweek of 5-h sleep opportunities followed by ad libitum weekend recovery sleep and then 2 days with 5-h sleep opportunities). Blood for metabolomics was collected on the simulated Monday immediately following the weekend. Nine machine learning models, including a machine learning ensemble, were built to classify samples from SR versus CON. Notably, SR showed decreased glycerophospholipids and sphingolipids versus CON. The machine learning ensemble showed the highest G-mean performance and classified 50% of the WR samples as insufficient sleep. Our findings show insufficient sleep and recovery sleep influence the plasma metabolome and suggest more than one weekend of recovery sleep may be necessary for the identified biomarkers to return to healthy adequate sleep levels.

The Influence of Preanalytical Biospecimen Handling on the Measurement of B Vitamers, Amino Acids, and Other Metabolites in Blood

Introduction: Sample handling can influence biomarker measurement and introduce variability when combining data from multiple studies or study sites. To inform the development of blood collection protocols within a multisite cohort study, we directly quantified concentrations of 54 biomarkers in blood samples subjected to different handling conditions. Materials and Methods: We obtained serum, lithium heparin plasma, and EDTA plasma from 20 adult volunteers. Tubes of chilled whole blood were either centrifuged and processed within 2 hours of collection (the "reference standard") or were stored with cool packs for 24 or 48 hours; centrifuged before and/or after this delay; or collected in tubes with/without gel separators. We used linear mixed models with random intercepts to estimate geometric mean concentrations and relative percent differences across the conditions. Results: Compared to the reference standard tubes, concentrations of many biomarkers changed after processing delays, but changes were often small. In serum, we observed large differences for B vitamers, glutamic acid (37% and 73% increases with 24- and 48-hour delays, respectively), glycine (12% and 23% increases), serine (16% and 27% increases), and acetoacetate (-19% and -26% decreases). Centrifugation timing and separator tube use did not affect concentrations of most biomarkers. Conclusion: Sample handling should be consistent across samples within an analysis. The length of processing delays should be recorded and accounted for when this is not feasible.

Chronic GvHD NIH Consensus Project Biology Task Force: evolving path to personalized treatment of chronic GvHD

Chronic graft-versus-host disease (cGvHD) remains a prominent barrier to allogeneic hematopoietic stem cell transplantion as the leading cause of nonrelapse mortality and significant morbidity. Tremendous progress has been achieved in both the understanding of pathophysiology and the development of new therapies for cGvHD. Although our field has historically approached treatment from an empiric position, research performed at the bedside and bench has elucidated some of the complex pathophysiology of cGvHD. From the clinical perspective, there is significant variability of disease manifestations between individual patients, pointing to diverse biological underpinnings. Capitalizing on progress made to date, the field is now focused on establishing personalized approaches to treatment. The intent of this article is to concisely review recent knowledge gained and formulate a path toward patient-specific cGvHD therapy.

Evaluation of Small Molecules in Blank EDTA Plasma Tubes and Optimization of Metabolomic Workflow for Biomarker Studies Using Plasma Samples

As the first step of metabolomic analysis in biomarker identification studies, various types of blood collection tubes are used in clinical practice. However, little attention is paid to potential contamination caused by the blank tube itself. Here, we evaluated small molecules in blank EDTA plasma tubes through LC-MS-based untargeted metabolomic analysis and identified small molecules with markedly varied levels among different production batches or specifications. Our data demonstrate possible contamination and data interference caused by blank EDTA plasma tubes when employing large clinical cohorts for biomarker identification. Therefore, we propose a workflow of filtering metabolites in blank tubes prior to statistical analysis to improve the fidelity of biomarker identification.

Comprehensive Lipidomic Workflow for Multicohort Population Phenotyping Using Stable Isotope Dilution Targeted Liquid Chromatography-Mass Spectrometry

Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, requiring advanced methods that are accurate and robust to batch-to-batch and interday analytical variation. Herein, an optimized comprehensive targeted workflow for plasma and serum lipid quantification is presented, combining stable isotope internal standard dilution, automated sample preparation, and ultrahigh performance liquid chromatography-tandem mass spectrometry with rapid polarity switching to target 1163 lipid species spanning 20 subclasses. The resultant method is robust to common sources of analytical variation including blood collection tubes, hemolysis, freeze-thaw cycles, storage stability, analyte extraction technique, interinstrument variation, and batch-to-batch variation with 820 lipids reporting a relative standard deviation of <30% in 1048 replicate quality control plasma samples acquired across 16 independent batches (total injection count = 6142). However, sample hemolysis of ≥0.4% impacted lipid concentrations, specifically for phosphatidylethanolamines (PEs). Low interinstrument variability across two identical LC-MS systems indicated feasibility for intra/inter-lab parallelization of the assay. In summary, we have optimized a comprehensive lipidomic protocol to support rigorous analysis for large-scale, multibatch applications in precision medicine. The mass spectrometry lipidomics data have been deposited to massIVE: data set identifiers MSV000090952 and 10.25345/C5NP1WQ4S.

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).

Defective determination of synthetic cathinones in blood for forensic investigation

Antemortem specimens are sometimes the sole sources available for forensic investigation, and samples collected in nonideal ways are inevitably employed to achieve toxicological analysis. It is essential to assess the effects of blood collection tubes on the recoveries of emerging synthetic cathinones (SC) to estimate actual drug concentrations, and no such systematic investigations have been previously carried out. Seventy-one SC with various LogP values were employed to examine commonly used blood collection tubes, including plasma tubes, serum tubes and gel-containing tubes in recoveries which determined by a reliable LC-MS/MS method. Significantly poor recoveries for hydrophobic SC were obtained using serum separating tubes (SST). Notably, the suppressed recoveries in SST can be reversed by adding anticoagulants. Adding a procoagulant to a plasma separating tube (PST) considerably reduced recoveries, which indicated that clotting processes in the presence of polymeric gels contributed to poor recoveries of these hydrophobic drugs. In this study, we find that clotting formation in the presence of polymeric gels could significantly affect the determination of hydrophobic drugs. However, in real-world scenarios, nonideal collection methods are inevitably employed for antemortem specimens. Thus, it is important to rigorously interpret forensic toxicological results, especially for susceptible species.

Metabolomic analysis-identified 2-hydroxybutyric acid might be a key metabolite of severe preeclampsia

This study set out to determine the key metabolite changes underlying the pathophysiology of severe preeclampsia (PE) using metabolic analysis. We collected sera from 10 patients with severe PE and from 10 healthy pregnant women of the same trimester and analyzed them using liquid chromatography mass spectrometry. A total of 3,138 differential metabolites were screened, resulting in the identification of 124 differential metabolites. Kyoto encyclopedia of genes and genomes pathway analysis revealed that they were mainly enriched in the following metabolic pathways: central carbon metabolism in cancer; protein digestion and absorption; aminoacyl-transfer RNA biosynthesis; mineral absorption; alanine, aspartate, and glutamate metabolism; and prostate cancer. After analysis of 124 differential metabolites, 2-hydroxybutyric acid was found to be the most critical differential metabolite, and its use allowed the differentiation of women with severe PE from healthy pregnant women. In summary, our analysis revealed that 2-hydroxybutyric acid is a potential key metabolite for distinguishing severe PE from healthy controls and is also a marker for the early diagnosis of severe PE, thus allowing early intervention.

Plasma Oxylipins and Their Precursors Are Strongly Associated with COVID-19 Severity and with Immune Response Markers

COVID-19 is characterised by a dysregulated immune response, that involves signalling lipids acting as mediators of the inflammatory process along the innate and adaptive phases. To promote understanding of the disease biochemistry and provide targets for intervention, we applied a range of LC-MS platforms to analyse over 100 plasma samples from patients with varying COVID-19 severity and with detailed clinical information on inflammatory responses (>30 immune markers). The second publication in a series reports the results of quantitative LC-MS/MS profiling of 63 small lipids including oxylipins, free fatty acids, and endocannabinoids. Compared to samples taken from ward patients, intensive care unit (ICU) patients had 2−4-fold lower levels of arachidonic acid (AA) and its cyclooxygenase-derived prostanoids, as well as lipoxygenase derivatives, exhibiting negative correlations with inflammation markers. The same derivatives showed 2−5-fold increases in recovering ward patients, in paired comparison to early hospitalisation. In contrast, ICU patients showed elevated levels of oxylipins derived from poly-unsaturated fatty acids (PUFA) by non-enzymatic peroxidation or activity of soluble epoxide hydrolase (sEH), and these oxylipins positively correlated with markers of macrophage activation. The deficiency in AA enzymatic products and the lack of elevated intermediates of pro-resolving mediating lipids may result from the preference of alternative metabolic conversions rather than diminished stores of PUFA precursors. Supporting this, ICU patients showed 2-to-11-fold higher levels of linoleic acid (LA) and the corresponding fatty acyl glycerols of AA and LA, all strongly correlated with multiple markers of excessive immune response. Our results suggest that the altered oxylipin metabolism disrupts the expected shift from innate immune response to resolution of inflammation.

On Methods for the Measurement of the Apelin Receptor Ligand Apelin

Apelin exists in many isoforms, both in the circulation and in specific tissues. Apelin peptides have a short half-life but preservation before measurement is scarcely studied. Reproducible mass spectrometry methods to specifically measure a broad range of apelinergic peptide isoforms are currently lacking. A sample protocol to conserve apelinergic peptides in the preanalytical phase and a high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method to measure apelinergic isoforms was developed. Apelin was measured in plasma. For validation, human embryonic kidney (HEK) cells transfected with cDNA for preproapelin were used. Results were compared with a validated radioimmunoassay (RIA) method. Acidifying plasma to pH 2.5 improves post-sampling stability of apelin. HPLC-MS/MS was unable to detect apelin isoforms in plasma of healthy volunteers (n = 16) and chronic kidney disease patients (n = 4). RIA could detect apelin in concentrations between 71 and 263 fmol/l in 10 healthy volunteers. An optimized preanalytical protocol was developed. A sensitive and specific HPLC-MS/MS method failed to detect apelin in human plasma. Apelin-36 was detected in HEK cells transfected with cDNA for preproapelin. Currently, RIA with relatively selective antibodies is the best alternative for the measurement of apelin but novel sensitive and specific methods are needed.

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.

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.

Metabolomics in asthma: A platform for discovery

Asthma, characterized by airway hyperresponsiveness, inflammation and remodeling, is a chronic airway disease with complex etiology. Severe asthma is characterized by frequent exacerbations and poor therapeutic response to conventional asthma therapy. A clear understanding of cellular and molecular mechanisms of asthma is critical for the discovery of novel targets for optimal therapeutic control of asthma. Metabolomics is emerging as a powerful tool to elucidate novel disease mechanisms in a variety of diseases. In this review, we summarize the current status of knowledge in asthma metabolomics at systemic and cellular levels. The findings demonstrate that various metabolic pathways, related to energy metabolism, macromolecular biosynthesis and redox signaling, are differentially modulated in asthma. Airway smooth muscle cell plays pivotal roles in asthma by contributing to airway hyperreactivity, inflammatory mediator release and remodeling. We posit that metabolomic profiling of airway structural cells, including airway smooth muscle cells, will shed light on molecular mechanisms of asthma and airway hyperresponsiveness and help identify novel therapeutic targets.

Assessment of Circulating Nucleic Acids in Cancer: From Current Status to Future Perspectives and Potential Clinical Applications

Current approaches for cancer detection and characterization are based on radiological procedures coupled with tissue biopsies, despite relevant limitations in terms of overall accuracy and feasibility, including relevant patients' discomfort. Liquid biopsies enable the minimally invasive collection and analysis of circulating biomarkers released from cancer cells and stroma, representing therefore a promising candidate for the substitution or integration in the current standard of care. Despite the potential, the current clinical applications of liquid biopsies are limited to a few specific purposes. The lack of standardized procedures for the pre-analytical management of body fluids samples and the detection of circulating biomarkers is one of the main factors impacting the effective advancement in the applicability of liquid biopsies to clinical practice. The aim of this work, besides depicting current methods for samples collection, storage, quality check and biomarker extraction, is to review the current techniques aimed at analyzing one of the main circulating biomarkers assessed through liquid biopsy, namely cell-free nucleic acids, with particular regard to circulating tumor DNA (ctDNA). ctDNA current and potential applications are reviewed as well.

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.

Study Protocol: Design and Implementation of the Pediatric Liver Transplantation Biobank

Background: Early treatment of neonatal biliary atresia (BA) and other end-stage liver diseases can delay or prevent the necessity of liver transplantation (LT). The establishment of a standardized clinical pediatric liver transplantation (PLT) biobank is the prerequisite for scientific research, which helps to provide a qualified sample resource platform for research. Methods: Following standardized procedures to establish biobanks, the operational processes and quality control system were formulated. Liver tissue, blood, and stool samples undergoing LT were regularly collected, managed, and stored. Systematic management was conducted in collected specimens and corresponding clinical information. Results: Since implementation in August 2018, we have enrolled 49 unique subjects (0-18 years of age); the biobank contains nearly 3000 biospecimen aliquots. The most common LT diagnosis is BA (61.23%). Conclusion: The establishment of this biobank is a valuable resource that incorporates detailed clinical and biological information. It will help accelerate the pace of PLT discovery research. ClinicalTrials.gov ID: NCT04477967.

Large-scale plasma-metabolome analysis identifies potential biomarkers of psoriasis and its clinical subtypes

BACKGROUND

Psoriasis is an immune-mediated skin disease for which the crosstalk between genetic and environmental factors is responsible. To date, no definitive diagnostic criteria for psoriasis yet, and specific biomarkers are required.

OBJECTIVE

We performed metabolome analysis to identify metabolite biomarkers of psoriasis and its subtypes such as psoriatic arthritis (PsA) and cutaneous psoriasis (PsC).

METHODS

We constructed metabolomics profiling of 130 plasma samples (42 PsA patients, 50 PsC patients, and 38 healthy controls) using a nontargeted metabolomics approach.

RESULTS

Psoriasis-control association tests showed that one metabolite (ethanolamine phosphate) was significantly increased in psoriasis samples than in the controls, whereas three metabolites decreased (false discovery rate [FDR] < 0.05; XA0019, nicotinic acid, and 20α-hydroxyprogesterone). In the association test between PsA and PsC, tyramine significantly increased in PsA than in PsC, whereas mucic acid decreased (FDR < 0.05). Molecular pathway analysis of the PsA-PsC association test identified enrichment of vitamin digestion and absorption pathway in PsC (P = 1.3 × 10^-4). Correlation network analyses elucidated that a subnetwork centered on aspartate was constructed among the psoriasis-associated metabolites; meanwhile, the major subnetwork among metabolites with differences between PsA and PsC was primarily formed from saturated fatty acids.

CONCLUSION

Our large-scale metabolome analysis highlights novel characteristics of plasma metabolites in psoriasis and the differences between PsA and PsC, which could be used as potential biomarkers of psoriasis and its clinical subtypes. These findings contribute to our understanding of psoriasis pathophysiology.

Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.

Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules

Background: Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. Materials and Methods: Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV1]/forced vital capacity [FVC], and FEV1 percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions. Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics. Conclusion: A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors.

Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples

Quantitative nuclear magnetic resonance (NMR) spectroscopy of blood plasma is widely used to investigate perturbed metabolic processes in human diseases. The reliability of biochemical data derived from these measurements is dependent on the quality of the sample collection and exact preparation and analysis protocols. Here, we describe systematically, the impact of variations in sample collection and preparation on information recovery from quantitative proton (1H) NMR spectroscopy of human blood plasma and serum. The effects of variation of blood collection tube sizes and preservatives, successive freeze-thaw cycles, sample storage at -80 °C, and short-term storage at 4 and 20 °C on the quantitative lipoprotein and metabolite patterns were investigated. Storage of plasma samples at 4 °C for up to 48 h, freezing at -80 °C and blood sample collection tube choice have few and minor effects on quantitative lipoprotein profiles, and even storage at 4 °C for up to 168 h caused little information loss. In contrast, the impact of heat-treatment (56 °C for 30 min), which has been used for inactivation of SARS-CoV-2 and other viruses, that may be required prior to analytical measurements in low level biosecurity facilities induced marked changes in both lipoprotein and low molecular weight metabolite profiles. It was conclusively demonstrated that this heat inactivation procedure degrades lipoproteins and changes metabolic information in complex ways. Plasma from control individuals and SARS-CoV-2 infected patients are differentially altered resulting in the creation of artifactual pseudo-biomarkers and destruction of real biomarkers to the extent that data from heat-treated samples are largely uninterpretable. We also present several simple blood sample handling recommendations for optimal NMR-based biomarker discovery investigations in SARS CoV-2 studies and general clinical biomarker research.

Increased levels of plasma nucleotides in patients with rheumatoid arthritis

Novel biomarkers of rheumatoid arthritis (RA), in addition to antibodies against cyclic citrullinated peptides, are required. Metabolome analysis is a promising approach to identify metabolite biomarkers for clinical diagnosis. We adopted a comprehensive non-targeted metabolomics approach combining capillary electrophoresis time-of-flight mass spectrometry (TOFMS) and liquid chromatography TOFMS. We constructed metabolomics profiling of 286 plasma samples of a Japanese population [92 RA patients, 13 systemic lupus erythematosus (SLE) patients and 181 healthy controls). RA case–control association tests showed that seven metabolites exhibited significantly increased levels in RA samples compared with controls (P < 1.0 × 10⁻⁴; UTP, ethanolamine phosphate, ATP, GDP, ADP, 6-aminohexanoic acid and taurine), whereas one exhibited a decreased level (xanthine). The plasma levels of these eight metabolites were not significantly different between seropositive and seronegative RA patients (P > 0.05; n = 68 and 24, respectively). The four nucleotide levels (UTP, ATP, GDP and ADP) were significantly higher in the non-treatment patients in comparison between patients with and without treatment (P < 0.014; n = 57 and 35, respectively). Furthermore, we found that none of the four nucleotide levels showed significant differences in SLE case–control association tests (P > 0.2; 13 patients with SLE and the 181 shared controls) and psoriatic arthritis (PsA) case–control association tests (P > 0.11; 42 patients with PsA and 38 healthy controls), indicating disease specificity in RA. In conclusion, our large-scale metabolome analysis demonstrated the increased plasma nucleotide levels in RA patients, which could be used as potential clinical biomarkers of RA, especially for seronegative RA.

Developing preliminary blood metabolomics-based biomarkers of insufficient sleep in humans

STUDY OBJECTIVE

Identify small molecule biomarkers of insufficient sleep using untargeted plasma metabolomics in humans undergoing experimental insufficient sleep.

METHODS

We conducted a crossover laboratory study where 16 normal-weight participants (eight men; age 22 ± 5 years; body mass index < 25 kg/m2) completed three baseline days (9 hours sleep opportunity per night) followed by 5-day insufficient (5 hours sleep opportunity per night) and adequate (9 hours sleep opportunity per night) sleep conditions. Energy balanced diets were provided during baseline, with ad libitum energy intake provided during the insufficient and adequate sleep conditions. Untargeted plasma metabolomics analyses were performed using blood samples collected every 4 hours across the final 24 hours of each condition. Biomarker models were developed using logistic regression and linear support vector machine (SVM) algorithms.

RESULTS

The top-performing biomarker model was developed by linear SVM modeling, consisted of 65 compounds, and discriminated insufficient versus adequate sleep with 74% overall accuracy and a Matthew's Correlation Coefficient of 0.39. The compounds in the top-performing biomarker model were associated with ATP Binding Cassette Transporters in Lipid Homeostasis, Phospholipid Metabolic Process, Plasma Lipoprotein Remodeling, and sphingolipid metabolism.

CONCLUSION

We identified potential metabolomics-based biomarkers of insufficient sleep in humans. Although our current biomarkers require further development and validation using independent cohorts, they have potential to advance our understanding of the negative consequences of insufficient sleep, improve diagnosis of poor sleep health, and could eventually help identify targets for countermeasures designed to mitigate the negative health consequences of insufficient sleep.

Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

PURPOSE OF REVIEW

Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer.

RECENT FINDINGS

Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

Intensive Care Unit Delirium in Surgical Patients Is Associated with Upregulation in Tryptophan Metabolism

INTRODUCTION

In intensive care unit (ICU) patients, delirium is frequent, occurs early in ICU admission, and is associated with poor outcomes. Risk models based on clinical factors have shown variable performance in terms of predictive ability. Identification of a candidate biomarker that associates with delirium may lead to a better understanding of disease mechanism, validation biomarker studies, and the ability to develop targeted interventions for prevention and treatment of delirium. This study analyzed metabolite concentrations early in the course of ICU admission to assess the association with delirium onset.

METHODS

Within 24 hours of ICU admission, blood samples for global and targeted metabolomics analyses in adult surgical ICU patients were collected prospectively. Metabolites were determined using mass spectrometry/ultra-high-pressure liquid chromatography and analyzed in patients with delirium and a group of controls matched on age, sex, and admission Sequential Organ Function Assessment (SOFA) score.

RESULTS

Patients in the study (65 per group) were a mean age of 59 years, had a median SOFA score of 6, and were most commonly admitted to the ICU following major trauma. In the delirium group, median onset of delirium was 3 (interquartile range 1-6) days, and the most common delirium subtype was mixed (56%). Kynurenic acid was significantly increased, and tryptophan concentration was significantly decreased in the delirium group (p=0.04). The ratio of kynurenine-to-tryptophan concentration was significantly higher in the delirium group (p=0.005).

CONCLUSIONS

Evidence of upregulation was found in the tryptophan metabolic pathway in delirious patients because tryptophan concentrations were lower, tryptophan metabolites were higher, and the kynurenine-to-tryptophan ratio was increased. These findings suggest a role of increased inflammation and accumulation of neurotoxic metabolites in the pathogenesis of ICU delirium. Future studies should target this pathway to validate metabolites in the tryptophan pathway as risk biomarkers in patients with ICU delirium.

"Lipidomics": Mass spectrometric and chemometric analyses of lipids

Lipids are ubiquitous in the human organism and play essential roles as components of cell membranes and hormones, for energy storage or as mediators of cell signaling pathways. As crucial mediators of the human metabolism, lipids are also involved in metabolic diseases, cardiovascular and renal diseases, cancer and/or hepatological and neurological disorders. With rapidly growing evidence supporting the impact of lipids on both the genesis and progression of these diseases as well as patient wellbeing, the characterization of the human lipidome has gained high interest and importance in life sciences and clinical diagnostics within the last 15 years. This is mostly due to technically advanced molecular identification and quantification methods, mainly based on mass spectrometry. Mass spectrometry has become one of the most powerful tools for the identification of lipids. New lipidic mediators or biomarkers of diseases can be analysed by state-of-the art mass spectrometry techniques supported by sophisticated bioinformatics and biostatistics. The lipidomic approach has developed dramatically in the realm of life sciences and clinical diagnostics due to the available mass spectrometric methods and in particular due to the adaptation of biostatistical methods in recent years. Therefore, the current knowledge of lipid extraction methods, mass-spectrometric approaches, biostatistical data analysis, including workflows for the interpretation of lipidomic high-throughput data, are reviewed in this manuscript.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.