The influence of EDTA and citrate anticoagulant addition to human plasma on information recovery from NMR-based metabolic profiling studies

Recommendations for sample selection, collection and preparation for NMR-based metabolomics studies of blood

BACKGROUND

Metabolic profiling of blood metabolites, particularly in plasma and serum, is vital for studying human diseases, human conditions, drug interventions and toxicology. The clinical significance of blood arises from its close ties to all human cells and facile accessibility. However, patient-specific variables such as age, sex, diet, lifestyle and health status, along with pre-analytical conditions (sample handling, storage, etc.), can significantly affect metabolomic measurements in whole blood, plasma, or serum studies. These factors, referred to as confounders, must be mitigated to reveal genuine metabolic changes due to illness or intervention onset.

REVIEW OBJECTIVE

This review aims to aid metabolomics researchers in collecting reliable, standardized datasets for NMR-based blood (whole/serum/plasma) metabolomics. The goal is to reduce the impact of confounding factors and enhance inter-laboratory comparability, enabling more meaningful outcomes in metabolomics studies.

KEY CONCEPTS

This review outlines the main factors affecting blood metabolite levels and offers practical suggestions for what to measure and expect, how to mitigate confounding factors, how to properly prepare, handle and store blood, plasma and serum biosamples and how to report data in targeted NMR-based metabolomics studies of blood, plasma and serum.

Speciation of Potentially Carcinogenic Trace Nickel(II) Ion Levels in Human Saliva: A Sequential Metabolomics-Facilitated High-Field 1H NMR Investigation

Introduction/Objectives: Since the biological activities and toxicities of 'foreign' and/or excess levels of metal ions are predominantly determined by their precise molecular nature, here we have employed high-resolution 1H NMR analysis to explore the 'speciation' of paramagnetic Ni(II) ions in human saliva, a potentially rich source of biomolecular Ni(II)-complexants/chelators. These studies are of relevance to the in vivo corrosion of nickel-containing metal alloy dental prostheses (NiC-MADPs) in addition to the dietary or adverse toxicological intake of Ni(II) ions by humans. Methods: Unstimulated whole-mouth human saliva samples were obtained from n = 12 pre-fasted (≥8 h) healthy participants, and clear whole-mouth salivary supernatants (WMSSs) were obtained from these via centrifugation. Microlitre aliquots of stock aqueous Ni(II) solutions were sequentially titrated into WMSS samples via micropipette. Any possible added concentration-dependent Ni(II)-mediated pH changes therein were experimentally controlled. 1H NMR spectra were acquired on a JEOL JNM-ECZ600R/S1 spectrometer. Results: Univariate and multivariate (MV) metabolomics and MV clustering analyses were conducted in a sequential stepwise manner in order to follow the differential effects of increasing concentrations of added Ni(II). The results acquired showed that important Ni(II)-responsive biomolecules could be clustered into distinguishable patterns on the basis of added concentration-dependent responses of their resonance intensities and line widths. At low added concentrations (71 µmol/L), low-WMSS-level N-donor amino acids (especially histidine) and amines with relatively high stability constants for this paramagnetic metal ion were the most responsive (severe resonance broadenings were observed). However, at higher Ni(II) concentrations (140-670 µmol/L), weaker carboxylate O-donor ligands such as lactate, formate, succinate, and acetate were featured as major Ni(II) ligands, a consequence of their much higher WMSS concentrations, which were sufficient for them to compete for these higher Ni(II) availabilities. From these experiments, the metabolites most affected were found to be histidine ≈ methylamines > taurine ≈ lactate ≈ succinate > formate > acetate ≈ ethanol ≈ glycine ≈ N-acetylneuraminate, although they predominantly comprised carboxylato oxygen donor ligands/chelators at the higher added Ni(II) levels. Removal of the interfering effects arising from the differential biomolecular compositions of the WMSS samples collected from different participants and those from the effects exerted by a first-order interaction effect substantially enhanced the statistical significance of the differences observed between the added Ni(II) levels. The addition of EDTA to Ni(II)-treated WMSS samples successfully reversed these resonance modifications, an observation confirming the transfer of Ni(II) from the above endogenous complexants to this exogenous chelator to form the highly stable diamagnetic octahedral [Ni(II)-EDTA] complex (Kstab = 1.0 × 1019 M-1). Conclusions: The results acquired demonstrated the value of linking advanced experimental design and multivariate metabolomics/statistical analysis techniques to 1H NMR analysis for such speciation studies. These provided valuable molecular information regarding the identities of Ni(II) complexes in human saliva, which is relevant to trace metal ion speciation and toxicology, the in vivo corrosion of NiC-MADPs, and the molecular fate of ingested Ni(II) ions in this biofluid. The carcinogenic potential of these low-molecular-mass Ni(II) complexes is discussed.

Preanalytical (Mis)Handling of Plasma Investigated by 1H NMR Metabolomics

The preanalytical handling of plasma, how it is drawn, processed, and stored, influences its composition. Samples in biobanks often lack this information and, consequently, important information about their quality. Especially metabolite concentrations are affected by preanalytical handling, making conclusions from metabolomics studies particularly sensitive to misinterpretations. The perturbed metabolite profile, however, also offers an attractive choice for assessing the preanalytical history from the measured data. Here we show that it is possible using Orthogonal Projections to Latent Structures Discriminative Analysis to divide plasma NMR data into a multivariate "original sample space" suitable for further less biased metabolomics analysis and an orthogonal "preanalytical handling space" describing the changes occurring from preanalytical mishandling. Apart from confirming established preanalytical effects on metabolite levels, e.g., the consequent changes in glucose, lactate, ornithine, and pyruvate, the sample preparation protocol involved methanol precipitation which allowed the observation of reversible changes in short-chain fatty acid concentrations as a function of temperature.

Metabolomics: Unveiling biological matrices in precision nutrition and health

Precision nutrition, an expanding field at the intersection of nutrition science and personalized medicine, is rapidly evolving with metabolomics integration. Metabolomics, facilitated by advanced technologies like mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, facilitates comprehensive profiling of metabolites across diverse biological samples. From the perspective of health care systems, precision nutrition gains relevance due to the substantial impact of prevalent non-communicable diseases (NCDs) on societal well-being, which is directly linked with dietary habits and eating behavior. Furthermore, biomarker products derived from metabolomics have been utilized in Europe, the USA, and Brazil to understand metabolic dysregulations and tailor diets accordingly. Despite its burgeoning status, metabolomics holds great potential in revolutionizing nutritional science, particularly with the integration of artificial intelligence and machine learning, offering novel insights into personalized dietary interventions and disease prediction. This narrative review emphasizes the transformative impact of metabolomics in precision and delineates avenues for future research and application, paving the way for a more tailored and practical approach to nutrition management.

An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples

Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.

Synthesis and Design of Hybrid Metalloporphyrin Polymers Based on Palladium (II) and Copper (II) Cations and Axial Complexes of Pyridyl-Substituted Sn(IV)Porphyrins with Octopamine

Supramolecular metalloporphyrin polymers formed by binding tetrapyrrolic macrocycle peripheral nitrogen atoms to Pd(II) cations and Sn(IV)porphyrins extra-ligands reaction centers to Cu(II) cations were obtained and identified. The structure and the formation mechanism of obtained hydrophobic Sn(IV)-porphyrin oligomers and polymers in solution were established, and their resistance to UV radiation and changes in solution temperature was studied. It was shown that the investigated polyporphyrin nanostructures are porous materials with predominance cylindrical mesopores. Density functional theory (DFT) was used to geometrically optimize the experimentally obtained supramolecular porphyrin polymers. The sizes of unit cells in porphyrin tubular structures were determined and coincided with the experimental data. The results obtained can be used to create highly porous materials for separation, storage, transportation, and controlled release of substrates of different nature, including highly volatile, explosive, and toxic gases.

NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)

Each year, bovine respiratory disease (BRD) results in significant economic loss in the cattle sector, and novel metabolic profiling for early diagnosis represents a promising tool for developing effective measures for disease management. Here, ¹H-nuclear magnetic resonance (¹H-NMR) spectra were used to characterize metabolites from blood plasma collected from male dairy calves (n = 10) intentionally infected with two of the main BRD causal agents, bovine respiratory syncytial virus (BRSV) and Mannheimia haemolytica (MH), to generate a well-defined metabolomic profile under controlled conditions. In response to infection, 46 metabolites (BRSV = 32, MH = 33) changed in concentration compared to the uninfected state. Fuel substrates and products exhibited a particularly strong effect, reflecting imbalances that occur during the immune response. Furthermore, ¹H-NMR spectra from samples from the uninfected and infected stages were discriminated with an accuracy, sensitivity, and specificity ≥ 95% using chemometrics to model the changes associated with disease, suggesting that metabolic profiles can be used for further development, understanding, and validation of novel diagnostic tools.

NMR technique revealed the metabolic interference mechanism of the combined exposure to cadmium and tributyltin in grass carp larvae

Widespread human activity has resulted in the presence of different pollutants in the aquatic environment that does not exist in isolation. The study of the effects of contamination of aquatic organisms is of great significance. To assess the individual and combined toxicity of cadmium (Cd) and tributyltin (TBT) to aquatic organisms, juvenile grass carp (Ctenopharyngodon idella) were exposed to Cd (2.97 mg/L), TBT (7.5 μg/L), and their mixture MIX. The biological response was evaluated by nuclear magnetic resonance (NMR) analysis of plasma metabolites. Plasma samples at 1, 2, 4, 8, 16, 32, and 48 days post-exposure were analyzed using detection by NMR technique. The typical correlation analysis (CCA) analysis revealed that TBT had the greatest effect on plasma metabolism, followed by MIX and Cd. The interference pathway to grass carp was similar to that of TBT and MIX. Both Cd and TBT exposure alone or in combination can lead to metabolic abnormalities in TCA cycle-related pathways and interfere with energy metabolism. These results provide more detailed information for the metabolic study of pollutants and data for assessing the health risks of Cd, TBT, and MIX at the metabolic level.

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.

Trans- and Multigenerational Maternal Social Isolation Stress Programs the Blood Plasma Metabolome in the F3 Generation

Metabolic risk factors are among the most common causes of noncommunicable diseases, and stress critically contributes to metabolic risk. In particular, social isolation during pregnancy may represent a salient stressor that affects offspring metabolic health, with potentially adverse consequences for future generations. Here, we used proton nuclear magnetic resonance (¹H NMR) spectroscopy to analyze the blood plasma metabolomes of the third filial (F3) generation of rats born to lineages that experienced either transgenerational or multigenerational maternal social isolation stress. We show that maternal social isolation induces distinct and robust metabolic profiles in the blood plasma of adult F3 offspring, which are characterized by critical switches in energy metabolism, such as upregulated formate and creatine phosphate metabolisms and downregulated glucose metabolism. Both trans- and multigenerational stress altered plasma metabolomic profiles in adult offspring when compared to controls. Social isolation stress increasingly affected pathways involved in energy metabolism and protein biosynthesis, particularly in branched-chain amino acid synthesis, the tricarboxylic acid cycle (lactate, citrate), muscle performance (alanine, creatine phosphate), and immunoregulation (serine, threonine). Levels of creatine phosphate, leucine, and isoleucine were associated with changes in anxiety-like behaviours in open field exploration. The findings reveal the metabolic underpinnings of epigenetically heritable diseases and suggest that even remote maternal social stress may become a risk factor for metabolic diseases, such as diabetes, and adverse mental health outcomes. Metabolomic signatures of transgenerational stress may aid in the risk prediction and early diagnosis of non-communicable diseases in precision medicine approaches.

Metabolomic Profile in Patients with Malignant Disturbances of the Prostate: An Experimental Approach

Purpose To identify metabolites in humans that can be associated with the presence of malignant disturbances of the prostate.

Methods In the present study, we selected male patients aged between 46 and 82 years who were considered at risk of prostate cancer due to elevated levels of prostate-specific antigen (PSA) or abnormal results on the digital rectal examination. All selected patients came from two university hospitals (Hospital Universitario del Valle and Clínica Rafael Uribe Uribe) and were divided into 2 groups: cancer (12 patients) and non-cancer (20 patients). Cancer was confirmed by histology, and none of the patients underwent any previous treatment. Standard protocols were applied to all the collected blood samples. The resulting plasma samples were kept at -80°C, and a profile of each one was acquired by nuclear magnetic resonance (NMR) using established experiments. Multivariate analyses were applied to this dataset, first to establish the quality of the data and identify outliers, and then, to model the data.

Results We included 12 patients with cancer and 20 without it. Two patients were excluded due to contamination with ethanol. The remaining ones were used to build an Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model (including 15 non-cancer and 10 cancer patients), with acceptable discrimination (Q2 = 0.33). This model highlighted the role of lactate and lipids, with a positive association of these two metabolites and prostate cancer.

Conclusions The primary discriminative metabolites between patients with and without prostate cancer were lactate and lipids. These might be the most reliable biomarkers to trace the development of cancer in the prostate.

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.

Impact of the pre-examination phase on multicenter metabolomic studies

The development of metabolomics in clinical applications has been limited by the lack of validation in large multicenter studies. Large population cohorts and their biobanks are a valuable resource for acquiring insights into molecular disease mechanisms. Nevertheless, most of their collections are not tailored for metabolomics and have been created without specific attention to the pre-analytical requirements for high-quality metabolome assessment. Thus, comparing samples obtained by different pre-analytical procedures remains a major challenge. Here, ¹H NMR-based analyses are used to demonstrate how human serum and plasma samples collected with different operating procedures within several large European cohort studies from the Biobanking and Biomolecular Resources Infrastructure - Large Prospective Cohorts (BBMRI-LPC) consortium can be easily revealed by supervised multivariate statistical analyses at the initial stages of the process, to avoid biases in the downstream analysis. The inter-biobank differences are discussed in terms of deviations from the validated CEN/TS 16945:2016 / ISO 23118:2021 norms. It clearly emerges that biobanks must adhere to the evidence-based guidelines in order to support wider-scale application of metabolomics in biomedicine, and that NMR spectroscopy is informative in comparing the quality of different sample sources in multi cohort/center studies.

Role of hypertension in kidney transplant recipients

Cardiovascular events are one of the leading causes of mortality in kidney transplant recipients. Hypertension is the most common comorbidity accompanying chronic kidney disease, with prevalence remaining as high as 90% even after kidney transplantation. It is often poorly controlled. Abnormal blood pressure profiles, such as masked or white-coat hypertension, are also extremely common in these patients. The pathophysiology of blood pressure elevation in kidney transplant recipients is complex and includes transplantation-specific risk factors, which are added to the traditional or chronic kidney disease-related factors. Despite these observations, hypertension management has been an under-researched area in kidney transplantation. Thus, relevant evidence derives either from studies in the general population or from small trials in kidney transplant recipients. Based on the relevant guidelines in the general population, lifestyle modifications should probably be applied as the first step of hypertension management in kidney transplant recipients. The optimal pharmacological management of hypertension in kidney transplant recipients is also not clear. Dihydropyridine calcium channel blockers are commonly used as first line agents because of their lack of adverse effects on the kidney, while other antihypertensive drug classes are under-utilised due to fear of the possible haemodynamic consequences on renal function. This review summarizes the existing data on the pathophysiology, diagnosis, prognostic significance and management of hypertension in kidney transplantation.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

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.

Pre-analytical considerations in biomarker research: focus on cardiovascular disease

Clinical biomarker research is growing at a fast pace, particularly in the cardiovascular field, due to the demanding requirement to provide personalized precision medicine. The lack of a distinct molecular signature for each cardiovascular derangement results in a one-size-fits-all diagnostic and therapeutic approach, which may partially explain suboptimal outcomes in heterogeneous cardiovascular diseases (e.g., heart failure with preserved ejection fraction). A multidimensional approach using different biomarkers is quickly evolving, but it is necessary to consider pre-analytical variables, those to which a biological sample is subject before being analyzed, namely sample collection, handling, processing, and storage. Pre-analytical errors can induce systematic bias and imprecision, which may compromise research results, and are easy to avoid with an adequate study design. Academic clinicians and investigators must be aware of the basic considerations for biospecimen management and essential pre-analytical recommendations as lynchpin for biological material to provide efficient and valid data.

Improved Automated Quantification Algorithm (AQuA) and Its Application to NMR-Based Metabolomics of EDTA-Containing Plasma

We have recently presented an Automated Quantification Algorithm (AQuA) and demonstrated its utility for rapid and accurate absolute metabolite quantification in ¹H NMR spectra in which positions and line widths of signals were predicted from a constant metabolite spectral library. The AQuA quantifies based on one preselected signal per metabolite and employs library spectra to model interferences from other metabolite signals. However, for some types of spectra, the interspectral deviations of signal positions and line widths can be pronounced; hence, interferences cannot be modeled using a constant spectral library. We here address this issue and present an improved AQuA that handles interspectral deviations. The improved AQuA monitors and characterizes the appearance of specific signals in each spectrum and automatically adjusts the spectral library to model interferences accordingly. The performance of the improved AQuA was tested on a large data set from plasma samples collected using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant (n = 772). These spectra provided a suitable test system for the improved AQuA since EDTA signals (i) vary in intensity, position, and line width between spectra and (ii) interfere with many signals from plasma metabolites targeted for quantification (n = 54). Without the improvement, ca. 20 out of the 54 metabolites would have been overestimated. This included acetylcarnitine and ornithine, which are considered particularly difficult to quantify with ¹H NMR in EDTA-containing plasma. Furthermore, the improved AQuA performed rapidly (<10 s for all spectra). We believe that the improved AQuA provides a basis for automated quantification in other data sets where specific signals show interspectral deviations.

Qualitative and quantitative 1 H NMR spectroscopy for determination of divalent metal cation concentration in model salt solutions, food supplements, and pharmaceutical products by using EDTA as chelating agent

This paper introduces an ¹ H NMR method to identify individual divalent metal cations Be²⁺ , Mg²⁺ , Ca²⁺ , Sr²⁺ , Zn²⁺ , Cd²⁺ , Hg²⁺ , Sn²⁺ , and Pb²⁺ in aqueous salt solutions through their unique signal shift and coupling after complexation with the salt of ethylenediaminetetraacetic acid (EDTA). Furthermore, quantitative determination applied for the divalent metal cations Ca²⁺ , Mg²⁺ , Hg²⁺ , Sn²⁺ , Pb²⁺ , and Zn²⁺ (limit of quantification: 5-22 μg/ml) can be achieved using an excess of EDTA with aqueous model salt solutions. An internal standard is not required because a known excess of EDTA is added and the remaining free EDTA can be used to recalculate the quantity of chelated metal cations. The utility of the method is demonstrated for the analysis of divalent cations in some food supplements and in pharmaceutical products.

Fast quantitative 2D NMR for metabolomics and lipidomics: A tutorial

Nuclear magnetic resonance (NMR) is a well-known analytical technique for the analysis of complex mixtures. Its quantitative capability makes it ideally suited to metabolomics or lipidomics studies involving large sample collections of complex biological samples. To overcome the ubiquitous limitation of spectral overcrowding when recording 1D NMR spectra on such samples, the acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. Moreover, the experiment duration can be considerably reduced by applying fast acquisition methods. Here, we describe the general workflow to acquire fast quantitative 2D NMR spectra in the "omics" context. It is illustrated on three representative and complementary experiments: UF COSY, ZF-TOCSY with nonuniform sampling, and HSQC with nonuniform sampling. After giving some details and recommendations on how to apply this protocol, its implementation in the case of targeted and untargeted metabolomics/lipidomics studies is described.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

NMR for sample quality assessment in metabolomics

The EU Framework 7 project SPIDIA was the occasion for development of NMR approaches to evaluate the impact of different pre-analytical treatments on the quality of biological samples dedicated to metabolomics. Systematic simulation of different pre-analytical procedures was performed on urine and blood serum and plasma. Here we review the key aspects of these studies that have led to the development of CEN technical specifications, to be translated into ISO/IS in the course of the EU Horizon 2020 project SPIDIA4P. Inspired by the SPIDIA results, follow-up research was performed, extending the analysis to different sample types and to the different effects of long-term storage. The latter activity was in conjunction with the local European da Vinci Biobank. These results (which partially contributed to the ANNEX of CEN/TS 16945"MOLECULAR IN VITRO DIAGNOSTIC EXAMINATIONS - SPECIFICATIONS FOR PRE-EXAMINATION PROCESSES FOR METABOLOMICS IN URINE, VENOUS BLOOD SERUM AND PLASMA") are presented in detail.

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

Background: Metabolomics is emerging as a valuable tool in clinical science. However, one major challenge in clinical metabolomics is the limited use of standardized guidelines for sample collection and handling. In this study, we conducted a pilot analysis of serum and plasma to determine the effects of processing time and collection tube on the metabolome. Methods: Blood was collected in 3 tubes: Vacutainer serum separator tube (SST, serum), EDTA (plasma) and P100 (plasma) and stored at 4 degrees for 0, 0.5, 1, 2, 4 and 24 h prior to centrifugation. Compounds were extracted using liquid-liquid extraction to obtain a hydrophilic and a hydrophobic fraction and analyzed using liquid chromatography mass spectrometry. Differences among the blood collection tubes and sample processing time were evaluated (ANOVA, Bonferroni FWER ≤ 0.05 and ANOVA, Benjamini Hochberg FDR ≤ 0.1, respectively). Results: Among the serum and plasma tubes 93.5% of compounds overlapped, 382 compounds were unique to serum and one compound was unique to plasma. There were 46, 50 and 86 compounds affected by processing time in SST, EDTA and P100 tubes, respectively, including many lipids. In contrast, 496 hydrophilic and 242 hydrophobic compounds differed by collection tube. Forty-five different chemical classes including alcohols, sugars, amino acids and prenol lipids were affected by the choice of blood collection tube. Conclusion: Our results suggest that the choice of blood collection tube has a significant effect on detected metabolites and their overall abundances. Perhaps surprisingly, variation in sample processing time has less of an effect compared to collection tube; however, a larger sample size is needed to confirm this.

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.

Pharmacometabolomics analysis of plasma to phenotype clopidogrel high on treatment platelets reactivity in coronary artery disease patients

Dual antiplatelet therapy (DAPT) of clopidogrel and aspirin is crucial for coronary artery disease (CAD) patients undergoing percutaneous coronary intervention (PCI). However, some patients may endure clopidogrel high on treatment platelets reactivity (HTPR) which may cause thromboembolic events. Clopidogrel HTPR is multifactorial with some genetic and non-genetic factors contributing to it. We aimed to use nuclear magnetic resonance (1H NMR) pharmacometabolomics analysis of plasma to investigate this multifactorial and identify metabolic phenotypes and pathways associated with clopidogrel HTPR. Blood samples were collected from 71 CAD patients planned for interventional angiographic procedure (IAP) before the administration of clopidogrel 600 mg loading dose (LD) and 6 h after the LD. Platelets function testing was done 6 h post-LD using VerifyNow® P2Y12 assay. Pre-dose and post-dose plasma samples were analysed using 1H NMR. Multivariate statistical analysis was used to indicate the discriminating metabolites. Two metabotypes, each with 34 metabolites (pre-dose and post-dose) were associated with clopidogrel HTPR. Pathway analysis of these metabotypes revealed that aminoacyl-tRNA biosynthesis, nitrogen metabolism and glycine-serine-threonine metabolism are the most perturbed metabolic pathways associated with clopidogrel HTPR. Furthermore, the identified biomarkers indicated that clopidogrel HTPR is multifactorial where the metabolic phenotypes of insulin resistance, type two diabetes mellitus, obesity, gut-microbiota and heart failure are associated with it. Pharmacometabolomics analysis of plasma revealed new insights on the implicated metabolic pathways and the predisposing factors of clopidogrel HTPR.

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.

Understanding preanalytical variables and their effects on clinical biomarkers of oncology and immunotherapy

Identifying a suitable course of immunotherapy treatment for a given patient as well as monitoring treatment response is heavily reliant on biomarkers detected and quantified in blood and tissue biospecimens. Suboptimal or variable biospecimen collection, processing, and storage practices have the potential to alter clinically relevant biomarkers, including those used in cancer immunotherapy. In the present review, we summarize effects reported for immunologically relevant biomarkers and highlight preanalytical factors associated with specific analytical platforms and assays used to predict and gauge immunotherapy response. Given that many of the effects introduced by preanalytical variability are gene-, transcript-, and protein-specific, biospecimen practices should be standardized and validated for each biomarker and assay to ensure accurate results and facilitate clinical implementation of newly identified immunotherapy approaches.

The Plasma and Serum Metabotyping of Hepatocellular Carcinoma in a Nigerian and Egyptian Cohort using Proton Nuclear Magnetic Resonance Spectroscopy

BACKGROUND/AIMS

Previous studies have observed disturbances in the ¹H nuclear magnetic resonance (NMR) blood spectral profiles in malignancy. No study has metabotyped serum or plasma of hepatocellular carcinoma (HCC) patients from two diverse populations. We aimed to delineate the HCC patient metabotype from Nigeria (mostly hepatitis B virus infected) and Egypt (mostly hepatitis C virus infected) to explore lipid and energy metabolite alterations that may be independent of disease aetiology, diet and environment.

METHODS

Patients with HCC (53) and cirrhosis (26) and healthy volunteers (19) were recruited from Nigeria and Egypt. Participants provided serum or plasma samples, which were analysed using 600 MHz ¹H NMR spectroscopy with nuclear Overhauser enhancement spectroscopy pulse sequences. Median group spectra comparison and multivariate analysis were performed to identify regions of difference.

RESULTS

Significant differences between HCC patients and healthy volunteers were detected in levels of low density lipoprotein (P = 0.002), very low density lipoprotein (P < 0.001) and lactate (P = 0.03). N-acetylglycoproteins levels in HCC patients were significantly different from both healthy controls and cirrhosis patients (P < 0.001 and 0.001).

CONCLUSION

Metabotype differences were present, pointing to disturbed lipid metabolism and a switch from glycolysis to alternative energy metabolites with malignancy, which supports the Warburg hypothesis of tumour metabolism.

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling

Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA–MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.

Drawbacks of Dialysis Procedures for Removal of EDTA

Ethylenediaminetetraacetic acid (EDTA) is a chelating agent commonly used in protein purification, both to eliminate contaminating divalent cations and to inhibit protease activity. For a number of subsequent applications EDTA needs to be exhaustively removed. Most purification methods rely in extensive dialysis and/or gel filtration in order to exchange or remove protein buffer components, including metal chelators. We report here that dialysis protocols, even as extensive as those typically employed for protein refolding, may not effectively remove EDTA, which is reduced only by approximately two-fold and it also persists after spin-column gel filtration, as determined by NMR and by colorimetric methods. Remarkably, the most efficient removal was achieved by ultrafiltration, after which EDTA became virtually undetectable. These results highlight a potentially widespread source of experimental variability affecting free divalent cation concentrations in protein applications.

A Systematic Evaluation of Blood Serum and Plasma Pre-Analytics for Metabolomics Cohort Studies

The recent thriving development of biobanks and associated high-throughput phenotyping studies requires the elaboration of large-scale approaches for monitoring biological sample quality and compliance with standard protocols. We present a metabolomic investigation of human blood samples that delineates pitfalls and guidelines for the collection, storage and handling procedures for serum and plasma. A series of eight pre-processing technical parameters is systematically investigated along variable ranges commonly encountered across clinical studies. While metabolic fingerprints, as assessed by nuclear magnetic resonance, are not significantly affected by altered centrifugation parameters or delays between sample pre-processing (blood centrifugation) and storage, our metabolomic investigation highlights that both the delay and storage temperature between blood draw and centrifugation are the primary parameters impacting serum and plasma metabolic profiles. Storing the blood drawn at 4 °C is shown to be a reliable routine to confine variability associated with idle time prior to sample pre-processing. Based on their fine sensitivity to pre-analytical parameters and protocol variations, metabolic fingerprints could be exploited as valuable ways to determine compliance with standard procedures and quality assessment of blood samples within large multi-omic clinical and translational cohort studies.

Discovery of Infection Associated Metabolic Markers in Human African Trypanosomiasis

Human African trypanosomiasis (HAT) remains a major neglected tropical disease in Sub-Saharan Africa. As clinical symptoms are usually non-specific, new diagnostic and prognostic markers are urgently needed to enhance the number of identified cases and optimise treatment. This is particularly important for disease caused by Trypanosoma brucei rhodesiense, where indirect immunodiagnostic approaches have to date been unsuccessful. We have conducted global metabolic profiling of plasma from T.b.rhodesiense HAT patients and endemic controls, using 1H nuclear magnetic resonance (NMR) spectroscopy and ultra-performance liquid chromatography, coupled with mass spectrometry (UPLC-MS) and identified differences in the lipid, amino acid and metabolite profiles. Altogether 16 significantly disease discriminatory metabolite markers were found using NMR, and a further 37 lipid markers via UPLC-MS. These included significantly higher levels of phenylalanine, formate, creatinine, N-acetylated glycoprotein and triglycerides in patients relative to controls. HAT patients also displayed lower concentrations of histidine, sphingomyelins, lysophosphatidylcholines, and several polyunsaturated phosphatidylcholines. While the disease metabolite profile was partially consistent with previous data published in experimental rodent infection, we also found unique lipid and amino acid profile markers highlighting subtle but important differences between the host response to trypanosome infections between animal models and natural human infections. Our results demonstrate the potential of metabolic profiling in the identification of novel diagnostic biomarkers and the elucidation of pathogenetic mechanisms in this disease.

1H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells

A major obstacle of successful chemotherapy is the development of multidrug resistance (MDR) in the cancer cells, which is difficult to reverse. Metabolomic analysis, an emerging approach that has been increasingly applied in various fields, is able to reflect the unique chemical fingerprints of specific cellular processes in an organism. The assessment of such metabolite changes can be used to identify novel therapeutic biomarkers. In the present study, ¹H nuclear magnetic resonance (NMR) spectroscopy was used to analyze the extracellular metabolomic spectrum of the Tca8113 oral squamous carcinoma cell line, in which MDR was induced using the carboplatin (CBP) and pingyangmycin (PYM) chemotherapy drugs in vitro. The data were analyzed using the principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods. The results demonstrated that the extracellular metabolomic spectrum of metabolites such as glutamate, glycerophosphoethanol amine, α-Glucose and β-Glucose for the drug-induced Tca8113 cells was significantly different from the parental Tca8113 cell line. A number of biochemicals were also significantly different between the groups based on their NMR spectra, with drug-resistant cells presenting relatively higher levels of acetate and lower levels of lactate. In addition, a significantly higher peak was observed at δ 3.35 ppm in the spectrum of the PYM-induced Tca8113 cells. Therefore, ¹H NMR-based metabolomic analysis has a high potential for monitoring the formation of MDR during clinical tumor chemotherapy in the future.

mQTL.NMR: an integrated suite for genetic mapping of quantitative variations of (1)H NMR-based metabolic profiles

High-throughput (1)H nuclear magnetic resonance (NMR) is an increasingly popular robust approach for qualitative and quantitative metabolic profiling, which can be used in conjunction with genomic techniques to discover novel genetic associations through metabotype quantitative trait locus (mQTL) mapping. There is therefore a crucial necessity to develop specialized tools for an accurate detection and unbiased interpretability of the genetically determined metabolic signals. Here we introduce and implement a combined chemoinformatic approach for objective and systematic analysis of untargeted (1)H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate mQTL mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems. The mQTL.NMR package is freely available with its source code through the Comprehensive R/Bioconductor repository and its own website ( http://www.ican-institute.org/tools/ ). It represents a significant advance to facilitate untargeted metabolomic data processing and quantitative analysis and their genetic mapping.

Metabolomics in hypertension

Hypertension is the most prevalent chronic medical condition and a major risk factor for cardiovascular morbidity and mortality. In the majority of hypertensive cases, the underlying cause of hypertension cannot be easily identified because of the heterogeneous, polygenic and multi-factorial nature of hypertension. Metabolomics is a relatively new field of research that has been used to evaluate metabolic perturbations associated with disease, identify disease biomarkers and to both assess and predict drug safety and efficacy. Metabolomics has been increasingly used to characterize risk factors for cardiovascular disease, including hypertension, and it appears to have significant potential for uncovering mechanisms of this complex disease. This review details the analytical techniques, pre-analytical steps and study designs used in metabolomics studies, as well as the emerging role for metabolomics in gaining mechanistic insights into the development of hypertension. Suggestions as to the future direction for metabolomics research in the field of hypertension are also proposed.

An efficient high throughput metabotyping platform for screening of biomass willows

Future improvement of woody biomass crops such as willow and poplar relies on our ability to select for metabolic traits that sequester more atmospheric carbon into biomass, or into useful products to replace petrochemical streams. We describe the development of metabotyping screens for willow, using combined 1D 1H-NMR-MS. A protocol was developed to overcome 1D 1H-NMR spectral alignment problems caused by variable pH and peak broadening arising from high organic acid levels and metal cations. The outcome was a robust method to allow direct statistical comparison of profiles arising from source (leaf) and sink (stem) tissues allowing data to be normalised to a constant weight of the soluble metabolome. We also describe the analysis of two willow biomass varieties, demonstrating how fingerprints from 1D 1H-NMR-MS vary from the top to the bottom of the plant. Automated extraction of quantitative data of 56 primary and secondary metabolites from 1D 1H-NMR spectra was realised by the construction and application of a Salix metabolite spectral library using the Chenomx software suite. The optimised metabotyping screen in conjunction with automated quantitation will enable high-throughput screening of genetic collections. It also provides genotype and tissue specific data for future modelling of carbon flow in metabolic networks.

The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective

The ability to phenotype metabolic profiles in serum has increased substantially in recent years with the advent of metabolomics. Metabolomics is the study of the metabolome, defined as those molecules with an atomic mass less than 1.5 kDa. There are two main metabolomics methods: mass spectrometry (MS) and proton nuclear magnetic resonance (¹H NMR) spectroscopy, each with its respective benefits and limitations. MS has greater sensitivity and so can detect many more metabolites. However, its cost (especially when heavy labelled internal standards are required for absolute quantitation) and quality control is sub-optimal for large cohorts. ¹H NMR is less sensitive but sample preparation is generally faster and analysis times shorter, resulting in markedly lower analysis costs. ¹H NMR is robust, reproducible and can provide absolute quantitation of many metabolites. Of particular relevance to cardio-metabolic disease is the ability of ¹H NMR to provide detailed quantitative data on amino acids, fatty acids and other metabolites as well as lipoprotein subparticle concentrations and size. Early epidemiological studies suggest promise, however, this is an emerging field and more data is required before we can determine the clinical utility of these measures to improve disease prediction and treatment.

This review describes the theoretical basis of ¹H NMR; compares MS and ¹H NMR and provides a tabular overview of recent ¹H NMR-based research findings in the atherosclerosis field, describing the design and scope of studies conducted to date. ¹H NMR metabolomics-CVD related research is emerging, however further large, robustly conducted prospective, genetic and intervention studies are needed to advance research on CVD risk prediction and to identify causal pathways amenable to intervention.

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¹H NMR metabolomics is being increasingly applied to large cohort studies.

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Studies have identified potentially novel lipoprotein and metabolite predictors for CVD.

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Potential exists for the use of metabolomics in cardiovascular clinical practice.

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Current findings are too preliminary to translate into clinical recommendations.

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Further large scale studies are now needed to advance the field in a robust manner.

1H-NMR metabolomic biomarkers of poor outcome after hemorrhagic shock are absent in hibernators

Background

Hemorrhagic shock (HS) following trauma is a leading cause of death among persons under the age of 40. During HS the body undergoes systemic warm ischemia followed by reperfusion during medical intervention. Ischemia/reperfusion (I/R) results in a disruption of cellular metabolic processes that ultimately lead to tissue and organ dysfunction or failure. Resistance to I/R injury is a characteristic of hibernating mammals. The present study sought to identify circulating metabolites in the rat as biomarkers for metabolic alterations associated with poor outcome after HS. Arctic ground squirrels (AGS), a hibernating species that resists I/R injury independent of decreased body temperature (warm I/R), was used as a negative control.

Methodology/principal findings

Male Sprague-Dawley rats and AGS were subject to HS by withdrawing blood to a mean arterial pressure (MAP) of 35 mmHg and maintaining the low MAP for 20 min before reperfusing with Ringers. The animals’ temperature was maintained at 37±0.5°C for the duration of the experiment. Plasma samples were taken immediately before hemorrhage and three hours after reperfusion. Hydrophilic and lipid metabolites from plasma were then analyzed via ¹H–NMR from unprocessed plasma and lipid extracts, respectively. Rats, susceptible to I/R injury, had a qualitative shift in their hydrophilic metabolic fingerprint including differential activation of glucose and anaerobic metabolism and had alterations in several metabolites during I/R indicative of metabolic adjustments and organ damage. In contrast, I/R injury resistant AGS, regardless of season or body temperature, maintained a stable metabolic homeostasis revealed by a qualitative ¹H–NMR metabolic profile with few changes in quantified metabolites during HS-induced global I/R.

Conclusions/significance

An increase in circulating metabolites indicative of anaerobic metabolism and activation of glycolytic pathways is associated with poor prognosis after HS in rats. These same biomarkers are absent in AGS after HS with warm I/R.

1H NMR profiling as an approach to differentiate conventionally and organically grown tomatoes

This study describes the approach of (1)H NMR profiling for the authentication of organically produced tomatoes (Solanum lycopersicum). Overall, 361 tomato samples of two different cultivars and four different producers were regularly analyzed during a 7 month period. The results of principal component analysis showed a significant trend for the separation between organically and conventionally produced tomatoes (p < 0.001 using the t test). Linear discriminant analysis demonstrated good discrimination between the growing regimens, and external validation showed 100% correctly classified tomato samples. Further validation studies, however, also disclosed unexpected differences between individual producers, which interfere with the aim of predicting the cultivation method, yet the results indicate significant differences between (1)H NMR spectra of organically and conventionally grown tomatoes.

Human plasma stability during handling and storage: impact on NMR metabolomics

The stability of human plasma composition was investigated by NMR, considering different collection tubes, time at room temperature (RT), short- and long-term storage conditions and up to 5 consecutive freeze–thaw cycles.

Association between the metabolome and low bone mineral density in Taiwanese women determined by (1)H NMR spectroscopy

Osteoporosis is related to the alteration of specific circulating metabolites. However, previous studies on only a few metabolites inadequately explain the pathogenesis of this complex syndrome. To date, no study has related the metabolome to bone mineral density (BMD), which would provide an overview of metabolism status and may be useful in clinical practice. This cross-sectional study involved 601 healthy Taiwanese women aged 40 to 55 years recruited from MJ Health Management Institution between 2009 and 2010. Participants were classified according to high (2nd tertile plus 3rd tertile) and low (1st tertile) BMD groups. The plasma metabolome was evaluated by proton nuclear magnetic resonance spectroscopy ((1) H NMR). Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and logistic regression analysis were used to assess the association between the metabolome and BMD. The high and low BMD groups could be differentiated by PLS-DA but not PCA in postmenopausal women (Q(2)  = 0.05, ppermutation  = 0.04). Among postmenopausal women, elevated glutamine was significantly associated with low BMD (adjusted odds ratio [AOR] = 5.10); meanwhile, elevated lactate (AOR = 0.55), acetone (AOR = 0.51), lipids (AOR = 0.04), and very low-density lipoprotein (AOR = 0.49) protected against low BMD. To the best of our knowledge, this study is the first to identify a group of metabolites for characterizing low BMD in postmenopausal women using a (1) H NMR-based metabolomic approach. The metabolic profile may be useful for predicting the risk of osteoporosis in postmenopausal women at an early age.

Metabolomic profile of umbilical cord blood plasma from early and late intrauterine growth restricted (IUGR) neonates with and without signs of brain vasodilation

Objectives

To characterize via NMR spectroscopy the full spectrum of metabolic changes in umbilical vein blood plasma of newborns diagnosed with different clinical forms of intrauterine growth restriction (IUGR).

Methods

23 early IUGR cases and matched 23 adequate-for-gestational-age (AGA) controls and 56 late IUGR cases with 56 matched AGAs were included in this study. Early IUGR was defined as a birth weight <10^th centile, abnormal umbilical artery (UA) Doppler and delivery <35 weeks. Late IUGR was defined as a birth weight <10^th centile with normal UA Doppler and delivery >35 weeks. This group was subdivided in 18 vasodilated (VD) and 38 non-VD late IUGR fetuses. All AGA patients had a birth weight >10^th centile. ¹H nuclear magnetic resonance (NMR) metabolomics of the blood samples collected from the umbilical vein at delivery was obtained. Multivariate statistical analysis identified several metabolites that allowed the discrimination between the different IUGR subgroups, and their comparative levels were quantified from the NMR data.

Results

The NMR-based analysis showed increased unsaturated lipids and VLDL levels in both early and late IUGR samples, decreased glucose and increased acetone levels in early IUGR. Non-significant trends for decreased glucose and increased acetone levels were present in late IUGR, which followed a severity gradient when the VD and non-VD subgroups were considered. Regarding amino acids and derivatives, early IUGR showed significantly increased glutamine and creatine levels, whereas the amounts of phenylalanine and tyrosine were decreased in early and late-VD IUGR samples. Valine and leucine were decreased in late IUGR samples. Choline levels were decreased in all clinical subforms of IUGR.

Conclusions

IUGR is not associated with a unique metabolic profile, but important changes are present in different clinical subsets used in research and clinical practice. These results may help in characterizing comprehensively specific alterations underlying different IUGR subsets.

Metabolomic identification of diagnostic plasma biomarkers in humans with chronic heart failure

Chronic heart failure (CHF), as a progressive clinical syndrome, is characterized by failure of enough blood supply from the heart to meet the body's metabolic demands, and there is intense interest in identifying novel biomarkers that could make contributions to the diagnosis of CHF. Metabolomics, compared with current diagnostic approaches, could investigate many metabolic perturbations within biological systems. The overarching goal of the work discussed here is to apply a high-throughput approach to identify metabolic signatures and plasma diagnostic biomarkers underlying CHF by 1H-NMR spectroscopy. Plasma samples from 39 patients with CHF and 15 controls were analyzed by NMR spectroscopy. After processing the data, orthogonal partial least square discriminant analysis (OPLS-DA) was performed. The statistical model revealed good explained variance and predictability, and the diagnostic performance assessed by leave-one-out analysis exhibited 92.31% sensitivity and 86.67% specificity. The OPLS-DA score plots of spectra revealed good separation between case and control on the level of metabolites, and multiple biochemical changes indicated hyperlipidemia, alteration of energy metabolism and other potential biological mechanisms underlying CHF. It was concluded that the NMR-based metabolomics approach demonstrated good performance to identify diagnostic plasma markers and provided new insights into metabolic process related to CHF.