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A JEDI NMR pulse experiment incorporating relaxational, diffusional and J-modulation peak editing has been implemented for a low field (80 MHz proton resonance frequency) spectrometer system to measure quantitatively two recently discovered plasma markers of SARS-CoV-2 infection and general inflammation. JEDI spectra capture a unique signature of two biomarker signals from acetylated glycoproteins (Glyc) and the supramolecular phospholipid composite (SPC) signals that are relatively enhanced by the combination of relaxation, diffusion and J-editing properties of the JEDI experiment that strongly attenuate contributions from the other molecular species in plasma. The SPC/Glyc ratio data were essentially identical in the 600 MHz and 80 MHz spectra obtained (R2 = 0.97) and showed significantly different ratios for control (n = 28) versus SARS-CoV-2 positive patients (n = 29) (p = 5.2 × 10-8 and 3.7 × 10-8 respectively). Simplification of the sample preparation allows for data acquisition in a similar time frame to high field machines (∼4 min) and a high-throughput version with 1 min experiment time could be feasible. These data show that these newly discovered inflammatory biomarkers can be measured effectively on low field NMR instruments that do not not require housing in a complex laboratory environment, thus lowering the barrier to clinical translation of this diagnostic technology.

PURPOSE: Inhomogeneities in the static magnetic field (B0 ) deteriorate MRSI data quality by lowering the spectral resolution and SNR. MRSI with low spatial resolution is also prone to lipid bleeding. These problems are increasingly problematic at ultra-high fields. An approach to tackling these challenges independent of B0 -shim hardware is to increase the spatial resolution. Therefore, we investigated the effect of improved spatial resolution on spectral quality and quantification at 4 field strengths. METHODS: Whole-brain MRSI data was simulated for 3 spatial resolutions and 4 B0 s based on experimentally acquired MRI data and simulated free induction decay signals of metabolites and lipids. To compare the spectral quality and quantification, we derived SNR normalized to the voxel size (nSNR), linewidth and metabolite concentration ratios, their Cramer-Rao-lower-bounds (CRLBs), and the absolute percentage error (APE) of estimated concentrations compared to the gold standard for the whole-brain and 8 brain regions. RESULTS: At 7T, we found up to a 3.4-fold improved nSNR (in the frontal lobe) and a 2.8-fold reduced linewidth (in the temporal lobe) for 1 cm3 versus 0.25 cm3 resolution. This effect was much more pronounced at higher and less homogenous B0 (1.6-fold improved nSNR and 1.8-fold improved linewidth in the parietal lobe at 3T). This had direct implications for quantification: the volume of reliably quantified spectra increased with resolution by 1.2-fold and 1.5-fold (when thresholded by CRLBs or APE, respectively). CONCLUSION: MRSI data quality benefits from increased spatial resolution particularly at higher B0 , and leads to more reliable metabolite quantification. In conjunction with the development of better B0 shimming hardware, this will enable robust whole-brain MRSI at ultra-high field.

Purpose While PRESS is often employed to measure glutamate concentrations, MEGA-PRESS enables simultaneous Glx (glutamate and glutamine) and GABA measurements. This study aimed to compare validation, repeatability, and concordance of different approaches for glutamate quantification at 3 T to aid future studies in their selection of the appropriate sequence and quantification method. Materials and methods Nine phantoms with different glutamate and glutamine concentrations and five healthy participants were scanned twice to assess respectively the validation and repeatability of measurements with PRESS and MEGA-PRESS. To assess concordance between the different methods, results from 95 human participants were compared. PRESS, MEGA-PRESS (i.e. difference), and the MEGA-PRESS OFF spectra were analyzed with both LCModel and Gannet. Results In vitro, excellent agreement was shown between actual and measured glutamate concentrations for all measurements (r > 0.98). In vivo CVs were better for PRESS (2.9%) than MEGA-PRESS (4.9%) and MEGA-PRESS OFF (4.2%). However, the concordance between the sequences was low (PRESS and MEGA-PRESS OFF, r = 0.3) to modest (MEGA-PRESS versus MEGA-PRESS OFF, r = 0.8). Conclusion Both PRESS and MEGA-PRESS can be employed to measure in vivo glutamate concentrations, although PRESS shows a better repeatability. Comparisons between in vivo glutamate measures of different sequences however need to be interpreted cautiously.

Inborn errors of metabolism (IEMs) are rare diseases produced by the accumulation of abnormal amounts of metabolites, toxic to the newborn. When not detected on time, they can lead to irreversible physiological and psychological sequels or even demise. Metabolomics has emerged as an efficient and powerful tool for IEM detection in newborns, children, and adults with late onset. In here, we screened urine samples from a large set of neonates (470 individuals) from a homogeneous population (Basque Country), for the identification of congenital metabolic diseases using NMR spectroscopy. Absolute quantification allowed to derive a probability function for up to 66 metabolites that adequately describes their normal concentration ranges in newborns from the Basque Country. The absence of another 84 metabolites, considered abnormal, was routinely verified in the healthy newborn population and confirmed for all but 2 samples, of which one showed toxic concentrations of metabolites associated to ketosis and the other one a high trimethylamine concentration that strongly suggested an episode of trimethylaminuria. Thus, a non-invasive and readily accessible urine sample contains enough information to assess the potential existence of a substantial number (>70) of IEMs in newborns, using a single, automated and standardized H-1-NMR-based analysis. The authors thank the collaboration of the Basque Biobank/BioCruces Node www.biobancovasco.org for collecting the samples and data from the neonates included in this study. Support was provided from The Department of Industry, Tourism and Trade of the Government of the Autonomous Community of the Basque Country (Elkartek BG2015 & BG2017); grant from Agencia Estatal de Investigacion (Spain) CTQ2015-68756-R and for the Severo Ochoa Excellence Accreditation (SEV-2016-0644).

Large-scale metabolic profiling requires the development of novel economical high-throughput analytical methods to facilitate characterization of systemic metabolic variation in population phenotypes. We report a fit-for-purpose direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) method with time-of-flight detection for rapid targeted parallel analysis of over 40 urinary metabolites. The newly developed 2 min infusion method requires 10 000 24 h urine samples from the INTERMAP study in 12 weeks and >2200 spot urine samples from the ARIC study in

Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.

The aetiopathogenesis of cardiovascular diseases (CVD) is multifactorial. Adverse blood pressure (BP) is a major independent risk factor for epidemic CVD affecting about 40% of the adult population worldwide and resulting in significant morbidity and mortality. Metabolic phenotyping of biological fluids has proven its application in characterisin g low molecule weight metabolites providing novel insights into gene -environmental -gut microbiome interaction in relations to a disease state. In this review, we synthesi s e key results from the International Study of Macro/Micronutrients and Blood Pressure (INTERMAP) Study, a cross - sectional epidemiological study of 4,680 men and women aged 40 -59 years from Japan, the People’s Republic of China, the United Kingdom, and the United States. We describe the advancements we have made on: 1) analytical techniques for high throughput metabolic phenotyping; 2) statistical analyses for biomarker identification; 3) discovery of unique food - specific biomarkers; and 4) application of metabolome -wide association (MWA) studies to gain a better understanding into the molecular mechanisms of cross cultural and regional BP differences.

Objective: To evaluate potential variations in the plasma metabolomic profile of endometriosis patients as a consequence of pathophysiologic alterations associated with this disorder. Design: Prospective study. For each subject, a plasma sample was collected after overnight fasting and before surgery. Setting: University medical center. Patient(s): The clinical cohort included 50 endometriosis patients, diagnosed at early (n = 6) and advanced (n = 44) stages of the disease, and 23 healthy women. All volunteers underwent diagnostic laparoscopy to visually confirm the presence or absence of endometriotic lesions. Intervention(s): Metabolomic profiling of plasma samples based on 1H-nuclear magnetic resonance (NMR) spectroscopy in combination with statistical approaches. Main Outcome Measure(s): Comparative identification of metabolites present in plasma from endometriosis patients and healthy women. Result(s): The plasma metabolomic profile of endometriosis patients was characterized by increased concentration of valine, fucose, choline-containing metabolites, lysine/arginine, and lipoproteins and decreased concentration of creatinine compared with healthy women. Metabolic alterations identified in the plasma metabolomic profile of endometriosis patients correlate with pathophysiologic events previously described in the progression of this disease. Conclusion(s): The results highlight the potential of H-1-NMR-based metabolomics to characterize metabolic alterations associated with endometriosis in plasma samples. This information could be useful to get a better understanding of the molecular mechanisms involved in the pathogenesis of endometriosis, thus facilitating the noninvasive diagnosis of this pathology at early stages. (C) 2016 by American Society for Reproductive Medicine.

Plasma lipid concentrations cannot properly account for the complex interactions prevailing in lipoprotein (patho)physiology. Sequential ultracentrifugation (UCF) is the gold standard for physical lipoprotein isolations allowing for subsequent analyses of the molecular composition of the particles. Due to labor and cost issues, however, the UCF-based isolations are usually done only for VLDL, LDL, and HDL fractions; sometimes with the addition of intermediate density lipoprotein (IDL) particles and the fractionation of HDL into HDL(2) and HDL(3) (as done here; n = 302). We demonstrate via these data, with the lipoprotein lipid concentration and composition information combined, that the self-organizing map (SOM) analysis reveals a novel data-driven in silico phenotyping of lipoprotein metabolism beyond the experimentally available classifications. The SOM-based findings are biologically consistent with several well-known metabolic characteristics and also explain some apparent contradictions. The novelty is the inherent emergence of complex lipoprotein associations; e.g., the metabolic subgrouping of the associations between plasma LDL cholesterol concentrations and the structural subtypes of LDL particles. Importantly, lipoprotein concentrations cannot pinpoint lipoprotein phenotypes. It would generally be beneficial to computationally enhance the UCF-based lipoprotein data as illustrated here. Particularly, the compositional variations within the lipoprotein particles appear to be a fundamental issue with metabolic and clinical corollaries.

Objective: To investigate whether urine metabolomic profile can be used to identify biomarkers associated to endometriosis. Design: Prospective study. For each subject, a urine sample was collected after overnight fasting and before surgery. Setting: University medical center. Patient(s): The clinical cohort included 45 endometriosis patients, diagnosed at early (n = 6) and advanced (n = 39) stages of the disease, and 36 healthy women. All women underwent diagnostic laparoscopy to visually confirm the presence or absence of endometriotic lesions. Intervention(s): Metabolomic profiling of urine samples based on H-1-nuclear magnetic resonance (NMR) spectroscopy in combination with statistical approaches. Main Outcome Measure(s): Comparative identification of metabolites present in urine from endometriosis patients and healthy women. Result(s): The urine metabolomic profile of endometriosis patients exhibited higher concentrations of N-1-methyl-4-pyridone-5carboxamide, guanidinosuccinate, creatinine, taurine, valine, and 2-hydroxyisovalerate and decreased concentrations of lysine compared with healthy women. Most of these metabolites are involved in inflammation and oxidative stress processes. These pathophysiologic events had been previously described to be present in ectopic endometrial proliferation foci. Conclusion(s): Overall, the results demonstrate the potential of H-1-NMR-based metabolomics, a rapid and noninvasive approach, to identify metabolic changes associated to endometriosis in urine samples. This information could be useful to get a better understanding of the pathogenesis of endometriosis, thus providing support to the noninvasive diagnosis of this pathology. (C) 2015 by American Society for Reproductive Medicine.

Metabolic phenotyping corresponds to the large-scale quantitative and qualitative analysis of the metabolome i.e., the low-molecular weight

This paper reviews the main applications of NMR metabolomics of blood and urine in disease research, over the last 5 years. The broad range of disease types addressed attests the increasing interest within the academic and medical communities to explore the recognised potential of metabolomics to (1) provide insight into underlying disease pathogenesis and (2) unveil new metabolic markers for disease diagnosis and follow up. Importantly, most recent studies reveal an increasing awareness of possible limitations and pitfalls of the metabolomics approach, together with efforts for improved study design and statistical validation, which are crucial requisites for the sound development of NMR metabolomics and its progress into the clinical setting. (C) 2013 Elsevier B.V. All rights reserved.

The prediction of embryo viability by usual morphological analysis is currently unsatisfactory. New non-invasive techniques such as high-resolution nuclear magnetic resonance (1H-NMR) spectroscopy that allows assessment of metabolic profiling in spent culture media might help embryologists to predict embryo development. Individual microdrops of culture media were analysed after 24 h of embryo culture (from day 3 to day 4) by spectroscopy using a 1 mm microliter probe allowing analysis without sample dilution. Embryos were divided into two groups on day 5: non-arrested embryos (n = 19) and arrested embryos unable to reach the blastocyst stage (n = 20). Multivariate analysis techniques such as Principal Component Analysis (PCA) and Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) were performed to compare extracellular metabolite balance. 1H-NMR used in combination with a 1 mm probe suggested that in vitro cultured human embryos that have a high developmental potential modify their environment slightly compared to embryos that cease to develop. However, differences between the two groups did not reach statistical significance and multivariate statistical analysis did not allow clustering of the two groups. This study indicated that this technique would not be sufficiently powerful alone to provide information that might help to assess the developmental potential of individual embryos for in vitro fertilisation (IVF).

The lipid–leukocyte (LL) module is associated with, and reactive to, a wide variety of serum metabolites. The LL module appears to be a link between metabolism, adiposity, and inflammation. Serum metabolite concentrations themselves determine the connectedness of LL module., Comprehensive characterization of human tissues promises novel insights into the biological architecture of human diseases and traits. We assessed metabonomic, transcriptomic, and genomic variation for a large population-based cohort from the capital region of Finland. Network analyses identified a set of highly correlated genes, the lipid–leukocyte (LL) module, as having a prominent role in over 80 serum metabolites (of 134 measures quantified), including lipoprotein subclasses, lipids, and amino acids. Concurrent association with immune response markers suggested the LL module as a possible link between inflammation, metabolism, and adiposity. Further, genomic variation was used to generate a directed network and infer LL module's largely reactive nature to metabolites. Finally, gene co-expression in circulating leukocytes was shown to be dependent on serum metabolite concentrations, providing evidence for the hypothesis that the coherence of molecular networks themselves is conditional on environmental factors. These findings show the importance and opportunity of systematic molecular investigation of human population samples. To facilitate and encourage this investigation, the metabonomic, transcriptomic, and genomic data used in this study have been made available as a resource for the research community.

The medulla oblongata (MO) contains a high density of glycinergic synapses and a particularly high concentration of glycine The aims of this study were to measure directly in vivo the neurochemical profile, including glycine, in MO using a spin-echo-based H-1 MRS sequence at TE = 2 8 ms and to compare it with three other brain regions (cortex, striatum and hippocampus) in the rat Glycine was quantified in MO at TE = 2 8 ms with a Cramer-Rao lower bound (CRIB) of approximately 5% As a result of the relatively low level of glycine in the other three regions, the measurement of glycine was performed at TE = 20 ms, which provides a favorable J-modulation of overlapping myo-inositol resonance The other 14 metabolites composing the neurochemical profile were quantified in vivo in MO with CRLBs below 25% Absolute concentrations of metabolites in MO, such as glutamate, glutamine, gamma-aminobutyrate, taurine and glycine, were in the range of previous in vitro quantifications in tissue extracts Compared with the other regions, MO had a three-fold higher glycine concentration, and was characterised by reduced (p < 0 001) concentrations of glutamate (-50 +/- 4%), glutamine (-54 +/- 3%) and taurine (-78 +/- 3%) This study suggests that the functional specialisation of distinct brain regions is reflected in the neurochemical profile Copyright (C) 2010 John Wiley & Sons, Ltd

In the last decade, evidence has emerged indicating that the growth of a vast majority of tumors including gliomas is sustained by a subpopulation of cancer cells with stem cell properties called cancer initiating cells. These cells are able to initiate and propagate tumors and constitute only a fraction of all tumor cells. In the present study, we showed that intracerebral injection of cultured glioma-initiating cells into nude mice produced fast growing tumors showing necrosis and gadolinium enhancement in MR images, whereas gliomas produced by injecting freshly purified glioma-initiating cells grew slowly and showed no necrosis and very little gadolinium enhancement. Using proton localized spectroscopy at 14.1 Tesla, decreasing trends of N-acetylaspartate, glutamate and glucose concentrations and an increasing trend of glycine concentration were observed near the injection site after injecting cultured glioma-initiating cells. In contrast to the spectra of tumors grown from fresh cells, those from cultured cells showed intense peaks of lipids, increased absolute concentrations of glycine and choline-containing compounds, and decreased concentrations of glutamine, taurine and total creatine, when compared with a contralateral non-tumor-bearing brain tissue. A decrease in concentrations of N-acetylaspartate and -aminobutyrate was found in both tumor phenotypes after solid tumor formation. Further investigation is needed to determine the cause of the dissimilarities between the tumors grown from cultured glioma-initiating cells and those from freshly purified glioma-initiating cells, both derived from human glioblastomas. Copyright (C) 2011 John Wiley & Sons, Ltd.