Nuclear Magnetic Resonance Metabolomics Research Articles

Validation in type 2 diabetes of a metabolomic signature of all-cause mortality.

Mortality in type 2 diabetes is twice that of the normoglycemic population. Unravelling biomarkers that identify high-risk patients for referral to the most aggressive and costly prevention strategies is needed. To validate in type 2 diabetes the association with all-cause mortality of a 14-metabolite score (14-MS) previously reported in the general population and whether this score can be used to improve well-established mortality prediction models. This is a sub-study consisting of 600 patients from the "Sapienza University Mortality and Morbidity Event Rate" (SUMMER) study in diabetes, a prospective multicentre investigation on all-cause mortality in patients with type 2 diabetes. Metabolic biomarkers were quantified from serum samples using high-throughput proton nuclear magnetic resonance metabolomics. In type 2 diabetes, the 14-MS showed a significant (p<0.0001) association with mortality, which was lower (p<0.0001) than that reported in the general population. This difference was mainly due to two metabolites (histidine and ratio of polyunsaturated fatty acids to total fatty acids) with an effect size that was significantly (p=0.01) lower in diabetes than in the general population. A parsimonious 12-MS (i.e. lacking the 2 metabolites mentioned above) improved patient discrimination and classification of two well-established mortality prediction models (p<0.0001 for all measures). The metabolomic signature of mortality in the general population is only partially effective in type 2 diabetes. Prediction markers developed and validated in the general population must be revalidated if they are to be used in patients with diabetes.

Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables.

The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.

Multivariate modelling analysis for prediction of glycidyl esters and 3-monochloropropane-1,2-diol (3-MCPD) formation in periodically heated palm oil

Palm oil is a vegetable oil that is widely used for cooking and deep-frying because of its affordability. However, repeatedly heated palm oil is also prone to oxidation due to its significant content of unsaturated fatty acids and other chemical toxicants such as glycidyl esters and 3-monochloropropane-1,2-diol (3-MCPD). Initially, the physicochemical properties such as colour, viscosity, peroxide, p-anisidine and total oxidation (TOTOX) of periodically heated palm oil were investigated. Chemical profiling and fingerprinting of six different brands of palm cooking oil during heating cycles between 90 and 360 min were conducted using Fourier transform infrared (FTIR) and 1H Nuclear Magnetic Resonance (NMR) metabolomics. In addition, the multivariate analysis was employed to evaluate the 1H NMR spectroscopic pattern of repeatedly heated palm oil with the corresponding physicochemical properties. The FTIR metabolomics showed significant different of the chemical fingerprinting subjected to heating duration, which in agreement with the result of 1H NMR metabolomics. Partial least squares (PLS) model revealed that most of the physicochemical properties of periodically heated palm oil are positively correlated (R2 values of 0.98–0.99) to their spectroscopic pattern. Based on the findings, the color of the oils darkened with increased heating time. The peroxide value (PV), p-anisidine value (p-AnV), and total oxidation (TOTOX) values increased significantly due to degradation of unsaturated compounds and oxidation products formed. We identified targeted metabolites (probable carcinogens) such as 3-monochloropropane-1,2-diol (3-MCPD) and glycidyl ester (GE), indicating the conversion of 3-MCPD to GE in repeatedly heated oils based on PCA and OPLSDA models. Our correlation analysis of NMR and physicochemical properties has shown that the conversion of 3-MCPD to GE was significantly increased from 180 to 360 min cooking time. The combination spectroscopic techniques with physicochemical properties are a reliable and robust methods to evaluate the characteristics, stability and chemical's structure changes of periodically heated palm oil, which may contribute to probable carcinogens development. This study has proven that combination of NMR and physicochemical analysis may predict the formation of the probable carcinogens of heated cooking oil over time which emphasizing the need to avoid certain heating cycles to mitigate formation of probable carcinogens during cooking process.

Influence of Different Types, Utilization Times, and Volumes of Aging Barrels on the Metabolite Profile of Red Wine Revealed by 1H-NMR Metabolomics Approach.

It is well recognized that the aging process is a critical step in winemaking because it induces substantial chemical changes linked to the organoleptic properties and stability of the finished wines. Therefore, this study aimed to investigate the influence of different types, utilization times, and volumes of aging barrels on the metabolite profile of red wines, produced from Thai-grown Shiraz grapes, using a non-targeted proton nuclear magnetic resonance (1H-NMR) metabolomics approach. As a result, 37 non-volatile polar metabolites including alcohols, amino acids, organic acids, carbohydrates and low-molecular-weight phenolics were identified. Chemometric analysis allowed the discrimination of wine metabolite profiles associated with different types of aging containers (oak barrels vs. stainless-steel tanks), as well as the utilization times (2, 6 and >10 years old) and volumes (225, 500 and 2000 L) of the wooden barrels employed. Significant variations in the concentration of formate, fumarate, pyruvate, succinate, citrate, gallate, acetate, tyrosine, phenylalanine, histidine, γ-aminobutyrate, methionine and choline were statistically suggested as indicators accountable for the discrimination of samples aged under different conditions. These feature biomarkers could be applied to manipulate the use of aging containers to achieve the desired wine maturation profiles.

Maintaining immune homeostasis with Coptis Chinensis water extract to mitigate sepsis severity via modulating gut microbiome and metabolism

Sepsis arises from an uncontrolled inflammatory response to infection that can lead to organ failure. The gut microbiome is increasingly recognized as a key modulator of sepsis progression. This study investigated whether Coptis chinensis water extract (CCWE) could attenuate sepsis by modulating the gut microbiome and immune response. A rat model of sepsis induced by cecum ligation and perforation was used. 16 S ribosomal ribonucleic acid (rRNA) sequencing, proton nuclear magnetic resonance (1H NMR) metabolomics and flow cytometry assays were used to evaluate microbial, metabolic and immune profiles. CCWE treatment reversed sepsis-induced loss of beneficial bacteria like Firmicutes and Bacteroidetes and restored gut microbial balance. CCWE increased short-chain fatty acids, carnitine and phenylacetate, which provide energy and curb inflammation. By enhancing immune homeostasis and maintaining regulatory T cells (Tregs), CCWE treatment also exerted bidirectional regulation on T cells for initially suppressing hyperactivation then enabling recovery. Overall, CCWE may benefit sepsis by regulating the gut-microbiome-immune axis. By restoring microbiome balance, improving metabolism, and modulating immunity, CCWE treatment shows potential for alleviating sepsis severity and progression. The increases in beneficial bacteria, Tregs, and anti-inflammatory metabolites coupled with decreases in opportunistic pathogens likely contributed collectively to CCWE's protective effects. CCWE may emerge as an alternative or adjunctive option for managing disorders of dangerous inflammation like sepsis. Future research should explore CCWE's mechanisms of action clinically to determine its potential as a safe, effective means of modulating health through natural regulation of the gut microbiome and immune function.

Myo-inositol enhances the resistance and alleviates inflammation of Cyprinus carpio to Aeromonas hydrophila infection

The metabolic state is closely related to the host's anti-infection ability, and metabolic modulation has been proved as a powerful strategy to improve the therapy for bacterial infection. The present study investigated the metabolic profiling between susceptible and resistant Cyprinus carpio upon Aeromonas hydrophila infection based on nuclear magnetic resonance (NMR) metabolomics approach, and myo-inositol as a metabolite with the most significant change, may be a candidate biomarker for host anti-infection ability. Consistent with our expectations, exogenous myo-inositol potentiated C. carpio against A. hydrophila infection in a dose-dependent manner. In addition, myo-inositol pre-treatment could inhibit the inflammatory response and alleviate oxidative damage of liver caused by bacterial infection. Furthermore, the underlying mechanism of myo-inositol enhances the resistance was investigated. Exogenous myo-inositol triggered metabolic shift, including alanine, aspartate and glutamate metabolism and arginine biosynthesis, and myo-inositol remarkably induced iNOS expression, iNOS activity and promote the production of nitrogen oxide (NO). The study disclosed a metabolic approach to enhance a host's ability to fight bacterial infection.

Differentiation of patients with and without prostate cancer using urine 1 H NMR metabolomics.

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. For its detection, serum prostate-specific antigen (PSA) screening is commonly used, despite its lack of specificity, high false positive rate, and inability to discriminate indolent from aggressive PCa. Following increases in serum PSA levels, clinicians often conduct prostate biopsies with or without advanced imaging. Nuclear magnetic resonance (NMR)-based metabolomics has proven to be promising for advancing early-detection and elucidation of disease progression, through the discovery and characterization of novel biomarkers. This retrospective study of urine-NMR samples, from prostate biopsy patients with and without PCa, identified several metabolites involved in energy metabolism, amino acid metabolism, and the hippuric acid pathway. Of note, lactate and hippurate-key metabolites involved in cellular proliferation and microbiome effects, respectively-were significantly altered, unveiling widespread metabolomic modifications associated with PCa development. These findings support urine metabolomics profiling as a promising strategy to identify new clinical biomarkers for PCa detection and diagnosis.

Association of tobacco smoke exposure with metabolic profile from childhood to early adulthood: the Special Turku Coronary Risk Factor Intervention Project.

To investigate the associations between passive tobacco smoke exposure and daily smoking with a comprehensive metabolic profile, measured repeatedly from childhood to adulthood. Study cohort was derived from the Special Turku Coronary Risk Factor Intervention Project (STRIP). Smoking status was obtained by questionnaire, while serum cotinine concentrations were measured using gas chromatography. Metabolic measures were quantified by nuclear magnetic resonance metabolomics at 9 (n = 539), 11 (n = 536), 13 (n = 525), 15 (n = 488), 17 (n = 455), and 19 (n = 409) years. Association of passive tobacco smoke exposure with metabolic profile compared participants who reported less-than-weekly smoking and had serum cotinine concentration <1 ng/mL (no exposure) with those whose cotinine concentration was ≥10 ng/mL (passive tobacco smoke exposure). Associations of daily smoking with metabolic profile in adolescence were analysed by comparing participants reporting daily smoking with those reporting no tobacco use and having serum cotinine concentrations <1 ng/mL. Passive tobacco smoke exposure was directly associated with the serum ratio of monounsaturated fatty acids to total fatty acids [β = 0.34 standard deviation (SD), (0.17-0.51), P < 0.0001] and inversely associated with the serum ratios of polyunsaturated fatty acids. Exposure to passive tobacco smoke was directly associated with very-low-density lipoprotein particle size [β = 0.28 SD, (0.12-0.45), P = 0.001] and inversely associated with HDL particle size {β = -0.21 SD, [-0.34 to -0.07], P = 0.003}. Daily smokers exhibited a similar metabolic profile to those exposed to passive tobacco smoke. These results persisted after adjusting for body mass index, STRIP study group allocation, dietary target score, pubertal status, and parental socio-economic status. Both passive and active tobacco smoke exposures during childhood and adolescence are detrimentally associated with circulating metabolic measures indicative of increased cardio-metabolic risk.

Metabolomic profiling of CSF and blood serum elucidates general and sex-specific patterns for mild cognitive impairment and Alzheimer's disease patients.

Beta-amyloid (Abeta) and tau protein in cerebrospinal fluid (CSF) are established diagnostic biomarkers for Alzheimer's disease (AD). However, these biomarkers may not the only ones existing parameters that reflect Alzheimer's disease neuropathological change. The use of quantitative metabolomics approach could provide novel insights into dementia progression and identify key metabolic alterations in CSF and serum. In the present study, we quantified a set of 45 metabolites in CSF (71 patients) and 27 in serum (76 patients) in patients with mild cognitive impairment (MCI), AD, and controls using nuclear magnetic resonance (NMR)-based metabolomics. We found significantly reduced CSF (1.32-fold, p = 0.0195) and serum (1.47-fold, p = 0.0484) levels of the ketone body acetoacetate in AD and MCI patients. Additionally, we found decreased levels (1.20-fold, p = 0.0438) of the branched-chain amino acid (BCAA) valine in the CSF of AD patients with increased valine degradation pathway metabolites (such as 3-hydroxyisobutyrate and α-ketoisovalerate). Moreover, we discovered that CSF 2-hydroxybutyrate is dramatically reduced in the MCI patient group (1.23-fold, p = 0.039). On the other hand, vitamin C (ascorbate) was significantly raised in CSF of these patients (p = 0.008). We also identified altered CSF protein content, 1,5-anhydrosorbitol and fructose as further metabolic shifts distinguishing AD from MCI. Significantly decreased serum levels of the amino acid ornithine were seen in the AD dementia group when compared to healthy controls (1.36-fold, p = 0.011). When investigating the effect of sex, we found for AD males the sign of decreased 2-hydroxybutyrate and acetoacetate in CSF while for AD females increased serum creatinine was identified. Quantitative NMR metabolomics of CSF and serum was able to efficiently identify metabolic changes associated with dementia groups of MCI and AD patients. Further, we showed strong correlations between these changes and well-established metabolomic and clinical indicators like Abeta.

Identification of Salivary Metabolic Signatures Associated with Primary Sjögren's Disease.

Sjögren's disease (SjD) is the second most prevalent autoimmune disorder that involves chronic inflammation of exocrine glands. Correct diagnosis of primary SjD (pSjD) can span over many years since disease symptoms manifest only in advanced stages of salivary and lachrymal glandular destruction, and consensus diagnostic methods have critical sensitivity and selectivity limitations. Using nuclear magnetic resonance (NMR) spectroscopy, we determined the composition of metabolites in unstimulated saliva samples from 30 pSjD subjects and 30 participants who do not have Sjögren's disease (non-Sjögren's control group, NS-C). Thirty-four metabolites were quantified in each sample, and analysis was conducted on both non-normalized (concentration) and normalized metabolomics data from all study participants (ages 23-78) and on an age-restricted subset of the data (ages 30-70) while applying false discovery rate correction in determining data significance. The normalized data of saliva samples from all study participants, and of the age-restricted subset, indicated significant increases in the levels of glucose, glycerol, taurine, and lactate, as well as significant decreases in the levels of 5-aminopentanoate, acetate, butyrate and propionate, in subjects with pSjD compared to subjects in the NS-C group. Additionally, a significant increase in choline was found only in the age-restricted subset, and a significant decrease in fucose was found only in the whole study population in normalized data of saliva samples from the pSjD group compared to the NS-C group. Metabolite concentration data of saliva samples from all study participants, but not from the age-restricted subset, indicated significant increases in the levels of glucose, glycerol, taurine, and lactate in subjects with pSjD compared to controls. The study showed that NMR metabolomics can be implemented in defining salivary metabolic signatures that are associated with disease status, and can contribute to differential analysis between subjects with pSjD and those who are not affected with this disease, in the clinic.

Tumor Lipid Signatures Are Descriptive of Acquisition of Therapy Resistance in an Endocrine-Related Breast Cancer Mouse Model.

The lipid metabolism adaptations of estrogen and progesterone receptor-positive breast cancer tumors from a mouse syngeneic model are investigated in relation to differences across the transition from hormone-dependent (HD) to hormone-independent (HI) tumor growth and the acquisition of endocrine therapy (ET) resistance (HIR tumors). Results are articulated with reported polar metabolome results to complete a metabolic picture of the above transitions and suggest markers of tumor progression and aggressiveness. Untargeted nuclear magnetic resonance metabolomics was used to analyze tumor and mammary tissue lipid extracts. Tumor progression (HD-HI-HIR) was accompanied by increased nonesterified cholesterol forms and phospholipids (phosphatidylcholine, phosphatidylethanolamine, sphingomyelins, and plasmalogens) and decreased relative contents of triglycerides and fatty acids. Predominating fatty acids became shorter and more saturated on average. These results were consistent with gradually more activated cholesterol synthesis, β-oxidation, and phospholipid biosynthesis to sustain tumor growth, as well as an increase in cholesterol (possibly oxysterol) forms. Particular compound levels and ratios were identified as potential endocrine tumor HD-HI-HIR progression markers, supporting new hypotheses to explain acquired ET resistance.

Identification of dipeptidyl peptidase-4 inhibitory compounds of Paederia foetida twigs by nuclear magnetic resonance metabolomics, in silico models, and liquid chromatography–mass spectrometry profiling

Identification of dipeptidyl peptidase-4 inhibitory compounds of Paederia foetida twigs by nuclear magnetic resonance metabolomics, in silico models, and liquid chromatography–mass spectrometry profiling

Discrimination of Sayamakaori and Yabukita Which Are Original Plant Source of Japanese Green Tea and Identification of Specific Compounds for the Former by Nuclear Magnetic Resonance Metabolomics Techniques Combined with Isolation by Chromatography Methods

Camellia sinensis leaves are one of the most economically important crops, and various tea cultivars are available in Japan. In the present study, we compared the constituents of Camellia sinensis var. sinensis cv. Yabukita (“Yabukita”), a standard tea cultivar, and those of C. sinensis var. sinensis cv. Sayamakaori (“Sayamakaori”), grown in Saitama Prefecture, using proton nuclear magnetic resonance (1H-NMR) metabolomics technology. Principal component analysis and hierarchical cluster analysis of 1H-NMR spectroscopic data of the MeOH extracts of C. sinensis leaves revealed differences in the metabolic profiles of each tea cultivar. Chromatographic separation based on 1H-NMR spectral data showed that kaempferol 3- O-β-D-glucopyranosyl-(1 → 3)-α-L-rhamnopyranosyl-(1 → 6)-β-D-glucopyranoside was a distinct component of “Sayamakaori”. This compound can be used as maker for the identification of “Sayamakaori” and the maintenance of its quality.

Plasma metabolomics by nuclear magnetic resonance reveals biomarkers and metabolic pathways associated with the control of HIV-1 infection/progression.

How the human body reacts to the exposure of HIV-1 is an important research goal. Frequently, HIV exposure leads to infection, but some individuals show natural resistance to this infection; they are known as HIV-1-exposed but seronegative (HESN). Others, although infected but without antiretroviral therapy, control HIV-1 replication and progression to AIDS; they are named controllers, maintaining low viral levels and an adequate count of CD4+ T lymphocytes. Biological mechanisms explaining these phenomena are not precise. In this context, metabolomics emerges as a method to find metabolites in response to pathophysiological stimuli, which can help to establish mechanisms of natural resistance to HIV-1 infection and its progression. We conducted a cross-sectional study including 30 HESN, 14 HIV-1 progressors, 14 controllers and 30 healthy controls. Plasma samples (directly and deproteinized) were analyzed through Nuclear Magnetic Resonance (NMR) metabolomics to find biomarkers and altered metabolic pathways. The metabolic profile analysis of progressors, controllers and HESN demonstrated significant differences with healthy controls when a discriminant analysis (PLS-DA) was applied. In the discriminant models, 13 metabolites associated with HESN, 14 with progressors and 12 with controllers were identified, which presented statistically significant mean differences with healthy controls. In progressors, the metabolites were related to high energy expenditure (creatinine), mood disorders (tyrosine) and immune activation (lipoproteins), phenomena typical of the natural course of the infection. In controllers, they were related to an inflammation-modulating profile (glutamate and pyruvate) and a better adaptive immune system response (acetate) associated with resistance to progression. In the HESN group, with anti-inflammatory (lactate and phosphocholine) and virucidal (lactate) effects which constitute a protective profile in the sexual transmission of HIV. Concerning the significant metabolites of each group, we identified 24 genes involved in HIV-1 replication or virus proteins that were all altered in progressors but only partially in controllers and HESN. In summary, our results indicate that exposure to HIV-1 in HESN, as well as infection in progressors and controllers, affects the metabolism of individuals and that this affectation can be determined using NMR metabolomics.

The distribution of dietary choline intake and serum choline levels in Australian women during pregnancy and associated early life factors

BackgroundMaternal dietary choline has a central role in foetal brain development and may be associated with later cognitive function. However, many countries are reporting lower than recommended intake of choline during pregnancy.MethodsDietary choline was estimated using food frequency questionnaires in pregnant women participating in population-derived birth cohort, the Barwon Infant Study (BIS). Dietary choline is reported as the sum of all choline-containing moieties. Serum total choline-containing compounds (choline-c), phosphatidylcholine and sphingomyelin were measured using nuclear magnetic resonance metabolomics in the third trimester. The main form of analysis was multivariable linear regression.ResultsThe mean daily dietary choline during pregnancy was 372 (standard deviation (SD) 104) mg/day. A total of 236 women (23%) had adequate choline intake (440 mg/day) based on the Australian and New Zealand guidelines, and 27 women (2.6%) took supplemental choline (ge 50 mg/dose) daily during pregnancy. The mean serum choline-c in pregnant women was 3.27 (SD 0.44) mmol/l. Ingested choline and serum choline-c were not correlated (R2) = − 0.005, p = 0.880. Maternal age, maternal weight gain in pregnancy, and a pregnancy with more than one infant were associated with higher serum choline-c, whereas gestational diabetes and environmental tobacco smoke during preconception and pregnancy were associated with lower serum choline-c. Nutrients or dietary patterns were not associated with variation in serum choline-c.ConclusionIn this cohort, approximately one-quarter of women met daily choline recommendations during pregnancy. Future studies are needed to understand the potential impact of low dietary choline intake during pregnancy on infant cognition and metabolic intermediaries.

#5650 METABOLIC BLOOD BIOMARKER PROFILING FOR CHRONIC KIDNEY DISEASE PREDICTION – EVIDENCE FROM 275,000 INDIVIDUALS IN THE UK BIOBANK

Abstract Background and Aims Early identification of individuals at high risk of developing chronic kidney disease and other chronic conditions is essential for targeted prevention. Here, we assess the utility of metabolic blood biomarkers in predicting the onset of chronic kidney disease in over 250,000 individuals from UK Biobank, beyond established risk factors and polygenic risk scores. Method Circulating blood biomarkers, including lipids, fatty acids, amino acids, glycolysis metabolites and inflammation markers were measured by a low-cost nuclear magnetic resonance (NMR) metabolomics assay in around 275,000 plasma samples from the UK Biobank. During a prospective 10-year follow-up, over 6000 incident chronic kidney disease events were obtained from national health registries. Using multivariable regression modelling, we derived a 10-year risk score for chronic kidney disease onset. We assessed the predictive performance of the biomarker risk score in comparison to standard risk factors (age, sex, prevalent diabetes, body mass index, systolic and diastolic blood pressure, smoking status and use of cholesterol lowering medication), clinical chemistry measurements (HbA1c, LDL and HDL cholesterol, total cholesterol and eGFR) and polygenic risk scores. We evaluated the performance of the risk score in two practical scenarios: in a general population screening setting and in a clinical use case of stratifying kidney disease risk among prevalent type 2 diabetics. We further illustrate applications of the biomarker risk scores in predicting the risk of other chronic diseases, such as cardiovascular outcomes, in general population settings. Results In the general population screening setting, adding the metabolic biomarkers to a simple model of standard risk factors substantially improved the predictive performance, with area under the receiver operating characteristic curve (AUC) increasing from 0.74 to 0.82. When compared to more comprehensive model including also clinical chemistry measurements and polygenic risk scores, the improvements were also significant, albeit more modest, with the AUC increasing from 0.81 to 0.83. Even more substantial gains were observed in a clinical use case scenario of stratifying risk of chronic kidney disease onset among prevalent type 2 diabetics. Among diabetics with mildly to moderately decreased kidney function at the time of blood sampling (eGFR 60-90), adding the metabolic biomarkers in a comprehensive model including standard risk factors, clinical chemistry measurements and polygenic risk scores increased the AUC from 0.60 to 0.70. Conclusion Circulating metabolic biomarkers enhanced the prediction of chronic kidney disease onset beyond standard risk factors and polygenic risk scores. From a translational perspective, this may complement the identification of high-risk individuals in both general population and clinical settings beyond current risk factor assessment, while simultaneously informing on the risk of other chronic diseases.

The combined analysis of urine and blood metabolomics profiles provides an accurate prediction of the training and competitive status of Chinese professional swimmers.

Objective: The purpose of this study was to employ metabolomics for the analysis of urine metabolites in swimmers, with the aim of establishing models for assessing their athletic status and competitive potential. Furthermore, the study sought to compare the identification efficacy of multi-component (urine and blood) model versus single-component (urine or blood) models, in order to determine the optimal approach for evaluating training and competitive status. Methods: A total of 187 Chinese professional swimmers, comprising 103 elite and 84 sub-elite level athletes, were selected as subjects for this study. Urine samples were obtained from each participant and subjected to nuclear magnetic resonance (NMR) metabolomics analysis. Significant urine metabolites were screened through multivariable logistic regression analysis, and an identification model was established. Based on the previously established model of blood metabolites, this study compared the discriminative and predictive performance of three models: either urine or blood metabolites model and urine + blood metabolites model. Results: Among 39 urine metabolites, 10 were found to be significantly associated with the athletic status of swimmers (p < 0.05). Of these, levels of 2-KC, cis-aconitate, formate, and LAC were higher in elite swimmers compared to sub-elite athletes, while levels of 3-HIV, creatinine, 3-HIB, hippurate, pseudouridine, and trigonelline were lower in elite swimmers. Notably, 2-KC and 3-HIB exhibited the most substantial differences. An identification model was developed to estimate physical performance and athletic level of swimmers while adjusting for different covariates and including 2-KC and 3-HIB. The urine metabolites model showed an area under the curve (AUC) of 0.852 (95% CI: 0.793-0.912) for discrimination. Among the three identification models tested, the combination of urine and blood metabolites showed the highest performance than either urine or blood metabolites, with an AUC of 0.925 (95% CI: 0.888-0.963). Conclusion: The two urine metabolites, 2-KC and 3-HIV, can serve as significant urine metabolic markers to establish a discrimination model for identifying the athletic status and competitive potential of Chinese elite swimmers. Combining two screened urine metabolites with four metabolites reported exhibiting significant differences in blood resulted in improved predictive performance compared to using urine metabolites alone. These findings indicate that combining blood and urine metabolites has a greater potential for identifying and predicting the athletic status and competitive potential of Chinese professional swimmers.

MagMet: A fully automated web server for targeted nuclear magnetic resonance metabolomics of plasma and serum.

Nuclear magnetic resonance (NMR) spectral analysis of biofluids can be a time-consuming process, requiring the expertise of a trained operator. With NMR becoming increasingly popular in the field of metabolomics, there is a growing need to change this paradigm and to automate the process. Here we introduce MagMet, an online web server, that automates the processing and quantification of 1D 1 H NMR spectra from biofluids-specifically, human serum/plasma metabolites, including those associated with inborn errors of metabolism (IEM). MagMet uses a highly efficient data processing procedure that performs automatic Fourier Transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking/peak alignment. MagMet then uses the peak positions, linewidth information, and J-couplings from its own specially prepared standard metabolite reference spectral NMR library of 85 serum/plasma compounds to identify and quantify compounds from experimentally acquired NMR spectra of serum/plasma. MagMet employs linewidth adjustment for more consistent quantification of metabolites from higher field instruments and incorporates a highly efficient data processing procedure for more rapid and accurate detection and quantification of metabolites. This optimized algorithm allows the MagMet webserver to quickly detect and quantify 58 serum/plasma metabolites in 2.6 min per spectrum (when processing a dataset of 50-100 spectra). MagMet's performance was also assessed using spectra collected from defined mixtures (simulating other biofluids), with >100 previously measured plasma spectra, and from spiked serum/plasma samples simulating known IEMs. In all cases, MagMet performed with precision and accuracy matching the performance of human spectral profiling experts. MagMet is available at http://magmet.ca.

Comparative MS- and NMR-Based Metabolome Mapping of Egyptian Red and White Squill Bulbs F. Liliaceae and in Relation to Their Cytotoxic Effect.

Urginea maritima L. (squill) species is widely spread at the Mediterranean region as two main varieties, i.e., white squill (WS) and red squill (RS), that are recognized for several health potentials. The major secondary metabolite classes of the squill are cardiac glycosides, mainly, bufadienolides, flavonoids, and anthocyanins. Herein, a multiplex MS and NMR metabolomics approach targeting secondary and aroma compounds in WS and RS was employed for varieties classification. Solid-phase micro extraction-gas chromatography/mass spectroscopy (SPME-GC/MS), ultra-high-performance liquid chromatography/mass spectrometry (UPLC/MS), as well as nuclear magnetic resonance (NMR) provided fingerprinting and structural confirmation of the major metabolites for both types of the squill. For comparison of the different platforms' classification potential, multivariate data analysis was employed. While Bufadienolides, viz. "hydroxy-scilliglaucosidin-O-rhamnoside, desacetylscillirosidin-O-rhamnoside and bufotalidin-O-hexoside" as well as oxylipids, were enriched in WS, flavonoids, i.e., dihydro-kaempferol-O-hexoside and its aglycon, taxifolin derivative, were predominant in RS. A cytotoxicity screening against three cancer cell lines, including breast adenocarcinoma (MCF-7), lung (A-549), and ovarian (SKOV-3) cell lines was conducted. Results revealed that WS was more effective on A-549 and SKOV-3 cell lines (WS IC50 0.11 and 0.4 µg/mL, respectively) owing to its abundance of bufadienolides, while RS recorded IC50 (MCF7 cell line) 0.17 µg/mL since is is rich inflavonoids.

Characterization of Physicochemical, Biological, and Chemical Changes Associated with Coconut Milk Fermentation and Correlation Revealed by 1H NMR-Based Metabolomics.

Fermentation of milk enhances its nutritional and biological activity through the improvement of the bioavailability of nutrients and the production of bioactive compounds. Coconut milk was fermented with Lactiplantibacillus plantarum ngue16. The aim of this study was to evaluate the effect of fermentation and cold storage for 28 days on physicochemical characteristics, shelf life, and antioxidant and antibacterial activities of coconut milk as well as its proximate and chemical compositions. The pH of fermented milk decreased from 4.26 to 3.92 on the 28th day during cold storage. The viable cell count of lactic acid bacteria (LAB) in fermented coconut milk was significantly increased during fermentation and cold storage period (1 to 14 days), reaching 6.4 × 108 CFU/mL, and then decreased significantly after 14 days to 1.6 × 108 CFU/mL at 28 days. Yeast and molds in fermented coconut milk were only detected on the 21st and 28th days of cold storage, which ranged from 1.7 × 102 to 1.2 × 104 CFU/mL, respectively. However, the growth of coliforms and E. coli was observed on the 14th until the 28th day of cold storage. The fermented coconut milk demonstrated strong antibacterial activity against Staphylococcus aureus, Bacillus subtilis, Escherichia coli, Cronobacter sakazakii, Bacillus cereus, and Salmonella typhimurium compared to fresh coconut milk. Fermented coconut milk had the greatest 1,1-diphenyl-2-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) values, with 67.1% and 61.961 mmol/g at day 14 of cold storage, respectively. Forty metabolites were detected in fermented and pasteurized coconut milk by proton nuclear magnetic resonance (1H NMR) metabolomics. The principal component analysis (PCA) showed clear difference between the fermented and pasteurized coconut milk as well as the studied cold storage days. The metabolites responsible for this variation were ethanol, valine, GABA, arginine, lactic acid, acetoin, alanine, phenylalanine, acetic acid, methionine, acetone, pyruvate, succinic acid, malic acid, tryptophan, uridine, uracil, and cytosin, which were higher in fermented coconut milk. However, sugars and other identified compounds were higher in fresh coconut milk. The findings of this study show that fermentation of coconut milk with L. plantarum ngue16 had high potential benefits to extending its shelf life and improved biological activities as well as other beneficial nutrients.