Search

The associations of plasma metabolites with adverse cardiovascular (CV) outcomes are still underexplored and may be useful in CV risk stratification. We performed a systematic review and meta-analysis to establish correlations between blood metabolites and adverse CV outcomes in patients with heart failure (HF). Four cohorts were included, involving 83 metabolites and 37 metabolite ratios, measured in 1158 HF patients. Hazard ratios (HR) of 42 metabolites and 3 metabolite ratios, present in at least two studies, were combined through meta-analysis. Higher levels of histidine (HR 0.74, 95% CI [0.64; 0.86]) and tryptophan (HR 0.82 [0.71; 0.96]) seemed protective, whereas higher levels of symmetric dimethylarginine (SDMA) (HR 1.58 [1.30; 1.93]), N-methyl-1-histidine (HR 1.56 [1.27; 1.90]), SDMA/arginine (HR 1.38 [1.14; 1.68]), putrescine (HR 1.31 [1.06; 1.61]), methionine sulfoxide (HR 1.26 [1.03; 1.52]), and 5-hydroxylysine (HR 1.25 [1.05; 1.48]) were associated with a higher risk of CV events. Our findings corroborate important associations between metabolic imbalances and a higher risk of CV events in HF patients. However, the lack of standardization and data reporting hampered the comparison of a higher number of studies. In a future clinical scenario, metabolomics will greatly benefit from harmonizing sample handling, data analysis, reporting, and sharing.

Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1 H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.

Although silver nanoparticles (AgNPs) are widely disseminated and show great potential in the biomedical field, there is a recognized need to better understand their action at the metabolic and functional levels. In this work, we have used NMR metabolomics, together with conventional clinical chemistry and histological examination, to characterize multi-organ and systemic metabolic responses to AgNPs intravenously administered to mice at 8 mg/kg body weight (a dose not eliciting overt toxicity). The major target organs of AgNPs accumulation, liver and spleen, showed the greatest metabolic changes, in a clear 2-stage response. In particular, the liver of dosed mice was found to switch from glycogenolysis and lipid storage, at 6 h postinjection, to glycogenesis and lipolysis, at subsequent times up to 48 h. Moreover, metabolites related to antioxidative defense, immunoregulation and detoxification seemed to play a crucial role in avoiding major hepatic damage. The spleen showed several early changes, including depletion of several amino acids, possibly reflecting impairment of hemoglobin recycling, while only a few differences remained at 48 h postinjection. In the heart, the metabolic shift towards TCA cycle intensification and increased ATP production possibly reflected a beneficial adaptation to the presence of AgNPs. On the other hand, the TCA cycle appeared to be down regulated in the lungs of injected mice, which showed signs of inflammation. Thekidneys showed the mildest metabolic response to AgNPs. Overall, this study has shown that NMR metabolomics is a powerful tool to monitor invivo metabolic responses to nanoparticles, revealing unforeseen effects., (© The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Purpose: To differentiate the plasma metabolomic profile of patients with age related macular degeneration (AMD) from that of controls, by Nuclear Magnetic Resonance (NMR) spectroscopy., Methods: Two cohorts (total of 396 subjects) representative of central Portugal and Boston, USA phenotypes were studied. For each cohort, subjects were grouped according to AMD stage (early, intermediate and late). Multivariate analysis of plasma NMR spectra was performed, followed by signal integration and univariate analysis., Results: Small changes were detected in the levels of some amino acids, organic acids, dimethyl sulfone and specific lipid moieties, thus providing some biochemical information on the disease. The possible confounding effects of gender, smoking history and age were assessed in each cohort and found to be minimal when compared to that of the disease. A similar observation was noted in relation to age-related comorbidities. Furthermore, partially distinct putative AMD metabolite fingerprints were noted for the two cohorts studied, reflecting the importance of nutritional and other lifestyle habits in determining AMD metabolic response and potential biomarker fingerprints. Notably, some of the metabolite changes detected were noted as potentially differentiating controls from patients diagnosed with early AMD., Conclusion: For the first time, this study showed metabolite changes in the plasma of patients with AMD as compared to controls, using NMR. Geographical origins were seen to affect AMD patients´ metabolic profile and some metabolites were found to be valuable in potentially differentiating controls from early stage AMD patients. Metabolomics has the potential of identifying biomarkers for AMD, and further work in this area is warranted.

RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n = 42) and controls (n = 49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced.

Metabolic biomarkers of pre- and postdiagnosis gestational diabetes mellitus (GDM) were sought, using nuclear magnetic resonance (NMR) metabolomics of maternal plasma and corresponding lipid extracts. Metabolite differences between controls and disease were identified through multivariate analysis of variable selected (1)H NMR spectra. For postdiagnosis GDM, partial least squares regression identified metabolites with higher dependence on normal gestational age evolution. Variable selection of NMR spectra produced good classification models for both pre- and postdiagnostic GDM. Prediagnosis GDM was accompanied by cholesterol increase and minor increases in lipoproteins (plasma), fatty acids, and triglycerides (extracts). Small metabolite changes comprised variations in glucose (up regulated), amino acids, betaine, urea, creatine, and metabolites related to gut microflora. Most changes were enhanced upon GDM diagnosis, in addition to newly observed changes in low-Mw compounds. GDM prediction seems possible exploiting multivariate profile changes rather than a set of univariate changes. Postdiagnosis GDM is successfully classified using a 26-resonance plasma biomarker. Plasma and extracts display comparable classification performance, the former enabling direct and more rapid analysis. Results and putative biochemical hypotheses require further confirmation in larger cohorts of distinct ethnicities.

This work presents the first NMR metabolomics study of maternal plasma during pregnancy, including correlation between plasma and urine metabolites. The expected decrease in circulating amino acids early in pregnancy was confirmed with six amino acids being identified as required by the fetus in larger extents. Newly observed changes in citrate, lactate, and dimethyl sulfone suggested early adjustments in energy and gut microflora metabolisms. Alterations in creatine levels were also noted, in addition to creatinine variations reflecting alterations in glomerular filtration rate. Regarding plasma macromolecules, HDL and LDL+VLDL levels were confirmed to increase throughout pregnancy, although at different rates and accompanied by increases in fatty acid chain length and degree of unsaturation. Correlation studies suggested (a) an inverse relationship between lipoproteins (HDL and LDL+VLDL) and albumin, with a possible direct correlation to excreted (unassigned) pregnancy markers resonating at δ 0.55 and δ 0.63, (b) a direct link between LDL+VLDL and N-acetyl-glycoproteins, together with excreted marker at δ 0.55, and (c) correlation of plasma albumin with particular circulating and excreted metabolites. These results have unveiled specific lipoprotein/protein metabolic aspects of pregnancy with impact on the excreted metabolome and, therefore, provide an interesting lead for the further understanding of pregnancy metabolism.

Lung tumour subtyping, particularly the distinction between adenocarcinoma (AdC) and squamous cell carcinoma (SqCC), is a critical diagnostic requirement. In this work, the metabolic signatures of lung carcinomas were investigated through (1)H NMR metabolomics, with a view to provide additional criteria for improved diagnosis and treatment planning. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (NMR) spectroscopy was used to analyse matched tumour and adjacent control tissues from 56 patients undergoing surgical excision of primary lung carcinomas. Multivariate modeling allowed tumour and control tissues to be discriminated with high accuracy (97% classification rate), mainly due to significant differences in the levels of 13 metabolites. Notably, the magnitude of those differences were clearly distinct for AdC and SqCC: major alterations in AdC were related to phospholipid metabolism (increased phosphocholine, glycerophosphocholine and phosphoethanolamine, together with decreased acetate) and protein catabolism (increased peptide moieties), whereas SqCC had stronger glycolytic and glutaminolytic profiles (negatively correlated variations in glucose and lactate and positively correlated increases in glutamate and alanine). Other tumour metabolic features were increased creatine, glutathione, taurine and uridine nucleotides, the first two being especially prominent in SqCC and the latter in AdC. Furthermore, multivariate analysis of AdC and SqCC profiles allowed their discrimination with a 94% classification rate, thus showing great potential for aiding lung tumours subtyping. Overall, this study has provided new, clear evidence of distinct metabolic signatures for lung AdC and SqCC, which can potentially impact on diagnosis and provide important leads for future research on novel therapeutic targets or imaging tracers., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

This work describes an exploratory NMR metabonomic study of second trimester maternal urine and plasma, in an attempt to characterize the metabolic changes underlying prenatal disorders and identify possible early biomarkers. Fetal malformations have the strongest metabolic impact in both biofluids, suggesting effects due to hypoxia (leading to hypoxanthine increased excretion) and a need for enhanced gluconeogenesis, with higher ketone bodies (acetone and 3-hydroxybutyric acid) production and TCA cycle demand (suggested by glucogenic amino acids and cis-aconitate overproduction). Choline and nucleotide metabolisms also seem affected and a distinct plasma lipids profile is observed for mothers with fetuses affected by central nervous system malformations. Urine from women who subsequently develop gestational diabetes mellitus exhibits higher 3-hydroxyisovalerate and 2-hydroxyisobutyrate levels, probably due to altered biotin status and amino acid and/or gut metabolisms (the latter possibly related to higher BMI values). Other urinary changes suggest choline and nucleotide metabolic alterations, whereas lower plasma betaine and TMAO levels are found. Chromosomal disorders and pre-preterm delivery groups show urinary changes in choline and, in the latter case, in 2-hydroxyisobutyrate. These results show that NMR metabonomics of maternal biofluids enables the noninvasive detection of metabolic changes associated to prenatal disorders, thus unveiling potential disorder biomarkers.

In this study, ¹H NMR-based metabonomics has been applied, for the first time to our knowledge, to investigate lung cancer metabolic signatures in urine, aiming at assessing the diagnostic potential of this approach and gaining novel insights into lung cancer metabolism and systemic effects. Urine samples from lung cancer patients (n = 71) and a control healthy group (n = 54) were analyzed by high resolution ¹H NMR (500 MHz), and their spectral profiles subjected to multivariate statistics, namely, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Projections to Latent Structures (OPLS)-DA. Very good discrimination between cancer and control groups was achieved by multivariate modeling of urinary profiles. By Monte Carlo Cross Validation, the classification model showed 93% sensitivity, 94% specificity and an overall classification rate of 93.5%. The possible confounding influence of other factors, namely, gender and age, have also been modeled and found to have much lower predictive power than the presence of the disease. Moreover, smoking habits were found not to have a dominating influence over class discrimination. The main metabolites contributing to this discrimination, as highlighted by multivariate analysis and confirmed by spectral integration, were hippurate and trigonelline (reduced in patients), and β-hydroxyisovalerate, α-hydroxyisobutyrate, N-acetylglutamine, and creatinine (elevated in patients relatively to controls). These results show the valuable potential of NMR-based metabonomics for finding putative biomarkers of lung cancer in urine, collected in a minimally invasive way, which may have important diagnostic impact, provided that these metabolites are found to be specifically disease-related.

An NMR-metabonomic study of malformed fetuses was carried out through human amniotic fluid (HAF) analysis. Over 70 compounds were detected in control HAF by NMR. Possible confounding variables (fetus gender and gestational and maternal ages) were shown not to induce detectable compositional trends in the control group considered. Malformed fetuses showed variations in glucose, some amino acids and organic acids and proteins. In tandem with enzymatic assays, these NMR results suggest that changes in gycolysis and gluconeogenesis as well as kidney underdevelopment occur in the malformed fetuses studied here.

Although silver nanoparticles (AgNPs) are widely disseminated and show great potential in the biomedical field, there is a recognized need to better understand their action at the metabolic and functional levels. In this work, we have used NMR metabolomics, together with conventional clinical chemistry and histological examination, to characterize multi-organ and systemic metabolic responses to AgNPs intravenously administered to mice at 8mg/kg body weight (a dose not eliciting overt toxicity). Themajor target organs of AgNPs accumulation, liver and spleen, showed the greatestmetabolic changes, in a clear 2-stage response. In particular, the liver of dosed mice was found to switch fromglycogenolysis and lipid storage, at 6h postinjection, to glycogenesis and lipolysis, at subsequent times up to 48 h. Moreover, metabolites related to antioxidative defense, immunoregulation and detoxification seemed to play a crucial role in avoiding major hepatic damage. The spleen showed several early changes, including depletion of several amino acids, possibly reflecting impairment of hemoglobin recycling, while only a few differences remained at 48h postinjection. In the heart, the metabolic shift towards TCA cycle intensification and increased ATP production possibly reflected a beneficial adaptation to the presence of AgNPs. On the other hand, the TCA cycle appeared to be down regulated in the lungs of injected mice, which showed signs of inflammation. The kidneys showed the mildestmetabolic response to AgNPs. Overall, this study has shown that NMRmetabolomics is a powerful tool to monitor in vivo metabolic responses to nanoparticles, revealing unforeseen effects. [ABSTRACT FROM AUTHOR]

Highlights: [•] NMR and multivariate analysis measure metabolic impact of lettuce growth and mancozeb exposure. [•] Expanded leaves show Krebs cycle and anti-oxidant defence mechanisms activation. [•] Mancozeb exposure leads to marked disturbances in amino acid and other metabolisms. [•] Mancozeb exposure emphasises activated Krebs cycle and anti-oxidant defence mechanisms. [ABSTRACT FROM AUTHOR]