Metabolomics with multi-block modelling of mass spectrometry and nuclear magnetic resonance in order to discriminate Haplosclerida marine sponges.

A comprehensive metabolomic strategy, integrating 1H NMR and MS-based multi-block modelling in conjunction with multi-informational molecular networking, has been developed to discriminate sponges of the order Haplosclerida, well known for being taxonomically contentious. An in-house collection of 33 marine sponge samples belonging to three families (Callyspongiidae, Chalinidae, Petrosiidae) and four different genera (Callyspongia, Haliclona, Petrosia, Xestospongia) was investigated using LC-MS/MS, molecular networking, and the annotations processes combined with NMR data and multivariate statistical modelling. The combination of MS and NMR data into supervised multivariate models led to the discrimination of, out of the four genera, three groups based on the presence of metabolites, not necessarily previously described in the Haplosclerida order. Although these metabolomic methods have already been applied separately, it is the first time that a multi-block untargeted approach using MS and NMR has been combined with molecular networking and statistically analyzed, pointing out the pros and cons of this strategy.

NMR metabolomic profiles associated with long-term risk of prostate cancer.

INTRODUCTION: Prostate cancer is a multifactorial disease whose aetiology is still not fully understood. Metabolomics, by measuring several hundred metabolites simultaneously, could enhance knowledge on the metabolic changes involved and the potential impact of external factors. OBJECTIVES: The aim of the present study was to investigate whether pre-diagnostic plasma metabolomic profiles were associated with the risk of developing a prostate cancer within the following decade. METHODS: A prospective nested case-control study was set up among the 5141 men participant of the SU.VI.MAX cohort, including 171 prostate cancer cases, diagnosed between 1994 and 2007, and 171 matched controls. Nuclear magnetic resonance (NMR) metabolomic profiles were established from baseline plasma samples using NOESY1D and CPMG sequences. Multivariable conditional logistic regression models were computed for each individual NMR signal and for metabolomic patterns derived using principal component analysis. RESULTS: Men with higher fasting plasma levels of valine (odds ratio (OR) = 1.37 [1.07-1.76], p = .01), glutamine (OR = 1.30 [1.00-1.70], p = .047), creatine (OR = 1.37 [1.04-1.80], p = .02), albumin lysyl (OR = 1.48 [1.12-1.95], p = .006 and OR = 1.51 [1.13-2.02], p = .005), tyrosine (OR = 1.40 [1.06-1.85], p = .02), phenylalanine (OR = 1.39 [1.08-1.79], p = .01), histidine (OR = 1.46 [1.12-1.88], p = .004), 3-methylhistidine (OR = 1.37 [1.05-1.80], p = .02) and lower plasma level of urea (OR = .70 [.54-.92], p = .009) had a higher risk of developing a prostate cancer during the 13 years of follow-up. CONCLUSIONS: This exploratory study highlighted associations between baseline plasma metabolomic profiles and long-term risk of developing prostate cancer. If replicated in independent cohort studies, such signatures may improve the identification of men at risk for prostate cancer well before diagnosis and the understanding of this disease.

Associations between untargeted plasma metabolomic signatures and gut microbiota composition in the Milieu Intérieur population of healthy adults.

Host-microbial co-metabolism products are being increasingly recognised to play important roles in physiological processes. However, studies undertaking a comprehensive approach to consider host-microbial metabolic relationships remain scarce. Metabolomic analysis yielding detailed information regarding metabolites found in a given biological compartment holds promise for such an approach. This work aimed to explore the associations between host plasma metabolomic signatures and gut microbiota composition in healthy adults of the Milieu Intérieur study. For 846 subjects, gut microbiota composition was profiled through sequencing of the 16S rRNA gene in stools. Metabolomic signatures were generated through proton NMR analysis of plasma. The associations between metabolomic variables and α- and ß-diversity indexes and relative taxa abundances were tested using multi-adjusted partial Spearman correlations, permutational ANOVA and multivariate associations with linear models, respectively. A multiple testing correction was applied (Benjamini-Hochberg, 10 % false discovery rate). Microbial richness was negatively associated with lipid-related signals and positively associated with amino acids, choline, creatinine, glucose and citrate (-0·133 ≤ Spearman's ρ ≤ 0·126). Specific associations between metabolomic signals and abundances of taxa were detected (twenty-five at the genus level and nineteen at the species level): notably, numerous associations were observed for creatinine (positively associated with eleven species and negatively associated with Faecalibacterium prausnitzii). This large-scale population-based study highlights metabolites associated with gut microbial features and provides new insights into the understanding of complex host-gut microbiota metabolic relationships. In particular, our results support the implication of a 'gut-kidney axis'. More studies providing a detailed exploration of these complex interactions and their implications for host health are needed.

Nuclear magnetic resonance-based serum metabolomic analysis reveals different disease evolution profiles between septic shock survivors and non-survivors.

BACKGROUND: Septic shock is the most severe phase of sepsis and is associated with high rates of mortality. However, early stage prediction of septic shock outcomes remains difficult. Metabolomic techniques have emerged as a promising tool for improving prognosis. METHODS: Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models separating the serum metabolomes of survivors from those of non-survivors were established with samples obtained at the intensive care unit (ICU) admission (H0) and 24 h later (H24). For 51 patients with available H0 and H24 samples, multi-level modeling was performed to provide insight into different metabolic evolutions that occurred between H0 and H24 in the surviving and non-surviving patients. Relative quantification and receiver operational characteristic curves (ROC) were applied to estimate the predictability of key discriminatory metabolites for septic shock mortality. RESULTS: Metabolites that were involved in energy supply and protein breakdown were primarily responsible for differentiating survivors from non-survivors. This was not only seen in the H0 and H24 discriminatory models, but also in the H0-H24 paired models. Reanalysis of extra H0-H24 paired samples in the established multi-level model demonstrated good performance of the model for the classification of samplings. According to the ROC results, nine discriminatory metabolites defined consistently from the unpaired model and the H0-H24 time-trend change (ΔH24-H0) show good prediction of mortality. These results suggest that NMR-based metabolomic analysis is useful for a better overall assessment of septic shock patients. CONCLUSIONS: Dysregulation of the metabolites identified by this study is associated with poor outcomes for septic shock. Evaluation of these compounds during the first 24 h after ICU admission in the septic shock patient may be helpful for estimating the severity of cases and for predicting outcomes. TRIAL REGISTRATION: All human serum samples were collected and stored, provided by the "center of biologic resources for liver disease", in Jean Verdier Hospital, Bondy, France (BB-0033-00027).

Surface enhanced Raman scattering sensor for highly sensitive and selective detection of ochratoxin A.

The trace detection of toxic compounds in complex matrices is a major concern, in particular when it comes to mycotoxins in food. We developed a highly sensitive and specific SERS sensor for the detection of ochratoxin A using a simple rough gold film as a substrate. When adding the analyte, we observed spectral variations related to the interaction of the analyte with the specific aptamer used as a bioreceptor. Using a partial least squares regression method, our sensor is able to detect concentrations down to the picomolar range, which is much lower than the minimum legal concentration allowed in food products. Moreover, we demonstrate the accurate detection of the analyte in a wide concentration range from the picomolar up to the micromolar level. The detection was validated with negative detection tests using deoxynivalenol and bovine serum albumin.

Sequential Serum Metabolomic Profiling after Radiofrequency Ablation of Hepatocellular Carcinoma Reveals Different Response Patterns According to Etiology.

Radiofrequency ablation (RFA) is commonly performed as a curative approach in patients with hepatocellular carcinoma (HCC); however, the risk of tumor recurrence is difficult to predict due to a lack of reliable clinical and biological markers, and identification of new biomarkers poses a major challenge for improving prognoses. Metabolomics is a promising technique that may lead to the identification and characterization of new disease fingerprints. The objective of the present study was to explore, preoperatively and at various time points post-RFA, the metabolic profile of serum samples from HCC patients to identify factors associated with treatment response and recurrence. Sequential sera obtained before and after RFA procedures for 120 patients with HCC due to cirrhosis were investigated using nuclear magnetic resonance metabolomics. A multilevel orthogonal projection to latent structure analysis was used to discriminate intraindividual metabolic changes in response to RFA treatment. Recurrence-free survival differed depending on the underlying cause of cirrhosis. The statistical model showed significant differences depending on whether the liver disease had a viral or nonviral etiology before RFA intervention (explained variance of R(2)Y = 0.89 and predictability of Q(2)Y = 0.34). These profiles were also associated with specific and distinct metabolic responses after RFA.

NMR metabolomics for assessment of exercise effects with mouse biofluids.

Exercise modulates the metabolome in urine or blood as demonstrated previously for humans and animal models. Using nuclear magnetic resonance (NMR) metabolomics, the present study compares the metabolic consequences of an exhaustive exercise at peak velocity (Vp) and at critical velocity (Vc) on mice. Since small-volume samples (blood and urine) were collected, dilution was necessary to acquire NMR spectra. Consequently, specific processing methods were applied before statistical analysis. According to the type of exercise (control group, Vp group and Vc group), 26 male mice were divided into three groups. Mice were sacrificed 2 h after the end of exercise, and urine and blood samples were drawn from each mouse. Proton NMR spectra were acquired with urine and deproteinized blood. The NMR data were aligned with the icoshift method and normalised using the probabilistic quotient method. Finally, data were analysed with the orthogonal projection of latent-structure analysis. The spectra obtained with deproteinized blood can neither discriminate the control mice from exercised mice nor discriminate according to the duration of the exercise. With urine samples, a significant statistical model can be estimated when comparing the control mice to both groups, Vc and Vp. The best model is obtained according to the exercise duration with all mice. Taking into account the spectral regions having the highest correlations, the discriminant metabolites are allantoin, inosine and branched-chain amino acids. In conclusion, metabolomic profiles assessed with NMR are highly dependent on the exercise. These results show that urine samples are more informative than blood samples and that the duration of the exercise is a more important parameter to influence the metabolomic status than the exercise velocity.

Plasma Metabolomic and Lipidomic Profiling of Metabolic Dysfunction-Associated Fatty Liver Disease in Humans Using an Untargeted Multiplatform Approach.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disorder that is implicated in dysregulations in multiple biological pathways, orchestrated by interactions between genetic predisposition, metabolic syndromes and environmental factors. The limited knowledge of its pathogenesis is one of the bottlenecks in the development of prognostic and therapeutic options for MAFLD. Moreover, the extent to which metabolic pathways are altered due to ongoing hepatic steatosis, inflammation and fibrosis and subsequent liver damage remains unclear. To uncover potential MAFLD pathogenesis in humans, we employed an untargeted nuclear magnetic resonance (NMR) spectroscopy- and high-resolution mass spectrometry (HRMS)-based multiplatform approach combined with a computational multiblock omics framework to characterize the plasma metabolomes and lipidomes of obese patients without (n = 19) or with liver biopsy confirmed MAFLD (n = 63). Metabolite features associated with MAFLD were identified using a metabolome-wide association study pipeline that tested for the relationships between feature responses and MAFLD. A metabolic pathway enrichment analysis revealed 16 pathways associated with MAFLD and highlighted pathway changes, including amino acid metabolism, bile acid metabolism, carnitine shuttle, fatty acid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and steroid metabolism. These results suggested that there were alterations in energy metabolism, specifically amino acid and lipid metabolism, and pointed to the pathways being implicated in alerted liver function, mitochondrial dysfunctions and immune system disorders, which have previously been linked to MAFLD in human and animal studies. Together, this study revealed specific metabolic alterations associated with MAFLD and supported the idea that MAFLD is fundamentally a metabolism-related disorder, thereby providing new perspectives for diagnostic and therapeutic strategies.

Metabolomic approach by 1H NMR spectroscopy of serum for the assessment of chronic liver failure in patients with cirrhosis.

Assessment of chronic liver failure (CLF) in cirrhotic patients is needed to make therapeutic decisions. A biological score is usually performed, using the Model for End-Stage Liver Disease (MELD), to evaluate CLF. Nevertheless, MELD does not take into account metabolic perturbations produced by liver-function impairment. In contrast, metabolomics can investigate many metabolic perturbations within biological systems. The purpose of this study was to assess whether metabolomic profiles of serum, obtained by proton NMR spectroscopy from cirrhotic patients, are affected by the severity of CLF. An orthogonal projection to latent-structure analysis was performed to compare MELD scores and NMR spectra of 124 patients with cirrhosis. The statistical model obtained showed a good explained variance (R(2)X = 0.87 and R(2)Y = 0.86) and a good predictability (Q(2)Y = 0.64). Metabolomic profiles showed significant differences regarding various metabolites depending of severity of CLF: levels of high-density lipoprotein and phosphocholine resonances were significantly higher in patients with mild CLF compared to severe CLF. Other metabolites such as lactate, pyruvate, glucose, amino acids, and creatinine were significantly higher in patients with severe CLF than mild CLF. Our conclusion is that metabolomic NMR analysis provides new insights into metabolic processes related to the severity of hepatic function impairment in cirrhosis.

Plasma Metabolomics for Discovery of Early Metabolic Markers of Prostate Cancer Based on Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry.

BACKGROUND: The prevention and early screening of PCa is highly dependent on the identification of new biomarkers. In this study, we investigated whether plasma metabolic profiles from healthy males provide novel early biomarkers associated with future risk of PCa. METHODS: Using the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, we identified plasma samples collected from 146 PCa cases up to 13 years prior to diagnosis and 272 matched controls. Plasma metabolic profiles were characterized using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). RESULTS: Orthogonal partial least squares discriminant analysis (OPLS-DA) discriminated PCa cases from controls, with a median area under the receiver operating characteristic curve (AU-ROC) of 0.92 using a 1000-time repeated random sub-sampling validation. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) identified the top 10 most important metabolites (p < 0.001) discriminating PCa cases from controls. Among them, phosphate, ethyl oleate, eicosadienoic acid were higher in individuals that developed PCa than in the controls during the follow-up. In contrast, 2-hydroxyadenine, sphinganine, L-glutamic acid, serotonin, 7-keto cholesterol, tiglyl carnitine, and sphingosine were lower. CONCLUSION: Our results support the dysregulation of amino acids and sphingolipid metabolism during the development of PCa. After validation in an independent cohort, these signatures may promote the development of new prevention and screening strategies to identify males at future risk of PCa.

Early-onset pneumonia following bag-mask ventilation versus endotracheal intubation during cardiopulmonary resuscitation: A substudy of the CAAM trial.

AIM: Early-onset pneumonia (EOP) is a common in-hospital complication in survivors of out-of-hospital cardiac arrest. In this substudy of the CAAM trial, we aimed to compare whether bag mask ventilation (BMV) compared to endotracheal intubation (ETI) performed during cardiopulmonary resuscitation increases the risk of developing EOP. METHODS: Adult patients from the CAAM trial that survived beyond 12 h of hospitalization were included. Information about in-hospital management and outcome of study subjects was systematically collected. Our primary aim was to compare the incidence of EOP in the BMV and ETI group using a series of bivariate analysis adjusting for one variable at a time and a logistic regression controlled for survival beyond 96 h, age, gender, catecholamine administration, no flow time, and initial shockable rhythm. RESULTS: Of 627 patients from the CAAM trial that survived to hospital admission, 409 patients were hospitalized beyond 12 h and thus included (202 randomized to BMV and 20 7 randomized to ETI). Patients in the BMV group had a significantly longer period of unsecured airway during prehospital cardiopulmonary resuscitation (BMV (median): 33 min; ETI (median): 17 min, p < 0.0001). No significant difference in the development of EOP according to airway management was identified on univariate analysis (BMV: 53%, ETI: 53%, Odds Ratio 1.0 [0.7-1.5], p = 1.0). We found no difference in the development of EOP according to airway management in the series of bivariate analyses or in the multivariable regression analysis either. CONCLUSION: In this substudy of the CAAM trial, development of early-onset pneumonia in out-of-hospital cardiac arrest survivors did not depend on airway management technique during CPR.

A First Step Toward Unraveling the Energy Metabolism in Endurance Horses: Comparison of Plasma Nuclear Magnetic Resonance Metabolomic Profiles Before and After Different Endurance Race Distances.

Endurance racing places high demands on energy metabolism pathways. Metabolomics can be used to investigate biochemical responses to endurance exercise in humans, laboratory animals, and horses. Although endurance horses have previously been assessed in the field (i.e., during races) using broad-window Nuclear Magnetic Resonance metabolomics, these studies included several different race locations, race distances, age classes, and race statuses (finisher or elimination). The present NMR metabolomics study focused on 40 endurance horses racing in three race categories over 90, 120, or 160 km. The three races took place in the same location. Given that energy metabolism is closely related to exercise intensity and duration (and therefore distance covered), the study's objective was to determine whether the metabolic pathways recruited during the race varied as a function of the total ride distance. For each horse, a plasma sample was collected the day before the race, and another was collected at the end of the race. Sixteen, 15, and 9 horses raced over 90, 120, and 160 km, respectively. Proton NMR spectra (500 MHz) were acquired for these 80 plasma samples. After processing, the spectra were divided into bins representing the NMR variables and then classified using orthogonal projection on latent structure models supervised by the sampling time (pre- or post-race) or the distance covered. The models revealed that the post-race metabolomic profiles are associated to the total ride distance groups. By combining biochemical assay results and NMR data in multiblock models, we further showed that enzymatic activities and metabolites are significantly associated to the race category. In the highest race category (160 km), there appears to be a metabolic switch from carbohydrate consumption to lipid consumption in order to maintain glycaemia. Furthermore, signs of protein breakdown were more apparent in the longest race category. The metabolic shift seen in the different racing categories could be related to a mixture of three important factors that are the ride distance, the training status and the inherited endurance capacity of the various horses competing.

Nuclear magnetic resonance metabolic fingerprint of bevacizumab in mutant IDH1 glioma cells.

Background Malignant gliomas are rapidly growing tumours that extensively invade the brain and have bad prognosis. Our study was performed to assess the metabolic effects of bevacizumab on the glioma cells carrying the IDH1 mutation, a mutation, associated with better prognosis and treatment outcome. Bevacizumab is known to inhibit tumour growth by neutralizing the biological activity of vascular endothelial growth factor (VEGF). However, the direct effects of bevacizumab on tumour cells metabolism remain poorly known. Materials and methods The immunoassay and MTT assay were used to assess the concentration of secreted VEGF and cell viability after bevacizumab exposure. Metabolomic studies on cells were performed using high resolution magic angle spinning spectroscopy (HRMAS). Results mIDH1-U87 cells secreted VEGF (13 ng/mL). Regardless, bevacizumab had no cytotoxic effect, even after a 72h exposure and with doses as high as 1 mg/mL. Yet, HRMAS analysis showed a significant effect of bevacizumab (0.1 mg/mL) on the metabolic phenotype of mIDH1-U87 cells with elevation of 2-hydroxyglutarate and changes in glutamine group metabolites (alanine, glutamate, glycine) and lipids (polyunsaturated fatty acids [PUFA], glycerophosphocholine, and phosphocholine). Conclusions In mIDH1-U87 cells, changes in glutamine group metabolites and lipids were identified as metabolic markers of bevacizumab treatment. These data support the possibility of a functional tricarboxylic acid cycle that runs in reductive manner, as a probable mechanism of action of bevacizumab in IDH1 mutated gliomas and propose a new target pathway for effective treatment of malignant gliomas.

NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer.

Background: Combination of metabolomics and epidemiological approaches opens new perspectives for ground-breaking discoveries. The aim of the present study was to investigate for the first time whether plasma untargeted metabolomic profiles, established from a simple blood draw from healthy women, could contribute to predict the risk of developing breast cancer within the following decade and to better understand the aetiology of this complex disease. Methods: A prospective nested case-control study was set up in the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, including 206 breast cancer cases diagnosed during a 13-year follow-up and 396 matched controls. Untargeted nuclear magnetic resonance (NMR) metabolomic profiles were established from baseline plasma samples. Multivariable conditional logistic regression models were computed for each individual NMR variable and for combinations of variables derived by principal component analysis. Results: Several metabolomic variables from 1D NMR spectroscopy were associated with breast cancer risk. Women characterized by higher fasting plasma levels of valine, lysine, arginine, glutamine, creatine, creatinine and glucose, and lower plasma levels of lipoproteins, lipids, glycoproteins, acetone, glycerol-derived compounds and unsaturated lipids had a higher risk of developing breast cancer. P-values ranged from 0.00007 [odds ratio (OR)T3vsT1=0.37 (0.23-0.61) for glycerol-derived compounds] to 0.04 [ORT3vsT1=1.61 (1.02-2.55) for glutamine]. Conclusion: This study highlighted associations between baseline NMR plasma metabolomic signatures and long-term breast cancer risk. These results provide interesting insights to better understand complex mechanisms involved in breast carcinogenesis and evoke plasma metabolic disorders favourable for carcinogenesis initiation. This study may contribute to develop screening strategies for the identification of at-risk women for breast cancer well before symptoms appear.

Use of nuclear magnetic resonance spectroscopy to assess renal dysfunction after hypertonic-hyperoncotic resuscitation in rats.

BACKGROUND: The aim of this study was to evaluate the renal tolerance of a hypertonic-hyperoncotic solution (HHS) administration during uncontrolled hemorrhagic shock (UHS). METHODS: UHS was produced in rats by a preliminary bleed followed by tail amputation. Hydroxyethylstarch (HHS) 200/0.5 6% in NaCl 7.2% was administered to the HHS groups (n = 20) and normal saline (NS) to the NS group (n = 20). Infusion rates were adjusted to prevent mean arterial pressure (MAP) from falling either below 40 mm Hg in the HHS40 (n = 10) and NS40 groups (n = 10), or below 80 mm Hg in the HHS80 (n = 10) and NS80 groups (n = 10). Data obtained were compared with a sham group and a no resuscitation (NR) group. Nephrotoxicity was evaluated by nuclear magnetic resonance analysis in urine samples. RESULTS: Survival was 60% in the NS40 group and 40% in the NS80 group, 70% in the HHS40 group, and 60% in the HHS80 group (p = not significant). Within and between target groups of 40 mm Hg MAP and 80 mm Hg MAP, there was no significant difference in survival. The mean values of renal metabolites to creatinine (ct) ratios were not significantly different among the six groups. Principal component analysis showed that the HHS80 group was characterized by an increase in allantoin/ct and urea/ct ratios demonstrating acute renal dysfunction and failure of nitrogen metabolism. CONCLUSION: In prolonged UHS, an infusion of HHS may not increase the rate of survival. HHS infusion in normotensive resuscitation appears to be associated with renal toxicity.

The impact of moderate altitude on exercise metabolism in recreational sportsmen: a nuclear magnetic resonance metabolomic approach.

Although it is known that altitude impairs performance in endurance sports, there is no consensus on the involvement of energy substrates in this process. The objective of the present study was to determine whether the metabolomic pathways used during endurance exercise differ according to whether the effort is performed at sea level or at moderate altitude (at the same exercise intensity, using proton nuclear magnetic resonance, 1H NMR). Twenty subjects performed two 60-min endurance exercise tests at sea level and at 2150 m at identical relative intensity on a cycle ergometer. Blood plasma was obtained from venous blood samples drawn before and after exercise. 1H NMR spectral analysis was then performed on the plasma samples. A multivariate statistical technique was applied to the NMR data. The respective relative intensities of the sea level and altitude endurance tests were essentially the same when expressed as a percentage of the maximal oxygen uptake measured during the corresponding incremental maximal exercise test. Lipid use was similar at sea level and at altitude. In the plasma, levels of glucose, glutamine, alanine, and branched-chain amino acids had decreased after exercise at altitude but not after exercise at sea level. The decrease in plasma glucose and free amino acid levels observed after exercise at altitude indicated that increased involvement of the protein pathway was necessary but not sufficient for the maintenance of glycaemia. Metabolomics is a powerful means of gaining insight into the metabolic changes induced by exercise at altitude.

Nuclear magnetic resonance metabolomics and human liver diseases: The principles and evidence associated with protein and carbohydrate metabolism.

During the last decade, metabolomics has become widely used in the field of human diseases. Numerous studies have demonstrated that this is a powerful technique for improving the understanding, diagnosis and management of various types of liver disease, such as acute and chronic liver diseases, and liver transplantation. Nuclear magnetic resonance (NMR) spectroscopy is one of the two most commonly applied methods for metabolomics. The aim of the present review was to investigate the results from recent key publications focusing on aspects of protein and carbohydrate metabolism. The review includes existing procedures, which are currently used for NMR data acquisition and statistical analysis. In addition, notable results obtained by these studies on protein and carbohydrate metabolism concerning human liver diseases are presented.

Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy.

Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis.

Cardiopulmonary resuscitation duration and survival in out-of-hospital cardiac arrest patients.

AIM: Relationship between cardiopulmonary arrest and resuscitation (CPR) durations and survival after out-of-hospital cardiac arrest (OHCA) remain unclear. Our primary aim was to determine the association between survival without neurologic sequelae and cardiac arrest intervals in the setting of witnessed OHCA. METHODS: We analyzed 27,301 non-traumatic, witnessed OHCA patients in France included in the national registry from June 1, 2011 through December 1, 2015. We analyzed cardiac arrest intervals, designated as no-flow (NF; from collapse to start of CPR) and low-flow (LF; from start of CPR to cessation of resuscitation) in relation to 30-day survival without sequelae. We determined the influence of recognized prognostic factors (age, gender, initial rhythm, location of cardiac arrest) on this relation. RESULTS: For the entire cohort, the area delimited by a value of NF greater than 12min (95% confidence interval: 11-13min) and LF greater than 33min (95% confidence interval: 29-45min), yielded a probability of 30-day survival of less than 1%. These sets of values were greatly influenced by initial cardiac arrest rhythm, age, sex and location of cardiac arrest. Extended CPR duration (greater than 40min) in the setting of initial shockable cardiac rhythm is associated with greater than 1% survival with NF less than 18min. The NF interval was highly influential on the LF interval regardless of outcome, whether return of spontaneous circulation (p<0.001) or death (p<0.001). CONCLUSION: NF duration must be considered in determining CPR duration in OHCA patients. The knowledge of (NF, LF) curves as function of age, initial rhythm, location of cardiac arrest or gender may aid in decision-making vis-à-vis the termination of CPR or employment of advanced techniques.

Nuclear magnetic resonance based metabolomics and liver diseases: Recent advances and future clinical applications.

Metabolomics is defined as the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. It is an "omics" technique that is situated downstream of genomics, transcriptomics and proteomics. Metabolomics is recognized as a promising technique in the field of systems biology for the evaluation of global metabolic changes. During the last decade, metabolomics approaches have become widely used in the study of liver diseases for the detection of early biomarkers and altered metabolic pathways. It is a powerful technique to improve our pathophysiological knowledge of various liver diseases. It can be a useful tool to help clinicians in the diagnostic process especially to distinguish malignant and non-malignant liver disease as well as to determine the etiology or severity of the liver disease. It can also assess therapeutic response or predict drug induced liver injury. Nevertheless, the usefulness of metabolomics is often not understood by clinicians, especially the concept of metabolomics profiling or fingerprinting. In the present work, after a concise description of the different techniques and processes used in metabolomics, we will review the main research on this subject by focusing specifically on in vitro proton nuclear magnetic resonance spectroscopy based metabolomics approaches in human studies. We will first consider the clinical point of view enlighten physicians on this new approach and emphasis its future use in clinical "routine".