Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease.

Bicuspid aortic valve (BAV) is the most common congenital heart defect responsible for valvular and aortic complications in affected patients. Causes and mechanisms of this pathology are still elusive and thus the lack of early detection biomarkers leads to challenges in its diagnosis and prevention of associated cardiovascular anomalies. The aim of this study was to explore the potential use of urine Nuclear Magnetic Resonance (NMR) metabolomics to evaluate a molecular fingerprint of BAV. Both multivariate and univariate statistical analyses were performed to compare the urinary metabolome of 20 patients with BAV with that of 24 matched controls. Orthogonal partial least squared discriminant analysis (OPLS-DA) showed statistically significant discrimination between cases and controls, suggesting seven metabolites (3-hydroxybutyrate, alanine, betaine, creatine, glycine, hippurate, and taurine) as potential biomarkers. Among these, glycine, hippurate and taurine individually displayed medium sensitivity and specificity by receiver operating characteristic (ROC) analysis. Pathway analysis indicated two metabolic pathways likely perturbed in BAV subjects. Possible contributions of gut microbiota activity and energy imbalance are also discussed. These results constitute encouraging preliminary findings in favor of the use of urine-based metabolomics for early diagnosis of BAV.

1H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI).

BACKGROUND: Urinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis. METHODS: Urine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The 1H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve. RESULTS: Orthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R2Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity. CONCLUSIONS: Acetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations.

Impact of Early Postnatal Nutrition on the NMR Urinary Metabolic Profile of Infant.

NMR-based metabolomics was used to compare the metabolic urinary profiles of exclusively breast-fed term infants (n = 11) with those of a double-blinded controlled trial with 49 formula-fed term newborns randomized to receive either an infant formula enriched by functional ingredients (n = 24) or a standard formula (n = 25). Anthropometric measurements and urine samples were taken at enrollment (within the first month of life), at around 60 days of life, and at the end of study period (average age of 130 days). The metabolic profiles were examined in relation to time and diet strategy. A common age-dependent modification of the urine metabolome was observed for the three types of nutrition, mainly characterized by similar temporal trends of choline, betaine, myoinositol, taurine, and citrate. Contrariwise, differences in the metabolic profiles were identified according to the type of diet (human versus formula milk), while no significant difference was observed between the two formulas. These modifications are discussed mainly in terms of the different milk compositions. Despite the low number of enrolled infants (n = 60), these findings pointed out the potential of the metabolomics approach for neonatal nutritional science, in particular to provide important contributions to the optimization of formula milk.

Statistical Health Monitoring Applied to a Metabolomic Study of Experimental Hepatocarcinogenesis: An Alternative Approach to Supervised Methods for the Identification of False Positives.

In a typical metabolomics experiment, two or more conditions (e.g., treated versus untreated) are compared, in order to investigate the potential differences in the metabolic profiles. When dealing with complex biological systems, a two-class classification is often unsuitable, since it does not consider the unpredictable differences between samples (e.g., nonresponder to treatment). An approach based on statistical process control (SPC), which is able to monitor the response to a treatment or the development of a pathological condition, is proposed here. Such an approach has been applied to an experimental hepatocarcinogenesis model to discover early individual metabolic variations associated with a different response to the treatment. Liver study was performed by nuclear magnetic resonance (NMR) spectroscopy, followed by multivariate statistical analysis. By this approach, we were able to (1) identify which treated samples have a significantly different metabolic profile, compared to the control (in fact, as confirmed by immunohistochemistry, the method correctly classified 7 responders and 3 nonresponders among the 10 treated animals); (2) recognize, for each individual sample, the metabolites that are out of control (e.g., glutathione, acetate, betaine, and phosphocholine). The first point could be used for classification purposes, and the second point could be used for a better understanding of the mechanisms underlying the early phase of carcinogenesis. The statistical control approach can be used for diagnosis (e.g., healthy versus pathological, responder versus nonresponder) and for generation of an individual metabolic profile, leading to a better understanding of the individual pathological processes and to a personalized diagnosis and therapy.

Discovery of biochemical biomarkers for aggression: A role for metabolomics in psychiatry.

Human aggression encompasses a wide range of behaviors and is related to many psychiatric disorders. We introduce the different classification systems of aggression and related disorders as a basis for discussing biochemical biomarkers and then present an overview of studies in humans (published between 1990 and 2015) that reported statistically significant associations of biochemical biomarkers with aggression, DSM-IV disorders involving aggression, and their subtypes. The markers are of different types, including inflammation markers, neurotransmitters, lipoproteins, and hormones from various classes. Most studies focused on only a limited portfolio of biomarkers, frequently a specific class only. When integrating the data, it is clear that compounds from several biological pathways have been found to be associated with aggressive behavior, indicating complexity and the need for a broad approach. In the second part of the paper, using examples from the aggression literature and psychiatric metabolomics studies, we argue that a better understanding of aggression would benefit from a more holistic approach such as provided by metabolomics. © 2016 Wiley Periodicals, Inc.

1H NMR metabolite fingerprinting as a new tool for body fluid identification in forensic science.

In this feasibility study, we propose, for the first time, (1)H NMR spectroscopy coupled with mathematical strategies as a valid tool for body fluid (BF) trace identification in forensic science. In order to assess the ability of this approach to identify traces composed either by a single or by two different BFs, samples of blood, urine, saliva, and semen were collected from different donors, and binary mixtures were prepared. (1)H NMR analyses were carried out for all samples. Spectral data of the whole set were firstly submitted to unsupervised principal component analysis (PCA); it showed that samples of the same BF cluster well on the basis of their characterizing molecular components and that mixtures exhibit intermediate characteristics among BF typologies. Furthermore, samples were divided into a training set and a test set. An average NMR spectral profile for each typology of BF was obtained from the training set and validated as representative of each BF class. Finally, a fitting procedure, based on a system of linear equations with the four obtained average spectral profiles, was applied to the test set and the mixture samples; it showed that BFs can be unambiguously identified, even as components of a mixture. The successful use of this mathematical procedure has the advantage, in forensics, of overcoming bias due to the analyst's personal judgment. We therefore propose this combined approach as a valid, fast, and non-destructive tool for addressing the challenges in the identification of composite traces in forensics.

Is the body composition development in premature infants associated with a distinctive nuclear magnetic resonance metabolomic profiling of urine?

OBJECTIVE: Preterm infants' body composition at term-corrected age differs from that of term infants but appears to be similar at the age of 3 months. The aim of this study was to compare the metabolomic pattern of preterm infants at term and at 3 months with that of term infants and to determine its association with body composition development. METHOD: We designed a pilot study. Growth and body composition were evaluated by an air displacement plethysmography system in 13 preterm infants and seven term newborns at term and at 3 months of corrected age. Urine samples were collected at the same time points and analysed by nuclear magnetic resonance. RESULTS: At term-corrected age, preterm infants showed a higher fat mass percentage compared with that of term newborns, whereas at 3 months of corrected age, the body composition parameters were similar between the groups. At the first time point, nuclear magnetic resonance analysis showed a urinary increase in choline/phosphocholine, betaine and glucose in preterm infants. At the second time point, the preterm group exhibited a urinary increase in choline/phosphocholine and a decrease in betaine. CONCLUSIONS: The increased urinary excretion of choline, a betaine precursor, could reflect a potential altered metabolism in preterm infants.

Metabolomic profile of systemic sclerosis patients.

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterized by vascular lesions, immunological alterations and diffuse fibrosis of the skin and internal organs. Since recent evidence suggests that there is a link between metabolomics and immune mediated disease, serum metabolic profile of SSc patients and healthy controls was investigated by 1H-NMR and GC-MS techniques. The results indicated a lower level of aspartate, alanine, choline, glutamate, and glutarate in SSc patients compared with healthy controls. Moreover, comparing patients affected by limited SSc (lcSSc) and diffuse SSc (dcSSc), 6 discriminant metabolites were identified. The multivariate analysis performed using all the metabolites significantly different revealed glycolysis, gluconeogenesis, energetic pathways, glutamate metabolism, degradation of ketone bodies and pyruvate metabolism as the most important networks. Aspartate, alanine and citrate yielded a high area under receiver-operating characteristic (ROC) curves (AUC of 0.81; CI 0.726-0.93) for discriminating SSc patients from controls, whereas ROC curve generated with acetate, fructose, glutamate, glutamine, glycerol and glutarate (AUC of 0.84; CI 0.7-0.98) discriminated between lcSSc and dcSSc. These results indicated that serum NMR-based metabolomics profiling method is sensitive and specific enough to distinguish SSc from healthy controls and provided a feasible diagnostic tool for the diagnosis and classification of the disease.

Urinary 1H-NMR Metabolomics in the First Week of Life Can Anticipate BPD Diagnosis.

Despite the advancements in medical knowledge and technology, the etiopathogenesis of bronchopulmonary dysplasia (BPD) is not yet fully understood although oxidative stress seems to play a role, leading to a very demanding management of these patients by the neonatologist. In this context, metabolomics can be useful in understanding, diagnosing, and treating this illness since it is one of the newest omics science that analyzes the metabolome of an individual through the investigation of biological fluids such as urine and blood. In this study, 18 patients admitted to the Neonatal Intensive Care Unit of the Cagliari University Hospital were enrolled. Among them, 11 patients represented the control group and 7 patients subsequently developed BPD. A sample of urine was collected from each patient at 7 days of life and analyzed through 1H-NMR coupled with multivariate statistical analysis. The discriminant metabolites between the 2 groups noted were alanine, betaine, trimethylamine-N-oxide, lactate, and glycine. Utilizing metabolomics, it was possible to detect the urinary metabolomics fingerprint of neonates in the first week of life who subsequently developed BPD. Future studies are needed to confirm these promising results suggesting a possible role of microbiota and oxidative stress, and to apply this technology in clinical practice.

Metabolomic analysis of urine with Nuclear Magnetic Resonance spectroscopy in patients with idiopathic sudden sensorineural hearing loss: A preliminary study.

OBJECTIVE: Idiopathic sudden sensorineural hearing loss is a frequent emergency, with unknown aetiology and usually treated with empiric therapy. Steroids represent the only validated treatment but prognosis is unpredictable and the possibility to select the patients who will not respond to steroids could avoid unnecessary treatments. Metabolomic profiling of the biofluids target the analysis of the final product of genic expression and enzymatic activity, defining the biochemical phenotype of a whole biologic system. METHODS: We studied the metabolomics of the urine of a cohort of patients with idiopathic sudden sensorineural hearing loss, correlating the metabolic profiles with the clinical outcomes. Metabolomic profiling of urine samples was performed by 1H Nuclear Magnetic Resonance spectroscopy in combination with multivariate statistical approaches. RESULTS: 26 patients were included in the study: 5 healthy controls, 13 patients who did not recover after treatment at 6 months while the remaining 8 patients recovered from the hearing loss. The orthogonal partial least square-discriminant analysis score plot showed a significant separation between the two groups, responders and non-responders after steroid therapy, R2Y of 0.83, Q2 of 0.38 and p value <0.05. The resulting metabolic profiles were characterized by higher levels of urinary B-Alanine, 3-hydroxybutyrate and Trimethylamine N-oxide, and lower levels of Citrate and Creatinine in patients with worst outcome. CONCLUSION: Idiopathic sudden sensorineural hearing loss is a specific disease with unclear systemic changes, but our data suggest that there are different types of this disorder or patients predisposed to effective action of steroids allowing the recover after treatment.

The urinary 1 H-NMR metabolomics profile of an italian autistic children population and their unaffected siblings.

Autism spectrum disorders (ASD) make a dishomogeneous group of psychiatric diseases having either genetic and environmental components, including changes of the microbiota. The rate of diagnosis, based on a series of psychological tests and observed behavior, dramatically increased in the past few decades. Currently, no biological markers are available and the pathogenesis is not defined. The purpose of this study was to evaluate the potential use of 1 H-NMR metabolomics to analyze the global biochemical signature of ASD patients (n = 21) and controls (n = 21), these being siblings of autistic patients. A multivariate model has been used to extrapolate the variables of importance. The discriminating urinary metabolites were identified; in particular, significantly increased levels of hippurate, glycine, creatine, tryptophan, and d-threitol and decreased concentrations of glutamate, creatinine, lactate, valine, betaine, and taurine were observed in ASD patients. Based on the identified discriminant metabolites, the attention was focused on two possible mechanisms that could be involved in ASD: oxidative stress conditions and gut microflora modifications. In conclusion, nuclear magnetic resonance-based metabolomics analysis of the urine seems to have the potential for the identification of a metabolic fingerprint of ASD phenotypes and appears to be suitable for further investigation of the disease mechanisms. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1058-1066. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Urinary metabolomics of pregnant women at term: a combined GC/MS and NMR approach.

BACKGROUND: Physiological changes leading to parturition are not completely understood while clinical diagnosis of labour is still retrospective. Gas chromatography mass spectrometry (GC/MS) and nuclear magnetic resonance spectroscopy (NMR) represent two of the main analytical platforms used in clinical metabolomics. Metabolomics might help us to improve our knowledge about the biochemical mechanisms underlying labour. METHODS: Urine samples (n = 59), collected from pregnant women at term of gestation before and/or after the onset of labour, were analysed by GC/MS and NMR techniques in order to identify the metabolic profile. Both GC/MS and NMR data matrices containing the identified metabolites were analysed by multivariate statistical techniques in order to characterise the discriminant variables between labour (L) and not labour (NL) status. RESULTS: 18 potential metabolites (11 with (1)H-NMR, eight with GC-MS: glycine was relevant in both) were found discriminant in urine of women during labour. Taken together, the identified metabolites produced a composite biomarker pattern, a sort of barcode, capable of differentiating between labour and not labour conditions. Major discriminant metabolites for NMR and GC/MS analysis were: alanine, glycine, acetone, 3-hydroxybutiyric acid, 2,3,4-trihydroxybutyric acid and succinic acid, giving a urine metabolite signature on the late phase of labour. CONCLUSIONS: The metabolomics analysis evidenced clusters of metabolites involved in labour condition able to discriminate between urine samples collected before the onset and during labour, potentially offering the promise of a robust screening test.

Urinary metabolomics of bronchopulmonary dysplasia (BPD): preliminary data at birth suggest it is a congenital disease.

OBJECTIVE: Bronchopulmonary dysplasia (BPD) or chronic lung disease is one of the principal causes of mortality and morbidity in preterm infants. Early identification of infants at the greater risk of developing BPD may allow a targeted approach for reducing disease severity and complications. The trigger cause of the disease comprehends the impairment of the alveolar development and the increased angiogenesis. Nevertheless, the molecular pathways characterizing the disease are still unclear. Therefore, the use of the metabolomics technique, due to the capability of identifying instantaneous metabolic perturbation, might help to recognize metabolic patterns associated with the condition. METHODS: The purpose of this study is to compare urinary metabolomics at birth in 36 newborns with a gestational age below 29 weeks and birth weight <1500 g (very low birth weight - VLBW), admitted in Neonatal Intensive Care Unit (NICU) divided into two groups: the first group (18 cases) consisting of newborns who have not yet developed the disease, but who will subsequently develop it and the second group (18 controls) consisting of newborns not affected by BPD. Urine samples were collected within 24-36 h of life and immediately frozen at -80 °C. RESULTS: The (1)H-NMR spectra were analyzed using a partial least squares discriminant analysis (PLS-DA) model coupled with orthogonal Signal Correction. Using this approach it was possible with urine at birth to discriminate newborns that will be later have a diagnosis of BPD with a high statistics power. In particular, we found five important discriminant metabolites in urine in BPD newborns: lactate, taurine, TMAO, myoinositol (which increased) and gluconate (which decreased). CONCLUSION: These preliminary results seem to be promising for the identification of predictor's biomarkers characterizing the BPD condition. These data may suggest that BPD is probably the result of an abnormal development (respiratory bud, vascular tree, hypodysplasia of pneumocytes) and could be considered a congenital disease (genetics plus intrauterine epigenetics). Early identification of infants at the greater risk of developing BPD may allow a targeted approach for reducing disease severity and complications.

Urinary (1)H-NMR and GC-MS metabolomics predicts early and late onset neonatal sepsis.

The purpose of this article is to study one of the most significant causes of neonatal morbidity and mortality: neonatal sepsis. This pathology is due to a bacterial or fungal infection acquired during the perinatal period. Neonatal sepsis has been categorized into two groups: early onset if it occurs within 3-6 days and late onset after 4-7 days. Due to the not-specific clinical signs, along with the inaccuracy of available biomarkers, the diagnosis is still a major challenge. In this regard, the use of a combined approach based on both nuclear magnetic resonance ((1)H-NMR) and gas-chromatography-mass spectrometry (GC-MS) techniques, coupled with a multivariate statistical analysis, may help to uncover features of the disease that are still hidden. The objective of our study was to evaluate the capability of the metabolomics approach to identify a potential metabolic profile related to the neonatal septic condition. The study population included 25 neonates (15 males and 10 females): 9 (6 males and 3 females) patients had a diagnosis of sepsis and 16 were healthy controls (9 males and 7 females). This study showed a unique metabolic profile of the patients affected by sepsis compared to non-affected ones with a statistically significant difference between the two groups (p = 0.05).

Metabolomics in paediatric oncology: a potential still to be exploited.

Oncology is a branch of medicine in rapid evolution in the attempt to find innovative methods for early diagnosis and a better understanding of tumoral processes leading to the development of new therapies. Metabolomics is the emerging discipline among the "omics" sciences which makes it possible to further expand our knowledge concerning cancer biology. Different studies have revealed the potential role of metabolomics in gaining an understanding of pathophysiological processes in cancer, improving tumor staging, characterizing tumors and searching for biomarkers predictive of therapeutic responses. However, to date there are few works aimed at gaining deeper insights into infantile oncology through metabolomics.

A metabolomic study of preterm human and formula milk by high resolution NMR and GC/MS analysis: preliminary results.

OBJECTIVE: The aim of the present study was to investigate the metabolic profile of preterm human breast milk (HBM) by using a metabolomic approach. METHODS: NMR spectroscopy and GC/MS were used to analyze the water-soluble and lipid fractions extracted from milk samples obtained from mothers giving birth at 26-36 weeks of gestation. For the sake of comparison, preterm formula milk was also studied. RESULTS: The multivariate statistical analysis of the data evidenced biochemical variability both between preterm HBM and commercial milk and within the group of HBM samples. CONCLUSIONS: The preliminary results of this study suggest that metabolomics may provide a promising tool to study aspects related to the nutrition and health of preterm infant.

Clinical metabolomics and urinary NGAL for the early prediction of chronic kidney disease in healthy adults born ELBW.

BACKGROUND: Clinical metabolomics is a recent "omic" technology which is defined as a global holistic overview of the personal metabolic status (fingerprinting). This technique allows to prove metabolic differences in different groups of people with the opportunity to explore interactions such as genotype-phenotype and genotype-environment type, whether normal or pathological. AIM: To study chronic kidney injury 1) using urine metabolomic profiles of young adults born extremely low-birth weight (ELBW) and 2) correlating a biomarker of kidney injury, urinary neutrophil gelatinase-associated lipocalin (NGAL), in order to confirm the metabolomic injury profile. METHOD: Urine samples were collected from a group of 18 people (mean: 24-year-old, std: 4.27) who were born with ELBW and a group of 13 who were born at term appropriate for gestational age (AGA) as control (mean 25-year-old, std: 5.15). Urine samples were analyzed by (1)H-nuclear magnetic resonance spectroscopy, and then submitted to unsupervised and supervised multivariate analysis. Urine NGAL (uNGAL) was measured using ARCHITECT (ABBOTT diagnostic NGAL kit). RESULTS: With a multivariate approach and using a supervised analysis method, PLS-DA, (partial least squares discriminant analysis) we could correlate ELBW metabolic profiles with uNGAL concentration. Conversely, uNGAL could not be correlated to AGA. CONCLUSIONS: This study demonstrates the relevance of the metabolomic technique as a predictive tool of the metabolic status of exELBW. This was confirmed by the use of uNGAL as a biomarker which may predict a subclinical pathological process in the kidney such as chronic kidney disease.

Metabolomics in newborns with intrauterine growth retardation (IUGR): urine reveals markers of metabolic syndrome.

To date, we have little knowledge on the overall metabolic status of neonates with intrauterine growth retardation (IUGR). In the last few years, the analysis of metabolomics has assumed an important clinical role in identifying "disorders" in the metabolic profile of patients. The aim of this work has been to analyze the urine metabolic profiles of neonates with IUGR and compare them with controls to define the metabolic patterns associated with this pathology. To our knowledge, this is the first study of metabolomics performed on neonates with IUGR. Recruited for the study were 26 neonates with IUGR diagnosed in the neonatal period and with weight at birth below the 10th percentile and 30 neonates of proper gestational weight at birth (controls). In the first 24 hours (prior to feeding) (T1) and about 4 days after birth (T2), a urine sample was taken non-invasively from each neonate. The samples were then frozen at -80°C up to the time of the analysis by proton nuclear magnetic resonance spectroscopy (1H-NMR). The data contained in the NMR spectra obtained from the single samples were statistically analyzed using the Principal Components Analysis and the Partial Least Squares-Discriminate Analysis. By means of a multivariate analysis of the NMR spectra obtained, it was possible to highlight the differences between the two groups (IUGRs and controls) owing to the presence of different metabolic patterns. The discriminants in the urine metabolic profiles derived essentially from significant differences in certain metabolites such as: myo-inositol, sarcosine, creatine and creatinine. The metabolomic analysis showed different urine metabolic profiles between neonates with IUGR and controls and made it possible to identify the molecules responsible for such differences.

A metabolomic approach in an experimental model of hypoxia-reoxygenation in newborn piglets: urine predicts outcome.

Perinatal asphyxia is one of the leading causes of morbidity and mortality in the neonatal period. Response to oxygen treatment is unpredictable and the optimum concentration of oxygen in neonatal resuscitation is still a matter of debate among neonatologists. A metabolomic approach was used to characterize the metabolic profiles of newborn hypoxic-reoxygenated piglets. Urine samples were collected from newborn piglets (n = 40) undergoing hypoxia followed by resuscitation at different oxygen concentrations (ranging from 18% to 100%) and analyzed by ¹H NMR spectroscopy. Despite reoxygenation 7 piglets, out of 10 which became asystolic, did not respond to resuscitation. Profiles of the ¹H NMR spectra were submitted to unsupervised (principal component analysis) and supervised (partial least squares-discriminant analysis) multivariate analysis. The supervised analyses showed differences in the metabolic profile of the urine collected before the induction of hypoxia between survivors and deaths. Metabolic variations were observed in the urine of piglets treated with different oxygen concentrations comparing T0 (basal value) and end of the experiment (resuscitation). Some of the individual metabolites discriminating between these groups were urea, creatinine, malonate, methylguanidine, hydroxyisobutyric acid. The metabolomic approach appears a promising tool for investigating newborn hypoxia over time, for monitoring the response to the treatment with different oxygen concentrations, and might lead to a tailored management of the disorder.