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Lymphoma defines a group of different diseases. This study examined pre-treatment plasma samples from 66 adult patients (aged 20-74) newly diagnosed with any lymphoma subtype, and 96 frequency matched population controls. We used gas chromatography-mass spectrometry (GC-MS) to compare the metabolic profile by case/control status and across the major lymphoma subtypes. We conducted univariate and multivariate analyses, and partial least square discriminant analysis (PLS-DA). When compared to the controls, statistically validated models were obtained for diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and Hodgkin lymphoma (HL), but not follicular lymphoma (FL). The metabolomic analysis highlighted interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects: Important metabolites, such as hypoxanthine and elaidic acid, were more abundant in all lymphoma subtypes. The small sample size of the individual lymphoma subtypes prevented obtaining PLS-DA validated models, although specific peculiar features of each subtype were observed; for instance, fatty acids were most represented in MM and HL patients, while 2-aminoadipic acid, 2-aminoheptanedioic acid, erythritol, and threitol characterized DLBCL and CLL. Metabolomic analysis was able to highlight interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects. Further studies are warranted to understand whether the peculiar metabolic patterns observed might serve as early biomarkers of lymphoma., Competing Interests: The authors declare no conflict of interest.

Objective: Chorioamnionitis is a leading cause of preterm birth worldwide, with higher incidence at lower gestational ages. An early and reliable diagnosis of histological chorioamnionitis (HCA) in preterm infants may be helpful in guiding postnatal management, especially the administration of prophylactic antibiotics to prevent early-onset sepsis. The main aim of this study was to investigate metabolomic analysis of urines collected in the first 24 hours of life as diagnostic tool of HCA., Methods: Gestational age-, birth weight-, delivery mode- and sex- matched (1:2) preterm neonates (< 35 weeks' gestation) born to mothers with or without HCA were enrolled from an observational study. Gas chromatography-mass spectrometry (GC-MS)-based metabolomic analysis was performed on urine samples non-invasively collected in the first 24 hours of life. Univariate analysis, partial least square discriminant analysis (PLS-DA) and its associated variable importance in projection (VIP) score were performed. The most affected metabolic pathways were examined by Metabolite Sets Enrichment Analysis (MSEA)., Results: Fifteen cases (mean GA 30.2 ± 3.8 weeks, mean BW 1415 ± 471.9 grams) and 30 controls (mean GA 30.2 ± 2.9 weeks, mean BW 1426 ± 569.8 grams) were enrolled. Following univariate analysis, 29 metabolites had a significantly different concentration between cases and controls. The supervised PLS-DA model confirmed a separation between the two groups. Only gluconic acid, an oxidation product of glucose, was higher in cases than in controls. All other VIP metabolites were more abundant in the control group. Glutamate metabolism, mitochondrial electron transport chain, citric acid cycle, galactose metabolism, and fructose and mannose degradation metabolism were the most significantly altered pathways (P < 0.01)., Conclusions: For the first time, urinary metabolomics was able to discriminate neonates born to mothers with and without HCA. The identification of specifically altered metabolic pathways may be helpful in understanding metabolic derangement following chorioamnionitis.

Background: Human cytomegalovirus (HCMV) is one of the most frequent risk of viral infections during pregnancy. The aim of this study was to evaluate the metabolic profile in amniotic fluid (AF) samples obtained from HCMV-infected, and uninfected fetuses in order to elucidate changes in metabolic pathways during congenital HCMV infection and to recognize new potential diagnostic and/or prognostic biomarkers., Methods: A retrospective cohort study was conducted on 63 pregnant women: 20 contracted primary HCMV infection during pregnancy and, subsequently, transmitted the virus to the fetus (transmitters); 20 contracted the infection without transmitting the virus to the fetus (non-transmitters); 23 who underwent amniocentesis for cytogenetic-based diagnosis were considered controls. Metabolomics analysis was performed by using the hyphenated technique Gas chromatography-mass spectrometry (GC-MS) followed by a multivariate statistical approach. Four PLS-DA models were generated: controls vs. transmitters; controls vs. non-transmitters; transmitters vs. non-transmitters; and asymptomatic infected vs. symptomatic infected newborns. Subsequently, these models were exploited for network mapping., Results: Compared with controls, HCMV transmitters showed significantly increased levels in glutamine, glycine, serine, pyruvic acid, threonine, threonic acid, and cystine; conversely, unknown U1715 and U1804, glutamic acid, U1437, fructose, sugar-like A203003 and A203005, and tyrosine levels were found decreased. In non-transmitters, glutamine, serine, glycine, threonic acid, threonine, 1-monostearin, urea, and cystine were found increased, while sorbitol, unknown U1804, sugar-like A203003, U1751, xylitol, leucine and fructose were decreased. The comparison between transmitters and non-transmitters did not produce a statistically significant model. Unlike controls' profile, a common feature of HCMV infected subjects (transmitters and non-transmitters) was the activation of glutamine-glutamate and pyrimidine metabolic pathways. In addition, a clusterization for asymptomatic vs. symptomatic outcome was also observed due to alteration of fatty acids biosynthesis., Conclusions: Metabolomics approach could highlight the significant modification of maternal and placental status during HCMV infection for both transmitter and non-transmitter subjects. A further separation was observed for asymptomatic vs. symptomatic HCMV congenital infections model. Therefore, metabolomics may be a promising tool to improve the accuracy of an early diagnosis, and the management of HCMV pregnancy-related infections., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Amniotic fluid (AF) is a biological fluid in which metabolite transport is regulated by the placenta, the permeable skin, fetal lung egress and gastric fluid. During pregnancy, the composition of AF changes from similar to the interstitial fluid of the mother, to a more complex system, influenced by the fetus's urine. Since AF reflects the mother's and the fetus's health status at the same time, it may be an important diagnostic tool for a wider spectrum of clinical conditions. Indeed, the metabolic characterization of AF in relation to pathological occurrences may lead to the discovery of new biomarkers for a better clinical practice. For this reason, metabolomics may be the most suitable strategy for this task. In this review, research works on metabolomic AF analysis are discussed according to the morbidity of interest, being preterm birth/labor, gestational age and diabetes and fetal malformations, along with a number of other important studies.

Perinatal asphyxia is defined as an oxygen deprivation that occurs around the time of birth, and may be caused by several perinatal events. This medical condition affects some four million neonates worldwide per year, causing the death of one million subjects. In most cases, infants successfully recover from hypoxia episodes; however, some patients may develop HIE, leading to permanent neurological conditions or impairment of different organs and systems. Given its multifactor dependency, the timing, severity and outcome of this disease, mainly assessed through Sarnat staging, are of difficult evaluation. Moreover, although the latest newborn resuscitation guideline suggests the use of a 21% oxygen concentration or room air, such an approach is still under debate. Therefore, the pathological mechanism is still not clear and a golden standard treatment has yet to be defined. In this context, metabolomics, a new discipline that has described important perinatal issues over the last years, proved to be a useful tool for the monitoring, the assessment, and the identification of potential biomarkers associated with asphyxia events. This review covers metabolomics research on perinatal asphyxia condition, examining in detail the studies reported both on animal and human models.

Objective: A supervised multivariate model to classify the metabolome alterations between autistic spectrum disorders (ASD) patients and controls, siblings of autistic patients, has been realized and used to realize a network model of the ASD patients' metabolome., Methods: In our experiment we propose a quantification of urinary metabolites with the Mass Spectroscopy technique couple to Gas Chromatography. A multivariate model has been used to extrapolate the variables of importance for a network model of interaction between metabolites. In this way we are able to propose a network-based approach to ASD description., Results: Children with autistic disease composing our studied population showed elevated concentration of several organic acids and sugars. Interactions among diet, intestinal flora and genes may explain such findings. Among them, the 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid has been previously described as altered in autistic subjects. Other metabolites increased are 3,4-dihydroxybutyric acid, glycolic acid and glycine, cis-aconitic acid; phenylalanine, tyrosine, p-hydroxyphenylacetic acid, and homovanillic acid are all involved in the tyrosine pathway leading to neurotransmitter cathecolamine., Conclusion: GC-MS-based metabolomic analysis of the urinary metabolome suggests to have the required sensitivity and specificity to gain insight into ASD phenotypes and aid a personalized network-based medicine approach.

Objective: Metabolomics is a new "omics" platform aimed at high-throughput identification, quantification and characterization of small-molecule metabolites. The metabolomics approach has been successfully applied to the classification different physiological states and identification of perturbed biochemical pathways. The purpose of the current investigation is the application of metabolomics to explore biological mechanisms which may lead to the onset of metabolic syndrome in adulthood., Methods: We evaluated differences in metabolites in the urine collected within 12 h from 23 infants with IUGR (IntraUterine Growth Restriction), or LGA (Large for Gestational Age), compared to control infants (10 patients defined AGA: Appropriate for Gestational Age). Urinary metabolites were quantified by GC-MS and used to highlight similarities between the two metabolic diseases and identify metabolic markers for their predisposition. Quantified metabolites were analyzed using a multivariate statistics coupled with receiver operator characteristic curve (ROC) analysis of identified biomarkers., Results: Urinary myo-inositol was the most important discriminant between LGA + IUGR and control infants, and displayed an area under the ROC curve = 1., Conclusion: We postulate that the increase in plasma and consequently urinary inositol may constitute a marker of altered glucose metabolism during fetal development in both IUGR and LGA newborns.