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.
6 October 2014 in The Journal of Maternal-Fetal & Neonatal Medicine
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. 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. 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. 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.
2 October 2012 in The Journal of Maternal-Fetal & Neonatal Medicine
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.
Though vagus nerve stimulation (VNS) is an important option in pharmaco-resistant epilepsy, its mechanism of action remains unclear. The observation that VNS desynchronised the EEG activity in animals suggested that this mechanism could be involved in VNS antiepileptic effects in humans. Indeed VNS decreases spiking bursts, whereas its effects on the EEG background remain uncertain. The objective of the present study is to investigate how VNS affects local and inter regional syncronization in different frequencies in pharmaco-resistant partial epilepsy. Digital recordings acquired in 11 epileptic subjects 1 year and 1 week before VNS surgery were compared with that obtained 1 month and 1 year after VNS activation. Power spectrum and synchronization were then analyzed and compared with an epileptic group of 10 patients treated with AEDs only. VNS decreases the synchronization of theta frequencies (P < 0.01), whereas it increases gamma power spectrum and synchronization (< 0.001 and 0.01, respectively). The reduction of theta frequencies and the increase in power spectrum and synchronization of gamma bands can be related to VNS anticonvulsant mechanism. In addition, gamma modulation could also play a seizure-independent role in improving attentional performances. These results suggest that some antiepileptic mechanisms affected by VNS can be modulated by or be the reflection of EEG changes.
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.
12 September 2015 in Journal of Translational Medicine
Heart failure (HF) is characterized by a series of adaptive changes in energy metabolism. The use of metabolomics enables the parallel assessment of a wide range of metabolites. In this study, we appraised whether metabolic changes correlate with HF severity, assessed as an impairment of functional contractility, and attempted to interpret the role of metabolic changes in determining systolic dysfunction. A 500 MHz proton nuclear magnetic resonance (1H-NMR)-based analysis was performed on blood samples from three groups of individuals: 9 control subjects (Group A), 9 HF patients with mild to moderate impairment of left ventricle ejection fraction (LVEF: 41.9 ± 4.0 %; Group B), and 15 HF patients with severe LVEF impairment (25.3 ± 10.3 %; Group C). In order to create a descriptive model of HF, a supervised orthogonal projection on latent structures discriminant analysis (OPLS-DA) was applied using speckle tracking-derived longitudinal strain rate as the Y-variable in the multivariate analysis. OPLS-DA identified three metabolic clusters related to the studied groups achieving good values for R2 [R2(X) = 0.64; R2(Y) = 0.59] and Q2 (0.39). The most important metabolites implicated in the clustering were 2-hydroxybutyrate, glycine, methylmalonate, and myo-inositol. The results demonstrate the suitability of metabolomics in combination with functional evaluation techniques in HF staging. This innovative tool should facilitate investigation of perturbed metabolic pathways in HF and their correlation with the impairment of myocardial function.