Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut–brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

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 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.

Several biochemical markers have been proposed over the past years to manage critically ill newborns with acute inflammation and sepsis. The state of the art in diagnosing and monitoring neonatal sepsis, severe sepsis and septic shock consists of the measurement of plasma C-reactive protein (CRP) and procalcitonin (PCT) at the onset and in the course of the disease. CRP and PCT in combination are clinically significant in diagnosing and monitoring septic newborns; however, CRP and PCT have a very limited value for risk stratification and in predicting outcome. The availability of commercial methods for the automated measurement of the soluble CD14 subtype presepsin (sCD14-ST) and lipopolysaccharide binding protein (LBP) represent a challenge for the evaluation in clinical practice of reliable markers of neonatal sepsis, specifically for the very early diagnosis, the classification into class of severity, and the prediction of complications and death.

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.