The Human Breast Milk Metabolome in Overweight and Obese Mothers.

Pre-pregnancy body mass index (BMI) is a major relevance factor, since maternal overweight and obesity can impair the pregnancy outcome and represent risk factors for several neonatal, childhood, and adult conditions, including excessive weight gain, cardiovascular disease, diabetes mellitus, and even behavioral disorders. Currently, breast milk (BM) composition in such category of mothers was not completely defined. In this field, metabolomics represents the ideal technology, able to detect the whole profile of low molecular weight molecules in BM. Limited information is available on human BM metabolites differences in overweight or obese compared to lean mothers. Analyzing all the metabolomics studies published on Medline in English language, this review evaluated the effects that 8 specific types of metabolites found altered by maternal overweight and obesity (nucleotide derivatives, 5-methylthioadenosine, sugar-alcohols, acylcarnitine and amino acids, polyamines, mono-and oligosaccharides, lipids) can exert on the risk of offspring obesity development and other potentially associated health outcomes and complications. However, metabolites variations in samples collected from overweight and obese mothers and the potentially correlated effects highlighted below still need further investigations and should be confirmed in future metabolomics studies on larger samples. Finally, the positive or negative influence of maternal overweight and obesity on the offspring, potentially exerted by breastfeeding, should be analyzed in close correlation with maternal age, genetic and environmental factors, including diet, and taking into account the interactions occurring between BM metabolites and lactobiome. The evaluation of all the factors affecting BM metabolites in overweight and obese mothers can lead to the comprehensive description of such biofluid and the related effects on breastfed subjects, potentially highlighting personalized needs of BM supplementation or short- and long-term prevention strategies to optimize offspring health.

Soluble CD14 subtype (sCD14-ST) presepsin in premature and full term critically ill newborns with sepsis and SIRS.

Neonatal sepsis still remains a major cause of morbidity and mortality in neonatal intensive care unit (NICU). Recently, soluble CD14 subtype (sDC14-ST) also named presepsin, was proposed as an effective biomarker for diagnosing, monitoring, and assessing the risk of neonatal sepsis and septic shock. The aim of this study was to investigate the diagnostic accuracy of sCD14-ST presepsin in diagnosing neonatal bacterial sepsis and in discriminating non-bacterial systemic inflammatory response syndrome (SIRS) from bacterial sepsis. This study involved 65 critically ill full-term and preterm newborns admitted to the neonatal intensive care unit (NICU), divided into three groups: 25 newborns with bacterial neonatal sepsis (group A); 15 newborns with a diagnosis of non-bacterial SIRS and with no localizing source of bacterial infection (group B); and 25 babies with no clinical or bacteriological signs of systemic or local infection receiving routine NICU care, most of them treated with phototherapy for neonatal jaundice (group C). A total of 102 whole blood samples were collected, 40 in group A, 30 in group B and 32 in group C. In 10 babies included in group A, sCD14-ST presepsin was also measured in an additional second blood sample collected 3 days after the start of antibiotic treatment. sCD14-ST presepsin was measured by a commercially available chemiluminescent enzyme immunoassay (CLEIA) optimized on an automated immunoassay analyzer. Statistical analysis was performed by means of MedCalc® statistical package; receiver operating characteristic (ROC) analysis was computed, and the area under the ROC curve (AUC) was used to evaluate the ability of sCD14-ST to discriminate neonatal bacterial sepsis from non-bacterial SIRS. Blood sCD14-ST presepsin levels were found significantly higher in bacterial sepsis when compared with controls (p<0.0001); similarly, they were higher in non-bacterial SIRS when compared with controls (p<0.0001). However, no statistically significant difference was found between bacterial sepsis and non-bacterial SIRS (p=0.730). In our population, CRP and sCD14-ST did not correlate with each other. ROC analysis revealed that sCD14-ST presepsin has an area under the curve (AUC) of 0.995 (95% C.I.: 0.941-1.00) greater than that of CRP (0.827; 95% C.I.: 0.72-0.906). Similarly, in the group of babies with non-infectious SIRS, sCD14-ST AUC was greater than CRP AUC (0.979; 95% C.I.: 0.906-0.999 versus 0.771; 95% C.I.: 0.647-0.868). In controls, preliminary reference intervals for sCD14-ST ranged 223.4-599.7 ng/L, being significantly different from those previously published elsewhere. In conclusion, sCD14-ST presepsin could be introduced in clinical practice as a diagnostic tool for improving the management of neonatal sepsis and non-bacterial SIRS.

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.