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Early Hum Dev. 2014 Mar;90 Suppl 1:S78-83. doi: 10.1016/S0378-3782(14)70024-6.

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

Author information

1
Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, Azienda Ospedaliera Universitaria, University of Cagliari, Cagliari, Italy. Electronic address: vafanos@tiscali.it.
2
Department of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy.
3
Operative Unit of Pediatrics and Neonatal Intensive Therapy, Mother and Child Department, University of Palermo, Palermo, Italy.
4
Neonatal Unit and Neonatal Intensive Care Unit, Maternal-Infant Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Laboratory of Neonatal Immunology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
5
Department of Maternal, Fetal and Neonatal Health, C. Arrigo Children's Hospital, Alessandria, Italy.
6
Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, Azienda Ospedaliera Universitaria, University of Cagliari, Cagliari, Italy.
7
Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy.
8
Neonatal Unit and Neonatal Intensive Care Unit, Maternal-Infant Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
9
Laboratory of Neonatal Immunology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.

Abstract

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).

KEYWORDS:

Metabolomics; Neonatal infections; Newborn; Sepsis

PMID:
24709468
DOI:
10.1016/S0378-3782(14)70024-6
[Indexed for MEDLINE]

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