2020-06-11T21:32:33Z
2020-06-11T21:32:33Z
2019-11-15
2020-06-11T21:32:33Z
Abstract: Congenital human cytomegalovirus (HCMV) infection is the most common mother-to-child transmitted infection in the developed world. Certain aspects of its management remain a challenge. Urinary metabolic profiling is a promising tool for use in pediatric conditions. The aim of this study was to investigate the urinary metabolic profile in HCMV-infected infants and controls during acute care hospitalization. Urine samples were collected from 53 patients at five hospitals participating in the Spanish congenital HCMV registry. Thirty-one cases of HCMV infection and 22 uninfected controls were included. Proton nuclear magnetic resonance (1H-NMR) spectra were obtained using NOESYPR1D pulse sequence. The dataset underwent orthogonal projection on latent structures discriminant analysis to identify candidate variables affecting the urinary metabolome: HCMV infection, type of infection, sex, chronological age, gestational age, type of delivery, twins, and diet. Statistically significant discriminative models were obtained only for HCMV infection (p = 0.03) and chronological age (p < 0.01). No significant differences in the metabolomic profile were found between congenital and postnatal HCMV infection. When the HCMV-infected group was analyzed according to chronological age, a statistically significant model was obtained only in the neonatal group (p = 0.01), with the differentiating metabolites being betaine, glycine, alanine, and dimethylamine. Despite the considerable variation in urinary metabolic profiles in a real-life setting, clinical application of metabolomics to the study of HCMV infection seems feasible.
Article
Published version
English
Infeccions per citomegalovirus; Pediatria; Metabolòmica; Cytomegalovirus infections; Pediatrics; Metabolomics
MDPI
Reproducció del document publicat a: https://doi.org/10.3390/metabo9120288
Metabolites, 2019, vol. 9, p. 288
https://doi.org/10.3390/metabo9120288
cc-by (c) Frick, Marie Antoinette et al., 2019
http://creativecommons.org/licenses/by/3.0/es