Abstract
A number of metabolic abnormalities have been observed in pregnancies complicated by intrauterine growth restriction (IUGR). Metabolic fingerprinting and clinical metabolomics have recently been proposed as tools to investigate individual phenotypes beyond genomes and proteomes and to advance hypotheses on the genesis of diseases. Non-targeted metabolomic profiling was employed to study fetal and/or placental metabolism alterations in IUGR fetuses by liquid chromatography high-resolution mass spectrometry (LC-HRMS) analysis of cord blood collected soon after birth. Samples were collected from 22 IUGR and 21 appropriate for gestational age (AGA) fetuses. Birth weight differed significantly between IUGR and AGA fetuses (p < 0.001). Serum samples were immediately obtained and deproteinized by mixing with methanol at room temperature and centrifugation; supernatants were lyophilized and reconstituted in water for analysis. LC-HRMS analyses were performed on an Orbitrap mass spectrometer linked to a Surveyor Plus LC. Samples were injected into a 1.0 × 150-mm Luna C18 column. Spectra were collected in full-scan mode at a resolution of approximately 30,000. Data were acquired over the m/z range of 50-1,000, with measurements performed in duplicate. To observe metabolic variations between the two sets of samples, LC-HRMS data were analyzed by a principal component analysis model. Many features (e.g., ionic species with specific retention times) differed between the two classes of samples: among these, the essential amino acids phenylalanine, tryptophan, and methionine were identified by comparison with available databases. Logistic regression coupled to a receiver-operating characteristic curve identified a cut-off value for phenylalanine and tryptophan, which gave excellent discrimination between IUGR and AGA fetuses. Non-targeted LC-HRMS analysis of cord blood collected at birth allowed the identification of significant differences in relative abundances of essential amino acids between IUGR and AGA fetuses, emerging as a promising tool for studying metabolic alterations.
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