Second Trimester Maternal Urine for the Diagnosis of Trisomy 21 and Prediction of Poor Pregnancy Outcomes

• Type: Article in International Scientific Journal
• Date: 2013

Authors

Diaz, SO (Author) Other; Barros, AS (Author) Other; Goodfellow, BJ (Author) Other; Duarte, IF (Author) Other; Galhano, E (Author) Other; Pita, C (Author) Other; Almeida, MD (Author) Other; Carreira, IM (Author) Other; Gil, AM (Author) Other

Journal

Title: Journal of Proteome Research

Vol. 12

Pages: 2946-2957

ISSN: 1535-3893

Publisher: American Chemical Society

Indexing

ISI Web of Knowledge - 70 Citations

Scopus - 74 Citations

Other information

Authenticus ID: P-005-04H

DOI: 10.1021/pr4002355

Abstract (EN): Given the recognized lack of prenatal clinical methods for the early diagnosis of preterm delivery, intrauterine growth restriction, preeclampsia and gestational diabetes mellitus, and the continuing need for optimized diagnosis methods for specific chromosomal disorders (e.g., trisomy 21) and fetal malformations, this work sought specific metabolic signatures of these conditions in second trimester maternal urine, using 'H Nuclear Magnetic Resonance (H-1 NMR) metabolomics. Several variable importance to the projection (VIP)- and b-coefficient-based variable selection methods were tested, both individually and through their intersection, and the resulting data sets were analyzed by partial least-squares discriminant analysis (PLS-DA) and submitted to Monte Carlo cross validation (MCCV) and permutation tests to evaluate model predictive power. The NMR data subsets produced significantly improved PLS-DA models for all conditions except for pre-premature rupture of membranes. Specific urinary metabolic signatures were unveiled for central nervous system malformations, trisomy 21, preterm delivery, gestational diabetes, intrauterine growth restriction and preeclampsia, and biochemical interpretations were proposed. This work demonstrated, for the first time, the value of maternal urine profiling as a complementary means of prenatal diagnostics and early prediction of several poor pregnancy outcomes.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12