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    Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy.

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    Authors
    Horgan, Richard P
    Broadhurst, David I
    Walsh, Sarah K
    Dunn, Warwick B
    Brown, Marie
    Roberts, Claire T
    North, Robyn A
    McCowan, Lesley M
    Kell, Douglas B
    Baker, Philip N
    Kenny, Louise C
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    Affiliation
    The Anu Research Centre, Department of Obstetrics and Gynaecology, University, College Cork, Cork University Maternity Hospital, Cork, Ireland.
    Issue Date
    2012-01-31T16:43:04Z
    MeSH
    Animals
    Chromatography, Liquid
    Female
    Fetal Growth Retardation
    Humans
    Infant, Newborn
    *Infant, Small for Gestational Age
    Mass Spectrometry
    Models, Animal
    Multivariate Analysis
    Pregnancy
    Pregnancy Trimester, First/*metabolism
    ROC Curve
    Rats
    Rats, Sprague-Dawley
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    Citation
    J Proteome Res. 2011 Aug 5;10(8):3660-73. Epub 2011 Jun 29.
    Journal
    Journal of proteome research
    URI
    http://hdl.handle.net/10147/206219
    DOI
    10.1021/pr2002897
    PubMed ID
    21671558
    Abstract
    Being born small for gestational age (SGA) confers increased risks of perinatal morbidity and mortality and increases the risk of cardiovascular complications and diabetes in later life. Accumulating evidence suggests that the etiology of SGA is usually associated with poor placental vascular development in early pregnancy. We examined metabolomic profiles using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) in three independent studies: (a) venous cord plasma from normal and SGA babies, (b) plasma from a rat model of placental insufficiency and controls, and (c) early pregnancy peripheral plasma samples from women who subsequently delivered a SGA baby and controls. Multivariate analysis by cross-validated Partial Least Squares Discriminant Analysis (PLS-DA) of all 3 studies showed a comprehensive and similar disruption of plasma metabolism. A multivariate predictive model combining 19 metabolites produced by a Genetic Algorithm-based search program gave an Odds Ratio for developing SGA of 44, with an area under the Receiver Operator Characteristic curve of 0.9. Sphingolipids, phospholipids, carnitines, and fatty acids were among this panel of metabolites. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of SGA offers insight into disease pathogenesis and offers the promise of a robust presymptomatic screening test.
    Language
    eng
    ISSN
    1535-3907 (Electronic)
    1535-3893 (Linking)
    ae974a485f413a2113503eed53cd6c53
    10.1021/pr2002897
    Scopus Count
    Collections
    Cork University Maternity Hospital

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