Utility of metabolic profiling of serum in the diagnosis of pregnancy complications. (June 2018)

• Record Type: Journal Article
• Authors: Powell, Katie L.; Carrozzi, Anthony; Stephens, Alexandre S.; Tasevski, Vitomir; Morris, Jonathan M.; Ashton, Anthony W.; Dona, Anthony C.
• Is Part Of: Placenta. Volume 66(2018)
• Page Start: 65
• Page End: 73
• Publication Date: 2018-06
• Subjects: Biomarker -- Metabolomics -- Preeclampsia -- Intrauterine growth restriction -- Small for gestational age -- Nuclear magnetic resonance spectroscopy; PET Preeclampsia -- IUGR Intrauterine growth restriction -- SGA Small for gestational age -- NMR Nuclear magnetic resonance -- OPLS-DA Orthogonal partial least squares-discriminant analysis -- PCA Principal components analysis -- AUC Area under the curve -- ROC Receiver operating characteristic; Placenta -- Periodicals; Reproduction -- Periodicals; Placenta -- Periodicals; Placenta -- Périodiques; Reproduction -- Périodiques; 612.63
• DOI: 10.1016/j.placenta.2018.04.005
• Languages: English
• ISSNs: 0143-4004

Abstract

Abstract: Introduction: Currently there are no clinical screening tests available to identify pregnancies at risk of developing preeclampsia (PET) and/or intrauterine growth restriction (IUGR), both of which are associated with abnormal placentation. Metabolic profiling is now a stable analytical platform used in many laboratories and has successfully been used to identify biomarkers associated with various pathological states. Methods: We used nuclear magnetic resonance spectroscopy (NMR) to metabolically profile serum samples collected from 143 pregnant women at 26–41 weeks gestation with pregnancy outcomes of PET, IUGR, PET IUGR or small for gestational age (SGA) that were age-matched to normal pre/term pregnancies. Results: Spectral analysis found no difference in the measured metabolites from normal term, pre-term and SGA samples, and of 25 identified metabolites, only glutamate was marginally different between groups. Of the identified metabolites, 3-methylhistidine, creatinine, acetyl groups and acetate, were determined to be independent predictors of PET and produced area under the curves (AUC) = 0.938 and 0.936 for the discovery and validation sets. Only 3-hydroxybutyrate was determined to be an independent predictor of IUGR, however the model had low predictive power (AUC = 0.623 and 0.581 for the discovery and validation sets). Conclusions: A sub-panel of metabolites had strong predictive power for identifying PET samples in a validation dataset, however prediction of IUGR was more difficult using the identified metabolites. NMR based metabolomics can identify metabolites strongly associated with disease and has the potential to be useful in developing early clinical screening tests for at risk pregnancies. Highlights: Serum metabolites were similar between normal, healthy and SGA pregnancies. A sub-panel of metabolites had strong predictive power for identifying PET samples. NMR based metabolomics could be used to develop early clinical screening tests.