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Dis Markers. 2015;2015:857108. doi: 10.1155/2015/857108. Epub 2015 Jun 4.

First-Trimester Serum Acylcarnitine Levels to Predict Preeclampsia: A Metabolomics Approach.

Author information

1
Department of Obstetrics, Wilhelmina Children's Hospital, University Medical Centre Utrecht (UMCU), 3508 AB Utrecht, Netherlands.
2
Leiden Academic Centre for Drug Research, Division of Analytical Biosciences, Leiden University, 2300 RA Leiden, Netherlands ; Discovery & Exploratory BA, Pharmacokinetics, Dynamics & Metabolism, Discovery Sciences, Janssen Pharmaceutica, Beerse, Belgium.
3
Leiden Academic Centre for Drug Research, Division of Analytical Biosciences, Leiden University, 2300 RA Leiden, Netherlands.
4
Centre for Infectious Diseases Research, Diagnostics and Screening (IDS), National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, Netherlands.
5
Netherlands Metabolomics Centre, 3501 DE Utrecht, Netherlands.
6
Centre for Health Protection (GZB), National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, Netherlands.

Abstract

OBJECTIVE:

To expand the search for preeclampsia (PE) metabolomics biomarkers through the analysis of acylcarnitines in first-trimester maternal serum.

METHODS:

This was a nested case-control study using serum from pregnant women, drawn between 8 and 14 weeks of gestational age. Metabolites were measured using an UPLC-MS/MS based method. Concentrations were compared between controls (n = 500) and early-onset- (EO-) PE (n = 68) or late-onset- (LO-) PE (n = 99) women. Metabolites with a false discovery rate <10% for both EO-PE and LO-PE were selected and added to prediction models based on maternal characteristics (MC), mean arterial pressure (MAP), and previously established biomarkers (PAPPA, PLGF, and taurine).

RESULTS:

Twelve metabolites were significantly different between EO-PE women and controls, with effect levels between -18% and 29%. For LO-PE, 11 metabolites were significantly different with effect sizes between -8% and 24%. Nine metabolites were significantly different for both comparisons. The best prediction model for EO-PE consisted of MC, MAP, PAPPA, PLGF, taurine, and stearoylcarnitine (AUC = 0.784). The best prediction model for LO-PE consisted of MC, MAP, PAPPA, PLGF, and stearoylcarnitine (AUC = 0.700).

CONCLUSION:

This study identified stearoylcarnitine as a novel metabolomics biomarker for EO-PE and LO-PE. Nevertheless, metabolomics-based assays for predicting PE are not yet suitable for clinical implementation.

PMID:
26146448
PMCID:
PMC4471382
DOI:
10.1155/2015/857108
[Indexed for MEDLINE]
Free PMC Article

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