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Disease Markers
Volume 2015, Article ID 857108, 8 pages
Research Article

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

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

Received 13 April 2015; Accepted 27 May 2015

Academic Editor: Vincent Sapin

Copyright © 2015 Maria P. H. Koster et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 () and early-onset- (EO-) PE () or late-onset- (LO-) PE () 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.