Robust Early Pregnancy Prediction of Later Preeclampsia Using Metabolomic Biomarkers

Preeclampsia is a pregnancy-specific syndrome that causes substantial maternal and fetal morbidity and mortality. The etiology is incompletely understood, and there is no clinically useful screening test. Current metabolomic technologies have allowed the establishment of metabolic signatures of preeclampsia in early pregnancy. Here, a 2-phase discovery/validation metabolic profiling study was performed. In the discovery phase, a nested case-control study was designed, using samples obtained at 15±1 weeks' gestation from 60 women who subsequently developed preeclampsia and 60 controls taking part in the prospective Screening for Pregnancy Endpoints cohort study. Controls were proportionally population matched for age, ethnicity, and body mass index at booking. Plasma samples were analyzed using ultra performance liquid chromatography-mass spectrometry. A multivariate predictive model combining 14 metabolites gave an odds ratio for developing preeclampsia of 36 (95% CI: 12 to 108), with an area under the receiver operator characteristic curve of 0.94. These findings were then validated using an independent case-control study on plasma obtained at 15±1 weeks from 39 women who subsequently developed preeclampsia and 40 similarly matched controls from a participating center in a different country. The same 14 metabolites produced an odds ratio of 23 (95% CI: 7 to 73) with an area under receiver operator characteristic curve of 0.92. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of preeclampsia offers insight into disease pathogenesis and offers the tantalizing promise of a robust presymptomatic screening test.

[1]  G. ter Riet,et al.  Methods of prediction and prevention of pre-eclampsia: systematic reviews of accuracy and effectiveness literature with economic modelling. , 2008, Health technology assessment.

[2]  Royston Goodacre,et al.  Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data , 2005, Bioinform..

[3]  Joshua D. Knowles,et al.  Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. , 2009, Analytical chemistry.

[4]  D. Luthy,et al.  Maternal plasma lipid concentrations in early pregnancy and risk of preeclampsia. , 2004, American journal of hypertension.

[5]  Kypros H Nicolaides,et al.  First-Trimester Prediction of Hypertensive Disorders in Pregnancy , 2009, Hypertension.

[6]  Ronald Eugene Shaffer,et al.  Multi‐ and Megavariate Data Analysis. Principles and Applications, I. Eriksson, E. Johansson, N. Kettaneh‐Wold and S. Wold, Umetrics Academy, Umeå, 2001, ISBN 91‐973730‐1‐X, 533pp. , 2002 .

[7]  Douglas B. Kell,et al.  Metabolomics and Machine Learning: Explanatory Analysis of Complex Metabolome Data Using Genetic Programming to Produce Simple, Robust Rules , 2004, Molecular Biology Reports.

[8]  R. Goodacre,et al.  Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis , 2003, Springer US.

[9]  R. Romero,et al.  A prospective cohort study of the value of maternal plasma concentrations of angiogenic and anti-angiogenic factors in early pregnancy and midtrimester in the identification of patients destined to develop preeclampsia , 2009, The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians.

[10]  K. Lim,et al.  Circulating angiogenic factors and the risk of preeclampsia. , 2004, The New England journal of medicine.

[11]  James R. Schott,et al.  Principles of Multivariate Analysis: A User's Perspective , 2002 .

[12]  I. Sargent,et al.  Latest Advances in Understanding Preeclampsia , 2005, Science.

[13]  K. Nicolaides,et al.  Maternal serum placental protein 13 at 11–13 weeks of gestation in preeclampsia , 2009, Prenatal diagnosis.

[14]  Age K. Smilde,et al.  Direct orthogonal signal correction , 2001 .

[15]  D. Kell,et al.  Mass Spectrometry Tools and Metabolite-specific Databases for Molecular Identification in Metabolomics , 2009 .

[16]  Wojtek J. Krzanowski,et al.  Principles of multivariate analysis : a user's perspective. oxford , 1988 .

[17]  D. Kell,et al.  High-throughput classification of yeast mutants for functional genomics using metabolic footprinting , 2003, Nature Biotechnology.

[18]  D. Edwards,et al.  Statistical Analysis of Gene Expression Microarray Data , 2003 .

[19]  A. Hingorani,et al.  Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis , 2007, BMJ : British Medical Journal.

[20]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[21]  I. Wilson,et al.  A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. , 2006, The Analyst.

[22]  Royston Goodacre,et al.  Evolutionary computation for the interpretation of metabolomic data. , 2003 .

[23]  Olli Simell,et al.  Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes , 2008, The Journal of experimental medicine.

[24]  N. Perkins,et al.  The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. , 2006, American journal of epidemiology.

[25]  H. Wold Soft Modelling by Latent Variables: The Non-Linear Iterative Partial Least Squares (NIPALS) Approach , 1975, Journal of Applied Probability.

[26]  R. A. van den Berg,et al.  Centering, scaling, and transformations: improving the biological information content of metabolomics data , 2006, BMC Genomics.

[27]  J. van der Greef,et al.  The art and practice of systems biology in medicine: mapping patterns of relationships. , 2007, Journal of proteome research.

[28]  Douglas B. Kell,et al.  Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning , 2005, Metabolomics.

[29]  D. Kell,et al.  Detection and Identification of Novel Metabolomic Biomarkers in Preeclampsia , 2008, Reproductive Sciences.

[30]  James J. Walker,et al.  Pre-eclampsia , 2000, The Lancet.

[31]  D. Kell,et al.  Metabolic profiling of serum using Ultra Performance Liquid Chromatography and the LTQ-Orbitrap mass spectrometry system. , 2008, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[32]  R. Hariharan,et al.  Genetic characterization of 2006–2008 isolates of Chikungunya virus from Kerala, South India, by whole genome sequence analysis , 2009, Virus Genes.

[33]  Timothy M. D. Ebbels,et al.  Genetic algorithms for simultaneous variable and sample selection in metabonomics , 2009, Bioinform..

[34]  S. Wold,et al.  Some recent developments in PLS modeling , 2001 .

[35]  Douglas B. Kell,et al.  Statistical strategies for avoiding false discoveries in metabolomics and related experiments , 2007, Metabolomics.

[36]  Age K. Smilde,et al.  UvA-DARE ( Digital Academic Repository ) Assessment of PLSDA cross validation , 2008 .

[37]  M. Sculpher,et al.  The effectiveness and cost-effectiveness of minimal access surgery amongst people with gastro-oesophageal reflux disease - a UK collaborative study. The REFLUX trial. , 2008, Health technology assessment.

[38]  R. Abagyan,et al.  XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. , 2006, Analytical chemistry.

[39]  D. Kell,et al.  Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. , 2004, BioEssays : news and reviews in molecular, cellular and developmental biology.

[40]  Martin W. Bunder,et al.  The inconsistency of , 1976, Journal of Symbolic Logic.

[41]  B. Sibai,et al.  Soluble endoglin and other circulating antiangiogenic factors in preeclampsia. , 2006, The New England journal of medicine.

[42]  F. Malone,et al.  Quad Screen as a Predictor of Adverse Pregnancy Outcome , 2005, Obstetrics and gynecology.

[43]  J. Higgins,et al.  The detection, investigation and management of hypertension in pregnancy: full consensus statement , 2000, The Australian & New Zealand journal of obstetrics & gynaecology.

[44]  Douglas B. Kell,et al.  Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry , 1997 .