| Literature DB >> 18790389 |
David M Carty1, Christian Delles, Anna F Dominiczak.
Abstract
Preeclampsia is a major cause of maternal morbidity and mortality worldwide. Despite decades of research into the condition, the ability of clinicians to predict preeclampsia prior to the onset of symptoms has not improved significantly. In this review, we will examine the pathophysiology underlying preeclampsia and will look at potential biomarkers for early prediction and diagnosis. In addition, we will explore potential future areas of research into the condition.Entities:
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Year: 2008 PMID: 18790389 PMCID: PMC2577131 DOI: 10.1016/j.tcm.2008.07.002
Source DB: PubMed Journal: Trends Cardiovasc Med ISSN: 1050-1738 Impact factor: 6.677
Figure 1Biomarkers of pre-eclampsia. Biomarkers of preeclampsia are grouped into four major categories (modified from data from Conde-Agudelo et al. 2004). Production and levels of biomarkers are ultimately dependent on genetic factors and therefore genomic studies are likely to detect genetic variants associated with preeclampsia. However, in contrast to the static genome, the proteome is dynamic. Whereas the genome will not change during pregnancy or pregnancy-associated conditions such as preeclampsia, the proteome will change. This is indicated by the double-headed arrow. Proteomic and metabolomic studies will therefore reflect a large number of biomarkers and their actual levels and will more accurately predict risk than genomic studies.
Figure 2Capillary electrophoresis online coupled to mass spectrometry. Urine samples are prepared for analysis, polypeptides are separated by capillary electrophoresis and directly sprayed into electrospray ionization–time of flight mass spectrometry. Data are evaluated using specific software solutions. Each polypeptide is defined by its accurate mass and normalized CE migration time. Signal intensity serves as measure of the relative abundance. The data are stored as peak lists summarizing the information in a database. The process is demonstrated for use of CE–mass spectrometry to in the diagnosis of coronary artery disease. The lower panel shows a coronary artery disease-specific polypeptide pattern. The top panel is modified with permission from Electrophoresis 2007;28:1407-1417Sniehotta et al., 2007. Sniehotta et al: CE - a multifunctional application for clinical diagnosis. Electrophoresis. 2007. Volume 28. Pages 1407-1417. Copyright Wiley-VCH Verlag GmbH & Co. KGaA. Reproduced with permission.