M7.4 Uncovering the metabolomic signature of intrauterine growth restriction in early pregnancy: search, discovery and validation

Abstract

M7.3 – Table 1. Log2 (EXP) fold change data shown asmedian ratio (IQR) for each sample compared to the reference sample. Proteins A-C cannot be named in the abstract for confidentiality reasons Protein Technical replicate Control Control PE PE Control PE 22/40 22/40 22/40 22/40 26/40 26/40 Fibrinogen alpha 0.92 (0.64-1.31) 0.42 (0.23-0.75) 0.37 (0.22-0.64) 1.33 (0.76-2.34) 1.21 (0.69-2.12) 0.44 (0.27-0.73) 0.76 (0.41-1.41) Apo E 1.00 (0.70-1.42) 0.48 (0.31-0.72) 0.53 (0.32-0.86) 0.76 (0.54-1.09) 0.77 (0.52-1.14) 0.56 (0.39-0.81) 1.14 (0.70-1.87) PAPP-A 1.04 (0.82-1.33) 0.71 (0.44-1.15) 0.72 (0.41-1.27) 0.58 (0.36-0.92) 0.63 (0.49-0.82) 1.07 (0.74-1.55) 1.09 (0.74-1.59) Apo C3 0.89 (0.51-1.57) 0.28 (0.15-0.51) 0.28 (0.16-0.48) 0.49 (0.19-1.26) 0.39 (0.27-0.56) 0.50 (0.31-0.82) 1.13 (0.76-1.66) Apo C2 0.89 (0.51-1.57) 0.28 (0.15-0.51) 0.28 (0.16-0.48) 0.49 (0.19-1.26) 0.39 (0.27-0.56) 0.50 (0.31-0.82) 1.13 (0.76-1.66) Protein A 1.02 (0.85-1.21) 0.79 (0.70-0.90) 0.69 (0.90-0.54) 1.15 (0.99-1.34) 1.15 (0.95-1.38) 0.61 (0.48-0.79) 1.20 (0.96-1.50) Protein B 0.83 (0.61-1.13) 0.56 (0.36-0.86) 0.44 (0.33-0.59) 0.89 (0.54-1.49) 0.92 (0.45-1.86) 0.53 (0.43-0.64) 1.46 (0.69-3.08) Protein C 0.94 (0.75-1.17) 0.59 (0.49-0.71) 0.56 (0.35-0.89) 0.94 (0.72-1.23) 0.98 (0.85-1.14) 0.60 (0.50-0.72) 1.03 (0.77-1.38) in three independent studies (a) venous cord blood plasma from normal babies and babies with IUGR, (b) plasma from a rat model of fetal growth restriction: reduced uterine perfusion pressure (RUPP) rat, (c) plasma samples obtained at 15±1 weeks gestation from women who subsequently delivered an IUGR baby and matched controls. All samples were analyzed using Ultra Performance Liquid Chromatography coupled to a LTQ-Orbitrap Mass Spectrometer. In both the cord blood and RUPP studies there was comprehensive disruption of plasma metabolism due to IUGR. Multivariate predictive models gave area under the Receiver Operator Characteristic (AuROC) curve of 1 in both cases. Disruption was specific to lipid and amino acid metabolism. When the time-of-disease biomarker signature of cord blood was validated using the pre-symptomatic 15-week maternal blood, a multivariate predictive model with AuROC of 0.96 was produced. This is the first time any clear biomarkers for IUGR have been discovered using any technology. A pre-symptomatic predictive test at 15 weeks gestation will have a significant impact on clinical care, allowing scarce resources to be concentrated on those at greatest risk. M9.1 Paternal factors involved in the causation of preeclampsia Gus Dekker1,2 , Claire Roberts2, Denise Furness2, Prabha Andraweera2. 1Lyell McEwin Hospital 2Robinson Institute, University of Adelaide, Australia Preeclampsia is often considered as just a maternal disease with variable degrees of fetal involvement. More and more the unique immunogenetic maternal-paternal relationship is appreciated, and as such also the specific “genetic conflict” that is characteristic of haemochorial placentation. From that perspective, preeclampsia can also be seen as a disease of an individual couple with primarily maternal and fetal manifestations. The maternal and fetal genomes perform different roles during development. Heritable paternal, rather than maternal, imprinting of the genome is necessary for normal trophoblast development. Large population studies have estimated that 35% of the variance in susceptibility to preeclampsia was attributable to maternal genetic effects, 20% to fetal genetic effects (with similar contributions of both parents), 13% to the couple effect, less than 1% to shared sibling environment and 32% to unmeasured factors. Not one of these large population studies involved a real focus on the paternal contribution to preeclampsia which is demonstrated by: 1. The effect of the length of sexual relationship. 2. The concept of primipaternity versus primigravidity. 3. Existence of the so-called ’dangerous’ father which has been demonstrated in various large population studies. It is currently unknown how the father exerts this effect. Possible mechanisms include seminal cytokine levels and their effect on maternal immune deviation, specific paternal HLA characteristics and specific paternal SNP’s (in particular in the paternally expressed genes affecting placentation). Several large cohort studies, including the large international SCOPE consortium, have identified several paternal SNP’s with strong associations with preeclampsia