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Abstract Background A substantial proportion of maternal pregnancy complications, adverse birth outcomes and neurodevelopmental delay in children may be attributable to high maternal pre-pregnancy Body Mass Index (BMI). However, BMI alone is insufficient for the identification of all at-risk mothers and children as many women with non-obesity(

Abstract Background Preeclampsia (PE) is a life-threatening disease characterized by elevated blood pressure and proteinuria. Predictive biomarkers of PE are needed, especially those predicting PE in early pregnancy. The aim of this pilot study was to identify urine proteins that could be candidates for new non-invasive markers for PE. Methods Urine samples at three time points of pregnancy (12–14, 18–20 and 26–28 weeks of gestation) were prospectively collected from high-risk women who subsequently developed PE (n = 7), high-risk women who did not develop PE (n = 6), and women without known risk factors for PE (n = 4). The samples were analyzed using mass spectrometry and we subsequently quantified 361 proteins used for further analysis. Rigorous statistical analysis with multiple methods was performed to identify biomarker candidates. Results Of the clinical risk factors analyzed, pre-pregnancy body mass index (BMIBP) was found to be the most important predictor of PE. We identified multiple proteins that correlated with BMIBP and could improve the prediction of PE in combination with BMIBP. Other statistical analyses identified six proteins that each could differentiate women who subsequently developed PE from those who did not at all three time points. Conclusions We identified multiple urine proteins that could be used to predict PE in combination with BMIBP. We also identified six proteins that are strong candidates for predicting PE already in early pregnancy.

Abstract Background The proportion of hyperglycosylated human chorionic gonadotropin (hCG-h) to total human chorionic gonadotropin (%hCG-h) during the first trimester is a promising biomarker for prediction of early-onset pre-eclampsia. We wanted to evaluate the performance of clinical risk factors, mean arterial pressure (MAP), %hCG-h, hCGβ, pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PlGF) and mean pulsatility index of the uterine artery (Uta-PI) in the first trimester in predicting pre-eclampsia (PE) and its subtypes early-onset, late-onset, severe and non-severe PE in a high-risk cohort. Methods We studied a subcohort of 257 high-risk women in the prospectively collected Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) cohort. Multivariate logistic regression was used to construct the prediction models. The first model included background variables and MAP. Additionally, biomarkers were included in the second model and mean Uta-PI was included in the third model. All variables that improved the model fit were included at each step. The area under the curve (AUC) was determined for all models. Results We found that lower levels of serum PlGF concentration were associated with early-onset PE, whereas lower %hCG-h was associated with the late-onset PE. Serum PlGF was lower and hCGβ higher in severe PE, while %hCG-h and serum PAPP-A were lower in non-severe PE. By using multivariate regression analyses the best prediction for all PE was achieved with the third model: AUC was 0.66, and sensitivity 36% at 90% specificity. Third model also gave the highest prediction accuracy for late-onset, severe and non-severe PE: AUC 0.66 with 32% sensitivity, AUC 0.65, 24% sensitivity and AUC 0.60, 22% sensitivity at 90% specificity, respectively. The best prediction for early-onset PE was achieved using the second model: AUC 0.68 and 20% sensitivity at 90% specificity. Conclusions Although the multivariate models did not meet the requirements to be clinically useful screening tools, our results indicate that the biomarker profile in women with risk factors for PE is different according to the subtype of PE. The heterogeneous nature of PE results in difficulty to find new, clinically useful biomarkers for prediction of PE in early pregnancy in high-risk cohorts. Trial registration International Standard Randomised Controlled Trial number ISRCTN14030412, Date of registration 6/09/2007, retrospectively registered.

Abstract Background A recent study has shown that it is possible to accurately estimate gestational age (GA) at birth from the DNA methylation (DNAm) of fetal umbilical cord blood/newborn blood spots. This DNAm GA predictor may provide additional information relevant to developmental stage. In 814 mother-neonate pairs, we evaluated the associations between DNAm GA and a number of maternal and offspring characteristics. These characteristics reflect prenatal environmental adversity and are expected to influence newborn developmental stage. Results DNAm GA acceleration (GAA; i.e., older DNAm GA than chronological GA) of the offspring at birth was associated with maternal age of over 40 years at delivery, pre-eclampsia and fetal demise in a previous pregnancy, maternal pre-eclampsia and treatment with antenatal betamethasone in the index pregnancy, lower neonatal birth size, lower 1-min Apgar score, and female sex. DNAm GA deceleration (GAD; i.e., younger DNAm GA than chronological GA) of the offspring at birth was associated with insulin-treated gestational diabetes mellitus (GDM) in a previous pregnancy and Sjögren’s syndrome. These findings were more accentuated when the DNAm GA calculation was based on the raw difference between DNAm GA and GA than on the residual from the linear regression of DNAm GA on GA. Conclusions Our findings show that variations in the DNAm GA of the offspring at birth are associated with a number of maternal and offspring characteristics known to reflect exposure to prenatal environmental adversity. Future studies should be aimed at determining if this biological variation is predictive of developmental adversity.