Pregnancy associated-plasma protein-A (PAPP-A) and alfa-fetoprotein (AFP) associated with placental abruption

Correlation between elevated maternal serum alpha-fetoprotein and ischemic placental disease: a retrospective cohort study

BACKGROUND

To evaluate the correlation between elevated maternal serum alpha-fetoprotein (AFP) in the second trimester and ischemic placental disease (IPD).

METHODS

A retrospective cohort study was conducted to analyze the data of 22,574 pregnant women who delivered in the Department of Obstetrics at Hangzhou Women's Hospital from 2018 to 2020, and were screened for maternal serum AFP and free beta-human chorionic gonadotropin (free β-hCG) in the second trimester. The pregnant women were divided into two groups: elevated maternal serum AFP group (n = 334, 1.48%); and normal group (n = 22,240, 98.52%). Mann-Whitney U-test or Chi-square test was used for continuous or categorical data. Modified Poisson regression analysis was used to calculate the relative risk (RR) and 95% confidence interval (CI) of the two groups.

RESULTS

The AFP MoM and free β-hCG MoM in the elevated maternal serum AFP group were higher than the normal group (2.25 vs. 0.98, 1.38 vs. 1.04) and the differences were all statistically significant (all P < .001). Placenta previa, hepatitis B virus carrying status of pregnant women, premature rupture of membranes (PROM), advanced maternal age (≥35 years), increased free β-hCG MoM, female infants, and low birth weight (RR: 2.722, 2.247, 1.769, 1.766, 1.272, 0.624, 2.554 respectively) were the risk factors for adverse maternal pregnancy outcomes in the elevated maternal serum AFP group.

CONCLUSIONS

Maternal serum AFP levels during the second trimester can monitor IPD, such as IUGR, PROM, and placenta previa. Maternal women with high serum AFP levels are more likely to deliver male fetuses and low birth weight infants. Finally, the maternal age (≥35 years) and hepatitis B carriers also increased maternal serum AFP significantly.

Establishment and validation of a predictive model of preeclampsia based on transcriptional signatures of 43 genes in decidua basalis and peripheral blood

Preeclampsia (PE) has an increasing incidence worldwide, and there is no gold standard for prediction. Recent progress has shown that abnormal decidualization and impaired vascular remodeling are essential to PE pathogenesis. Therefore, it is of great significance to analyze the decidua basalis and blood changes of PE to explore new methods. Here, we performed weighted gene co-expression network analysis based on 9553 differentially expressed genes of decidua basalis data (GSE60438 includes 25 cases of PE and 23 non-cases) from Gene Expression Omnibus to screen relevant module-eigengenes (MEs). Among them, MEblue and MEgrey are the most correlated with PE, which contains 371 core genes. Subsequently, we applied the logistic least absolute shrinkage and selection operator regression, screened 43 genes most relevant to prediction from the intersections of the 371 genes and training set (GSE48424 includes 18 cases of PE and 18 non-cases) genes, and built a predictive model. The specificity and sensitivity are illustrated by receiver operating characteristic curves, and the stability was verified by two validation sets (GSE86200 includes 12 cases of PE and 48 non-cases, and GSE85307 includes 47 cases of PE and 110 non-cases). The results demonstrated that our predictive model shows good predictions, with an area under the curve of 0.991 for the training set, 0.874 and 0.986 for the validation sets. Finally, we found the 43 key marker genes in the model are closely associated with the clinically accepted predictive molecules, including FLT1, PIGF, ENG and VEGF. Therefore, this predictive model provides a potential approach for PE diagnosis and treatment.

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

OBJECTIVE

Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.

STUDY DESIGN

A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.

RESULTS

The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).

CONCLUSION

The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.

First trimester prediction of preeclampsia

Preeclampsia (PE) is a serious pregnancy-related condition that causes severe maternal and fetal morbidity and mortality. Within the recent years, there has been an increasing focus in predicting PE at the end of the first trimester of pregnancy. In this review, literature published between 2011 and 2015 was evaluated. In a total of six biomarker algorithms, for first and early second trimester, the prediction of preeclampsia is discussed. In addition, one randomized clinical trial was included. Several algorithms were based on placental biomarkers such as pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PLGF), and soluble FMS-like tyrosine kinase 1 (s-FLT-1). The algorithms containing these biomarkers showed a high prediction rate (PR) for early onset PE, ranging from 44 to 92 % at 5 % false positive rate (FPR). New biomarkers suggest an alternative model based on free HbF and the heme scavenger alpha-1-microglobulin (A1M) with a prediction rate of 69 % at an FPR of 5 %. Interestingly, this model performs well without uterine artery Doppler pulsatility index (UtAD-PI), which is an advantage particularly if the screening method were to be implemented in developing countries. The randomized clinical trial showed a clear reduction in early onset PE as well as reducing preterm PE if identified high-risk pregnancies were treated with low-dose aspirin. In conclusion, PE prediction is now possible through several prediction algorithms and prophylaxis is beneficial in high-risk cases.