» Articles » PMID: 28964327

Performance of First Trimester Biochemical Markers and Mean Arterial Pressure in Prediction of Early-onset Pre-eclampsia

Overview
Journal Metabolism
Specialty Endocrinology
Date 2017 Oct 2
PMID 28964327
Citations 9
Authors
Affiliations
Soon will be listed here.
Abstract

Objective: To develop a predictive risk model for early-onset pre-eclampsia (EO-PE) using maternal characteristics, combined screening markers, previously reported biomarkers for PE and mean arterial pressure (MAP).

Methods: This retrospective study was conducted at Oulu University hospital between 2006 and 2010. Maternal serum from first trimester combined screening was further analyzed for alpha fetoprotein (AFP), placental growth factor (PlGF), soluble tumor necrosis factor receptor-1 (sTNFR1), retinol binding protein-4 (RBP4), a disintegrin and metalloprotease-12 (ADAM12), soluble P-selectin (sP-selectin), follistatin like-3 (FSTL3), adiponectin, angiopoietin-2 (Ang-2) and sex hormone binding globulin (SHBG). First, the training sample set with 29 cases of EO-PE and 652 controls was developed to study whether these biomarkers separately or in combination with prior risk (maternal characteristics, first trimester pregnancy associated plasma protein-A (PAPP-A) and free beta human chorionic gonadotrophin (fβ-hCG)) could be used to predict the development of EO-PE. Second, the developed risk models were validated with a test sample set of 42 EO-PE and 141 control subjects. For the test set MAP data was also available.

Results: Single marker statistically significant (ANOVA p<0.05) changes between control and EO-PE pregnancies were observed with AFP, RBP4 and sTNFR1 with both training and test sample sets. Based on the test sample set performances, the best detection rate, 47% for a 10% false positive rate, was achieved with PlGF and sTNFR1 added with prior risk and MAP.

Conclusion: Based on our results, the best first trimester biomarkers to predict the subsequent EO-PE were AFP, PlGF, RBP4 and sTNFR1. The risk models that performed best for the prediction of EO-PE included prior risk, MAP, sTNFR1 and AFP or PlGF or RBP4.

Citing Articles

Pro- and anti-inflammatory cytokines and growth factors in patients undergoing fertilization procedure treated with prednisone.

Piekarska K, Dratwa M, Radwan P, Radwan M, Bogunia-Kubik K, Nowak I Front Immunol. 2023; 14:1250488.

PMID: 37744353 PMC: 10511889. DOI: 10.3389/fimmu.2023.1250488.


Maternal Serum Activin A, Inhibin A and Follistatin-Related Proteins across Preeclampsia: Insights into Their Role in Pathogenesis and Prediction.

Barrero J, Villamil-Camargo L, Imaz J, Arciniegas-Villa K, Rubio-Romero J J Mother Child. 2023; 27(1):119-133.

PMID: 37595293 PMC: 10438925. DOI: 10.34763/jmotherandchild.20232701.d-23-00002.


Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia-A Prospective Study.

Melinte-Popescu A, Vasilache I, Socolov D, Melinte-Popescu M J Clin Med. 2023; 12(2).

PMID: 36675347 PMC: 9865606. DOI: 10.3390/jcm12020418.


Predictive ability of serum advanced glycation end products at 11 to 13 weeks of gestation for early-onset preeclampsia.

Goto M, Yamagishi S, Matsui T, Koide K, Takita H, Tokunaka M AJOG Glob Rep. 2022; 2(2):100052.

PMID: 36275494 PMC: 9563657. DOI: 10.1016/j.xagr.2022.100052.


Human placenta-based genome-wide mRNA sequencing to identify TEK/IGF1/CSF1/ANGPT2 as crucial segments in the pathogenesis of pre-eclampsia.

Wang L, Zhang L, Fan Y, Peng Y, Song D, Fu J Front Genet. 2022; 13:944932.

PMID: 36160014 PMC: 9493102. DOI: 10.3389/fgene.2022.944932.