scholarly journals Prenatal pre-eclampsia screening using maternal characteristics, maternal serum PAPP-A and PlGF in the first trimester

2022 ◽  
Vol 226 (1) ◽  
pp. S158
Tianhua Huang ◽  
Shamim Rashid ◽  
Ellen Mak-Tam ◽  
Megan Priston ◽  
Clare Gibbons ◽  
2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Maria P. H. Koster ◽  
Rob J. Vreeken ◽  
Amy C. Harms ◽  
Adrie D. Dane ◽  
Sylwia Kuc ◽  

Objective. To expand the search for preeclampsia (PE) metabolomics biomarkers through the analysis of acylcarnitines in first-trimester maternal serum.Methods. This was a nested case-control study using serum from pregnant women, drawn between 8 and 14 weeks of gestational age. Metabolites were measured using an UPLC-MS/MS based method. Concentrations were compared between controls (n=500) and early-onset- (EO-) PE (n=68) or late-onset- (LO-) PE (n=99) women. Metabolites with a false discovery rate <10% for both EO-PE and LO-PE were selected and added to prediction models based on maternal characteristics (MC), mean arterial pressure (MAP), and previously established biomarkers (PAPPA, PLGF, and taurine).Results. Twelve metabolites were significantly different between EO-PE women and controls, with effect levels between −18% and 29%. For LO-PE, 11 metabolites were significantly different with effect sizes between −8% and 24%. Nine metabolites were significantly different for both comparisons. The best prediction model for EO-PE consisted of MC, MAP, PAPPA, PLGF, taurine, and stearoylcarnitine (AUC = 0.784). The best prediction model for LO-PE consisted of MC, MAP, PAPPA, PLGF, and stearoylcarnitine (AUC = 0.700).Conclusion. This study identified stearoylcarnitine as a novel metabolomics biomarker for EO-PE and LO-PE. Nevertheless, metabolomics-based assays for predicting PE are not yet suitable for clinical implementation.

2015 ◽  
Vol 9 ◽  
pp. CMRH.S21865 ◽  
Anna Yliniemi ◽  
Kaarin Makikallio ◽  
Teemu Korpimaki ◽  
Heikki Kouru ◽  
Jaana Marttala ◽  

Objective To evaluate the efficacy of first-trimester markers–-pregnancy-associated plasma protein A (PAPPA), free human chorionic gonadotropin β (fhCGβ), alpha-fetoprotein (AFP), placental growth factor (PlGF), and soluble tumor necrosis factor receptor-1 (sTNFR1) together with maternal characteristics (MC) for prediction of early-onset preeclampsia (EOPE). Methods During 2005-2010, the abovementioned biomarkers were analyzed with logistic regression analysis in 64 EOPE and 752 control subjects to determine whether these biomarkers separately and in combination with MC would predict development of EOPE. Results PAPPA, fhCGβ, and PlGF levels were lower, whereas AFP and sTNFR1 levels were higher in mothers with EOPE compared to controls. The combination of all markers with MC (age, weight, and smoking status) detected 48% of the mothers with EOPE, with a 10% false-positive rate (FPR). Conclusions First-trimester maternal serum levels of PAPPA, fhCGβ, AFP, PlGF, and sTNFR1, together with MC, are predictive of development of subsequent EOPE. These markers, along with MC, form a suitable panel for predicting EOPE.

2021 ◽  
Vol 225 (02) ◽  
pp. 125-128
Hasan Eroğlu ◽  
Nazan Vanlı Tonyalı ◽  
Gokcen Orgul ◽  
Derya Biriken ◽  
Aykan Yucel ◽  

Abstract Purpose To evaluate the usability of first-trimester maternal serum ProBNP levels in the prediction of intrauterine growth restriction (IUGR). Methods In this prospective study, blood samples taken from 500 women who applied to our polyclinic for routine serum aneuploidy screening between the 11–14th gestational weeks were centrifuged. The obtained plasma samples were placed in Eppendorf tubes and stored at −80+°C. For the final analysis, first-trimester maternal serum ProBNP levels of 32 women diagnosed with postpartum IUGR and 32 healthy women randomly selected as the control group were compared. FGR was defined as estimated fetal weight below the 10th percentile for the gestational age. Results The mean ProBNP levels were statistically and significantly higher in the women with intrauterine growth restriction (113.73±94.69 vs. 58.33±47.70 pg/mL, p<0.01). At a cut-off level of 50.93, ProBNP accurately predicted occurrence of IUGR (AUC+= 0.794 (95% confidence interval 0.679–0.910), p+= 0.001) with sensitivity and specificity rates of 78.1 and 69.0%, respectively. Conclusion First-trimester serum ProBNP level was significantly higher in women who developed IUGR compared to healthy controls. First-trimester ProBNP level can be used as a potential marker to predict the development of IUGR in pregnant women.

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 2012
Lisa Daneels ◽  
Dries S. Martens ◽  
Soumia Arredouani ◽  
Jaak Billen ◽  
Gudrun Koppen ◽  

Nutrition is important during pregnancy for offspring health. Gestational vitamin D intake may prevent several adverse outcomes and might have an influence on offspring telomere length (TL). In this study, we want to assess the association between maternal vitamin D intake during pregnancy and newborn TL, as reflected by cord blood TL. We studied mother–child pairs enrolled in the Maternal Nutrition and Offspring’s Epigenome (MANOE) cohort, Leuven, Belgium. To calculate the dietary vitamin D intake, 108 women were asked to keep track of their diet using the seven-day estimated diet record (EDR) method. TL was assessed in 108 cord blood using a quantitative real-time PCR method. In each trimester of pregnancy, maternal serum 25-hydroxyvitamin D (25-OHD) concentration was measured. We observed a positive association (β = 0.009, p-value = 0.036) between newborn average relative TL and maternal vitamin D intake (diet + supplement) during the first trimester. In contrast, we found no association between average relative TL of the newborn and mean maternal serum 25-OHD concentrations during pregnancy. To conclude, vitamin D intake (diet + supplements), specifically during the first trimester of pregnancy, is an important factor associated with TL at birth.

2009 ◽  
Vol 201 (6) ◽  
pp. S87
Yan Zhong ◽  
Mark Longtine ◽  
Jean Schoenborn ◽  
Methodius Tuuli ◽  
Linda Odibo ◽  

2015 ◽  
Vol 43 (3) ◽  
Rinat Gabbay-Benziv ◽  
Lauren E. Doyle ◽  
Miriam Blitzer ◽  
Ahmet A. Baschat

AbstractTo predict gestational diabetes mellitus (GDM) or normoglycemic status using first trimester maternal characteristics.We used data from a prospective cohort study. First trimester maternal characteristics were compared between women with and without GDM. Association of these variables with sugar values at glucose challenge test (GCT) and subsequent GDM was tested to identify key parameters. A predictive algorithm for GDM was developed and receiver operating characteristics (ROC) statistics was used to derive the optimal risk score. We defined normoglycemic state, when GCT and all four sugar values at oral glucose tolerance test, whenever obtained, were normal. Using same statistical approach, we developed an algorithm to predict the normoglycemic state.Maternal age, race, prior GDM, first trimester BMI, and systolic blood pressure (SBP) were all significantly associated with GDM. Age, BMI, and SBP were also associated with GCT values. The logistic regression analysis constructed equation and the calculated risk score yielded sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 62%, 13.8%, and 98.3% for a cut-off value of 0.042, respectively (ROC-AUC – area under the curve 0.819, CI – confidence interval 0.769–0.868). The model constructed for normoglycemia prediction demonstrated lower performance (ROC-AUC 0.707, CI 0.668–0.746).GDM prediction can be achieved during the first trimester encounter by integration of maternal characteristics and basic measurements while normoglycemic status prediction is less effective.

2007 ◽  
Vol 30 (4) ◽  
pp. 543-544
M. Ryynanen ◽  
Y. Valinen ◽  
J. Ignatius ◽  
P. Laitinen ◽  
T. Ahola ◽  

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