Literature DB >> 25122928

Early pregnancy prediction of preeclampsia in nulliparous women, combining clinical risk and biomarkers: the Screening for Pregnancy Endpoints (SCOPE) international cohort study.

Louise C Kenny1, Michael A Black2, Lucilla Poston2, Rennae Taylor2, Jenny E Myers2, Philip N Baker2, Lesley M McCowan2, Nigel A B Simpson2, Gus A Dekker2, Claire T Roberts2, Kelline Rodems2, Brian Noland2, Michael Raymundo2, James J Walker2, Robyn A North2.   

Abstract

More than half of all cases of preeclampsia occur in healthy first-time pregnant women. Our aim was to develop a method to predict those at risk by combining clinical factors and measurements of biomarkers in women recruited to the Screening for Pregnancy Endpoints (SCOPE) study of low-risk nulliparous women. Forty-seven biomarkers identified on the basis of (1) association with preeclampsia, (2) a biological role in placentation, or (3) a role in cellular mechanisms involved in the pathogenesis of preeclampsia were measured in plasma sampled at 14 to 16 weeks' gestation from 5623 women. The cohort was randomly divided into training (n=3747) and validation (n=1876) cohorts. Preeclampsia developed in 278 (4.9%) women, of whom 28 (0.5%) developed early-onset preeclampsia. The final model for the prediction of preeclampsia included placental growth factor, mean arterial pressure, and body mass index at 14 to 16 weeks' gestation, the consumption of ≥3 pieces of fruit per day, and mean uterine artery resistance index. The area under the receiver operator curve (95% confidence interval) for this model in training and validation cohorts was 0.73 (0.70-0.77) and 0.68 (0.63-0.74), respectively. A predictive model of early-onset preeclampsia included angiogenin/placental growth factor as a ratio, mean arterial pressure, any pregnancy loss <10 weeks, and mean uterine artery resistance index (area under the receiver operator curve [95% confidence interval] in training and validation cohorts, 0.89 [0.78-1.0] and 0.78 [0.58-0.99], respectively). Neither model included pregnancy-associated plasma protein A, previously reported to predict preeclampsia in populations of mixed parity and risk. In nulliparous women, combining multiple biomarkers and clinical data provided modest prediction of preeclampsia.
© 2014 American Heart Association, Inc.

Entities:  

Keywords:  biological markers; diagnosis; preeclampsia; pregnancy

Mesh:

Substances:

Year:  2014        PMID: 25122928     DOI: 10.1161/HYPERTENSIONAHA.114.03578

Source DB:  PubMed          Journal:  Hypertension        ISSN: 0194-911X            Impact factor:   10.190


  78 in total

1.  Increased circulating levels of Epidermal Growth Factor-like Domain 7 in pregnant women affected by preeclampsia.

Authors:  Micol Massimiani; Lauretta A Lacko; Clare S Burke Swanson; Silvia Salvi; Lissenya B Argueta; Sascia Moresi; Sergio Ferrazzani; Shari E Gelber; Rebecca N Baergen; Nicola Toschi; Luisa Campagnolo; Heidi Stuhlmann
Journal:  Transl Res       Date:  2018-12-25       Impact factor: 7.012

Review 2.  Biosensors for Detection of Human Placental Pathologies: A Review of Emerging Technologies and Current Trends.

Authors:  Jia Liu; Babak Mosavati; Andrew V Oleinikov; E Du
Journal:  Transl Res       Date:  2019-05-20       Impact factor: 7.012

3.  Iron status, body size, and growth in the first 2 years of life.

Authors:  Elaine K McCarthy; Carol Ní Chaoimh; Louise C Kenny; Jonathan O'B Hourihane; Alan D Irvine; Deirdre M Murray; Mairead E Kiely
Journal:  Matern Child Nutr       Date:  2017-04-27       Impact factor: 3.092

4.  Reduced expression of the epidermal growth factor signaling system in preeclampsia.

Authors:  D R Armant; R Fritz; B A Kilburn; Y M Kim; J K Nien; N J Maihle; R Romero; R E Leach
Journal:  Placenta       Date:  2014-12-27       Impact factor: 3.481

5.  Differences in uterine artery blood flow and fetal growth between the early and late onset of pregnancy-induced hypertension.

Authors:  Takashi Mitsui; Hisashi Masuyama; Jota Maki; Shoko Tamada; Yumika Hirano; Eriko Eto; Etsuko Nobumoto; Kei Hayata; Yuji Hiramatsu
Journal:  J Med Ultrason (2001)       Date:  2016-06-28       Impact factor: 1.314

Review 6.  First trimester screening for pre-eclampsia.

Authors:  Stefan C Kane
Journal:  Obstet Med       Date:  2016-05-14

Review 7.  Dietary Protein: Mechanisms Influencing Hypertension and Renal Disease.

Authors:  John Henry Dasinger; Daniel J Fehrenbach; Justine M Abais-Battad
Journal:  Curr Hypertens Rep       Date:  2020-02-03       Impact factor: 5.369

8.  Impact of maternal, antenatal and birth-associated factors on iron stores at birth: data from a prospective maternal-infant birth cohort.

Authors:  E K McCarthy; L C Kenny; J O B Hourihane; A D Irvine; D M Murray; M E Kiely
Journal:  Eur J Clin Nutr       Date:  2016-12-21       Impact factor: 4.016

9.  Childhood dietary patterns and body composition at age 6 years: the Children of SCOPE study.

Authors:  Angela C Flynn; John M D Thompson; Kathryn V Dalrymple; Clare Wall; Shahina Begum; Jaijus Pallippadan Johny; Wayne S Cutfield; Robyn North; Lesley M E McCowan; Keith M Godfrey; Edwin A Mitchell; Lucilla Poston
Journal:  Br J Nutr       Date:  2020-02-26       Impact factor: 3.718

10.  Preconception Blood Pressure and Its Change Into Early Pregnancy: Early Risk Factors for Preeclampsia and Gestational Hypertension.

Authors:  Carrie J Nobles; Pauline Mendola; Sunni L Mumford; Robert M Silver; Keewan Kim; Victoria C Andriessen; Matthew Connell; Lindsey Sjaarda; Neil J Perkins; Enrique F Schisterman
Journal:  Hypertension       Date:  2020-08-03       Impact factor: 10.190

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