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Metabolic syndrome and pre-eclampsia

Authors :
Chahinda Ghossein-Doha
Mieke Cathy Elisabeth Hooijschuur
A. A. M. Zandbergen
S.M.J. van Kuijk
Abraham A. Kroon
Marc E. A. Spaanderman
P. W. De Leeuw
Internal Medicine
Obstetrie & Gynaecologie
MUMC+: MA Med Staf Artsass Interne Geneeskunde (9)
RS: GROW - R4 - Reproductive and Perinatal Medicine
Interne Geneeskunde
MUMC+: MA Alg Interne Geneeskunde (9)
RS: CARIM - R3.02 - Hypertension and target organ damage
MUMC+: KIO Kemta (9)
Epidemiologie
RS: CAPHRI - R2 - Creating Value-Based Health Care
MUMC+: MA Medische Staf Obstetrie Gynaecologie (9)
RS: Carim - V02 Hypertension and target organ damage
RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience
Source :
Ultrasound in Obstetrics and Gynecology, 54(1), 64-71. John Wiley & Sons Ltd., Ultrasound in Obstetrics & Gynecology, 54(1), 64-71. Wiley
Publication Year :
2019

Abstract

Objective To evaluate the association between different pre-eclampsia (PE) phenotypes and the development of metabolic syndrome postpartum, in order to identify the subgroup of formerly pre-eclamptic women with a worse cardiovascular risk profile requiring tailored postpartum follow-up. Methods This was a cohort study of 1102 formerly pre-eclamptic women in whom cardiovascular and cardiometabolic evaluation was performed at least 3 months postpartum. Women were divided into four subgroups based on PE resulting in delivery before 34 weeks (early-onset (EO)) or at or after 34 weeks (late onset (LO)) of gestation and whether they delivered a small-for-gestational-age (SGA) neonate. Metabolic syndrome was diagnosed as the presence of hyperinsulinemia along with two or more of: body mass index >= 30 kg/m(2); dyslipidemia; hypertension; and microalbuminuria or proteinuria. Data were compared between groups using ANOVA after Bonferroni correction. Odds ratios (OR) were calculated using logistic regression to determine the association between metabolic syndrome and the four subgroups. We constructed receiver-operating characteristics curves and computed the area under the curve (AUC) to quantify the ability of different obstetric variables to distinguish between women who developed metabolic syndrome and those who did not. Results The prevalence of metabolic syndrome was higher in women with EO-PE and SGA (25.8%) than in those with EO-PE without SGA (14.7%) (OR 2.01 (95% CI, 1.34-3.03)) and approximately five-fold higher than in women with LO-PE with SGA (5.6%) (OR 5.85 (95% CI, 2.60-13.10)). In women with LO-PE, the prevalence of metabolic syndrome did not differ significantly between women with and those without SGA. Multivariate analysis revealed that a history of SGA, a history of EO-PE and systolic blood pressure at the time of screening are the best predictors of developing metabolic syndrome postpartum. The AUC of the model combining these three variables was 74.6% (95% CI, 70.7-78.5%). The probability of the presence of metabolic syndrome was calculated as: P = 1/(1 + e(-LP)), where LP is linear predictor = -8.693 + (0.312 x SGA (yes = 1)) + (0.507 x EO-PE (yes = 1)) + (0.053 x systolic blood pressure). Conclusions The incidence of metabolic syndrome postpartum was associated more strongly with EO-PE in combination with SGA as compared with LO-PE or EO-PE without SGA. Both time of onset of PE and fetal growth affect the risk of metabolic syndrome after a pre-eclamptic pregnancy. Copyright (c) 2018 ISUOG. Published by John Wiley & Sons Ltd.

Details

ISSN :
09607692
Volume :
54
Issue :
1
Database :
OpenAIRE
Journal :
Ultrasound in Obstetrics and Gynecology
Accession number :
edsair.doi.dedup.....836a0ebccb081e322d583230a2b1c668
Full Text :
https://doi.org/10.1002/uog.20126