Ultrasound estimated subcutaneous and visceral adipose tissue thicknesses and risk of pre-eclampsia

Abstract Early identification of high-risk pregnancies enables identification of those who would benefit from aspirin prophylaxis and increased surveillance for pre-eclampsia. A high body mass index (BMI) is a well-known predictor for pre-eclampsia. However, if abdominal adipose tissue distribution...

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Main Authors: Heidrun Pétursdóttir Maack, Inger Sundström Poromaa, Linda Lindström, Ajlana Mulic-Lutvica, Katja Junus, Anna-Karin Wikström
Format: article
Language:EN
Published: Nature Portfolio 2021
Subjects:
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Online Access:https://doaj.org/article/f5e5116e39da470babf1d50b81162b23
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Summary:Abstract Early identification of high-risk pregnancies enables identification of those who would benefit from aspirin prophylaxis and increased surveillance for pre-eclampsia. A high body mass index (BMI) is a well-known predictor for pre-eclampsia. However, if abdominal adipose tissue distribution is associated with pre-eclampsia is limited investigated. Subcutaneous adipose tissue (SAT) thickness and visceral adipose tissue (VAT) thickness were measured by ultrasound on 3777 women at around 18 gestational weeks. SAT thickness was measured from the skin to linea alba and VAT from linea alba to the anterior aortic wall. The risk of developing pre-eclampsia (de novo hypertension at ≥ 20 gestational weeks in combination with proteinuria) was evaluated by logistic regression and expressed as odds ratio (OR) with 95% confidence intervals (CI). The risk of pre-eclampsia increased by 79% for every cm in SAT thickness (OR 1.79; 95% CI 1.48–2.17) and by 23% for every cm VAT thickness (OR 1.23; 95% CI 1.11–1.35). After adjustment for maternal age, parity, BMI, smoking and country of birth, the association between SAT thickness and pre-eclampsia remained (AOR 1.35; 95% CI 1.02–1.79). Greater SAT thickness measured with second trimester ultrasound is associated with increased risk of developing pre-eclampsia. The measurement may improve prediction models for pre-eclampsia.