Related Experiment Video
Updated: Aug 18, 2026
Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound (30/45MHZ) System
Published on: May 5, 2018
Early prediction model for hypertensive disorders in pregnancy using cardiovascular microRNAs and maternal clinical
Ilona Hromadnikova1, Katerina Kotlabova1, Ladislav Krofta2
1Dpt. of Molecular Biology and Cell Pathology, Gynaecology and Obstetrics Clinic, Institute for the Care of Mother and Child, Third Faculty of Medicine, Charles University, Prague, Czechia.
The goal of the study was to establish an efficient first-trimester prediction model for gestational hypertension (GH) and preeclampsia (PE). GH is defined as new-onset hypertension without proteinuria or end-organ dysfunction, whereas PE involves hypertension accompanied by proteinuria and/or systemic maternal complications. Current first-trimester screening strategies are primarily based on the Fetal Medicine Foundation (FMF) algorithm, which combines maternal characteristics with biophysical and biochemical markers (e.g., mean arterial pressure, uterine artery Doppler, and PlGF) to estimate the risk of early-onset PE. The retrospective nested case-control study was performed on whole peripheral blood leukocyte lysates collected between November 2012 and March 2020 during the first trimester of gestation. The cohort included pregnancies that later developed GH (n = 83) or PE (n = 66), and 80 randomly selected controls matched by sample storage time. Multiparametric models were constructed using the levels of circulating microRNAs aberrantly expressed during early gestation together with maternal clinical characteristics identified as significant risk factors. A first-trimester prediction model for PE combining the levels of six selected microRNAs with six maternal clinical characteristics-age, body mass index, infertility treatment, nulliparity, prior PE, and any autoimmune disease-identified 78.79% of PE pregnancies at 10.0% false positive rate (FPR). Adding first-trimester FMF screening results for PE and/or fetal growth restriction (FGR) and spontaneous preterm birth further increased predictive performance to 84.85% at 10.0% FPR. The GH prediction model, integrating the levels of miR-181a-5p with five maternal clinical characteristics (age, BMI, infertility treatment, nulliparity, and any autoimmune disease), identified 62.65% of GH cases at 10% FPR, increasing to 69.88% when FMF screening results were included. Importantly, the dysregulated microRNAs identified in early gestation were linked to immune-regulatory and angiogenic pathways at the maternal-fetal interface, supporting the biological plausibility of their involvement in the early pathogenesis of GH and PE. These findings suggest that combining peripheral blood microRNA profiles with maternal clinical characteristics offers a promising new approach for early prediction of GH and PE. Larger prospective studies are needed to validate these pilot results and assess clinical utility.
The goal of the study was to establish an efficient first-trimester prediction model for gestational hypertension (GH) and preeclampsia (PE). GH is defined as new-onset hypertension without proteinuria or end-organ dysfunction, whereas PE involves hypertension accompanied by proteinuria and/or systemic maternal complications. Current first-trimester screening strategies are primarily based on the Fetal Medicine Foundation (FMF) algorithm, which combines maternal characteristics with biophysical and biochemical markers (e.g., mean arterial pressure, uterine artery Doppler, and PlGF) to estimate the risk of early-onset PE. The retrospective nested case-control study was performed on whole peripheral blood leukocyte lysates collected between November 2012 and March 2020 during the first trimester of gestation. The cohort included pregnancies that later developed GH (n = 83) or PE (n = 66), and 80 randomly selected controls matched by sample storage time. Multiparametric models were constructed using the levels of circulating microRNAs aberrantly expressed during early gestation together with maternal clinical characteristics identified as significant risk factors. A first-trimester prediction model for PE combining the levels of six selected microRNAs with six maternal clinical characteristics-age, body mass index, infertility treatment, nulliparity, prior PE, and any autoimmune disease-identified 78.79% of PE pregnancies at 10.0% false positive rate (FPR). Adding first-trimester FMF screening results for PE and/or fetal growth restriction (FGR) and spontaneous preterm birth further increased predictive performance to 84.85% at 10.0% FPR. The GH prediction model, integrating the levels of miR-181a-5p with five maternal clinical characteristics (age, BMI, infertility treatment, nulliparity, and any autoimmune disease), identified 62.65% of GH cases at 10% FPR, increasing to 69.88% when FMF screening results were included. Importantly, the dysregulated microRNAs identified in early gestation were linked to immune-regulatory and angiogenic pathways at the maternal-fetal interface, supporting the biological plausibility of their involvement in the early pathogenesis of GH and PE. These findings suggest that combining peripheral blood microRNA profiles with maternal clinical characteristics offers a promising new approach for early prediction of GH and PE. Larger prospective studies are needed to validate these pilot results and assess clinical utility.
