We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
LGC Clinical Diagnostics

Download Mobile App




Early-Pregnancy Urine Test Could Predict Preeclampsia

By LabMedica International staff writers
Posted on 12 Dec 2022
Print article
Image: Researchers have discovered biomarkers that predict a common and severe pregnancy complication (Photo courtesy of Pexels)
Image: Researchers have discovered biomarkers that predict a common and severe pregnancy complication (Photo courtesy of Pexels)

Preeclampsia is a dangerous complication of pregnancy and one of the top three causes of maternal death worldwide. Characterized by high blood pressure late in pregnancy, it affects 3% to 5% of pregnancies in the U.S. and up to 8% of pregnancies worldwide. Preeclampsia can lead to eclampsia, an obstetric emergency linked to seizures, strokes, permanent organ damage and death. At present, preeclampsia can be diagnosed only in the second half of pregnancy, and the sole treatment is to deliver the baby, putting infants at risk from premature birth. Now, researchers have discovered biomarkers in the blood and urine of women with preeclampsia that could lead to a low-cost test to predict the condition months before a pregnant woman shows symptoms. Predictive testing would enable better pregnancy monitoring and the development of more effective treatments.

To figure out which biological signals could provide an early warning system for preeclampsia, researchers at Stanford Medicine (Stanford, CA, USA) collected biological samples from pregnant women who did and did not develop preeclampsia. They conducted highly detailed analyses of all the samples, measuring changes in as many biological signals as possible, then zeroing in on a small set of the most useful predictive signals. The research team collected biological samples at two or three points in pregnancy (early, mid and late) in 49 women, of whom 29 developed preeclampsia during their pregnancies and 20 did not. The participants were selected from a larger cohort of women who had donated biological samples for pregnancy research at Stanford Medicine.

For each time point, the participants gave blood, urine and vaginal swab samples. The samples were used to measure six types of biological signals: all cell-free RNA in blood plasma, a measure of which genes are active; all proteins in plasma; all metabolic products in plasma; all metabolic products in urine; all fat-like molecules in plasma; and all microbes/bacteria in vaginal swabs. The scientists also conducted measurements of all immune cells in plasma in a subset of 19 of the participants. Using the resulting thousands of measurements, as well as information about which participants developed preeclampsia and when in pregnancy each sample was collected, the scientists used machine learning to determine which biological signals best predicted who progressed to preeclampsia.

They aimed to identify a small set of signals detectable in the first 16 weeks of pregnancy that could form the basis for a simple, low-cost diagnostic test feasible to use in low-, middle- and high-income countries. To estimate the accuracy of the machine learning models, the researchers initially constructed the models with data from the discovery cohort, then confirmed the results by testing their performance on data from women in the validation cohort. A prediction model using a set of nine urine metabolites was highly accurate, the researchers found. These urine markers, in samples collected before week 16 of pregnancy, strongly predicted who later developed preeclampsia. The performance of the test was measured by a statistical standard used in machine learning known as area under the characteristic curve. An AUC of 1 for a test with two possible outcomes indicates perfect prediction, whereas an AUC of 0.5 indicates no predictive value, the same as the results obtained from a coin toss. For the urine markers, the AUC was 0.88 in the discovery cohort and 0.83 in the validation cohort, indicating high prediction capability.

Measuring the same set of urine metabolites in samples collected throughout pregnancy produced similar predictive power, with an AUC of 0.89 in the discovery cohort and 0.87 in the validation cohort. The researchers confirmed that their model had stronger predictive power than using only clinical features linked to a pregnant woman’s preeclampsia risk, such as chronic hypertension, high body mass index and carrying twins. A set of nine proteins measured in blood performed almost as strongly, with an AUC of 0.84. The researchers also created a predictive model that combined participants’ clinical features with urine metabolites, which enabled them to predict preeclampsia starting early in pregnancy with an AUC of 0.96. The clinical features in the combined model are data that are already collected as part of standard medical records, such as patients’ age, height, body mass index and pre-pregnancy hypertension.

“We used a number of cutting-edge technologies on Stanford University’s campus to analyze preeclampsia at an unprecedented level of biological detail,” said the study’s senior author Nima Aghaeepour, PhD, associate professor of pediatrics and of anesthesiology, perioperative and pain medicine. “We learned that a urine test fairly early on during pregnancy has a strong statistical power for predicting preeclampsia.”

Related Links:
Stanford Medicine

Gold Member
Chagas Disease Test
CHAGAS Cassette
Verification Panels for Assay Development & QC
Seroconversion Panels
New
Anti-HHV-6 IgM Assay
anti-HHV-6 IgM ELISA (semiquant.)
New
Aspergillus Test
REALQUALITY Aspergillus

Print article

Channels

Clinical Chemistry

view channel
Image: The tiny clay-based materials can be customized for a range of medical applications (Photo courtesy of Angira Roy and Sam O’Keefe)

‘Brilliantly Luminous’ Nanoscale Chemical Tool to Improve Disease Detection

Thousands of commercially available glowing molecules known as fluorophores are commonly used in medical imaging, disease detection, biomarker tagging, and chemical analysis. They are also integral in... Read more

Immunology

view channel
Image: The cancer stem cell test can accurately choose more effective treatments (Photo courtesy of University of Cincinnati)

Stem Cell Test Predicts Treatment Outcome for Patients with Platinum-Resistant Ovarian Cancer

Epithelial ovarian cancer frequently responds to chemotherapy initially, but eventually, the tumor develops resistance to the therapy, leading to regrowth. This resistance is partially due to the activation... Read more

Microbiology

view channel
Image: The lab-in-tube assay could improve TB diagnoses in rural or resource-limited areas (Photo courtesy of Kenny Lass/Tulane University)

Handheld Device Delivers Low-Cost TB Results in Less Than One Hour

Tuberculosis (TB) remains the deadliest infectious disease globally, affecting an estimated 10 million people annually. In 2021, about 4.2 million TB cases went undiagnosed or unreported, mainly due to... Read more

Pathology

view channel
Image: The ready-to-use DUB enzyme assay kits accelerate routine DUB activity assays without compromising data quality (Photo courtesy of Adobe Stock)

Sensitive and Specific DUB Enzyme Assay Kits Require Minimal Setup Without Substrate Preparation

Ubiquitination and deubiquitination are two important physiological processes in the ubiquitin-proteasome system, responsible for protein degradation in cells. Deubiquitinating (DUB) enzymes contain around... Read more

Technology

view channel
Image: The HIV-1 self-testing chip will be capable of selectively detecting HIV in whole blood samples (Photo courtesy of Shutterstock)

Disposable Microchip Technology Could Selectively Detect HIV in Whole Blood Samples

As of the end of 2023, approximately 40 million people globally were living with HIV, and around 630,000 individuals died from AIDS-related illnesses that same year. Despite a substantial decline in deaths... Read more

Industry

view channel
Image: The collaboration aims to leverage Oxford Nanopore\'s sequencing platform and Cepheid\'s GeneXpert system to advance the field of sequencing for infectious diseases (Photo courtesy of Cepheid)

Cepheid and Oxford Nanopore Technologies Partner on Advancing Automated Sequencing-Based Solutions

Cepheid (Sunnyvale, CA, USA), a leading molecular diagnostics company, and Oxford Nanopore Technologies (Oxford, UK), the company behind a new generation of sequencing-based molecular analysis technologies,... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.