Researchers at UCLA Health and the UCLA School of Dentistry have found that a rapid, noninvasive urine test may potentially identify patients at risk for common yet serious pregnancy complications before symptoms occur.
Their findings, published this month in Springer Nature, could lead to development of a point-of-care method to quickly and more easily assess patient risk for gestational diabetes, preeclampsia and pregnancy hypertension as part of routine prenatal care.
Pregnancy complications such as gestational diabetes and preeclampsia can cause serious health problems for expectant moms and babies. Typically, these conditions are found later in pregnancy, after body changes have occurred. Researchers believe earlier screening could aid physicians in monitoring pregnancies and making earlier care decisions.
"Many existing screening tools can be costly, invasive, require specialized training, or lack sufficient accuracy. We sought out to identify a more efficient way to predict which patients are at risk of developing gestational diabetes or preeclampsia during the early stages of their pregnancy, said Sherin Devaskar, MD, distinguished professor of pediatrics at the David Geffen School of Medicine at UCLA and senior author of the study. “Our findings, if validated in larger studies, may set the stage for deploying this technology at the point of care for real-time decision-making,” Devaskar said.
Devaskar and colleagues tested a technology called electric field-induced release and measurement, EFIRM, which looks for molecular signals in urine, including RNA transcripts and proteins. The EFIRM analyses were conducted in collaboration with Fang Wei, co-principal investigator and inventor of this technology and adjunct professor at the UCLA School of Dentistry. This technology is designed to work quickly and does not require complex sample preparation, making it a potentially useful tool for clinical settings.
Their study included 56 pregnant women. The researchers collected urine samples during the participants’ pregnancies and studied cell-free RNA using RNA sequencing, a next-generation technique to identify and quantify RNA molecules from the urine samples. They then deployed the EFIRM technology to measure selected RNA transcripts and proteins directly from the samples.
They found that these signals helped identify which patients would later develop gestational diabetes and preeclampsia during their pregnancies. Key results included:
- In first-trimester samples, one panel of RNA markers could help identify patients who later developed gestational diabetes.
- A separate panel could help identify patients who later developed preeclampsia.
- The test also showed strong ability to rule out these risks in the overall group.
Of the 50-plus participants, 12 later developed gestational diabetes, another dozen developed preeclampsia and 11 developed gestational hypertension, with 6 demonstrating fetal growth restriction. Fifteen participants had pregnancies without these complications and served as study controls.
These findings build on previous research from Devaskar and colleagues that looked for early pregnancy biomarkers in blood. By extending their work to urine, the researchers hope to identify a less-invasive and more patient-friendly technique that does not require repeated blood draws during pregnancy.
“These early findings give us reason to be hopeful because this novel technology yields rapid measurements that are easily obtained,” Dr. Devaskar said, adding that the process is entirely noninvasive.
Although the results are encouraging, she cautioned that their research is still in its infancy phase and must be validated.
“We have to validate these findings in a much larger study to ensure that our discovery is applicable to a much larger group of patients,” she explained.
Authors
The complete list of authors is noted in the paper.
Funding
This research was supported by Grants HD-R01-100015 (SUD, FW), HD-U01-087221 (SUD, CJ, KS), HD-R01-089714 (SUD) from NICHD/NIH, and NIH/NCATS UL1TR000124 supported SUD (UCLA CTSI). FW is supported by grants from NIH-NCI U01CA233370, CA206126-01A1 (UH2/UH3). NIH/NCATS 1U18TR003778-01.
Disclosures
None of the authors reported relevant financial or non-financial competing interests for disclosure.
