A study of pregnant women's blood RNA has found specific molecular profiles that identify women at risk of pre-eclampsia. These insights can identify complications before a woman experiences symptoms.
The study, published today in Nature, involved researchers from King's and Guy's and St Thomas' NHS Foundation Trust in partnership with Mirvie. The study examines genetic material found in blood samples that can predict pregnancy complications such as pre-eclampsia.
Pre-eclampsia effects up to 1 in 12 pregnancies and is a significant cause of maternal morbidity. It is also a cause of a higher risk of cardiovascular disease. Most cases of pre-eclampsia are diagnosed when the mother experiences symptoms in the third trimester. This study could widen the window of detection and lead to quicker intervention.
I am delighted to be involved in this important collaborative effort to develop a new tool to predict pre-eclampsia."
Rachel Tribe, Professor, Department of Women and Children's Health, King's College London
"Using a cutting-edge sequencing approach, we were able to detect cell free RNA (cfRNA) in the blood of pregnant women. These provided a molecular signature that can be used to identify women at risk of pre-eclampsia.
She added: Excitingly, this requires only a single blood sample and has potential to identify women at risk much earlier in pregnancy so that they can be more closely monitored and treated by the clinicians involved."
Researchers took 2500 blood samples from eight prospectively collected cohorts that included multiple ethnicities, nationalities, socioeconomic contexts and geographic locations. They then examined the anonymised cfRNA profiles – signals from the fetus and pregnant mother's tissues – that reflect fetal development and healthy pregnancy progression. This provided a non-invasive window into maternal and fetal health.
In this study, researchers show the cfRNA signals which deviate from those of a healthy pregnancy. One single blood sample could reliably identify women at risk of developing preeclampsia months prior to the presentation of the disease. Using machine learning to analyse tens of thousands of RNA messages from the mother, baby and placenta, the Mirvie RNA platform can identify 75% of women who go on to develop preeclampsia. Researchers hope this test can be widened to investigate other pregnancy complications, such as preterm birth.
Professor Tribe added: "Because the study drew upon samples for a diverse group of women, including participants recruited across King's Health Partners, the molecular signature is very reliable and has potential to outperform currently available tests.
'We are now focused on ongoing clinical research to further validate these results and improve the understanding of other pregnancy complications. As a scientist, it was also extremely interesting to see that the molecular signature tells us something about mechanisms associated with health in pregnancy and complications including preeclampsia; such knowledge will aid development of treatment strategies in the future."
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