Researchers at Stanford School of Medicine have discovered biomarkers in the blood and urine of pregnant people that would help in predicting preeclampsia, a common and severe complication that can occur during pregnancy.
Preeclampsia is commonly recognised by high blood pressure late in pregnancy and affects between 3% and 5% of pregnancies in the US and up to 8% of pregnancies across the world.
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The condition can lead to eclampsia, an obstetric emergency that can cause strokes, permanent organ damage, seizures and death.
Currently, preeclampsia can only be diagnosed in the second half of pregnancy with delivery of the baby the sole available treatment.
To identify biological signals that might provide an early warning, the researchers’ team collected samples from pregnant people who did and did not develop the condition.
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All the samples were analysed in detail and changes in as many biological signals as possible were measured. Following this, a small set of useful predictive signals was zeroed in on.
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By GlobalDataStanford School of Medicine paediatrics and anesthesiology, perioperative and pain medicine associate professor Nima Aghaeepour said: “We used a number of cutting-edge technologies on Stanford University’s campus to analyse preeclampsia at an unprecedented level of biological detail.
“We learned that a urine test fairly early on during pregnancy has a strong statistical power for predicting preeclampsia.”
The samples were used to measure six types of biological signals, including all metabolic products in plasma; all fat-like molecules in plasma; all microbes/bacteria in vaginal swabs; all proteins in plasma; all cell-free RNA in blood plasma; and all metabolic products in urine.
Researchers used machine learning to determine the biological signals that could best predict the individuals who would progress to preeclampsia.
By combining the clinical features of participants with the urine metabolites, the researchers also created a predictive model that allowed them to predict preeclampsia early in pregnancy.
