Researchers at Stanford School of Medicine have discovered biomarkers in the blood and urine of women with Preeclampsia, a serious complication that occurs during pregnancy.

The discovery is said to support the development of a low-cost test to predict the condition a few months before a pregnant woman shows symptoms.

Preeclampsia is characterised by high blood pressure late in pregnancy and may lead to eclampsia, which is linked to seizures, strokes, permanent organ damage and death.

Currently, the condition can be diagnosed only in the second half of pregnancy, and the sole treatment is to deliver the baby, putting infants at risk of premature birth.

Its predictive testing is anticipated to facilitate better pregnancy monitoring and the development of more effective treatments.

Stanford Medicine senior research scientist in paediatrics, and the study co-lead author Ivana Marić said: “The advantage of predicting early in pregnancy who will get preeclampsia is that we could follow moms more closely for early symptoms.

“In addition, taking low-dose aspirin starting early in pregnancy may lower preeclampsia rates in women at risk for the condition, but pinpointing who could benefit has been challenging.

“There is really a need to identify those pregnancies to prevent tragic outcomes for mothers, and preterm births for babies, which can be very dangerous.”

The team at Stanford Medicine has collected samples from pregnant women who did and did not develop preeclampsia, to identify an early warning system for preeclampsia.

All the samples were analysed in detail, measuring changes in as many biological signals as possible, and then zeroing in on a small set of the most useful predictive signals.

The samples were used to measure six types of biological signals, all cell-free RNA in blood plasma, all proteins in plasma, all metabolic products in the urine, all fat-like molecules in plasma; and all microbes/bacteria in vaginal swabs.

In addition, the scientists measured all immune cells in plasma.

They applied machine learning to the resulting measurements, and information about participants, to determine which biological signals best predicted preeclampsia.