A discovery by Stanford School of Medicine researchers of biomarkers within the blood and urine of girls with a dangerous complication of pregnancy may lead to a low-cost test to predict the condition.

The findings, which published online Dec. 9 in Patterns, lay the groundwork for predicting preeclampsia — one among the highest three causes of maternal death worldwide — months before a pregnant woman shows symptoms. Predictive testing would enable higher pregnancy monitoring and the event of simpler treatments.

Preeclampsia is characterised by hypertension late in pregnancy. It affects 3% to five% of pregnancies in the US and as much as 8% of pregnancies worldwide, and it might probably result in eclampsia, an obstetric emergency linked to seizures, strokes, everlasting organ damage and death. At present, preeclampsia will be diagnosed only within the second half of pregnancy, and the only real treatment is to deliver the infant, putting infants in danger from premature birth.

The advantage of predicting early in pregnancy who will get preeclampsia is that we could follow mothers more closely for early symptoms.”

Ivana Marić, PhD, study’s co-lead writer, senior research scientist in pediatrics, Stanford Medicine

As well as, taking low-dose aspirin starting early in pregnancy may lower preeclampsia rates in women in danger for the condition, but pinpointing who may gain advantage has been difficult, Marić said.

“There is absolutely a must discover those pregnancies to forestall tragic outcomes for moms, and preterm births for babies, which will be very dangerous.”

Marić shares lead authorship of the study with Kévin Contrepois, PhD, former scientific director of the Stanford Medicine Metabolic Health Center. The study’s senior authors are Nima Aghaeepour, PhD, associate professor of pediatrics and of anesthesiology, perioperative and pain medicine; Brice Gaudilliere, MD, PhD, associate professor of anesthesiology, perioperative and pain medicine; and David Stevenson, MD, professor of pediatrics and director of the Stanford Prematurity Research Center, which supported the research.

“While you reduce preeclampsia, you furthermore may likely reduce preterm birth,” Stevenson said. “It is a double whammy of excellent impacts.”

To determine which biological signals could provide an early warning system for preeclampsia, the Stanford Medicine research team collected biological samples from pregnant women who did and didn’t develop preeclampsia. They conducted highly detailed analyses of all of the samples, measuring changes in as many biological signals as possible, then zeroing in on a small set of probably the most useful predictive signals.

“We used various cutting-edge technologies on Stanford University’s campus to research preeclampsia at an unprecedented level of biological detail,” Aghaeepour said. “We learned that a urine test fairly early on while pregnant has a powerful statistical power for predicting preeclampsia.”

Measuring all the things that changes in pregnancy

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 didn’t. The participants were chosen from a bigger cohort of girls who had donated biological samples for pregnancy research at Stanford Medicine.

For every time point, the participants gave blood, urine and vaginal swab samples. The samples were used to measure six forms of biological signals: all cell-free RNA in blood plasma, a measure of which genes are lively; 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 1000’s of measurements, in addition to details about which participants developed preeclampsia and when in pregnancy each sample was collected, the scientists used machine learning to find out which biological signals best predicted who progressed to preeclampsia.

Source:

Journal reference:

Marić, I., et al. (2022) Early prediction and longitudinal modeling of preeclampsia from multiomics. Patterns. doi.org/10.1016/j.patter.2022.100655.