A discovery by Stanford School of Medicine researchers of biomarkers in the blood and urine of women with a dangerous complication of pregnancy could 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 of the top three causes of maternal death worldwide — months before a pregnant woman shows symptoms. Predictive testing would enable better pregnancy monitoring and the development of more effective treatments.
Preeclampsia is characterized by high blood pressure late in pregnancy. It affects 3% to 5% of pregnancies in the United States and up to 8% of pregnancies worldwide, and it can lead to eclampsia, an obstetric emergency linked to seizures, strokes, permanent organ damage and death. At present, preeclampsia can be diagnosed only in the second half of pregnancy, and the sole treatment is to deliver the baby, putting infants at risk from premature birth.
The advantage of predicting early in pregnancy who will get preeclampsia is that we could follow moms more closely for early symptoms.”
Ivana Marić, PhD, study’s co-lead author, senior research scientist in pediatrics, Stanford Medicine
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, Marić said.
“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.”
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.
“When you reduce preeclampsia, you also likely reduce preterm birth,” Stevenson said. “It’s a double whammy of good impacts.”
To figure out 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 did not develop preeclampsia. They conducted highly detailed analyses of all the samples, measuring changes in as many biological signals as possible, then zeroing in on a small set of the most useful predictive signals.
“We used a number of cutting-edge technologies on Stanford University’s campus to analyze preeclampsia at an unprecedented level of biological detail,” Aghaeepour said. “We learned that a urine test fairly early on during pregnancy has a strong statistical power for predicting preeclampsia.”
Measuring everything 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 did not. The participants were selected from a larger cohort of women who had donated biological samples for pregnancy research at Stanford Medicine.
For each time point, the participants gave blood, urine and vaginal swab samples. The samples were used to measure six types of biological signals: all cell-free RNA in blood plasma, a measure of which genes are active; 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 thousands of measurements, as well as information about which participants developed preeclampsia and when in pregnancy each sample was collected, the scientists used machine learning to determine which biological signals best predicted who progressed to preeclampsia.
Marić, I., et al. (2022) Early prediction and longitudinal modeling of preeclampsia from multiomics. Patterns. doi.org/10.1016/j.patter.2022.100655.