A New Study from Metabolomic Diagnostics Demonstrates the Potential for Blood Biomarkers to Improve Preeclampsia Risk Stratification in Pregnant Women
A new study from Metabolomic Diagnostics shows the ability metabolite biomarkers, along with patient clinical features and mathematical modeling to improve the assessment of preeclampsia risk.
Preeclampsia, a potentially life-threatening condition that can occur during pregnancy, remains a significant concern in maternal healthcare. Early detection and prediction of preeclampsia are crucial for managing and mitigating its impact on both the mother and the baby. Recent research has shed light on how a combination of metabolite biomarkers, alongside established biomarkers, can significantly enhance the prediction of preterm preeclampsia in pregnant individuals.
Current screening for preeclampsia primarily relies on a combination of maternal demographic data, medical history, and three biomarkers: serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index. While this approach identifies approximately 75% of women who may develop preterm preeclampsia, there is a recognized need for further improvements in screening accuracy.
The study sought to explore whether incorporating specific metabolite biomarkers could enhance the prediction of preterm preeclampsia. The study involved a large cohort of singleton pregnancies and analyzed blood samples collected during early pregnancy. Liquid chromatography-mass spectrometry was used to quantify the levels of 50 metabolites previously associated with pregnancy complications.
The research utilized a multi-step modeling approach to develop prediction models for preterm preeclampsia. These steps included normalizing predictors, creating models for different patient groups based on body mass index (BMI) and/or race, selecting relevant classifiers, and aggregating them into a final prediction model. Three predictor panels were considered: one including placental growth factor and metabolites, another adding mean arterial pressure to the mix, and a third including uterine artery pulsatility index alongside the previous components.
The results of the study demonstrated several key findings:
- Improved Prediction Models: All three prediction models incorporating metabolite biomarkers showed enhanced predictive performance compared to the reference model based solely on established biomarkers.
- Increased Detection Rates: The prediction model that included placental growth factor, mean arterial pressure, and metabolites exhibited a 15% increase in detection rates over the reference model. This improvement was observed across different patient groups, including Black and White patients, as well as those with normal weight and obesity. Notably, the overweight group did not experience the same level of improvement.
- Maternal Phenotyping: The study highlighted the importance of classifying pregnant individuals based on maternal characteristics, such as BMI and race, in achieving improved prediction accuracy. This suggests that considering these factors can play a significant role in enhancing the prediction of obstetrical syndromes like preeclampsia.
The research demonstrates the potential of metabolite biomarkers to enhance the accuracy of preterm preeclampsia prediction alongside established biomarkers. The study’s findings also emphasize the value of maternal phenotyping, such as considering BMI and race, in tailoring prediction models to different patient groups. These developments hold promise for improving prenatal care and ultimately enhancing maternal and fetal health outcomes by enabling earlier intervention and management of preeclampsia.
Based upon the findings, Metabalomic Diagnostics is planning on developing a blood based test utilizing the metabolite biomarkers to improve screening for preeclampsia.