LARGE-SCALE PROTEOMICS REVEALS NEW CANDIDATE BIOMARKERS OF PREECLAMPSIA

Preeclampsia is one of the great obstetrical syndromes affecting approximately 5% of pregnancies worldwide. Arising from placental dysfunction, it remains a leading causes of maternal and fetal morbidity and mortality, and is associated with long-term cardiovascular, endothelial and angiogenic abnormalities in both the mother and child. Early prediction is therefore critical to enable timely interventions that may prevent disease development and improve pregnancy outcomes. Recent advances in proteomics now allow for quantification of thousands of proteins, offering new opportunities for biomarker discovery. In this symposium presentation, we will highlight findings from several studies conducted by the maternal-fetal interaction research group at the Oslo University Hospital that leverage large-scale proteomic profiling to predict and classify preeclampsia. Using machine-learning-based models, we have identified multiple novel biomarker candidates and evaluated their predictive performance across several gestational time point to determine how early in pregnancy these biomarkers can detect preeclampsia. Importantly, by analyzing multiple independent pregnancy cohorts from Norway, Sweden, and the United States, we have identified biomarker signatures that demonstrate robustness and potential applicability across diverse demographic groups. Furthermore, analysis on paired afferent and efferent placental vessels have enabled us to assess the placental contribution to circulating biomarker profiles. Together, these results underscore the promise of proteomics-driven approaches for improving early prediction, biological understanding and clinical detection of preeclampsia.