Presentation Details
| Agnostic identification of plasma biomarkers for postpartum hemorrhage risk Stephanie E.Reitsma1, Julia R.Barsoum2, Kirk C.Hansen3, Monika Dzieciatkowska3, Andra H.James4, Kjersti M.Aagaard5, Homa K.Ahmadzia2, Alisa S.Wolberg1. 1Department of Pathology and Laboratory Medicine and UNC Blood Research Center, University of North Carolina School of Medicine, Chapel Hill, NC, USA.2Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, The George Washington University School of Medicine and Health Science, Washington, DC, USA.3Biochemistry and Molecular Genetics, The University of Colorado Anschutz Medical Campus, Aurora, CO, USA.4Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, and Department of Medicine under Hematology, Duke University School of Medicine, Durham, NC, USA.5Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA |
Abstract
Background. Postpartum hemorrhage (PPH) is difficult to predict, is associated with significant maternal morbidity, and is the leading cause of maternal mortality worldwide. The identification of maternal biomarkers that can predict increased PPH risk would enhance clinical care and may uncover mechanisms that lead to PPH. Objective. This study employed agnostic proteomic profiling of pre-delivery maternal plasma samples to identify differentially abundant proteins in controls and PPH cases. Study Design. Maternal pre-delivery plasma samples were procured from a cohort of >60,000 participants in a single institution perinatal repository. PPH was defined as a decrease in hematocrit of ≥10% or receipt of transfusion within 24 hours of delivery. PPH cases (N=30) were matched by maternal age- and delivery mode (vaginal or cesarean) with controls (N=57). Mass spectrometry was used to identify differentially abundant proteins using Intensity Based Absolute Quantitation (iBAQ). Statistically significant differences between groups were defined by a p-value of <0.05 after controlling for multiple comparisons. Results. By study design, cases and controls did not differ in race, ethnicity, gestational age at delivery, blood type, or pre-delivery platelet count. Cases had slightly, but significantly lower pre- and post-delivery hematocrit and hemoglobin. Mass spectrometry detected 1140 proteins, including 74 proteins for which relative abundance differed significantly between cases and controls (fold change >1.15, P<0.05). Of these differentially abundant plasma proteins, most had likely liver or placental origins. Gene ontology term analysis mapped to protein clusters involved in responses to wound healing, stress response, and host immune defense. Pregnancy is associated with significant changes in the levels of circulating coagulation and antifibrinolytic proteins. It is therefore interesting that despite the bleeding presentation, our agnostic approach taken in the present study showed that the relative abundance of most detected coagulation and fibrinolysis proteins did not differ between groups. Significantly differentially abundant proteins with the highest fold change (periostin, prostaglandin D2 synthase, and several serine protease inhibitors) did not correlate with pre-delivery hematocrit, hemoglobin, or platelet count but were predictive of PPH with logistic regression modeling revealing good-to-excellent area under the operator receiver characteristic curves (AUROC 0.802-0.874). Incorporating pre-delivery hemoglobin and hematocrit with our target proteins improved PPH predictability (AUROC 0.74 to 0.9) for periostin and prostaglandin D2 synthase, respectfully. Conclusion. Agnostic analysis of pre-delivery maternal plasma samples identified differentially abundant proteins in controls and PPH cases. Several of these proteins are known to participate in biologically plausible pathways for PPH risk. Our analyses suggest these proteins have potential value for predicting PPH. These findings identify candidate protein biomarkers for future validation and mechanistic studies.
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