OC 51.3 - Agnostic identification of plasma biomarkers for postpartum hemorrhage risk

Tuesday, June 25, 2024

10:00 – 10:15 ICT

Room: Ballroom B1

Co-Author(s)

Stéphanie E. Reitsma

Postdoc
University of North Carolina School of Medicine
Chapel Hill, North Carolina, United States
JB

Julia R. Barsoum

Medical resident
The George Washington University School of Medicine and Health Science
Washington, District of Columbia, United States
KH

Kirk C. Hansen

Professor
University of Colorado Anschutz Medical Campus, School of Medicine, Department of Biochemistry and Molecular Genetics
Aurora, Colorado, United States
MD

Monika Dzieciatkowska

Postdoc
The University of Colorado Anschutz Medical Campus
Aurora, Colorado, United States
Andra H. James

Professor Emeritus
Duke University School of Medicine
Durham, North Carolina, United States
KA

Kjersti M. Aagaard

Professor
Baylor College of Medicine
Houston, Texas, United States

Presenting Author(s)

Homa K. Ahmadzia, MD, MPH

Associate Professor Division of Maternal-Fetal Medicine, Deputy Director Perinatal Research Unit
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Inova Health System, 3300 Gallows Rd, Falls Church, VA, USA
Falls Church, Virginia, United States

Co-Author(s)

Alisa S. Wolberg, PhD

Professor
University of North Carolina
Chapel Hill, North Carolina, United States

Background: Postpartum hemorrhage (PPH) is difficult to predict clinically so identification of biomarkers that can predict increased PPH risk would enhance clinical care and may uncover mechanisms that lead to PPH.

Aims: This study employed agnostic proteomic profiling of maternal plasma samples to identify differentially abundant proteins in controls and PPH cases.

Methods: Maternal pre-delivery plasma samples were from a cohort of >60,000 participants in a single institution biorepository. 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: 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). Gene ontology term analysis mapped to protein clusters involved in responses to wound healing, stress response, and host immune defense. Despite the bleeding presentation, 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) 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(s): Agnostic analysis of maternal plasma samples identified differentially abundant proteins in controls and PPH cases. Several of these proteins participate in biologically plausible pathways for PPH risk. These findings identify candidate protein biomarkers for future validation and mechanistic studies.