Agnostic identification of plasma biomarkers for postpartum hemorrhage risk

Stéphanie E. Reitsma, Department of Pathology and Laboratory Medicine and UNC Blood Research Center, University of North Carolina School of Medicine, Chapel Hill, NC.
Julia R. Barsoum, Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The George Washington University School of Medicine and Health Science, Washington DC.
Kirk C. Hansen, Biochemistry and Molecular Genetics, The University of Colorado Anschutz Medical Campus, Aurora, CO.
Alexa M. Sassin, Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX.
Monika Dzieciatkowska, Biochemistry and Molecular Genetics, The University of Colorado Anschutz Medical Campus, Aurora, CO.
Andra H. James, Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology Duke University School of Medicine, Durham, NC; Department of Medicine under Hematology, Duke University School of Medicine, Durham, NC.
Kjersti M. Aagaard, Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX.
Homa K. Ahmadzia, Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The George Washington University School of Medicine and Health Science, Washington DC. Electronic address: homa.adhadzia@inova.org.
Alisa S. Wolberg, Department of Pathology and Laboratory Medicine and UNC Blood Research Center, University of North Carolina School of Medicine, Chapel Hill, NC. Electronic address: alisa_wolberg@med.unc.edu.

Document Type

Journal Article

Publication Date

5-6-2024

Journal

American journal of obstetrics and gynecology

DOI

10.1016/j.ajog.2024.04.050

Keywords

biomarker; hemostasis; maternal mortality; postpartum hemorrhage; proteomics

Abstract

BACKGROUND: Postpartum hemorrhage 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 postpartum hemorrhage risk would enhance clinical care and may uncover mechanisms that lead to postpartum hemorrhage. OBJECTIVE: This retrospective case-control study employed agnostic proteomic profiling of maternal plasma samples to identify differentially abundant proteins in controls and postpartum hemorrhage cases. STUDY DESIGN: Maternal plasma samples were procured from a cohort of >60,000 participants in a single institution's perinatal repository. Postpartum hemorrhage was defined as a decrease in hematocrit of ≥10% or receipt of transfusion within 24 hours after delivery. Postpartum hemorrhage cases (n=30) were matched by maternal age and delivery mode (vaginal or cesarean) with controls (n=56). Mass spectrometry was used to identify differentially abundant proteins using integrated peptide peak areas. Statistically significant differences between groups were defined as P<.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 predelivery platelet count. Cases had slightly but significantly lower predelivery and postdelivery hematocrit and hemoglobin. Mass spectrometry detected 1140 proteins, including 77 proteins for which relative abundance differed significantly between cases and controls (fold change >1.15, P<.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. Significantly differentially abundant proteins with the highest fold change (prostaglandin D2 synthase, periostin, and several serine protease inhibitors) did not correlate with predelivery hematocrit or hemoglobin but identified postpartum hemorrhage cases with logistic regression modeling revealing good-to-excellent area under the operator receiver characteristic curves (0.802-0.874). Incorporating predelivery hemoglobin with these candidate proteins further improved the identification of postpartum hemorrhage cases. CONCLUSION: Agnostic analysis of maternal plasma samples identified differentially abundant proteins in controls and postpartum hemorrhage cases. Several of these proteins are known to participate in biologically plausible pathways for postpartum hemorrhage risk and have potential value for predicting postpartum hemorrhage. These findings identify candidate protein biomarkers for future validation and mechanistic studies.

Department

Obstetrics and Gynecology

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