Predictive Modeling of Preeclampsia Subtypes Utilizing First-Trimester Circulating Exosomal MicroRNAs

International Journal of Obstetrics and Gynecology | Vol. 14, No. 9, September 2026 | pp. 1059–1070

DOI: 10.46882/2026/IJOG/000569

Original Research Article

Title: Predictive Modeling of Preeclampsia Subtypes Utilizing First-Trimester Circulating Exosomal MicroRNAs

Names of Authors: Camille Laurent¹, Ananya Sen²

Authors’ Affiliations:
¹ Inserm U1139, Faculté de Pharmacie de Paris, Université Paris Cité, Paris, France
² Department of Biophysics and Molecular Biology, University of Calcutta, Kolkata, India

Abstract:
Early-onset preeclampsia (< 34 weeks) and late-onset preeclampsia (≥ 34 weeks) present distinct placental phenotypes, requiring separate biomarker frameworks for early gestational tracking. This prospective longitudinal study explored the diagnostic utility of profiling circulating placental exosomal microRNAs (miRNAs) in the first trimester to predict specific preeclampsia subtypes. Plasma samples were collected from 520 asymptomatic singleton pregnancies between 11 and 13⁺⁶ weeks. Exosomes were isolated via ultracentrifugation, and miRNA expression levels were quantified using quantitative real-time polymerase chain reaction (qPCR). Patients were followed until delivery, with 28 developing early-onset and 42 developing late-onset preeclampsia. Differential expression analysis revealed that early-onset preeclampsia was independently characterized by a significant upregulation of exosomal miR-210 and miR-517a (p < 0.001). Conversely, late-onset preeclampsia demonstrated distinct elevations in miR-155 expressions (p = 0.004). A machine learning Random Forest model incorporating the first-trimester exosomal miRNA signatures alongside maternal mean arterial pressure achieved an Area Under the Curve (AUC) of 0.94 for predicting early-onset preeclampsia, yielding a sensitivity of 89.3% and a specificity of 92.1%. Its predictive power for late-onset disease was moderate (AUC = 0.81). Quantified shifts in first-trimester maternal plasma exosomal microRNA patterns provide a high-precision, non-invasive molecular index capable of differentiating preeclampsia phenotypes long before clinical signs manifest.

Keywords: Preeclampsia Subtypes; Exosomes; MicroRNA; Liquid Biopsy; First-Trimester Screening; Biomarkers.

Manuscript Timeline: Received: July 12, 2026; Revised: September 04, 2026; Accepted: September 16, 2026; Published: September 26, 2026.

Citation: Laurent C, Sen A. Predictive Modeling of Preeclampsia Subtypes Utilizing First-Trimester Circulating Exosomal MicroRNAs. International Journal of Obstetrics and Gynecology. 2026; 14(9): 1059–1070.

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