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Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models.

Grégoire Thomas1, Argyro Syngelaki2, Karam Hamed2

  • 1SQU4RE, Lokeren, Belgium (Dr Thomas); Metabolomic Diagnostics, Cork, Ireland (Drs Thomas, Panigassi, and Tuytten).

American Journal of Obstetrics & Gynecology MFM
|September 26, 2023
PubMed
Summary

New metabolite biomarkers significantly improve early prediction of preterm preeclampsia when combined with existing markers. Stratifying by maternal characteristics like BMI enhances detection rates, offering a more accurate screening tool for this critical pregnancy complication.

Keywords:
algorithmsbiomarkersfirst-trimester screeningphenotypespredictionpreeclampsiapregnancypreterm

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Area of Science:

  • Obstetrics and Gynecology
  • Biomarker Discovery
  • Maternal-Fetal Medicine

Background:

  • Preeclampsia screening is vital in antenatal care, with current tests identifying ~75% of preterm cases using placental growth factor, mean arterial pressure, and uterine artery pulsatility index.
  • Further improvements necessitate additional biomarkers, as recent findings link specific metabolites to preterm preeclampsia, with prediction varying by maternal body mass index (BMI).

Purpose of the Study:

  • To investigate if metabolite biomarkers enhance preterm preeclampsia prediction across three screening scenarios based on biomarker availability.
  • To assess the impact of combining metabolites with placental growth factor (PlGF), mean arterial pressure (MAP), and uterine artery pulsatility index (UtAPI).

Main Methods:

  • An observational case-control study at King's College Hospital, London, involving 1635 controls and 106 preterm preeclampsia cases.
  • Liquid chromatography-mass spectrometry quantified 50 metabolites in plasma, with prediction models developed using combinatorial modeling and bagging, stratified by BMI and race.
  • Performance evaluated using area under the receiver operating characteristic curve (AUC) and detection rate at 10% false-positive rate.

Main Results:

  • New prediction models incorporating metabolites showed significantly higher AUC and detection rates compared to reference models across all three scenarios.
  • The PlGF+MAP+metabolites model achieved a 15% increase in detection rate (0.58 vs. 0.43), significantly improving prediction in Black (14%) and White (19%) patients, and normal-weight (18.5–25 BMI) and obese (≥30 BMI) groups.
  • Metabolites were selected across models, with 21 contributing to at least two models, demonstrating their consistent utility.

Conclusions:

  • Metabolite biomarkers, when combined with established markers (PlGF, MAP, UtAPI), significantly improve early preterm preeclampsia prediction.
  • Maternal phenotyping (BMI, race) is crucial for optimizing prediction, highlighting its role in improving screening for obstetrical syndromes like preeclampsia.