Machine Learning and Blood-Targeted Proteomics Enable Early Prediction and Etiological Discrimination of Hypertensive Pregnancy Disorders

Imperfect first-trimester screening for hypertensive disorders of pregnancy (HDP) means many high-risk women miss the window for preventive aspirin, and the biological heterogeneity of HDPs is overlooked. This study aimed to leverage first-trimester serum proteomics to create a more precise tool for predicting preeclampsia (PE) and differentiating it from other HDPs. A prospective nested case-control study (<i>n</i> = 172) was conducted using targeted liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS) proteomic profiling of 115 proteins. Machine learning (ML) methods were used to develop classifiers from the proteomic data. The signature predictive of PE was characterized by dysregulation of the complement and coagulation cascades (<i>F10</i>, <i>C8A</i>, <i>C1QA</i>, <i>SERPING1</i>, <i>VTN</i>). The profile differentiating gestational hypertension (GAH) from chronic hypertension (CAH) was linked to lipid metabolism (<i>HRG</i>, <i>APOA4</i>, <i>APOC2</i>). An 18-protein support vector machine (SVM) model for predicting PE demonstrated exceptional performance, with 94% sensitivity and 100% specificity, significantly outperforming the standard Fetal Medicine Foundation (FMF) screening algorithm. Pathway analysis confirmed that PE is associated with early activation of innate immunity and coagulation pathways, while GAH is linked to a pregnancy-induced metabolic response. A targeted serum proteomic combined with ML approach represents a new perspective diagnostic tool with strong potential to personalize monitoring for women at the highest risk for specific hypertensive pregnancy complications.