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

ABSTRACT: 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 (n = 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 (F10, C8A, C

SUBMITTER: Starodubtseva N 

PROVIDER: S-EPMC12897837 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature