ArticleBMC pregnancy and childbirth2025
Early prediction of preeclampsia from clinical, multi-omics and laboratory data using random forest model.
Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Beyond the 'Pregnancy Black Box': a global roadmap for artificial intelligence-driven pharmacogenomics in maternal-neonatal health.The pharmacogenomics journal · 2026Review
- Extracellular Vesicle-Associated Non-Coding RNAs in Preeclampsia: Mechanistic Insights, Biomarker Discovery, and Emerging Nanomedicine Concepts.International journal of nanomedicine · 2026Review
- Association of Placental Growth Factor with the risk of adverse pregnancy outcomes: a prospective cohort study in Chinese pregnant women.Frontiers in endocrinology · 2025Article
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28 authors.
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Abstract
backgroundPredicting preeclampsia (PE) within the first 16 weeks of gestation is difficult due to various risk factors, poorly understood causes and likely multiple pathogenic phenotypes of preeclampsia.
objectivesIn this study, we aimed to develop prediction models for early-onset preeclampsia (EPE) and late-onset preeclampsia (LPE) respectively using clinical data, metabolome and proteome analyses on plasma samples and laboratory data.
methodsWe retrospectively recruited 56 EPE, 50 LPE patients and 92 normotensive controls from three tertiary hospitals and used clinical and laboratory data in early pregnancy. Models for EPE and LPE were fitted with the use of patient' clinical, multi-omics and laboratory data.
resultsBy comparing multi-omics and laboratory test variables between EPE, LPE and healthy controls, we identified sets of differentially expressed biomarkers, including 49 and 33 metabolites, 28 and 36 proteins as well as 5 and 7 laboratory variables associated with EPE and LPE respectively. Using the random forest algorithm, we developed a prediction model using seven clinical factors, seven metabolites, five laboratory test variables. The model yielded the highest accuracy for EPE prediction with good sensitivity (87.5%, 95% confidence interval [CI]: 67.64%-97.34%) and specificity (94.1%, 95% CI: 80.32%-99.28%). We also developed a prediction model that exhibited high accuracy in separating LPE from controls (sensitivity: 66.67%, 95% CI: 43.03%-85.41%; specificity: 94.12%, 95% CI: 80.32%-99.28%) using seven clinical factors, five metabolites and eight proteins.
conclusionOur study has identified a set of significant omics and laboratory features for PE prediction. The established models yielded high prediction performance for preeclampsia risk from clinical, multi-omics and laboratory information.
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