Preeclampsia (PE) is a serious pregnancy complication characterized by hypertension and multi-organ dysfunction after 20 weeks of gestation, posing significant risks to both mothers and infants. Early identification of women at high risk enables timely preventive interventions, yet established screening protocols, such as the Fetal Medicine Foundation (FMF) clinical model, exhibit moderate sensitivity during the first trimester. Since routine laboratory tests reflect early metabolic and inflammatory changes associated with disease development, they may provide an accessible and cost-effective strategy for improving PE prediction.
In a study published in Genes & Diseases , researchers from Soochow University, The University of Chicago Medical Center, Anhui Medical University, Nanjing Medical University, Chongqing Medical University, and Fudan University developed machine learning models integrating clinical risk profiles with laboratory biomarkers, using cell-free DNA (cfDNA) profiling to investigate underlying physiological alterations.
The researchers identified 26 laboratory indicators that differed significantly between women who later developed preeclampsia and those with uncomplicated pregnancies. These included markers of liver and kidney function, lipid metabolism, inflammation, electrolytes, and insulin resistance. Threshold analyses further demonstrated nonlinear relationships between several biomarkers—including gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), triglyceride-glucose (TyG) index, C-reactive protein (CRP), magnesium, iron, and high-density lipoprotein cholesterol (HDL-C)—and subsequent PE risk, suggesting that clinically meaningful cut-off values could improve individualized risk assessment.
Using Boruta feature selection and multiple machine learning algorithms, the investigators developed optimized prediction models for both preterm and term PE. For preterm disease, the AdaBoost model achieved the best performance, while a logistic regression model performed best for term PE. Importantly, combining routine laboratory markers with the existing FMF screening strategy significantly improved predictive accuracy. The integrated model achieved an AUC of 0.954 for preterm PE and 0.913 for term PE, substantially outperforming the FMF model alone and markedly increasing detection rates at the same false-positive threshold. SHAP analyses further identified inflammatory and metabolic markers, including UAR, TyG index, prealbumin, GGT, and CRP, as major contributors to prediction.
To investigate the biological basis of these findings, the researchers analyzed cell-free DNA promoter profiles and identified early transcriptomic alterations associated with immune dysregulation, oxidative stress, metabolic dysfunction, and abnormal placental angiogenesis. These observations support the concept that preeclampsia originates from systemic physiological disturbances well before clinical symptoms appear, rather than representing an isolated placental disorder.
Overall, this study demonstrates that integrating inexpensive first-trimester laboratory tests with machine learning substantially improves early prediction of both preterm and term preeclampsia. By combining routinely available clinical data with explainable artificial intelligence and mechanistic transcriptomic analyses, the findings provide a practical, cost-effective framework for earlier identification of high-risk pregnancies and may facilitate more personalized preventive strategies, particularly in resource-limited healthcare settings.
Reference
Title of Original Paper: First-trimester preeclampsia prediction model via integrative machine learning of maternal risk profiles and laboratory markers
Journal: Genes & Diseases
Genes & Diseases is a journal for molecular and translational medicine. The journal primarily focuses on publishing investigations on the molecular bases and experimental therapeutics of human diseases. Publication formats include full length research article, review article, short communication, correspondence, perspectives, commentary, views on news, and research watch.
DOI: https://doi.org/10.1016/j.gendis.2026.102075
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