Angiogenic Imbalance Defines Multisystem Phenotypes of Preeclampsia: A Phenotype-Oriented Cohort Study

血管生成失衡定义了先兆子痫的多系统表型:一项以表型为导向的队列研究

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Abstract

BACKGROUND: Preeclampsia is a heterogeneous multisystem disorder characterized by endothelial dysfunction and angiogenic imbalance. While the sFlt-1/PlGF ratio is widely used for diagnostic purposes, its role in defining biological phenotypes of preeclampsia remains insufficiently explored. This study aimed to investigate whether angiogenic imbalance is associated with distinct multisystem phenotypes of preeclampsia and with perinatal outcomes. METHODS: We conducted a retrospective cohort study including 320 pregnant women, of whom 68 were diagnosed with preeclampsia. Multisystem phenotypes were defined using laboratory markers reflecting renal, hepatic, and hematologic involvement. The sFlt-1/PlGF ratio was compared across phenotypes. Associations with gestational age at delivery, birth weight, Apgar score, and neonatal intensive care unit (NICU) admission were evaluated. Receiver operating characteristic (ROC) analysis assessed the discriminatory performance of the sFlt-1/PlGF ratio for identifying the renal-dominant phenotype. RESULTS: The mean sFlt-1/PlGF ratio was higher in preeclampsia compared to normotensive pregnancies (58.5 ± 20.3 vs. 34.6 ± 15.9). Within preeclampsia, the renal-dominant phenotype showed the highest ratio (66.0 ± 22.5), followed by hepatic (55.9 ± 18.2) and hematologic phenotypes (52.0 ± 16.8). The renal phenotype was associated with earlier delivery (34.6 weeks), lower birth weight (2196 g), higher NICU admission (10.7%), and lower Apgar scores. The sFlt-1/PlGF ratio demonstrated moderate discrimination for the renal phenotype (AUC = 0.69). CONCLUSIONS: Angiogenic imbalance varies across multisystem phenotypes of preeclampsia and is associated with meaningful perinatal differences. The sFlt-1/PlGF ratio may contribute to phenotype-based risk stratification, supporting a move toward precision obstetrics. Prospective studies are needed to validate phenotype-oriented classification models.

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