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Life sciences · Journal article
Hypertension in Pregnancy · October 13, 2026
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Hypertensive disorders of pregnancy (HDP) encompass chronic hypertension, gestational hypertension, preeclampsia, and preeclampsia superimposed on chronic hypertension, affecting about 5% to 10% of pregnancies worldwide and remaining a major cause of maternal and perinatal morbidity and mortality. Their burden is increasing with rising obesity, advanced maternal age, evolving diagnostic criteria, and marked socioeconomic and racial-ethnic disparities. This review summarises current evidence on the epidemiology, pathophysiology, biomarkers, clinical management, and long-term outcomes of HDP, highlighting recent advances and unresolved questions. Mechanistically, HDP is characterised by systemic endothelial dysfunction and angiogenic imbalance, particularly involving soluble fms-like tyrosine kinase-1 (sFlt-1), placental growth factor (PlGF), and soluble endoglin (sEng), together with immune maladaptation, oxidative stress, and genetic and environmental contributors such as endocrine-disrupting chemicals (EDCs) and sleep-disordered breathing (SDB). The sFlt-1/PlGF ratio is among the most validated tools for short-term risk assessment in suspected preeclampsia, while retinal microvascular imaging may provide a non-invasive marker of systemic microvascular dysfunction. Clinically, trial evidence supports treating mild chronic hypertension to below 140/90 mmHg to reduce adverse outcomes without compromising foetal growth. Long-term maternal cardiovascular and metabolic risks vary according to HDP phenotype, severity, gestational age at onset, and associated preterm delivery or foetal growth restriction, whereas offspring neurodevelopmental associations are largely observational and may be influenced by prematurity and shared familial or socioeconomic factors. Experimental strategies supported primarily by preclinical or early translational evidence include PPARγ agonists, placenta-targeted nanomedicine, and transcutaneous auricular vagus nerve stimulation. Machine learning combined with longitudinal multi-omics data may support precision risk stratification and individualised management.