UC Irvine

Hypertensive disorders of pregnancy (HDP) comprise a spectrum of syndromes that range in severity from gestational hypertension and pre-eclamplsia (PE) to eclampsia, as well as chronic hypertension and chronic hypertension with superimposed PE. HDP occur in 2% to 10% of pregnant women worldwide, and impose a substantial burden on maternal and fetal/infant health. Cardiovascular disease (CVD) is the leading cause of death in women. The high prevalence of non-obstructive coronary artery disease and the lack of an efficient diagnostic workup make the identification of CVD in women challenging. Accumulating evidence suggests that a previous history of PE is consistently associated with future CVD risk. Moreover, PE as a maladaptation to pregnancy-induced hemodynamic and metabolic stress may also be regarded as a "precision" testing result that predicts future cardiovascular risk. Therefore, the development of PE provides a tremendous, early opportunity that may lead to changes in maternal and infant future well-being. However, the underlying pathogenesis of PE is not precise, which warrants precision medicine-based approaches to establish a more precise definition and reclassification. In this review, we proposed a stage-specific, PE-targeted algorithm, which may provide novel hypotheses that bridge the gap between Big Data-generating approaches and clinical translational research in terms of PE prediction and prevention, clinical treatment, and long-term CVD management.