Abstract
Hypertensive disorders of pregnancy are associated with a higher risk of later cardiovascular disease, but the mechanistic links are unknown. We recruited two groups of women, one during pregnancy and another at least two years after delivery, including both cases (with a hypertensive disorder of pregnancy) and controls (with a normotensive pregnancy). We measured metabolites using liquid chromatography-mass spectroscopy and applied machine learning to identify metabolomic signatures at three time points: antepartum, postpartum, and mid-life. The mean ages of the pregnancy cohort (58 cases, 46 controls) and the mid-life group (71 cases, 74 controls) were 33.8 and 40.8 years, respectively. The levels of 157 metabolites differed significantly between the cases and the controls antepartum, including 19 acylcarnitines, 12 gonadal steroids, 11 glycerophospholipids, nine fatty acids, six vitamin D metabolites, and four corticosteroids. The machine learning model developed using all antepartum metabolite levels discriminated well between the cases and the controls antepartum (c-index = 0.96), postpartum (c-index = 0.63), and in mid-life (c-index = 0.60). Levels of 10,20-dihydroxyeicosanoic acid best distinguished the cases from the controls both antepartum and postpartum. These data suggest that the pattern of differences in metabolites found antepartum continues to distinguish women who had a hypertensive disorder of pregnancy from women with a normotensive pregnancy for years after delivery.