AIMS Little is known about how pregnancy complications and cardiovascular disease (CVD) risk are associated, specifically among ethnic minorities. In this study we examined this association in women from six… Click to show full abstract
AIMS Little is known about how pregnancy complications and cardiovascular disease (CVD) risk are associated, specifically among ethnic minorities. In this study we examined this association in women from six ethnic groups, and the potential value of pregnancy complications as eligibility criterion for CVD risk screening. METHODS We conducted a cross-sectional study combining obstetric history from the Dutch perinatal registry with data on cardiovascular risk up to 15 years after pregnancy from the multi-ethnic HELIUS study. We included 2,466 parous women of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan origin. Associations were studied across ethnicities and predictive value of pregnancy complications for CVD risk factors above traditional eligibility criteria for CVD risk screening was assessed using Poisson regression. RESULTS History of hypertensive disorders of pregnancy and preterm birth were associated with higher prevalence of chronic hypertension and chronic kidney disease across most groups (prevalence ratio 1.6-1.9). Gestational diabetes mellitus was associated with increased type 2 diabetes mellitus risk, particularly in ethnic minority groups (prevalence ratio 4.5-7.7). Associations did not significantly differ across ethnic groups. The prediction models did not improve substantially after adding pregnancy complications to traditional eligibility criteria for CVD risk screening. CONCLUSION History of hypertensive disorders of pregnancy, preterm birth and gestational diabetes mellitus is associated with CVD risk factors in parous women, without evidence of a differential association across ethnic groups. However, addition of pregnancy complications to traditional eligibility criteria for CVD risk screening does not substantially improve the prediction of prevalent CVD risk factors.
               
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