Women with multiple gestations have an increased risk of development of hypertension in the future

BMC Pregnancy Childbirth. 2021 Jul 16;21(1):510. doi: 10.1186/s12884-021-03992-2.

Abstract

Background: Multiple gestations are associated with an increased incidence of preeclampsia. However, there exists no evidence for an association between multiple gestations and development of hypertension(HTN) later in life. This study aimed to determine whether multiple gestations are associated with HTN beyond the peripartum period.

Methods: In this retrospective nationwide population-based study, women who delivered a baby between January 1, 2007, and December 31, 2008, and underwent a national health screening examination within one year prior to their pregnancy were included. Subsequently, we tracked the occurrence of HTN during follow-up until December 31, 2015, using International Classification of Diseases-10th Revision codes.

Results: Among 362,821 women who gave birth during the study period, 4,944 (1.36%) women had multiple gestations. The cumulative incidence of HTN was higher in multiple gestations group compared with singleton group (5.95% vs. 3.78%, p < 0.01, respectively). On the Cox proportional hazards models, the risk of HTN was increased in women with multiple gestations (HR 1.35, 95% CI 1.19, 1.54) compared with those with singleton after adjustment for age, primiparity, preeclampsia, atrial fibrillation, body mass index, blood pressure, diabetes mellitus, high total cholesterol, abnormal liver function test, regular exercise, and smoking status.

Conclusions: Multiple gestations are associated with an increased risk of HTN later in life. Therefore, guidelines for the management of high-risk patients after delivery should be established.

Keywords: Hypertension; Multiple gestation; Preeclampsia.

MeSH terms

  • Adult
  • Female
  • Humans
  • Hypertension / epidemiology*
  • Incidence
  • Kaplan-Meier Estimate
  • Pregnancy
  • Pregnancy, Multiple / statistics & numerical data*
  • Proportional Hazards Models
  • Republic of Korea / epidemiology
  • Retrospective Studies