Genetic predictors of blood pressure traits are associated with preeclampsia

Elizabeth A Jasper1,2,3,4, Jacklyn N Hellwege5,3,6, Joseph H Breeyear5

  • 1Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 600, Rm 616, Nashville, TN, 37203, USA.

Scientific Reports
|July 30, 2024
PubMed

Insights

Polygenic scores for blood pressure traits are associated with preeclampsia, indicating shared genetic influences. However, these scores did not improve clinical prediction models for the condition.

Area of Science:

  • Genetics
  • Obstetrics
  • Cardiovascular Medicine

Background:

  • Preeclampsia is a serious pregnancy complication with unclear causes but high heritability.
  • Hypertension after 20 weeks of gestation defines preeclampsia, a leading cause of maternal and neonatal mortality.

Purpose of the Study:

  • To investigate the shared genetic architecture between blood pressure traits and preeclampsia.
  • To assess the association of polygenic scores (PGS) for blood pressure with preeclampsia risk.

Main Methods:

  • Utilized genetic data from Black and White individuals from BioVU and Penn Medicine Biobank.
  • Defined preeclampsia using ICD codes and analyzed associations with diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PGS.
  • Conducted meta-analysis across ancestry groups and evaluated prediction model performance.

Main Results:

  • All tested polygenic scores (DBP, SBP, PP) showed a significant association with preeclampsia in cross-ancestry meta-analysis.
  • The addition of polygenic scores to existing clinical models did not enhance their predictive accuracy for preeclampsia.
  • Significant associations were observed: ORDBP=1.10, ORSBP=1.16, ORPP=1.14.

Conclusions:

  • Genetic factors influencing blood pressure regulation in the general population also contribute to preeclampsia susceptibility.
  • While genetic links exist, polygenic scores alone are not sufficient for improving preeclampsia prediction in clinical settings.

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