Polygenic Risk Score and Risk Factors for Preeclampsia and Gestational Hypertension

Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.

Genetic predictors of blood pressure traits are associated with preeclampsia

Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. Mechanisms leading to preeclampsia are unclear; however, there is evidence of high heritability. We evaluated the association of polygenic scores (PGS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Non-Hispanic Black and White reproductive age females with pregnancy indications and genotypes were obtained from Vanderbilt University's BioVU, Electronic Medical Records and Genomics network, and Penn Medicine Biobank. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PGS were acquired from Giri et al. Associations between preeclampsia and each PGS were evaluated separately by race and data source before subsequent meta-analysis. Ten-fold cross validation was used for prediction modeling. In 3504 Black and 5009 White included individuals, the rate of preeclampsia was 15.49%. In cross-ancestry meta-analysis, all PGSs were associated with preeclampsia (ORDBP = 1.10, 95% CI 1.02-1.17, p = 7.68 × 10-3; ORSBP = 1.16, 95% CI 1.09-1.23, p = 2.23 × 10-6; ORPP = 1.14, 95% CI 1.07-1.27, p = 9.86 × 10-5). Addition of PGSs to clinical prediction models did not improve predictive performance. Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.

Patterns of Gestational Hypertension or Preeclampsia Across 2 Pregnancies in Relationship to Chronic Hypertension Development: A Retrospective Cohort Study

BACKGROUND

Gestational hypertension (GHTN) and preeclampsia are established risk indicators for chronic hypertension. While recurrence is associated with a greater risk, it is unclear whether there are differences in risk when these gestational complications occur for the first time in an earlier pregnancy versus first occurrence in a subsequent one. We hypothesized that the absence of recurrence reflects a transition toward a lower hypertension risk trajectory, whereas a new occurrence in a later pregnancy indicates a transition toward elevated risk.

METHODS AND RESULTS

We analyzed linked data in Quebec, Canada, from public health care insurance administrative databases and birth, stillbirth, and death registries. Our retrospective cohort study included mothers with 2 singleton deliveries between April 1990 and December 2012. The primary exposure was patterns of GHTN or preeclampsia across 2 pregnancies (GHTN/preeclampsia in neither, first only, second only, or both). The outcome was incident chronic hypertension. We performed an adjusted multivariable Cox regression analysis. Among 431 980 women with 2 singleton pregnancies, 27 755 developed hypertension during the follow-up period. Compared with those without GHTN/preeclampsia, those with GHTN/preeclampsia only in the first pregnancy had a 2.7-fold increase in hazards (95% CI, 2.6-2.8), those with GHTN/preeclampsia only in the second had a 4.9-fold increase (95% CI, 4.6-5.1), and those with GHTN/preeclampsia in both pregnancies experienced a 7.3-fold increase (95% CI, 6.9-7.6). Patterns and estimates were similar when we considered GHTN and preeclampsia separately.

CONCLUSIONS

The magnitude of hypertension risk is associated with the number and sequence of GHTN/preeclampsia-affected pregnancies. Considering both allows more personalized risk estimates.

Editorial: Precision Medicine in Obstetrics: Pregnancy Complication

Millions of women give birth every year worldwide [...].