Increased Risk of Preeclampsia in Women With a Genetic Predisposition to Elevated Blood Pressure

Urinary protein and coagulation-fibrinolysis indicators in preeclampsia: Expression and significance

This study investigates the expression and significance of urinary protein and coagulation-fibrinolysis indicators in preeclampsia, categorized into mild preeclampsia (109 cases) and severe preeclampsia (97 cases) based on disease severity. Additionally, 110 patients with gestational hypertension (gestational hypertension group) were included for comparative analysis. General information, laboratory indicators, urinary protein, and coagulation-fibrinolysis indicator levels were collected for each group. Significant differences were observed in blood pressure among groups (P < .05), while uric acid, serum creatinine, aspartate transaminase, alanine transaminase, and triglycerides showed no significant differences (P > .05). Total cholesterol, triglycerides, and low-density Lipoprotein levels in severe preeclampsia were higher than those in mild preeclampsia and gestational hypertension groups, whereas high-density lipoprotein, albumin, and platelet levels were lower in severe preeclampsia. No significant differences were observed in prothrombin time or D-dimer levels among groups (P > .05). Urinary protein, urinary protein quantification, activated partial thromboplastin time, thrombin time, and fibrinogen were identified as influencing factors for adverse maternal and infant outcomes in severe preeclampsia patients. The study concludes that urinary protein and coagulation-fibrinolysis indicators are elevated in preeclampsia, particularly in severe preeclampsia cases, suggesting their potential use as diagnostic influencing factors for severe preeclampsia.

Sex-specific genetic architecture of blood pressure

The genetic and genomic basis of sex differences in blood pressure (BP) traits remain unstudied at scale. Here, we conducted sex-stratified and combined-sex genome-wide association studies of BP traits using the UK Biobank resource, identifying 1,346 previously reported and 29 new BP trait-associated loci. Among associated loci, 412 were female-specific (Pfemale ≤ 5 × 10-8; Pmale > 5 × 10-8) and 142 were male-specific (Pmale ≤ 5 × 10-8; Pfemale > 5 × 10-8); these sex-specific loci were enriched for hormone-related transcription factors, in particular, estrogen receptor 1. Analyses of gene-by-sex interactions and sexually dimorphic effects identified four genomic regions, showing female-specific associations with diastolic BP or pulse pressure, including the chromosome 13q34-COL4A1/COL4A2 locus. Notably, female-specific pulse pressure-associated loci exhibited enriched acetylated histone H3 Lys27 modifications in arterial tissues and a female-specific association with fibromuscular dysplasia, a female-biased vascular disease; colocalization signals included Chr13q34: COL4A1/COL4A2, Chr9p21: CDKN2B-AS1 and Chr4q32.1: MAP9 regions. Sex-specific and sex-biased polygenic associations of BP traits were associated with multiple cardiovascular traits. These findings suggest potentially clinically significant and BP sex-specific pleiotropic effects on cardiovascular diseases.

Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies

BACKGROUND

Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed.

METHODS

We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk.

RESULTS

Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score.

CONCLUSIONS

Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

Pre-pregnancy body mass index combined with peripheral blood PLGF, DCN, LDH, and UA in a risk prediction model for pre-eclampsia

Objective

This study analyzes the levels of peripheral blood placental growth factor (PLGF), body mass index (BMI), decorin (DCN), lactate dehydrogenase (LDH), uric acid (UA), and clinical indicators of patients with preeclampsia (PE), and establishes a predictive risk model of PE, which can provide a reference for early and effective prediction of PE.

Methods

81 cases of pregnant women with PE who had regular prenatal checkups and delivered in Jinshan Branch of Shanghai Sixth People's Hospital from June 2020 to December 2022 were analyzed, and 92 pregnant women with normal pregnancies who had their antenatal checkups and delivered at the hospital during the same period were selected as the control group. Clinical data and peripheral blood levels of PLGF, DCN, LDH, and UA were recorded, and the two groups were subjected to univariate screening and multifactorial logistic regression analysis. Based on the screening results, the diagnostic efficacy of PE was evaluated using the receiver operating characteristic (ROC) curve. Risk prediction nomogram model was constructed using R language. The Bootstrap method (self-sampling method) was used to validate and produce calibration plots; the decision curve analysis (DCA) was used to assess the clinical benefit rate of the model.

Results

There were statistically significant differences in age, pre-pregnancy BMI, gestational weight gain, history of PE or family history, family history of hypertension, gestational diabetes mellitus, and history of renal disease between the two groups (P < 0.05). The results of multifactorial binary logistic stepwise regression revealed that peripheral blood levels of PLGF, DCN, LDH, UA, and pre-pregnancy BMI were independent influences on the occurrence of PE (P < 0.05). The area under the curve of PLGF, DCN, LDH, UA levels and pre-pregnancy BMI in the detection of PE was 0.952, with a sensitivity of 0.901 and a specificity of 0.913, which is better than a single clinical diagnostic indicator. The results of multifactor analysis were constructed as a nomogram model, and the mean absolute error of the calibration curve of the modeling set was 0.023, suggesting that the predictive probability of the model was generally compatible with the actual value. DCA showed the predictive model had a high net benefit in the range of 5% to 85%, suggesting that the model has clinical utility value.

Conclusion

The occurrence of PE is related to the peripheral blood levels of PLGF, DCN, LDH, UA and pre-pregnancy BMI, and the combination of these indexes has a better clinical diagnostic value than a single index. The nomogram model constructed by using the above indicators can be used for the prediction of PE and has high predictive efficacy.

Hypertensive Disorders in Pregnancy: Global Burden From 1990 to 2019, Current Research Hotspots and Emerging Trends

Hypertensive disorders in pregnancy (HDP) constitute a worldwide health problem for pregnant women and their infants. This study provided HDP burden over 1990 to 2019 by region and age distribution, and predicted changes in related values for the next 25 years. We then conducted an econometric analysis of the author distribution, collaborative networks, keyword burst clustering, and spatio-temporal analysis of HDP-related publications from 2012 to 2022 to access current scientific developments and hotspots. The number of pregnant women with HDP has been increasing over the past 30 years, with regional and age-stratified differences in the burden of disease. Additionally, projections suggest an increase of deaths due to maternal HDP among adolescents younger than 20 years. Current research is mostly centered on pre-eclampsia, with hot keywords including trophoblast, immune tolerance, frozen-thawed embryo transfer, aspirin, gestational diabetes association, and biomarkers. Researches on the pathological mechanism, classification, and subtypes of HDP need to be further advanced.

Blood pressure, arterial stiffness, and cardiovascular risk profiles in 8-12-year-old children following preeclampsia (FINNCARE-study)

OBJECTIVES

The aim was to study if children following preeclampsia (PE) develop alterations in blood pressure (BP) and arterial stiffness already early in life, and how this is associated with gestational, perinatal and child cardiovascular risk profiles.

METHODS

One hundred eighty-two PE (46 early-onset with diagnosis before 34 gestational weeks, and 136 late-onset) and 85 non-PE children were assessed 8-12 years from delivery. Office and 24-h ambulatory BP, body composition, anthropometrics, lipids, glucose, inflammatory markers, and tonometry-derived pulse wave velocity (PWV) and central BPs were assessed.

RESULTS

Office BP, central BPs, 24-h systolic BP (SBP) and pulse pressure (PP) were higher in PE compared with non-PE. Early-onset PE children had the highest SBP, SBP-loads, and PP. SBP nondipping during night-time was common among PE. The higher child 24-h mean SBP among PE was explained by maternal SBP at first antenatal visit and prematurity (birth weight or gestational weeks), but child 24-h mean PP remained related with PE and child adiposity after adjustments. Central and peripheral PWVs were elevated in late-onset PE subgroup only and attributed to child age and anthropometrics, child and maternal office SBP at follow-up, but relations with maternal antenatal SBPs and prematurity were not found. There were no differences in body anthropometrics, composition, or blood parameters.

CONCLUSIONS

PE children develop an adverse BP profile and arterial stiffness early in life. PE-related BP is related with maternal gestational BP and prematurity, whereas arterial stiffness is determined by child characteristics at follow-up. The alterations in BP are pronounced in early-onset PE.Clinical Trial Registration information: https://clinicaltrials.gov/ct2/show/NCT04676295ClinicalTrials.gov Identifier: NCT04676295.

Genome-wide polygenic risk scores for hypertensive disease during pregnancy can also predict the risk for long-term cardiovascular disease

BACKGROUND

Hypertensive disorders during pregnancy are associated with the risk of long-term cardiovascular disease after pregnancy, but it has not yet been determined whether genetic predisposition for hypertensive disorders during pregnancy can predict the risk for long-term cardiovascular disease.

OBJECTIVE

This study aimed to evaluate the risk for long-term atherosclerotic cardiovascular disease according to polygenic risk scores for hypertensive disorders during pregnancy.

STUDY DESIGN

Among UK Biobank participants, we included European-descent women (n=164,575) with at least 1 live birth. Participants were divided according to genetic risk categorized by polygenic risk scores for hypertensive disorders during pregnancy (low risk, score ≤25th percentile; medium risk, score 25th∼75th percentile; high risk, score >75th percentile), and were evaluated for incident atherosclerotic cardiovascular disease, defined as the new occurrence of one of the following: coronary artery disease, myocardial infarction, ischemic stroke, or peripheral artery disease.

RESULTS

Among the study population, 2427 (1.5%) had a history of hypertensive disorders during pregnancy, and 8942 (5.6%) developed incident atherosclerotic cardiovascular disease after enrollment. Women with high genetic risk for hypertensive disorders during pregnancy had a higher prevalence of hypertension at enrollment. After enrollment, women with high genetic risk for hypertensive disorders during pregnancy had an increased risk for incident atherosclerotic cardiovascular disease, including coronary artery disease, myocardial infarction, and peripheral artery disease, compared with those with low genetic risk, even after adjustment for history of hypertensive disorders during pregnancy.

CONCLUSION

High genetic risk for hypertensive disorders during pregnancy was associated with increased risk for atherosclerotic cardiovascular disease. This study provides evidence on the informative value of polygenic risk scores for hypertensive disorders during pregnancy in prediction of long-term cardiovascular outcomes later in life.

Polygenic prediction of preeclampsia and gestational hypertension

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR-CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.

Associations of polygenic risk scores for preeclampsia and blood pressure with hypertensive disorders of pregnancy

BACKGROUND

Preexisting hypertension increases risk for preeclampsia. We examined whether a generic blood pressure polygenic risk score (BP-PRS), compared with a preeclampsia-specific polygenic risk score (PE-PRS), could better predict hypertensive disorders of pregnancy.

METHODS

Our study sample included 141 298 genotyped FinnGen study participants with at least one childbirth and followed from 1969 to 2021. We calculated PRSs for SBP and preeclampsia using summary statistics for greater than 1.1 million single nucleotide polymorphisms.

RESULTS

We observed 8488 cases of gestational hypertension (GHT) and 6643 cases of preeclampsia. BP-PRS was associated with GHT [multivariable-adjusted hazard ratio for 1SD increase in PRS (hazard ratio 1.38; 95% CI 1.35-1.41)] and preeclampsia (1.26, 1.23-1.29), respectively. The PE-PRS was also associated with GHT (1.16; 1.14-1.19) and preeclampsia (1.21, 1.18-1.24), but with statistically more modest magnitudes of effect (P = 0.01). The model c-statistic for preeclampsia improved when PE-PRS was added to clinical risk factors (P = 4.6 × 10-15). Additional increment in the c-statistic was observed when BP-PRS was added to a model already including both clinical risk factors and PE-PRS (P = 1.1 × 10-14).

CONCLUSION

BP-PRS is strongly associated with hypertensive disorders of pregnancy. Our current observations suggest that the BP-PRS could capture the genetic architecture of preeclampsia better than the current PE-PRSs. These findings also emphasize the common pathways in the development of all BP disorders. The clinical utility of a BP-PRS for preeclampsia prediction warrants further investigation.

Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores

Background

Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20 weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed.

Methods

We identified a cohort of N=1,125 pregnant individuals who delivered between 05/2015-05/2022 at Mass General Brigham hospitals with available electronic health record (EHR) data and linked genetic data. Using clinical EHR data and systolic blood pressure polygenic risk scores (SBP PRS) derived from a large genome-wide association study, we developed machine learning (xgboost) and linear regression models to predict preeclampsia risk.

Results

Pregnant individuals with an SBP PRS in the top quartile had higher blood pressures throughout pregnancy compared to patients within the lowest quartile SBP PRS. In the first trimester, the most predictive model was xgboost, with an area under the curve (AUC) of 0.73. Adding the SBP PRS to the models improved the performance only of the linear regression model from AUC 0.70 to 0.71; the predictive power of other models remained unchanged. In late pregnancy, with data obtained up to the delivery admission, the best performing model was xgboost using clinical variables, which achieved an AUC of 0.91.

Conclusions

Integrating clinical and genetic factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented in clinical practice to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

Cross-Generational Impact of Innate Immune Memory Following Pregnancy Complications

Pregnancy complications can have long-term negative effects on the health of the affected mothers and their children. In this review, we highlight the underlying inflammatory etiologies of common pregnancy complications and discuss how aberrant inflammation may lead to the acquisition of innate immune memory. The latter can be described as a functional epigenetic reprogramming of innate immune cells following an initial exposure to an inflammatory stimulus, ultimately resulting in an altered response following re-exposure to a similar inflammatory stimulus. We propose that aberrant maternal inflammation associated with complications of pregnancy increases the cross-generational risk of developing noncommunicable diseases (i.e., pregnancy complications, cardiovascular disease, and metabolic disease) through a process mediated by innate immune memory. Elucidating a role for innate immune memory in the cross-generational health consequences of pregnancy complications may lead to the development of novel strategies aimed at reducing the long-term risk of disease.