Clustering Longitudinal Blood Pressure Trajectories to Examine Heterogeneity in Outcomes Among Preeclampsia Cases and Controls

Causal relationship between iron status and preeclampsia-eclampsia: a Mendelian randomization analysis

BACKGROUND

Preeclampsia/eclampsia is a severe pregnancy-related disorder associated with hypertension and organ damage. While observational studies have suggested a link between maternal iron status and preeclampsia/eclampsia, the causal relationship remains unclear. The aim of this study was to investigate the genetic causality between iron status and preeclampsia/eclampsia using large-scale genome-wide association study (GWAS) summary data and Mendelian randomization (MR) analysis.

METHODS

Summary data for the GWAS on preeclampsia/eclampsia and genetic markers related to iron status were obtained from the FinnGen Consortium and the IEU genetic databases. The "TwoSampleMR" software package in R was employed to test the genetic causality between these markers and preeclampsia/eclampsia. The inverse variance weighted (IVW) method was primarily used for MR analysis. Heterogeneity, horizontal pleiotropy, and potential outliers were evaluated for the MR analysis results.

RESULTS

The random-effects IVW results showed that ferritin (OR = 1.11, 95% CI: .89-1.38, p = .341), serum iron (OR = .90, 95% CI: .75-1.09, p = .275), TIBC (OR = .98, 95% CI: .89-1.07, p = .613), and TSAT (OR = .94, 95% CI: .83-1.07, p = .354) have no genetic causal relationship with preeclampsia/eclampsia. There was no evidence of heterogeneity, horizontal pleiotropy, or possible outliers in our MR analysis (p > .05).

CONCLUSIONS

Our study did not detect a genetic causal relationship between iron status and preeclampsia/eclampsia. Nonetheless, this does not rule out a relationship between the two at other mechanistic levels.

Physiologic blood pressure patterns in pregnancies with mild chronic hypertension

OBJECTIVES

To assess physiologic blood pressure (BP) changes throughout pregnancy in patients with mild chronic hypertension (CHTN) who do and do not develop preeclampsia (PEC), compared to patients with normal BP.

STUDY DESIGN

Retrospective cohort of singleton gestations with CHTN at a single tertiary center from 2000 to 2014 and a randomly selected cohort of patients without CHTN and normal pregnancy outcomes (NML) in the same time period with BP measurements available <12 weeks gestational age.

MAIN OUTCOME MEASURES

The primary outcome was gestational age (GA) at nadir of systolic and diastolic BP. Secondary outcomes included perinatal death, umbilical cord pH, maternal and neonatal length of stay, GA at delivery, and mode of delivery. Quadratic mixed models were used to estimate SBP and DBP throughout gestation.

RESULTS

Of 367 pregnancies with CHTN, 268 (73%) had CHTN without PEC and 99 (27%) had CHTN with PEC; 198 NML pregnancies were used as a comparison group. The median GA nadir for patients in the NML, CHTN without PEC, and CHTN with PEC for SBP were 20, 24, and 21, respectively. For DBP, the median GA nadir were 22, 24, and 21 for patients in the NML, CHTN without PEC, and CHTN with PEC cohorts, respectively. Adverse secondary outcomes were more frequent in patients with CHTN who developed PEC.

CONCLUSIONS

BP trajectories in pregnancy are different between patients with CHTN with PEC, CHTN without PEC, and patients with normal BP. These findings may be useful in assessing patients' risks for developing preeclampsia during pregnancy.

Notch Signaling: An Emerging Paradigm in the Pathogenesis of Reproductive Disorders and Diverse Pathological Conditions

The highly conserved Notch pathway, a pillar of juxtacrine signaling, orchestrates intricate intercellular communication, governing diverse developmental and homeostatic processes through a tightly regulated cascade of proteolytic cleavages. This pathway, culminating in the migration of the Notch intracellular domain (NICD) to the nucleus and the subsequent activation of downstream target genes, exerts a profound influence on a plethora of molecular processes, including cell cycle progression, lineage specification, cell-cell adhesion, and fate determination. Accumulating evidence underscores the pivotal role of Notch dysregulation, encompassing both gain and loss-of-function mutations, in the pathogenesis of numerous human diseases. This review delves deep into the multifaceted roles of Notch signaling in cellular dynamics, encompassing proliferation, differentiation, polarity maintenance, epithelial-mesenchymal transition (EMT), tissue regeneration/remodeling, and its intricate interplay with other signaling pathways. We then focus on the emerging landscape of Notch aberrations in gynecological pathologies predisposing individuals to infertility. By highlighting the exquisite conservation of Notch signaling in Drosophila and its power as a model organism, we pave the way for further dissection of disease mechanisms and potential therapeutic interventions through targeted modulation of this master regulatory pathway.

Temporal patterns of pre- and post-natal target organ damage associated with hypertensive pregnancy: a systematic review

AIMS

Hypertensive pregnancy is associated with increased risks of developing a range of vascular disorders in later life. Understanding when hypertensive target organ damage first emerges could guide optimal timing of preventive interventions. This review identifies evidence of hypertensive target organ damage across cardiac, vascular, cerebral, and renal systems at different time points from pregnancy to postpartum.

METHODS AND RESULTS

Systematic review of Ovid/MEDLINE, EMBASE, and ClinicalTrials.gov up to and including February 2023 including review of reference lists. Identified articles underwent evaluation via a synthesis without meta-analysis using a vote-counting approach based on direction of effect, regardless of statistical significance. Risk of bias was assessed for each outcome domain, and only higher quality studies were used for final analysis. From 7644 articles, 76 studies, including data from 1 742 698 pregnancies, were identified of high quality that reported either blood pressure trajectories or target organ damage during or after a hypertensive pregnancy. Left ventricular hypertrophy, white matter lesions, proteinuria, and retinal microvasculature changes were first evident in women during a hypertensive pregnancy. Cardiac, cerebral, and retinal changes were also reported in studies performed during the early and late post-partum period despite reduction in blood pressure early postpartum. Cognitive dysfunction was first reported late postpartum.

CONCLUSION

The majority of target organ damage reported during a hypertensive pregnancy remains evident throughout the early and late post-partum period despite variation in blood pressure. Early peri-partum strategies may be required to prevent or reverse target organ damage in women who have had a hypertensive pregnancy.

An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine Learning

Background

Preeclampsia is a pregnancy-specific disease characterized by new onset hypertension after 20 weeks of gestation that affects 2-8% of all pregnancies and contributes to up to 26% of maternal deaths. Despite extensive clinical research, current predictive tools fail to identify up to 66% of patients who will develop preeclampsia. We sought to develop a tool to longitudinally predict preeclampsia risk.

Methods

In this retrospective model development and validation study, we examined a large cohort of patients who delivered at six community and two tertiary care hospitals in the New England region between 02/2015 and 06/2023. We used sociodemographic, clinical diagnoses, family history, laboratory, and vital signs data. We developed eight datasets at 14, 20, 24, 28, 32, 36, 39 weeks gestation and at the hospital admission for delivery. We created linear regression, random forest, xgboost, and deep neural networks to develop multiple models and compared their performance. We used Shapley values to investigate the global and local explainability of the models and the relationships between the predictive variables.

Findings

Our study population (N=120,752) had an incidence of preeclampsia of 5.7% (N=6,920). The performance of the models as measured using the area under the curve, AUC, was in the range 0.73-0.91, which was externally validated. The relationships between some of the variables were complex and non-linear; in addition, the relative significance of the predictors varied over the pregnancy. Compared to the current standard of care for preeclampsia risk stratification in the first trimester, our model would allow 48.6% more at-risk patients to be identified.

Interpretation

Our novel preeclampsia prediction tool would allow clinicians to identify patients at risk early and provide personalized predictions, as well as longitudinal predictions throughout pregnancy.

Funding

National Institutes of Health, Anesthesia Patient Safety Foundation.

RESEARCH IN CONTEXT

Evidence before this study: Current tools for the prediction of preeclampsia are lacking as they fail to identify up to 66% of the patients who develop preeclampsia. We searched PubMed, MEDLINE, and the Web of Science from database inception to May 1, 2023, using the keywords "deep learning", "machine learning", "preeclampsia", "artificial intelligence", "pregnancy complications", and "predictive models". We identified 13 studies that employed machine learning to develop prediction models for preeclampsia risk based on clinical variables. Among these studies, six included biomarkers such as serum placental growth factor, pregnancy-associated plasma protein A, and uterine artery pulsatility index, which are not routinely available in our clinical practice; two studies were in diverse cohorts of more than 100 000 patients, and two studies developed longitudinal predictions using medical records data. However, most studies have limited depth, concerns about data leakage, overfitting, or lack of generalizability.Added value of this study: We developed a comprehensive longitudinal predictive tool based on routine clinical data that can be used throughout pregnancy to predict the risk of preeclampsia. We tested multiple types of predictive models, including machine learning and deep learning models, and demonstrated high predictive power. We investigated the changes over different time points of individual and group variables and found previously known and novel relationships between variables such as red blood cell count and preeclampsia risk.Implications of all the available evidence: Longitudinal prediction of preeclampsia using machine learning can be achieved with high performance. Implementation of an accurate predictive tool within the electronic health records can aid clinical care and identify patients at heightened risk who would benefit from aspirin prophylaxis, increased surveillance, early diagnosis, and escalation in care. These results highlight the potential of using artificial intelligence in clinical decision support, with the ultimate goal of reducing iatrogenic preterm birth and improving perinatal care.

Effects of Nifedipine and Labetalol Combined with Magnesium Sulfate on Blood Pressure Control, Blood Coagulation Function, and Maternal and Infant Outcome in Patients with Pregnancy-Induced Hypertension

Objective

The purpose is to investigate the influence of nifedipine, labetalol, and magnesium sulfate on blood pressure control, blood coagulation, and maternal and infant outcome in those suffering from pregnancy-induced hypertension (PIH).

Methods

From January 2019 to April 2021, 100 participants with PIH in our center were randomly assigned to a control group and a research group. As a control, nifedipine combined with magnesium sulfate was administered. Nifedipine, labetalol, and magnesium sulfate were administered to the research group. The curative effect, blood pressure level, blood coagulation function, vascular endothelial function, and pregnancy comparisons were made between the two groups.

Results

Based on the results of the study, the effective rate totaled 92.00%, while as for the control group, it was 80.0%, which indicates that there was a statistically significant difference between the effective rates of the research group and that of the control group, and the difference was statistically significant (P < 0.05). Blood pressure and blood coagulation function did not differ significantly between the two groups before treatment, and the difference was not statistically significant (P > 0.05). After treatment, both groups experienced a significant drop in systolic and diastolic blood pressure. After treatment, a higher PT index was found in the research group than in the control group. Likewise, the Fbg, D-D, and PLT were lower compared to those in the control group, and the difference was statistically significant (P < 0.05). Neither group had significantly different vascular endothelial function before treatment, and the difference was not statistically significant (P > 0.05). After treatment, the ET-1 of the two groups decreased, and the level of NO increased. There was a lower ET-1 in the research group than in the control group as well as a higher NO level in the research group than in the control group, and the difference was statistically significant (P < 0.05). Compared with the pregnancy outcome, in comparison to the controls, the research group had a higher vaginal delivery rate. Significantly, fewer cases of fetal distress, intrauterine asphyxia, and placental abruption were reported in the research group than in the control group, and the difference was statistically significant (P < 0.05).

Conclusion

Nifedipine, in combination with magnesium sulfate and labetalol, is effective at treating PIH, reducing blood pressure, improving blood coagulation, preventing cardiovascular events and vascular endothelial function, and further improve the pregnancy outcome.

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

Phenotype‐Directed Management of Hypertension in Pregnancy

Hypertensive disorders of pregnancy are among the most serious conditions that pregnancy care providers face; however, little attention has been paid to the concept of tailoring clinical care to reduce associated adverse maternal and perinatal outcomes based on the underlying disease pathogenesis. This narrative review discusses the integration of phenotype‐based clinical strategies in the management of high‐risk pregnant patients that are currently not common clinical practice: real‐time placental growth factor testing at Mount Sinai Hospital, Toronto and noninvasive hemodynamic monitoring to guide antihypertensive therapy at the University of Washington Medical Center, Seattle. Future work should focus on promoting more widespread integration of these novel strategies into obstetric care to improve outcomes of pregnancies at high risk of adverse maternal‐fetal outcomes from these complications of pregnancy.