Advancing human health in the decade ahead: pregnancy as a key window for discovery: A Burroughs Wellcome Fund Pregnancy Think Tank

Adiponectin and Glucocorticoids Modulate Risk for Preterm Birth: The Healthy Start Study

CONTEXT

Adiponectin is a potent uterine tocolytic that decreases with gestational age, suggesting it could be a maternal metabolic quiescence factor. Maternal stress can influence preterm birth risk, and adiponectin levels may be stress responsive.

OBJECTIVE

We characterized associations between adiponectin and glucocorticoids with preterm birth and modeled their predictive utility. We hypothesized maternal plasma adiponectin and cortisol are inversely related and lower adiponectin and higher cortisol associate with preterm birth.

METHODS

We performed a nested case-control study using biobanked fasting maternal plasma. We included low-risk singleton pregnancies, and matched 1:3 (16 preterm, 46 term). We quantified high molecular weight (HMW), low molecular weight (LMW), and total adiponectin using an enzyme-linked immunosorbent assay. We validated a high-performance liquid chromatography-tandem mass spectrometry serum assay for use in plasma, to simultaneously measure cortisol, cortisone, and 5 related steroid hormones. We used linear/logistic regression to compare group means and machine learning for predictive modeling.

RESULTS

The preterm group had lower mean LMW adiponectin (3.07 μg/mL vs 3.81 μg/mL at 15 weeks (w) 0 days (d), P = .045) and higher mean cortisone (34.4 ng/mL vs 29.0 ng/mL at 15w0d, P = .031). The preterm group had lower cortisol to cortisone and lower LMW adiponectin to cortisol ratios. We found HMW adiponectin, cortisol to cortisone ratio, cortisone, maternal height, age, and prepregnancy body mass index most strongly predicted preterm birth (area under the receiver operator curve = 0.8167). In secondary analyses, we assessed biomarker associations with maternal self-reported psychosocial stress. Lower perceived stress was associated with a steeper change in cortisone in the term group.

CONCLUSION

Overall, metabolic and stress biomarkers are associated with preterm birth in this healthy cohort. We identify a possible mechanistic link between maternal stress and metabolism for pregnancy maintenance.

Challenges and opportunities in perinatal public health: the utility of perinatal health inequality dashboards in addressing disparities in maternal and neonatal outcomes

INTRODUCTION

In clinical settings, digital dashboards display medical data, with the aim of identifying trends and signals. In so doing these contribute towards improving service delivery and care within hospitals. It is not clear whether the utility of perinatal health equity dashboards could be used to identify health inequality trends that could potentially impact on health service delivery, care and public health interventions. This study aims to evaluate the implementation of health inequality dashboards that address disparities in maternal and neonatal outcomes, with a specific focus on identifying key challenges encountered during their deployment and use in healthcare settings.

METHODS

Three databases, namely Embase, CINAHL, and Medline were searched to identify relevant studies in English Language published between 2010 and 2022. All findings were reported according to PRISMA guidelines for scoping reviews.

RESULTS

Of 670 identified articles, only 13 met the inclusion criteria. The study identified three key themes: dashboard functionality, data accuracy, and challenges in collecting health inequality data. Dashboards were used to visualize disparities, with functionalities focusing on specific audiences, contents, and utility. Issues with data completeness, standardization, and challenges in collecting consistent health inequality data, especially from diverse ethnic groups, hindered the accurate tracking of maternal and neonatal disparities.

CONCLUSION

The use of perinatal health inequality dashboards is a critical step forward in optimizing maternal and neonatal care by providing targeted interventions. However, further research is needed to assess their long-term impact on reducing health inequalities, while addressing challenges related to data accuracy, completeness, and standardization to improve their effectiveness.

Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China

BACKGROUND

Preterm birth (PTB) is a significant cause of neonatal mortality and long-term health issues. Accurate prediction and timely prevention of PTB are essential for reducing associated child mortality and morbidity. Traditional predictive methods face challenges due to heterogeneous risk factors and their interaction effects. This study aims to develop and evaluate six machine learning (ML) models to predict PTB using large-scale children survey data from Shenzhen, China, and to identify key predictors through Shapley Additive Explanations (SHAP) analysis.

METHODS

Data from 84,050 mother-child pairs, collected in 2021 and 2022, were processed and divided into training, validation, and test sets. Six ML models were tested: L1-Regularised Logistic Regression, Light Gradient Boosting Machine (LightGBM), Naive Bayes, Random Forests, Support Vector Machine, and Extreme Gradient Boosting (XGBoost). Model performance was evaluated based on discrimination, calibration and clinical utility. SHAP analysis was used to interpret the importance and impact of individual features on PTB prediction.

RESULTS

The XGBoost model demonstrated the best overall performance, with the area under the receiver operating characteristic curve (AUC) scores of 0.752 and 0.757 in the validation and test sets, respectively, along with favorable calibration and clinical utility. Key predictors identified were multiple pregnancies, threatened abortion, and maternal age of conception. SHAP analysis highlighted the positive impacts of multiple pregnancies and threatened abortion, as well as the negative impact of micronutrient supplementation on PTB.

CONCLUSION

Our study found that ML models, particularly XGBoost, show promise in accurately predicting PTB and identifying key risk factors. These findings provide the potential of ML for enhancing clinical interventions, personalizing prenatal care, and informing public health initiatives.

Into the Unknown: Navigating a Path as an Early-stage Physician-scientist in Obstetrics and Gynecology

This piece is a reflection of one early-stage physician-scientist's professional journey. It highlights a few challenges of navigating this path while calling for continued investment and support for physician-scientists to enhance maternal and child lifelong health.

Recent Advances in Genomic Studies of Gestational Duration and Preterm Birth

Preterm birth (PTB) is the leading cause of infant mortality and morbidity. For several decades, extensive epidemiologic and genetic studies have highlighted the significant contribution of maternal and offspring genetic factors to PTB. This review discusses the challenges inherent in conventional genomic analyses of PTB and underscores the importance of adopting nonconventional approaches, such as analyzing the mother-child pair as a single analytical unit, to disentangle the intertwined maternal and fetal genetic influences. We elaborate on studies investigating PTB phenotypes through 3 levels of genetic analyses: single-variant, multi-variant, and genome-wide variants.

Prenatal environmental exposures associated with sex differences in childhood obesity and neurodevelopment

BACKGROUND

Obesity and neurodevelopmental delay are complex traits that often co-occur and differ between boys and girls. Prenatal exposures are believed to influence children's obesity, but it is unknown whether exposures of pregnant mothers can confer a different risk of obesity between sexes, and whether they can affect neurodevelopment.

METHODS

We analyzed data from 1044 children from the HELIX project, comprising 93 exposures during pregnancy, and clinical, neuropsychological, and methylation data during childhood (5-11 years). Using exposome-wide interaction analyses, we identified prenatal exposures with the highest sexual dimorphism in obesity risk, which were used to create a multiexposure profile. We applied causal random forest to classify individuals into two environments: E1 and E0. E1 consists of a combination of exposure levels where girls have significantly less risk of obesity than boys, as compared to E0, which consists of the remaining combination of exposure levels. We investigated whether the association between sex and neurodevelopmental delay also differed between E0 and E1. We used methylation data to perform an epigenome-wide association study between the environments to see the effect of belonging to E1 or E0 at the molecular level.

RESULTS

We observed that E1 was defined by the combination of low dairy consumption, non-smokers' cotinine levels in blood, low facility richness, and the presence of green spaces during pregnancy (OR_interaction = 0.070, P = 2.59 × 10^-5). E1 was also associated with a lower risk of neurodevelopmental delay in girls, based on neuropsychological tests of non-verbal intelligence (OR_interaction = 0.42, P = 0.047) and working memory (OR_interaction = 0.31, P = 0.02). In line with this, several neurodevelopmental functions were enriched in significant differentially methylated probes between E1 and E0.

CONCLUSIONS

The risk of obesity can be different for boys and girls in certain prenatal environments. We identified an environment combining four exposure levels that protect girls from obesity and neurodevelopment delay. The combination of single exposures into multiexposure profiles using causal inference can help determine populations at risk.

Cell-free DNA in maternal blood and artificial intelligence: accurate prenatal detection of fetal congenital heart defects

BACKGROUND

DNA cytosine nucleotide methylation (epigenomics and epigenetics) is an important mechanism for controlling gene expression in cardiac development. Combined artificial intelligence and whole-genome epigenomic analysis of circulating cell-free DNA in maternal blood has the potential for the detection of fetal congenital heart defects.

OBJECTIVE

This study aimed to use genome-wide DNA cytosine methylation and artificial intelligence analyses of circulating cell-free DNA for the minimally invasive detection of fetal congenital heart defects.

STUDY DESIGN

In this prospective study, whole-genome cytosine nucleotide methylation analysis was performed on circulating cell-free DNA using the Illumina Infinium MethylationEPIC BeadChip array. Multiple artificial intelligence approaches were evaluated for the detection of congenital hearts. The Ingenuity Pathway Analysis program was used to identify gene pathways that were epigenetically altered and important in congenital heart defect pathogenesis to further elucidate the pathogenesis of isolated congenital heart defects.

RESULTS

There were 12 cases of isolated nonsyndromic congenital heart defects and 26 matched controls. A total of 5918 cytosine nucleotides involving 4976 genes had significantly altered methylation, that is, a P value of <.05 along with ≥5% whole-genome cytosine nucleotide methylation difference, in congenital heart defect cases vs controls. Artificial intelligence analysis of the methylation data achieved excellent congenital heart defect predictive accuracy (areas under the receiver operating characteristic curve, ≥0.92). For example, an artificial intelligence model using a combination of 5 whole-genome cytosine nucleotide markers achieved an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.87-1.0) with 98% sensitivity and 94% specificity. We found epigenetic changes in genes and gene pathways involved in the following important cardiac developmental processes: "cardiovascular system development and function," "cardiac hypertrophy," "congenital heart anomaly," and "cardiovascular disease." This lends biologic plausibility to our findings.

CONCLUSION

This study reported the feasibility of minimally invasive detection of fetal congenital heart defect using artificial intelligence and DNA methylation analysis of circulating cell-free DNA for the prediction of fetal congenital heart defect. Furthermore, the findings supported an important role of epigenetic changes in congenital heart defect development.

A Nested Case-Control Study of Allopregnanolone and Preterm Birth in the Healthy Start Cohort

Context

Chronic stress is a risk factor for preterm birth; however, objective measures of stress in pregnancy are limited. Maternal stress biomarkers may fill this gap. Steroid hormones and neurosteroids such as allopregnanolone (ALLO) play important roles in stress physiology and pregnancy maintenance and therefore may be promising for preterm birth prediction.

Objective

We evaluated maternal serum ALLO, progesterone, cortisol, cortisone, pregnanolone, and epipregnanolone twice in gestation to evaluate associations with preterm birth.

Methods

We performed a nested case-control study using biobanked fasting serum samples from the Healthy Start prebirth cohort. We included healthy women with a singleton pregnancy and matched preterm cases with term controls (1:1; N = 27 per group). We used a new HPLC-tandem mass spectrometry assay to quantify ALLO and five related steroids. We used ANOVA, Fisher exact, χ2, t test, and linear and logistic regression as statistical tests.

Results

Maternal serum ALLO did not associate with preterm birth nor differ between groups. Mean cortisol levels were significantly higher in the preterm group early in pregnancy (13w0d-18w0d; P < 0.05) and higher early pregnancy cortisol associated with increased odds of preterm birth (at 13w0d; odds ratio, 1.007; 95% CI, 1.0002-1.014). Progesterone, cortisone, pregnanolone, and epipregnanolone did not associate with preterm birth.

Conclusion

The findings from our pilot study suggest potential utility of cortisol as a maternal serum biomarker for preterm birth risk assessment in early pregnancy. Further evaluation using larger cohorts and additional gestational timepoints for ALLO and the other analytes may be informative.

Maternal factors during pregnancy influencing maternal, fetal, and childhood outcomes

Enhancing pregnancy health is known to improve the mother's and offspring's life-long well-being. The maternal environment, encompassing genetic factors, impacts of social determinants, the nutritional/metabolic milieu, and infections and inflammation, have immediate consequences for the in utero development of the fetus and long-term programming into childhood and adulthood. Moreover, adverse pregnancy outcomes such as preterm birth or preeclampsia, often attributed to the maternal environmental factors listed above, have been associated with poor maternal cardiometabolic health after pregnancy. In this BMC Medicine article collection, we explore a broad spectrum of maternal characteristics across pregnancy and postnatal phenotypes, anticipating substantial cross-fertilization of new understanding and shared mechanisms around diverse outcomes. Advances in the ability to leverage 'omics across different platforms (genome, transcriptome, proteome, metabolome, microbiome, lipidome), large high-dimensional population databases, and unique cohorts are generating exciting new insights: The first articles in this collection highlight the role of placental biomarkers of preterm birth, metabolic influences on fetal and childhood growth, and the impact of common pre-existing maternal disorders, obesity and smoking on pregnancy outcomes, and the child's health. As the collection grows, we look forward to seeing the connections emerge across maternal, fetal, and childhood outcomes that will foster new insights and preventative strategies for women.

Racism and perinatal health inequities research: where we have been and where we should go

For more than a century, substantial racial and ethnic inequities in perinatal health outcomes have persisted despite technical clinical advances and changes in public health practice that lowered the overall incidence of morbidity. Race is a social construct and not an inherent biologic or genetic reality; therefore, racial differences in health outcomes represent the consequences of structural racism or the inequitable distribution of opportunities for health along racialized lines. Clinicians and scientists in obstetrics and gynecology have a responsibility to work to eliminate health inequities for Black, Brown, and Indigenous birthing people, and fulfilling this responsibility requires actionable evidence from high-quality research. To generate this actionable evidence, the research community must realign paradigms, praxis, and infrastructure with an eye directed toward reproductive justice and antiracism. This special report offers a set of key recommendations as a roadmap to transform perinatal health research to achieve health equity. The recommendations are based on expert opinion and evidence presented at the State of the Science Research Symposium at the 41st Annual Pregnancy Meeting of the Society for Maternal-Fetal Medicine in 2021. Recommendations fall into 3 broad categories-changing research paradigms, reforming research praxis, and transforming research infrastructure-and are grounded in a historic foundation of the advances and shortcomings of clinical, public health, and sociologic scholarship in health equity. Changing the research paradigm requires leveraging a multidisciplinary perspective on structural racism; promoting mechanistic research that identifies the biologic pathways perturbed by structural racism; and utilizing conceptual models that account for racism as a factor in adverse perinatal outcomes. Changing praxis approaches to promote and engage multidisciplinary teams and to develop standardized guidelines for data collection will ensure that paradigm shifts center the historically marginalized voices of Black, Brown, and Indigenous birthing people. Finally, infrastructure changes that embed community-centered approaches are required to make shifts in paradigm and praxis possible. Institutional policies that break down silos and support true community partnership, and also the alignment of institutional, funding, and academic publishing objectives with strategic priorities for perinatal health equity, are paramount. Achieving health equity requires shifting the structures that support the ecosystem of racism that Black, Brown, and Indigenous birthing people must navigate before, during, and after childbearing. These structures extend beyond the healthcare system in which clinicians operate day-to-day, but they cannot be excluded from research endeavors to create the actionable evidence needed to achieve perinatal health equity.

Longitudinal Plasma Metabolomics Profile in Pregnancy-A Study in an Ethnically Diverse U.S. Pregnancy Cohort

Amino acids, fatty acids, and acylcarnitine metabolites play a pivotal role in maternal and fetal health, but profiles of these metabolites over pregnancy are not completely established. We described longitudinal trajectories of targeted amino acids, fatty acids, and acylcarnitines in pregnancy. We quantified 102 metabolites and combinations (37 fatty acids, 37 amino acids, and 28 acylcarnitines) in plasma samples from pregnant women in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies—Singletons cohort (n = 214 women at 10–14 and 15–26 weeks, 107 at 26–31 weeks, and 103 at 33–39 weeks). We used linear mixed models to estimate metabolite trajectories and examined variation by body mass index (BMI), race/ethnicity, and fetal sex. After excluding largely undetected metabolites, we analyzed 77 metabolites and combinations. Levels of 13 of 15 acylcarnitines, 7 of 25 amino acids, and 18 of 37 fatty acids significantly declined over gestation, while 8 of 25 amino acids and 10 of 37 fatty acids significantly increased. Several trajectories appeared to differ by BMI, race/ethnicity, and fetal sex although no tests for interactions remained significant after multiple testing correction. Future studies merit longitudinal measurements to capture metabolite changes in pregnancy, and larger samples to examine modifying effects of maternal and fetal characteristics.

Development and validation of an LC-MS/MS assay for the quantification of allopregnanolone and its progesterone-derived isomers, precursors, and cortisol/cortisone in pregnancy

Neuroactive steroids are potent neuromodulators that play a critical role in both maternal and fetal health during pregnancy. These stress-responsive compounds are reportedly low in women with perinatal depression and may be associated with poor pregnancy outcomes in animal models. Chronic stress is a risk factor for adverse birth outcomes. Simultaneous quantification of neuroactive steroids, in combination with stress hormones cortisol/cortisone, provides an opportunity to investigate the synergistic relationship of these analytes within the convenience of one assay. A simple, reliable, and sensitive method for quantifying these endogenous compounds is necessary for further research with the potential to advance clinical diagnostic tools during pregnancy. Analytes were extracted from serum with a simple protein precipitation using methanol and then separated and quantified using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). After online extraction, analytes were separated using an Agilent Poroschell 120, 50 × 4.6 mm, 2.7 μm particle size, EC-C18 analytical column. The reliable quantification range was from 0.78 to 1000 ng/mL. QC sample inter- and intraday trueness was between 90 and 110% while inter- and intraday imprecision was less than 10%. Extracted samples were stable up to 7 days at 4 °C and extraction recovery was above 95%. Serum samples from 54 women in pregnancy were analyzed using this method. Here, we provide a validated, fast, and specific assay with sufficient sensitivity that allows for simultaneous quantification of blood serum concentrations of allopregnanolone (3α-hydroxy-5α-pregnan-20-one), pregnanolone (3α-hydroxy-5β-pregnan-20-one), epipregnanolone (3β-hydroxy-5β-pregnan-20-one), pregnenolone, progesterone, cortisol, and cortisone in pregnancy for clinical study samples and clinical diagnostics.

Women and babies are dying from inertia: a collaborative framework for obstetrical drug development is urgently needed

Obstetrical complications, often referred to as the "great obstetrical syndromes," are among the most common global causes of mortality and morbidity in young women and their infants. However, treatments for these syndromes are underdeveloped compared with other fields of medicine and are urgently needed. This current paucity of treatments for obstetrical complications is a reflection of the challenges of drug development in pregnancy. The appetite of pharmaceutical companies to invest in research for obstetrical syndromes is generally reduced by concerns for maternal, fetal, and infant safety, poor definition, and high-risk regulatory paths toward product approval. Notably, drug candidates require large investments for development with an unguaranteed return on investment. Furthermore, the discovery of promising drug candidates is hampered by a poor understanding of the pathophysiology of obstetrical syndromes and their uniqueness to human pregnancies. This limits translational extrapolation and de-risking strategies in preclinical studies, as available for other medical areas, compounded with limited fetal safety monitoring to capture early prenatal adverse reactions. In addition, the ethical review committees are reluctant to approve the inclusion of pregnant women in trials, and in the absence of regulatory guidance in obstetrics, clinical development programs are subject to unpredictable regulatory paths. To develop effective and safe drugs for pregnancy complications, substantial commitment, and investment in research for innovative therapies are needed in parallel with the creation of an enabling ethical, legislative, and guidance framework. Solutions are proposed to enable stakeholders to work with a common set of expectations to facilitate progress in this medical discipline. Addressing this significant unmet need to advance maternal and possibly perinatal health requires the involvement of all stakeholders and specifically patients, couples, and clinicians facing pregnancy complications in the dearth of appropriate therapies. This paper focused on the key pharmaceutical research and development challenges to achieve effective and safe treatments for obstetrical syndromes.