Solving the Puzzle of Preterm Birth.

Solving the puzzle of preterm birth has been challenging and will require novel integrative solutions as preterm birth likely arises from many etiologies. It has been demonstrated that many sociodemographic and psychological determinants of preterm birth relate to its complex biology. It is this understanding that has enabled the development of a novel preventative strategy, which integrates the omics profile (genome, epigenome, transcriptome, proteome, metabolome, microbiome) with sociodemographic, environmental, and psychological determinants of individual pregnant people to solve the puzzle of preterm birth.

Predicting Preterm Birth Using Proteomics.

The complexity of preterm birth (PTB), both spontaneous and medically indicated, and its various etiologies and associated risk factors pose a significant challenge for developing tools to accurately predict risk. This review focuses on the discovery of proteomics signatures that might be useful for predicting spontaneous PTB or preeclampsia, which often results in PTB. We describe methods for proteomics analyses, proteomics biomarker candidates that have so far been identified, obstacles for discovering biomarkers that are sufficiently accurate for clinical use, and the derivation of composite signatures including clinical parameters to increase predictive power.

Identifying therapeutic candidates for endometriosis through a transcriptomics-based drug repositioning approach.

Existing medical treatments for endometriosis-related pain are often ineffective, underscoring the need for new therapeutic strategies. In this study, we applied a computational drug repurposing pipeline to stratified and unstratified disease signatures based on endometrial gene expression data to identify potential therapeutics from existing drugs, based on expression reversal. Of 3,131 unique genes differentially expressed by at least one of six endometriosis signatures, only 308 (9.8%) were in common; however, 221 out of 299 drugs identified, (73.9%) were shared. We selected fenoprofen, an uncommonly prescribed NSAID that was the top therapeutic candidate for further investigation. When testing fenoprofen in an established rat model of endometriosis, fenoprofen successfully alleviated endometriosis-associated vaginal hyperalgesia, a surrogate marker for endometriosis-related pain. These findings validate fenoprofen as a therapeutic that could be utilized more frequently for endometriosis and suggest the utility of the aforementioned computational drug repurposing approach for endometriosis.

Is it time for a precision health approach to the management of newborn hyperbilirubinemia?

Newborn hyperbilirubinemia during the first two weeks of life is one of most common problems requiring management decisions by a pediatrician. However, high bilirubin levels in the circulation have been associated with neurologic injury under a variety of conditions encountered in the newborn infant, such as hemolysis. The risk for developing dangerous hyperbilirubinemia is multifactorial and is determined by a complex set of factors related to a newborn infant's genetic capacities as well as intra- and extrauterine exposures. To this end, a precision health approach based on the integration of prenatal genetic and postnatal diagnostic measures might improve the management of neonatal hyperbilirubinemia.

Vascular health years after a hypertensive disorder of pregnancy: The EPOCH study.

BACKGROUND: Preeclampsia is associated with a two-fold increase in a woman's lifetime risk of developing atherosclerotic cardiovascular disease (ASCVD), but the reasons for this association are uncertain. The objective of this study was to examine the associations between vascular health and a hypertensive disorder of pregnancy among women ≥ 2 years postpartum. METHODS: Pre-menopausal women with a history of either a hypertensive disorder of pregnancy (cases: preeclampsia or gestational hypertension) or a normotensive pregnancy (controls) were enrolled. Participants were assessed for standard ASCVD risk factors and underwent vascular testing, including measurements of blood pressure, endothelial function, and carotid artery ultrasound. The primary outcomes were blood pressure, ASCVD risk, reactive hyperemia index measured by EndoPAT and carotid intima-medial thickness. The secondary outcomes were augmentation index normalized to 75 beats per minute and pulse wave amplitude measured by EndoPAT, and carotid elastic modulus and carotid beta-stiffness measured by carotid ultrasound. RESULTS: Participants had a mean age of 40.7 years and were 5.7 years since their last pregnancy. In bivariate analyses, cases (N = 68) were more likely than controls (N = 71) to have hypertension (18% vs 4%, P = .034), higher calculated ASCVD risk (0.6 vs 0.4, P = .02), higher blood pressures (systolic: 118.5 vs 111.6 mm Hg, P = .0004; diastolic: 75.2 vs 69.8 mm Hg, P = .0004), and higher augmentation index values (7.7 vs 2.3, P = .03). They did not, however, differ significantly in carotid intima-media thickness (0.5 vs 0.5, P = .29) or reactive hyperemia index (2.1 vs 2.1, P = .93), nor in pulse wave amplitude (416 vs 326, P = .11), carotid elastic modulus (445 vs 426, P = .36), or carotid beta stiffness (2.8 vs 2.8, P = .86). CONCLUSION: Women with a prior hypertensive disorder of pregnancy had higher ASCVD risk and blood pressures several years postpartum, but did not have more endothelial dysfunction or subclinical atherosclerosis.

Integrating evidence and causal mapping of factors that influence medication decision-making by pregnant women at risk of hypertensive disorder: protocol for a scoping review.

INTRODUCTION: In 2018, the American College of Obstetricians and Gynecologists recommended low-dose aspirin to prevent the onset of pre-eclampsia among women who were at high risk. Factors influencing women's acceptance of this recommendation span multiple sectors and levels. Understanding how these factors interact will help stakeholders design effective population-level intervention strategies. Our study aims to identify and map relationships among factors influencing the medication decisions of pregnant women at risk of hypertensive disorders. METHODS AND ANALYSIS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines will be followed for this review. A research librarian developed a comprehensive search strategy to retrieve published and unpublished English studies after 1 January 1980, involving factors that influence pregnant women's uptake and adherence to medication for gestational hypertensive disorders. This literature includes perceptions, patterns, acceptance, refusal, tendencies, probability and service utilisation. We will search PubMed, Embase, Web of Science and CINAHL. Reference lists of the selected papers will be searched manually to identify more relevant studies. A two-stage independent screening, consisting of title and abstract screening, followed by full-text screening, will be conducted by two independent reviewers to identify eligible articles. Extracted data will be recorded in a customised variable extraction form and input into a Microsoft Access database. The PRISMA-ScR will be used to guide the presentation of the results, which will be presented in a table and causal map to demonstrate the relationships between extracted variables and medication uptake and adherence. A conceptual simulation model will be formulated to validate the logic of the relationships between variables and identify knowledge gaps. Lastly, experts and stakeholders will be invited to critique and comment on the results. ETHICS AND DISSEMINATION: This study does not require ethical approval. The full review results will be presented at a relevant conference and submitted to a peer-reviewed scientific journal for publication.

Discovery of sparse, reliable omic biomarkers with Stabl.

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.

BACKGROUND: Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties. METHODS: Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their 3- to 5-year-old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist). RESULTS: We found that machine learning models of prenatal proteomics predict 5%-10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes. CONCLUSIONS: Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell-signaling pathways were enriched in relation to child temperament and psychopathology measures.

Integrative analysis of noncoding mutations identifies the druggable genome in preterm birth.

Preterm birth affects ~10% of pregnancies in the US. Despite familial associations, identifying at-risk genetic loci has been challenging. We built deep learning and graphical models to score mutational effects at base resolution via integrating the pregnant myometrial epigenome and large-scale patient genomes with spontaneous preterm birth (sPTB) from European and African American cohorts. We uncovered previously unidentified sPTB genes that are involved in myometrial muscle relaxation and inflammatory responses and that are regulated by the progesterone receptor near labor onset. We studied genomic variants in these genes in our recruited pregnant women administered progestin prophylaxis. We observed that mutation burden in these genes was predictive of responses to progestin treatment for preterm birth. To advance therapeutic development, we screened ~4000 compounds, identified candidate molecules that affect our identified genes, and experimentally validated their therapeutic effects on regulating labor. Together, our integrative approach revealed the druggable genome in preterm birth and provided a generalizable framework for studying complex diseases.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

The placental vasculature is affected by changes in gene expression and glycogen-rich cells in a diet-induced obesity mouse model.

Maternal obesity is a risk factor for pregnancy complications. Obesity caused by a high-fat diet (HFD) may alter maternal glucose/glycogen metabolism. Here, our objective was to investigate whether the placental vasculature is altered via changes in gene expression and glycogen-rich cells using a preclinical mouse model of diet-induced obesity. We subjected female FVB/N mice to one of three feeding regimens: regular chow (RC) given at preconception and during pregnancy (Control); RC given at preconception and then a HFD during pregnancy (HFD-P); or HFD initiated 4 weeks preconception and during pregnancy (HFD-PreCP). Daily food consumption and weekly maternal weights were recorded. Maternal blood glucose levels were measured at preconception and 4 gestational epochs (E6.5-E9.5, E10.5-E12.5, E13.5-E15.5, E16.5-E19.5). At E8.5-E16.5, total RNA in placentas were isolated for gene expression analyses. Placentas were also collected for HE and periodic acid Schiff's (PAS) staining and glycogen content assays. Dams in the HFD-P and HFD-PreCP groups gained significantly more weight than controls. Pre- and antenatal glucose levels were also significantly higher (15%-30%) in HFD-PreCP dams. Expression of several placental genes were also altered in HFD dams compared with controls. Consumption of the HFD also led to phenotypic and morphologic changes in glycogen trophoblasts (GlyTs) and uterine natural killer (uNK) cells. Alterations in vascularity were also observed in the labyrinth of HFD-PreCP placentas, which correlated with decreased placental efficiency. Overall, we observed that a HFD induces gestational obesity in mice, alters expression of placental genes, affects glucose homeostasis, and alters glycogen-positive GlyTs and uNK cells. All these changes may lead to impaired placental vascular development, and thus heighten the risk for pregnancy complications.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine.

BACKGROUND: Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures. OBJECTIVE: This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome. STUDY DESIGN: Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins. RESULTS: A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins. CONCLUSION: Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

Abrupt perturbation and delayed recovery of the vaginal ecosystem following childbirth.

The vaginal ecosystem is closely tied to human health and reproductive outcomes, yet its dynamics in the wake of childbirth remain poorly characterized. Here, we profile the vaginal microbiota and cytokine milieu of participants sampled longitudinally throughout pregnancy and for at least one year postpartum. We show that delivery, regardless of mode, is associated with a vaginal pro-inflammatory cytokine response and the loss of Lactobacillus dominance. By contrast, neither the progression of gestation nor the approach of labor strongly altered the vaginal ecosystem. At 9.5-months postpartum-the latest timepoint at which cytokines were assessed-elevated inflammation coincided with vaginal bacterial communities that had remained perturbed (highly diverse) from the time of delivery. Time-to-event analysis indicated a one-year postpartum probability of transitioning to Lactobacillus dominance of 49.4%. As diversity and inflammation declined during the postpartum period, dominance by L. crispatus, the quintessential health-associated commensal, failed to return: its prevalence before, immediately after, and one year after delivery was 41%, 4%, and 9%, respectively. Revisiting our pre-delivery data, we found that a prior live birth was associated with a lower odds of L. crispatus dominance in pregnant participants-an outcome modestly tempered by a longer ( > 18-month) interpregnancy interval. Our results suggest that reproductive history and childbirth in particular remodel the vaginal ecosystem and that the timing and degree of recovery from delivery may help determine the subsequent health of the woman and of future pregnancies.

A "Gold Standard" Test for Diagnosing and Quantifying Hemolysis in Neonates and Infants.

Identifying "gold standard" diagnostic tests can promote evidence-based neonatology practice. Hemolysis is a pathological shortening of the erythrocyte lifespan, differing from erythrocyte senescence in responsible mechanisms and clinical implications. Diagnosing hemolysis goes beyond a binary (yes vs. no) determination. It is characterized according to magnitude, and as acute vs. chronic, and genetically based vs. not. For neonates with significant hyperbilirubinemia or anemia, detecting hemolysis and quantifying its magnitude provides diagnostic clarity. The 2022 American Academy of Pediatrics (AAP) Clinical Practice Guideline on management of hyperbilirubinemia in the newborn states that hemolysis is a risk factor for developing significant hyperbilirubinemia and neurotoxicity. The guideline recommends identifying hemolysis from any cause, but specific guidance is not provided. A spectrum of laboratory tests has been endorsed as diagnostic methods for hemolysis. Herein we examine these laboratory tests and recommend one as the "gold standard" for diagnosing and quantifying hemolysis in neonates and infants.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy.

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.