Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset

Untargeted metabolomics and machine learning unveil the exposome and metabolism linked with the risk of early pregnancy loss

Early pregnancy loss (EPL) may result from exposure to emerging contaminants (ECs), although the underlying mechanisms remain poorly understood. This case-control study measured over 2000 serum features, including 37 ECs, 6 biochemicals, and 2057 endogenous metabolites, in serum samples collected from 48 EPL patients and healthy pregnant women. The median total concentration of targeted EC in the EPL group (65.9 ng/mL) was significantly higher than in controls (43.0 ng/mL; p < 0.05). Four machine learning algorithms were employed to identify key molecular features and develop EPL risk prediction models. A random forest model based on chemical data achieved a predictive accuracy of 95 %, suggesting a potential association between EPL and chemical exposure, with phthalic acid esters identified as significant contributors. Ninety-five potential metabolite biomarkers were selected, which were predominantly enriched in pathways related to spermidine and spermine biosynthesis, ubiquinone biosynthesis, and pantothenate and coenzyme A biosynthesis. C17-sphinganine was identified as a leading biomarker with an area under the curve of 0.93. Furthermore, exposure to bis(2-ethylhexyl)phthalate was linked to an increased risk of EPL by disrupting lipid metabolism. These findings indicate that combining untargeted metabolomics with machine learning approaches offers novel insights into the mechanisms of EPL related to EC exposure.

Application of Multiomics in Perinatology: A Metabolomics Integration-Focused Review

Precision medicine stems from a new approach to the prevention, diagnosis and treatment of patients, due to the shift in focus away from pathology and towards the uniqueness of the individual, personalising the diagnostic-therapeutic pathway. This paradigm shift has been made possible by the emergence of new high-throughput technologies capable of generating large amounts of data on multiple levels of a biological system, identifying pathology-related genes, transcripts, proteins and metabolites. Metabolomics plays a primary role in this context, providing, through non-invasive sampling, a very close image of the phenotype of the organism being studied by detecting metabolites, end products downstream of gene transcription, present in cells, tissues, organs and biological fluids. The enormous amount of data that these modern technologies make available, together with the need to elucidate the complex interplay of the various biological levels by combining data from distinct omics, has led to the need to employ advanced informatics techniques, among which artificial intelligence has recently emerged. These innovations are of great interest in the field of perinatology, representing an attempt to optimise the diagnostic timeline for the most critical newborns. In addition, they may contribute to the improvement of prevention strategies available to date. All these contributions prove to be crucial at very vulnerable life stages, allowing crucial intervention opportunities. In this review, we have analysed studies that have integrated metabolomics with at least one other omics in the perinatal field, attempting to highlight the usefulness of multiomics integration and the different methods employed.

Prediction of p-phenylenediamine antioxidant concentrations in human urine using machine learning models

p-phenylenediamine antioxidants (PPDs) are extensively used in rubber manufacturing for their potent antioxidative properties, but PPDs and 2-anilino-5-[(4-methylpentan-2yl)amino]cyclohexa-2,5-diene-1,4-dione (6PPDQ) pose potential environmental and health risks. Existing biomonitoring methods for assessing human exposure to PPDs are labor-intensive, costly, and provide limited data. Thus, there is a critical need to develop predictive models for evaluating PPDs and 6PPDQ exposure levels to facilitate health risk assessments. In this study, machine learning (ML) models were developed to predict the concentration of three PPDs and 6PPDQ in human urine samples. A total of 759 participants from three cities in Zhejiang Province, China, provided urine samples, which were analyzed for PPDs and 6PPDQ concentrations using liquid chromatography-tandem mass spectrometry. Eight ML models were employed to predict PPDs and 6PPDQ concentrations based on demographic and environmental exposure factors such as age, gender, body mass index (BMI), and occupation. N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) was the most frequently detected PPD (mean 3.03 ng/mL, range < LOD-18.65 ng/mL), followed by 6PPDQ (mean 2.76 ng/mL, range < LOD-20.85 ng/mL) and N-phenyl-N'-cyclohexyl-p-phenylenediamine (mean 2.04 ng/mL, range < LOD-10.22 ng/mL). Random forest model demonstrated the highest accuracy in predicting PPDs and 6PPDQ concentrations in human urine among the ML models evaluated. Through the application of these models, age, BMI, and occupation emerged as significant predictors of urinary PPDs and 6PPDQ concentrations. This research significantly contributes by using ML models to enhance exposure assessment accuracy and efficiency, providing a novel framework for future studies on environmental health risks related to PPDs and 6PPDQ.

Single-cell omics technologies - Fundamentals on how to create single-cell looking glasses for reproductive health

Over the last decade, in line with the goals of precision medicine to offer individualized patient care, various single-cell technologies measuring gene and proteomic expression in various tissues have rapidly advanced to study health and disease at the single cell level. Precisely understanding cell composition, position within tissues, signaling pathways, and communication can reveal insights into disease mechanisms and systemic changes during development, pregnancy, and gynecologic disorders across the lifespan. Single-cell technologies dissect the complex cellular compositions of reproductive tract tissues, providing insights into mechanisms behind reproductive tract dysfunction which impact wellness and quality of life. These technologies aim to understand basic tissue and organ functions and, clinically, to develop novel diagnostics, early disease biomarkers, and cell-targeted therapies for currently suboptimally-treated disorders. Increasingly, they are applied to pregnancy and pregnancy disorders, gynecologic malignancies, and uterine and ovarian physiology and aging, which are discussed in more detail in manuscripts in this special issue of AJOG. Here, we review recent applications of single-cell technologies to the study of gynecologic disorders and systemic biological adaptations during fetal development, pregnancy, and across a woman's lifespan. We discuss sequencing- and proteomic-based single-cell methods, as well as spatial transcriptomics and high-dimensional proteomic imaging, describing each technology's mechanism, workflow, quality control, and highlighting specific benefits, drawbacks, and utility in the context of reproductive medicine. We consider analytical methods for the high-dimensional single-cell data generated, highlighting statistical constraints and recent computational techniques for downstream clinical translation. Overall, current and evolving single-cell "looking glasses", or perspectives, have the potential to transform fundamental understanding of women's health and reproductive disorders and alter the trajectory of clinical practice and patient outcomes in the future.

Machine Learning Reveals Distinct Immunogenic Signatures of Th1 Imprinting in ART-Treated Individuals with HIV Following Repeated SARS-CoV-2 Vaccination

The human immune system is intrinsically variable and remarkably diverse across a population. The immune response to antigens is driven by a complex interplay of time-dependent interdependencies across components of the immune system. After repeated vaccination, the humoral and cellular arms of the immune response display highly heterogeneous dynamics, further complicating the attribution of a phenotypic outcome to specific immune system components. We employ a random forest (RF) approach to classify informative differences in immunogenicity between older people living with HIV (PLWH) on ART and an age-matched control group who received up to five SARS-CoV-2 vaccinations over 104 weeks. RFs identify immunological variables of importance, interpreted as evidence for Th1 imprinting, and suggest novel distinguishing immune features, such as saliva-based antibody screening, as promising diagnostic features towards classifying responses (whereas serum IgG is not). Additionally, we implement supervised and unsupervised Machine Learning methods to produce physiologically accurate synthetic datasets that conform to the statistical distribution of the original immunological data, thus enabling further data-driven hypothesis testing and model validation. Our results highlight the effectiveness of RFs in utilizing informative immune feature interdependencies for classification tasks and suggests broad impacts of ML applications for personalized vaccination strategies among high-risk populations.

Application of Predictive Analytics in Pregnancy, Birth, and Postpartum Nursing Care

ABSTRACT

Predictive analytics has emerged as a promising approach for improving reproductive health care and patient outcomes. During pregnancy and birth, the ability to accurately predict risks and complications could enable earlier interventions and reduce adverse events. However, there are challenges and ethical considerations for implementing predictive models in perinatal care settings. We introduce major concepts in predictive analytics and describe application of predictive modeling to perinatal care topics such as fertility, preeclampsia, labor onset, vaginal birth after cesarean, uterine rupture, induction outcomes, postpartum hemorrhage, and postpartum mood disorders. Although some predictive models have achieved adequate accuracy (AUC 0.7-0.9), most require additional external validation across diverse populations and practice settings. Bias, particularly racial bias, remains a key limitation of current models. Nurses and advanced practice nurses, including nurse practitioners certified registered nurse anesthetists, and nurse-midwives, play a vital role in ensuring high-quality data collection and communicating predictive model outputs to clinicians and users of the health care system. Addressing the ethical challenges and limitations of predictive analytics is imperative to equitably translate these tools to support patient-centered perinatal care.

The single-cell immune profile throughout gestation and its potential value for identifying women at risk for spontaneous preterm birth

Precisely timed immune adaptations, observed in the maternal circulation, underpin the notion of an immune clock of human pregnancy that supports its successful progression and completion at delivery. This immune clock is divided into three immunological phases, with the first phase starting at the time of conception and implantation, shifting into the second phase that supports homeostasis and tolerance throughout pregnancy, and culminating in the last phase of labor and parturition. Disruptions of this immune clock are reported in pregnancy complications such as spontaneous preterm birth. However, our understanding of the immune clock preceding spontaneous preterm birth remains scattered. In this review, we describe the chronology of maternal immune cell adaptations during healthy pregnancies and highlight its disruption in spontaneous preterm birth. With a focus on single-cell cytometric, proteomic and transcriptomic approaches, we review recent studies of term and spontaneous preterm pregnancies and discuss the need for future prospective studies aimed at tracking pregnancies longitudinally on a multi-omic scale. Such studies will be critical in determining whether spontaneous preterm pregnancies progress at an accelerated pace or follow a preterm-intrinsic pattern when compared to those delivered at term.

A multi-omics spatial framework for host-microbiome dissection within the intestinal tissue microenvironment

The intricate interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host and microbiome across multiple spatial modalities. We demonstrate MicroCart by investigating gut host and microbiome changes in a murine colitis model, using spatial proteomics, transcriptomics, and glycomics. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multi-omics.

A machine learning approach to leveraging electronic health records for enhanced omics analysis

Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electronic health record data and adaptively blending both early and late fusion strategies, COMET overcomes the limitations of existing multimodal machine learning methods. Using two independent datasets, we showed that COMET improved the predictive modelling performance and biological discovery compared with the analysis of omics data with traditional methods. By incorporating electronic health record data into omics analyses, COMET enables more precise patient classifications, beyond the simplistic binary reduction to cases and controls. This framework can be broadly applied to the analysis of multimodal omics studies and reveals more powerful biological insights from limited cohort sizes.

Deep learning model using continuous skin temperature data predicts labor onset

BACKGROUND

Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans. We investigated if continuous body temperature exhibits similar changes in women and whether these changes may be linked to hormonal status. Finally, we developed a deep learning model using temperature patterning to provide a daily forecast of time to labor onset.

METHODS

We evaluated patterns in continuous skin temperature data in 91 (n = 54 spontaneous labors) pregnant women using a wearable smart ring. In a subset of 28 pregnancies, we examined daily steroid hormone samples leading up to labor to analyze relationships among hormones and body temperature trajectory. Finally, we applied an autoencoder long short-term memory (AE-LSTM) deep learning model to provide a novel daily estimation of days until labor onset.

RESULTS

Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 37 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The input to the pipeline was 5-min skin temperature data from a gestational age of 240 days until the day of labor onset. During cross-validation AE-LSTM average error (true - predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor.

CONCLUSION

Continuous skin temperature reflects progression toward labor and hormonal change during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Towards a new taxonomy of preterm birth

Disease categories traditionally reflect a historical clustering of clinical phenotypes based on biologic and nonbiologic features. Multiomics approaches have striven to identify signatures to develop individualized categorizations through tests and/or therapies for 'personalized' medicine. Precision health classifies clinical syndromes into endotype clusters based on novel technological advancements, which can reveal insights into the etiologies of phenotypical syndromes. A new taxonomy of preterm birth should be considered in this context, as not all preterm infants of similar gestational ages are the same because most have different biologic vulnerabilities and hence different health trajectories. Even the choice of interventions may affect observed clinical conditions. Thus, a new taxonomy of prematurity would help to advance the field of neonatology, but also obstetrics and perinatology by adopting anticipatory and more targeted approaches to the care of preterm infants with the intent of preventing and treating some of the most common newborn pathologic conditions.

Optimized ultrasound neuromodulation for non-invasive control of behavior and physiology

Focused ultrasound can non-invasively modulate neural activity, but whether effective stimulation parameters generalize across brain regions and cell types remains unknown. We used focused ultrasound coupled with fiber photometry to identify optimal neuromodulation parameters for four different arousal centers of the brain in an effort to yield overt changes in behavior. Applying coordinate descent, we found that optimal parameters for excitation or inhibition are highly distinct, the effects of which are generally conserved across brain regions and cell types. Optimized stimulations induced clear, target-specific behavioral effects, whereas non-optimized protocols of equivalent energy resulted in substantially less or no change in behavior. These outcomes were independent of auditory confounds and, contrary to expectation, accompanied by a cyclooxygenase-dependent and prolonged reduction in local blood flow and temperature with brain-region-specific scaling. These findings demonstrate that carefully tuned and targeted ultrasound can exhibit powerful effects on complex behavior and physiology.

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 .

Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models

Translational biology posits a strong bi-directional link between clinical phenotypes and a patient's biological profile. By leveraging this bi-directional link, we can efficiently deconvolute pre-existing clinical information into biological profiles. However, traditional computational tools are limited in their ability to resolve this link because of the relatively small sizes of paired clinical-biological datasets for training and the high dimensionality/sparsity of tabular clinical data. Here, we use state-of-the-art foundation models (FMs) for electronic health record (EHR) data to generate proteomics profiles of pregnant patients, thereby deconvoluting pre-existing clinical information into biological profiles without the cost and effort of running large-scale traditional omics studies. We show that FM-derived representations of a patient's EHR data coupled with a fully connected neural network prediction head can generate 206 blood protein expression levels. Interestingly, these proteins were enriched for developmental pathways, while proteins not able to be generated from EHR data were enriched for metabolic pathways. Finally, we show a proteomic signature of gestational diabetes that includes proteins with established and novel links to gestational diabetes. These results showcase the power of FM-derived EHR representations in efficiently generating biological states of pregnant patients. This capability can revolutionize disease understanding and therapeutic development, offering a cost-effective, time-efficient, and less invasive alternative to traditional methods of generating proteomics.

The mark of success: The role of vaccine-induced skin scar formation for BCG and smallpox vaccine-associated clinical benefits

Skin scar formation following Bacille Calmette-Guérin (BCG) or smallpox (Vaccinia) vaccination is an established marker of successful vaccination and 'vaccine take'. Potent pathogen-specific (tuberculosis; smallpox) and pathogen-agnostic (protection from diseases unrelated to the intentionally targeted pathogen) effects of BCG and smallpox vaccines hold significant translational potential. Yet despite their use for centuries, how scar formation occurs and how local skin-based events relate to systemic effects that allow these two vaccines to deliver powerful health promoting effects has not yet been determined. We review here what is known about the events occurring in the skin and place this knowledge in the context of the overall impact of these two vaccines on human health with a particular focus on maternal-child health.

Novel biomarkers for prediction of atonic postpartum hemorrhage among 'low-risk' women in labor

Background

Postpartum hemorrhage (PPH) is the primary cause of maternal mortality globally, with uterine atony being the predominant contributing factor. However, accurate prediction of PPH in the general population remains challenging due to a lack of reliable biomarkers.

Methods

Using retrospective cohort data, we quantified 48 cytokines in plasma samples from 40 women diagnosed with PPH caused by uterine atony. We also analyzed previously reported hemogram and coagulation parameters related to inflammatory response. The least absolute shrinkage and selection operator (LASSO) and logistic regression were applied to develop predictive models. Established models were further evaluated and temporally validated in a prospective cohort.

Results

Fourteen factors showed significant differences between the two groups, among which IL2Rα, IL9, MIP1β, TNFβ, CTACK, prenatal Hb, Lymph%, PLR, and LnSII were selected by LASSO to construct predictive model A. Further, by logistic regression, model B was constructed using prenatal Hb, PLR, IL2Rα, and IL9. The area under the curve (AUC) values of model A in the training set, internal validation set, and temporal validation set were 0.846 (0.757-0.934), 0.846 (0.749-0.930), and 0.875 (0.789-0.961), respectively. And the corresponding AUC values for model B were 0.805 (0.709-0.901), 0.805 (0.701-0.894), and 0.901 (0.824-0.979). Decision curve analysis results showed that both nomograms had a high net benefit for predicting atonic PPH.

Conclusion

We identified novel biomarkers and developed predictive models for atonic PPH in women undergoing "low-risk" vaginal delivery, providing immunological insights for further exploration of the mechanism underlying atonic PPH.

Stress and Its Consequences-Biological Strain

Understanding the role of stress in pregnancy and its consequences is important, particularly given documented associations between maternal stress and preterm birth and other pathological outcomes. Physical and psychological stressors can elicit the same biological responses, known as biological strain. Chronic stressors, like poverty and racism (race-based discriminatory treatment), may create a legacy or trajectory of biological strain that no amount of coping can relieve in the absence of larger-scale socio-behavioral or societal changes. An integrative approach that takes into consideration simultaneously social and biological determinants of stress may provide the best insights into the risk of preterm birth. The most successful computational approaches and the most predictive machine-learning models are likely to be those that combine information about the stressors and the biological strain (for example, as measured by different omics) experienced during pregnancy.

The vaginal immunoproteome for the prediction of spontaneous preterm birth: A retrospective longitudinal study

Background

Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Most cases of preterm birth occur spontaneously and result from preterm labor with intact (spontaneous preterm labor [sPTL]) or ruptured (preterm prelabor rupture of membranes [PPROM]) membranes. The prediction of spontaneous preterm birth (sPTB) remains underpowered due to its syndromic nature and the dearth of independent analyses of the vaginal host immune response. Thus, we conducted the largest longitudinal investigation targeting vaginal immune mediators, referred to herein as the immunoproteome, in a population at high risk for sPTB.

Methods

Vaginal swabs were collected across gestation from pregnant women who ultimately underwent term birth, sPTL, or PPROM. Cytokines, chemokines, growth factors, and antimicrobial peptides in the samples were quantified via specific and sensitive immunoassays. Predictive models were constructed from immune mediator concentrations.

Results

Throughout uncomplicated gestation, the vaginal immunoproteome harbors a cytokine network with a homeostatic profile. Yet, the vaginal immunoproteome is skewed toward a pro-inflammatory state in pregnant women who ultimately experience sPTL and PPROM. Such an inflammatory profile includes increased monocyte chemoattractants, cytokines indicative of macrophage and T-cell activation, and reduced antimicrobial proteins/peptides. The vaginal immunoproteome has improved predictive value over maternal characteristics alone for identifying women at risk for early (<34 weeks) sPTB.

Conclusions

The vaginal immunoproteome undergoes homeostatic changes throughout gestation and deviations from this shift are associated with sPTB. Furthermore, the vaginal immunoproteome can be leveraged as a potential biomarker for early sPTB, a subset of sPTB associated with extremely adverse neonatal outcomes.

Funding

This research was conducted by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS) under contract HHSN275201300006C. ALT, KRT, and NGL were supported by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health.

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.

Elevated Plasma Levels of Arginines During Labor Among Women with Spontaneous Preterm Birth: A Prospective Cohort Study

PROBLEM

Preterm birth (PTB) is a leading cause of infant mortality and morbidity. The pathogenesis of PTB is complex and involves many factors, including socioeconomy, inflammation and infection. Asymmetric dimethylarginine, ADMA and symmetric dimethylarginine, SDMA are involved in labor as inhibitors of nitric oxide, a known relaxant of the uterine smooth muscles. Arginines are scarcely studied in relation to PTB and we aimed to investigate arginines (ADMA, SDMA and L-arginine) in women with spontaneous PTB and term birth.

METHODS OF THE STUDY

The study was based on data from the population-based, prospective cohort BASIC study conducted in Uppsala County, Sweden, between September 2009 and November 2018. Arginines were analyzed by Ultra-High Performance Liquid Chromatography using plasma samples taken at the onset of labor from women with spontaneous PTB (n = 34) and term birth (n = 45). We also analyzed the inflammation markers CRP, TNF-R1 and TNF-R2 and GDF-15.

RESULTS

Women with spontaneous PTB had higher plasma levels of ADMA (p < 0.001), and L-Arginine (p = 0.03). In addition, inflammation marker, TNF-R1 (p = 0.01) was higher in spontaneous PTB compared to term birth. Further, in spontaneous PTB, no significant correlations could be observed when comparing levels of arginines with inflammation markers, except ADMA versus CRP.

CONCLUSIONS

These findings provide novel evidence for the potential involvement of arginines in the pathogenesis of spontaneous PTB and it seems that arginine levels at labor vary independently of several inflammatory markers. Further research is warranted to investigate the potential of arginines as therapeutic targets in the prevention and management of spontaneous PTB.

Predicting Spontaneous Preterm Birth Using the Immunome

Throughout pregnancy, the maternal peripheral circulation contains valuable information reflecting pregnancy progression, detectable as tightly regulated immune dynamics. Local immune processes at the maternal-fetal interface and other reproductive and non-reproductive tissues are likely to be the pacemakers for this peripheral immune "clock." This cellular immune status of pregnancy can be leveraged for the early risk assessment and prediction of spontaneous preterm birth (sPTB). Systems immunology approaches to sPTB subtypes and cross-tissue (local and peripheral) interactions, as well as integration of multiple biological data modalities promise to improve our understanding of preterm birth pathobiology and identify potential clinically actionable biomarkers.

Computational Approaches for Connecting Maternal Stress to Preterm Birth

Multiple studies have hinted at a complex connection between maternal stress and preterm birth (PTB). This article describes the potential of computational methods to provide new insights into this relationship. For this, we outline existing approaches for stress assessments and various data modalities available for profiling stress responses, and review studies that sought either to establish a connection between stress and PTB or to predict PTB based on stress-related factors. Finally, we summarize the challenges of computational methods, highlighting potential future research directions within this field.

Computational Approaches for Predicting Preterm Birth and Newborn Outcomes

Preterm birth (PTB) and its associated morbidities are a leading cause of infant mortality and morbidity. Accurate predictive models and a better biological understanding of PTB-associated morbidities are critical in reducing their adverse effects. Increasing availability of multimodal high-dimensional data sets with concurrent advances in artificial intelligence (AI) have created a rich opportunity to gain novel insights into PTB, a clinically complex and multifactorial disease. Here, the authors review the use of AI to analyze 3 modes of data: electronic health records, biological omics, and social determinants of health metrics.

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.

Using Machine Learning to Construct the Blood-Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data

Permeabilities of various trace elements (TEs) through the blood-follicle barrier (BFB) play an important role in oocyte development. However, it has not been comprehensively described as well as its involved biological pathways. Our study aimed to construct a blood-follicle distribution model of the concerned TEs and explore their related biological pathways. We finally included a total of 168 women from a cohort of in vitro fertilization-embryo transfer conducted in two reproductive centers in Beijing City and Shandong Province, China. The concentrations of 35 TEs in both serum and follicular fluid (FF) samples from the 168 women were measured, as well as the multiomics features of the metabolome, lipidome, and proteome in both plasma and FF samples. Multiomics features associated with the transfer efficiencies of TEs through the BFB were selected by using an elastic net model and further utilized for pathway analysis. Various machine learning (ML) models were built to predict the concentrations of TEs in FF. Overall, there are 21 TEs that exhibited three types of consistent BFB distribution characteristics between Beijing and Shandong centers. Among them, the concentrations of arsenic, manganese, nickel, tin, and bismuth in FF were higher than those in the serum with transfer efficiencies of 1.19-4.38, while a reverse trend was observed for the 15 TEs with transfer efficiencies of 0.076-0.905, e.g., mercury, germanium, selenium, antimony, and titanium. Lastly, cadmium was evenly distributed in the two compartments with transfer efficiencies of 0.998-1.056. Multiomics analysis showed that the enrichment of TEs was associated with the synthesis and action of steroid hormones and the glucose metabolism. Random forest model can provide the most accurate predictions of the concentrations of TEs in FF among the concerned ML models. In conclusion, the selective permeability through the BFB for various TEs may be significantly regulated by the steroid hormones and the glucose metabolism. Also, the concentrations of some TEs in FF can be well predicted by their serum levels with a random forest model.

Proteomic studies of VEGFR2 in human placentas reveal protein associations with preeclampsia, diabetes, gravidity, and labor

VEGFR2 (Vascular endothelial growth factor receptor 2) is a central regulator of placental angiogenesis. The study of the VEGFR2 proteome of chorionic villi at term revealed its partners MDMX (Double minute 4 protein) and PICALM (Phosphatidylinositol-binding clathrin assembly protein). Subsequently, the oxytocin receptor (OT-R) and vasopressin V1aR receptor were detected in MDMX and PICALM immunoprecipitations. Immunogold electron microscopy showed VEGFR2 on endothelial cell (EC) nuclei, mitochondria, and Hofbauer cells (HC), tissue-resident macrophages of the placenta. MDMX, PICALM, and V1aR were located on EC plasma membranes, nuclei, and HC nuclei. Unexpectedly, PICALM and OT-R were detected on EC projections into the fetal lumen and OT-R on 20-150 nm clusters therein, prompting the hypothesis that placental exosomes transport OT-R to the fetus and across the blood-brain barrier. Insights on gestational complications were gained by univariable and multivariable regression analyses associating preeclampsia with lower MDMX protein levels in membrane extracts of chorionic villi, and lower MDMX, PICALM, OT-R, and V1aR with spontaneous vaginal deliveries compared to cesarean deliveries before the onset of labor. We found select associations between higher MDMX, PICALM, OT-R protein levels and either gravidity, diabetes, BMI, maternal age, or neonatal weight, and correlations only between PICALM-OT-R (p < 2.7 × 10-8), PICALM-V1aR (p < 0.006), and OT-R-V1aR (p < 0.001). These results offer for exploration new partnerships in metabolic networks, tissue-resident immunity, and labor, notably for HC that predominantly express MDMX.

A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment

The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.

An integrated Bayesian framework for multi-omics prediction and classification

With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.

Deep Learning Model Using Continuous Skin Temperature Data Predicts Labor Onset

Background

Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans.

Methods

We evaluated patterns in continuous skin temperature data in 91 pregnant women using a wearable smart ring. Additionally, we collected daily steroid hormone samples leading up to labor in a subset of 28 pregnancies and analyzed relationships among hormones and body temperature trajectory. Finally, we developed a novel autoencoder long-short-term-memory (AE-LSTM) deep learning model to provide a daily estimation of days until labor onset.

Results

Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 40 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The model was trained only on aggregate 5-minute skin temperature data starting at a gestational age of 240 until labor onset. During cross-validation AE-LSTM average error (true - predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor.

Conclusion

Continuous skin temperature reflects progression toward labor and hormonal status during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Recent Advances and Challenges in the Early Diagnosis and Treatment of Preterm Labor

Preterm birth (PTB) is the primary cause of neonatal mortality and long-term disabilities. The unknown mechanism behind PTB makes diagnosis difficult, yet early detection is necessary for controlling and averting related consequences. The primary focus of this work is to provide an overview of the known risk factors associated with preterm labor and the conventional and advanced procedures for early detection of PTB, including multi-omics and artificial intelligence/machine learning (AI/ML)- based approaches. It also discusses the principles of detecting various proteomic biomarkers based on lateral flow immunoassay and microfluidic chips, along with the commercially available point-of-care testing (POCT) devices and associated challenges. After briefing the therapeutic and preventive measures of PTB, this review summarizes with an outlook.

Multimodal machine learning for modeling infant head circumference, mothers' milk composition, and their shared environment

Links between human milk (HM) and infant development are poorly understood and often focus on individual HM components. Here we apply multi-modal predictive machine learning to study HM and head circumference (a proxy for brain development) among 1022 mother-infant dyads of the CHILD Cohort. We integrated HM data (19 oligosaccharides, 28 fatty acids, 3 hormones, 28 chemokines) with maternal and infant demographic, health, dietary and home environment data. Head circumference was significantly predictable at 3 and 12 months. Two of the most associated features were HM n3-polyunsaturated fatty acid C22:6n3 (docosahexaenoic acid, DHA; p = 9.6e-05) and maternal intake of fish (p = 4.1e-03), a key dietary source of DHA with established relationships to brain function. Thus, using a systems biology approach, we identified meaningful relationships between HM and brain development, which validates our statistical approach, gives credence to the novel associations we observed, and sets the foundation for further research with additional cohorts and HM analytes.

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.

Deciphering maternal-fetal cross-talk in the human placenta during parturition using single-cell RNA sequencing

Labor is a complex physiological process requiring a well-orchestrated dialogue between the mother and fetus. However, the cellular contributions and communications that facilitate maternal-fetal cross-talk in labor have not been fully elucidated. Here, single-cell RNA sequencing (scRNA-seq) was applied to decipher maternal-fetal signaling in the human placenta during term labor. First, a single-cell atlas of the human placenta was established, demonstrating that maternal and fetal cell types underwent changes in transcriptomic activity during labor. Cell types most affected by labor were fetal stromal and maternal decidual cells in the chorioamniotic membranes (CAMs) and maternal and fetal myeloid cells in the placenta. Cell-cell interaction analyses showed that CAM and placental cell types participated in labor-driven maternal and fetal signaling, including the collagen, C-X-C motif ligand (CXCL), tumor necrosis factor (TNF), galectin, and interleukin-6 (IL-6) pathways. Integration of scRNA-seq data with publicly available bulk transcriptomic data showed that placenta-derived scRNA-seq signatures could be monitored in the maternal circulation throughout gestation and in labor. Moreover, comparative analysis revealed that placenta-derived signatures in term labor were mirrored by those in spontaneous preterm labor and birth. Furthermore, we demonstrated that early in gestation, labor-specific, placenta-derived signatures could be detected in the circulation of women destined to undergo spontaneous preterm birth, with either intact or prelabor ruptured membranes. Collectively, our findings provide insight into the maternal-fetal cross-talk of human parturition and suggest that placenta-derived single-cell signatures can aid in the development of noninvasive biomarkers for the prediction of preterm birth.

SingPro: a knowledge base providing single-cell proteomic data

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

Association of the key immunological and hemodynamic determinants with cervix ripening in pregnant women

OBJECTIVE

Aim: To investigate a correlation between cervical ripening, the immunological features and the hemodynamic characteristics of the cervix during the preparation for vaginal labor.

PATIENTS AND METHODS

Materials and Methods: We examined 75 pregnant women at different gestational age. General clinical and immunological studies were conducted in order to check serum concentration of cytokines IL-6, IL-1β, and TNF-α. Ultrasound and Doppler study were used to determine resistance index and systolic-diastolic ratio of blood flow in the common uterine artery as well as the descending and ascending parts and cervical stromal arteries.

RESULTS

Results: Pregnant women with high cervical ripening score had high concentrations of the major proinflammatory cytokines (IL-1β, IL-6, and TNF-α). Analysis of the of the cervical blood flow indicators of the studied groups showed significant differences in the indices of vascular resistance in the vessels that feed the cervix. Our data showed a significant correlation between the cervix ripening and both the serum levels of the studied cytokines and the level of peripheral vascular resistance indices in the common uterine arteries of the cervix, and the blood flow indices in the cervical stromal vessels.

CONCLUSION

Conclusions: Our study shows that the process of preparing the woman's body for labor is associated with immunological adjustment and increased hemodynamics of the cervix. We report that cervical ripening is associated with the immunological components and hemodynamic parameters of the cervix at late-stage pregnancy. Measuring cervix ripening and the accompanied changes in cytokine levels and hemodynamic parameters will form a more accurate assessment of birth preparedness and labor complications.

Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

Preeclampsia is a life-threatening pregnancy disorder. Current clinical assays cannot predict the onset of preeclampsia until the late 2nd trimester, which often leads to poor maternal and neonatal outcomes. Here we show that Raman spectroscopy combined with machine learning in pregnant patient plasma enables rapid, highly sensitive maternal metabolome screening that predicts preeclampsia as early as the 1st trimester with >82% accuracy. We identified 12, 15 and 17 statistically significant metabolites in the 1st, 2nd and 3rd trimesters, respectively. Metabolic pathway analysis shows multiple pathways corresponding to amino acids, fatty acids, retinol, and sugars are enriched in the preeclamptic cohort relative to a healthy pregnancy. Leveraging Pearson's correlation analysis, we show for the first time with Raman Spectroscopy that metabolites are associated with several clinical factors, including patients' body mass index, gestational age at delivery, history of preeclampsia, and severity of preeclampsia. We also show that protein quantification alone of proinflammatory cytokines and clinically relevant angiogenic markers are inadequate in identifying at-risk patients. Our findings demonstrate that Raman spectroscopy is a powerful tool that may complement current clinical assays in early diagnosis and in the prognosis of the severity of preeclampsia to ultimately enable comprehensive prenatal care for all patients.

Multi-omics in stress and health research: study designs that will drive the field forward

Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals' stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.

Host-microbiome interactions in distinct subsets of preterm labor and birth

Preterm birth, the leading cause of perinatal morbidity, often follows premature labor, a syndrome whose prevention remains a challenge. To better understand the relationship between premature labor and host-microbiome interactions, we conducted a mechanistic investigation using three preterm birth models. We report that intra-amniotic delivery of LPS triggers inflammatory responses in the amniotic cavity and cervico-vaginal microenvironment, causing vaginal microbiome changes and signs of active labor. Intra-amniotic IL-1α delivery causes a moderate inflammatory response in the amniotic cavity but increasing inflammation in the cervico-vaginal space, leading to vaginal microbiome disruption and signs of active labor. Conversely, progesterone action blockade by RU-486 triggers local immune responses accompanying signs of active labor without altering the vaginal microbiome. Preterm labor facilitates ascension of cervico-vaginal bacteria into the amniotic cavity, regardless of stimulus. This study provides compelling mechanistic insights into the dynamic host-microbiome interactions within the cervico-vaginal microenvironment that accompany premature labor and birth.

Nutrient regulation of development and cell fate decisions

Diet contributes to health at all stages of life, from embryonic development to old age. Nutrients, including vitamins, amino acids, lipids and sugars, have instructive roles in directing cell fate and function, maintaining stem cell populations, tissue homeostasis and alleviating the consequences of aging. This Review highlights recent findings that illuminate how common diets and specific nutrients impact cell fate decisions in healthy and disease contexts. We also draw attention to new models, technologies and resources that help to address outstanding questions in this emerging field and may lead to dietary approaches that promote healthy development and improve disease treatments.

Plasma levels of arginines at term pregnancy in relation to mode of onset of labor and mode of childbirth

PROBLEM

The exact biochemical mechanisms that initiate labor are not yet fully understood. Nitric oxide is a potent relaxant of uterine smooth muscles until labor starts, and its precursor is L-arginine. Asymmetric (ADMA) and symmetric (SDMA) dimethylarginines, are potent NO-inhibitors. However, arginines (dimethylarginines and L-arginine) are scarcely studied in relation to labor and childbirth. We aimed to investigate arginines in women with spontaneous (SLVB) and induced (ILVB) term labor with vaginal birth and in women undergoing elective caesarean section (ECS).

METHOD OF STUDY

Women at gestational week 16-18 were recruited to the population-based prospective cohort study BASIC at the Uppsala University Hospital, Sweden. Plasma samples taken at start of labor were analyzed for arginines, from SLVB (n = 45), ILVB (n = 45), and ECS (n = 45), using Ultra-High Performance Liquid Chromatography. Between-group differences were assessed using Kruskal-Wallis and Mann-Whitney U-test.

RESULTS

Women with SLVB and ILVB had higher levels of ADMA (p < .0001), SDMA (p < .05) and lower L-arginines (p < .01), L-arginine/ADMA (p < .0001), and L-arginine/SDMA (p < .01, respectively <.001) compared to ECS. However, ILVB had higher ADMA (p < .0001) and lower L-arginine (p < .01), L-arginine/ADMA (p < .0001), and L-arginine/SDMA (p < .01) compared to SLVB. Results are adjusted for gestational length at birth and cervical dilatation at sampling.

CONCLUSION

Our novel findings of higher levels of dimethylarginines in term vaginal births compared to ECS give insights into the biochemical mechanisms of labor. These findings might also serve as a basis for further studies of arginines in complicated pregnancies and labor.

Siglecs family used by pathogens for immune escape may engaged in immune tolerance in pregnancy

The Siglecs family is a group of type I sialic acid-binding immunoglobulin-like receptors that regulate cellular signaling by recognizing sialic acid epitopes. Siglecs are predominantly expressed on the surface of leukocytes, where they play a crucial role in regulating immune activity. Pathogens can exploit inhibitory Siglecs by utilizing their sialic acid components to promote invasion or suppress immune functions, facilitating immune evasion. The establishing of an immune-balanced maternal-fetal interface microenvironment is essential for a successful pregnancy. Dysfunctional immune cells may lead to adverse pregnancy outcomes. Siglecs are important for inducing a phenotypic switch in leukocytes at the maternal-fetal interface toward a less toxic and more tolerant phenotype. Recent discoveries regarding Siglecs in the reproductive system have drawn further attention to their potential roles in reproduction. In this review, we primarily discuss the latest advances in understanding the impact of Siglecs as immune regulators on infections and pregnancy.

Predicting labor onset relative to the estimated date of delivery using smart ring physiological data

The transition from pregnancy into parturition is physiologically directed by maternal, fetal and placental tissues. We hypothesize that these processes may be reflected in maternal physiological metrics. We enrolled pregnant participants in the third-trimester (n = 118) to study continuously worn smart ring devices monitoring heart rate, heart rate variability, skin temperature, sleep and physical activity from negative temperature coefficient, 3-D accelerometer and infrared photoplethysmography sensors. Weekly surveys assessed labor symptoms, pain, fatigue and mood. We estimated the association between each metric, gestational age, and the likelihood of a participant's labor beginning prior to (versus after) the clinical estimated delivery date (EDD) of 40.0 weeks with mixed effects regression. A boosted random forest was trained on the physiological metrics to predict pregnancies that naturally passed the EDD versus undergoing onset of labor prior to the EDD. Here we report that many raw sleep, activity, pain, fatigue and labor symptom metrics are correlated with gestational age. As gestational age advances, pregnant individuals have lower resting heart rate 0.357 beats/minute/week, 0.84 higher heart rate variability (milliseconds) and shorter durations of physical activity and sleep. Further, random forest predictions determine pregnancies that would pass the EDD with accuracy of 0.71 (area under the receiver operating curve). Self-reported symptoms of labor correlate with increased gestational age and not with the timing of labor (relative to EDD) or onset of spontaneous labor. The use of maternal smart ring-derived physiological data in the third-trimester may improve prediction of the natural duration of pregnancy relative to the EDD.

Noninvasive Prenatal Testing Using Circulating DNA and RNA: Advances, Challenges, and Possibilities

Prenatal screening using sequencing of circulating cell-free DNA has transformed obstetric care over the past decade and significantly reduced the number of invasive diagnostic procedures like amniocentesis for genetic disorders. Nonetheless, emergency care remains the only option for complications like preeclampsia and preterm birth, two of the most prevalent obstetrical syndromes. Advances in noninvasive prenatal testing expand the scope of precision medicine in obstetric care. In this review, we discuss advances, challenges, and possibilities toward the goal of providing proactive, personalized prenatal care. The highlighted advances focus mainly on cell-free nucleic acids; however, we also review research that uses signals from metabolomics, proteomics, intact cells, and the microbiome. We discuss ethical challenges in providing care. Finally, we look to future possibilities, including redefining disease taxonomy and moving from biomarker correlation to biological causation.

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.

Maternal Vaccination to Prevent Adverse Pregnancy Outcomes: An Underutilized Molecular Immunological Intervention?

Adverse pregnancy outcomes including maternal mortality, stillbirth, preterm birth, intrauterine growth restriction cause millions of deaths each year. More effective interventions are urgently needed. Maternal immunization could be one such intervention protecting the mother and newborn from infection through its pathogen-specific effects. However, many adverse pregnancy outcomes are not directly linked to the infectious pathogens targeted by existing maternal vaccines but rather are linked to pathological inflammation unfolding during pregnancy. The underlying pathogenesis driving such unfavourable outcomes have only partially been elucidated but appear to relate to altered immune regulation by innate as well as adaptive immune responses, ultimately leading to aberrant maternal immune activation. Maternal immunization, like all immunization, impacts the immune system beyond pathogen-specific immunity. This raises the possibility that maternal vaccination could potentially be utilised as a pathogen-agnostic immune modulatory intervention to redirect abnormal immune trajectories towards a more favourable phenotype providing pregnancy protection. In this review we describe the epidemiological evidence surrounding this hypothesis, along with the mechanistic plausibility and present a possible path forward to accelerate addressing the urgent need of adverse pregnancy outcomes.

Increased late preterm birth risk and altered uterine blood flow upon exposure to heat stress

BACKGROUND

Climate change, in particular the exposure to heat, impacts on human health and can trigger diseases. Pregnant people are considered a vulnerable group given the physiological changes during pregnancy and the potentially long-lasting consequences for the offspring. Evidence published to date on higher risk of pregnancy complications upon heat stress exposure are from geographical areas with high ambient temperatures. Studies from geographic regions with temperate climates are sparse; however, these areas are critical since individuals may be less equipped to adapt to heat stress. This study addresses a significant gap in knowledge due to the temperature increase documented globally.

METHODS

Birth data of singleton pregnancies (n = 42,905) from a tertiary care centre in Hamburg, Germany, between 1999 and 2021 were retrospectively obtained and matched with climate data from the warmer season (March to September) provided by the adjacent federal meteorological station of the German National Meteorological Service to calculate the relative risk of heat-associated preterm birth. Heat events were defined by ascending temperature percentiles in combination with humidity over exposure periods of up to 5 days. Further, ultrasound data documented in a longitudinal prospective pregnancy cohort study (n = 612) since 2012 were used to identify pathophysiological causes of heat-induced preterm birth.

FINDINGS

Both extreme heat and prolonged periods of heat exposure increased the relative risk of preterm birth (RR: 1.59; 95% CI: 1.01-2.43; p = 0.045; RR: 1.20; 95% CI: 1.02-1.40; p = 0.025). We identified a critical period of heat exposure during gestational ages 34-37 weeks that resulted in increased risk of late preterm birth (RR: 1.67; 95% CI: 1.14-1.43; p = 0.009). Pregnancies with a female fetus were more prone to heat stress-associated preterm birth. We found heat exposure was associated with altered vascular resistance within the uterine artery.

INTERPRETATION

Heat stress caused by high ambient temperatures increases the risk of preterm birth in a geographical region with temperate climate. Prenatal routine care should be revised in such regions to provide active surveillance for women at risk.

FUNDING

Found in acknowledgements.

Prematurely delivering mothers show reductions of lachnospiraceae in their gut microbiomes

BACKGROUND

Preterm birth is the leading cause of perinatal morbidity and mortality. Despite evidence shows that imbalances in the maternal microbiome associates to the risk of preterm birth, the mechanisms underlying the association between a perturbed microbiota and preterm birth remain poorly understood.

METHOD

Applying shotgun metagenomic analysis on 80 gut microbiotas of 43 mothers, we analyzed the taxonomic composition and metabolic function in gut microbial communities between preterm and term mothers.

RESULTS

Gut microbiome of mothers delivering prematurely showed decreased alpha diversity and underwent significant reorganization, especially during pregnancy. SFCA-producing microbiomes, particularly species of Lachnospiraceae, Ruminococcaceae, and Eubacteriaceae, were significantly depleted in preterm mothers. Lachnospiraceae and its species were the main bacteria contributing to species' differences and metabolic pathways.

CONCLUSION

Gut microbiome of mothers delivering prematurely has altered and demonstrates the reduction of Lachnospiraceae.

Multi-Omics Profiling for Health

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.