Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries

Identification of differentially expressed non-coding RNAs in the plasma of women with preterm birth

This study aimed to identify differentially expressed non-coding RNAs (ncRNAs) associated with preterm birth (PTB) and determine biological pathways being influenced in the context of PTB. We processed cell-free RNA sequencing data and identified seventeen differentially expressed (DE) ncRNAs that could be involved in the onset of PTB. Per the validation via customized RT-qPCR, the recorded variations in expressions of eleven ncRNAs were concordant with the in-silico analyses. The results of this study provide insights into the role of DE ncRNAs and their impact on pregnancy-related biological pathways that could lead to PTB. Further studies are required to elucidate the precise mechanisms by which these DE ncRNAs contribute to adverse pregnancy outcomes (APOs) and their potential as diagnostic biomarkers.

Parkinson's disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction

Recent studies demonstrate that Parkinson's disease (PD) is associated with dysregulated metabolic flux through the kynurenine pathway (KP), in which tryptophan is converted to kynurenine (KYN), and KYN is subsequently metabolized to neuroactive compounds quinolinic acid (QA) and kynurenic acid (KA). Here, we used mass-spectrometry to compare blood and cerebral spinal fluid (CSF) KP metabolites between 158 unimpaired older adults and 177 participants with PD. We found increased neuroexcitatory QA/KA ratio in both plasma and CSF of PD participants associated with peripheral and cerebral inflammation and vitamin B6 deficiency. Furthermore, increased QA tracked with CSF tau, CSF soluble TREM2 (sTREM2) and severity of both motor and non-motor PD clinical symptoms. Finally, PD patient subgroups with distinct KP profiles displayed distinct PD clinical features. These data validate the KP as a site of brain and periphery crosstalk, integrating B-vitamin status, inflammation and metabolism to ultimately influence PD clinical manifestation.

Neonatal microbiome in the multiomics era: development and its impact on long-term health

The neonatal microbiome has been the focus of considerable research over the past two decades and studies have added fascinating information in terms of early microbial patterns and how these relate to various disease processes. One difficulty with the interpretation of these relationships is that such data is associative and provides little in terms of proof of causality or the underpinning mechanisms. Integrating microbiome data with other omics such as the proteome, inflammatory mediators, and the metabolome is an emerging approach to address this gap. Here we discuss these omics, their integration, and how they can be applied to improve our understanding, treatment, and prevention of disease. IMPACT: This review introduces the concept of multiomics in neonatology and how emerging technologies can be integrated improve understanding, treatment, and prevention of disease. We highlight considerations for performing multiomic research in neonates and the need for validation in separate cohorts and/or relevant model systems. We summarise how the use of multiomics is expanding and lay out steps to bring this to the clinic to enable precision medicine.

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.

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.

Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review

BACKGROUND

The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies.

OBJECTIVE

This scoping review aims to collate (1) the existing research in Bangladesh's health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research.

METHODS

MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English.

RESULTS

With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%).

CONCLUSIONS

This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh's health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.

Parkinson's disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction

Parkinson's disease (PD) is a complex multisystem disorder clinically characterized by motor, non-motor, and premotor manifestations. Pathologically, PD involves neuronal loss in the substantia nigra, striatal dopamine deficiency, and accumulation of intracellular inclusions containing aggregates of α-synuclein. Recent studies demonstrate that PD is associated with dysregulated metabolic flux through the kynurenine pathway (KP), in which tryptophan is converted to kynurenine (KYN), and KYN is subsequently metabolized to neuroactive compounds quinolinic acid (QA) and kynurenic acid (KA). This multicenter study used highly sensitive liquid chromatography-tandem mass-spectrometry to compare blood and cerebral spinal fluid (CSF) KP metabolites between 158 unimpaired older adults and 177 participants with PD. Results indicate that increased neuroexcitatory QA/KA ratio in both plasma and CSF of PD participants associated with peripheral and cerebral inflammation and vitamin B6 deficiency. Furthermore, increased QA tracked with CSF tau and severity of both motor and non-motor PD clinical dysfunction. Importantly, plasma and CSF kynurenine metabolites classified PD participants with a high degree of accuracy (AUC = 0.897). Finally, analysis of metabolite data revealed subgroups with distinct KP profiles, and these were subsequently found to display distinct PD clinical features. Together, these data further support the hypothesis that the KP serves as a site of brain and periphery crosstalk, integrating B-vitamin status, inflammation and metabolism to ultimately influence PD clinical manifestation.

Reassessing acquired neonatal intestinal diseases using unsupervised machine learning

BACKGROUND

Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning.

METHODS

Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis.

RESULTS

Five clusters of intestinal injury were identified: Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster.

CONCLUSION

Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases.

IMPACT

Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.

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.

The association of maternal factors with the neonatal microbiota and health

The human microbiome plays a crucial role in human health. However, the influence of maternal factors on the neonatal microbiota remains obscure. Herein, our observations suggest that the neonatal microbiotas, particularly the buccal microbiota, change rapidly within 24-48 h of birth but begin to stabilize by 48-72 h after parturition. Network analysis clustered over 200 maternal factors into thirteen distinct groups, and most associated factors were in the same group. Multiple maternal factor groups were associated with the neonatal buccal, rectal, and stool microbiotas. Particularly, a higher maternal inflammatory state and a lower maternal socioeconomic position were associated with a higher alpha diversity of the neonatal buccal microbiota and beta diversity of the neonatal stool microbiota was influenced by maternal diet and cesarean section by 24-72 h postpartum. The risk of admission of a neonate to the newborn intensive care unit was associated with preterm birth as well as higher cytokine levels and probably higher alpha diversity of the maternal buccal microbiota.

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.

Overview of the Global and US Burden of Preterm Birth

Preterm birth (PTB) is the leading cause of morbidity and mortality in children globally, yet its prevalence has been difficult to accurately estimate due to unreliable methods of gestational age dating, heterogeneity in counting, and insufficient data. The estimated global PTB rate in 2020 was 9.9% (95% confidence interval: 9.1, 11.2), which reflects no significant change from 2010, and 81% of prematurity-related deaths occurred in Africa and Asia. PTB prevalence in the United States in 2021 was 10.5%, yet with concerning racial disparities. Few effective solutions for prematurity prevention have been identified, highlighting the importance of further research.

Ethics of Predicting and Preventing Preterm Birth

Preterm birth (PTB) occurs disproportionately among women who are minoritized and who live and work in poverty. This disadvantage occurs as a result of societal norms and policies that affect how people are treated and determine their access to a broad range of resources. Research that takes social context into account offers the best opportunity for identifying approaches to prevent PTB. The experience and knowledge of women from groups experiencing high rates of PTB can provide important insights for research design and for determining the feasibility and acceptability of potential interventions.

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.

Estimating Gestational Age and Prediction of Preterm Birth Using Metabolomics Biomarkers

Preterm birth (PTB) is a leading cause of morbidity and mortality in children aged under 5 years globally, especially in low-resource settings. It remains a challenge in many low-income and middle-income countries to accurately measure the true burden of PTB due to limited availability of accurate measures of gestational age (GA), first trimester ultrasound dating being the gold standard. Metabolomics biomarkers are a promising area of research that could provide tools for both early identification of high-risk pregnancies and for the estimation of GA and preterm status of newborns postnatally.

Current Status of Newborn Bloodspot Screening Worldwide 2024: A Comprehensive Review of Recent Activities (2020-2023)

Newborn bloodspot screening (NBS) began in the early 1960s based on the work of Dr. Robert "Bob" Guthrie in Buffalo, NY, USA. His development of a screening test for phenylketonuria on blood absorbed onto a special filter paper and transported to a remote testing laboratory began it all. Expansion of NBS to large numbers of asymptomatic congenital conditions flourishes in many settings while it has not yet been realized in others. The need for NBS as an efficient and effective public health prevention strategy that contributes to lowered morbidity and mortality wherever it is sustained is well known in the medical field but not necessarily by political policy makers. Acknowledging the value of national NBS reports published in 2007, the authors collaborated to create a worldwide NBS update in 2015. In a continuing attempt to review the progress of NBS globally, and to move towards a more harmonized and equitable screening system, we have updated our 2015 report with information available at the beginning of 2024. Reports on sub-Saharan Africa and the Caribbean, missing in 2015, have been included. Tables popular in the previous report have been updated with an eye towards harmonized comparisons. To emphasize areas needing attention globally, we have used regional tables containing similar listings of conditions screened, numbers of screening laboratories, and time at which specimen collection is recommended. Discussions are limited to bloodspot screening.

Metabolomic analysis of the human placenta reveals perturbations in amino acids, purine metabolites, and small organic acids in spontaneous preterm birth

Spontaneous preterm delivery presents one of the most complex challenges in obstetrics and is a leading cause of perinatal morbidity and mortality. Although it is a common endpoint for multiple pathological processes, the mechanisms governing the etiological complexity of spontaneous preterm birth and the placental responses are poorly understood. This study examined placental tissues collected between May 2019 and May 2022 from a well-defined cohort of women who experienced spontaneous preterm birth (n = 72) and healthy full-term deliveries (n = 30). Placental metabolomic profiling of polar metabolites was performed using Ultra-High Performance Liquid Chromatography/Mass Spectrometry (UHPLC/MS) analysis. The resulting data were analyzed using multi- and univariate statistical methods followed by unsupervised clustering. A comprehensive metabolomic evaluation of the placenta revealed that spontaneous preterm birth was associated with significant changes in the levels of 34 polar metabolites involved in intracellular energy metabolism and biochemical activity, including amino acids, purine metabolites, and small organic acids. We found that neither the preterm delivery phenotype nor the inflammatory response explain the reported differential placental metabolome. However, unsupervised clustering revealed two molecular subtypes of placentas from spontaneous preterm pregnancies exhibiting differential enrichment of clinical parameters. We also identified differences between early and late preterm samples, suggesting distinct placental functions in early spontaneous preterm delivery. Altogether, we present evidence that spontaneous preterm birth is associated with significant changes in the level of placental polar metabolites. Dysregulation of the placental metabolome may underpin important (patho)physiological mechanisms involved in preterm birth etiology and long-term neonatal outcomes.

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

The PRotective Effect of Maternal Immunisation on preTerm birth: characterising the Underlying mechanisms and Role in newborn immune function: the PREMITUR study protocol

Maternal immunisation, a low cost and high efficacy intervention is recommended for its pathogen specific protection. Evidence suggests that maternal immunisation has another significant impact: reduction of preterm birth (PTB), the single greatest cause of childhood morbidity and mortality globally. Our overarching question is: how does maternal immunisation modify the immune system in pregnant women and/or their newborn to reduce adverse pregnancy outcomes and enhance the newborn infant's capacity to protect itself from infectious diseases during early childhood? To answer this question we are conducting a multi-site, prospective observational cohort study collecting maternal and infant biological samples at defined time points during pregnancy and post-partum from nulliparous women. We aim to enrol 400 women and determine the immune trajectory in pregnancy and the impact of maternal immunisation (including influenza, pertussis and/or COVID-19 vaccines) on this trajectory. The results are expected to identify areas that can be targeted for future intervention studies.

The Role of Genetics in Preterm Birth

Preterm birth (PTB), defined as the birth of a child before 37 completed weeks gestation, affects approximately 11% of live births and is the leading cause of death in children under 5 years. PTB is a complex disease with multiple risk factors including genetic variation. Much research has aimed to establish the biological mechanisms underlying PTB often through identification of genetic markers for PTB risk. The objective of this review is to present a comprehensive and updated summary of the published data relating to the field of PTB genetics. A literature search in PubMed was conducted and English studies related to PTB genetics were included. Genetic studies have identified genes within inflammatory, immunological, tissue remodeling, endocrine, metabolic, and vascular pathways that may be involved in PTB. However, a substantial proportion of published data have been largely inconclusive and multiple studies had limited power to detect associations. On the contrary, a few large hypothesis-free approaches have identified and replicated multiple novel variants associated with PTB in different cohorts. Overall, attempts to predict PTB using single "-omics" datasets including genomic, transcriptomic, and epigenomic biomarkers have been mostly unsuccessful and have failed to translate to the clinical setting. Integration of data from multiple "-omics" datasets has yielded the most promising results.

Identifying placental metabolic biomarkers of preterm birth using nuclear magnetic resonance of intact tissue samples

INTRODUCTION

Our understanding of the etiology of preterm birth (PTB) is incomplete; however, recent evidence has found a strong association between placental dysfunction and PTB. Altered placental metabolism may precede placental dysfunction and therefore the study of placental metabolic profiles could identify early biomarkers of PTB. In this study, we evaluated the placental metabolome in PTB in intact tissue samples using nuclear magnetic resonance (NMR) and spectral editing.

METHODS

Placental tissue samples were collected from nine term pregnancies and nine preterm pregnancies (<37 weeks' gestation). 1H NMR experiments on unprocessed tissue samples were performed using a high field magnet (500 MHz spectrometer) and a comprehensive multiphase NMR probe. The relative concentrations of 23 metabolites were corrected for gestational age and compared between groups.

RESULTS

The relative concentration of valine, glutamate and creatine were significantly decreased while alanine, choline and glucose were elevated in placentas from PTB pregnancies compared to controls (p < 0.05). Multivariate analysis using principal component analysis showed the PTB and control groups were significantly separated (p < 0.0001) and pathway analysis identified perturbations in the glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis and valine, leucine and isoleucine biosynthesis pathways.

CONCLUSION

PTB is associated with significant alterations in placental metabolism. This study helps improve our understanding of the etiology of PTB. It also highlights the potential for small molecule metabolites to serve as placental metabolic biomarkers to aid in the prediction and diagnosis of PTB. The results can be translated to clinical use via in utero magnetic resonance spectroscopy.

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.

Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics

Preterm birth (PTB) is the leading cause of infant deaths globally. Current clinical measures often fail to identify women who may deliver preterm. Therefore, accurate screening tools are imperative for early prediction of PTB. Here, we show that Raman spectroscopy is a promising tool for studying biological interfaces, and we examine differences in the maternal metabolome of the first trimester plasma of PTB patients and those that delivered at term (healthy). We identified fifteen statistically significant metabolites that are predictive of the onset of PTB. Mass spectrometry metabolomics validates the Raman findings identifying key metabolic pathways that are enriched in PTB. We also show that patient clinical information alone and protein quantification of standard inflammatory cytokines both fail to identify PTB patients. We show for the first time that synergistic integration of Raman and clinical data guided with machine learning results in an unprecedented 85.1% accuracy of risk stratification of PTB in the first trimester that is currently not possible clinically. Correlations between metabolites and clinical features highlight the body mass index and maternal age as contributors of metabolic rewiring. Our findings show that Raman spectral screening may complement current prenatal care for early prediction of PTB, and our approach can be translated to other patient-specific biological interfaces.

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.

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.

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.

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Introduction

Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.

Aim

To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.

Methodology

Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.

Current state

ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.

Future challenges

To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.

Conclusion

The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles

BACKGROUND

Preterm birth (PTB) is the main driver of newborn deaths. The identification of pregnancies at risk of PTB remains challenging, as the incomplete understanding of molecular mechanisms associated with PTB. Although several transcriptome studies have been done on the placenta and plasma from PTB women, a comprehensive description of the RNA profiles from plasma and placenta associated with PTB remains lacking.

METHODS

Candidate markers with consistent trends in the placenta and plasma were identified by implementing differential expression analysis using placental tissue and maternal plasma RNA-seq datasets, and then validated by RT-qPCR in an independent cohort. In combination with bioinformatics analysis tools, we set up two protein-protein interaction networks of the significant PTB-related modules. The support vector machine (SVM) model was used to verify the prediction potential of cell free RNAs (cfRNAs) in plasma for PTB and late PTB.

RESULTS

We identified 15 genes with consistent regulatory trends in placenta and plasma of PTB while the full term birth (FTB) acts as a control. Subsequently, we verified seven cfRNAs in an independent cohort by RT-qPCR in maternal plasma. The cfRNA ARHGEF28 showed consistence in the experimental validation and performed excellently in prediction of PTB in the model. The AUC achieved 0.990 for whole PTB and 0.986 for late PTB.

CONCLUSIONS

In a comparison of PTB versus FTB, the combined investigation of placental and plasma RNA profiles has shown a further understanding of the mechanism of PTB. Then, the cfRNA identified has the capacity of predicting whole PTB and late PTB.

Maternal Plasma RNA in First Trimester Nullipara for the Prediction of Spontaneous Preterm Birth ≤ 32 Weeks: Validation Study

The first-trimester prediction of spontaneous preterm birth (sPTB) has been elusive, and current screening is heavily dependent on obstetric history. However, nullipara lack a relevant history and are at higher risk for spontaneous (s)PTB ≤ 32 weeks compared to multipara. No available objective first-trimester screening test has proven a fair predictor of sPTB ≤ 32 weeks. We questioned whether a panel of maternal plasma cell-free (PCF) RNAs (PSME2, NAMPT, APOA1, APOA4, and Hsa-Let-7g) previously validated at 16-20 weeks for the prediction of sPTB ≤ 32 weeks might be useful in first-trimester nullipara. Sixty (60) nulliparous women (40 with sPTB ≤ 32 weeks) who were free of comorbidities were randomly selected from the King's College Fetal Medicine Research Institute biobank. Total PCF RNA was extracted and the expression of panel RNAs was quantitated by qRT-PCR. The analysis employed, primarily, multiple regression with the main outcome being the prediction of subsequent sPTB ≤ 32 weeks. The test performance was judged by the area under the curve (AUC) using a single threshold cut point with observed detection rates (DRs) at three fixed false positive rates (FPR). The mean gestation was 12.9 ± 0.5 weeks (range 12.0-14.1 weeks). Two RNAs were differentially expressed in women destined for sPTB ≤ 32 weeks: APOA1 (p < 0.001) and PSME2 (p = 0.05). APOA1 testing at 11-14 weeks predicted sPTB ≤ 32 weeks with fair to good accuracy. The best predictive model generated an AUC of 0.79 (95% CI 0.66-0.91) with observed DRs of 41%, 61%, and 79% for FPRs of 10%, 20%, and 30%, including crown-rump length, maternal weight, race, tobacco use, and age.

Evidence for human milk as a biological system and recommendations for study design-a report from "Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)" Working Group 4

Human milk contains all of the essential nutrients required by the infant within a complex matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is a source of bioactive components, living cells and microbes that facilitate the transition to life outside the womb. Our ability to fully appreciate the importance of this matrix relies on the recognition of short- and long-term health benefits and, as highlighted in previous sections of this supplement, its ecology (i.e., interactions among the lactating parent and breastfed infant as well as within the context of the human milk matrix itself). Designing and interpreting studies to address this complexity depends on the availability of new tools and technologies that account for such complexity. Past efforts have often compared human milk to infant formula, which has provided some insight into the bioactivity of human milk, as a whole, or of individual milk components supplemented with formula. However, this experimental approach cannot capture the contributions of the individual components to the human milk ecology, the interaction between these components within the human milk matrix, or the significance of the matrix itself to enhance human milk bioactivity on outcomes of interest. This paper presents approaches to explore human milk as a biological system and the functional implications of that system and its components. Specifically, we discuss study design and data collection considerations and how emerging analytical technologies, bioinformatics, and systems biology approaches could be applied to advance our understanding of this critical aspect of human biology.

Causes of preterm birth: Genetic factors in preterm birth and preterm infant phenotypes

AIM

The aim is to provide an overview of recent research on genetic factors that influence preterm birth in the context of neonatal phenotypic assessment.

METHODS

This is a nonsystematic review of the recent scientific literature.

RESULTS

Maternal and fetal genetic diversity and rare genome variants are linked with crucial immune response sites. In addition, more frequent in preterm neonates, de novo variants may lead to attention deficits, hyperactivity, autism spectrum disorders, and infertility of both sexes later in life. Environmental factors may also greatly burden fetal, and consequently, neonatal development and neurodevelopment through a failure in the fetal epigenome reprogramming process and even influence the initiation of spontaneous preterm pregnancy termination. Minimally invasive analysis of the transcription factors associated with preterm birth helps elucidate labor mechanisms and predict its timing. We also provide valuable summaries of genomic and transcriptomic factors that contribute to preterm birth.

CONCLUSIONS

Investigation of the human genome, epigenome, and transcriptome helps to identify molecular mechanisms linked with preterm delivery and premature newborn clinical appearance in early and late neonatal life and even predict developmental outcomes. Further studies are needed to fully understand the implications of genetic changes in preterm births. These data could be used to develop targeted interventions aimed at selecting the most effective individual treatment and rehabilitation plan.

The influence of maternal factors on the neonatal microbiome and health

The human microbiome plays an essential role in human health. However, the influence of maternal factors on the neonatal microbiome remains obscure. Herein, our observations suggest that the neonatal buccal microbiome is similar to the maternal buccal microbiome, but the neonatal gastrointestinal microbiome develops a unique composition at an early stage. The low complexity of the neonatal buccal microbiome is a hallmark of maternal and neonatal health, but that of the neonatal gastrointestinal microbiome is associated with maternal inflammation-related metabolites. Microbial infections in the maternal reproductive tract universally impact the complexity of the neonatal microbiomes, and the body site is most important in modulating the composition of the neonatal microbiomes. Additionally, maternal lipids attenuated the adverse influence of several maternal factors on the neonatal microbiomes. Finally, admission of neonates to the newborn intensive care unit is associated with sub-optimal states of the maternal buccal and rectal microbiomes and maternal health.

From research cohorts to the patient - a role for "omics" in diagnostics and laboratory medicine?

Human pathologies are complex and might benefit from a more holistic diagnostic approach than currently practiced. Omics is a concept in biological research that aims to comprehensively characterize and quantify large numbers of biological molecules in complex samples, e.g., proteins (proteomics), low molecular weight molecules (metabolomics), glycans (glycomics) or amphiphilic molecules (lipidomics). Over the past decades, respective unbiased discovery approaches have been intensively applied to investigate functional physiological and pathophysiological relationships in various research study cohorts. In the context of clinical diagnostics, omics approaches seem to have potential in two main areas: (i) biomarker discovery i.e. identification of individual marker analytes for subsequent translation into diagnostics (as classical target analyses with conventional laboratory techniques), and (ii) the readout of complex, higher-dimensional signatures of diagnostic samples, in particular by means of spectrometric techniques in combination with biomathematical approaches of pattern recognition and artificial intelligence for diagnostic classification. Resulting diagnostic methods could potentially represent a disruptive paradigm shift away from current one-dimensional (i.e., single analyte marker based) laboratory diagnostics. The underlying hypothesis of omics approaches for diagnostics is that complex, multigenic pathologies can be more accurately diagnosed via the readout of "omics-type signatures" than with the current one-dimensional single marker diagnostic procedures. While this is indeed promising, one must realize that the clinical translation of high-dimensional analytical procedures into routine diagnostics brings completely new challenges with respect to long-term reproducibility and analytical standardization, data management, and quality assurance. In this article, the conceivable opportunities and challenges of omics-based laboratory diagnostics are discussed.

Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.

Multiomics, artificial intelligence, and precision medicine in perinatology

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

Roles of the Microbiota of the Female Reproductive Tract in Gynecological and Reproductive Health

The microbiome of the female reproductive tract defies the convention that high biodiversity is a hallmark of an optimal ecosystem. Although not universally true, a homogeneous vaginal microbiome composed of species of Lactobacillus is generally associated with health, whereas vaginal microbiomes consisting of other taxa are generally associated with dysbiosis and a higher risk of disease. The past decade has seen a rapid advancement in our understanding of these unique biosystems. Of particular interest, substantial effort has been devoted to deciphering how members of the microbiome of the female reproductive tract impact pregnancy, with a focus on adverse outcomes, including but not limited to preterm birth. Herein, we review recent research efforts that are revealing the mechanisms by which these microorganisms of the female reproductive tract influence gynecologic and reproductive health of the female reproductive tract.

Untargeted Metabolome Analysis of Alcohol-Exposed Pregnancies Reveals Metabolite Differences That Are Associated with Infant Birth Outcomes

Prenatal alcohol exposure can produce offspring growth deficits and is a leading cause of neurodevelopmental disability. We used untargeted metabolomics to generate mechanistic insight into how alcohol impairs fetal development. In the Western Cape Province of South Africa, 52 women between gestational weeks 5-36 (mean 18.5 ± 6.5) were recruited, and they provided a finger-prick fasting bloodspot that underwent mass spectrometry. Metabolomic data were analyzed using partial least squares-discriminant analyses (PLS-DA) to identify metabolites that correlated with alcohol exposure and infant birth outcomes. Women who consumed alcohol in the past seven days were distinguished by a metabolite profile that included reduced sphingomyelins, cholesterol, and pregnenolones, and elevated fatty acids, acyl and amino acyl carnitines, and androsterones. Using PLS-DA, 25 of the top 30 metabolites differentiating maternal groups were reduced by alcohol with medium-chain free fatty acids and oxidized sugar derivatives having the greatest influence. A separate ortho-PLS-DA analysis identified a common set of 13 metabolites that were associated with infant length, weight, and head circumference. These included monoacylglycerols, glycerol-3-phosphate, and unidentified metabolites, and most of their associations were negative, implying they represent processes having adverse consequences for fetal development.

Human Plasma Proteome During Normal Pregnancy

The human plasma proteome is underexplored despite its potential value for monitoring health and disease. Herein, using a recently developed aptamer-based platform, we profiled 7288 proteins in 528 plasma samples from 91 normal pregnancies (Gene Expression Omnibus identifier GSE206454). The coefficient of variation was <20% for 93% of analytes (median 7%), and a cross-platform correlation for selected key angiogenic and anti-angiogenic proteins was significant. Gestational age was associated with changes in 953 proteins, including highly modulated placenta- and decidua-specific proteins, and they were enriched in biological processes including regulation of growth, angiogenesis, immunity, and inflammation. The abundance of proteins corresponding to RNAs specific to populations of cells previously described by single-cell RNA-Seq analysis of the placenta was highly modulated throughout gestation. Furthermore, machine learning-based prediction of gestational age and of time from sampling to term delivery compared favorably with transcriptomic models (mean absolute error of 2 weeks). These results suggested that the plasma proteome may provide a non-invasive readout of placental cellular dynamics and serve as a blueprint for investigating obstetrical disease.

On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review

A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.

Infections and Pregnancy: Effects on Maternal and Child Health

Pregnancy causes physiological and immunological adaptations that allow the mother and fetus to communicate with precision in order to promote a healthy pregnancy. At the same time, these adaptations may make pregnant women more susceptible to infections, resulting in a variety of pregnancy complications; those pathogens may also be vertically transmitted to the fetus, resulting in adverse pregnancy outcomes. Even though the placenta has developed a robust microbial defense to restrict vertical microbial transmission, certain microbial pathogens have evolved mechanisms to avoid the placental barrier and cause congenital diseases. Recent mechanistic studies have begun to uncover the striking role of the maternal microbiota in pregnancy outcomes. In this review, we discuss how microbial pathogens overcome the placental barrier to cause congenital diseases. A better understanding of the placental control of fetal infection should provide new insights into future translational research.

Signature for Pain Recovery IN Teens (SPRINT): protocol for a multisite prospective signature study in chronic musculoskeletal pain

INTRODUCTION

Current treatments for chronic musculoskeletal (MSK) pain are suboptimal. Discovery of robust prognostic markers separating patients who recover from patients with persistent pain and disability is critical for developing patient-specific treatment strategies and conceiving novel approaches that benefit all patients. Given that chronic pain is a biopsychosocial process, this study aims to discover and validate a robust prognostic signature that measures across multiple dimensions in the same adolescent patient cohort with a computational analysis pipeline. This will facilitate risk stratification in adolescent patients with chronic MSK pain and more resourceful allocation of patients to costly and potentially burdensome multidisciplinary pain treatment approaches.

METHODS AND ANALYSIS

Here we describe a multi-institutional effort to collect, curate and analyse a high dimensional data set including epidemiological, psychometric, quantitative sensory, brain imaging and biological information collected over the course of 12 months. The aim of this effort is to derive a multivariate model with strong prognostic power regarding the clinical course of adolescent MSK pain and function.

ETHICS AND DISSEMINATION

The study complies with the National Institutes of Health policy on the use of a single internal review board (sIRB) for multisite research, with Cincinnati Children's Hospital Medical Center Review Board as the reviewing IRB. Stanford's IRB is a relying IRB within the sIRB. As foreign institutions, the University of Toronto and The Hospital for Sick Children (SickKids) are overseen by their respective ethics boards. All participants provide signed informed consent. We are committed to open-access publication, so that patients, clinicians and scientists have access to the study data and the signature(s) derived. After findings are published, we will upload a limited data set for sharing with other investigators on applicable repositories.

TRIAL REGISTRATION NUMBER

NCT04285112.

Evaluation of a Maternal Plasma RNA Panel Predicting Spontaneous Preterm Birth and Its Expansion to the Prediction of Preeclampsia

Preterm birth is the principal contributor to neonatal death and morbidity worldwide. We previously described a plasma cell-free RNA panel that between 16 and 20 weeks of pregnancy had potential to predict spontaneous preterm birth (sPTB) ≤ 32 weeks caused by preterm labor (PTL) or preterm premature rupture of membranes (PPROM). The present study had three objectives: (1) estimate the RNA panel prognostic accuracy for PTL/PPROM ≤ 32 weeks in a larger series; (2) improve accuracy by adding clinical characteristics to the predictive model; and (3) examine the association of the RNA panel with preeclampsia. We studied 289 women from Memphis TN prospectively sampled 16.0-20.7 weeks and found: (1) PSME2 and Hsa-Let 7g were differentially expressed in cases of PTL/PPROM ≤ 32 weeks and together provided fair predictive accuracy with AUC of 0.76; (2) combining the two RNAs with clinical characteristics improved good predictive accuracy for PTL/PPROM ≤ 32 weeks (AUC 0.83); (3) NAMPT and APOA1 were differentially expressed in women with 'early-onset preeclampsia' (EOP) and together provided good predictive accuracy with AUC of 0.89; and (4) combining the two RNAs with clinical characteristics provided excellent predictive accuracy (AUC 0.96). Our findings suggest an underlying common pathophysiological relationship between PTL/PPROM ≤ 32 weeks and EOP and open inroads for the prognostication of high-risk pregnancies.