Mode of delivery predicts postpartum maternal leukocyte telomere length.

BACKGROUND: Recent studies have suggested that pregnancy accelerates biologic aging, yet little is known about how biomarkers of aging are affected by events during the peripartum period. Given that immune shifts are known to occur following surgery, we explored the relation between mode of delivery and postpartum maternal leukocyte telomere length (LTL), a marker of biologic aging. STUDY DESIGN: Postpartum maternal blood samples were obtained from a prospective cohort of term, singleton livebirths without hypertensive disorders or peripartum infections between 2012 and 2018. The primary outcome was postpartum LTLs from one blood sample drawn between postpartum week 1 and up to 6 months postpartum, measured from thawed frozen peripheral blood mononuclear cells using quantitative PCR in basepairs (bp). Multivariable linear regression models compared LTLs between vaginal versus cesarean births, adjusting for age, body mass index, and nulliparity as potential confounders. Analyses were conducted in two mutually exclusive groups: those with LTL measured postpartum week 1 and those measured up to 6 months postpartum. Secondarily, we compared multiomics by mode of delivery using machine-learning methods to evaluate whether other biologic changes occurred following cesarean. These included transcriptomics, metabolomics, microbiomics, immunomics, and proteomics (serum and plasma). RESULTS: Of 67 included people, 50 (74.6 %) had vaginal and 17 (25.4 %) had cesarean births. LTLs were significantly shorter after cesarean in postpartum week 1 (5755.2 bp cesarean versus 6267.8 bp vaginal, p = 0.01) as well as in the later draws (5586.6 versus 5945.6 bp, p = 0.04). After adjusting for confounders, these differences persisted in both week 1 (adjusted beta -496.1, 95 % confidence interval [CI] -891.1, -101.1, p = 0.01) and beyond (adjusted beta -396.8; 95 % CI -727.2, -66.4. p = 0.02). Among the 15 participants who also had complete postpartum multiomics data available, there were predictive signatures of vaginal versus cesarean births in transcriptomics (cell-free [cf]RNA), metabolomics, microbiomics, and proteomics that did not persist after false discovery correction. CONCLUSION: Maternal LTLs in postpartum week 1 were nearly 500 bp shorter following cesarean. This difference persisted several weeks postpartum, even though other markers of inflammation had normalized. Mode of delivery should be considered in any analyses of postpartum LTLs and further investigation into this phenomenon is warranted.

Association of pregnancy complications and postpartum maternal leukocyte telomeres in two diverse cohorts: a nested case-control study.

BACKGROUND: Biologic strain such as oxidative stress has been associated with short leukocyte telomere length (LTL), as well as with preeclampsia and spontaneous preterm birth, yet little is known about their relationships with each other. We investigated associations of postpartum maternal LTL with preeclampsia and spontaneous preterm birth. METHODS: This pilot nested case control study included independent cohorts of pregnant people with singleton gestations from two academic institutions: Cohort 1 (hereafter referred to as Suburban) were enrolled prior to 20 weeks' gestation between 2012 and 2018; and Cohort 2 (hereafter referred to as Urban) were enrolled at delivery between 2000 and 2012. Spontaneous preterm birth or preeclampsia were the selected pregnancy complications and served as cases. Cases were compared with controls from each study cohort of uncomplicated term births. Blood was collected between postpartum day 1 and up to 6 months postpartum and samples were frozen, then simultaneously thawed for analysis. Postpartum LTL was the primary outcome, measured using quantitative polymerase chain reaction (PCR) and compared using linear multivariable regression models adjusting for maternal age. Secondary analyses were done stratified by mode of delivery and self-reported level of stress during pregnancy. RESULTS: 156 people were included; 66 from the Suburban Cohort and 90 from the Urban Cohort. The Suburban Cohort was predominantly White, Hispanic, higher income and the Urban Cohort was predominantly Black, Haitian, and lower income. We found a trend towards shorter LTLs among people with preeclampsia in the Urban Cohort (6517 versus 6913 bp, p = 0.07), but not in the Suburban Cohort. There were no significant differences in LTLs among people with spontaneous preterm birth compared to term controls in the Suburban Cohort (6044 versus 6144 bp, p = 0.64) or in the Urban Cohort (6717 versus 6913, p = 0.37). No differences were noted by mode of delivery. When stratifying by stress levels in the Urban Cohort, preeclampsia was associated with shorter postpartum LTLs in people with moderate stress levels (p = 0.02). CONCLUSION: Our exploratory results compare postpartum maternal LTLs between cases with preeclampsia or spontaneous preterm birth and controls in two distinct cohorts. These pilot data contribute to emerging literature on LTLs in pregnancy.

Solving the Puzzle of Preterm Birth.

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

Predicting Preterm Birth Using Proteomics.

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

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

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

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

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

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

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

Advances to diminish global newborn kernicterus mortality.

OBJECTIVE: Initiatives, "Every Newborn Action Plans" and "Sustainable Developmental Goals," are profoundly shaping global infant mortality trends. Concurrently, professional organizations recommended curricula to prevent extreme hyperbilirubinemia (EHB) sequelae. Therefore we assessed if these efforts have successfully decreased EHB-related mortality over time. STUDY DESIGN: We used the Global Burden of Diseases 2019 database to determine neonatal and infant mortality and the burden of kernicterus from 1990-2019. RESULTS: Globally, kernicterus accounted for 2.8 million infant deaths and trended downwards significantly from 1990 to 2019. By 2019, kernicterus-related mortality was 4 and 293 per million livebirths in high (HICs) and low income countries (LICs), respectively. 82% of deaths occurred in LICs and lower-middle income-countries. Average declines of mortality rates were 6.2% and 3.0% for HICs and LICs, respectively. CONCLUSIONS: Kernicterus-related mortality has been effectively reduced to <5 per million in HICs. Skills and knowledge transfer can potentially transform frontline services to bridge discordant kernicteric outcomes worldwide.

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.

Gestational Dating by Urine Metabolic Profile at High Resolution Weekly Sampling Timepoints: Discovery and Validation.

Background: Pregnancy triggers longitudinal metabolic alterations in women to allow precisely-programmed fetal growth. Comprehensive characterization of such a "metabolic clock" of pregnancy may provide a molecular reference in relation to studies of adverse pregnancy outcomes. However, a high-resolution temporal profile of metabolites along a healthy pregnancy remains to be defined. Methods: Two independent, normal pregnancy cohorts with high-density weekly urine sampling (discovery: 478 samples from 19 subjects at California; validation: 171 samples from 10 subjects at Alabama) were studied. Urine samples were profiled by liquid chromatography-mass spectrometry (LC-MS) for untargeted metabolomics, which was applied for gestational age dating and prediction of time to delivery. Results: 5,473 urinary metabolic features were identified. Partial least-squares discriminant analysis on features with robust signals (n = 1,716) revealed that the samples were distributed on the basis of the first two principal components according to their gestational age. Pathways of bile secretion, steroid hormone biosynthesis, pantohenate, and CoA biosynthesis, benzoate degradation, and phenylpropanoid biosynthesis were significantly regulated, which was collectively applied to discover and validate a predictive model that accurately captures the chronology of pregnancy. With six urine metabolites (acetylcholine, estriol-3-glucuronide, dehydroepiandrosterone sulfate, α-lactose, hydroxyexanoy-carnitine, and l-carnitine), models were constructed based on gradient-boosting decision trees to date gestational age in high accordance with ultrasound results, and to accurately predict time to delivery. Conclusion: Our study characterizes the weekly baseline profile of the human pregnancy metabolome, which provides a high-resolution molecular reference for future studies of adverse pregnancy outcomes.