Prediction of preterm birth with and without preeclampsia using mid-pregnancy immune and growth-related molecular factors and maternal characteristics

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.

Key roles of glial cells in the encephalopathy of prematurity

Across the globe, approximately one in 10 babies are born preterm, that is, before 37 weeks of a typical 40 weeks of gestation. Up to 50% of preterm born infants develop brain injury, encephalopathy of prematurity (EoP), that substantially increases their risk for developing lifelong defects in motor skills and domains of learning, memory, emotional regulation, and cognition. We are still severely limited in our abilities to prevent or predict preterm birth. No longer just the "support cells," we now clearly understand that during development glia are key for building a healthy brain. Glial dysfunction is a hallmark of EoP, notably, microgliosis, astrogliosis, and oligodendrocyte injury. Our knowledge of glial biology during development is exponentially expanding but hasn't developed sufficiently for development of effective neuroregenerative therapies. This review summarizes the current state of knowledge for the roles of glia in infants with EoP and its animal models, and a description of known glial-cell interactions in the context of EoP, such as the roles for border-associated macrophages. The field of perinatal medicine is relatively small but has worked passionately to improve our understanding of the etiology of EoP coupled with detailed mechanistic studies of pre-clinical and human cohorts. A primary finding from this review is that expanding our collaborations with computational biologists, working together to understand the complexity of glial subtypes, glial maturation, and the impacts of EoP in the short and long term will be key to the design of therapies that improve outcomes.

Association of maternal prenatal copper concentration with gestational duration and preterm birth: a multicountry meta-analysis

BACKGROUND

Copper (Cu), an essential trace mineral regulating multiple actions of inflammation and oxidative stress, has been implicated in risk for preterm birth (PTB).

OBJECTIVES

This study aimed to determine the association of maternal Cu concentration during pregnancy with PTB risk and gestational duration in a large multicohort study including diverse populations.

METHODS

Maternal plasma or serum samples of 10,449 singleton live births were obtained from 18 geographically diverse study cohorts. Maternal Cu concentrations were determined using inductively coupled plasma mass spectrometry. The associations of maternal Cu with PTB and gestational duration were analyzed using logistic and linear regressions for each cohort. The estimates were then combined using meta-analysis. Associations between maternal Cu and acute-phase reactants (APRs) and infection status were analyzed in 1239 samples from the Malawi cohort.

RESULTS

The maternal prenatal Cu concentration in our study samples followed normal distribution with mean of 1.92 μg/mL and standard deviation of 0.43 μg/mL, and Cu concentrations increased with gestational age up to 20 wk. The random-effect meta-analysis across 18 cohorts revealed that 1 μg/mL increase in maternal Cu concentration was associated with higher risk of PTB with odds ratio of 1.30 (95% confidence interval [CI]: 1.08, 1.57) and shorter gestational duration of 1.64 d (95% CI: 0.56, 2.73). In the Malawi cohort, higher maternal Cu concentration, concentrations of multiple APRs, and infections (malaria and HIV) were correlated and associated with greater risk of PTB and shorter gestational duration.

CONCLUSIONS

Our study supports robust negative association between maternal Cu and gestational duration and positive association with risk for PTB. Cu concentration was strongly correlated with APRs and infection status suggesting its potential role in inflammation, a pathway implicated in the mechanisms of PTB. Therefore, maternal Cu could be used as potential marker of integrated inflammatory pathways during pregnancy and risk for PTB.

Assessing for prenatal risk factors associated with infant neurologic morbidity using a multivariate analysis

OBJECTIVE

To characterize the biochemical and demographic profiles of pregnant people with maternal immune activation (MIA) and identify the prenatal characteristics associated with neurologic morbidity in offspring.

STUDY DESIGN

This was a retrospective cohort study of 602 mother-infant dyads with births between 2009 and 2010 in California. Multivariable logistic regression was used to build a MIA vulnerability profile including mid-pregnancy biochemical markers and maternal demographic characteristics, and its relationship with infant neurologic morbidity was examined.

RESULTS

Of the 602 mother-infant dyads, 80 mothers and 61 infants had diagnoses suggestive of MIA and neurologic morbidity, respectively. Our model, including two demographic and seven biochemical characteristics, identified mothers with MIA with good performance (AUC:0.814; 95% CI:0.7-0.8). Three demographic and five inflammatory markers together identified 80% of infants with neurological morbidity (AUC:0.802, 95% CI:0.7-0.8).

CONCLUSION

Inflammatory environment in mothers with pre-existing risk factors like obesity, poverty, and prematurity renders offspring more susceptible to neurologic morbidities.

Decreased Adiponectin Levels in Early Pregnancy Are Associated with High Risk of Prematurity for African American Women

The relationship of low maternal serum adiponectin levels with preterm delivery among a multi-ethnic group has not been extensively investigated. We examined ethnic differences in cytokine/adipokine profiles and whether they contribute to several adverse pregnancy outcomes, particularly preterm delivery. Data and samples were from a large prospective observational cohort (n = 1776) of young, generally healthy pregnant women (African American 36.4%, Hispanic 48.0%, Caucasian 15.6%). Serum cytokine/adipokine concentrations were measured at entry (mean gestational age of 16.83 weeks) using the Liminex xMap Technology. Multivariable analyses were performed. A significant difference in adiponectin level was observed among ethnic groups. African Americans had a decreased adiponectin and increased resistin levels compared to Hispanics and Caucasians (p < 0.05 to p < 0.0001 for each). Decreased adiponectin (lowest quartile) was positively associated with preterm delivery independent of usual risk factors (adjusted odds ratio (AOR) 1.46, 95% confidence interval (CI) 1.05, 2.04 for all preterm and AOR 1.84, 95% CI 1.07, 3.17 for early preterm births). The results were unchanged when women with preeclampsia were excluded. Similar results were observed in African Americans. Decreased adiponectin levels were not related to preterm birth in either Hispanics or Caucasians. Lower adiponectin levels were also significantly associated with an increased risk of developing gestational diabetes (AOR 1.72, 95% CI 1.05, 2.84) and preeclampsia (AOR 1.45, 95% CI 1.00, 2.14) in the whole cohort and in Caucasians. We did not find any consistent relationships between the other markers with outcome variables. Dysregulation in maternal adiponectin at early gestation is associated with an increased risk of preterm delivery. An ethnic difference in adiponectin levels may contribute to a higher preterm delivery rate in African American women.

Is there a maternal blood biomarker that can predict spontaneous preterm birth prior to labour onset? A systematic review

INTRODUCTION

The ability to predict spontaneous preterm birth (sPTB) prior to labour onset is a challenge, and it is currently unclear which biomarker(s), may be potentially predictive of sPTB, and whether their predictive power has any utility. A systematic review was conducted to identify maternal blood biomarkers of sPTB.

METHODS

This study was conducted according to PRISMA protocol for systematic reviews. Four databases (MEDLINE, EMBASE, CINAHL, Scopus) were searched up to September 2021 using search terms: "preterm labor", "biomarker" and "blood OR serum OR plasma". Studies assessing blood biomarkers prior to labour onset against the outcome sPTB were eligible for inclusion. Risk of bias was assessed based on the Newcastle Ottawa scale. Increased odds of sPTB associated with maternal blood biomarkers, as reported by odds ratios (OR), or predictive scores were synthesized. This review was not prospectively registered.

RESULTS

Seventy-seven primary research articles met the inclusion criteria, reporting 278 unique markers significantly associated with and/or predictive of sPTB in at least one study. The most frequently investigated biomarkers were those measured during maternal serum screen tests for aneuploidy, or inflammatory cytokines, though no single biomarker was clearly predictive of sPTB based on the synthesized evidence. Immune and signaling pathways were enriched within the set of biomarkers and both at the level of protein and gene expression.

CONCLUSION

There is currently no known predictive biomarker for sPTB. Inflammatory and immune biomarkers show promise, but positive reporting bias limits the utility of results. The biomarkers identified may be more predictive in multi-marker models instead of as single predictors. Omics-style studies provide promising avenues for the identification of novel (and multiple) biomarkers. This will require larger studies with adequate power, with consideration of gestational age and the heterogeneity of sPTB to identify a set of biomarkers predictive of sPTB.

AI aided analysis on saliva crystallization of pregnant women for accurate estimation of delivery date and fetal status

Saliva contains similar molecular components to serum. Analysis of saliva can provide important diagnostic information about the body. Here we report an artificial intelligence (AI) aided home-based method that can let pregnant women perform daily monitoring on their pregnant status and accurate prediction on their delivery date by the pattern analysis of their salivary crystals. The method was developed based on the information obtained from our investigation on the saliva samples of 170 pregnant women about the correlation of the salivary crystal pattern with pregnant age and fetal status. It demonstrated that the patterns of salivary crystallization could act as indicators of the pregnant age, fetal state, and some medical conditions of pregnant women. On this basis, with the aid of AI recognition and analysis of the fractal dimension and some characteristic crystals in the salivary crystallization, we performed estimation on the delivery date in both quantitative and qualitative manners. The accuracy of the prediction on 15 pregnant women was satisfactory: 100 % delivering in the predicted week, 93.3 % within the estimated three days, and 86.7 % on the day as the prediction. We also developed a simple smartphone-based AI-aided salivary crystal imaging and analysis device as an auxiliary means to let pregnant women monitor their fetal status daily at home and predict their delivery date with adequate accuracy.

Association of Maternal Immune Activation during Pregnancy and Neurologic Outcomes in Offspring

OBJECTIVE

To evaluate neurologic morbidity among offspring during their first year of life in association with prenatal maternal immune activation (MIA), using an inclusive definition.

STUDY DESIGN

This retrospective cohort study included singletons born in California between 2011 and 2017. MIA was defined by International Classification of Diseases diagnosis of infection, autoimmune disorder, allergy, asthma, atherosclerosis, or malignancy during pregnancy. Neurologic morbidity in infants was defined by International Classification of Diseases diagnosis of intraventricular hemorrhage, periventricular leukomalacia, seizures, abnormal neurologic examination, or abnormal neurologic imaging. Outcomes of delayed developmental milestones during the first year of life were also explored. Risk of neurologic morbidity in offspring was approximated for women with and without MIA using log link binary regression.

RESULTS

Demographic characteristics among 3 004 166 mother-infant dyads with or without MIA were similar in both groups. Rate of preterm delivery in mothers with MIA (9.4%) was significantly higher than those without MIA (5.6%). Infants of mothers with MIA were more likely to experience neurologic morbidities across all gestational ages. Adjusted relative risk (95% CI) in the exposed infants was 2.0 (1.9-2.1) for abnormal neurologic examination; 1.6 (1.5-1.7) for seizures, and 1.6 (1.4-1.8) for periventricular leukomalacia.

CONCLUSIONS

Our results demonstrate that MIA during pregnancy may be associated with considerably higher risk of neurologic morbidity in offspring.

Using principal component analysis to examine associations of early pregnancy inflammatory biomarker profiles and adverse birth outcomes

OBJECTIVE

Inflammation as a risk factor for preterm birth is well-established. The primary objective of this analysis was to examine whether individual cytokines versus a composite indicator of mid-pregnancy inflammation are significantly associated with risk for adverse birth outcomes.

STUDY DESIGN

A multi-site prospective study was conducted in a socio-demographically diverse cohort of 610 pregnant participants. At a study visit between 12 and 20 6/7 weeks' gestation, low-grade inflammation was measured via log-transformed serum concentrations of the biomarkers IFN-γ, IL-10, IL-13, IL-6, IL-8, TNF-α, and CRP. Principal component analysis (PCA) was used to identify underlying dimensions of inflammatory activity from the seven biomarkers measured. Gestational age and birth weight at delivery were obtained from medical chart review. The associations between inflammatory profiles and birth outcomes were assessed via linear and logistic regression models. Results were compared with those from individual inflammatory biomarkers, and model fit was assessed using Akaike's Information Criterion (AIC).

RESULTS

Principal component analysis analysis yielded a two-factor solution, with the first factor (IF1) composed of IL-8, IL-10, IL-13, IFN-ɣ, and TNF-α, and the second factor (IF2) containing IL-6 and CRP. When adjusted for race, education, BMI, smoking status, gestational age at time of blood draw, and study site, a one standard deviation (SD) increase in IF1 remained significantly associated with a decrease in standardized gestational age (β = -.13, 95% CI: -.21, -.05) and an increase in odds of preterm delivery (OR = 1.46, 95% CI: 1.13, 1.88) (Table 3). A one SD increase in IF2 was similarly associated with a decrease in standardized gestational age at delivery (β = -.13, 95% CI: -.23, -.04) and an increase in odds of preterm delivery (OR: 1.46, 95% CI: 1.04, 2.05). Neither IF1 nor IF2 was associated with measures of fetal growth. AIC identified that IL-6 was a slightly better fit for length of gestation compared to either composite measure, though all performed similarly.

CONCLUSION

Independent of known sociodemographic risk factors, an elevated mid-pregnancy inflammatory profile was associated with a nearly 50% increase in odds of preterm delivery. The composite performed similarly to IL-6. These results suggest that maternal low-grade inflammation is a risk factor for preterm delivery, and that mid-pregnancy inflammatory biomarkers may be useful in predicting risk for preterm delivery.

Association of maternal prenatal selenium concentration and preterm birth: a multicountry meta-analysis

BACKGROUND

Selenium (Se), an essential trace mineral, has been implicated in preterm birth (PTB). We aimed to determine the association of maternal Se concentrations during pregnancy with PTB risk and gestational duration in a large number of samples collected from diverse populations.

METHODS

Gestational duration data and maternal plasma or serum samples of 9946 singleton live births were obtained from 17 geographically diverse study cohorts. Maternal Se concentrations were determined by inductively coupled plasma mass spectrometry analysis. The associations between maternal Se with PTB and gestational duration were analysed using logistic and linear regressions. The results were then combined using fixed-effect and random-effect meta-analysis.

FINDINGS

In all study samples, the Se concentrations followed a normal distribution with a mean of 93.8 ng/mL (SD: 28.5 ng/mL) but varied substantially across different sites. The fixed-effect meta-analysis across the 17 cohorts showed that Se was significantly associated with PTB and gestational duration with effect size estimates of an OR=0.95 (95% CI: 0.9 to 1.00) for PTB and 0.66 days (95% CI: 0.38 to 0.94) longer gestation per 15 ng/mL increase in Se concentration. However, there was a substantial heterogeneity among study cohorts and the random-effect meta-analysis did not achieve statistical significance. The largest effect sizes were observed in UK (Liverpool) cohort, and most significant associations were observed in samples from Malawi.

INTERPRETATION

While our study observed statistically significant associations between maternal Se concentration and PTB at some sites, this did not generalise across the entire cohort. Whether population-specific factors explain the heterogeneity of our findings warrants further investigation. Further evidence is needed to understand the biologic pathways, clinical efficacy and safety, before changes to antenatal nutritional recommendations for Se supplementation are considered.

Prediction and Prevention of Spontaneous Preterm Birth: ACOG Practice Bulletin, Number 234

Preterm birth is among the most complex and important challenges in obstetrics. Despite decades of research and clinical advancement, approximately 1 in 10 newborns in the United States is born prematurely. These newborns account for approximately three-quarters of perinatal mortality and more than one half of long-term neonatal morbidity, at significant social and economic cost (1-3). Because preterm birth is the common endpoint for multiple pathophysiologic processes, detailed classification schemes for preterm birth phenotype and etiology have been proposed (4, 5). In general, approximately one half of preterm births follow spontaneous preterm labor, about a quarter follow preterm prelabor rupture of membranes (PPROM), and the remaining quarter of preterm births are intentional, medically indicated by maternal or fetal complications. There are pronounced racial disparities in the preterm birth rate in the United States. The purpose of this document is to describe the risk factors, screening methods, and treatments for preventing spontaneous preterm birth, and to review the evidence supporting their roles in clinical practice. This Practice Bulletin has been updated to include information on increasing rates of preterm birth in the United States, disparities in preterm birth rates, and approaches to screening and prevention strategies for patients at risk for spontaneous preterm birth.

Data-Driven Modeling of Pregnancy-Related Complications

A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.

Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation

Background

Preterm delivery is a common pregnancy complication that can result in significant neonatal morbidity and mortality. Limited tools exist to predict preterm birth, and none to predict neonatal morbidity, from early in pregnancy. The objective of this study was to determine if the progesterone metabolites 11-deoxycorticosterone (DOC) and 16-alpha hydroxyprogesterone (16α-OHP), when combined with patient demographic and obstetric history known during the pregnancy, are predictive of preterm delivery-associated neonatal morbidity, neonatal length of stay, and risk for spontaneous preterm delivery prior to 32 weeks’ gestation.

Methods and findings

We conducted a cohort study of pregnant women with plasma samples collected as part of Building Blocks of Pregnancy Biobank at the Indiana University School of Medicine. The progesterone metabolites, DOC and 16α-OHP, were quantified by mass spectroscopy from the plasma of 58 pregnant women collected in the late first trimester/early second trimester. Steroid levels were combined with patient demographic and obstetric history data in multivariable logistic regression models. The primary outcome was composite neonatal morbidity as measured by the Hassan scale. Secondary outcomes included neonatal length of stay and spontaneous preterm delivery prior to 32 weeks’ gestation. The final neonatal morbidity model, which incorporated antenatal corticosteroid exposure and fetal sex, was able to predict high morbidity (Hassan score ≥ 2) with an area under the ROC curve (AUROC) of 0.975 (95% CI 0.932, 1.00), while the model without corticosteroid and fetal sex predictors demonstrated an AUROC of 0.927 (95% CI 0.824, 1.00). The Hassan score was highly correlated with neonatal length of stay (p<0.001), allowing the neonatal morbidity model to also predict increased neonatal length of stay (53 [IQR 22, 76] days vs. 4.5 [2, 31] days, above and below the model cut point, respectively; p = 0.0017). Spontaneous preterm delivery prior to 32 weeks’ gestation was also predicted with an AUROC of 0.94 (95% CI 0.869, 1.00).

Conclusions

Plasma levels of DOC and 16α-OHP in early gestation can be combined with patient demographic and clinical data to predict significant neonatal morbidity, neonatal length of stay, and risk for very preterm delivery, though validation studies are needed to verify these findings. Early identification of pregnancies at risk for preterm delivery and neonatal morbidity allows for timely implementation of multidisciplinary care to improve perinatal outcomes.

Maternal cardiovascular disease risk factors as predictors of preterm birth in California: a case-control study

OBJECTIVE

To determine whether maternal cardiovascular disease (CVD) risk factors predict preterm birth.

DESIGN

Case control.

SETTING

California hospitals.

PARTICIPANTS

868 mothers with linked demographic information and biospecimens who delivered singleton births from July 2009 to December 2010.

METHODS

Logistic regression analysis was employed to calculate odds ratios for the associations between maternal CVD risk factors before and during pregnancy (including diabetes, hypertensive disorders and cholesterol levels) and preterm birth outcomes.

PRIMARY OUTCOME

Preterm delivery status.

RESULTS

Adjusting for the other maternal CVD risk factors of interest, all categories of hypertension led to increased odds of preterm birth, with the strongest magnitude observed in the pre-eclampsia group (adjusted OR (aOR), 13.49; 95% CI 6.01 to 30.27 for preterm birth; aOR, 10.62; 95% CI 4.58 to 24.60 for late preterm birth; aOR, 17.98; 95% CI 7.55 to 42.82 for early preterm birth) and chronic hypertension alone for early preterm birth (aOR, 4.58; 95% CI 1.40 to 15.05). Diabetes (types 1 and 2 and gestational) was also associated with threefold increased risk for preterm birth (aOR, 3.06; 95% CI 1.12 to 8.41). A significant and linear dose response was found between total and low-density lipoprotein (LDL) cholesterol and aORs for late and early preterm birth, with increasing cholesterol values associated with increased risk (likelihood χ2 differences of 8.422 and 8.019 for total cholesterol for late and early, and 9.169 and 10.896 for LDL for late and early, respectively). Receiver operating characteristic curves using these risk factors to predict late and early preterm birth produced C statistics of 0.601 and 0.686.

CONCLUSION

Traditional CVD risk factors are significantly associated with an increased risk of preterm birth; these findings reinforce the clinical importance of integrating obstetric and cardiovascular risk assessment across the healthcare continuum in women.

Alterations in endogenous progesterone metabolism associated with spontaneous very preterm delivery

STUDY QUESTION

Do maternal serum levels of progesterone metabolites early in pregnancy correspond to an increased risk for very preterm delivery prior to 32 weeks?

SUMMARY ANSWER

Maternal serum levels of 11-deoxycorticosterone (DOC) measured during the late first trimester or early second trimester correlate with an increased risk for preterm delivery prior to 32 weeks, and the correlation becomes stronger when the ratio of DOC to 16-alpha-hydroxyprogesterone was measured.

WHAT IS KNOWN ALREADY

Progesterone is a pro-gestational steroid hormone that has been shown to decrease the risk of preterm birth in some pregnant women. Progesterone is metabolized by the body into various metabolites including members of the mineralocorticoid and glucocorticoid families. Our group has previously demonstrated that some progesterone metabolites enhance myometrial contractility in an ex vivo system, while others result in myometrial relaxation. The current exploratory study was designed to determine if pre-specified metabolites of progesterone measured early in pregnancy were associated with a woman's risk for delivery prior to 32 weeks, which is referred to as a very preterm delivery.

STUDY DESIGN SIZE DURATION

The Building Blocks of Pregnancy Biobank (BBPB) is a biorepository at Indiana University (IU) that follows women prospectively through their pregnancy. A variety of biospecimens are collected at various time points during a woman's pregnancy. Women participating in the IU BBPB who were enrolled after 8 weeks' gestation with pregnancy outcome data were eligible for participation.

PARTICIPANTS/MATERIALS SETTING METHODS

Women delivering prior to 37 weeks (preterm) and at or after 37 weeks (term) who had blood samples collected during the late first trimester/early second trimester and/or during the early third trimester were identified. These samples were then processed for mass spectroscopy, and the amount of progesterone and progesterone metabolites in the samples were measured. Mean values of each measured steroid metabolite were calculated and compared among women delivering at less than 32 weeks, less than 37 weeks and greater than or equal to 37 weeks. Receiver operating characteristic (ROC) curves were constructed and threshold levels determined for each compound to identify a level above or below which best predicted a woman's risk for delivery prior to 32 and prior to 37 weeks. Mann-Whitney U nonparametric testing with Holm-Bonferroni correction for multiple comparisons was utilized to identify steroid ratios that could differentiate women delivering spontaneously at less than 32 weeks from all other pregnancies.

MAIN RESULTS AND THE ROLE OF CHANCE

Steroid hormone levels and pregnancy outcome data were available for 93 women; 28 delivering prior to 32 weeks, 40 delivering between 32 0/7 and 36 6/7 weeks and 25 delivering at or greater than 37 weeks: the mean gestational age at delivery within the three groups was 27.0, 34.4 and 38.8 weeks, respectively. Among women delivering spontaneously at less than 37 weeks, maternal 11-deoxycorticosterone (DOC) levels drawn in the late first trimester/early second trimester were significantly associated with spontaneous preterm delivery prior to 32 weeks; a threshold level of 47.5 pg/ml had 78% sensitivity, 73% specificity and an AUC of 0.77 (P = 0.044). When DOC levels were analyzed as a ratio with other measured steroid hormones, the ratio of DOC to 16-alpha-hydroxyprogesterone among women delivering spontaneously prior to 37 weeks was able to significantly discriminate women delivering prior to 32 weeks from those delivering at or greater than 32 weeks, with a threshold value of 0.2 with 89% sensitivity, 91% specificity and an AUC of 0.92 (P = 0.002). When the entire study cohort population was considered, including women delivering at term and women having an iatrogenic preterm delivery, the ratio of DOC to 16-alpha-hydroxyprogesterone was able to discriminate women delivering spontaneously prior to 32 weeks from the rest of the population at a threshold of 0.18 and 89% sensitivity, 59% specificity and an AUC of 0.81 (P = 0.003).

LIMITATIONS REASONS FOR CAUTION

This is a discovery study, and the findings have not been validated on an independent cohort. To mitigate issues with multiple comparisons, we limited our study to pre-specified metabolites that are most representative of the major metabolic pathways for progesterone, and adjustments for multiple comparisons were made.

WIDER IMPLICATIONS OF THE FINDINGS

Spontaneous preterm birth is increasingly being recognized to represent a common end pathway for a number of different disease phenotypes that include infection, inflammation, premature rupture of the membranes, uterine over distension, cervical insufficiency, placental dysfunction and genetic predisposition. In addition to these phenotypes, longitudinal changes in the maternal-fetal hypothalamic-pituitary-adrenal (HPA) axis also likely contribute to a significant proportion of the disease burden of spontaneous preterm birth. Here, we demonstrate that differential production of steroid metabolites is associated with very early preterm birth. The identified biomarkers may hint at a pathophysiologic mechanism and changes in the maternal-fetal dyad that result in preterm delivery. The early identification of abnormal changes in HPA axis metabolites may allow for targeted interventions that reverse the aberrant steroid metabolic profile to a more favorable one, thereby decreasing the risk for early delivery. Further research is therefore required to validate and extend the results presented here.

STUDY FUNDING/COMPETING INTERESTS

Funding for this study was provided from the Office of the Vice Chancellor for Research at IUPUI, 'Funding Opportunities for Research Commercialization and Economic Success (FORCES) grant'.Both A.S.P. and C.A.G. are affiliated with Nixxi, a biotech startup. The remaining authors report no conflict of interest.

TRIAL REGISTRATION NUMBER

Not applicable.

Effects of Selective Exclusion of Patients on Preterm Birth Test Performance

The need to reduce the rate of preterm delivery and the recent emergence of technologies that measure hundreds of biological analytes (eg, genomics, transcriptomics, metabolomics, proteomics; collectively referred to as "omics approaches") have led to proliferation of potential diagnostic biomarkers. On review of the literature, a concern must be raised regarding experimental design and data analysis reporting. Specifically, inaccurate performance has often been reported after selective exclusion of patients around the definition boundary of preterm birth. For example, authors may report the performance of a preterm delivery predictor by using patients who delivered early preterm compared with deliveries at 37 weeks of gestation or greater. A key principle that must be maintained during the development of any predictive test is to communicate performance for all patients for whom the test will be applicable clinically (ie, the intended-use population), which for prediction of preterm birth includes patients delivering throughout the spectrum of gestational ages, as this is what is to be predicted, and not known at the time of testing. Using biomarker data collected from the U.S.-based Proteomic Assessment of Preterm Risk clinical trial, we provide examples where the area under the receiver operating characteristic curve for the same test artifactually improves from 0.68 (for preterm delivery at less than 37 weeks of gestation) or 0.76 (for preterm delivery at less than 32 weeks of gestation) to 0.91 when patients who deliver late preterm are excluded. We review this phenomenon in this commentary and offer recommendations for clinicians and investigators going forward. FUNDING SOURCE:: Sera Prognostics.

Genetic Risk Scores for Maternal Lipid Levels and Their Association with Preterm Birth

Maternal lipid profiles are associated with risk for preterm birth (PTB), although the lipid component and effect size are inconsistent between studies. It is also unclear whether these associations are the result of excessive changes in lipid metabolism during pregnancy or genetic variability in genes controlling basal lipid metabolism. This study investigates the association between genetic risk scores (GRS) for four lipid components (high-density lipoprotein [HDL-C], low-density lipoprotein [LDL-C], triacylglycerols [TAG], and total cholesterol [TC]) with risk for PTB. Subjects included 954 pregnant women from California for whom second trimester serum samples were available, of which 479 gave birth preterm and 475 gave birth at term. We genotyped 96 single-nucleotide polymorphisms, which were selected from genome-wide association studies of lipid levels in adult populations. Lipid-specific GRS were constructed for HDL-C, LDL-C, TAG, and TC. The associations between GRS and PTB were analyzed using logistic regression. A higher HDL-C GRS was associated with increased risk for PTB overall and spontaneous PTB. Higher TAG and TC GRS were associated with decreased risk for PTB overall and spontaneous PTB. This study identifies counter-intuitive associations between lipid GRS and spontaneous PTB. Further replication studies are needed to confirm these findings, but they suggest that our current scientific understanding of the relationship between lipid metabolism, PTB, and genetics is incomplete.

The Association of Polymorphisms in Circadian Clock and Lipid Metabolism Genes With 2nd Trimester Lipid Levels and Preterm Birth

Deregulation of the circadian system in humans and animals can lead to various adverse reproductive outcomes due to genetic mutations and environmental factors. In addition to the clock, lipid metabolism may also play an important role in influencing reproductive outcomes. Despite the importance of the circadian clock and lipid metabolism in regulating birth timing few studies have examined the relationship between circadian genetics with lipid levels during pregnancy and their relationship with preterm birth (PTB). In this study we aimed to determine if single nucleotide polymorphisms (SNPs) in genes from the circadian clock and lipid metabolism influence 2^nd trimester maternal lipid levels and if this is associated with an increased risk for PTB. We genotyped 72 SNPs across 40 genes previously associated with various metabolic abnormalities on 930 women with 2^nd trimester serum lipid measurements. SNPs were analyzed for their relationship to levels of total cholesterol, high density lipoprotein (HDL), low density lipoprotein (LDL) and triglycerides (TG) using linear regression. SNPs were also evaluated for their relationship to PTB using logistic regression. Five SNPs in four genes met statistical significance after Bonferroni correction (p < 1.8 × 10^-4) with one or more lipid levels. Of these, four SNPs were in lipid related metabolism genes: rs7412 in APOE with total cholesterol, HDL and LDL, rs646776 and rs599839 in CELSR2-PSRC1-SORT1 gene cluster with total cholesterol, HDL and LDL and rs738409 in PNPLA3 with HDL and TG and one was in a circadian clock gene: rs228669 in PER3 with TG. Of these SNPs only PER3 rs228669 was marginally associated with PTB (p = 0.02). In addition, PER3 rs228669 acts as an effect modifier on the relationship between TG and PTB.

A systematic review and meta-analysis indicates long-term risk of chronic and end-stage kidney disease after preeclampsia

Preeclampsia is a pregnancy-related syndrome of variable severity, classically characterized by acute kidney involvement, with hypertension and/or proteinuria and reduced kidney function. Once considered a self-limited disease healed by delivery, it is now acknowledged that preeclampsia can affect cardiovascular and kidney health in the long term. The entity of risk has not been established and consequently follow-up policies have not been defined. Here we undertook a systematic review to gain better insights into the need for post-preeclampsia follow-up. Articles published between January 2000 and March 2018 were selected, dealing with at least 20 preeclampsia patients, with follow-up of 4 years or more (MEDLINE, Embase, and Cochrane Library). No quality selection or language restriction was performed. Of the 10,510 titles and abstracts originally considered, 21 papers were selected, providing information on 110,803 cases with and 2,680,929 controls without preeclampsia, with partial overlap between studies on the same databases. Heterogeneity was high, and a random meta-analytic model selected. The increase in risk of end stage renal disease after preeclampsia was significant (meta-analytic risk ratios (95% confidence interval) 6.35 (2.73-14.79)); the risk of albuminuria and chronic kidney disease increased but statistical significance was not reached (4.31 (0.95-19.58) and 2.03 (0.58-7.32), respectively). Translating meta-analytic risk into the number of patients who need follow-up to detect one adverse event, 310 patients with preeclampsia are needed to identify one woman with end stage renal disease or four to identify one woman with albuminuria. Heterogeneity in definitions, insufficient follow-up and incomplete recruitment may account for discrepancies. Thus, preeclampsia significantly increases the risk of end stage renal disease. However, there is lack of sufficient data to show a relationship between preeclampsia, albuminuria and chronic kidney disease, underlining the need for further prospective studies.

Second trimester inflammatory and metabolic markers in women delivering preterm with and without preeclampsia

OBJECTIVE

Inflammatory and metabolic pathways are implicated in preterm birth and preeclampsia. However, studies rarely compare second trimester inflammatory and metabolic markers between women who deliver preterm with and without preeclampsia.

STUDY DESIGN

A sample of 129 women (43 with preeclampsia) with preterm delivery was obtained from an existing population-based birth cohort. Banked second trimester serum samples were assayed for 267 inflammatory and metabolic markers. Backwards-stepwise logistic regression models were used to calculate odds ratios.

RESULTS

Higher 5-α-pregnan-3β,20α-diol disulfate, and lower 1-linoleoylglycerophosphoethanolamine and octadecanedioate, predicted increased odds of preeclampsia.

CONCLUSIONS

Among women with preterm births, those who developed preeclampsia differed with respect metabolic markers. These findings point to potential etiologic underpinnings for preeclampsia as a precursor to preterm birth.

Neuroprotection of the preterm brain

Despite notable advances in the care and survival of preterm infants, a significant proportion of preterm neonates will have life-long cognitive, behavioral, and motor deficits, and robustly effective neuroprotective strategies are still missing. These therapies must target the pathophysiologic mechanisms observed in contemporaneous infants and rely on modern epidemiology, imaging, and experimental models and assessment techniques. Two drugs, magnesium sulfate and caffeine, are already in use in several units, and although their targets are apnea of prematurity and myometrial contractility (respectively), they do offer improved odds of positive outcomes. Nevertheless, these drugs have limited efficacy, and NICU-to-NICU administration varies greatly. As such, there is an obvious need for additional specific neurotherapeutic strategies to further enhance the outcome of this very fragile population of neonates. The chapter reviews these issues, highlights bottlenecks that need to be solved for meaningful progress in the field, and proposes future innovative avenues for intervention, including delayed interventions.

Knowledge Gaps and Emerging Research Areas in Intrauterine Growth Restriction-Associated Brain Injury

Intrauterine growth restriction (IUGR) is a complex global healthcare issue. Concerted research and clinical efforts have improved our knowledge of the neurodevelopmental sequelae of IUGR which has raised the profile of this complex problem. Nevertheless, there is still a lack of therapies to prevent the substantial rates of fetal demise or the constellation of permanent neurological deficits that arise from IUGR. The purpose of this article is to highlight the clinical and translational gaps in our knowledge that hamper our collective efforts to improve the neurological sequelae of IUGR. Also, we draw attention to cutting-edge tools and techniques that can provide novel insights into this disorder, and technologies that offer the potential for better drug design and delivery. We cover topics including: how we can improve our use of crib-side monitoring options, what we still need to know about inflammation in IUGR, the necessity for more human post-mortem studies, lessons from improved integrated histology-imaging analyses regarding the cell-specific nature of magnetic resonance imaging (MRI) signals, options to improve risk stratification with genomic analysis, and treatments mediated by nanoparticle delivery which are designed to modify specific cell functions.