Association of early-preterm birth with abnormal levels of routinely collected first- and second-trimester biomarkers

doi:10.1016/j.ajog.2013.02.012

American Journal of Obstetrics and Gynecology

Volume 208, Issue 6, June 2013, Pages 492.e1-492.e11

https://doi.org/10.1016/j.ajog.2013.02.012 Get rights and content

Objective

The purpose of this study was to examine the relationship between typically measured prenatal screening biomarkers and early-preterm birth in euploid pregnancies.

Study Design

The study included 345 early-preterm cases (<30 weeks of gestation) and 1725 control subjects who were drawn from a population-based sample of California pregnancies who had both first- and second-trimester screening results. Logistic regression analyses were used to compare patterns of biomarkers in cases and control subjects and to develop predictive models. Replicability of the biomarker early-preterm relationships that was revealed by the models was evaluated by examination of the frequency and associated adjusted relative risks (RRs) for early-preterm birth and for preterm birth in general (<37 weeks of gestation) in pregnancies with identified abnormal markers compared with pregnancies without these markers in a subsequent independent California cohort of screened pregnancies (n = 76,588).

Results

The final model for early-preterm birth included first-trimester pregnancy-associated plasma protein A in the ≤5th percentile, second-trimester alpha-fetoprotein in the ≥95th percentile, and second-trimester inhibin in the ≥95th percentile (odds ratios, 2.3–3.6). In general, pregnancies in the subsequent cohort with a biomarker pattern that were found to be associated with early-preterm delivery in the first sample were at an increased risk for early-preterm birth and preterm birth in general (<37 weeks of gestation; adjusted RR, 1.6–27.4). Pregnancies with ≥2 biomarker abnormalities were at particularly increased risk (adjusted RR, 3.6–27.4).

Conclusion

When considered across cohorts and in combination, abnormalities in routinely collected biomarkers reveal predictable risks for early-preterm birth.

Section snippets

Materials and Methods

Evaluation of early-preterm biomarker relationships was undertaken in 2 independent datasets; one set was used for model building (the “training” study set), and one set was used for model testing (the “testing” study set). The training study set included 345 early-preterm singleton cases (<30 weeks of gestation) and 1725 term singleton pregnancies (control subjects) with expected dates of delivery in September 2009 through December 2010. These cases and control subjects were drawn from 497,023

Results

Early-preterm cases and term control subjects in the training study set were mostly Hispanic (43.2% and 40.5%, respectively) and were between 18 and 34 years old (72.8% and 72.1%, respectively). Pregnancies that resulted in early-preterm birth were more likely than term control subjects to be black, regardless of early-preterm grouping (spontaneous labor, medically indicated, or combined; OR, 2.5 and 4.3; Table 1).

Cases in the training study set (with spontaneous or medically indicated preterm

Comment

This study explored population-level screening and clinical data from 2 separate population cohorts to investigate whether preterm birth (overall and by medically indicated and spontaneous labor subgroups) was associated with single and multiple biomarker abnormalities. This study showed increased risks for early-preterm birth when the first-trimester PAPP-A level was low and/or when second-trimester AFP and/or inhibin levels were high, irrespective of whether the preterm birth was spontaneous

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The timely identification of nulliparas at high risk of adverse fetal and neonatal outcomes during pregnancy is crucial for initiating clinical interventions to prevent perinatal complications. Although machine learning methods have been applied to predict preterm birth and other pregnancy complications, many models do not provide explanations of their predictions, limiting the clinical use of the model.
This study aimed to develop interpretable prediction models for a composite adverse perinatal outcome (stillbirth, neonatal death, estimated Combined Apgar score of <10, or preterm birth) at different points in time during the pregnancy and to evaluate the marginal predictive value of individual predictors in the context of a machine learning model.
This was a secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be data, a prospective cohort study in which 10,038 nulliparous pregnant individuals with singleton pregnancies were enrolled. Here, interpretable prediction models were developed using L1-regularized logistic regression for adverse perinatal outcomes using data available at 3 study visits during the pregnancy (visit 1: 6 0/7 to 13 6/7 weeks of gestation; visit 2: 16 0/7 to 21 6/7 weeks of gestation; visit 3: 22 0/7 to 29 6/7 weeks of gestation). We identified the important predictors for each model using SHapley Additive exPlanations, a model-agnostic method of computing explanations of model predictions, and evaluated the marginal predictive value of each predictor using the DeLong test.
Our interpretable machine learning model had an area under the receiver operating characteristic curves of 0.617 (95% confidence interval, 0.595–0.639; all predictor variables at visit 1), 0.652 (95% confidence interval, 0.631–0.673; all predictor variables at visit 2), and 0.673 (95% confidence interval, 0.651–0.694; all predictor variables at visit 3). For all visits, the placental biomarker inhibin A was a valuable predictor, as including inhibin A resulted in better performance in predicting adverse perinatal outcomes (P<.001, all visits). At visit 1, endoglin was also a valuable predictor (P<.001). At visit 2, free beta human chorionic gonadotropin (P=.001) and uterine artery pulsatility index (P=.023) were also valuable predictors. At visit 3, cervical length was also a valuable predictor (P<.001).
Despite various advances in predictive modeling in obstetrics, the accurate prediction of adverse perinatal outcomes remains difficult. Interpretable machine learning can help clinicians understand how predictions are made, but barriers exist to the widespread clinical adoption of machine learning models for adverse perinatal outcomes. A better understanding of the evolution of risk factors for adverse perinatal outcomes throughout pregnancy is necessary for the development of effective interventions.
Placental transcriptomic signatures of spontaneous preterm birth
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Other studies have identified unique molecular signatures in early-preterm infants (both spontaneous and medically indicated).44,45 A study involving biomarkers collected during the first trimester screening identified a unique subset of biomarkers associated with early PTB.44 Another multi-omics assessment of maternal whole blood identified unique differences in the methylome and transcriptome associated with very-early preterm labor (<28 weeks’ gestation), but that were not significantly associated with preterm vs term labor or other preterm phenotypes.45
Spontaneous preterm birth accounts for most preterm births and leads to significant morbidity in the newborn and childhood period. This subtype of preterm birth represents an increasing proportion of all preterm births when compared with medically indicated preterm birth, yet it is understudied in omics analyses. The placenta is a key regulator of fetal and newborn health, and the placental transcriptome can provide insight into pathologic changes that lead to spontaneous preterm birth.
This analysis aimed to identify genes for which placental expression was associated with spontaneous preterm birth (including early preterm and late preterm birth).
The ECHO PATHWAYS consortium extracted RNA from placental samples collected from the Conditions Affecting Neurocognitive Development and Learning in Early Childhood and the Global Alliance to Prevent Prematurity and Stillbirth studies. Placental transcriptomic data were obtained by RNA sequencing. Linear models were fit to estimate differences in placental gene expression between term birth and spontaneous preterm birth (including gestational age subgroups defined by the American College of Obstetricians and Gynecologists). Models were adjusted for numerous confounding variables, including labor status, cohort, and RNA sequencing batch. This analysis excluded patients with induced labor, chorioamnionitis, multifetal gestations, or medical indications for preterm birth. Our combined cohort contained gene expression data for 14,023 genes in 48 preterm and 540 term samples. Genes and pathways were considered statistically significantly different at false discovery rate–adjusted P value of <.05.
In total, we identified 1728 genes for which placental expression was associated with spontaneous preterm birth with more differences in expression in early preterm samples than late preterm samples when compared with full-term samples. Of those, 9 genes were significantly decreased in both early and late spontaneous preterm birth, and the strongest associations involved placental expression of IL1B, ALPL, and CRLF1. In early and late preterm samples, we observed decreased expression of genes involved in immune signaling, signal transduction, and endocrine function.
This study provides a comprehensive assessment of the differences in the placental transcriptome associated with spontaneous preterm birth with robust adjustment for confounding. Results of this study are in alignment with the known etiology of spontaneous preterm birth, because we identified multiple genes and pathways for which the placental and chorioamniotic membrane expression was previously associated with prematurity, including IL1B. We identified decreased expression in key signaling pathways that are essential for placental growth and function, which may be related to the etiology of spontaneous preterm birth. We identified increased expression of genes within metabolic pathways associated exclusively with early preterm birth. These signaling and metabolic pathways may provide clinically targetable pathways and biomarkers. The findings presented here can be used to understand underlying pathologic changes in premature placentas, which can inform and improve clinical obstetrics practice.
Early pregnancy prediction of gestational diabetes mellitus risk using prenatal screening biomarkers in nulliparous women
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To evaluate the clinical utility of first and second trimester prenatal screening biomarkers for early pregnancy prediction of gestational diabetes mellitus (GDM) risk in nulliparous women.
We conducted a population-based cohort study of nulliparous women participating in the California Prenatal Screening Program from 2009 to 2011 (n = 105,379). GDM was ascertained from hospital discharge records or birth certificates. Models including maternal characteristics and prenatal screening biomarkers were developed and validated. Risk stratification and reclassification were performed to assess clinical utility of the biomarkers.
Decreased levels of first trimester pregnancy-associated plasma protein A (PAPP-A) and increased levels of second trimester unconjugated estriol (uE₃) and dimeric inhibin A (INH) were associated with GDM. The addition of PAPP-A only and PAPP-A, uE₃, and INH to maternal characteristics resulted in small, yet significant, increases in area under the receiver operating characteristic curve (AUC) (maternal characteristics only: AUC 0.714 (95% CI 0.703–0.724), maternal characteristics + PAPP-A: AUC 0.718 (95% CI 0.707–0.728), maternal characteristics + PAPP-A, uE₃, and INH: AUC 0.722 (0.712–0.733)); however, no net improvement in classification was observed.
PAPP-A, uE₃, and INH have limited clinical utility for prediction of GDM risk in nulliparous women. Utility of other readily accessible clinical biomarkers in predicting GDM risk warrants further investigation.
Understanding periviable birth: A microeconomic alternative to the dysregulation narrative
2019, Social Science and Medicine
Periviable infants (i.e., those born in the 20th through 26th weeks of gestation) suffer much morbidity and approximately half die in the first year of life. Attempts to explain and predict these births disproportionately invoke a “dysregulation” narrative. Research inspired by this narrative has not led to efficacious interventions. The clinical community has, therefore, urged novel approaches to the problem. We aim to provoke debate by offering the theory, inferred from microeconomics, that risk tolerant women carry, without cognitive involvement, high risk fetuses farther into pregnancy than do other women. These extended high-risk pregnancies historically ended in stillbirth but modern obstetric practices now convert a fraction to periviable births. We argue that this theory deserves testing because it suggests inexpensive and noninvasive screening for pregnancies that might benefit from the costly and invasive interventions clinical research will likely devise.
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Moreover, there is controversy about the association of abnormal concentrations of uE3 with unfavorable perinatal outcomes. For example, no correlation between elevated uE3 concentration and PTB was reported by Jelliffe-Pawlowski et al., however, Oslen et al. observed a direct linkage between high maternal serum uE3 concentrations and a higher incidence of PTB [10,11]. On the other hand, limited data have been published regarding the role of triple screen for Down syndrome in second-trimester as a predicting tool for pregnancy outcomes in Chinese pregnant women.
We examined the associations between Down's serum screening analytes and pregnancy outcomes in Chinese women.
A retrospective cohort study of 2470 pregnant women was conducted. Maternal serum triple tests (AFP, fβ-hCG, uE3), maternal characteristics and pregnancy outcomes were recorded from our prenatal screening and hospitalization information system, respectively.
The elevated concentration of uE3 in the early-second trimester was associated with increased risk of LGA infants and macrosomia, decreased risk of PE and small SGA infants (for LGA: OR: 1.34, 95% CI: 1.09–1.65; for macrosomia: OR:1.39, 95% CI: 1.08–1.78; for PE: OR: 0.61, 95% CI: 0.40–0.95; for SGA: OR: 0.35, 95% CI: 0.25–0.49). The increased ratio of AFP/uE3 was associated with reduced risk of GDM in the study populations (BMI ≥ 25; OR: 0.96, 95% CI: 0.0.93–1.00). The higher ratio of AFP/fβ-hCG + uE3 associated with increased risk of SGA infants and ICP in these subjects (BMI ≥ 25) was also observed (for SGA: OR: 1.11, 95% CI: 1.03–1.18; for ICP: OR: 1.27, 95% CI: 1.06–1.53).
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: Funded in part by grants from the National Institutes of Health/National Heart, Lung and Blood Institute (RC2 HL101748) and the March of Dimes Prematurity Center, Stanford University School of Medicine, Stanford, CA.

: The authors report no conflict of interest.

: Cite this article as: Jelliffe-Pawlowski LL, Shaw GM, Currier RJ, et al. Association of early-preterm birth with abnormal levels of routinely collected first- and second-trimester biomarkers. Am J Obstet Gynecol 2013;208:492.e1-11.

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ResearchGeneticsAssociation of early-preterm birth with abnormal levels of routinely collected first- and second-trimester biomarkers

Objective

Study Design

Results

Conclusion

Section snippets

Materials and Methods

Results

Comment

Placenta

Placenta

Placenta

Am J Obstet Gynecol

Am J Obstet Gynecol

Clin Perinatol

Int J Gynaecol Obstet

Am J Obstet Gynecol

Am J Obstet Gynecol

Biochem Biophys Res Commun

Eur J Obstet Gynecol Reprod Biol

Obstet Gynecol

Am J Obstet Gynecol

Corticotrophin-releasing hormone directly and preferentially stimulates dehydroepiandrosterone sulfate secretion by human fetal adrenal cortical cells

J Clin Endocrinol Metab

On the role of human chorionic gonadotropin (hCG) in the embryo-endometrial microenvironment: implications for differentiation and implantation

Semin Reprod Med

Intrauterine infection and preterm delivery

N Engl J Med

Modulation of PAPP-A expression by PPARgamma in human first trimester trophoblast

Placenta

TGF-beta superfamily expression and actions in the endometrium and placenta

Reproduction

Physiology of alpha-fetoprotein as a biomarker for perinatal distress: relevance to adverse pregnancy outcome

Exp Biol Med (Maywood )

Defective implantation and placentation: laying the blueprint for pregnancy complications

Reprod Biomed Online

Parturition

N Engl J Med

PPARgamma and early human placental development

Curr Med Chem

Patterns of plasma corticotropin-releasing hormone, progesterone, estradiol, and estriol change and the onset of human labor

J Clin Endocrinol Metab

Increased nuchal translucency in euploid fetuses: what should we be telling the parents?

Prenat Diagn

Research
Genetics
Association of early-preterm birth with abnormal levels of routinely collected first- and second-trimester biomarkers