Development and validation of a spontaneous preterm delivery predictor in asymptomatic women

Saade, George R.; Boggess, Kim; Sullivan, Scott; Markenson, Glenn; Iams, Jay D.; Coonrod, Dean V.; Pereira, Leonardo; Esplin, M. Sean; Cousins, Larry; Lam, Garrett; Hoffman, Matthew; Severinsen, Robert D.; Pugmire, Trina; Flick, Jeff; Fox, Angela; Lueth, Amir J.; Rust, Sharon R.; Mazzola, Emanuele; Hsu, ChienTing; Dufford, Max T.; Bradford, Chad; Ichetovkin, Ilia; Fleischer, Tracey C.; Polpitiya, Ashoka D.; Critchfield, Gregory C.; Kearney, Paul; Boniface, J. Jay; Hickok, Durlin E.

doi:10.1016/j.ajog.2016.02.001

Cited by 106 publications

(151 citation statements)

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“…High-dimensional biology techniques allow characterization of the genome (54,55,125,126), transcriptome (55,(127)(128)(129)(130)(131)(132)(133)(134)(135)(136)(137), proteome (55,125,127,(138)(139)(140)(141)(142)(143)(144)(145)(146)(147)(148), lipidome (149)(150)(151)(152)(153)(154)(155), glucome (156,157), metabolome (6,55,128,(158)(159)(160)(161)(162)(163), and cytome (164)(165)(166)(167)(168)(169). Major trust of the new biology and medicine has been to gather the information derived from these platforms to optimize diagnosis, prognosi...…”

Section: Introductionmentioning

confidence: 99%

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

Romero

Erez

Maymon

et al. 2017

American Journal of Obstetrics and Gynecology

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Objective Pregnancy is accompanied by dramatic physiologic changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age, and determine biological processes that are perturbed in normal pregnancy. Materials and methods A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age (AGA) without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median=5,) were profiled to measure the abundance of 1,125 proteins using multiplex assays. Linear mixed effects models with polynomial splines were used to model protein abundance as a function of gestational age, and significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having: 1) more than 1.5 fold change between 8 and 40 weeks of gestation; and 2) a false discovery rate (FDR) adjusted p-value <0.1. Gene ontology enrichment analysis was employed to identify biological processes over-represented among the proteins that changed with advancing gestation. Results 1) Ten percent (112/1,125) of the profiled proteins changed in abundance as a function of gestational age; 2) of the 1,125 proteins analyzed Glypican-3, sialic acid-binding immunoglobulin-type lectins (Siglec)-6, placental growth factor (PlGF), C-C motif (CCL)-28, carbonic anhydrase 6, Prolactin (PRL), interleukin-1 receptor 4 (IL-1 R4), dual specificity mitogen-activated protein kinase 4 (MP2K4) and pregnancy-associated plasma protein-A (PAPP-A) had more than 5 fold change in abundance across gestation. These 9 proteins are known to be involved in a wide range of both physiologic and pathologic processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc.; and 3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis and leukocyte migration (FDR=10%). Conclusions The plasma proteome of normal pregnancy demonstrates dramatic changes in both magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy, development of biomarkers to differentiate normal vs. abnormal pregnancy, and determine the response to interventions.

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Section: Introductionmentioning

confidence: 99%

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

Romero

Erez

Maymon

et al. 2017

American Journal of Obstetrics and Gynecology

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“…While levels of PAPP-A have been found diminished in maternal plasma during the first trimester [11], a variety of other biomarkers studied including free β-hCG have not been found predictive [12]. Development of a single diagnostic assay, particularly one implemented at an early time point, remains an important goal [13, 14]. …”

Section: Introductionmentioning

confidence: 99%

Early first trimester peripheral blood cell microRNA predicts risk of preterm delivery in pregnant women: Proof of concept

Winger¹,

Reed²,

2017

PLoS ONE

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ObjectiveWe investigated the capacity of first trimester peripheral blood mononuclear cell (PBMC) microRNA to determine risk of spontaneous preterm birth among pregnant women.Study designThe study included 39 pregnant women with the following delivery outcomes: 25 with a full term delivery (38–42 weeks gestation) 14 with spontaneous preterm birth (<38 weeks gestation). Of the 14 women experiencing spontaneous preterm birth, 7 delivered at 34-<38 weeks gestation (late preterm) and 7 delivered at <34 weeks gestation (early preterm). Samples were collected at a mean of 7.9±3.0 weeks gestation. Quantitative rtPCR was performed on 30 selected microRNAs. MicroRNA Risk Scores were calculated and Area-Under the Curve-Receiver-Operational-Characteristic (AUC-ROC) curves derived.ResultsThe AUC-ROC for the group delivering preterm (<38 weeks) was 0.95 (p>0.0001). The AUC-ROC for early preterm group (<34 weeks) was 0.98 (p<0.0001) and the AUC-ROC for the late preterm group (34-<38 weeks) was 0.92 (p<0.0001).ConclusionQuantification of first trimester peripheral blood PBMC MicroRNA may provide sensitive and specific prediction of spontaneous preterm birth in pregnant women. Larger studies are needed for confirmation.

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“…The cfRNA results can be compared attempts to estimate preterm risk using mass spectroscopic measurements of the ratio of two proteins (SHBG and IBP4) (25). Our method not only yields higher mean accuracy for comparable sample sizes in the validation cohorts (AUC=0.74 (cfRNA), AUC=0.67(IBP4/SHBG)), but also has several advantages, specifically broader applicability and cost.…”

Section: Resultsmentioning

confidence: 99%

A Noninvasive Molecular Clock for Fetal Development Predicts Gestational Age and Preterm Delivery

Ngo¹,

Moufarrej²,

Rasmussen³

et al. 2017

Preprint

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We performed a high time-resolution, longitudinal study of normal pregnancy development by measuring cell-free RNA (cfRNA) in blood from women during each week of pregnancy. Analysis of tissue-specific transcripts in these samples enabled us to follow fetal and placental development with high resolution and sensitivity, and also to detect gene-specific responses of the maternal immune system to pregnancy. We established a "clock" for normal pregnancy development and enabled a direct molecular approach to determine expected delivery dates with comparable accuracy to ultrasound, creating the basis for a portable, inexpensive fetal dating method. We also identified a related gene set that accurately discriminated women at risk for spontaneous preterm delivery up to two months in advance of labor, forming the basis of a potential screening test for risk of preterm delivery.

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Development and validation of a spontaneous preterm delivery predictor in asymptomatic women

Cited by 106 publications

References 50 publications

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

Early first trimester peripheral blood cell microRNA predicts risk of preterm delivery in pregnant women: Proof of concept

A Noninvasive Molecular Clock for Fetal Development Predicts Gestational Age and Preterm Delivery

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