Biomarkers Of Spontaneous Preterm Birth Research Articles

Early pregnancy biomarker discovery study for spontaneous preterm birth

(1) Objectivediscover new candidate biomarkers for spontaneous preterm birth in early pregnancy samples. When fully clinically validated, early pregnancy biomarkers for sPTB give the possibility to intervene or monitor high-risk pregnancies more intensively through, as example, pelvic exams, ultrasound or sonographic cervical length surveillance. (2) Study designEarly pregnancy serum samples of eight spontaneous extreme and very preterm birth cases (<32 weeks of gestational age) without any symptoms of preeclampsia and fetal growth restriction and eight uncomplicated pregnancies were analyzed by liquid chromatography mass spectrometry (LC-MS). Thirteen proteins, which were differentially expressed according to the LC-MS data, were subsequently selected for confirmation by enzyme-linked immunosorbent assay (ELISA). (3) ResultsDifferential expression of four candidate biomarkers was confirmed by ELISA with decreased early pregnancy levels of gelsolin and fibulin-1 and increased levels of c-reactive protein and complement C5 in the preterm birth group. (4) ConclusionsThe confirmed candidate biomarkers are all to some extent related to inflammatory pathways and/or the complement system. This supports the hypothesis that both play a role in extreme and very preterm birth without any symptoms of preeclampsia and fetal growth restriction. The predictive value of complement C5, c-reactive protein, fibulin-1 and gelsolin should, therefore, be validated in another cohort with early pregnancy samples.

Integrated analysis of transcriptomic datasets to identify placental biomarkers of spontaneous preterm birth

IntroductionPreterm birth (PTB) remains the leading cause of neonatal morbidity and mortality in the United States. The mechanisms underlying spontaneous PTB (SPTB) involve multiple physiological processes and molecular transformations at the level of the placenta. This study aimed to identify consistent molecular correlates in the placenta linked with SPTB by cross-examining publicly available transcriptomic datasets within two publicly available repositories. MethodsThe National Center for Biotechnology Information and the European Bioinformatics Institute were queried, and relevant datasets were independently normalized, and then merged based on similarity in design. Differentially expressed genes between SPTB and term delivery (TD) were identified using a fixed effects linear model (p < 0.0001) and were evaluated for enrichment of biological processes and pathways. In general, global signatures associated with SPTB were unique to each study. ResultsA total of three datasets were used in the meta-analysis to assess the placental transcriptome in SPTB (11 samples) as compared to TD (15 samples). We identified 174 differentially expressed genes consistently correlated with SPTB across all studies, including previously proposed and new candidate biomarkers of SPTB. Differentially expressed genes were significantly enriched for master regulatory pathways relevant to placental development and disease, including chromatin organization and cellular response to stress. DiscussionIdentification of differentially expressed genes and associated pathways across multiple studies may identify transcriptomic biomarkers that can be applied in clinical investigations of SPTB and provide researchers enhanced insight into the underlying etiologies of SPTB.

The amniotic fluid cell-free transcriptome in spontaneous preterm labor

The amniotic fluid (AF) cell-free RNA was shown to reflect physiological and pathological processes in pregnancy, but its value in the prediction of spontaneous preterm delivery is unknown. Herein we profiled cell-free RNA in AF samples collected from women who underwent transabdominal amniocentesis after an episode of spontaneous preterm labor and subsequently delivered within 24 h (n = 10) or later (n = 28) in gestation. Expression of known placental single-cell RNA-Seq signatures was quantified in AF cell-free RNA and compared between the groups. Random forest models were applied to predict time-to-delivery after amniocentesis. There were 2385 genes differentially expressed in AF samples of women who delivered within 24 h of amniocentesis compared to gestational age-matched samples from women who delivered after 24 h of amniocentesis. Genes with cell-free RNA changes were associated with immune and inflammatory processes related to the onset of labor, and the expression of placental single-cell RNA-Seq signatures of immune cells was increased with imminent delivery. AF transcriptomic prediction models captured these effects and predicted delivery within 24 h of amniocentesis (AUROC = 0.81). These results may inform the development of biomarkers for spontaneous preterm birth.

A Comprehensive Integrative Review of the Factors Associated with Spontaneous Preterm Birth, Its Prevention and Prediction, Including Metabolomic Markers.

Preterm birth is a major maternal complication that has a great impact on perinatal and neonatal health, with consequences suffered during childhood and adulthood. Little is known about its etiology and development, resulting in poor screening, prediction and preventive methods. The present integrative review discusses the current knowledge regarding some risk factors for preterm birth, the differences between screening and prediction methods, the limitations of some current preventive interventions, the importance of applying standardized concepts for exposures and outcomes, and why it is important to develop more accurate and reproducible methods to predict preterm birth. In addition, the authors introduce the concept of metabolomics and the technology involved in this technique, and discuss about how it has become a promising approach to identify biomarkers for spontaneous preterm birth.

Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression

In this work, we provide a computational method of regularized logistic regression for discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data. The successful identification of SPTB biomarkers will greatly benefit the interference of infant gestational age for reducing the risks of pregnant women and preemies. In recent years, various approaches have been proposed for the feature selection of identifying the subset of meaningful genes that can achieve accurate classification for disease samples from controls. Here, we comprehensively summarize the regularized logistic regression with seven effective penalties developed for the selection of strongly indicative genes of SPTB from microarray data. We compare their properties and assess their classification performances in multiple datasets. It shows that elastic net, lasso, L1/2 and SCAD penalties get the better performance than others and can be successfully used to identify biomarkers of SPTB. Particularly, we make a functional enrichment analysis on these biomarkers and construct a logistic regression classifier based on them. The classifier generates an indicator of preterm risk score (PRS) for predicting SPTB. Based on the trained predictor, we verify the identified biomarkers on an independent dataset. The biomarkers achieve the AUC value of 0.933 in the SPTB classification. The results demonstrate the effectiveness and efficiency of the built-up strategy of biomarker discovery with regularized logistic regression. Obviously, the proposed method of discovering biomarkers for SPTB can be easily extended for other complex diseases.

Cervicovaginal fluid proteomic analysis to identify potential biomarkers for preterm birth

Spontaneous preterm birth is a leading cause of neonatal morbidity and mortality. Early identification of at-risk women by reliable screening tests could reduce the spontaneous preterm birth rate, but conventional methods such as obstetrical history and maternal cervical length screening identify only a minority of spontaneous preterm birth cases. Cervicovaginal fluid might prove to be a useful, readily available biological fluid for identifying spontaneous preterm birth biomarkers. The objective of the study was to identify cervicovaginal fluid biomarkers of early spontaneous preterm birth in a high-risk cohort of pregnant women with a history of spontaneous preterm birth using targeted and shotgun proteomic analyses. A nested case control study (cases were spontaneous preterm birth <34 weeks in the current pregnancy; controls were spontaneous labor and delivery at 39-41 weeks) was performed using cervicovaginal fluid samples collected at 3 study visits (100/7 to 186/7 weeks, 190/7 to 236/7 weeks, and 280/7 to 316/7 weeks). All participants had a history of at least 1 prior spontaneous preterm birth. Targeted proteomic analysis was performed using a stable isotope-labeled proteome derived from endocervical and vaginal mucosal cells. This served as a standard to quantitate candidate protein levels in individual cervicovaginal fluid samples from the second and third study visits using liquid chromatography-multiple reaction monitoring mass spectrometry. The ratio of endogenous peptide area/stable isotope-labeled proteome-derived peptide area was used to measure levels of 42 peptides in 22 proteins. To maximize biomarker discovery in the cervicovaginal fluid samples, shotgun proteomic analysis also was performed utilizing liquid chromatography and ion trap mass spectrometry. A validation study was performed in second-trimester cervicovaginal fluid samples from an independent study group (12 spontaneous preterm birth cases, 19 term delivery controls) using enzyme-linked immunosorbent assay for 5 proteins expressed at higher levels in spontaneous preterm birth cases compared with controls in targeted or shotgun proteomic analyses. For targeted proteomics, cervicovaginal fluid samples from 33 cases and 32 controls at 190/7 to 236/7 weeks and 16 cases and 14 controls at 280/7 to 316/7 weeks from the same pregnancies were analyzed. When samples were compared between cases and controls, the relative abundance of 5 proteins was greater (P= .02-.05) in cases at both visits, while the relative abundance of 1 protein was lower (P= .03) in cases at both visits. For shotgun proteomics analyses, cervicovaginal fluid samples were pooled for 9 spontaneous preterm birth cases and 9 term delivery controls at each study visit. Shotgun proteomics yielded 28 proteins that were detected at levels >2 times higher and 1 protein that was detected at a level <0.5 times lower in spontaneous preterm birth cases compared with controls at all 3 study visits. Validation enzyme-linked immunosorbent assay for 5 proteins that were detected at higher levels in cervicovaginal fluid samples from spontaneous preterm birth cases compared with term delivery controls in proteomics analyses did not demonstrate statistically significant differences between spontaneous preterm birth cases and controls. Potential biomarkers of spontaneous preterm birth were identified by targeted and shotgun proteomics analyses in cervicovaginal fluid samples from high-risk, asymptomatic women. Many of the proteins detected at higher levels in cervicovaginal fluid samples from spontaneous preterm birth cases are extracellular matrix proteins and/or regulate cell membrane physiology. These proteins have substantial biological interest, but validation enzyme-linked immunosorbent assay for 5 of these proteins did not yield clinically useful biomarkers for spontaneous preterm birth.

Biomarkers of spontaneous preterm birth: a systematic review of studies using multiplex analysis.

Despite decades of research on risk indicators of spontaneous preterm birth (PTB), reliable biomarkers are still not available to screen or diagnose high-risk pregnancies. Several biomarkers in maternal and fetal compartments have been mechanistically linked to PTB, but none of them are reliable predictors of pregnancy outcome. This systematic review was conducted to synthesize the knowledge on PTB biomarkers identified using multiplex analysis. Three electronic databases (PubMed, EMBASE and Web of Science) were searched for studies in any language reporting the use of multiplex assays for maternal biomarkers associated with PTB published from January 2005 to March 2014. Retrieved citations (3631) were screened, and relevant studies (33) were selected for full-text reading. Ten studies were included in the review. Forty-two PTB-related proteins were reported, and RANTES and IL-10 (three studies) followed by MIP-1β, GM-CSF, Eotaxin, and TNF-RI (two studies) were reported more than once in maternal serum. However, results could not be combined due to heterogeneity in type of sample, study population, assay, and analysis methods. By this systematic review, we conclude that multiplex assays are a potential technological advancement for identifying biomarkers of PTB, although no single or combination of biomarkers could be identified to predict PTB risk.

Circulating serum-derived microparticles provide novel proteomic biomarkers of spontaneous preterm birth.

The purpose of this study was to determine whether the proteomic biosignature of circulating microparticles in maternal serum obtained in the second trimester could identify pregnancies that result in spontaneous preterm birth (SPTB). Microparticles were isolated from blinded biorepository-sourced serum samples from 48 pregnant women at 15 to 17 weeks of gestation. Microparticle proteins were extracted and analyzed using label-free liquid chromatography/mass spectrometry. Peptide features were analyzed to assess the association of specific protein patterns with subjects delivering at term (≥ 37 weeks gestation; n = 24) and those experiencing SPTB (≤ 34 weeks gestation; n = 24). We found 99 proteins that had statistically significant differences in signal intensity between term and SPTB women in both first (n = 26) and second (n = 22) singleton gestation pregnancy cohorts. Additional evaluation identified 18 biomarkers that met criteria for further priority evaluation (12 preterm, 6 term). Pathway analysis showed that differentiating SPTB biomarker proteins were predominantly associated with inflammation and cell injury, while differentiating term biomarkers were associated with cell growth and hematological parameters. This study shows for the first time that the proteomic content of serum microparticles isolated in the second trimester can identify with a high degree of accuracy pregnancies that result in SPTB.

Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth

To develop classification models of demographic/clinical factors and biomarker data from spontaneous preterm birth in African Americans and Caucasians. Secondary analysis of biomarker data using multivariate adaptive regression splines (MARS), a supervised machine learning algorithm method. Analysis of data on 36 biomarkers from 191 women was reduced by MARS to develop predictive models for preterm birth in African Americans and Caucasians. Maternal plasma, cord plasma collected at admission for preterm or term labor and amniotic fluid at delivery. Data were partitioned into training and testing sets. Variable importance, a relative indicator (0-100%) and area under the receiver operating characteristic curve (AUC) characterized results. Multivariate adaptive regression splines generated models for combined and racially stratified biomarker data. Clinical and demographic data did not contribute to the model. Racial stratification of data produced distinct models in all three compartments. In African Americans maternal plasma samples IL-1RA, TNF-α, angiopoietin 2, TNFRI, IL-5, MIP1α, IL-1β and TGF-α modeled preterm birth (AUC train: 0.98, AUC test: 0.86). In Caucasians TNFR1, ICAM-1 and IL-1RA contributed to the model (AUC train: 0.84, AUC test: 0.68). African Americans cord plasma samples produced IL-12P70, IL-8 (AUC train: 0.82, AUC test: 0.66). Cord plasma in Caucasians modeled IGFII, PDGFBB, TGF-β1 , IL-12P70, and TIMP1 (AUC train: 0.99, AUC test: 0.82). Amniotic fluid in African Americans modeled FasL, TNFRII, RANTES, KGF, IGFI (AUC train: 0.95, AUC test: 0.89) and in Caucasians, TNF-α, MCP3, TGF-β3 , TNFR1 and angiopoietin 2 (AUC train: 0.94 AUC test: 0.79). Multivariate adaptive regression splines models multiple biomarkers associated with preterm birth and demonstrated racial disparity.

Proteomic Biomarkers for Spontaneous

This review aimed to identify, synthesize, and analyze the findings of studies on proteomic biomarkers for spontaneous preterm birth (PTB). Three electronic databases (Medline, Embase, and Scopus) were searched for studies in any language reporting the use of proteomic biomarkers for PTB published between January 1994 and December 2012. Retrieved citations were screened, and relevant studies were selected for full-text reading, in triplicate. The search yielded 529 citations, 51 were selected for full-text reading and 8 studies were included in the review. A total of 64 dysregulated proteins were reported. Only 14-3-3 protein sigma, annexin A5, protein S100-A8, protein S100-A12, and inter-α-trypsin inhibitor heavy chain H4 were reported in more than 1 study, but results could not be combined due to heterogeneity in type of sample and analytical platform. In conclusion, according to the existing literature, there are no specific proteomic biomarkers capable of accurately predicting PTB.

487: Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth

Objective. To develop classification models of demographic/clinical factors and biomarker data from spontaneous preterm birth in African Americans and Caucasians. Design. Secondary analysis of biomarker data using multivariate adaptive regression splines (MARS), a supervised machine learning algorithm method. Setting. Analysis of data on 36 biomarkers from 191 women was reduced by MARS to develop predictive models for preterm birth in African Americans and Caucasians. Samples. Maternal plasma, cord plasma collected at admission for preterm or term labor and amniotic fluid at delivery. Methods. Data were partitioned into training and testing sets. Variable importance, a relative indicator (0–100%) and area under the receiver operating characteristic curve (AUC) characterized results. Results. Multivariate adaptive regression splines generated models for combined and racially stratified biomarker data. Clinical and demographic data did not contribute to the model. Racial stratification of data produced distinct models in all three compartments. In African Americans maternal plasma samples IL-1RA, TNF-a, angiopoietin 2, TNFRI, IL-5, MIP1a, IL-1b and TGF-a modeled preterm birth (AUC train: 0.98, AUC test: 0.86). In Caucasians TNFR1, ICAM-1 and IL-1RA contributed to the model (AUC train: 0.84, AUC test: 0.68). African Americans cord plasma samples produced IL-12P70, IL-8 (AUC train: 0.82, AUC test: 0.66). Cord plasma in Caucasians modeled IGFII, PDGFBB, TGF-b1, IL-12P70, and TIMP1 (AUC train: 0.99, AUC test: 0.82). Amniotic fluid in African Americans modeled FasL, TNFRII, RANTES, KGF, IGFI (AUC train: 0.95, AUC test: 0.89) and in Caucasians, TNF-a, MCP3, TGF-b3, TNFR1 and angiopoietin 2 (AUC train: 0.94 AUC test: 0.79). Conclusions. Multivariate adaptive regression splines models multiple biomarkers associated with preterm birth and demonstrated racial disparity.

Biomarkers of Spontaneous Preterm Birth: An Overview of The Literature in the Last Four Decades

Understanding spontaneous preterm birth ([PTB] < 37 weeks) is difficult due to heterogeneities associated with multitudes of risk factors and pathophysiological pathways. Several biomarkers are routinely used clinically for predicting preterm labor; however, these factors are either nonspecific or detected too late. Systematic review of literature on PTB biomarkers in the last 40 years to map out the existing knowledge and gaps in understanding PTB biomarkers. Five electronic databases were searched for human studies on PTB biomarkers published in any language between 1965 and 2008. The phenotype of interest for final data extraction was exclusively spontaneous PTB with no rupture of membranes. Data extraction included (a) general characteristics of the study (clinical setting, period, and study design), (b) study/participant characteristics (inclusion and exclusion criteria, race/ethnicity, number of participants, gestational age at sampling, (c) characteristics of the biomarker (type, rationale for its selection, type of biological sample, and assay used, and (d) concentration of biomarkers in cases and controls. The search yielded 7255 citations and data were extracted from 217 articles which met our inclusion and exclusion criteria. A total of 116 different biomarkers were reported and these were assayed 578 times in the 217 included studies. Over two thirds of the 217 studies were performed on North American or European populations. No reliable biomarkers emerged as a risk predictor of PTB. Identifying similar studies on biomarkers for the prediction of PTB was a very challenging task due heterogeneities in study design, sampling issues (types, timing and processing), assay methods, and analyses. Major areas of concern identified in this review include poor phenotype definition, nonideal study designs and poor rationale for biomarker selection and assays and population stratification issues.

Serum biomarkers of spontaneous preterm birth

Serum biomarkers of spontaneous preterm birth

Serum biomarkers of spontaneous preterm birth

The rationale for the prediction of spontaneous preterm birth (sPTB) is three-fold. First, delineating those factors predictive of PTB, we may obtain a better understanding of the mechanisms and biologic pathways that lead to spontaneous preterm parturition. Second, the use of predictors of sPTB permits the identification of a group of women at the highest risk for whom an intervention may be tested and for whom intervention is most critically needed. The third motivation for the prediction of sPTB is a corollary of the second; by identifying women at low risk of PTB, unnecessary, costly, and sometime hazardous interventions might be avoided. With respect to the source of biomarkers, there are two fundamental compartments of interest: the maternal and the fetal. The maternal compartment may be subdivided into serum, saliva, urine, cervix, and vagina. The fetal compartment may be subdivided into cord blood and amniotic fluid. This brief review will focus solely on the maternal serum compartment. The maternal serum compartment offers several distinct theoretical advantages. Obtaining maternal serum is less invasive than obtaining amniotic fluid or fetal blood. Maternal blood is drawn routinely at several points in time during prenatal care, and thus, a serum biomarker could be incorporated into the usual provision of care. Using maternal serum for biomarkers of PTB also facilitates central processing and analysis. After minimal processing at the point of care, blood specimens may be transported to a central facility for processing and analysis. This model of central processing and analysis has already been successfully incorporated into the aspects of routine prenatal care, such as serum screening for open neural tube defects and serum screening for aneuploidy (1, 2). Serum biomarkers of sPTB may be categorized by using the notion of the syndrome of PTB (3) and the four distinct pathways to PTB forwarded by Lockwood and Kuczynski (4): activation of maternal or fetal hypothalamic–pituitary–adrenal axis (e.g. corticotropin-releasing hormone), upper genital tract infection (e.g. defensins and tumor necrosis factor-α), decidual hemorrhage (e.g. thrombin–anti-thrombin III complex), and pathologic uterine overdistension. To date, no serum biomarkers of pathologic uterine overdistension have been described. Given the known pathophysiologic heterogeneity of sPTB, the clinical utility of any individual biomarker for predicting PTB is limited. To that end, Goldenberg and colleagues sought to evaluate a ‘multiple marker test’ for PTB in The Preterm Prediction Study of the Maternal–Fetal Medicine Units Network of the National Institutes of Child Health and Human Development (5). This was a nested case-control study performed within a cohort of 2929 women with a singleton gestation recruited from the general obstetric population of the 10 participating centers. Women, in this study, underwent serial assessment of serum, cervical, vaginal, ultrasound, and historical markers or risk factors. The serum biomarkers considered in this study have been listed below. The markers associated with sPTB at <32 weeks were α-fetoprotein, alkaline phosphatase, granulocyte-macrophage colony-stimulating factor, and defensins. Only α-fetoprotein and alkaline phosphatase were related to sPTB at <35 weeks. Importantly, the overlap between the biomarkers was small, supporting the notion that there are several heterogeneous pathways to sPTB. In fact, the utilization of the serum markers in concert improved the predictive ability. Any one ‘positive’ serum marker had 81% sensitivity and 78% specificity for sPTB at <32 weeks and 60% sensitivity and 73% specificity for sPTB at <35 weeks. Because this was a nested case-control study, the true positive and negative predictive values are uncertain. No individual serum biomarker of sPTB is useful when used alone in an symptomatic low-risk obstetric population. The notion of a ‘multiple marker’ approach may improve the predictive ability, but no prediction model, to date, provides adequate utility to justify routine clinical use. The future development of an effective prediction model may permit the evaluation of targeted therapies. Future directions for research in this arena will involve the optimization and validation of multiple markers in diverse prospective cohorts. Novel marker development will be facilitated by the use of high-throughput proteomic and metabolomic technologies and the consideration of more complex modeling techniques, such as neural networks and artificial intelligence. Corticotropin-releasing hormone, α-fetoprotein, alkaline phosphatase, β2-macroglobulin, ferritin, ICAM-1, interleukin-6, C-reactive protein, cortisol, lactoferrin, defensins, relaxin, interleukin-10, granulocyte-macrophage colony-stimulating factor, and activin.