Prediction of Preterm Birth: Maternal Characteristics, Ultrasound Markers, and Biomarkers: An Updated Overview.
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ABSTRACT:
SUBMITTER: Oskovi Kaplan ZA
PROVIDER: S-EPMC6199875 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Publications
Prediction of Preterm Birth: Maternal Characteristics, Ultrasound Markers, and Biomarkers: An Updated Overview.
Oskovi Kaplan Zeynep Asli ZA Ozgu-Erdinc A Seval AS
Journal of pregnancy 20181010
There is not a single or combined screening method for preterm birth with high sensitivity which will truly identify the women at risk for preterm birth while also with high specificity to prevent unnecessary interventions and high treatment costs. Measurement of cervical length is the most cost-effective method that is used in clinical practice. Bedside tests have also been developed for detecting markers like fetal fibronectin, insulin-like growth factor binding protein-1 (IGFBP-1), interleuki ...[more]
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Project description:ObjectiveTo examine the relationship between maternal characteristics, serum biomarkers and preterm birth (PTB) by spontaneous and medically indicated subtypes.DesignPopulation-based cohort.SettingCalifornia, United States of America.PopulationFrom a total population of 1 004 039 live singleton births in 2009 and 2010, 841 665 pregnancies with linked birth certificate and hospital discharge records were included.MethodsCharacteristics were compared for term and preterm deliveries by PTB subtype using logistic regression and odds ratios adjusted for maternal characteristics and obstetric factors present in final stepwise models and 95% confidence intervals. First-trimester and second-trimester serum marker levels were analysed in a subset of 125 202 pregnancies with available first-trimester and second-trimester serum biomarker results.Main outcome measurePTB by subtype.ResultsIn fully adjusted models, ten characteristics and three serum biomarkers were associated with increased risk in each PTB subtype (Black race/ethnicity, pre-existing hypertension with and without pre-eclampsia, gestational hypertension with pre-eclampsia, pre-existing diabetes, anaemia, previous PTB, one or two or more previous caesarean section(s), interpregnancy interval ? 60 months, low first-trimester pregnancy-associated plasma protein A, high second-trimester ?-fetoprotein, and high second-trimester dimeric inhibin A). These risks occurred in 51.6-86.2% of all pregnancies ending in PTB depending on subtype. The highest risk observed was for medically indicated PTB <32 weeks in women with pre-existing hypertension and pre-eclampsia (adjusted odds ratio 89.7, 95% CI 27.3-111.2).ConclusionsOur findings suggest a shared aetiology across PTB subtypes. These commonalities point to targets for further study and exploration of risk reduction strategies.Tweetable abstractFindings suggest a shared aetiology across preterm birth subtypes. Patterns may inform risk reduction efforts.
| S-EPMC4704442 | biostudies-literature
Project description:OBJECTIVE:To evaluate if mid-pregnancy immune and growth-related molecular factors predict preterm birth (PTB) with and without (±) preeclampsia. STUDY DESIGN:Included were 400 women with singleton deliveries in California in 2009-2010 (200 PTB and 200 term) divided into training and testing samples at a 2:1 ratio. Sixty-three markers were tested in 15-20 serum samples using multiplex technology. Linear discriminate analysis was used to create a discriminate function. Model performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS:Twenty-five serum biomarkers along with maternal age <34 years and poverty status identified >80% of women with PTB?±?preeclampsia with best performance in women with preterm preeclampsia (AUC?=?0.889, 95% confidence interval (0.822-0.959) training; 0.883 (0.804-0.963) testing). CONCLUSION:Together with maternal age and poverty status, mid-pregnancy immune and growth factors reliably identified most women who went on to have a PTB?±?preeclampsia.
| S-EPMC6089890 | biostudies-literature
Project description:IntroductionPrediction models may contribute to personalized risk-based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models.Material and methodsPrediction models based on routinely collected maternal parameters obtainable during first 16 weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web-based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation. Clinical value was evaluated by means of decision curve analysis and calculating classification accuracy for different risk thresholds.ResultsFour studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation, respectively. A subanalysis showed that the models discriminated poorly (AUC 0.51-0.56) for nulliparous women. Although we recalibrated the models, two models retained evidence of overfitting. The decision curve analysis showed low clinical benefit for the best performing models.ConclusionsThis review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth.
| S-EPMC6099449 | biostudies-literature
Project description:Preterm birth (PTB) is a major public health challenge, and novel, sensitive approaches to predict PTB are still evolving. Epigenomic markers are being explored as biomarkers of PTB because of their molecular stability compared to gene expression. This approach is also relatively new compared to gene-based diagnostics, which relies on mutations or single nucleotide polymorphisms. The fundamental principle of epigenome diagnostics is that epigenetic reprogramming in the target tissue (e.g. placental tissue) might be captured by more accessible surrogate tissue (e.g. blood) using biochemical epigenome assays on circulating DNA that incorporate methylation, histone modifications, nucleosome positioning, and/or chromatin accessibility. Epigenomic-based biomarkers may hold great potential for early identification of the majority of PTBs that are not associated with genetic variants or mutations. In this review, we discuss recent advances made in the development of epigenome assays focusing on its potential exploration for association and prediction of PTB. We also summarize population-level cohort studies conducted in the USA and globally that provide opportunities for genetic and epigenetic marker development for PTB. In addition, we summarize publicly available epigenome resources and published PTB studies. We particularly focus on ongoing genome-wide DNA methylation and epigenome-wide association studies. Finally, we review the limitations of current research, the importance of establishing a comprehensive biobank, and possible directions for future studies in identifying effective epigenome biomarkers to enhance health outcomes for pregnant women at risk of PTB and their infants.
| S-EPMC7293830 | biostudies-literature
Project description:We reviewed the evidence for three theories of how preconceptional psychosocial stress could act as a contributing determinant of excess preterm birth risk among African American women: early life developmental plasticity and epigenetic programming of adult neuroendocrine systems; blunting, weathering, or dysfunction of neuroendocrine and immune function in response to chronic stress activation through the life course; individuals' adoption of risky behaviors such as smoking as a response to stressful stimuli.Basic science, clinical, and epidemiologic studies indexed in MEDLINE and Web of Science databases on preconceptional psychosocial stress, preterm birth and race were reviewed.Mixed evidence leans towards modest associations between preconceptional chronic stress and preterm birth (for example common odds ratios of 1.2-1.4), particularly in African American women, but it is unclear whether this association is causal or explains a substantial portion of the Black-White racial disparity in preterm birth. The stress-preterm birth association may be mediated by hypothalamic-pituitary-adrenal axis dysfunction and susceptibility to bacterial vaginosis, although these mechanisms are incompletely understood. Evidence for the role of epigenetic or early life programming as a determinant of racial disparities in preterm birth risk is more circumstantial.Preconceptional stress, directly or in interaction with host genetic susceptibility or infection, remains an important hypothesized risk factor for understanding and reducing racial disparities in preterm birth. Future studies that integrate adequately sized epidemiologic samples with measures of stress, infection, and gene expression, will advance our knowledge and allow development of targeted interventions.
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Project description:Maternal lipid profiles during pregnancy are associated with risk for preterm birth. This study investigates the association between maternal dyslipidemia and subsequent preterm birth among pregnant women in the state of California. Births were identified from California birth certificate and hospital discharge records from 2007-2012 (N = 2,865,987). Preterm birth was defined as <37 weeks completed gestation and dyslipidemia was defined by diagnostic codes. Subtypes of preterm birth were classified as preterm premature rupture of membranes (PPROM), spontaneous labor, and medically indicated, according to birth certificate data and diagnostic codes. The association between dyslipidemia and preterm birth was tested with logistic regression. Models were adjusted for maternal age at delivery, race/ethnicity, hypertension, pre-pregnancy body mass index, insurance type, and education. Maternal dyslipidemia was significantly associated with increased odds of preterm birth (adjusted OR: 1.49, 95%CI: 1.39, 1.59). This finding was consistent across all subtypes of preterm birth, including PPROM (adjusted OR: 1.54, 95%CI: 1.34, 1.76), spontaneous (adjusted OR: 1.51, 95%CI: 1.39, 1.65), and medically indicated (adjusted OR: 1.454, 95%CI: 1.282, 1.649). This study suggests that maternal dyslipidemia is associated with increased risk for all types of preterm birth.
| S-EPMC6303099 | biostudies-literature
Project description:Preterm birth is the major cause of newborn and infant mortality affecting nearly one in every ten live births. This study was designed to develop an epigenetic biomarker for susceptibility of preterm birth using buccal cells from the mother, father, and child (triads). MeDIP-seq was used to identify differential DNA methylation regions (DMRs) using a comparison of control term birth versus preterm birth triads. Epigenetic DMR associations with preterm birth were identified for both the mother and father that were distinct and suggest potential epigenetic contributions from both parents. The mother (165 DMRs) and female child (136 DMRs) at p<1e-04 had the highest number of DMRs and were highly similar suggesting potential epigenetic inheritance of the epimutations. The male child had negligible DMR associations. The DMR associated genes for each group involve previously identified preterm birth associated genes.
2022-03-09 | GSE194227 | GEO
Project description:BackgroundPreterm 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 findingsWe 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).ConclusionsPlasma 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.
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Project description:Endogenous signaling molecules derived from lipids, peptides, and DNA, are important regulators of physiological processes during pregnancy. The effect of their collective impact on preterm birth (delivery < 37 weeks gestation) is understudied. We aimed to characterize the associations and predictive capacity of an extensive panel of eicosanoids, immune biomarkers, oxidative stress markers, and growth factors towards preterm birth and its subtypes. We conducted a cross-sectional study of pregnant women (recruited < 15 weeks gestation) in the LIFECODES birth cohort, which included 58 cases of preterm birth and 115 controls that delivered term. Among the cases there were 31 cases who had a spontaneous preterm birth (cases who had spontaneous preterm labor and/or preterm premature rupture of membranes) and 25 that had preterm birth associated with aberrant placentation (cases who had preeclampsia and/or intrauterine growth restriction) and 2 cases that could not be sufficiently categorized as either. We analyzed single biomarker associations with each preterm birth outcome using multiple logistic regression. Adaptive elastic-net was implemented to perform a penalized multiple logistic regression on all biomarkers simultaneously to identify the most predictive biomarkers. We then organized biomarkers into biological groups and by enzymatic pathways and applied adaptive elastic-net and random forest to evaluate the accuracy of each group for predicting preterm birth cases. The majority of associations we observed were for spontaneous preterm birth, and adaptive elastic-net identified 5-oxoeicosatetraenoic acid, resolvin D1, 5,6-epoxy-eicsatrienoic acid, and 15-deoxy-12,14-prostaglandin J2 as most predictive. Overall, lipid biomarkers performed the best at separating cases from controls compared to other biomarker categories (adaptive elastic-net AUC = 0.78 [0.62, 0.94], random forest AUC = 0.84 [0.72, 0.96]). Among the enzymatic pathways that differentiate eicosanoid metabolites, we observed the highest prediction of overall preterm birth by lipoxygenase metabolites using random forest (AUC = 0.83 [0.69, 0.96]), followed by cytochrome p450 metabolites using adaptive elastic-net (AUC = 0.74 [0.52, 0.96]). In this study we translate biological hypothesis into the language of modern machine learning. Many lipid biomarkers were highly associated with overall and spontaneous preterm birth. Among eicosanoids, lipoxygenase and cytochrome p450 products performed best in identifying overall and spontaneous preterm birth. The combination of lipid biomarkers may have good utility in clinical settings to predict preterm birth.
| S-EPMC6863859 | biostudies-literature
Project description:Background:Air pollution has been associated with hypertension and preterm birth. We examined if prenatal exposure to air pollutants was associated with gestational hypertension and if its association with preterm birth was modified by maternal hypertension. Methods:Data were from birth certificates and hospital discharge records of 252,205 women in San Joaquin Valley of California from 2000-2006. Air quality data were assigned from 24-hour averages of nitrogen dioxide (NO2), particulate matter <10?m (PM10) and <2.5?m (PM2.5), and carbon monoxide (CO) for different averaging periods over pregnancy. We estimated odds of preterm birth and multiplicative interaction between each pollutant and hypertensive disorder. Results:Among normotensive women, odds of preterm birth were slightly higher for higher exposure to all pollutants over the entire pregnancy. Patterns were similar among women with a hypertensive disorder. Among 32-36 week births there was effect modification for exposure to NO2 and CO during the first trimester with higher odds among hypertensive women, and PM2.5 and CO during the last six weeks with higher odds among normotensive women. For 28-31 week births, there was effect modification by hypertensive status for PM10 exposure for entire pregnancy, first, and second trimester with hypertensive women consistently having lower odds of preterm birth than normotensive. Conclusion:There was some evidence of effect modification in the direction counter to our hypothesis for exposure to PM10 and early preterm birth, and CO and PM2.5 at the end of pregnancy, but overall, hypertension did not modify the relationship between pollution and preterm birth.
| S-EPMC7015251 | biostudies-literature