Abstract
Introduction
A short cervix (cervical length <25 mm) in the midtrimester (18–24 weeks) of pregnancy is a powerful predictor of spontaneous preterm delivery. Although the biological mechanisms of cervical change during pregnancy have been the subject of extensive investigation, little is known about whether genes influence the length of the cervix, or the extent to which genetic factors contribute to premature cervical shortening. Defining the genetic architecture of cervical length is foundational to understanding the aetiology of a short cervix and its contribution to an increased risk of spontaneous preterm delivery.
Methods/analysis
The proposed study is designed to characterise the genetic architecture of cervical length and its genetic relationship to gestational age at delivery in a large cohort of Black/African American women, who are at an increased risk of developing a short cervix and delivering preterm. Repeated measurements of cervical length will be modelled as a longitudinal growth curve, with parameters estimating the initial length of the cervix at the beginning of pregnancy, and its rate of change over time. Genome-wide complex trait analysis methods will be used to estimate the heritability of cervical length growth parameters and their bivariate genetic correlation with gestational age at delivery. Polygenic risk profiling will assess maternal genetic risk for developing a short cervix and subsequently delivering preterm and evaluate the role of cervical length in mediating the relationship between maternal genetic variation and gestational age at delivery.
Ethics/dissemination
The proposed analyses will be conducted using deidentified data from participants in an IRB-approved study of longitudinal cervical length who provided blood samples and written informed consent for their use in future genetic research. These analyses are preregistered with the Center for Open Science using the AsPredicted format and the results and genomic summary statistics will be published in a peer-reviewed journal.
Keywords: maternal medicine, fetal medicine, obstetrics, perinatology, genetics, statistics & research methods
Strengths and limitations of this study.
This study will be the first to characterise the genetic architecture of cervical length and its longitudinal change during pregnancy.
This study will be the first to estimate the bivariate genetic correlation between cervical length and gestational age at delivery.
While the study cohort is not large enough to identify individual genetic variants associated with cervical length, it is well powered to analyse aggregate genome-wide summary statistics in order to estimate trait heritability and bivariate genetic correlations, and to develop a polygenic risk score to identify women with the highest risk of developing a short cervix and delivering preterm.
The study cohort predominately comprises women who self-identify as Black/African American; although the findings of this study may not be generalisable to women from other populations or ancestry groups, they could improve screening and clinical care for a population of women who are disproportionally affected by health disparities in preterm birth and perinatal outcomes.
Background and introduction
A short cervix (cervical length <25 mm) in the midtrimester (18–24 weeks) of pregnancy is a powerful predictor of maternal risk for delivering preterm (<37 weeks),1–29 and the only biomarker for spontaneous preterm birth that can be coupled with an effective clinical intervention.30–47 Preterm birth and prematurity-related conditions are the leading causes of perinatal morbidity and mortality worldwide48–51 and in the USA,52 53 where there is a pronounced and persistent racial disparity in the incidence of preterm birth and its associated health outcomes.54–59 Although the rate of medically indicated preterm births is on the rise,48 51 58 60 most preterm births are idiopathic, and occur spontaneously.51 60 Thus, prevention of spontaneous preterm birth remains a major public health priority.56 58 61 62 A better understanding of the primary pathogenic mechanisms contributing to spontaneous preterm birth is needed to develop effective strategies for reducing the morbidity and mortality associated with prematurity.
Despite extensive evidence of a genetic contribution to gestational age at birth,63–83 there has been little success identifying specific genetic variants that influence the timing of labour and delivery.84 The difficulty in gene discovery may be due, in part, to the syndromic nature of spontaneous preterm birth.85 86 Multiple mechanistic pathways in both the mother and the fetus—each influenced by unique genes and environmental risk factors—are thought to contribute to the premature onset of labour.49 58 60 87 For this reason, statistical genetic methods may be more successful at identifying genes associated with individual risk factors, rather than the final common outcome of spontaneous preterm birth. A genetic study of cervical length could prove highly informative, given that the length of the cervix is an easily measured, quantitative trait that is highly correlated with risk for spontaneous preterm delivery.88
Biomechanical properties of the cervix
The uterine cervix has two opposing functions during pregnancy: first, it must remain firmly closed to prevent intrauterine infection, spontaneous abortion, or preterm delivery; and second, at the onset of labour, it must open to allow successful parturition.89 90 These changes are reflected in the histology,91–94 biochemistry94–97 and biomechanical properties98–100 of the cervix. While the uterine corpus is predominantly composed of smooth muscle (ie, myometrium), the uterine cervix is fundamentally a connective tissue.89 90 Smooth muscle cells constitute approximately 10% of the cervix stroma, with the remainder comprising collagen and elastin fibres, interspersed with cervical fibroblasts.89 90 The structural integrity of the cervical stroma is essential for carrying a pregnancy to term, and relies on the strength and organisation of the fibrous network structure, rather than the contractile strength of smooth muscle.98–100 Remodelling of this collagen-rich, connective tissue is a complex process which begins early in pregnancy, and culminates with softening, effacement and dilation of the cervix at parturition.91–97
The length of the cervix is defined as the distance between the external os and the functional internal os,88 and can be easily measured by transvaginal ultrasonography over the course of a pregnancy.101–103 Estimates for the mean length of the cervix in the midtrimester vary between 35 and 45 mm, depending on the population,1 2 9–11 88 104–109 with cervical lengths shorter than 25 mm before 24 weeks meeting the clinical definition of a short cervix.2 88 Typically, the cervix progressively shortens with increasing gestational age,2 decreasing between 0.1–0.3 mm per week after 15 weeks of gestation.8 110–112
Relationship between cervical length and spontaneous preterm birth
A short cervix in the midtrimester is associated with a sixfold increase in the risk of preterm delivery.2 The shorter the cervix, and the earlier in pregnancy the shortening occurs, the higher the risk for spontaneous preterm delivery.1–29 In women with a cervical length <25 mm, every additional 1 mm of cervical shortening is associated with a 3% increase in the odds of spontaneous preterm delivery.113 114 The rate of change in cervical length is also significantly associated with an increased risk of preterm delivery,2 115–118 independent of the initial measurement.
Mean cervical length in the midtrimester is significantly shorter—and the incidence of a short cervix is more than twice as high—among Black women living in North America and Europe, compared with women from other racial and ethnic backgrounds.104 106–109 119 Cervical shortening begins at an earlier gestational age, and occurs more rapidly, among Black/African American mothers,120 121 and midtrimester cervical length is more predictive of risk for spontaneous preterm delivery for Black/African American women, compared with white/Caucasian American women.108 122 While structural and social risk factors are strongly associated with an increased risk of premature cervical shortening and spontaneous preterm delivery among Black/African American women,119–122 the observed variation in mean midtrimester cervical length among women from different continental ancestry groups raises the question of whether population-level differences in the frequency of risk alleles may also be contributing to the risk of premature cervical shortening and subsequent risk for spontaneous preterm delivery.71 123
A biometrical genetic approach to the study of cervical length
Sonographic measurements of cervical length in the midtrimester and gestational age at delivery are two quantitative phenotypic traits that can be approximated by a normal distribution.2 9–11 114 These sampling distributions are characterised by mean and variance statistics, which provide an estimate for interindividual differences within each trait in the population. The relative contributions of genetic and environmental factors influencing population-level variation in these traits can be estimated using biometric modelling techniques in a genetically informative cohort. The concept of heritability describes the proportion of the total phenotypic variation in a trait that can be attributed to genetic variation among individuals in the population.124–126
The broad-sense heritability of spontaneous preterm birth is estimated between 25% and 40%,65 66 68 70 76 78 80–82 and can be separated into fetal and maternal components explaining 11%–35% and 13%–20% of the phenotypic variance, respectively.78 80 81 Heritability estimates vary significantly by population, although variation in gestational age at birth among ancestry groups can be primarily attributed to sociodemographic and environmental factors.127 Although the maternal and fetal genetic contributions to spontaneous preterm delivery are well described in the literature, there is no estimate for the heritability of cervical length, and very little is known about how genes influence the length and rate of change of the cervix during pregnancy.69 72 84 128–130
Classical twin and family studies are the most common methods for estimating trait heritability through comparison of genotypic and phenotypic similarities between pairs of family members, stratified by their genetic relatedness.124–126 131 For instance, observing an increased phenotypic correlation among monozygotic twin pairs (who are genetically identical) compared with dizygotic twin pairs (who share, on average, 50% of their genetic material) would indicate the contribution of genetic factors to trait variance. A recent meta-analysis of 17 804 traits from 2748 twin studies published in the last 50 years estimates an average heritability of 49% across all categories of complex human traits.131 The average heritability of traits specifically related to female reproduction is estimated at 45%, with 144 of the 164 studied traits consistent with a simple and parsimonious model in which all trait resemblance between twins can be attributed to additive genetic influences.131
No large twin or family cohorts to date have collected data on cervical length, and the lack of a heritability estimate discourages large scale genetic studies aiming to identify the contribution of individual genes to cervical length and its rate of change during pregnancy.124–126 A solution exists in modern statistical methods for estimating heritability using genome-wide association data from large, population-based cohorts of unrelated individuals.132–134 Genome-wide complex trait analysis (GCTA) can be used to estimate the proportion of phenotypic variation in a population that is attributable to common genetic variants, in the form of single nucleotide polymorphisms (SNPs) at millions of positions across the genome.132–134 SNP-based heritability estimation is based on the same fundamental concept as twin and family methods; that is, the correlation between shared genotypes and shared phenotypes. If the degree of genetic similarity between pairs of individuals is positively correlated with the degree of phenotypic similarity between individuals, this suggests that genetic variation contributes to phenotypic variation in the trait.132–134 While twin and family-based methods use theoretically derived estimates of genetic relatedness based on known pedigrees, SNP-based methods estimate the degree of genetic similarity between individuals empirically from the observed genotypic SNP data.132–134 The estimated coefficient of genetic similarity between two individuals—that is, the mean number of shared alleles for all genotyped SNPs, weighted by the frequency of each allele in the population—is represented in a genetic relationship matrix (GRM), which contains a single value for each pair of individuals in the cohort. Instead of testing for an association between the phenotype and the genotype at each SNP independently, the GRM is used to estimate the phenotypic variance explained by genetic variation across all genotyped SNPs simultaneously.132–134 Although SNP-based heritability estimates are often lower than those reported by classical twin studies due to methodological limitations, they can be used to approximate the lower bound of genetic contributions to phenotypic variance and help contextualise the results of genome-wide association studies.124–126
A simple extension of these methods can be used to estimate the coheritability, or genetic covariance, between two traits.135 Just as phenotypic variance can be partitioned into genetic and environmental components to estimate heritability, phenotypic covariance between two traits can also be decomposed into its constituent genetic and environmental components.136 137 A strong genetic correlation between cervical length and gestational age at delivery would suggest that some of the same genes influence the expression of both traits, and that assessing the genetic risk for one trait would allow estimation of the genetic risk for the second trait via cross-trait polygenic analyses.138–140
Although a genetic correlation between cervical length and gestational age at delivery would suggest an underlying genetic aetiology shared between the two traits, it would not reveal any information about the causal mechanisms that lead to the observed correlation. Indeed, cross-sectional association-based analysis methods have limited power to unravel the mechanistic pathways that underly complex diseases. However, because genetic variants are fixed at conception, and therefore not subject to the question of reverse causation, they can be used to test the direction of causality in an observed association between an intermediate phenotype or modifiable risk factor, such as cervical length, and a clinically relevant outcome, such as spontaneous preterm delivery. A mediation model can be constructed within the structural equation modelling (SEM) framework to model the relationship between predictor and outcome variables, both directly and an indirectly, mediated by a third, intermediary variable.141–144 SEM can be used to test whether the same genetic factors influence both traits independently (ie, horizontal pleiotropy), or if unique genetic factors influence cervical length, which then mediates the risk for spontaneous preterm delivery through a short or rapidly shortening cervix during pregnancy. Understanding if, and how, maternal genetic risk for spontaneous preterm delivery is mediated by cervical change during pregnancy may improve the predictive value of midtrimester cervical length for use in universal screening programmes, and inform clinical interventions for women at high risk for spontaneous preterm delivery associated with a short cervix.
Methods and proposed analysis
Objective
The primary aim of this study is to characterise the genetic architecture of cervical length and its bivariate genetic correlation with gestational age at delivery. The hypotheses are as follows: (1) maternal genetic variation contributes directly to variance in cervical length and its rate of change during pregnancy; (2) there is a bivariate genetic correlation between cervical length/change and gestational age at delivery; and (3) cervical length/change causally mediates a portion of the maternal genetic contribution to gestational age at delivery.
Study start and end dates
The proposed study will use phenotypic data and biological specimens collected from women enrolled under the protocol entitled Biological Markers of Disease in the Prediction of Preterm Delivery, Preeclampsia and Intra-Uterine Growth Restriction: A Longitudinal Study (NCT00340899) between November 2005 and Novermber 2016. Research activities specific to this project began in August 2017 (research design and planning). DNA samples isolated from banked biological specimens provided by the 5000 selected participants were sent to a commercial laboratory for genotyping via low-pass whole genome sequencing in May 2021. Genotyping is projected to be completed by February 2022. Statistical analysis following the proposed protocol will begin once genotyping is complete.
Study participants
This study involves women enrolled in a longitudinal study of cervical length at the Center for Advanced Obstetrical Care and Research (CAOCR) at Hutzel Women’s Hospital in Detroit, Michigan. The center in affiliated with Wayne State University and the Detroit Medical Center, and is an integral part of the Perinatal Research Branch of The Eunice Kennedy Shriver National Institute of Child Health and Human Development (National Institutes of Health, U.S. Department of Health and Human Services). This research was approved by the Institutional Review Boards of Wayne State University (WSU IRB#110605MP2F) and NICHD/NIH/DHHS (OH97-CH-N067). All study participants were enrolled between 2005 and 2017 and provided written informed consent before the collection of demographic or clinical information, images, or biological samples. From a set of 8226 pregnancies with serial cervical length measurements available, 5971 pregnancies were selected on the basis of the following criteria: a singleton pregnancy, at least 2 cervical length measurements performed between 8 and 40 weeks of gestation, availability of a blood sample and consent to its use in future genetic research, and availability of relevant demographic and clinical characteristics (weight, height, age, parity, etc). Women with a medically induced preterm delivery or a termination during study participation were excluded from the current analysis. Additional exclusion criteria include a history of cervical trauma or any serious medical conditions (such as severe chronic hypertension or renal insufficiency, congestive heart disease, chronic respiratory insufficiency, etc). Biological samples from 5000 participants meeting these criteria were selected for genotyping. The study cohort of 5000 women comprises women who self-identify as Black/African (4640 women, 93%), Caucasian/white (139 women, 3%), or Biracial/other racial identity (220 women, 4%).
Patient and public involvement
Patient/public input was not consulted regarding the design, conduct, reporting or dissemination plans of our research.
Data collection and outcome measures
Demographic characteristics, relevant medical history and pregnancy outcome data were obtained for each participant via medical record abstraction. Maternal peripheral blood samples were collected from each participant during their enrolment period in the original study. DNA extracted from buffy coat isolations has been sent to a commercial laboratory for low-pass whole genome sequencing (sequencing depth 1×).
Cervical length is measured in millimetres (mm) using a transvaginal 12–3 MHz ultrasound endocavitary probe (SuperSonic Imagine).102 Serial cervical length measurements were obtained between 8 and 40 weeks of gestation when patients were seen for prenatal care visits in the CAOCR clinic. Cervical stiffness was also assessed in some women via cervical elastography, by measuring the percentage of displacement or deformation of cervical tissue during manual application of oscillatory pressure. The primary outcome measure, gestational age at delivery, is measured from the first day of a woman’s last menstrual period and confirmed by ultrasound.
Statistical methods and approach
Relationships among repeated measures of cervical length during pregnancy will be modelled as a longitudinal growth curve, with parameters describing the initial length of the cervix and its rate of change over time. The intercept parameter (intercept term (INT)) will represent the best estimate for the baseline measurement of cervical length in early pregnancy, while the slope parameter (SLP) will represent the linear rate of change in cervical length during pregnancy. Higher order parametric terms can also be incorporated to model non-linear growth. Individual growth trajectories and parameters will be estimated for each participant in the cohort, and associations between INT, SLP and the outcome variable will be tested to determine if the starting value or rate of change of cervical length varies significantly with respect to gestational age at delivery.
Quality control for genomic data will conform to the current best practices for the platform used for genotyping. Each individual will be empirically assigned to an ancestry group using genome-wide molecular variation and an external reference panel of known ancestry. If a small number of outliers are identified, they can be excluded from analysis. Otherwise, analyses can be performed within empirically assigned ancestry groups and then meta-analysed. This approach has the advantage of minimising genomic inflation and sample loss due to exclusion of low frequency, unknown or admixed ancestry groups. The open-source, whole-genome association analysis toolset PLINK will be used to test for association between genetic variants (eg, SNPs) and individual estimates of cervical length growth parameters as quantitative traits,145 for the purpose of constructing a polygenic risk score (PRS) for a short cervix. The PRS method will be used to aggregate the effects of many moderately associated SNPs across the genome for multiple significance thresholds (eg, p<0.00001, p<0.0001, p<0.001, etc) and the phenotypic variance accounted for by SNPs within each threshold will be calculated.146 The PRS approach will allow further characterisation of the genetic architecture of cervical length by estimating the total number of genetic loci influencing phenotypic variance and the effect sizes and frequency of risk alleles in the population. Furthermore, a PRS can be used to infer genetic overlap between two traits, such as cervical length and gestational age at delivery, and to predict associated phenotypes based on a genetic profile.139 140 147 148
The genomic-relatedness-based restricted maximum-likelihood (GREML) method from the GCTA framework will be used to estimate the SNP-based heritabilities of cervical length growth parameters and gestational age at delivery within the sample, and calculate the genetic correlation between these phenotypes.132–134 136 The SEM framework will be used to test for mediation of the relationship between maternal genetic variation and gestational age at delivery. A mediation model will assess the effects of common genetic variants (SNPs and/or PRS) on gestational age at birth, both directly and indirectly, mediated through cervical length growth parameters (INT and SLP). The standard criteria of a p value less than 0.05 will be used to draw inferences. A false discovery rate correction will be applied based on iterations of the model used.
Power calculation
The estimation of statistical power was performed for the questions outlined in the study objectives for a sample size of 5000 participants. While individual SNP association tests will be performed, this study lacks the statistical power to identify the contribution of a single SNP while controlling for multiple tests at the genome-wide level (ie, family-wise error rate of 5×10−8). The summary statistics from individual SNP association tests will be used to estimate the aggregate GCTA and PRS statistical summaries as previously described. Statistical power for GCTA methods followed the approach as described by the GREML power calculator.149 We estimate 80% power to detect a cervical length heritability of 0.16 or greater using the GCTA method (as previously mentioned, the average heritability female reproductive traits is around 0.45, based on 164 estimates from twin studies131). For the bivariate GCTA approach, we estimate 80% power to detect a genetic correlation between cervical length and gestational age at delivery of 0.36 or greater, and 80% power to estimate a mediating role of cervical length on the relationship between the PRS and gestational age at delivery for a wide range of scenarios in which the proportion of this direct effect that is mediated ranges from 10% to 100%.
Discussion
This study is designed to decipher the genetic architecture of cervical length and its genetic relationship to spontaneous preterm birth in a large cohort of Black/African American women with longitudinal cervical length measurements across pregnancy. Characterising the number, effect size and population frequency of genetic variants influencing cervical changes during pregnancy is essential to inform a mechanistic understanding of cervical shortening and its contribution to maternal liability for preterm birth.
This project has the potential to identify genetic factors contributing to the increased incidence of spontaneous preterm birth and the higher relative risk for preterm delivery associated with a short cervix in Black/African American women. The development of a PRS for assessing maternal genetic liability to cervical shortening and subsequent risk for spontaneous preterm birth could aid in clinical risk assessment for women of African ancestry, and help identify high risk women who could benefit from early intervention to prevent preterm delivery.150 A PRS would be particularly useful for primigravida, who have no medical history of pregnancy to inform their risk for spontaneous preterm delivery, and who may not receive cervical length screening as standard of care in the absence of other known risk factors. Rapid assessment of a patient’s genetic risk for developing a short cervix and delivering preterm could help identify additional patients who would benefit from effective clinical interventions, such as vaginal administration of progesterone for the prevention of spontaneous preterm birth.
Cervical length screening by transvaginal ultrasound is the best available technique for predicting and preventing preterm birth when paired with the administration of vaginal progesterone in patients with a short cervix. A simple extension of the methods described in this study design could be used to test for genetic moderation of the relationship between vaginal progesterone treatment and cervical length change during pregnancy. A pharmacogenomic study of the women in the cohort who were treated with vaginal progesterone due to a pregnancy complicated by a short cervix could be conducted to identify genetic alleles that modify the responsiveness of progesterone treatment in women with a short cervix and determine whether response to progesterone is informed by the developed PRS or individual genetic variants (SNPs).
Limitations
Although the study cohort is not large enough to identify individual genetic variants associated with cervical length change or gestational duration while controlling for multiple tests at the genome-wide level, it is well powered to analyse aggregate genome-wide summary statistics in order to estimate trait heritability and bivariate genetic correlations, and to develop a PRS to identify women with the highest risk of developing a short cervix and delivering preterm.
The study cohort predominately comprises women who self-identify as Black/African American; although the findings of this study may not be generalisable to women from other populations or ancestry groups, they could improve screening and clinical care for a population of women who are disproportionally affected by health disparities in preterm birth and perinatal outcomes. While heritability estimates and alleles/allele frequencies vary from population to population, we do not expect the underlying mechanistic relationships between genes, cervical change and gestational duration to differ substantially between our study population and other global populations. Thus, we hope that a better understanding of how genes contribute to cervical change during pregnancy and risk for preterm delivery can inform perinatal care for women from all populations across the globe.
The proposed study does not address the influence of fetal genes, which are predicted to play a role in the timing of birth. Furthermore, there may be overlapping fetal and maternal genes associated with cervical length and spontaneous preterm birth, given that matrix metabolism is implicated in both cervical ripening and changes in the fetal membranes preceding parturition.84 Fetal genetic variants may also promote preterm premature rupture of membranes,84 151–153 which often occurs in the setting of a prematurely shortened cervix.154–156 The proposed cohort has a rich biobank, including fetal blood samples, that will allow follow-up studies of the fetal genetic contributions to cervical length changes during pregnancy.
Although this study will examine maternal genetic contributions to the correlation between cervical length and gestational age at delivery, we do not discuss all of the environmental and sociodemographic factors that may also contribute.157 Additionally, population differences in vaginal microbiome states, which are associated with cervical inflammation,158–161 may also contribute to the association between cervical length and gestational age at birth.162 163 Cervicovaginal samples collected from the cohort will allow evaluation of the vaginal microbiome and its relationship to cervical length and the presence of proinflammatory cytokines.
Ethics approval
The Institutional Review Boards of Wayne State University and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)/National Institutes of Health/U.S. Department of Health and Human Services (Detroit, MI, USA) approved the study. Participants were enrolled under the protocols Biological Markers of Disease in the Prediction of Preterm Delivery, Preeclampsia and Intra-Uterine Growth Restriction: A Longitudinal Study (WSU IRB#110605MP2F and NICHD/NIH# OH97-CH-N067). All participants provided written informed consent for the collection of cervical length data and blood samples for future genetic research studies.
Supplementary Material
Acknowledgments
We gratefully acknowledge the study participants, and the staff of the Center for Advanced Obstetrical Care and Research of the Perinatology Research Branch, NICHD/NIH/DHHS, the Detroit Medical Center, and Wayne State University.
Footnotes
Twitter: @hopemwolf
Contributors: Study designed by TPY, JFS, SSH and RR. The initial manuscript written by HMW and TPY, JFS, RR, SSH, and ALT provided editing and review. Input on technical issues and methodological approaches was provided by SJL, BTW, ALT, NG-L and C-DH. All authors have read and approved the final manuscript.
Funding: This work is supported, in part, by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS); and, in part, with Federal funds from NICHD/NIH/DHHS under Contract No. HHSN275201300006C. RR contributed to this work as part of his official duties as an employee of the United States Federal Government. ALT and NG-L were also supported by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health.
Disclaimer: The funding agency had no role in design of the study; the collection, analysis or interpretation of the data; or in writing the manuscript.
Competing interests: None declared.
Provenance and peer review: Not commissioned; externally peer reviewed.
Ethics statements
Patient consent for publication
Not applicable.
References
- 1.Andersen HF, Nugent CE, Wanty SD, et al. Prediction of risk for preterm delivery by ultrasonographic measurement of cervical length. Am J Obstet Gynecol 1990;163:859–67. 10.1016/0002-9378(90)91084-p [DOI] [PubMed] [Google Scholar]
- 2.Iams JD, Goldenberg RL, Meis PJ. The length of the cervix and the risk of spontaneous premature delivery. N Engl J Med 1996;334:567–73. [DOI] [PubMed] [Google Scholar]
- 3.Goldenberg RL, Iams JD, Miodovnik M. The preterm prediction study: risk factors in twin gestations. Am J Obstet Gynecol 1996;175:1047–53. [DOI] [PubMed] [Google Scholar]
- 4.Berghella V, Tolosa JE, Kuhlman K, et al. Cervical ultrasonography compared with manual examination as a predictor of preterm delivery. Am J Obstet Gynecol 1997;177:723–30. 10.1016/s0002-9378(97)70259-x [DOI] [PubMed] [Google Scholar]
- 5.Imseis HM, Albert TA, Iams JD. Identifying twin gestations at low risk for preterm birth with a transvaginal ultrasonographic cervical measurement at 24 to 26 weeks' gestation. Am J Obstet Gynecol 1997;177:1149–55. 10.1016/s0002-9378(97)70032-2 [DOI] [PubMed] [Google Scholar]
- 6.Wennerholm UB, Holm B, Mattsby-Baltzer I, et al. Fetal fibronectin, endotoxin, bacterial vaginosis and cervical length as predictors of preterm birth and neonatal morbidity in twin pregnancies. Br J Obstet Gynaecol 1997;104:1398–404. 10.1111/j.1471-0528.1997.tb11010.x [DOI] [PubMed] [Google Scholar]
- 7.Goldenberg RL, Iams JD, Mercer BM, et al. The preterm prediction study: the value of new vs standard risk factors in predicting early and all spontaneous preterm births. NICHD MFMU network. Am J Public Health 1998;88:233–8. 10.2105/ajph.88.2.233 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Guzman ER, Mellon C, Vintzileos AM, et al. Longitudinal assessment of endocervical canal length between 15 and 24 weeks' gestation in women at risk for pregnancy loss or preterm birth. Obstet Gynecol 1998;92:31–7. 10.1016/s0029-7844(98)00120-3 [DOI] [PubMed] [Google Scholar]
- 9.Heath VC, Southall TR, Souka AP, et al. Cervical length at 23 weeks of gestation: prediction of spontaneous preterm delivery. Ultrasound Obstet Gynecol 1998;12:312–7. 10.1046/j.1469-0705.1998.12050312.x [DOI] [PubMed] [Google Scholar]
- 10.Hassan SS, Romero R, Berry SM, et al. Patients with an ultrasonographic cervical length < or =15 mm have nearly a 50% risk of early spontaneous preterm delivery. Am J Obstet Gynecol 2000;182:1458–67. 10.1067/mob.2000.106851 [DOI] [PubMed] [Google Scholar]
- 11.Hibbard JU, Tart M, Moawad AH. Cervical length at 16-22 weeks' gestation and risk for preterm delivery. Obstet Gynecol 2000;96:972–8. 10.1016/s0029-7844(00)01074-7 [DOI] [PubMed] [Google Scholar]
- 12.Guzman ER, Walters C, O'reilly-Green C, et al. Use of cervical ultrasonography in prediction of spontaneous preterm birth in twin gestations. Am J Obstet Gynecol 2000;183:1103–7. 10.1067/mob.2000.108896 [DOI] [PubMed] [Google Scholar]
- 13.Yang JH, Kuhlman K, Daly S, et al. Prediction of preterm birth by second trimester cervical sonography in twin pregnancies. Ultrasound Obstet Gynecol 2000;15:288–91. 10.1046/j.1469-0705.2000.00087.x [DOI] [PubMed] [Google Scholar]
- 14.Owen J, Yost N, Berghella V, et al. Mid-Trimester endovaginal sonography in women at high risk for spontaneous preterm birth. JAMA 2001;286:1340–8. 10.1001/jama.286.11.1340 [DOI] [PubMed] [Google Scholar]
- 15.Soriano D, Weisz B, Seidman DS, et al. The role of sonographic assessment of cervical length in the prediction of preterm birth in primigravidae with twin gestation conceived after infertility treatment. Acta Obstet Gynecol Scand 2002;81:39–43. 10.1046/j.0001-6349.2001.00466.x [DOI] [PubMed] [Google Scholar]
- 16.Vayssière C, Favre R, Audibert F, et al. Cervical length and funneling at 22 and 27 weeks to predict spontaneous birth before 32 weeks in twin pregnancies: a French prospective multicenter study. Am J Obstet Gynecol 2002;187:1596–604. 10.1067/mob.2002.127380 [DOI] [PubMed] [Google Scholar]
- 17.Honest H, Bachmann LM, Coomarasamy A, et al. Accuracy of cervical transvaginal sonography in predicting preterm birth: a systematic review. Ultrasound Obstet Gynecol 2003;22:305–22. 10.1002/uog.202 [DOI] [PubMed] [Google Scholar]
- 18.Gibson JL, Macara LM, Owen P, et al. Prediction of preterm delivery in twin pregnancy: a prospective, observational study of cervical length and fetal fibronectin testing. Ultrasound Obstet Gynecol 2004;23:561–6. 10.1002/uog.1048 [DOI] [PubMed] [Google Scholar]
- 19.Owen J, Yost N, Berghella V, et al. Can shortened midtrimester cervical length predict very early spontaneous preterm birth? Am J Obstet Gynecol 2004;191:298–303. 10.1016/j.ajog.2003.11.025 [DOI] [PubMed] [Google Scholar]
- 20.To MS, Skentou CA, Royston P, et al. Prediction of patient-specific risk of early preterm delivery using maternal history and sonographic measurement of cervical length: a population-based prospective study. Ultrasound Obstet Gynecol 2006;27:362–7. 10.1002/uog.2773 [DOI] [PubMed] [Google Scholar]
- 21.To MS, Fonseca EB, Molina FS, et al. Maternal characteristics and cervical length in the prediction of spontaneous early preterm delivery in twins. Am J Obstet Gynecol 2006;194:1360–5. 10.1016/j.ajog.2005.11.001 [DOI] [PubMed] [Google Scholar]
- 22.Crane JMG, Hutchens D. Transvaginal sonographic measurement of cervical length to predict preterm birth in asymptomatic women at increased risk: a systematic review. Ultrasound Obstet Gynecol 2008;31:579–87. 10.1002/uog.5323 [DOI] [PubMed] [Google Scholar]
- 23.Klein K, Gregor H, Hirtenlehner-Ferber K, et al. Prediction of spontaneous preterm delivery in twin pregnancies by cervical length at mid-gestation. Twin Res Hum Genet 2008;11:552–7. 10.1375/twin.11.5.552 [DOI] [PubMed] [Google Scholar]
- 24.Domin CM, Smith EJ, Terplan M. Transvaginal ultrasonographic measurement of cervical length as a predictor of preterm birth: a systematic review with meta-analysis. Ultrasound Q 2010;26:241–8. 10.1097/RUQ.0b013e3181fe0e05 [DOI] [PubMed] [Google Scholar]
- 25.Conde-Agudelo A, Romero R, Hassan SS, et al. Transvaginal sonographic cervical length for the prediction of spontaneous preterm birth in twin pregnancies: a systematic review and metaanalysis. Am J Obstet Gynecol 2010;203:128.e1–128.e12. 10.1016/j.ajog.2010.02.064 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Lim AC, Hegeman MA, Huis In 'T Veld MA, et al. Cervical length measurement for the prediction of preterm birth in multiple pregnancies: a systematic review and bivariate meta-analysis. Ultrasound Obstet Gynecol 2011;38:10–17. 10.1002/uog.9013 [DOI] [PubMed] [Google Scholar]
- 27.Barros-Silva J, Pedrosa AC, Matias A. Sonographic measurement of cervical length as a predictor of preterm delivery: a systematic review. J Perinat Med 2013;42:281–93. [DOI] [PubMed] [Google Scholar]
- 28.Conde-Agudelo A, Romero R. Predictive accuracy of changes in transvaginal sonographic cervical length over time for preterm birth: a systematic review and metaanalysis. Am J Obstet Gynecol 2015;213:789–801. 10.1016/j.ajog.2015.06.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kindinger LM, Poon LC, Cacciatore S, et al. The effect of gestational age and cervical length measurements in the prediction of spontaneous preterm birth in twin pregnancies: an individual patient level meta-analysis. BJOG 2016;123:877–84. 10.1111/1471-0528.13575 [DOI] [PubMed] [Google Scholar]
- 30.da Fonseca EB, Bittar RE, Carvalho MHB, et al. Prophylactic administration of progesterone by vaginal suppository to reduce the incidence of spontaneous preterm birth in women at increased risk: a randomized placebo-controlled double-blind study. Am J Obstet Gynecol 2003;188:419–24. 10.1067/mob.2003.41 [DOI] [PubMed] [Google Scholar]
- 31.Fonseca EB, Celik E, Parra M, et al. Progesterone and the risk of preterm birth among women with a short cervix. N Engl J Med 2007;357:462–9. 10.1056/NEJMoa067815 [DOI] [PubMed] [Google Scholar]
- 32.Cetingoz E, Cam C, Sakallı M, et al. Progesterone effects on preterm birth in high-risk pregnancies: a randomized placebo-controlled trial. Arch Gynecol Obstet 2011;283:423–9. 10.1007/s00404-009-1351-2 [DOI] [PubMed] [Google Scholar]
- 33.Hassan SS, Romero R, Vidyadhari D, et al. Vaginal progesterone reduces the rate of preterm birth in women with a sonographic short cervix: a multicenter, randomized, double-blind, placebo-controlled trial. Ultrasound Obstet Gynecol 2011;38:18–31. 10.1002/uog.9017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Aboulghar MM, Aboulghar MA, Amin YM, et al. The use of vaginal natural progesterone for prevention of preterm birth in IVF/ICSI pregnancies. Reprod Biomed Online 2012;25:133–8. 10.1016/j.rbmo.2012.03.013 [DOI] [PubMed] [Google Scholar]
- 35.Berghella V. Universal cervical length screening for prediction and prevention of preterm birth. Obstet Gynecol Surv 2012;67:653–8. 10.1097/OGX.0b013e318270d5b2 [DOI] [PubMed] [Google Scholar]
- 36.Romero R, Nicolaides K, Conde-Agudelo A, et al. Vaginal progesterone in women with an asymptomatic sonographic short cervix in the midtrimester decreases preterm delivery and neonatal morbidity: a systematic review and metaanalysis of individual patient data. Am J Obstet Gynecol 2012;206:124.e1–124.e19. 10.1016/j.ajog.2011.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Combs CA. Vaginal progesterone for asymptomatic cervical shortening and the case for universal screening of cervical length. Am J Obstet Gynecol 2012;206:101–3. 10.1016/j.ajog.2011.12.008 [DOI] [PubMed] [Google Scholar]
- 38.Committee on Practice Bulletins—Obstetrics, The American College of Obstetricians and Gynecologists . Practice Bulletin No. 130: prediction and prevention of preterm birth. Obstet Gynecol 2012;120:964–73. 10.1097/AOG.0b013e3182723b1b [DOI] [PubMed] [Google Scholar]
- 39.Society for Maternal-Fetal Medicine Publications Committee, with assistance of Vincenzo Berghella . Progesterone and preterm birth prevention: translating clinical trials data into clinical practice. Am J Obstet Gynecol 2012;206:376–86. 10.1016/j.ajog.2012.03.010 [DOI] [PubMed] [Google Scholar]
- 40.Conde-Agudelo A, Romero R, Nicolaides K, et al. Vaginal progesterone vs. cervical cerclage for the prevention of preterm birth in women with a sonographic short cervix, previous preterm birth, and singleton gestation: a systematic review and indirect comparison metaanalysis. Am J Obstet Gynecol 2013;208:42.e1–42.e18. 10.1016/j.ajog.2012.10.877 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Romero R, Yeo L, Miranda J, et al. A blueprint for the prevention of preterm birth: vaginal progesterone in women with a short cervix. J Perinat Med 2013;41:27–44. 10.1515/jpm-2012-0272 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Son M, Grobman WA, Ayala NK, et al. A universal mid-trimester transvaginal cervical length screening program and its associated reduced preterm birth rate. Am J Obstet Gynecol 2016;214:365.e1–365.e5. 10.1016/j.ajog.2015.12.020 [DOI] [PubMed] [Google Scholar]
- 43.Conde-Agudelo A, Romero R. Vaginal progesterone to prevent preterm birth in pregnant women with a sonographic short cervix: clinical and public health implications. Am J Obstet Gynecol 2016;214:235–42. 10.1016/j.ajog.2015.09.102 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Romero R, Conde-Agudelo A, El-Refaie W, et al. Vaginal progesterone decreases preterm birth and neonatal morbidity and mortality in women with a twin gestation and a short cervix: an updated meta-analysis of individual patient data. Ultrasound Obstet Gynecol 2017;49:303–14. 10.1002/uog.17397 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Vintzileos AM, Visser GHA. Interventions for women with mid-trimester short cervix: which ones work? Ultrasound Obstet Gynecol 2017;49:295–300. 10.1002/uog.17357 [DOI] [PubMed] [Google Scholar]
- 46.Conde-Agudelo A, Romero R, Da Fonseca E, et al. Vaginal progesterone is as effective as cervical cerclage to prevent preterm birth in women with a singleton gestation, previous spontaneous preterm birth, and a short cervix: updated indirect comparison meta-analysis. Am J Obstet Gynecol 2018;219:10–25. 10.1016/j.ajog.2018.03.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Romero R, Conde-Agudelo A, Da Fonseca E, et al. Vaginal progesterone for preventing preterm birth and adverse perinatal outcomes in singleton gestations with a short cervix: a meta-analysis of individual patient data. Am J Obstet Gynecol 2018;218:161–80. 10.1016/j.ajog.2017.11.576 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Blencowe H, Cousens S, Oestergaard MZ, et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet 2012;379:2162–72. 10.1016/S0140-6736(12)60820-4 [DOI] [PubMed] [Google Scholar]
- 49.Blencowe H, Cousens S, Chou D, et al. Born too soon: the global epidemiology of 15 million preterm births. Reprod Health 2013;10 Suppl 1:S2. 10.1186/1742-4755-10-S1-S2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Liu L, Oza S, Hogan D, et al. Global, regional, and national causes of child mortality in 2000-13, with projections to inform post-2015 priorities: an updated systematic analysis. Lancet 2015;385:430–40. 10.1016/S0140-6736(14)61698-6 [DOI] [PubMed] [Google Scholar]
- 51.World Health Organization . World health statistics 2018: monitoring health for the SDGs (sustainable development goals), 2018. WHO. Available: http://www.who.int/gho/publications/world_health_statistics/2018/en/ [Accessed 20 Nov 2018].
- 52.Callaghan WM, MacDorman MF, Rasmussen SA, et al. The contribution of preterm birth to infant mortality rates in the United States. Pediatrics 2006;118:1566–73. 10.1542/peds.2006-0860 [DOI] [PubMed] [Google Scholar]
- 53.Murphy SL, Xu J, Kochanek KD, et al. Mortality in the United States, 2017. NCHS Data Brief 2018:1–8. [PubMed] [Google Scholar]
- 54.Allen MC, Alexander GR, Tompkins ME, et al. Racial differences in temporal changes in newborn viability and survival by gestational age. Paediatr Perinat Epidemiol 2000;14:152–8. 10.1046/j.1365-3016.2000.00255.x [DOI] [PubMed] [Google Scholar]
- 55.Alexander GR, Kogan M, Bader D, et al. Us birth weight/gestational age-specific neonatal mortality: 1995-1997 rates for whites, Hispanics, and blacks. Pediatrics 2003;111:e61–6. 10.1542/peds.111.1.e61 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Saigal S, Doyle LW. An overview of mortality and sequelae of preterm birth from infancy to adulthood. Lancet 2008;371:261–9. 10.1016/S0140-6736(08)60136-1 [DOI] [PubMed] [Google Scholar]
- 57.Culhane JF, Goldenberg RL. Racial disparities in preterm birth. Semin Perinatol 2011;35:234–9. 10.1053/j.semperi.2011.02.020 [DOI] [PubMed] [Google Scholar]
- 58.Institute of Medicine (US) Committee on Understanding Premature Birth and Assuring Healthy Outcomes . Preterm birth: causes, consequences, and prevention. Washington, DC: National Academies Press (US), 2007. http://www.ncbi.nlm.nih.gov/books/NBK11362/ [PubMed] [Google Scholar]
- 59.Ely DM, Driscoll AK. Infant mortality in the United States, 2017: data from the period linked Birth/Infant death file. Natl Vital Stat Rep 2019;68:20. [PubMed] [Google Scholar]
- 60.Goldenberg RL, Culhane JF, Iams JD, et al. Epidemiology and causes of preterm birth. Lancet 2008;371:75–84. 10.1016/S0140-6736(08)60074-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Behrman RE, Butler AS, Institute of Medicine (U.S.) . Preterm birth: causes, consequences, and prevention. Washington, D.C.: National Academies Press, 2007. http://public.eblib.com/choice/publicfullrecord.aspx?p=3378223 [PubMed] [Google Scholar]
- 62.Althabe F, Howson CP, Kinney M. Born too soon: the global action report on preterm birth, 2012. Available: http://www.who.int/pmnch/media/news/2012/201204%5Fborntoosoon-report.pdf [Accessed 23 Jul 2018].
- 63.Porter TF, Fraser AM, Hunter CY, et al. The risk of preterm birth across generations. Obstet Gynecol 1997;90:63–7. 10.1016/S0029-7844(97)00215-9 [DOI] [PubMed] [Google Scholar]
- 64.Winkvist A, Mogren I, Högberg U. Familial patterns in birth characteristics: impact on individual and population risks. Int J Epidemiol 1998;27:248–54. 10.1093/ije/27.2.248 [DOI] [PubMed] [Google Scholar]
- 65.Clausson B, Lichtenstein P, Cnattingius S. Genetic influence on birthweight and gestational length determined by studies in offspring of twins. BJOG 2000;107:375–81. 10.1111/j.1471-0528.2000.tb13234.x [DOI] [PubMed] [Google Scholar]
- 66.Treloar SA, Macones GA, Mitchell LE. Genetic influences on premature parturition in an Australian twin sample. Twin Res Off J Int Soc Twin Stud 2000;3:80–2. [DOI] [PubMed] [Google Scholar]
- 67.Ward K, Argyle V, Meade M, et al. The heritability of preterm delivery. Obstet Gynecol 2005;106:1235–9. 10.1097/01.AOG.0000189091.35982.85 [DOI] [PubMed] [Google Scholar]
- 68.Lunde A, Melve KK, Gjessing HK, et al. Genetic and environmental influences on birth weight, birth length, head circumference, and gestational age by use of population-based parent-offspring data. Am J Epidemiol 2007;165:734–41. 10.1093/aje/kwk107 [DOI] [PubMed] [Google Scholar]
- 69.Chaudhari BP, Plunkett J, Ratajczak CK, et al. The genetics of birth timing: insights into a fundamental component of human development. Clin Genet 2008;74:493–501. 10.1111/j.1399-0004.2008.01124.x [DOI] [PubMed] [Google Scholar]
- 70.Kistka ZA-F, DeFranco EA, Ligthart L, et al. Heritability of parturition timing: an extended twin design analysis. Am J Obstet Gynecol 2008;199:43.e1–43.e5. 10.1016/j.ajog.2007.12.014 [DOI] [PubMed] [Google Scholar]
- 71.Menon R. Spontaneous preterm birth, a clinical dilemma: etiologic, pathophysiologic and genetic heterogeneities and racial disparity. Acta Obstet Gynecol Scand 2008;87:590–600. 10.1080/00016340802005126 [DOI] [PubMed] [Google Scholar]
- 72.Plunkett J, Muglia LJ. Genetic contributions to preterm birth: implications from epidemiological and genetic association studies. Ann Med 2008;40:167–79. 10.1080/07853890701806181 [DOI] [PubMed] [Google Scholar]
- 73.Wilcox AJ, Skjaerven R, Lie RT. Familial patterns of preterm delivery: maternal and fetal contributions. Am J Epidemiol 2008;167:474–9. 10.1093/aje/kwm319 [DOI] [PubMed] [Google Scholar]
- 74.Boyd HA, Poulsen G, Wohlfahrt J, et al. Maternal contributions to preterm delivery. Am J Epidemiol 2009;170:1358–64. 10.1093/aje/kwp324 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Plunkett J, Feitosa MF, Trusgnich M, et al. Mother's genome or maternally-inherited genes acting in the fetus influence gestational age in familial preterm birth. Hum Hered 2009;68:209–19. 10.1159/000224641 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Svensson AC, Sandin S, Cnattingius S, et al. Maternal effects for preterm birth: a genetic epidemiologic study of 630,000 families. Am J Epidemiol 2009;170:1365–72. 10.1093/aje/kwp328 [DOI] [PubMed] [Google Scholar]
- 77.Weinberg CR, Shi M. The genetics of preterm birth: using what we know to design better association studies. Am J Epidemiol 2009;170:1373–81. 10.1093/aje/kwp325 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.York TP, Strauss JF, Neale MC, et al. Estimating fetal and maternal genetic contributions to premature birth from multiparous pregnancy histories of twins using MCMC and maximum-likelihood approaches. Twin Res Hum Genet 2009;12:333–42. 10.1375/twin.12.4.333 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.Dolan SM, Hollegaard MV, Merialdi M. Synopsis of preterm birth genetic association studies: the preterm birth genetics knowledge base (PTBGene). Public Health Genomics Basel 2010;13:514–23. [DOI] [PubMed] [Google Scholar]
- 80.York TP, Eaves LJ, Lichtenstein P, et al. Fetal and maternal genes' influence on gestational age in a quantitative genetic analysis of 244,000 Swedish births. Am J Epidemiol 2013;178:543–50. 10.1093/aje/kwt005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Oberg AS, Frisell T, Svensson AC, et al. Maternal and fetal genetic contributions to postterm birth: familial clustering in a population-based sample of 475,429 Swedish births. Am J Epidemiol 2013;177:531–7. 10.1093/aje/kws244 [DOI] [PubMed] [Google Scholar]
- 82.York TP, Eaves LJ, Neale MC, et al. The contribution of genetic and environmental factors to the duration of pregnancy. Am J Obstet Gynecol 2014;210:398–405. 10.1016/j.ajog.2013.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Zhang G, Feenstra B, Bacelis J, et al. Genetic associations with gestational duration and spontaneous preterm birth. N Engl J Med 2017;377:1156–67. 10.1056/NEJMoa1612665 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Strauss JF, Romero R, Gomez-Lopez N, et al. Spontaneous preterm birth: advances toward the discovery of genetic predisposition. Am J Obstet Gynecol 2018;218:294–314. 10.1016/j.ajog.2017.12.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Ananth CV, Vintzileos AM. Epidemiology of preterm birth and its clinical subtypes. J Matern Fetal Neonatal Med 2006;19:773–82. 10.1080/14767050600965882 [DOI] [PubMed] [Google Scholar]
- 86.Romero R, Espinoza J, Kusanovic JP, et al. The preterm parturition syndrome. BJOG 2006;113 Suppl 3:17–42. 10.1111/j.1471-0528.2006.01120.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Romero R, Dey SK, Fisher SJ. Preterm labor: one syndrome, many causes. Science 2014;345:760–5. 10.1126/science.1251816 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Tommaso MD, Berghella V. Cervical length for the prediction and prevention of preterm birth. Expert Rev Obstet Gynecol 2013;8:345–55. [Google Scholar]
- 89.Ludmir J, Sehdev HM. Anatomy and physiology of the uterine cervix. Clin Obstet Gynecol 2000;43:433–9. 10.1097/00003081-200009000-00003 [DOI] [PubMed] [Google Scholar]
- 90.Nott JP, Bonney EA, Pickering JD. The structure and function of the cervix during pregnancy. Transl Res Anat 2016;2:1–7. [Google Scholar]
- 91.Leppert PC. Anatomy and physiology of cervical ripening. Clin Obstet Gynecol 1995;38:267–79. 10.1097/00003081-199506000-00009 [DOI] [PubMed] [Google Scholar]
- 92.Word RA, Li X-H, Hnat M, et al. Dynamics of cervical remodeling during pregnancy and parturition: mechanisms and current concepts. Semin Reprod Med 2007;25:069–79. 10.1055/s-2006-956777 [DOI] [PubMed] [Google Scholar]
- 93.Timmons B, Akins M, Mahendroo M. Cervical remodeling during pregnancy and parturition. Trends Endocrinol Metab 2010;21:353–61. 10.1016/j.tem.2010.01.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94.Winkler M, Rath W. Changes in the cervical extracellular matrix during pregnancy and parturition. J Perinat Med 1999;27:45–60. 10.1515/JPM.1999.006 [DOI] [PubMed] [Google Scholar]
- 95.Kelly RW. Inflammatory mediators and cervical ripening. J Reprod Immunol 2002;57:217–24. 10.1016/s0165-0378(02)00007-4 [DOI] [PubMed] [Google Scholar]
- 96.Mohan AR, Loudon JA, Bennett PR. Molecular and biochemical mechanisms of preterm labour. Semin Fetal Neonatal Med 2004;9:437–44. 10.1016/j.siny.2004.08.001 [DOI] [PubMed] [Google Scholar]
- 97.Yellon SM. Contributions to the dynamics of cervix remodeling prior to term and preterm birth. Biol Reprod 2017;96:13–23. 10.1095/biolreprod.116.142844 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.House M, Kaplan DL, Socrate S. Relationships between mechanical properties and extracellular matrix constituents of the cervical stroma during pregnancy. Semin Perinatol 2009;33:300–7. 10.1053/j.semperi.2009.06.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Myers KM, Feltovich H, Mazza E, et al. The mechanical role of the cervix in pregnancy. J Biomech 2015;48:1511–23. 10.1016/j.jbiomech.2015.02.065 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Fernandez M, House M, Jambawalikar S, et al. Investigating the mechanical function of the cervix during pregnancy using finite element models derived from high-resolution 3D MRI. Comput Methods Biomech Biomed Engin 2016;19:404–17. 10.1080/10255842.2015.1033163 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Andersen HF. Transvaginal and transabdominal ultrasonography of the uterine cervix during pregnancy. J Clin Ultrasound 1991;19:77–83. 10.1002/jcu.1870190204 [DOI] [PubMed] [Google Scholar]
- 102.Berghella V, Bega G, Tolosa JE, et al. Ultrasound assessment of the cervix. Clin Obstet Gynecol 2003;46:947–62. 10.1097/00003081-200312000-00026 [DOI] [PubMed] [Google Scholar]
- 103.Owen J, Iams JD, National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network . What we have learned about cervical ultrasound. Semin Perinatol 2003;27:194–203. 10.1016/S0146-0005(03)00021-1 [DOI] [PubMed] [Google Scholar]
- 104.Heath VC, Southall TR, Souka AP, et al. Cervical length at 23 weeks of gestation: relation to demographic characteristics and previous obstetric history. Ultrasound Obstet Gynecol 1998;12:304–11. 10.1046/j.1469-0705.1998.12050304.x [DOI] [PubMed] [Google Scholar]
- 105.Petrović D, Novakov-Mikić A, Mandić V. Socio-Demographic factors and cervical length in pregnancy. Med Pregl 2008;61:443–51. 10.2298/MPNS0810443P [DOI] [PubMed] [Google Scholar]
- 106.van der Ven AJ, van Os MA, Kleinrouweler CE, et al. Is cervical length associated with maternal characteristics? Eur J Obstet Gynecol Reprod Biol 2015;188:12–16. 10.1016/j.ejogrb.2015.02.032 [DOI] [PubMed] [Google Scholar]
- 107.Buck JN, Orzechowski KM, Berghella V. Racial disparities in cervical length for prediction of preterm birth in a low risk population. J Matern Fetal Neonatal Med 2017;30:1851–4. 10.1080/14767058.2016.1228056 [DOI] [PubMed] [Google Scholar]
- 108.Bligard K, Temming LA, Stout MJ, et al. 85: performance of cervical length screening in African American women. Am J Obstet Gynecol 2018;218:S62–3. 10.1016/j.ajog.2017.10.496 [DOI] [Google Scholar]
- 109.Temming LA, Rampersad RR, Lopez JD, et al. 431: differences in cervical length in African American and non-African American women. Am J Obstet Gynecol 2018;218:S262. 10.1016/j.ajog.2017.10.367 [DOI] [Google Scholar]
- 110.Arisoy R, Yayla M. Transvaginal sonographic evaluation of the cervix in asymptomatic singleton pregnancy and management options in short cervix. J Pregnancy 2012;2012:1–10. 10.1155/2012/201628 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.Bergelin I, Valentin L. Patterns of normal change in cervical length and width during pregnancy in nulliparous women: a prospective, longitudinal ultrasound study. Ultrasound Obstet Gynecol 2001;18:217–22. 10.1046/j.0960-7692.2001.00524.x [DOI] [PubMed] [Google Scholar]
- 112.Bergelin I, Valentin L. Normal cervical changes in parous women during the second half of pregnancy--a prospective, longitudinal ultrasound study. Acta Obstet Gynecol Scand 2002;81:31–8. 10.1046/j.0001-6349.2001.00311.x [DOI] [PubMed] [Google Scholar]
- 113.Moroz LA, Simhan HN. Rate of sonographic cervical shortening and biologic pathways of spontaneous preterm birth. Am J Obstet Gynecol 2014;210:555.e1–555.e5. 10.1016/j.ajog.2013.12.037 [DOI] [PubMed] [Google Scholar]
- 114.Moroz LA, Simhan HN. Rate of sonographic cervical shortening and the risk of spontaneous preterm birth. Am J Obstet Gynecol 2012;206:234.e1–234.e5. 10.1016/j.ajog.2011.11.017 [DOI] [PubMed] [Google Scholar]
- 115.Naim A, Haberman S, Burgess T, et al. Changes in cervical length and the risk of preterm labor. Am J Obstet Gynecol 2002;186:887–9. 10.1067/mob.2002.123058 [DOI] [PubMed] [Google Scholar]
- 116.Melamed N, Pittini A, Hiersch L, et al. Serial cervical length determination in twin pregnancies reveals 4 distinct patterns with prognostic significance for preterm birth. Am J Obstet Gynecol 2016;215:476.e1–476.e11. 10.1016/j.ajog.2016.05.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 117.Melamed N, Pittini A, Hiersch L, et al. Do serial measurements of cervical length improve the prediction of preterm birth in asymptomatic women with twin gestations? Am J Obstet Gynecol 2016;215:616.e1–616.e14. 10.1016/j.ajog.2016.06.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 118.Wang L. Value of serial cervical length measurement in prediction of spontaneous preterm birth in post-conization pregnancy without short mid-trimester cervix. Sci Rep 2018;8:15305. 10.1038/s41598-018-33537-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 119.Harville EW, Miller KS, Knoepp LR. Racial and social predictors of longitudinal cervical measures: the cervical ultrasound study. J Perinatol 2017;37:335–9. 10.1038/jp.2016.240 [DOI] [PubMed] [Google Scholar]
- 120.Dijkstra K, Janssen HC, Kuczynski E, et al. Cervical length in uncomplicated pregnancy: a study of sociodemographic predictors of cervical changes across gestation. Am J Obstet Gynecol 1999;180:639–44. 10.1016/S0002-9378(99)70267-X [DOI] [PubMed] [Google Scholar]
- 121.Dijkstra K. Prediction of spontaneous preterm birth, 2002. Available: https://dspace.library.uu.nl/handle/1874/425 [Accessed 20 Nov 2019].
- 122.Harville EW, Knoepp LR, Wallace ME, et al. Cervical pathways for racial disparities in preterm births: the preterm prediction study. J Matern Fetal Neonatal Med 2019;32:1–7. 10.1080/14767058.2018.1484091 [DOI] [PubMed] [Google Scholar]
- 123.Menon R, Pearce B, Velez DR, et al. Racial disparity in pathophysiologic pathways of preterm birth based on genetic variants. Reprod Biol Endocrinol 2009;7:62. 10.1186/1477-7827-7-62 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 124.Visscher PM, Hill WG, Wray NR. Heritability in the genomics era--concepts and misconceptions. Nat Rev Genet 2008;9:255–66. 10.1038/nrg2322 [DOI] [PubMed] [Google Scholar]
- 125.Tenesa A, Haley CS. The heritability of human disease: estimation, uses and abuses. Nat Rev Genet 2013;14:139–49. 10.1038/nrg3377 [DOI] [PubMed] [Google Scholar]
- 126.Mayhew AJ, Meyre D. Assessing the heritability of complex traits in humans: methodological challenges and opportunities. Curr Genomics 2017;18:332–40. 10.2174/1389202918666170307161450 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 127.York TP, Strauss JF, Neale MC, et al. Racial differences in genetic and environmental risk to preterm birth. PLoS One 2010;5:e12391. 10.1371/journal.pone.0012391 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 128.Warren JE, Silver RM, Dalton J, et al. Collagen 1Α1 and transforming growth factor-β polymorphisms in women with cervical insufficiency. Obstet Gynecol 2007;110:619–24. 10.1097/01.AOG.0000277261.92756.1a [DOI] [PubMed] [Google Scholar]
- 129.Warren JE, Silver RM. Genetics of the cervix in relation to preterm birth. Semin Perinatol 2009;33:308–11. 10.1053/j.semperi.2009.06.003 [DOI] [PubMed] [Google Scholar]
- 130.Warren JE, Nelson LM, Stoddard GJ, et al. Polymorphisms in the promoter region of the interleukin-10 (IL-10) gene in women with cervical insufficiency. Am J Obstet Gynecol 2009;201:372.e1–372.e5. 10.1016/j.ajog.2009.05.022 [DOI] [PubMed] [Google Scholar]
- 131.Polderman TJC, Benyamin B, de Leeuw CA, et al. Meta-Analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet 2015;47:702–9. 10.1038/ng.3285 [DOI] [PubMed] [Google Scholar]
- 132.Yang J, Lee SH, Goddard ME, et al. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011;88:76–82. 10.1016/j.ajhg.2010.11.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 133.Vinkhuyzen AAE, Wray NR, Yang J, et al. Estimation and partition of heritability in human populations using whole-genome analysis methods. Annu Rev Genet 2013;47:75–95. 10.1146/annurev-genet-111212-133258 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 134.Yang J, Zeng J, Goddard ME, et al. Concepts, estimation and interpretation of SNP-based heritability. Nat Genet 2017;49:1304–10. 10.1038/ng.3941 [DOI] [PubMed] [Google Scholar]
- 135.Janssens MJJ. Co-heritability: its relation to correlated response, linkage, and pleiotropy in cases of polygenic inheritance. Euphytica 1979;28:601–8. 10.1007/BF00038926 [DOI] [Google Scholar]
- 136.Lee SH, Yang J, Goddard ME, et al. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 2012;28:2540–2. 10.1093/bioinformatics/bts474 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 137.Bulik-Sullivan B, Finucane HK, Anttila V, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 2015;47:1236–41. 10.1038/ng.3406 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 138.International Schizophrenia Consortium, Purcell SM, Wray NR, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009;460:748–52. 10.1038/nature08185 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 139.Dudbridge F. Power and predictive accuracy of polygenic risk scores. PLoS Genet 2013;9:e1003348. 10.1371/journal.pgen.1003348 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 140.Wray NR, Lee SH, Mehta D, et al. Research review: polygenic methods and their application to psychiatric traits. J Child Psychol Psychiatry 2014;55:1068–87. 10.1111/jcpp.12295 [DOI] [PubMed] [Google Scholar]
- 141.Judd CM, Kenny DA. Process analysis: estimating mediation in treatment evaluations. Eval Rev 1981;5:602–19. [Google Scholar]
- 142.James LR, Brett JM. Mediators, moderators, and tests for mediation. J Appl Psychol 1984;69:307–21. 10.1037/0021-9010.69.2.307 [DOI] [Google Scholar]
- 143.Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51:1173–82. 10.1037/0022-3514.51.6.1173 [DOI] [PubMed] [Google Scholar]
- 144.Judd CM, Kenny DA, McClelland GH. Estimating and testing mediation and moderation in within-subject designs. Psychol Methods 2001;6:115–34. 10.1037/1082-989X.6.2.115 [DOI] [PubMed] [Google Scholar]
- 145.Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559–75. 10.1086/519795 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 146.Choi SW, Heng Mak TS, O’Reilly PF. A guide to performing polygenic risk score analyses. bioRxiv. 10.1101/416545 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 147.Palla L, Dudbridge F. A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait. Am J Hum Genet 2015;97:250–9. 10.1016/j.ajhg.2015.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 148.Dudbridge F. Polygenic epidemiology. Genet Epidemiol 2016;40:268–72. 10.1002/gepi.21966 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 149.Visscher PM, Hemani G, Vinkhuyzen AAE, et al. Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples. PLoS Genet 2014;10:e1004269. 10.1371/journal.pgen.1004269 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 150.Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nat Rev Genet 2018;19:581–90. 10.1038/s41576-018-0018-x [DOI] [PubMed] [Google Scholar]
- 151.Modi BP, Teves ME, Pearson LN, et al. Rare mutations and potentially damaging missense variants in genes encoding fibrillar collagens and proteins involved in their production are candidates for risk for preterm premature rupture of membranes. PLoS One 2017;12:e0174356. 10.1371/journal.pone.0174356 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 152.Modi BP, Teves ME, Pearson LN, et al. Mutations in fetal genes involved in innate immunity and host defense against microbes increase risk of preterm premature rupture of membranes (PPROM). Mol Genet Genomic Med 2017;5:720–9. 10.1002/mgg3.330 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 153.Anum EA, Hill LD, Pandya A, et al. Connective tissue and related disorders and preterm birth: clues to genes contributing to prematurity. Placenta 2009;30:207–15. 10.1016/j.placenta.2008.12.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 154.Mercer BM, Goldenberg RL, Meis PJ, et al. The preterm prediction study: prediction of preterm premature rupture of membranes through clinical findings and ancillary testing. Am J Obstet Gynecol 2000;183:738–45. 10.1067/mob.2000.106766 [DOI] [PubMed] [Google Scholar]
- 155.Odibo AO, Talucci M, Berghella V. Prediction of preterm premature rupture of membranes by transvaginal ultrasound features and risk factors in a high-risk population. Ultrasound Obstet Gynecol 2002;20:245–51. 10.1046/j.1469-0705.2002.00759.x [DOI] [PubMed] [Google Scholar]
- 156.Odibo AO, Berghella V, Reddy U, et al. Does transvaginal ultrasound of the cervix predict preterm premature rupture of membranes in a high-risk population? Ultrasound Obstet Gynecol 2001;18:223–7. 10.1046/j.1469-0705.2001.00419.x [DOI] [PubMed] [Google Scholar]
- 157.Bortoletto TG, Silva TV, Borovac-Pinheiro A, et al. Cervical length varies considering different populations and gestational outcomes: results from a systematic review and meta-analysis. PLoS One 2021;16:e0245746. 10.1371/journal.pone.0245746 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 158.Ravel J, Gajer P, Abdo Z, et al. Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci U S A 2011;108 Suppl 1:4680–7. 10.1073/pnas.1002611107 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 159.Fettweis JM, Brooks JP, Serrano MG, et al. Differences in vaginal microbiome in African American women versus women of European ancestry. Microbiology 2014;160:2272–82. 10.1099/mic.0.081034-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 160.Fettweis JM, Serrano MG, Brooks JP, et al. The vaginal microbiome and preterm birth. Nat Med 2019;25:1012–21. 10.1038/s41591-019-0450-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 161.Serrano MG, Parikh HI, Brooks JP, et al. Racioethnic diversity in the dynamics of the vaginal microbiome during pregnancy. Nat Med 2019;25:1001–11. 10.1038/s41591-019-0465-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 162.Romero R, Gonzalez R, Sepulveda W, et al. Infection and labor. VIII. microbial invasion of the amniotic cavity in patients with suspected cervical incompetence: prevalence and clinical significance. Am J Obstet Gynecol 1992;167:1086–91. 10.1016/s0002-9378(12)80043-3 [DOI] [PubMed] [Google Scholar]
- 163.Gomez R, Romero R, Nien JK, et al. A short cervix in women with preterm labor and intact membranes: a risk factor for microbial invasion of the amniotic cavity. Am J Obstet Gynecol 2005;192:678–89. 10.1016/j.ajog.2004.10.624 [DOI] [PubMed] [Google Scholar]
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