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Harris Wellbeing Preterm Birth Research Centre, Department of Women's and Children's Health, University of Liverpool, Liverpool Women’s Hospital, Liverpool, United Kingdom
Harris Wellbeing Preterm Birth Research Centre, Department of Women's and Children's Health, University of Liverpool, Liverpool Women’s Hospital, Liverpool, United KingdomWolfson Centre for Personalised Medicine, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdom
Harris Wellbeing Preterm Birth Research Centre, Department of Women's and Children's Health, University of Liverpool, Liverpool Women’s Hospital, Liverpool, United Kingdom
Division of Human Genetics, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Department of Pediatrics and University of Cincinnati College of Medicine, Cincinnati, OH, United States
Division of Human Genetics, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Department of Pediatrics and University of Cincinnati College of Medicine, Cincinnati, OH, United States
Division of Human Genetics, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Department of Pediatrics and University of Cincinnati College of Medicine, Cincinnati, OH, United States
Division of Human Genetics, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Department of Pediatrics and University of Cincinnati College of Medicine, Cincinnati, OH, United States
Harris Wellbeing Preterm Birth Research Centre, Department of Women's and Children's Health, University of Liverpool, Liverpool Women’s Hospital, Liverpool, United Kingdom
Harris Wellbeing Preterm Birth Research Centre, Department of Women's and Children's Health, University of Liverpool, Liverpool Women’s Hospital, Liverpool, United KingdomWolfson Centre for Personalised Medicine, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdom
Division of Human Genetics, Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Department of Pediatrics and University of Cincinnati College of Medicine, Cincinnati, OH, United States
Harris Wellbeing Preterm Birth Research Centre, Department of Women's and Children's Health, University of Liverpool, Liverpool Women’s Hospital, Liverpool, United Kingdom
Low second trimester maternal selenium levels associate with spontaneous PTB risk.
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Selenium not sufficiently predictive in the context of personalised medicine.
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First GWAS using maternal Se levels as continuous data investigating recurrent sPTB.
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Low maternal Se levels in women with recurrent sPTB associated with variant in FOXN3 gene.
Abstract
Objective
To establish if low maternal selenium (Se) was associated with sPTB in women with recurrent sPTB and identify genetic link with maternal Se levels.
Design
Nested case-control study.
Setting
Tertiary Maternity Hospital.
Population
Plasma and whole blood from pregnant women with history of early sPTB/PPROM < 34+0 and European ancestry were obtained at 20 weeks (range 15–24 weeks). ‘Cases’ were recurrent PTB/PPROM < 34+0 weeks and term (≥37+0) deliveries were classified as ‘high-risk controls.’ Women with previous term births and index birth ≥ 39 weeks were ‘low-risk controls’.
Methods
Maternal plasma Se measured by ICP-MS was used as a continuous phenotype in a GWAS analysis. Se was added to a logistic regression model using PTB predictor variables.
Main outcome measures
Maternal Se concentration, recurrent early sPTB/PPROM.
Results
53/177 high-risk women had a recurrent sPTB/PPROM < 34+0weeks and were 2.7 times more likely to have a Se level < 83.3 ppm at 20weeks of pregnancy compared with low-risk term controls (n = 179), (RR 2.7, 95%CI 1.5–4.8; p = .001). One SNP from a non-coding region (FOXN3 intron variant, rs55793422) reached genome-wide significance level (p = 3.73E−08). Targeted analysis of Se gene variant did not show difference between preterm and term births. (χ2 test, OR = 0.95; 95%CI = 0.59–1.56; p = 0.82). When Se levels were added to a clinical prediction model, only an additional 5% of cases (n = 3) and 0.6% (n = 1) of controls were correctly identified.
Conclusions
Low plasma Se is associated with sPTB risk but is not sufficiently predictive at individual patient level. We did not find a genetic association between maternal Se levels and Se-related genes.
Selenium (Se) is of importance to human wellbeing through its incorporation into numerous selenoproteins involved in antioxidant, anti-inflammatory and anti-apoptotic functions [
]. Therefore, it has been difficult to ascertain whether low maternal Se is a cause of sPTB or simply an association secondary to the inflammation related pathophysiology of sPTB. Se has gained more interest recently as a potential causative agent for sPTB after a genome wide association study (GWAS) discovered that the EEFSEC gene variant associated with risk for sPTB [
]. EEFSEC (Eukaryotic Elongation Factor, SElenoCysteine-TRNA Specific) is a protein coding gene responsible for the translation factor necessary for the incorporation of selenocysteine into proteins [
]. It is not clear how the EEFSEC gene polymorphisms affect selenoproteins in practice, or if this gene could be used as a way to inform supplementation of Se in women at risk.
We hypothesised that women with recurrent early sPTB < 34 weeks gestation would be more likely to have a genetic marker for sPTB than women with no previous sPTB or only one previous sPTB followed by term birth. We aimed to establish if low maternal Se was significantly associated with recurrent sPTB, and if so, to determine whether maternal Se phenotype was associated with genetic factors.
Methods
Study population
From 1st April 2012 until 31st December 2017, 541 pregnant women with singleton pregnancies enrolled into “Development of novel biomarkers for prediction of preterm labour in a high-risk population” study (REC reference: 11/NW/0720) at the Liverpool Women’s Hospital (LWH), UK. All participants gave medical histories, transvaginal cervical length (CL) and donated blood samples in the midtrimester from 16 to 24 weeks.
Selection criteria from two obstetric populations at high and low risk of sPTB is shown in Fig. 1. ‘High-risk’ consisted of women with a history of sPTB or preterm prelabour rupture of membranes (PPROM) at 16+0–33+6 weeks. ‘Low-risk’ included women with previous term delivery. Hospital records were used to ascertain delivery information.
Fig. 1Participant Selection. Two obstetric populations were used to recruit women. The first was women at high-risk of sPTB based on their history of previous sPTB. The second population was used to represent “normality” and consisted of women with a history of term birth only. ‘Cases’ consisted of women with recurrent sPTB or PPROM < 34 weeks. Control groups included women who achieved term delivery (defined as >37 weeks in the high-risk group and >39 weeks in the low-risk cohort).
Three groups of women were retained for further analysis: i) cases (recurrent early sPTB/PPROM < 34+0 weeks), ii) high-risk sPTB controls and iii) low-risk controls.
High-risk controls were high-risk women who had a subsequent healthy term birth (≥37+0 weeks) without preventative PTB treatment (progesterone, cerclage or pessary) (Fig. 1). Low-risk controls were selected to represent the ‘normal healthy’ obstetric population, and included only women who had a subsequent healthy term birth (≥39+0 weeks). Many infants born at ≤38 weeks of gestation experience an increase in neonatal mortality and lifetime morbidity related to immaturity of one or more organs when compared with infants born at 39 weeks or greater [
]. The definition of a healthy term birth being anything at or greater than 37 weeks does not correspond with functional maturity. For this reason, we, in agreement with others [
] believe defining healthy term births as occurring at 39 weeks more appropriate. All pregnancies were classified independently by obstetricians with PTB prevention experience AC and AS or LG using hospital case notes. When there was a discrepancy between reviewers, the case was reviewed by third researcher, ZA, until the team reached a consensus on classification.
Sample size for recruitment was calculated based on a PTB rate <34 weeks of 17–20%, from annual audit of clinic outcomes. Approximately 140 high risk women could be recruited in 3 years (n = 25 PTB < 34 weeks), demonstrated by pilot recruitment. Based on the assumption that 80% of term births (controls) were recruited, an AUC of 0.9 could be achieved with 50 cases. ROC curves were generated using the R package 'pROC' [
Maternal blood samples were taken in a 6 ml BD vacutainer® tubes containing EDTA. Plasma was separated within one hour and stored at −80 °C. Samples were transferred to the March of Dimes Prematurity Research Center Ohio Collaborative at the Cincinnati Children’s Hospital Medical Center, USA for Se measurement by inductively coupled plasma mass spectrometry (ICP-MS) using Agilent 7700 ICP-MS. This team remained blinded to the clinical details during the analysis phase.
Genome wide association study
DNA was extracted from whole blood using Chemagen instrument with M-PVA Magnetic bead technology (PerkinElmer, UK). Genotyping was performed using the UK Biobank Axiom™ Array (Affymetrix/Thermo Fisher). PLINK 1.9 software [
] Samples with high or low heterozygosity rates (±3 standard deviations from the mean) or close relatedness (pi-hat score >0.2) were excluded. Since combining genetic information across ethnicities can result in false positive findings from population stratification within genetically distinct populations, individuals not genetically assigned to European ancestry (CEU) population based on the HapMap data were excluded [
]. We had insufficient numbers from other ethnicities to adjust our GWAS analysis for their inclusion. A total of 618,283 SNPs were uploaded onto the Michigan Imputation Server for phasing chromosome 1–22 using Eagle v2.3 and imputation (using the minimac3 algorithm) against the Haplotype Reference Consortium (HRC r1.1 2016) panel [
Analysis included all PTB cases (n = 53) and all term (n = 303) controls. After inverse-rank normalisation, maternal Se levels were used as a continuous outcome in the association analysis completed with SNPtest v2.5 [
]. To control for cohort genetic variance, principal components were generated and the components displaying largest variance were selected as covariates.
Interrogation of Se gene polymorphisms
TaqMan real-time PCR was used to validate the cohort for the presence of EEFSEC polymorphism associated with sPTB in a recent GWAS [
] (rs2955117; assay ID: C____196708_20, Thermo Fisher, UK). Analysis included all sPTB (n = 53) and all term births (n = 311). Fixed-effects model analysis and forest plot were generated using R package ‘metafor’ [
] to describe differences in odds ratio (OR) and 95% confidence intervals (CI) for this SNP between the recurrent sPTB population and the population described by Zhang et al. [
] In addition, associations with genes reported in literature were targeted in our analyses.
Se predictive modelling
A binomial logistic regression analysis was performed testing the prediction accuracy of four clinical variables available to the clinician in the PTB prevention clinic; 1) history of sPTB/PPROM, 2) CL at sampling, 3) smoking status and 4) history of cervical surgery, on the likelihood of delivering at term or <34 weeks (dichotomous variable). Analysis included all PTB cases (n = 53) and all term birth (low and high-risk term births, n = 296) controls where demographic data was completely available. Se was analysed independently, then subsequently added as a predictor variable to determine the difference the addition of Se would make to the prediction of the dependent variable. To assess model accuracy, the predictive model was split into training (80% data) and testing (20% data) subsets and area under the receiver operating curve (AUROC) and 95% CI were recorded.
Statistical analysis
Normalisation methods were applied to account for batch effects. IBM SPSS Statistics for Windows, v.25 (IBM Corp., Armonk, N.Y., USA) was used for the descriptive statistics and logistic regression analysis. One-way analysis of variance (ANOVA) test was used for continuous normally distributed data, Kruskal-Wallace test for continuous, non-normally distributed data and Fishers exact test or χ2 for categorical data analyses where appropriate. Nominal 2-sided P values are reported, with a P < .05 considered significant in all analyses, apart from the GWAS, where P < 5 × 10−8 was used. For the binomial logistic regression predictive model, the R package pROC [
For the final analysis, 356 women were included (Fig. 1) n = 53 cases, n = 124 high-risk controls and n = 179 low-risk controls. Baseline characteristics and pregnancy outcomes are shown in Table 1. There was a lower prevalence of smokers in the low-risk control group, compared with the high-risk group. There was a significant statistical difference in the gestational age at sampling among the groups reflecting a difference in average sampling of one to two days, however this does not make a clinically detectable difference to Se concentrations [
P value calculated by Independent Samples Kruskal-Wallis for continuous, not normally distributed data. The significance level is 0.05. CL = cervical length. IQR – interquartile range.
P value calculated by Independent Samples Kruskal-Wallis for continuous, not normally distributed data. The significance level is 0.05. CL = cervical length. IQR – interquartile range.
P value calculated by Independent Samples Kruskal-Wallis for continuous, not normally distributed data. The significance level is 0.05. CL = cervical length. IQR – interquartile range.
P value calculated by ANOVA for continuous, normally distributed data,
a P value calculated by Fishers exact test or X2 for categorical data analyses unless otherwise noted.
b P value calculated by ANOVA for continuous, normally distributed data,
c P value calculated by Independent Samples Kruskal-Wallis for continuous, not normally distributed data. The significance level is 0.05. CL = cervical length. IQR – interquartile range.
Women with early sPTB/PPROM had a lower median Se level (73 ppm) than either the high-risk control group (78.7 ppm) or the low-risk controls (83.3 ppm). (Fig. 2) Participants with recurrent sPTB were 2.7 times more likely to have a Se level below median at 20 weeks of pregnancy (RR 2.7, 95% CI 1.5–4.8; p = .001). When comparing women within the high-risk population, women with Se levels below the median for the normal population (<83.3 ppm) were 1.5 times more likely to have a recurrent early sPTB. However, the difference did not reach statistical significance with the 95% CI just crossing one (RR 1.5, 95% CI 0.9–2.7).
Fig. 2Second Trimester Maternal Selenium Levels (ppb) of Cases (n = 53, blue) and Controls (n = 124 red, n = 179 green). The median levels of each group are marked by a vertical line inside the box. The box spans the interquartile range. The whiskers outside the box extend to the highest and lowest observations. Outliers are shown by dots and stars. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
One intronic SNP, rs55793422 in the FOXN3 gene was associated with low Se levels in women with sPTB at genome-wide significance level (p = 3.73E−08). (Fig. 3)
Fig. 3Manhattan Plot of Selenium Genome Wide Association Analysis for 301 Participants across all Autosomes. The Manhattan plot shows the strength of association of each tested SNP (shown as individual dots) with maternal selenium levels in a GWAS analysis, plotted as -log10 (Pvalue) on the y-axis against corresponding variant position on the x-axis. A sentinel SNP rs55793422, which corresponds to a non-coding region of the FOXN3 intron variant, crosses the red line denoting genome-wide significance (P < 5 × 10−8). The blue line denotes a suggestive significance threshold (P < 1.0 × 10−5). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Chi-square analysis of EEFSEC allele frequency using TaqMan chemistry didn’t reach statistically significant difference when preterm and term births were compared (OR = 0.95; 95% CI = 0.59, 1.56; p = 0.82). We checked several SNPs in genes previously reporting an association with Se levels, none of which reached statistical significance (Table S1). When we compared the SNP located in the EEFSEC gene previously published by Zhang et al. with our population we found no association, although the confidence intervals are wide, and we cannot rule out a false positive finding (Fig. 4).
Table S1Genes and polymorphisms from the literature that have been associated with variability in plasma selenium levels. Literature searches were undertaken to investigate genetic factors implicated in selenium levels variability. We then compared the results from the literature with our GWAS p-values using selenium levels as a continuous trait in the linear regression analysis.
Fig. 4Forest plot demonstrating the association of SNP rs2955117 with spontaneous preterm birth < 37 weeks. Odds ratio and 95% confidence intervals are presented for the high-risk Liverpool, UK population and the published Zhang et al population.
A logistic regression was performed to ascertain the effects of previous history of sPTB/PPROM < 34 weeks, CL at sampling, smoking and cervical surgery on the likelihood that participants have PTB. The logistic regression model was statistically significant, χ2 = 124.828, p < .0005. The model explained 53.0% (Nagelkerke R2) of the variance in pregnancy outcome and correctly classified 48.1% of cases and 97.3% of controls. When Se levels were added to the model, the model correctly classified 53.8% of cases and 97.6% of controls. Decreasing CL at sampling and decreasing Se levels were both significantly associated with an increased likelihood of having sPTB/PPROM < 34 weeks. The adjusted OR for Se plasma levels was 1.05 (95% CI 1.02–1.09). Fig. 5 demonstrates the area under the receiver operating characteristic curves (AUC ROC) for the training data and testing data for Se alone, clinical covariates alone and Se plus clinical covariates. For Se alone, the training data (80% of the data) produced an AUC = 0.62 (95% CI 0.53–0.71). The test data (remaining 20% data) showed comparable poor performance with an AUC = 0.65 (95% CI = 0.56–0.74). Adding Se to the prediction using clinical variables alone produces AUC = 0.92; 95% CI: 0.88–0.95(80% training data) and AUC = 0.95; 95% CI: 0.90–1 (20% test data). Although this appears to be good prediction, Se has not significantly added to the prediction offered by clinical variables alone (Fig. 5).
Fig. 5ROC curves of PTB predictive modelling using logistic regression. Logistic regression was used to assess the prediction power of plasma selenium levels plus clinical covariates. To test model accuracy the cohort was split into A. the testing subset (80% of data) and B. the testing set (20% of data).
This prospective, single-center, cohort study evaluated maternal genetic variants associated with second trimester maternal Se levels. Additionally, we assessed the predictive ability of Se levels in women with recurrent sPTB. Although there is an association between lower second trimester maternal Se levels and sPTB, we could not confirm a genetic link between Se and any Se related SNP or gene. We did, however, find an association with a SNP in FOXN3 gene.
Strengths and limitations
Our work has several strengths over prior published literature. All cases are stringently phenotyped ensuring a homogenous case group with all ‘iatrogenic’ cases of PTB removed thus reducing ‘noise’ in the dataset. The cases were all collected prospectively in a single centre and classified by the same PTB clinical researchers ensuring a clean dataset that frequently cannot be attained in retrospective or multicentre studies. The mean age of gestation for sPTB is 31 weeks and 5 days, which is lower than most sPTB studies. This study has 53 women with recurrent PTB which would potentially select for cases with a genetic cause of sPTB.
Despite these strengths, the single-centre design limits our sample size which is a weakness for the GWAS analysis. Our GWAS only includes women of Caucasian descent. We cannot control for daily Se intake between populations during this study as information on Se consumption was not recorded. Additionally, there is no external cohort for validation of these findings.
Results in context
To our knowledge, this is the first GWAS study that used maternal Se levels as continuous data linked with PTB and investigates a recurrent sPTB population. Other studies have found low maternal Se in women who have PTB, often associating to gestational hypertension or preeclampsia [
]. No studies have examined a population of women at high risk of sPTB. In a study of 1129 Dutch pregnant women, Se was prospectively tested at 12 weeks and 60 (5.3%) had PTB. Those in the lowest quartile of serum Se had a two-fold greater risk of PTB than controls, even after adjustment for the inclusion of pre-eclampsia cases (n = 13) [
]. Only 10 cases occurred before 33 weeks’ gestation. Given the heterogenous nature of the classification and relatively late gestation of PTB, we aimed to validate these finding in spontaneous PTB/PPROM < 34 weeks.
We found the median Se level for women who have a history of healthy term births is higher in the midtrimester (83 ppm) than women who have a history of sPTB/PPROM (78 ppm). The median Se levels are even lower for women with recurrent PTB (73.3 ppm). This gave us some encouragement that Se might predict women at highest risk of sPTB. Unfortunately, on an individual level this remains a poor predictor of sPTB, adding only 5% improved prediction of sPTB/PPROM cases over existing predictive clinical markers.
Our main aim was to confirm a genetic link between maternal Se levels as a continuous phenotype and maternal genotype. If an EEFSEC variant is disproportionally represented in women who have sPTB, as discovered in a recent GWAS [
] – there should be even greater selection of this variant in a population of women with recurrent sPTB. If EEFSEC is linked to low maternal Se, performing a GWAS using maternal Se levels as the continuous outcome would select for SNPs associated with Se genes and validate the genetic link with Se in our predominantly Caucasian population. Unfortunately, no such association was found in our population.
The only SNP that reached genome wide significance (FOXN3 intron variant) was in a noncoding gene region of a gene that did not demonstrate any immediate relation to Se. This gene is a protein coding gene which acts as a transcriptional repressor that may be involved in DNA damage-inducible cell cycle arrests (checkpoints) [
] and should be investigated further for a relationship to Se or selenoprotein regulation. Additionally, further research to understand the association between low maternal Se and sPTB is required.
Our targeted analysis of the EEFSEC variant (rs2955117) demonstrated no difference between recurrent PTBs and term births, suggesting this gene may not be linked to PTB in our high-risk cohort. It is possible for this to be a ‘false-negative’ result due to a small sample size.
Our data do not confirm a genetic link for EEFSEC gene or other Se related genes in recurrent sPTB in a Caucasian population. This suggests either other genetic pathways are implicated, or environmental factors play a bigger role than previously thought. This is reinforced by a study by Monagi et al. (unpublished data) [
Monagi NK, Xu H, Khanam R, Khan W, Deb S, Pervin J, et al. Association of maternal prenatal selenium concentration and preterm birth: a multi-country meta-analysis. (Submitted for publication) 2021.
] which compared this Liverpool cohort in a consortium of 9946 pregnancies from 17 geographically diverse populations to determine the association of maternal Se concentration and PTB risk. Although Se concentrations followed a normal distribution, the mean varied substantially across different sites. Interestingly, the Liverpool cohort demonstrated the largest effect size but was also the only population to have a substantial number of women with recurrent PTB. In another UK cohort collected in Oxford, a more affluent region of the UK, the mean Se concentration was 10 ng/ml higher and did not associate with PTB risk or duration [
Monagi NK, Xu H, Khanam R, Khan W, Deb S, Pervin J, et al. Association of maternal prenatal selenium concentration and preterm birth: a multi-country meta-analysis. (Submitted for publication) 2021.
]. This may reflect regional dietary patterns related to differences in deprivation status or culture which may confound the association between Se and PTB risk. Although we did find an association with low maternal Se and sPTB, until the causes for this are understood we would not recommend maternal Se supplements. We are not aware of any evidence that supplementation can directly prevent sPTB. A possibility of harm should also be born in mind as recently highlighted by a secondary analysis of randomised trial of omega 3 supplementation for PTB prevention [
Omega-3 fatty acid supplementation in pregnancy—baseline omega-3 status and early preterm birth: exploratory analysis of a randomised controlled trial.
Low plasma Se is associated with sPTB risk but is not sufficiently predictive in the context of personalised medicine. We could not determine a genetic association between maternal Se levels and EEFSEC or any other Se related SNP or gene in a population of women with recurrent sPTB, but we identified, in an unbiased GWAS analysis a new candidate gene FOXN3 that may be linked to Se levels.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We thank Lord and Lady Harris and Wellbeing of Women who funded this project along with support from the March of Dimes Prematurity Research Center Ohio Collaborative and the Bill and Melinda Gates Foundation (OPP1175128). Dr Borna Poljak, Dr Jelena Ivandic and Dr Silvia Mammarella all assisted with recruitment of samples and Dr Jane Harrold, Sarah Northey, Dr Lawrence McEvoy and the staff at the LWH and Wolfson centre laboratory. We would like to thank Oxford Genomics Centre at the Wellcome Centre for Human Genetics for processing our DNA samples on the Applied Biosystems™ UK Biobank Axiom™ array (Thermo Fisher). We thank all the women who participated in this research study.
Contribution to authorship
ZA, LJM, AA, BMM, AC, AS conceived the study and obtained funding. AC, LG, AS, AA, BMM, ZA, LJM, NM, GZ, JC, EB, NM, JG contributed to the protocol, AC, LG, AS recruited participants. JL, EB, LJM developed the selenium measurement methods. AC, LG, AS extracted the clinical data. AC, JG extracted DNA. GZ, NM, EB, JL, JC, AA, JG, performed laboratory analysis (Se or GWAS). AC, GZ, NM, JC, JG, AA, BMM performed data processing and statistical analysis. AC wrote the initial draft and ZA, LJM, JC, LG, AA, JG, BMM contributed to data interpretation and revised the paper. All authors approved the final manuscript.
Details of ethics approval
The study was approved by North West Research Ethics Committee – Liverpool Central, reference 11/NW/0720 on 4 November 2011.
Funding
The prospective cohort study was funded by Wellbeing of Women as part of a charitable donation from Lord and Lady Harris to establish the Harris-Wellbeing PTB Centre, University of Liverpool. The selenium analysis was performed at the University of Cincinnati, Ohio, United States and was supported by the March of Dimes Prematurity Research Center Ohio Collaborative and the Bill and Melida Gates Foundation (OPP1175128).
Monagi NK, Xu H, Khanam R, Khan W, Deb S, Pervin J, et al. Association of maternal prenatal selenium concentration and preterm birth: a multi-country meta-analysis. (Submitted for publication) 2021.
Omega-3 fatty acid supplementation in pregnancy—baseline omega-3 status and early preterm birth: exploratory analysis of a randomised controlled trial.
Angharad Care, MRCOG PhD is a National Institute of Health Research (NIHR) Academic Clinical Lecturer at the University of Liverpool and an Obstetric and Gynaecology Specialty Registrar at Liverpool Women’s Hospital, UK. Her research has been focussed around preterm birth prediction and prevention, including finding new predictive biomarkers for preterm birth to better identify women at risk, clinical trials of preventative therapies and Cochrane systematic reviews.
Juhi Kumari Gupta completed her undergraduate and master’s training in Genetics (MBiolSci), and has recently been awarded her PhD from the University of Liverpool. During her thesis Juhi developed expertise in analysis of large “omics” data using statistical and machine learning approaches. She has gained a range of laboratory experience in molecular and cell biology, as well as working with multi-disciplinary teams to deliver translational research.
Dr Goodfellow is an obstetrics and gynaecology trainee and a clinical research fellow in preterm birth in Liverpool Women’s Hospital, UK. Dr Goodfellow has been involved in the Liverpool Biomarkers of Preterm Birth study, working on participant involvement and data analysis with particular emphasis on the contribution of the vaginal microbiota to preterm birth.
Dr. Zhang is an associate professor at the Cincinnati Children’s Hospital Medical Center. His training background is in statistical and population genetics. He has extensive experience in the genetic data analysis of human pregnancy phenotypes. He conducted a genome-wide association (GWA) study and identified genetic variants associated with gestational length and preterm birth. He developed a novel Mendelian randomization method for the casual inference between parental phenotype and pregancy outcomes in offspring.
Dr.Monangi is a Neonatal-Perinatal Medicine faculty and a member of the Center for Prevention of Prematurity at Cincinnati Children's Hospital. He is currently co-leading the International consortium of Selenium, Genetics and Preterm birth and co-investigator of the MOMI Consortium supported by Bill and Melinda Gates foundation. His current focus is to curate several omics datasets to perform multi scale analysis for identifying molecular signatures of the influence of environment and nutritional factors on adverse pregnancy outcomes in diverse populations. His goal is to study the role of nutrition/environmental exposures and social determinants on global maternal-child health.
Elizabeth Belling is a Research Assistant at Cincinnati Children’s Hospital Medical Center. She is a member of the Center for Prevention of Prematurity and the International Consortium of Selenium, Genetics, and Preterm Birth. She is currently working on a project studying the association between heavy metals and biochemical analytes such as maternal selenium concentrations and preterm birth. Elizabeth graduated in 2018 with a Bachelor of Science in Biochemistry from Miami University.
Julio A. Landero Figueroa PhD. Bioanalytical Chemist Graduated Summa Cum Laude from the University of Guanajuato, MX. Specialist in Metallomics, the comprehensive study of metals in biological systems, with 15 years of experience in the development of novel approaches for elemental analysis and chemical speciation using chromatography coupled to atomic mass spectrometry. Research Assistant Professor in the Chemistry Department and Pharmacology & Systems Biology at the University of Cincinnati with interest in the role of toxic and essential metals and metalloids in human disease with over 60 publications and 1200 citations.
Joanne Chappell B.S. is the Director of administrative and financial operations of the March of Dimes Prematurity Center Ohio Collaborative and International consortium for selenium, genetics and preterm birth supported by Bill and Melinda Gates Foundation. Joanne Chappell coordinates and administers all the programs at Center for Prevention of Prematurity at Cincinnati Children’s Hospital designed to discover the unknown causes of preterm birth. Joanne graduated with Bachelor of Science in Accounting and Finance from Missouri State University.
Dr Andrew Sharp is a Senior Lecturer in Obstetrics at the University of Liverpool. He is co-lead for the preterm birth clinic and lead for fetal growth and multiple pregnancy at Liverpool Women’s Hospital, one of the largest maternity units in Europe. His research interests include clinical trials, biomarker discovery in a number of maternity conditions including fetal growth, induction of labour, preterm birth and multiple pregnancy.
Ana is Professor of Pharmacology and Personalised Medicine and the Head of Department of Pharmacology and Therapeutics at the University of Liverpool. Ana’s research has been focused on molecular pharmacology and pharmacogenetics. She has been working on several projects on discovery of genetic predisposing factors for adverse drug reactions including drug-induced hypersensitivity, hepatotoxicity, antipsychotic drug-induced agranulocytosis and statin-induced myotoxicity using high throughput genotyping and sequencing methodologies. In addition, she was leading several pharmacology and systems biology projects in reproductive medicine and pregnancy including the multiomic approach to preterm birth. To date, she has published over 130 peer-reviewed research papers.
Bertram is a Professor of Statistical Genetics at the and Research Group Leader at the International Max Plank Research School of Psychiatry and holds a post at the Institute of Translational Medicine, University of Liverpool. His main interests lie in the development and application of new statistical and informatics methodology to unravel the architecture of traits and diseases, with a special focus on, but not restricted to, neurologic and psychiatric disorders and phenotypes. To this end he is trying to marry statistical genetics and machine learning methods, extending the scope of analysis beyond pure genetics. As a consequence of the heavy computational loads many of these methods are facing, he is very interested in algorithmic and generally computational advances, e.g. OpenCL, GPGPU implementations on the one hand, and faster algorithms, even approximate or two-stage designs, on the other hand.
Louis Muglia, MD PhD is President and CEO of the Burroughs Wellcome Fund, an independent nonprofit research foundation and Adjunct Professor of Pediatrics at Cincinnati Children’s Hospital. The goal of the Muglia laboratory has been to understand the molecular machinery comprising the biological clock that determines the timing for birth, and how this is shaped by the environment, to prevent or better treat human preterm delivery utilizing genetic and comparative genomic approaches. Among Dr. Muglia’s achievements are more than 280 publications and election to the US National Academy of Medicine and to the Finnish Academy of Science and Letters.
Zarko Alfirevic is Professor of Fetal and Maternal Medicine and Associate Pro-Vice Chancellor at the Faculty of Health and Life Sciences, University of Liverpool. He is Director of Fetal Medicine Unit and Harris Wellbeing PTB Research Centre at Liverpool Women’s Hospital, one of the largest stand-alone maternity hospitals in Europe with more than 8000 births per annum. His research interests are evidence based medicine and clinical trials in high risk obstetrics. He is Co-ordinating Editor of the Cochrane Pregnancy and Childbirth Group, has over 290 publications listed in PubMed and speaks regularly at international meetings on topics related to PTB, fetal growth restriction, induction of labour and evidence based medicine.
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