Evidence that geographic variation in genetic ancestry associates with uterine fibroids.

Uterine fibroids disproportionately impact Black women. Evidence suggests Black women have earlier onset and higher cumulative risk. This risk disparity may be due an imbalance of risk alleles in one parental geographic ancestry subgroup relative to others. We investigated ancestry proportions for the 1000 Genomes phase 3 populations clustered into six geographic groups for association with fibroid traits in Black women (n = 583 cases, 797 controls) and White women (n = 1195 cases, 1164 controls). Global ancestry proportions were estimated using ADMIXTURE. Dichotomous (fibroids status and multiple fibroid status) and continuous outcomes (volume and largest dimension) were modeled for association with ancestry proportions using logistic and linear regression adjusting for age. Effect estimates are reported per 10% increase in genetically inferred ancestry proportion. Among Black women, West African (WAFR) ancestry was associated with fibroid risk, East African ancestry was associated with risk of multiple fibroids, Northern European (NEUR) ancestry was protective for multiple fibroids, Southern European ancestry was protective for fibroids and multiple fibroids, and South Asian (SAS) ancestry was positively associated with volume and largest dimension. In White women, NEUR ancestry was protective for fibroids, SAS ancestry was associated with fibroid risk, and WAFR ancestry was positively associated with volume and largest dimension. These results suggest that a proportion of fibroid risk and fibroid trait racial disparities are due to genetic differences between geographic groups. Further investigation at the local ancestry and single variant levels may yield novel insights into disease architecture and genetic mechanisms underlying ethnic disparities in fibroid risk.

Newborn metabolic vulnerability profile identifies preterm infants at risk for mortality and morbidity.

BACKGROUND: Identifying preterm infants at risk for mortality or major morbidity traditionally relies on gestational age, birth weight, and other clinical characteristics that offer underwhelming utility. We sought to determine whether a newborn metabolic vulnerability profile at birth can be used to evaluate risk for neonatal mortality and major morbidity in preterm infants. METHODS: This was a population-based retrospective cohort study of preterm infants born between 2005 and 2011 in California. We created a newborn metabolic vulnerability profile wherein maternal/infant characteristics along with routine newborn screening metabolites were evaluated for their association with neonatal mortality or major morbidity. RESULTS: Nine thousand six hundred and thirty-nine (9.2%) preterm infants experienced mortality or at least one complication. Six characteristics and 19 metabolites were included in the final metabolic vulnerability model. The model demonstrated exceptional performance for the composite outcome of mortality or any major morbidity (AUC 0.923 (95% CI: 0.917-0.929). Performance was maintained across mortality and morbidity subgroups (AUCs 0.893-0.979). CONCLUSIONS: Metabolites measured as part of routine newborn screening can be used to create a metabolic vulnerability profile. These findings lay the foundation for targeted clinical monitoring and further investigation of biological pathways that may increase the risk of neonatal death or major complications in infants born preterm. IMPACT: We built a newborn metabolic vulnerability profile that could identify preterm infants at risk for major morbidity and mortality. Identifying high-risk infants by this method is novel to the field and outperforms models currently in use that rely primarily on infant characteristics. Utilizing the newborn metabolic vulnerability profile for precision clinical monitoring and targeted investigation of etiologic pathways could lead to reductions in the incidence and severity of major morbidities associated with preterm birth.

Genetic predictors of blood pressure traits are associated with preeclampsia.

Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. Mechanisms leading to preeclampsia are unclear; however, there is evidence of high heritability. We evaluated the association of polygenic scores (PGS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Non-Hispanic Black and White reproductive age females with pregnancy indications and genotypes were obtained from Vanderbilt University's BioVU, Electronic Medical Records and Genomics network, and Penn Medicine Biobank. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PGS were acquired from Giri et al. Associations between preeclampsia and each PGS were evaluated separately by race and data source before subsequent meta-analysis. Ten-fold cross validation was used for prediction modeling. In 3504 Black and 5009 White included individuals, the rate of preeclampsia was 15.49%. In cross-ancestry meta-analysis, all PGSs were associated with preeclampsia (ORDBP = 1.10, 95% CI 1.02-1.17, p = 7.68 × 10-3; ORSBP = 1.16, 95% CI 1.09-1.23, p = 2.23 × 10-6; ORPP = 1.14, 95% CI 1.07-1.27, p = 9.86 × 10-5). Addition of PGSs to clinical prediction models did not improve predictive performance. Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.

Challenges and Opportunities for Data Science in Women's Health.

The intersection of women's health and data science is a field of research that has historically trailed other fields, but more recently it has gained momentum. This growth is being driven not only by new investigators who are moving into this area but also by the significant opportunities that have emerged in new methodologies, resources, and technologies in data science. Here, we describe some of the resources and methods being used by women's health researchers today to meet challenges in biomedical data science. We also describe the opportunities and limitations of applying these approaches to advance women's health outcomes and the future of the field, with emphasis on repurposing existing methodologies for women's health.

Association of genetically-predicted placental gene expression with adult blood pressure traits.

OBJECTIVE: Blood pressure is a complex, polygenic trait, and the need to identify prehypertensive risks and new gene targets for blood pressure control therapies or prevention continues. We hypothesize a developmental origins model of blood pressure traits through the life course where the placenta is a conduit mediating genomic and nongenomic transmission of disease risk. Genetic control of placental gene expression has recently been described through expression quantitative trait loci (eQTL) studies which have identified associations with childhood phenotypes. METHODS: We conducted a transcriptome-wide gene expression analysis estimating the predicted gene expression of placental tissue in adult individuals with genome-wide association study (GWAS) blood pressure summary statistics. We constructed predicted expression models of 15 154 genes from reference placenta eQTL data and investigated whether genetically-predicted gene expression in placental tissue is associated with blood pressure traits using published GWAS summary statistics. Functional annotation of significant genes was generated using FUMA. RESULTS: We identified 18, 9, and 21 genes where predicted expression in placenta was significantly associated with systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP), respectively. There were 14 gene-tissue associations (13 unique genes) significant only in placenta. CONCLUSIONS: In this meta-analysis using S-PrediXcan and GWAS summary statistics, the predicted expression in placenta of 48 genes was statistically significantly associated with blood pressure traits. Notable findings included the association of FGFR1 expression with increased SBP and PP. This evidence of gene expression variation in placenta preceding the onset of adult blood pressure phenotypes is an example of extreme preclinical biological changes which may benefit from intervention.

Tutorial: Using Community Engagement Studios to Enhance Pharmacogenetic Study Design for Maximizing Enrollment of Diverse Children and Pregnant People.

Most pharmacogenetic research is conducted in adult, non-pregnant populations of European ancestry. Study of more diverse and special populations is necessary to validate findings and improve health equity. However, there are significant barriers to recruitment of diverse populations for genetic studies, such as mistrust of researchers due to a history of unethical research and ongoing social inequities. Engaging communities and understanding community members' perspectives may help to overcome these barriers and improve research quality. Here, we highlight one method for engaging communities, the Community Engagement Studio (CES), a consultative session that allows researchers to obtain guidance and feedback based on community members' lived experiences. We also provide an example of its use in pharmacogenetic studies. In designing a survey study of knowledge and attitudes around pharmacogenetic testing among children with chronic conditions and pregnant individuals, we sought input from diverse community stakeholders through CESs at Vanderbilt University Medical Center. We participated in two CESs with community stakeholders representing study target populations. Our goals were to learn specific concerns about pharmacogenetic testing and preferred recruitment strategies for these communities. Concerns were expressed about how genetic information would be used beyond the immediate study. Participants emphasized the importance of clarity and transparency in communication to overcome participation hesitancy and mistrust of the study team. Recruitment strategy recommendations ranged from informal notices posted in healthcare settings to provider referrals. The CES enabled us to modify our recruitment methods and research materials to better communicate with populations currently under-represented in pharmacogenetics research.

Genetically-predicted placental gene expression is associated with birthweight and adult body mass index.

The placenta is critical to human growth and development and has been implicated in health outcomes. Understanding the mechanisms through which the placenta influences perinatal and later-life outcomes requires further investigation. We evaluated the relationships between birthweight and adult body mass index (BMI) and genetically-predicted gene expression in human placenta. Birthweight genome-wide association summary statistics were obtained from the Early Growth Genetics Consortium (N = 298,142). Adult BMI summary statistics were obtained from the GIANT consortium (N = 681,275). We used S-PrediXcan to evaluate associations between the outcomes and predicted gene expression in placental tissue and, to identify genes where placental expression was exclusively associated with the outcomes, compared to 48 other tissues (GTEx v7). We identified 24 genes where predicted placental expression was significantly associated with birthweight, 15 of which were not associated with birthweight in any other tissue. One of these genes has been previously linked to birthweight. Analyses identified 182 genes where placental expression was associated with adult BMI, 110 were not associated with BMI in any other tissue. Eleven genes that had placental gene expression levels exclusively associated with BMI have been previously associated with BMI. Expression of a single gene, PAX4, was associated with both outcomes exclusively in the placenta. Inter-individual variation of gene expression in placental tissue may contribute to observed variation in birthweight and adult BMI, supporting developmental origins hypothesis.

The validity of hospital diagnostic and procedure codes reflecting morbidity in preterm neonates born <32 weeks gestation.

OBJECTIVE: To determine the validity of diagnostic hospital billing codes for complications of prematurity in neonates <32 weeks gestation. STUDY DESIGN: Retrospective cohort data from discharge summaries and clinical notes (n = 160) were reviewed by trained, blinded abstractors for the presence of intraventricular hemorrhage (IVH) grades 3 or 4, periventricular leukomalacia (PVL), necrotizing enterocolitis (NEC), stage 3 or higher, retinopathy of prematurity (ROP), and surgery for NEC or ROP. Data were compared to diagnostic billing codes from the neonatal electronic health record. RESULTS: IVH, PVL, ROP and ROP surgery had strong positive predictive values (PPV > 75%) and excellent negative predictive values (NPV > 95%). The PPVs for NEC (66.7%) and NEC surgery (37.1%) were low. CONCLUSION: Diagnostic hospital billing codes were observed to be a valid metric to evaluate preterm neonatal morbidities and surgeries except in the instance of more ambiguous diagnoses such as NEC and NEC surgery.

Genetic Predictors of Blood Pressure Traits are Associated with Preeclampsia.

Background: Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. The mechanisms leading to preeclampsia are unclear; however, there is evidence that preeclampsia is highly heritable. We evaluated the association of polygenic risk scores (PRS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Methods: Participants were obtained from Vanderbilt University's BioVU, the Electronic Medical Records and Genomics network, and the Penn Medicine Biobank. Non-Hispanic Black and White females of reproductive age with indications of pregnancy and genotype information were included. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PRS were obtained from Giri et al 2019. Associations between preeclampsia and each PRS were evaluated separately by race and study population before evidence was meta-analyzed. Prediction models were developed and evaluated using 10-fold cross validation. Results: In the 3,504 Black and 5,009 White individuals included, the rate of preeclampsia was 15.49%. The DBP and SBP PRSs were associated with preeclampsia in Whites but not Blacks. The PP PRS was significantly associated with preeclampsia in Blacks and Whites. In trans-ancestry meta-analysis, all PRSs were associated with preeclampsia (OR DBP =1.10, 95% CI=1.02-1.17, p =7.68×10 -3 ; OR SBP =1.16, 95% CI=1.09-1.23, p =2.23×10 -6 ; OR PP =1.14, 95% CI=1.07-1.27, p =9.86×10 -5 ). However, addition of PRSs to clinical prediction models did not improve predictive performance. Conclusions: Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.

Genetics of varicose veins reveals polygenic architecture and genetic overlap with arterial and venous disease.

Varicose veins represent a common cause of cardiovascular morbidity, with limited available medical therapies. Although varicose veins are heritable and epidemiologic studies have identified several candidate varicose vein risk factors, the molecular and genetic basis remains uncertain. Here we analyzed the contribution of common genetic variants to varicose veins using data from the Veterans Affairs Million Veteran Program and four other large biobanks. Among 49,765 individuals with varicose veins and 1,334,301 disease-free controls, we identified 139 risk loci. We identified genetic overlap between varicose veins, other vascular diseases and dozens of anthropometric factors. Using Mendelian randomization, we prioritized therapeutic targets via integration of proteomic and transcriptomic data. Finally, topological enrichment analyses confirmed the biologic roles of endothelial shear flow disruption, inflammation, vascular remodeling and angiogenesis. These findings may facilitate future efforts to develop nonsurgical therapies for varicose veins.

Targeted newborn metabolomics: prediction of gestational age from cord blood.

OBJECTIVE: Our study sought to determine whether metabolites from a retrospective collection of banked cord blood specimens could accurately estimate gestational age and to validate these findings in cord blood samples from Busia, Uganda. STUDY DESIGN: Forty-seven metabolites were measured by tandem mass spectrometry or enzymatic assays from 942 banked cord blood samples. Multiple linear regression was performed, and the best model was used to predict gestational age, in weeks, for 150 newborns from Busia, Uganda. RESULTS: The model including metabolites and birthweight, predicted the gestational ages within 2 weeks for 76.7% of the Ugandan cohort. Importantly, this model estimated the prevalence of preterm birth <34 weeks closer to the actual prevalence (4.67% and 4.00%, respectively) than a model with only birthweight which overestimates the prevalence by 283%. CONCLUSION: Models that include cord blood metabolites and birth weight appear to offer improvement in gestational age estimation over birth weight alone.

Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa.

Background: Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature. Methods: A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes. Results: With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24 (95% confidence interval (CI) = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births (gestational age <37 weeks), yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature. Conclusions: In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs.

Perinatal determinants of growth trajectories in children born preterm.

BACKGROUND: A growing amount of evidence indicates in utero and early life growth has profound, long-term consequences for an individual's health throughout the life course; however, there is limited data in preterm infants, a vulnerable population at risk for growth abnormalities. OBJECTIVE: To address the gap in knowledge concerning early growth and its determinants in preterm infants. METHODS: A retrospective cohort study was performed using a population of preterm (< 37 weeks gestation) infants obtained from an electronic medical record database. Weight z-scores were acquired from discharge until roughly two years corrected age. Linear mixed effects modeling, with random slopes and intercepts, was employed to estimate growth trajectories. RESULTS: Thirteen variables, including maternal race, hypertension during pregnancy, preeclampsia, first trimester body mass index, multiple status, gestational age, birth weight, birth length, head circumference, year of birth, length of birth hospitalization stay, total parenteral nutrition, and dextrose treatment, were significantly associated with growth rates of preterm infants in univariate analyses. A small percentage (1.32% - 2.07%) of the variation in the growth of preterm infants can be explained in a joint model of these perinatal factors. In extremely preterm infants, additional variation in growth trajectories can be explained by conditions whose risk differs by degree of prematurity. Specifically, infants with periventricular leukomalacia or retinopathy of prematurity experienced decelerated rates of growth compared to infants without such conditions. CONCLUSIONS: Factors found to influence growth over time in children born at term also affect growth of preterm infants. The strength of association and the magnitude of the effect varied by gestational age, revealing that significant heterogeneity in growth and its determinants exists within the preterm population.

Gestational vitamin D and offspring risk of multiple sclerosis: a systematic review and meta-analysis.

PURPOSE: Our objective was to systematically review and meta-analyze studies that assessed the association between gestational vitamin D levels and risk of multiple sclerosis (MS) in offspring. METHODS: Embase and Pubmed databases were searched from inception to May 2018. Original, observational studies that investigated both clinically defined MS (in offspring) and vitamin D levels in utero or shortly after birth were included. Two reviewers independently abstracted data and assessed the quality of studies using the Newcastle-Ottawa Quality Assessment Scale. Summary effect estimates and 95% confidence intervals were calculated with random effects models using inverse variance weighting. Determinants of heterogeneity were evaluated. RESULTS: Four case-control studies of moderate to low risk of bias were included. Summary effect estimates of the effect of higher levels of gestational vitamin D on risk of offspring MS demonstrated a significant protective effect in random effects (OR: 0.63, 95% CI: 0.47, 0.84) models and in a stratified analysis based on study quality. Factors identified as determinants of heterogeneity were the definitions of vitamin D deficiency, the characteristics of study participants, and the quality of the study. CONCLUSIONS: Sufficient levels of vitamin D during pregnancy may be protective against offspring's development of multiple sclerosis later in life.

Genetic Risk Scores for Maternal Lipid Levels and Their Association with Preterm Birth.

Maternal lipid profiles are associated with risk for preterm birth (PTB), although the lipid component and effect size are inconsistent between studies. It is also unclear whether these associations are the result of excessive changes in lipid metabolism during pregnancy or genetic variability in genes controlling basal lipid metabolism. This study investigates the association between genetic risk scores (GRS) for four lipid components (high-density lipoprotein [HDL-C], low-density lipoprotein [LDL-C], triacylglycerols [TAG], and total cholesterol [TC]) with risk for PTB. Subjects included 954 pregnant women from California for whom second trimester serum samples were available, of which 479 gave birth preterm and 475 gave birth at term. We genotyped 96 single-nucleotide polymorphisms, which were selected from genome-wide association studies of lipid levels in adult populations. Lipid-specific GRS were constructed for HDL-C, LDL-C, TAG, and TC. The associations between GRS and PTB were analyzed using logistic regression. A higher HDL-C GRS was associated with increased risk for PTB overall and spontaneous PTB. Higher TAG and TC GRS were associated with decreased risk for PTB overall and spontaneous PTB. This study identifies counter-intuitive associations between lipid GRS and spontaneous PTB. Further replication studies are needed to confirm these findings, but they suggest that our current scientific understanding of the relationship between lipid metabolism, PTB, and genetics is incomplete.

The Association of Polymorphisms in Circadian Clock and Lipid Metabolism Genes With 2nd Trimester Lipid Levels and Preterm Birth.

Deregulation of the circadian system in humans and animals can lead to various adverse reproductive outcomes due to genetic mutations and environmental factors. In addition to the clock, lipid metabolism may also play an important role in influencing reproductive outcomes. Despite the importance of the circadian clock and lipid metabolism in regulating birth timing few studies have examined the relationship between circadian genetics with lipid levels during pregnancy and their relationship with preterm birth (PTB). In this study we aimed to determine if single nucleotide polymorphisms (SNPs) in genes from the circadian clock and lipid metabolism influence 2nd trimester maternal lipid levels and if this is associated with an increased risk for PTB. We genotyped 72 SNPs across 40 genes previously associated with various metabolic abnormalities on 930 women with 2nd trimester serum lipid measurements. SNPs were analyzed for their relationship to levels of total cholesterol, high density lipoprotein (HDL), low density lipoprotein (LDL) and triglycerides (TG) using linear regression. SNPs were also evaluated for their relationship to PTB using logistic regression. Five SNPs in four genes met statistical significance after Bonferroni correction (p < 1.8 × 10-4) with one or more lipid levels. Of these, four SNPs were in lipid related metabolism genes: rs7412 in APOE with total cholesterol, HDL and LDL, rs646776 and rs599839 in CELSR2-PSRC1-SORT1 gene cluster with total cholesterol, HDL and LDL and rs738409 in PNPLA3 with HDL and TG and one was in a circadian clock gene: rs228669 in PER3 with TG. Of these SNPs only PER3 rs228669 was marginally associated with PTB (p = 0.02). In addition, PER3 rs228669 acts as an effect modifier on the relationship between TG and PTB.

First trimester prenatal screening biomarkers and gestational diabetes mellitus: A systematic review and meta-analysis.

Biomarkers commonly assessed in prenatal screening have been associated with a number of adverse perinatal and birth outcomes. However, it is not clear whether first trimester measurements of prenatal screening biomarkers are associated with subsequent risk of gestational diabetes mellitus (GDM). We aimed to systematically review and statistically summarize studies assessing the relationship between first trimester prenatal screening biomarker levels and GDM development. We comprehensively searched PubMed/MEDLINE, EMBASE, CINAHL, and Scopus (from inception through January 2018) and manually searched the reference lists of all relevant articles. We included original, published, observational studies examining the association of first trimester pregnancy associated plasma protein-A (PAPP-A) and/or free ß-human chorionic gonadotropin (free ß-hCG) levels with GDM diagnosis. Mean differences were calculated comparing PAPP-A and free ß-hCG multiples of median (MoM) levels between women who developed GDM and those who did not and were subsequently pooled using two-sided random-effects models. Our meta-analysis of 13 studies on PAPP-A and nine studies on free ß-hCG indicated that first trimester MoM levels for both biomarkers were lower in women who later developed GDM compared to women who remained normoglycemic throughout pregnancy (MD -0.17; 95% CI -0.24, -0.10; MD -0.04; 95% CI -0.07-0.01). There was no evidence for between-study heterogeneity among studies on free ß-hCG (I2 = 0%). A high level of between-study heterogeneity was detected among the studies reporting on PAPP-A (I2 = 90%), but was reduced after stratifying by geographic location, biomarker assay method, and timing of GDM diagnosis. Our meta-analysis indicates that women who are diagnosed with GDM have lower first trimester levels of both PAPP-A and free ß-hCG than women who remain normoglycemic throughout pregnancy. Further assessment of the predictive capacity of these biomarkers within large, diverse populations is needed.