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Uterine leiomyomata, or fibroids, are common gynecological tumors causing pelvic and menstrual symptoms that can negatively affect quality of life and child-bearing desires. As fibroids grow, symptoms can intensify and lead to invasive treatments that are less likely to preserve fertility. Identifying individuals at highest risk for fibroids can aid in access to earlier diagnoses. Polygenic risk scores (PRS) quantify genetic risk to identify those at highest risk for disease. Utilizing the PRS software PRS-CSx and publicly available genome-wide association study (GWAS) summary statistics from FinnGen and Biobank Japan, we constructed a multi-ancestry (META) PRS for fibroids. We validated the META PRS in two cross-ancestry cohorts. In the cross-ancestry Electronic Medical Record and Genomics (eMERGE) Network cohort, the META PRS was significantly associated with fibroid status and exhibited 1.11 greater odds for fibroids per standard deviation increase in PRS (95% confidence interval [CI]: 1.05 - 1.17, p = 5.21x10-5). The META PRS was validated in two BioVU cohorts: one using ICD9/ICD10 codes and one requiring imaging confirmation of fibroid status. In the ICD cohort, a standard deviation increase in the META PRS increased the odds of fibroids by 1.23 (95% CI: 1.15 - 1.32, p = 9.68x10-9), while in the imaging cohort, the odds increased by 1.26 (95% CI: 1.18 - 1.35, p = 2.40x10-11). We subsequently constructed single ancestry PRS for FinnGen (European ancestry [EUR]) and Biobank Japan (East Asian ancestry [EAS]) using PRS-CS and discovered a nominally significant association in the eMERGE cohort within fibroids and EAS PRS but not EUR PRS (95% CI: 1.09 - 1.20, p = 1.64x10-7). These findings highlight the strong predictive power of multi-ancestry PRS over single ancestry PRS. This study underscores the necessity of diverse population inclusion in genetic research to ensure precision medicine benefits all individuals equitably.

The following sections are included: Overview, Advancing multi-ancestry genetic research, Integrating social determinants of health to enhance genetic risk models, Methods to detect and mitigate disparities, Addressing Disparities in Adverse Drug Reactions, Conclusion, Acknowledgments,References.

Uterine leiomyomata (fibroids, UFs) are common, benign tumors in females, having an estimated prevalence of up to 80%. They are fibrous masses growing within the myometrium leading to chronic symptoms like dysmenorrhea, abnormal uterine bleeding, anemia, severe pelvic pain, and infertility. Hypertension (HTN) is a common risk factor for UFs, though less prevalent in premenopausal individuals. While observational studies have indicated strong associations between UFs and HTN, the biological mechanisms linking the two conditions remain unclear. Understanding the relationship between HTN and UFs is crucial because UFs and HTN lead to substantial comorbidities adversely impacting female health. Identifying the common underlying biological mechanisms can improve treatment strategies for both conditions. To clarify the genetic and causal relationships between UFs and BP, we conducted a bidirectional, two-sample Mendelian randomization (MR) analysis and evaluated the genetic correlations across BP traits and UFs. We used data from a multi-ancestry genome-wide association study (GWAS) meta-analysis of UFs (44,205 cases and 356,552 controls), and data from a cross-ancestry GWAS meta-analysis of BP phenotypes (diastolic BP [DBP], systolic BP [SBP], and pulse pressure [PP], N=447,758). We evaluated genetic correlation of BP phenotypes and UFs with linkage disequilibrium score regression (LDSC). LDSC results indicated a positive genetic correlation between DBP and UFs (Rg=0.132, p<5.0x10-5), and SBP and UFs (Rg=0.063, p<2.5x10-2). MR using UFs as the exposure and BP traits as outcomes indicated a relationship where UFs increases DBP (odds ratio [OR]=1.20, p<2.7x10-3). Having BP traits as exposures and UFs as the outcome showed that DBP and SBP increase risk for UFs (OR =1.04, p<2.2x10-3; OR=1.00, p<4.0x10-2; respectively). Our results provide evidence of shared genetic architecture and pleiotropy between HTN and UFs, suggesting common biological pathways driving their etiologies. Based on these findings, DBP appears to be a stronger risk factor for UFs compared to SBP and PP.

We report the findings of a genome-wide association study (GWAS) meta-analysis of endometriosis consisting of a large portion (31%) of non-European samples across 14 biobanks worldwide as part of the Global Biobank Meta-Analysis Initiative (GBMI). We identified 45 significant loci using a wide phenotype definition, seven of which are previously unreported and detected first genome-wide significant locus ( POLR2M ) among only African-ancestry. Our narrow phenotypes and surgically confirmed case definitions for endometriosis analyses replicated the known loci near CDC42 , SKAP1 , and GREB1 . Through this large ancestry stratified analyses, we document heritability estimates in range of 10-12% for all ancestral groups. Thirty-eight loci had at least one variant in the credible set after fine-mapping. An imputed transcriptome-wide association study (TWAS) identified 11 associated genes (two previously unreported), while the proteome-wide association study (PWAS) suggests significant association of R-spondin 3 (RSPO3) with wide endometriosis, which plays a crucial role in modulating the Wnt signaling pathway. Our diverse, comprehensive GWAS, coupled with integrative -omics analysis, identifies critical roles of immunopathogenesis, Wnt signaling, and balance between proliferation, differentiation, and migration of endometrial cells as hallmarks for endometriosis. These interconnected pathways and risk factors underscore a complex, multi-faceted etiology of endometriosis, suggesting multiple targets for precise and effective therapeutic interventions.

While most pregnancies are affected by nausea and vomiting, hyperemesis gravidarum (HG) is at the severe end of the clinical spectrum and is associated with dehydration, undernutrition, and adverse maternal, fetal, and child outcomes. Herein we performed a multi-ancestry genome-wide association study (GWAS) of severe nausea and vomiting of pregnancy of 10,974 cases and 461,461 controls across European, Asian, African, and Latino ancestries. We identified ten significantly associated loci, of which six were novel ( SLITRK1 , SYN3 , IGSF11 , FSHB , TCF7L2 , and CDH9) , and confirmed previous genome-wide significant associations with risk genes GDF15 , IGFBP7 , PGR , and GFRAL . In a spatiotemporal analysis of placental development, GDF15 and TCF7L2 were expressed primarily in extra villous trophoblast, and using a weighted linear model of maternal, paternal, and fetal effects, we confirmed opposing effects for GDF15 between maternal and fetal genotype. Conversely, IGFBP7 and PGR were primarily expressed in developing maternal spiral arteries during placentation, with effects limited to the maternal genome. Risk loci were found to be under significant evolutionary selection, with the strongest effects on nausea and vomiting mid-pregnancy. Selected loci were associated with abnormal pregnancy weight gain, pregnancy duration, birth weight, head circumference, and pre-eclampsia. Potential roles for candidate genes in appetite, insulin signaling, and brain plasticity provide new pathways to explore etiological mechanisms and novel therapeutic avenues., Competing Interests: Competing interests The authors declare the existence of financial/non-financial competing interests. MF: Hyperemesis Education and Research (HER) Foundation (voluntary, unpaid Board member and Research Director); Harmonia Healthcare (CSO, stock, paid consultant); NGM Biosciences (stock, paid consultant); Foundation for Women’s Health (Board member, voluntary, unpaid).

Objectives: To address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies., Materials and Methods: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities, utilizing data from Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. It offers interactive and multifaceted visualizations for exploring multimorbidity. Incorporating an enhanced version of associationSubgraphs, PheMIME also enables dynamic analysis and inference of disease clusters, promoting the discovery of complex multimorbidity patterns. A case study on schizophrenia demonstrates its capability for generating interactive visualizations of multimorbidity networks within and across multiple systems. Additionally, PheMIME supports diverse multimorbidity-based discoveries, detailed further in online case studies., Results: The PheMIME is accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial and multiple case studies for demonstration are available at https://prod.tbilab.org/PheMIME&#95;supplementary&#95;materials/. The source code can be downloaded from https://github.com/tbilab/PheMIME., Discussion: PheMIME represents a significant advancement in medical informatics, offering an efficient solution for accessing, analyzing, and interpreting the complex and noisy real-world patient data in electronic health records., Conclusion: PheMIME provides an extensive multimorbidity knowledge base that consolidates data from three EHR systems, and it is a novel interactive tool designed to analyze and visualize multimorbidities across multiple EHR datasets. It stands out as the first of its kind to offer extensive multimorbidity knowledge integration with substantial support for efficient online analysis and interactive visualization., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Black women experience a disproportionate impact of uterine fibroids compared to White women, including earlier diagnosis, higher frequency, and more severe symptoms. The etiology underlying this racial disparity remains elusive., Objective: The aim of this study was to evaluate the molecular differences in normal myometrium (fibroid-free uteri) and at-risk myometrium (fibroid-containing uteri) tissues in Black and White women., Study Design: We conducted whole-genome RNA-seq on normal and at-risk myometrium tissues obtained from both self-identified Black and White women (not Hispanic or Latino) to determine global gene expression profiles and to conduct enriched pathway analyses (n=3 per group). We initially assessed the differences within the same type of tissue (normal or at-risk myometrium) between races. Subsequently, we analyzed the transcriptome of normal myometrium compared to at-risk myometrium in each race and determined the differences between them. We validated our findings through real-time PCR (sample size range=5-12), western blot (sample size range=5-6), and immunohistochemistry techniques (sample size range=9-16)., Results: The transcriptomic analysis revealed distinct profiles between Black and White women in normal and at-risk myometrium tissues. Interestingly, genes and pathways related to extracellular matrix and mechanosensing were more enriched in normal myometrium from Black than White women. Transcription factor enrichment analysis detected greater activity of the serum response transcription factor positional motif in normal myometrium from Black compared to White women. Furthermore, we observed increased expression levels of myocardin-related transcription factor-serum response factor and the serum response factor in the same comparison. In addition, we noted increased expression of both mRNA and protein levels of vinculin, a target gene of the serum response factor, in normal myometrium tissues from Black women as compared to White women. Importantly, the transcriptomic profile of normal to at-risk myometrium conversion differs between Black and White women. Specifically, we observed that extracellular matrix-related pathways are involved in the transition from normal to at-risk myometrium and that these processes are exacerbated in Black women. We found increased levels of Tenascin C, type I collagen alpha 1 chain, fibronectin, and phospho-p38 MAPK (Thr180/Tyr182, active) protein levels in at-risk over normal myometrium tissues from Black women, whereas such differences were not observed in samples from White women., Conclusion: These findings indicate that the racial disparities in uterine fibroids may be attributed to heightened production of extracellular matrix in the myometrium in Black women, even before the tumors appear. Future research is needed to understand early life determinants of the observed racial differences., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Acute kidney injury (AKI) is a common and devastating complication of hospitalization. Here, we identified genetic loci associated with AKI in patients hospitalized between 2002-2019 in the Million Veteran Program and data from Vanderbilt University Medical Center's BioVU. AKI was defined as meeting a modified KDIGO Stage 1 or more for two or more consecutive days or kidney replacement therapy. Control individuals were required to have one or more qualifying hospitalizations without AKI and no evidence of AKI during any other observed hospitalizations. Genome-wide association studies (GWAS), stratified by race, adjusting for sex, age, baseline estimated glomerular filtration rate (eGFR), and the top ten principal components of ancestry were conducted. Results were meta-analyzed using fixed effects models. In total, there were 54,488 patients with AKI and 138,051 non-AKI individuals included in the study. Two novel loci reached genome-wide significance in the meta-analysis: rs11642015 near the FTO locus on chromosome 16 (obesity traits) (odds ratio 1.07 (95% confidence interval, 1.05-1.09)) and rs4859682 near the SHROOM3 locus on chromosome 4 (glomerular filtration barrier integrity) (odds ratio 0.95 (95% confidence interval, 0.93-0.96)). These loci colocalized with previous studies of kidney function, and genetic correlation indicated significant shared genetic architecture between AKI and eGFR. Notably, the association at the FTO locus was attenuated after adjustment for BMI and diabetes, suggesting that this association may be partially driven by obesity. Both FTO and the SHROOM3 loci showed nominal evidence of replication from diagnostic-code-based summary statistics from UK Biobank, FinnGen, and Biobank Japan. Thus, our large GWA meta-analysis found two loci significantly associated with AKI suggesting genetics may explain some risk for AKI., (Published by Elsevier Inc.)

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 (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 ). 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., (© 2024. The Author(s).)

Variability in quantitative traits has clinical, ecological, and evolutionary significance. Most genetic variants identified for complex quantitative traits have only a detectable effect on the mean of trait. We have developed the mean-variance test (MVtest) to simultaneously model the mean and log-variance of a quantitative trait as functions of genotypes and covariates by using estimating equations. The advantages of MVtest include the facts that it can detect effect modification, that multiple testing can follow conventional thresholds, that it is robust to non-normal outcomes, and that association statistics can be meta-analyzed. In simulations, we show control of type I error of MVtest over several alternatives. We identified 51 and 37 previously unreported associations for effects on blood-pressure variance and mean, respectively, in the UK Biobank. Transcriptome-wide association studies revealed 633 significant unique gene associations with blood-pressure mean variance. MVtest is broadly applicable to studies of complex quantitative traits and provides an important opportunity to detect novel loci., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 American Society of Human Genetics. All rights reserved.)

Background: Nineteen genomic regions have been associated with high-grade serous ovarian cancer (HGSOC). We used data from the Ovarian Cancer Association Consortium (OCAC), Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA), UK Biobank (UKBB), and FinnGen to identify novel HGSOC susceptibility loci and develop polygenic scores (PGS)., Methods: We analyzed >22 million variants for 398,238 women. Associations were assessed separately by consortium and meta-analysed. OCAC and CIMBA data were used to develop PGS which were trained on FinnGen data and validated in UKBB and BioBank Japan., Results: Eight novel variants were associated with HGSOC risk. An interesting discovery biologically was finding that TP53 3'-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76×10 -9 ). The optimal PGS included 64,518 variants and was associated with an odds ratio of 1.46 (95%CI:1.37-1.54) per standard deviation in the UKBB validation (AUROC curve=0.61, 95%CI:0.59-0.62)., Conclusions: This study represents the largest GWAS for HGSOC to date. The results highlight that improvements in imputation reference panels and increased sample sizes can identify HGSOC associated variants that previously went undetected, resulting in improved PGS. The use of updated PGS in cancer risk prediction algorithms will then improve personalized risk prediction for HGSOC.

Competing Interests: Financial Disclosure Digna R. Velez Edwards has served as a Member of the Board of Directors for American Society of Human Genetics. Todd L. Edwards did not report any potential conflicts of interest.

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings., (© 2024. The Author(s).)

There is a desire in research to move away from the concept of race as a clinical factor because it is a societal construct used as an imprecise proxy for geographic ancestry. In this study, we leverage the biobank from Vanderbilt University Medical Center, BioVU, to investigate relationships between genetic ancestry proportion and the clinical phenome. For all samples in BioVU, we calculated six ancestry proportions based on 1000 Genomes references: eastern African (EAFR), western African (WAFR), northern European (NEUR), southern European (SEUR), eastern Asian (EAS), and southern Asian (SAS). From PheWAS, we found phecode categories significantly enriched neoplasms for EAFR, WAFR, and SEUR, and pregnancy complication in SEUR, NEUR, SAS, and EAS (p < 0.003). We then selected phenotypes hypertension (HTN) and atrial fibrillation (AFib) to further investigate the relationships between these phenotypes and EAFR, WAFR, SEUR, and NEUR using logistic regression modeling and non-linear restricted cubic spline modeling (RCS). For EAS and SAS, we chose renal failure (RF) for further modeling. The relationships between HTN and AFib and the ancestries EAFR, WAFR, and SEUR were best fit by the linear model (beta p < 1x10-4 for all) while the relationships with NEUR were best fit with RCS (HTN ANOVA p = 0.001, AFib ANOVA p < 1x10-4). For RF, the relationship with SAS was best fit with a linear model (beta p < 1x10-4) while RCS model was a better fit for EAS (ANOVA p < 1x10-4). In this study, we identify relationships between genetic ancestry and phenotypes that are best fit with non-linear modeling techniques. The assumption of linearity for regression modeling is integral for proper fitting of a model and there is no knowing a priori to modeling if the relationship is truly linear.

Gestational weight gain (GWG) is linked to adverse outcomes in pregnant persons and offspring. The Early Growth Genetics Consortium previously identified genetic variants contributing to GWG from fetal and maternal genomes. However, their biologic mechanisms and tissue-specificity are unknown. We evaluated the association between genetically predicted gene expression in relevant maternal (subcutaneous and visceral adipose, breast, uterus, and whole blood) tissues from GTEx (v7) and fetal (placenta) tissue and early, late, and total GWG using S-PrediXcan. We tested for pathway enrichment using the GENE2FUNC module from Functional Mapping and Annotation of Genome-Wide Association Studies. After Bonferroni correction, we found no associations between maternal or fetal gene expression and GWG. Among nominally significant ( P  < 0.05) maternal genes, there was enrichment of several biological pathways, including metabolic processes, secretion, and intracellular transport, that varied across pregnancy. These results indicate the likely influence of diverse pathways, varying by tissue and weeks of gestation, on GWG., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2024.)

Many researchers in genetics and social science incorporate information about race in their work. However, migrations (historical and forced) and social mobility have brought formerly separated populations of humans together, creating younger generations of individuals who have more complex and diverse ancestry and race profiles than older age groups. Here, we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and impact health outcomes. We evaluated variation in genetic ancestry over 100 birth years in a cohort of 35,842 individuals with electronic health record (EHR) information in the Southeastern United States. Using the software STRUCTURE, we analyzed 2,678 ancestrally informative markers relative to three ancestral clusters (African, East Asian, and European) and observed rising levels of admixture for all clinically-defined race groups since 1990. Most race groups also exhibited increases in heterozygosity and long-range linkage disequilibrium over time, further supporting the finding of increasing admixture in young individuals in our cohort. These data are consistent with United States Census information from broader geographic areas and highlight the changing demography of the population. This increased diversity challenges classic approaches to studies of genotype-phenotype relationships which motivated us to explore the relationship between heterozygosity and disease diagnosis. Using a phenome-wide association study approach, we explored the relationship between admixture and disease risk and found that increased admixture resulted in protective associations with female reproductive disorders and increased risk for diseases with links to autoimmune dysfunction. These data suggest that tendencies in the United States population are increasing ancestral complexity over time. Further, these observations imply that, because both prevalence and severity of many diseases vary by race groups, complexity of ancestral origins influences health and disparities.

Diseases such as uterine leiomyomata (fibroids and benign tumors of the uterus) and keloids (raised scars) may share common etiology. Fibroids and keloids can co-occur in individuals, and both are highly heritable, suggesting they may share common genetic risk factors. Fibroproliferative diseases are common and characterized by scarring and overgrowth of connective tissue, impacting multiple organ systems. These conditions both have racial disparities in prevalence, with the highest prevalence observed among individuals of African ancestry. Several fibroproliferative diseases are more severe and common in populations of sub-Saharan Africa. This mini-review aims to provide a broad overview of the current knowledge of the evolutionary origins and causes of fibroproliferative diseases. We also discuss current hypotheses proposing that the increased prevalence of these diseases in African-derived populations is due to the selection for profibrotic alleles that are protective against helminth infections and provide examples from knowledge of uterine fibroid and keloid research.

Background: Genetic polymorphisms have been associated with risk of anti-tuberculosis treatment toxicity. We characterized associations with adverse events and treatment failure/recurrence among adults treated for tuberculosis in Brazil., Methods: Participants were followed in Regional Prospective Observational Research in Tuberculosis (RePORT)-Brazil. We included persons with culture-confirmed drug-susceptible pulmonary tuberculosis who started treatment between 2015-2019, and who were evaluable for pharmacogenetics. Treatment included 2 months of isoniazid, rifampin or rifabutin, pyrazinamide, and ethambutol, then 4 months of isoniazid and rifampin or rifabutin, with 24 month follow-up. Analyses included 43 polymorphisms in 20 genes related to anti-tuberculosis drug hepatotoxicity or pharmacokinetics. Whole exome sequencing was done in a case-control toxicity subset., Results: Among 903 participants in multivariable genetic association analyses, NAT2 slow acetylator status was associated with increased risk of treatment-related grade 2 or greater adverse events, including hepatotoxicity. Treatment failure/recurrence was more likely among NAT2 rapid acetylators, but not statistically significant at the 5% level. A GSTM1 polymorphism (rs412543) was associated with increased risk of treatment-related adverse events, including hepatotoxicity. SLCO1B1 polymorphisms were associated with increased risk of treatment- related hepatoxicity and treatment failure/recurrence. Polymorphisms in NR1/2 were associated with decreased risk of adverse events and increased risk of failure/recurrence. In whole exome sequencing, hepatotoxicity was associated with a polymorphism in VTI1A , and the genes METTL17 and PRSS57 , but none achieved genome-wide significance., Conclusions: In a clinical cohort representing three regions of Brazil, NAT2 acetylator status was associated with risk for treatment-related adverse events. Additional significant polymorphisms merit investigation in larger study populations.

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.

Motivation: Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations., Results: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on schizophrenia., Availability and Implementation: The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME&#95;supplementary&#95;materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME., Data Availability Statement: The data underlying this article are available in the article and in its online web application or supplementary material., Competing Interests: Competing Interest Statement The authors have declared no competing interest.

Endometriosis is a chronic, complex disease for which there are vast disparities in diagnosis and treatment between sociodemographic groups. Clinical presentation of endometriosis can vary from asymptomatic disease-often identified during (in)fertility consultations-to dysmenorrhea and debilitating pelvic pain. Because of this complexity, delayed diagnosis (mean time to diagnosis is 1.7-3.6 years) and misdiagnosis is common. Early and accurate diagnosis of endometriosis remains a research priority for patient advocates and healthcare providers. Electronic health records (EHRs) have been widely adopted as a data source in biomedical research. However, they remain a largely untapped source of data for endometriosis research. EHRs capture diverse, real-world patient populations and care trajectories and can be used to learn patterns of underlying risk factors for endometriosis which, in turn, can be used to inform screening guidelines to help clinicians efficiently and effectively recognize and diagnose the disease in all patient populations reducing inequities in care. Here, we provide an overview of the advantages and limitations of using EHR data to study endometriosis. We describe the prevalence of endometriosis observed in diverse populations from multiple healthcare institutions, examples of variables that can be extracted from EHRs to enhance the accuracy of endometriosis prediction, and opportunities to leverage longitudinal EHR data to improve our understanding of long-term health consequences for all patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Penrod, Okeh, Velez Edwards, Barnhart, Senapati and Verma.)

Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings., Competing Interests: Conflict of Interest The authors have no conflicts of interest to declare.

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., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Females with polycystic ovary syndrome (PCOS), the most common endocrine disorder in women, have an increased risk of developing cardiometabolic disorders such as insulin resistance, obesity, and type 2 diabetes (T2D). While only diagnosable in females, males with a family history of PCOS can also exhibit a poor cardiometabolic profile. Therefore, we aimed to elucidate the role of sex in the cardiometabolic comorbidities observed in PCOS by conducting bidirectional genetic risk score analyses in both sexes. We first conducted a phenome-wide association study (PheWAS) using PCOS polygenic risk scores (PCOSPRS) to identify potential pleiotropic effects of PCOSPRS across 1,380 medical conditions recorded in the Vanderbilt University Medical Center electronic health record (EHR) database, in females and males. After adjusting for age and genetic ancestry, we found that European (EUR)-ancestry males with higher PCOSPRS were significantly more likely to develop hypertensive diseases than females at the same level of genetic risk. We performed the same analysis in an African (AFR)-ancestry population, but observed no significant associations, likely due to poor trans-ancestry performance of the PRS. Based on observed significant associations in the EUR-ancestry population, we then tested whether the PRS for comorbid conditions (e.g., T2D, body mass index (BMI), hypertension, etc.) also increased the odds of a PCOS diagnosis. Only BMIPRS and T2DPRS were significantly associated with a PCOS diagnosis in EUR-ancestry females. We then further adjusted the T2DPRS for measured BMI and BMIresidual (regressed on the BMIPRS and enriched for the environmental contribution to BMI). Results demonstrated that genetically regulated BMI primarily accounted for the relationship between T2DPRS and PCOS. Overall, our findings show that the genetic architecture of PCOS has distinct sex differences in associations with genetically correlated cardiometabolic traits. It is possible that the cardiometabolic comorbidities observed in PCOS are primarily explained by their shared genetic risk factors, which can be further influenced by biological variables including sex and BMI., Competing Interests: The authors declare they have no competing interests., (Copyright: © 2023 Actkins et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Epidemiologists face a unique challenge in measuring risk relationships involving time-varying exposures in early pregnancy. Each week in early pregnancy is distinct in its contribution to fetal development, and this period is commonly characterized by shifts in maternal behavior and, consequently, exposures. In this simulation study, we used alcohol as an example of an exposure that often changes during early pregnancy and miscarriage as an outcome affected by early exposures. Data on alcohol consumption patterns from more than 5,000 women in the Right From the Start cohort study (United States, 2000-2012) informed measures of the prevalence of alcohol exposure, the distribution of gestational age at cessation of alcohol use, and the likelihood of miscarriage by week of gestation. We then compared the bias and precision of effect estimates and statistical power from 5 different modeling approaches in distinct simulated relationships. We demonstrate how the accuracy and precision of effect estimates depended on alignment between model assumptions and the underlying simulated relationship. Approaches that incorporated data about patterns of exposure were more powerful and less biased than simpler models when risk depended on timing or duration of exposure. To uncover risk relationships in early pregnancy, it is critical to carefully define the role of exposure timing in the underlying causal hypothesis., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Purpose: Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk., Methods: To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report and will assess uptake of care recommendations after return of results., Results: GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022., Conclusion: Return of a novel report for communicating monogenic, polygenic, and family history-based risk factors will inform the benefits of integrated genetic risk assessment for routine health care., Competing Interests: Conflict of Interest N.S.A.-H. is an employee and equity holder of 23andMe; serves as a scientific advisory board member for Allelica, Inc; received personal fees from Genentech Inc, Allelica Inc, and 23andMe; received research funding from Akcea Therapeutics; and was previously employed by Regeneron Pharmaceuticals. T.W. has grant funding from Gilead Sciences, Inc. L.O. and T.R.-B are founders of a company developing MeTree. T.S., E.D.E., and E.H. are employees and stockholders of Invitae Corporation. E.M.M. has been a consultant for Avidity Bioscience, Amgen Inc, AstraZeneca, Cytokinetics, Invitae Corporation, Janssen Pharmaceuticals, Pfizer Inc, PepGen Inc, Tenaya Therapeutics, and Stealth BioTherapeutics Inc; she is also the founder of Ikaika Therapeutics. E.E.K. received personal fees from Illumina Inc, 23andMe, and Regeneron Pharmaceuticals and serves as a scientific advisory board member for Encompass Bioscience, Foresite Labs, and Galateo Bio. B.K. is an advisory board member and stockholder of Genome Medical. M.S. is a member of the Institutional Review Board of the All of Us Research Program. E.F.P. is a paid consultant for Allecia Inc. J.F.P. is a paid consultant for Natera Inc. All other authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background Cardiometabolic diseases are highly comorbid, but their relationship with female-specific or overwhelmingly female-predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross-trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross-trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score-based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross-trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female-specific health conditions.

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., (© 2022 The Authors. Clinical Pharmacology & Therapeutics © 2022 American Society for Clinical Pharmacology and Therapeutics.)

Endometriosis is a common condition associated with debilitating pelvic pain and infertility. A genome-wide association study meta-analysis, including 60,674 cases and 701,926 controls of European and East Asian descent, identified 42 genome-wide significant loci comprising 49 distinct association signals. Effect sizes were largest for stage 3/4 disease, driven by ovarian endometriosis. Identified signals explained up to 5.01% of disease variance and regulated expression or methylation of genes in endometrium and blood, many of which were associated with pain perception/maintenance (SRP14/BMF, GDAP1, MLLT10, BSN and NGF). We observed significant genetic correlations between endometriosis and 11 pain conditions, including migraine, back and multisite chronic pain (MCP), as well as inflammatory conditions, including asthma and osteoarthritis. Multitrait genetic analyses identified substantial sharing of variants associated with endometriosis and MCP/migraine. Targeted investigations of genetically regulated mechanisms shared between endometriosis and other pain conditions are needed to aid the development of new treatments and facilitate early symptomatic intervention., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

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.

Abdominal aortic aneurysms (AAA) are common enlargements of the abdominal aorta which can grow larger until rupture, often leading to death. Detection of AAA is often by ultrasonography and screening recommendations are mostly directed at men over 65 with a smoking history. Recent large-scale genome-wide association studies have identified genetic loci associated with AAA risk. We combined known risk factors, polygenic risk scores (PRS) and precedent clinical diagnoses from electronic health records (EHR) to develop predictive models for AAA, and compared performance against screening recommendations. The PRS included genome-wide summary statistics from the Million Veteran Program and FinnGen (10,467 cases, 378,713 controls of European ancestry), with optimization in Vanderbilt's BioVU and validated in the eMERGE Network, separately across both White and Black participants. Candidate diagnoses were identified through a temporally-oriented Phenome-wide association study in independent EHR data from Vanderbilt, and features were selected via elastic net. We calculated C-statistics in eMERGE for models including PRS, phecodes, and covariates using regression weights from BioVU. The AUC for the full model in the test set was 0.883 (95% CI 0.873-0.892), 0.844 (0.836-0.851) for covariates only, 0.613 (95% CI 0.604-0.622) when using primary USPSTF screening criteria, and 0.632 (95% CI 0.623-0.642) using primary and secondary criteria. Brier scores were between 0.003 and 0.023 for our models indicating good calibration, and net reclassification improvement over combined primary and secondary USPSTF criteria was 0.36-0.60. We provide PRS for AAA which are strongly associated with AAA risk and add to predictive model performance. These models substantially improve identification of people at risk of a AAA diagnosis compared with existing guidelines, with evidence of potential applicability in minority populations.

Motivation: Making sense of networked multivariate association patterns is vitally important to many areas of high-dimensional analysis. Unfortunately, as the data-space dimensions grow, the number of association pairs increases in O(n2); this means that traditional visualizations such as heatmaps quickly become too complicated to parse effectively., Results: Here, we present associationSubgraphs: a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network percolation and clustering. The goal is to provide an efficient investigation of association subgraphs, each containing a subset of variables with stronger and more frequent associations among themselves than the remaining variables outside the subset, by showing the entire clustering dynamics and providing subgraphs under all possible cutoff values at once. Particularly, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record and provide an online, interactive demonstration for exploring multimorbidity subgraphs., Availability and Implementation: An R package implementing both the algorithm and visualization components of associationSubgraphs is available at https://github.com/tbilab/associationsubgraphs. Online documentation is available at https://prod.tbilab.org/associationsubgraphs&#95;info/. A demo using a multimorbidity association matrix is available at https://prod.tbilab.org/associationsubgraphs-example/., (© The Author(s) 2022. Published by Oxford University Press.)

Objective: High maternal folic acid exposure has been studied as a risk factor for child asthma with inconclusive results. Folic acid supplementation that begins before pregnancy may propagate high exposures during pregnancy, particularly in regions with fortified food supplies. We investigated whether folic acid supplementation initiated periconceptionally is associated with childhood asthma in a US cohort., Materials and Methods: We re-contacted mother-child dyads previously enrolled in a prospective pregnancy cohort and included children age 4 to 8 years at follow-up ( n  = 540). Using first trimester interviews, we assessed whether initial folic acid-containing supplement (FACS) use occurred near/before estimated conception ("periconceptional") or after (during the "first trimester"). Follow-up questionnaires were used to determine if a child ever had an asthma diagnosis ("ever asthma") or asthma diagnosis with prevalent symptoms or medication use ("current asthma"). We examined associations between FACS initiation and asthma outcomes using logistic regression, excluding preterm births and adjusting for child age, sex, maternal race, maternal education, and parental asthma., Results: Approximately half of women initiated FACS use periconceptionally (49%). Nine percent of children had "ever asthma" and 6% had "current asthma." Periconceptional initiation was associated with elevated odds of ever asthma [adjusted odds ratio (95% Confidence Interval): 1.65 (0.87, 3.14)] and current asthma [1.87 (0.88, 4.01)], relative to first trimester initiation., Conclusion: We observed positive, but imprecisely estimated associations between periconceptional FACS initiation and child asthma. Folic acid prevents birth defects and is recommended. However, larger studies of folic acid dosing and timing, with consideration for childhood asthma, are needed.

Uterine fibroids (UF) are common pelvic tumors in women, heritable, and genome-wide association studies (GWAS) have identified ~ 30 loci associated with increased risk in UF. Using summary statistics from a previously published UF GWAS performed in a non-Hispanic European Ancestry (NHW) female subset from the Electronic Medical Records and Genomics (eMERGE) Network, we constructed a polygenic risk score (PRS) for UF. UF-PRS was developed using PRSice and optimized in the separate clinical population of BioVU. PRS was validated using parallel methods of 10-fold cross-validation logistic regression and phenome-wide association study (PheWAS) in a seperate subset of eMERGE NHW females (validation set), excluding samples used in GWAS. PRSice determined p t  < 0.001 and after linkage disequilibrium pruning (r 2  < 0.2), 4458 variants were in the PRS which was significant (pseudo-R 2  = 0.0018, p = 0.041). 10-fold cross-validation logistic regression modeling of validation set revealed the model had an area under the curve (AUC) value of 0.60 (95% confidence interval [CI] 0.58-0.62) when plotted in a receiver operator curve (ROC). PheWAS identified six phecodes associated with the PRS with the most significant phenotypes being 218 'benign neoplasm of uterus' and 218.1 'uterine leiomyoma' (p = 1.94 × 10 -23 , OR 1.31 [95% CI 1.26-1.37] and p = 3.50 × 10 -23 , OR 1.32 [95% CI 1.26-1.37]). We have developed and validated the first PRS for UF. We find our PRS has predictive ability for UF and captures genetic architecture of increased risk for UF that can be used in further studies., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy., Methods: Here, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth., Results: We find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system., Conclusions: By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings., (© 2022. The Author(s).)

Background: Collecting social determinants of health in electronic health records is time-consuming. Meanwhile, an Area Deprivation Index (ADI) aggregates sociodemographic information from census data. The objective of this study was to ascertain whether ADI is associated with stage of human papillomavirus (HPV)-related cancer at diagnosis., Methods: We tested for the association between the stage of HPV-related cancer presentation and ADI as well as the association between stage and the value of each census-based measure using ordered logistic regression, adjusting for age, race and sex., Results: Among 3247 cases of HPV-related cancers presenting to an urban academic medical center, the average age at diagnosis was 57. The average stage at diagnosis was Surveillance, Epidemiology and End Results Stage 3. In the study population, 43% of patients were female and 87% were white. In this study population, there was no association between stage of HPV-related cancer presentation and either aggregate or individual census variables., Conclusions: These results may reflect insufficient sample size, a lack of socio-demographic diversity in our population, or suggest that simplifying social determinants of health into a single geocoded index is not a reliable surrogate for assessing a patient's risk for HPV-related cancer., (© The Author(s) 2021. Published by Oxford University Press on behalf of Faculty of Public Health.)