{"paper_id":"d5ad2d18-c541-403c-ba9a-a1687515fed6","body_text":"Vol.:(0123456789)1 3\nh\nttps://doi.org/10.1007/s43032-022-00956-5\nREPRODUCTIVE GENETICS: PERSPECTIVE, OPINIONS AND COMMENTARIES\nRacial and Ethnic Variation in Genetic Susceptibility: Are Disparities \nin Infertility Prevalence and Outcomes more than Black and White?\nJerrine R. Morris1  · Torie Comeaux Plowden2 · Lisa J. Green3,4 · Digna R. Velez Edwards5,6 · Tia Jackson‑Bey7,8\nReceived: 21 January 2022 / Accepted: 19 April 2022 \n© The Author(s) 2022\nAbstract\nRace, as a social construct without a clear genetic underpinning, is frequently referenced in medicine as predictor of multiple \ndiseases including that of infertility. The authors will discuss how systematic racism can have downstream consequences \nranging from overt physician bias to use of medical algorithms that may potentiate the same disparities they attempt to nar-\nrow. Then, the authors explore the utility and pragmatic use of genetic ancestry to estimate disease prevalence, instead of \nracial categories. Finally, the authors explore how health inequities, rooted in systematic racism, can influence disease herit-\nability effectively advocating for research to disentangle the contributions of racism to genetic susceptibility in infertility.\nKeywords\n Genetic ances\ntry · Health disparities · Infertility · Heritability\nAs recently as 2020, the American Medical Association \n(AMA) voted to adopt policies underscoring race as a \nsocially constructed category [ 1]. This acknowledgment is \nin response to mounting evidence that race is not an inher -\nent biological trait and thus disparities in adverse health \noutcomes are due, to a large extent, to systemic racism and/\nor stressors resulting from racism [1]. Systemic racism is the \nnormalization and legitimization of a multitude of behaviors \nand factors that stem from historical, cultural, institutional, \nand interpersonal sources that tend to advantage White \npeople while perpetuating or worsening adverse outcomes \nfor people of color. As the medical community learns more \nabout the impact of the social determinants of health, these \ndiscoveries have challenged many of our traditionally held \nbeliefs and dogmas related to race and its impact on health. \nWhat is the utility, if any, of referencing race in medicine, in \nwomen’s health, and, more specifically, in infertility?\nWhen providers use race or ethnicity to withhold or alter \ntreatment, whether consciously or not, they become con-\nduits of the racism that perpetuates disparities in medicine. \nOvert racism is less accepted in modern medicine; however, \nfalse beliefs of biological differences based on race often \nconceal unconscious bias and perpetuate the influence of \nracism in medicine. Hoffman et al. found at least 50% of \nresidents and medical students held at least one false belief \nregarding biologic differences between White and Black \nAmericans. Medical trainees with a greater number of false \nbeliefs were less likely to recommend the appropriate treat-\nment for Black patients in this fictional case study [2]. Direct \nconsequences are seen in women’s reproductive health. The \nestimated prevalence of endometriosis is higher among \nWhite and Asian women and lower among Black women \n[3]. However, Black women are disproportionately treated \n * Jerrine R. Morris \n Jer\nrinemorris.md@gmail.com\n1 Department of Obstetrics, Gynecology, and Reproductive \nSciences, \nUniversity of California San Francisco, 499 \nIllinois Street, San Francisco, CA 94158, USA\n2 Department of Obstetrics and Gynecology, Walter Reed \nN\national Military Medical Center, Bethesda, MD, USA\n3 Department of Obstetrics and Gynecology, School \nof Medicine, U\nniversity of South Carolina, Greenville, SC, \nUSA\n4 Fertility Center of the Carolinas-Greenville, Greenville, SC, \nU\nSA\n5 Division of Quantitativ e Sciences, Department of Obstetrics \nand Gynecology, Vanderbilt University Medical Center, \nNashville, TN, USA\n6 Department of Biomedical Informatics, Vanderbilt University \nMedical Center\n, Nashville, TN, USA\n7 Reproductive Medicine Associates of New York, New York, \nNY\n, USA\n8 Department of Obstetrics and Gynecology and Reproductive \nScience, Division of R\neproductive Endocrinology \nand Infertility, Mount Sinai School of Medicine, New York, \nNY, USA\n/ Published online: 28 April 2022\nReproductive Sciences (2022) 29:2081–2083\n\n1 3\nfor (presumptive) pelvic inflammatory disease (PID) when \npresenting with chronic pelvic pain instead of considering \nthe full spectrum of pelvic pain etiologies, including endo-\nmetriosis. Thus, many Black women with endometriosis \nmay have a delayed diagnosis because they were thought to \nhave PID. The utilization of race by providers often enforces \nfalse narratives and implicit biases that disenfranchise per -\nsons of color.\nIn an era of increased algorithms, tools to quickly identify \npatients who may be at risk for an adverse outcome have \nemerged. Several of these tools utilize race and ethnicity \nto determine risk stratification, which has in many cases \nresulted in an inadvertent disparity of healthcare delivery. \nThe most notable in the field of women’s health is the vagi-\nnal birth after cesarean (VBAC) risk calculator. When used, \nself- or provider-reported Black race or Hispanic ethnicity \ndecreased one’s probability of a successful live birth. Thus, \nthese women would be less likely offered the opportunity for \nVBAC. This decrement was comparable to the net benefit \none could gain with a history of a prior vaginal delivery or \nVBAC [4]. Similarly, the National Cancer Institute Breast \nCancer Risk Assessment Tool, which is used to estimate a \nwoman’s risk of developing invasive breast cancer in the \nnext 5 years, includes race as a variable. While “validated” \nwithin different racial and ethnic populations, lower risk \nestimates are provided for all racial and ethnic minority \nwomen when compared to White women. This algorithm \nmay falsely reassure providers when seeing women of color \nleading to inadequate screening in nonwhite women [4]. Use \nof such inherently biased algorithms to counsel and/or treat \ndiverse patients often perpetuate rather than ameliorate dis-\nparities in women’s health.\nAccepting these limitations yet understanding the desire \nfor evidence-based treatment algorithms, is there ever a time \nwhere race/ethnicity should be considered? The answer to \nthis is — well — yes. However, instead of highlighting \nracial and ethnic differences, the focus should be shifted \ntowards identifying ancestral markers that influence disease \nprevalence. The limiting factor in the use of race as a sur -\nrogate marker for genetic ancestry is that in most studies, \nincluding those reflected in reproductive medicine, race is \nself-reported. Kaseniit et al. found self-reported race was \nan imperfect proxy for genetic ancestry as roughly (only) \n9% of patients who underwent genetic testing were found to \nhave concordance between their genetic ancestry and self-\nreported race. Concordance was lowest among those who \nself-reported Middle Eastern, Ashkenazi Jewish, and South-\nern European descent [5 ]. Furthermore, these algorithms \nseldomly originate from nonwhite populations. In reproduc-\ntive medicine, much of the current research is focused on \nways to optimize outcomes in in vitro fertilization (IVF) \nwhen treating infertility. Preimplantation genetic testing is \na technology which has been employed for this very reason \n— proponents of this technology argue for its use to detect \nstructural chromosomal abnormalities, reduce heritability \nof single gene disorders, and potentially limit transfer of \nchromosomally abnormal embryos [6 ]. A new technology \nhas since emerged — preimplantation testing with polygenic \nrisk scores (PRSs) [7]. While the opportunity to potentially \nrank-order euploid embryos may seem advantageous, these \ngenome-wide association studies have only been validated \nusing European ancestry thus extrapolating risks may not \nbe valid for populations without this shared ancestry [ 8]. \nHence, even when genetic ancestry is used in algorithms, \nexclusive validation using European ancestry will still limit \ntheir clinical applicability and may further widen disparities \nin nonwhite populations.\nCertain diseases like endometriosis and PCOS have \nincreased heritability within families but how it applies to \nself-reported racial groups may need to be reexamined. Other \nreproductive health diagnoses, such as uterine fibroids, have \nstronger ties along self-reported racial groups, but this is \nmore likely a consequence of genetic ancestry than physical \nattributes of race. Keaton et al. investigated genetic ances -\ntry proportions for populations clustered into six geographic \ngroups and found northern European ancestry to be protec-\ntive against fibroids while west African ancestry typically \nconferred increased risk of fibroid prevalence among Black \nand White women [9]. Similarly, genome-wide association \nstudies have explored single nucleotide polymorphisms \n(SNPs) associated with age at menopause. Japanese, Chi-\nnese, and African American women have all exhibited SNPs \nthat were not implicated (seen/observed/demonstrated) in \nEuropean populations [10].\nFinally, we are only just beginning to understand \nthe degree to which environmental exposures influence \ntransgenerational health. Diethylstilbestrol (DES) is an \nendocrine disruptor that is a well-known transplacental \npathogen. Exposure in utero leads to increased risks for \nreproductive tract anomalies, infertility, and clear cell ade-\nnocarcinoma. However, third-generation women exposed \nhave been shown to have an increased risk of preterm birth \nand menstrual irregularities as compared to their counter -\nparts, suggesting long-term effects that permeate multiple \ngenerations [11]. Through use of a rat model, studies have \nshown how in utero exposure to 2,3,7,8-tetrachlorodibenzo-\np-dioxin (TCDD), a common pollutant found in solid waste \nand often a contaminant of food products, can increase risk \nof reduced fertility and preterm birth among future off-\nspring [12]. Epigenetic alterations have been proposed as \nthe link between the effects of environmental exposures on \npoor reproductive health outcomes. In another example, US \nborne Black women have higher rates preterm birth (PTB) as \ncompared to both Foreign borne non-Hispanic Black women \nand White women [13, 14]. Vitamin D deficiency has been \nshown to be associated with spontaneous PTB. Interestingly, \n2082 Reproductive Sciences (2022) 29:2081–2083\n\n1 3\ntranscriptomic analyses have found overlapping gene dys -\nregulation during both vitamin D deficiency and PTB [15]. \nOne can postulate how disparate access to resources that \noften affect minority populations may incorrectly lead one \nto suspect a “genetic” cause of adverse reproductive health \noutcomes when, in fact, this transgenerational morbidity is \na result of systemic racism.\nHealth inequities exist in a complex, multi-factorial envi-\nronment and are related to differences in access to care, dif-\nferences in resources that promote health, and yes, due to the \nimpact of systematic racism that is pervasive in the USA. Sim-\nply reporting health disparities is not enough anymore—argu-\nably, it was never enough. Research needs to focus on how \ngenetic ancestry may affect a population’s disease susceptibil-\nity and can be effectively and equitably used to identify preven-\ntion and treatment strategies. Tools to validate one’s risk using \nancestral markers must originate from unique populations to \nincrease their generalizability and subsequent clinical utility. \nMeanwhile, all fields of medicine must take a stark look at sys-\ntematic inequities and work to dismantle them. Now is the time \nto move beyond our traditional categorizations of humans, \nsteeped in division and hierarchy, and to some extent rooted \nin white supremacy and patriarchy, to discover new paradigms \nfor improving health and medical care for all.\nAvailability of Data and Material N ot applicable.\nCode Availability\n N\not applicable.\nDeclarations \nEthics Approval N ot applicable.\nConsent to Participate\n N\not applicable.\nConsent for Publication\n N\not applicable.\nConflict of Interest\n The aut\nhors declare no competing interests.\nOpen Access\n This ar\nticle is licensed under a Creative Commons Attri-\nbution 4.0 International License, which permits use, sharing, adapta-\ntion, distribution and reproduction in any medium or format, as long \nas you give appropriate credit to the original author(s) and the source, \nprovide a link to the Creative Commons licence, and indicate if changes \nwere made. The images or other third party material in this article are \nincluded in the article's Creative Commons licence, unless indicated \notherwise in a credit line to the material. If material is not included in \nthe article's Creative Commons licence and your intended use is not \npermitted by statutory regulation or exceeds the permitted use, you will \nneed to obtain permission directly from the copyright holder. To view a \ncopy of this licence, visit http://\n \ncreat\n \niveco\n \nmmons.\n \norg/\n \nlicen\n \nses/\n \nby/4.\n \n0/.\nReferences\n 1 .  Association AM. Or ganizational strategic plan to embed racial \njustice and advance health equity. 2020.\n 2\n. Hoffman K, T\nrawalter S, Axt JR, Oliver N. Racial bias in pain \nassessment and treatment recommendations, and false beliefs \nabout biological differences between blacks and whites. Proc Natl \nAcad Sci USA. 2016;113(16):4296–301.\n 3\n. Bougie O, Y\nap Ma I, Sikora L, Flaxman T, Singh S. 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