The HIF2α polymorphism rs4953361 is associated with impaired glucose metabolism in Han Chinese women with infertility

The HIF2α polymorphism rs4953361 is associated with impaired glucose metabolism in Han Chinese women with infertility | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The HIF2α polymorphism rs4953361 is associated with impaired glucose metabolism in Han Chinese women with infertility Xiaoya Zheng, Jiani Ma, Min Hu, Jian Long, Qiang Wei, Wei Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1467179/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To evaluate HIF2α polymorphisms and glucose metabolism in a group of women with and without infertility. Design: Case–control study. Setting: Female infertility patients who visited this reproductive center and age-matched females without infertility who underwent routine health examinations from January 2020 to December 2021. Patients: The study group consisted of 148 women with infertility and 176 women without infertility as healthy controls. Intervention: We genotyped 29 single nucleotide polymorphisms (SNPs) of HIF2α by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based genotyping technology. The genetic associations were analyzed statistically. Main Outcome Measures: Allele frequency, genotype distribution and haplotype analysis of the HIF2α polymorphisms. BMI, glucose metabolism and insulin release were also measured. Results: Infertile women had elevated BMI, impaired glucose metabolism, and increased levels of plasma insulin compared to those of healthy women. SNP analysis of HIF2α revealed that the allele and genotype frequencies of rs4953361 were significantly associated with female infertility. Haplotype analysis of HIF2α polymorphism identified haplotypes TGG and TGA as being associated with female infertility. Women with the AA genotype of rs4953361 had a significantly higher BMI and postload plasma glucose and insulin levels than those of women with the GG genotype. Conclusion: This is the first study to report an association between HIF2α polymorphisms and female infertility and glucose and insulin metabolic disorder. These results require replication in larger populations but suggest that the HIF2α polymorphisms may play an important role in female obesity-related infertility. In this observational study, we did not report the results of a health care intervention on human participants. The study was approved by the Human Research Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. Clinical data and peripheral blood samples were collected only after explaining the objectives of the study and obtaining a signed informed consent form. Infertility HIF2α gene polymorphism obesity insulin Figures Figure 1 Figure 2 Figure 3 Introduction Infertility is a worldwide problem estimated to affect up to 8–12% of couples (as many as 186 million people) in both developed and developing countries( 1 ). Although male infertility contributes to most of the global childlessness, infertility remains a woman’s social burden( 2 ). Several factors have been claimed to affect women’s fertility, such as aging, neuroendocrine, infectious, immune, psychiatric, stress, and weight factors, as well as iatrogenic factors, previous interventions, and surgery( 3 ). In addition, several studies have revealed many genetic factors related to female infertility that indicate that there are associations between the development of female infertility and genetic polymorphisms( 4 , 5 ). Hypoxia, a status caused by reduced oxygen availability or an imbalance in oxygen consumption/supply, is a stress that affects many physiological and pathological processes( 6 ). Hypoxia inducible factor 2α (HIF2α) is a major transcription factor that responds to hypoxia and induces the expression of hypoxia-related genes such as vascular endothelial growth factor (VEGF) and erythropoietin( 7 ). Notably, HIF2α is strongly expressed in uterine stroma after embryo attachment( 8 ). Based on the expression patterns, a study reported that aberrant expression of HIF2α in the entire uterus of mice resulted in infertility, indicating the importance of uterine HIF2α in infertility( 9 ). As there is emerging evidence on the potential contribution of HIF2α in modulating female infertility, the current study aimed to explore the relationship between genetic polymorphisms of HIF2α and female infertility. Research Design And Methods Patients In this case–control study, 148 women with infertility who visited the reproductive center of our hospital from January 2020 to December 2021 were enrolled as the case group, and 176 age-matched healthy women without infertility who underwent routine health examinations during the same time were enrolled as the control group. Women with acute or chronic medical conditions, especially known autoimmune diseases and hypothalamic-pituitary diseases, were excluded. Clinical data and peripheral blood samples were collected only after explaining the objectives of the study and obtaining a signed informed consent form, as approved by the Human Research Ethics Committee of our hospital. Laboratory tests A 75-g oral glucose tolerance test (OGTT) and insulin release test (IRT) were performed in the endocrinology laboratory of our hospital as previously described(10). Blood plasma glucose was measured using the glucose oxidase method. Blood plasma insulin was measured by the chemiluminescence method. DNA extraction Peripheral blood was collected from each patient and control in an ethylene diamine tetraacetic acid (EDTA)-containing tube. Genomic DNA was extracted from peripheral nucleated cells by using a commercially available kit (Axygen, JA1705). HIF2α genotyping For detection of HIF2α polymorphisms, 29 tag SNPs of HIF2α were genotyped by using MALDI-TOF MS as previously described(11). Genotyping was performed using MALDI-TOF mass spectrometry (MassArray TM Nanodispenser, SAMSUNG). All reactions were designed in multiplexes of up to 29 tag SNPs by use of Assay Design v2.0 software (Agena Bioscience). The data were collected using the Mass ARRAY Compact System (Agena Bioscience). Detailed primer information in provided Supplementary Material 1. Statistical analysis Statistical analysis was performed by GraphPad Prism version 5.0 software. Continuous variables with a normal distribution are expressed as the mean ± standard deviation (mean± SD). Categorical variables are described as percentages (%), and all SNPs were tested for Hardy–Weinberg equilibrium with the chi-square test. Haploview software developed at the Massachusetts Institute of Technology (MIT) Media Lab by B. Fry ( http://acg.media.mit.edu/people/fry/ ) was used to perform the haplotype analysis(12). All statistical analyses were two-sided, and p<0.05 was considered statistically significant. Results According to the main objectives of the study, glucose metabolic characteristics were analyzed (Fig. 1 ). Comparative analysis of infertile women (n = 148; 29.3 ± 4.4 years) and aged-matched healthy women (n = 174; 29.1 ± 3.9 years) enabled us to assess whether the carrier status of the tested genetic variants may predispose to infertility. As expected, infertile women had a significantly higher BMI than that of healthy women (24.8 ± 4.7 vs. 22.0 ± 3.2, p < 0.05). Compared with healthy control women, the infertile women had significantly higher levels of plasma glucose and insulin after the OGTT, indicating that infertile women were prone to having obesity-associated impaired glucose metabolism and insulin release. For 29 tag SNPs of HIF2α, except for rs11694193, the observed and expected values of alleles and genotypes were in good agreement with Hardy–Weinberg equilibrium (p > 0.05). The allele and genotype frequencies of SNPs between the infertility and control group that were significantly different (p < 0.05) and close to significantly different (p < 0.1) are shown. Information on the remaining SNPs is provided in Additional files. The allele frequencies of the HIF2α polymorphisms rs2346176, rs4953361, and rs13412887 in the women with and without infertility are shown in Table 1 . We found a significant association between the rs4953361 polymorphism and infertility (P = 0.0098, OR = 1.505, 95% CI 1.103–2.053), suggesting that the rs4953361 polymorphism was related to female infertility. Table 1 Allele frequency analysis between women with and without infertility. rs Allele Infertility Control Chi 2 Pearson's p OR 95% CI rs2346176 T 77(0.260) 72(0.205) C 219(0.740) 280(0.795) 2.8062 0.0939 1.367 0.948–1.973 rs4953361 A 163(0.551) 158(0.449) G 133(0.449) 194(0.551) 6.6674 0.0098 1.505 1.103–2.053 rs13412887 G 60(0.203) 51(0.145) C 236(0.797) 301(0.855) 3.7863 0.0517 1.500 0.995–2.262 Note: OR: odds ratio; CI: confidence interval. P < 0.05 means statistically significant. The genotype frequencies of the HIF2α polymorphisms rs1867783, rs4953361, rs3768728, and rs13412887 in the women with and without infertility are shown in Table 2 and Table 3 . The genotype frequency distribution of rs4953361 between the two groups was significantly different (p = 0.0335). The frequency of the minor allele (homozygous GG) of rs4953361 in women with infertility was significantly lower than that in women without infertility (19.6% vs. 31.3%, p = 0.0171, OR = 0.536, 95% CI 0.320–0.898), indicating that the GG genotype of rs4953361 was a protective genotype for female infertility. Table 2 Genotype frequency distribution analysis between women with and without infertility. rs Genotype Infertility Control Chi 2 Pearson's p rs1867783 GG 106(0.716) 136(0.773) CG 42(0.284) 36(0.205) CC 0(0.000) 4(0.023) 5.8041 0.0549 rs4953361 GG 29(0.196) 55(0.313) AA 44(0.297) 37(0.210) AG 75(0.507) 84(0.477) 6.793 0.0335 rs3768728 TT 117(0.791) 151(0.858) CT 30(0.203) 21(0.119) CC 1(0.007) 4(0.023) 5.3217 0.0699 rs13412887 CG 46(0.311) 47(0.267) CC 95(0.642) 127(0.722) GG 7(0.047) 2(0.011) 5.0189 0.0813 Note: OR: odds ratio. P < 0.05 means statistically significant. Table 3 Comparative analysis of the frequency of minor allele homozygous and other genotypes between women with and without infertility. rs Genotype Infertility Control Chi 2 Pearson's p OR 95% CI rs1867783 CC 0(0.000) 4(0.023) CG + GG 148(1.000) 172(0.977) 3.4057 0.065 rs4145836 AA 3(0.020) 0(0.000) AG + GG 145(0.980) 176(1.000) 3.6009 0.0577 rs2346176 CT + CC 135(0.912) 169(0.960) TT 13(0.088) 7(0.040) 3.2068 0.0733 0.43 0.167–1.108 rs4953361 GG 29(0.196) 55(0.313) AG + AA 119(0.804) 121(0.688) 5.687 0.0171 0.536 0.320–0.898 rs13412887 CC + CG 141(0.953) 174(0.989) GG 7(0.047) 2(0.011) 3.8439 0.0499 0.232 0.047–1.132 rs11900910 CT + TT 125(0.845) 161(0.915) CC 23(0.155) 15(0.085) 3.8245 0.0505 0.506 0.254–1.011 rs7571218 GG 20(0.135) 13(0.074) AA + AG 128(0.865) 163(0.926) 3.2994 0.0693 1.959 0.939–4.088 Note: OR: odds ratio; CI: confidence interval. P < 0.05 means statistically significant. Haplotype analysis of the HIF2α polymorphisms identified that rs11675232, rs11692911, and rs4953361 constituted block 5 (Fig. 2 and Table 4 ). The haplotype frequency of TGG in women with infertility was significantly lower than that in women without infertility (44.8% vs. 54.2%, p = 0.0171). In contrast, the haplotype frequency of TGA in women with infertility was significantly higher than that in women without infertility (36% vs. 26.5%, p = 0.0093). These data suggested that rs4953361 was the most important polymorphism associated with female infertility. Table 4 Haplotype analysis between women with and without infertility. Block Haplotype Freq. Case, Control Ratio Counts Case,Control Frequencies Chi Square P Value Block 5 TGG 0.499 132.5 : 163.5, 190.6 : 161.4 0.448, 0.542 5.691 0.0171 TGA 0.308 106.5 : 189.5, 93.3 : 258.7 0.360, 0.265 6.76 0.0093 CAA 0.166 48.8 : 247.2, 58.7 : 293.3 0.165, 0.167 0.004 0.95 CGA 0.018 6.7 : 289.3, 5.0 : 347.0 0.023, 0.014 0.647 0.421 Haplotype analysis revealed two risk haplotypes for women infertility. rs11675232, rs11692911, and rs4953361 constitute block 5, and the distribution of haplotype TGG and TGA between the two groups is statistically significant (p < 0.05). In the subgroup analysis of the different genotypes at rs4953361, we found that women with the AA and AG genotypes had a significantly higher BMI than that of women with the GG genotype (23.87 ± 4.16 and 23.77 ± 3.87 vs. 22.37 ± 3.09, p = 0.0295). Women with the AA genotype had significantly higher plasma glucose levels at 120 min after OGTT than the levels of women with the GG genotype. Women with the AA genotype had significantly higher plasma insulin levels at 60 min and 120 min after OGTT than the levels of women with the GG genotype (Fig. 3 ). These results indicated that the rs4953361 polymorphism was associated with obesity-induced impaired glucose and insulin tolerance. Discussion In the present study, we hypothesized that HIF2α polymorphisms might be involved in the pathogenesis of female infertility. We examined 29 HIF2α SNPs in women with and without infertility, and assessed the association of the allele, genotype and haplotype frequencies with the risk of female infertility and glucose metabolism. To our knowledge, this is the first study to report an association between HIF2α polymorphisms and female infertility and impaired glucose metabolism. Here, we observed that elevated BMI, impaired glucose metabolism, and increased levels of plasma insulin were metabolic traits that were characteristic of infertile women. The increasing obesity epidemic, with its related metabolic disorders, is associated with an increased risk of many female reproductive conditions( 13 ). Obesity negatively affects female reproductive health, including increased risks of menstrual dysfunction, anovulation, and other fertility problems( 14 , 15 ). Reduced fertility in women who are overweight might be related to multiple endocrine, adipokine, and metabolic alterations that affect follicle growth, embryo development and implantation. Some previous studies have indicated that obese females had lower levels of estrogen( 16 ). Another recent study found that female obesity impaired in vitro fertilization outcome without affecting embryo quality( 17 ). The etiologies of obesity-related infertility remain unclear; however, one common underlying feature associated with obesity is hyperinsulinemia or insulin resistance (IR). Plasma levels of insulin are correlated with BMI, and insulin can regulate steroidogenesis in ovarian cells in vitro and in the stromal and follicular compartments of ovaries( 18 , 19 ). Xu et al. found elevated plasma insulin levels, along with increased glucose and insulin pathway dysfunction in overweight/obese women, which were associated with reduced follicle-stimulating hormone receptor (FSHR) expression, estrogen synthesis-related genes, and decreased estrogen production( 20 ). Regarding the analyzed HIF2α polymorphisms, among the 29 SNPs, the only significant association detected was between rs4953361 and female infertility. Not only were the allele and genotype distribution frequencies of rs4953361 significantly associated with female infertility, but the distribution of haplotypes containing this locus was also significantly associated with female infertility. These results suggested that rs4953361 of HIF2α was the most important polymorphism associated with female infertility. HIF has been postulated to be involved in follicle development due to the hypoxic microenvironment in follicles( 21 ). However, it has become clear that expression of HIF1α is regulated by pituitary hormones instead of hypoxia in follicles( 22 ). A previous study showed that the expression of HIF1α is primarily in the uterine luminal epithelium during the peri-implantation period. Interestingly, HIF2α is strongly expressed in the uterine stroma during peri-implantation and after embryo attachment( 8 ). To explore the functional roles of HIF2α in embryo implantation, Matsumoto et al. generated mice with uterine tissue-specific deletion of HIF1α and HIF2α. They found that mice with HIF2α deletion in the entire uterus were infertile, whereas mice with uterine deletion of HIF1α showed subfertility, indicating the importance of HIF2α in fertility. Uterine HIF2α contributes to successful implantation regardless of decidualization and the position of embryo attachment( 9 ). As an intron mutation, the mechanism of how the rs4953361 polymorphism affects female infertility still needs to be clarified in future studies. As an additional insight, we compared the BMI, glucose tolerance, and insulin release among different genotypes of rs4953361. We found that compared with women with the GG genotype, women with the AA genotype had a significantly higher BMI, two-hour postload plasma glucose, and one- to two-hour postload plasma insulin levels. These results indicated that the rs4953361 mutation on HIF2α might be associated with obesity and its related impaired glucose and insulin tolerance. The obesity-associated adipose tissue hypoxia response is largely mediated by HIFs. Adipocyte-specific HIF2α knockout exacerbated high-fat diet-induced inflammation and insulin resistance( 23 ). Adipocyte HIF2α is protective against maladaptation to obesity and metabolic dysregulation by promoting angiogenesis in both white adipose tissue and brown adipose tissue and by counteracting obesity-mediated brown adipose tissue dysfunction( 24 ). Macrophage HIF2α has been shown to counteract proinflammatory responses to relieve obesity-induced insulin resistance in adipose tissue( 25 , 26 ). Taken together, these studies indicate that HIF2α is an important regulator of obesity and insulin resistance. There are some limitations of this study that should be mentioned. First, we may not have enough power to detect potential association between the other 27 tag SNPs of HIF2α and female infertility for the limited sample size, or differences in genetic effect sizes. Second, the population included in this study is a single center in western China, the association between the rs4953361 polymorphism of HIF2α and female infertility and metabolic disorder still needs to be validated in other populations. Conclusion In conclusion, our data point to a possible association of the HIF2α polymorphism (rs4953361) with infertility in Chinese women. This finding clearly needs to be replicated in a larger and independent sample and in different populations. An association between HIF2α polymorphism and female infertility and obesity-associated glucose and insulin disorder would strongly support the hypothesis that HIF2α is an important regulator of obesity-related infertility. The mechanism of the effect of the HIF2α polymorphism rs4953361 on female infertility still needs to be clarified in future studies. Declarations Ethics approval and consent to participate The study was approved by the Human Research Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. A signed informed consent form was obtained from each participant. Clinical data and peripheral blood samples were collected only after explaining the objectives of the study. Consent for publication Written informed consents for publication were obtained from all of the participants. Availability of data and materials The datasets analysed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This project was supported by the National Natural Science Foundation of China Youth Program (Grant No.81900733) in design of the study, the Chongqing municipal health and Family Planning Commission Fund (Number: 2021MSXM013) in collection of data, Bethune Charity Foundation (G-X-2020-1107-14) in interpretation of data, and the National Key Clinical Specialties Construction Program of China (2011) in writing and publication the manuscript. Authors' contributions Xiaoya Zheng designed and wrote the manuscript; Jiani Ma and Min Hu collected the data; Jian Long and Qiang Wei analyzed the data; Wei Ren is the design consultant. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Inhorn MC, Patrizio P. Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Hum Reprod Update. 2015;21(4):411-26. Chu KY, Patel P, Ramasamy R. 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Female obesity impairs in vitro fertilization outcome without affecting embryo quality. Fertility and Sterility. 2010;93(2):447-54. Mukherjee D, Majumder S, Roy Moulik S, Pal P, Gupta S, Guha P, et al. Membrane receptor cross talk in gonadotropin-, IGF-I-, and insulin-mediated steroidogenesis in fish ovary: An overview. Gen Comp Endocrinol. 2017;240:10-8. Das D, Arur S. Conserved insulin signaling in the regulation of oocyte growth, development, and maturation. Mol Reprod Dev. 2017;84(6):444-59. Xu P, Huang BY, Zhan JH, Liu MT, Fu Y, Su YQ, et al. Insulin Reduces Reaction of Follicular Granulosa Cells to FSH Stimulation in Women With Obesity-Related Infertility During IVF. J Clin Endocrinol Metab. 2019;104(7):2547-60. Fadhillah, Yoshioka S, Nishimura R, Okuda K. Hypoxia promotes progesterone synthesis during luteinization in bovine granulosa cells. J Reprod Dev. 2014;60(3):194-201. Lee HC, Tsai SJ. Endocrine targets of hypoxia-inducible factors. J Endocrinol. 2017;234(1):R53-R65. Lee YS, Kim JW, Osborne O, Oh DY, Sasik R, Schenk S, et al. Increased adipocyte O2 consumption triggers HIF-1alpha, causing inflammation and insulin resistance in obesity. Cell. 2014;157(6):1339-52. Garcia-Martin R, Alexaki VI, Qin N, Rubin de Celis MF, Economopoulou M, Ziogas A, et al. Adipocyte-Specific Hypoxia-Inducible Factor 2alpha Deficiency Exacerbates Obesity-Induced Brown Adipose Tissue Dysfunction and Metabolic Dysregulation. Mol Cell Biol. 2016;36(3):376-93. Choe SS, Shin KC, Ka S, Lee YK, Chun JS, Kim JB. Macrophage HIF-2alpha ameliorates adipose tissue inflammation and insulin resistance in obesity. Diabetes. 2014;63(10):3359-71. Aouadi M. HIF-2alpha blows out the flames of adipose tissue macrophages to keep obesity in a safe zone. Diabetes. 2014;63(10):3169-71. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.doc Additional file 1. Allele frequency analysis of infertility and normal control women.The allele frequencies of 29 tag SNPs between the infertility and control group. Data with statistical significance was in bold. Additional file 2. Genotype frequency distribution analysis of infertility and normal control women.The genotype frequencies of 29 tag SNPs between the infertility and control group. Data with statistical significance was in bold. Additional file 3. Comparative analysis of the frequency of Major allele homozygous and heterozygous and other genotypes.The frequency of Major allele homozygous and heterozygous and other genotypes between the two groups. Data with statistical significance was in bold. Additional file 4. Haplotype analysisHaplotype analysis showed statistical significance was in bold. 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University","correspondingAuthor":false,"prefix":"","firstName":"Jiani","middleName":"","lastName":"Ma","suffix":""},{"id":93440380,"identity":"2ee19638-d356-4fcf-b0cc-1bf8b8147551","order_by":2,"name":"Min Hu","email":"","orcid":"","institution":"the First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Hu","suffix":""},{"id":93440381,"identity":"abd4dc0d-9740-4439-8538-db96bd8edd89","order_by":3,"name":"Jian Long","email":"","orcid":"","institution":"the First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Long","suffix":""},{"id":93440382,"identity":"f145ea6b-015c-4034-aef1-053515d4ccaf","order_by":4,"name":"Qiang Wei","email":"","orcid":"","institution":"Chongqing Jiulongpo District Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Wei","suffix":""},{"id":93440383,"identity":"f21833fb-a155-487c-a708-e2717805c3f9","order_by":5,"name":"Wei Ren","email":"","orcid":"","institution":"the First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2022-03-19 01:44:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1467179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1467179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19735696,"identity":"252b4e21-e568-497d-badf-d57824a24c39","added_by":"auto","created_at":"2022-03-29 13:57:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWomen with infertility have a higher BMI and impaired glucose metabolism\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA,B: Comparison of age and BMI between women with and without infertility.\u003c/p\u003e\u003cp\u003eC,D: Comparison of the oral glucose tolerance test (OGTT) and corresponding area under the curve (AUC) between women with and without infertility. \u003c/p\u003e\u003cp\u003eE,F: Comparison of the insulin release test (IRT) and corresponding AUC between women with and without infertility.\u003c/p\u003e\u003cp\u003e\u0026nbsp;*p\u0026lt;0.05 compared with the control.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1467179/v1/d60f36b6b341cce8ec26eacb.png"},{"id":19735694,"identity":"dbe887f6-7aa1-4f5c-a5b4-ad28148e0084","added_by":"auto","created_at":"2022-03-29 13:57:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHaplotype analysis\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eHaplotype analysis showed that rs11675232, rs11692911, and rs4953361 of HIF2α constituted block 5, and the distribution of haplotypes TGG and TGA between women with and without infertility was statistically significant (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1467179/v1/3ba70ed0e3aca6b0e01fcc75.png"},{"id":19736731,"identity":"ff74adc4-7955-4d0b-9ca2-cb2090546436","added_by":"auto","created_at":"2022-03-29 14:07:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139645,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBMI and glucose metabolism among different genotypes of rs4953361\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA: Comparison of BMI among different genotypes of rs4953361.\u003c/p\u003e\u003cp\u003eB,C: Comparison of the OGTT and IRT among different genotypes of rs4953361. \u003c/p\u003e\u003cp\u003e*p\u0026lt;0.05 compared with the GG genotype.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1467179/v1/0a91e4d8001bc19bc74411c6.png"},{"id":19736732,"identity":"af4b198c-d25b-4931-abea-c471999cb573","added_by":"auto","created_at":"2022-03-29 14:07:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060446,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1467179/v1/e64cc153-aabf-40aa-827a-e69809db761e.pdf"},{"id":19736483,"identity":"da51c3ac-4112-43c8-b4e3-7456ef51134b","added_by":"auto","created_at":"2022-03-29 14:02:49","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":290816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1. Allele frequency analysis of infertility and normal control women.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe allele frequencies of 29 tag SNPs between the infertility and control group. Data with statistical significance was in bold. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdditional file 2. Genotype frequency distribution analysis of infertility and normal control women.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe genotype frequencies of 29 tag SNPs between the infertility and control group. Data with statistical significance was in bold. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdditional file 3. Comparative analysis of the frequency of Major allele homozygous and heterozygous and other genotypes.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe frequency of Major allele homozygous and heterozygous and other genotypes between the two groups. Data with statistical significance was in bold. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdditional file 4. Haplotype analysis\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eHaplotype analysis showed statistical significance was in bold.\u003c/p\u003e","description":"","filename":"SupplementaryTables.doc","url":"https://assets-eu.researchsquare.com/files/rs-1467179/v1/32940f3cc9354a944566852f.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"The HIF2α polymorphism rs4953361 is associated with impaired glucose metabolism in Han Chinese women with infertility","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfertility is a worldwide problem estimated to affect up to 8\u0026ndash;12% of couples (as many as 186\u0026nbsp;million people) in both developed and developing countries(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although male infertility contributes to most of the global childlessness, infertility remains a woman\u0026rsquo;s social burden(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Several factors have been claimed to affect women\u0026rsquo;s fertility, such as aging, neuroendocrine, infectious, immune, psychiatric, stress, and weight factors, as well as iatrogenic factors, previous interventions, and surgery(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In addition, several studies have revealed many genetic factors related to female infertility that indicate that there are associations between the development of female infertility and genetic polymorphisms(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHypoxia, a status caused by reduced oxygen availability or an imbalance in oxygen consumption/supply, is a stress that affects many physiological and pathological processes(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Hypoxia inducible factor 2α (HIF2α) is a major transcription factor that responds to hypoxia and induces the expression of hypoxia-related genes such as vascular endothelial growth factor (VEGF) and erythropoietin(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Notably, HIF2α is strongly expressed in uterine stroma after embryo attachment(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Based on the expression patterns, a study reported that aberrant expression of HIF2α in the entire uterus of mice resulted in infertility, indicating the importance of uterine HIF2α in infertility(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs there is emerging evidence on the potential contribution of HIF2α in modulating female infertility, the current study aimed to explore the relationship between genetic polymorphisms of HIF2α and female infertility.\u003c/p\u003e"},{"header":"Research Design And Methods","content":"\u003ch2\u003ePatients\u003c/h2\u003e\n\u003cp\u003eIn this case\u0026ndash;control study, 148 women with infertility who visited the reproductive center of our hospital from January 2020 to December 2021 were enrolled as the case group, and 176 age-matched healthy women without infertility who underwent routine health examinations during the same time were enrolled as the control group. Women with acute or chronic medical conditions, especially known autoimmune diseases and hypothalamic-pituitary diseases, were excluded. Clinical data and peripheral blood samples were collected only after explaining the objectives of the study and obtaining a signed informed consent form, as approved by the Human Research Ethics Committee of our hospital.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eLaboratory tests\u003c/h2\u003e\n\u003cp\u003eA 75-g oral glucose tolerance test (OGTT) and insulin release test (IRT) were performed in the endocrinology laboratory of our hospital as previously described(10). Blood plasma glucose was measured using the glucose oxidase method. Blood plasma insulin was measured by the chemiluminescence method.\u003c/p\u003e\n\u003ch2\u003eDNA extraction\u003c/h2\u003e\n\u003cp\u003ePeripheral blood was collected from each patient and control in an ethylene diamine tetraacetic acid (EDTA)-containing tube. Genomic DNA was extracted from peripheral nucleated cells by using a commercially available kit (Axygen, JA1705).\u003c/p\u003e\n\u003ch2\u003eHIF2\u0026alpha; genotyping\u003c/h2\u003e\n\u003cp\u003eFor detection of HIF2\u0026alpha; polymorphisms, 29 tag SNPs of HIF2\u0026alpha; were genotyped by using MALDI-TOF MS as previously described(11). Genotyping was performed using MALDI-TOF mass spectrometry (MassArray\u003csup\u003eTM\u003c/sup\u003e Nanodispenser, SAMSUNG). All reactions were designed in multiplexes of up to 29 tag SNPs by use of Assay Design v2.0 software (Agena Bioscience). The data were collected using the Mass ARRAY Compact System (Agena Bioscience). Detailed primer information in provided Supplementary Material 1.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;Statistical analysis was performed by GraphPad Prism version 5.0 software. Continuous variables with a normal distribution are expressed as the mean \u0026plusmn; standard deviation (mean\u0026plusmn; SD). Categorical variables are described as percentages (%), and all SNPs were tested for Hardy\u0026ndash;Weinberg equilibrium with the chi-square test. Haploview software developed at the Massachusetts Institute of Technology (MIT) Media Lab by B. Fry (\u003ca href=\"http://acg.media.mit.edu/people/fry/\"\u003ehttp://acg.media.mit.edu/people/fry/\u003c/a\u003e) was used to perform the haplotype analysis(12). All statistical analyses were two-sided, and p\u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAccording to the main objectives of the study, glucose metabolic characteristics were analyzed (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Comparative analysis of infertile women (n\u0026thinsp;=\u0026thinsp;148; 29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 years) and aged-matched healthy women (n\u0026thinsp;=\u0026thinsp;174; 29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 years) enabled us to assess whether the carrier status of the tested genetic variants may predispose to infertility. As expected, infertile women had a significantly higher BMI than that of healthy women (24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 vs. 22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with healthy control women, the infertile women had significantly higher levels of plasma glucose and insulin after the OGTT, indicating that infertile women were prone to having obesity-associated impaired glucose metabolism and insulin release.\u003c/p\u003e\n\u003cp\u003eFor 29 tag SNPs of HIF2\u0026alpha;, except for rs11694193, the observed and expected values of alleles and genotypes were in good agreement with Hardy\u0026ndash;Weinberg equilibrium (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The allele and genotype frequencies of SNPs between the infertility and control group that were significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and close to significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) are shown. Information on the remaining SNPs is provided in Additional files.\u003c/p\u003e\n\u003cp\u003eThe allele frequencies of the HIF2\u0026alpha; polymorphisms rs2346176, rs4953361, and rs13412887 in the women with and without infertility are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. We found a significant association between the rs4953361 polymorphism and infertility (P\u0026thinsp;=\u0026thinsp;0.0098, OR\u0026thinsp;=\u0026thinsp;1.505, 95% CI 1.103\u0026ndash;2.053), suggesting that the rs4953361 polymorphism was related to female infertility.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAllele frequency analysis between women with and without infertility.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInfertility\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePearson\u0026apos;s p\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2346176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77(0.260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72(0.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219(0.740)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280(0.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.8062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.948\u0026ndash;1.973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ers4953361\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e163(0.551)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e158(0.449)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(0.449)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e194(0.551)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.6674\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0098\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.505\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.103\u0026ndash;2.053\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers13412887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60(0.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51(0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236(0.797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e301(0.855)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.995\u0026ndash;2.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: OR: odds ratio; CI: confidence interval. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 means statistically significant.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe genotype frequencies of the HIF2\u0026alpha; polymorphisms rs1867783, rs4953361, rs3768728, and rs13412887 in the women with and without infertility are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The genotype frequency distribution of rs4953361 between the two groups was significantly different (p\u0026thinsp;=\u0026thinsp;0.0335). The frequency of the minor allele (homozygous GG) of rs4953361 in women with infertility was significantly lower than that in women without infertility (19.6% vs. 31.3%, p\u0026thinsp;=\u0026thinsp;0.0171, OR\u0026thinsp;=\u0026thinsp;0.536, 95% CI 0.320\u0026ndash;0.898), indicating that the GG genotype of rs4953361 was a protective genotype for female infertility.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenotype frequency distribution analysis between women with and without infertility.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInfertility\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePearson\u0026apos;s p\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1867783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106(0.716)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136(0.773)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42(0.284)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36(0.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ers4953361\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e29(0.196)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e55(0.313)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(0.297)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e37(0.210)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e75(0.507)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e84(0.477)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.793\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0335\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3768728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117(0.791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151(0.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30(0.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21(0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.3217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers13412887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46(0.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47(0.267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95(0.642)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127(0.722)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.0189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: OR: odds ratio. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 means statistically significant.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparative analysis of the frequency of minor allele homozygous and other genotypes between women with and without infertility.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInfertility\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePearson\u0026apos;s p\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1867783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148(1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172(0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4145836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAG\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145(0.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176(1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.6009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2346176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135(0.912)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169(0.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13(0.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7(0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.167\u0026ndash;1.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ers4953361\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e29(0.196)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e55(0.313)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u0026thinsp;+\u0026thinsp;AA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e119(0.804)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e121(0.688)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.687\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0171\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.536\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.320\u0026ndash;0.898\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ers13412887\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u0026thinsp;+\u0026thinsp;CG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e141(0.953)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e174(0.989)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e7(0.047)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2(0.011)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.8439\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0499\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.232\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u0026ndash;1.132\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11900910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125(0.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161(0.915)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23(0.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15(0.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.254\u0026ndash;1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7571218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20(0.135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13(0.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u0026thinsp;+\u0026thinsp;AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128(0.865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163(0.926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.939\u0026ndash;4.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: OR: odds ratio; CI: confidence interval. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 means statistically significant.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eHaplotype analysis of the HIF2\u0026alpha; polymorphisms identified that rs11675232, rs11692911, and rs4953361 constituted block 5 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The haplotype frequency of TGG in women with infertility was significantly lower than that in women without infertility (44.8% vs. 54.2%, p\u0026thinsp;=\u0026thinsp;0.0171). In contrast, the haplotype frequency of TGA in women with infertility was significantly higher than that in women without infertility (36% vs. 26.5%, p\u0026thinsp;=\u0026thinsp;0.0093). These data suggested that rs4953361 was the most important polymorphism associated with female infertility.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHaplotype analysis between women with and without infertility.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlock\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHaplotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFreq.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCase, Control Ratio Counts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCase,Control Frequencies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.499\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e132.5 : 163.5, 190.6 : 161.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.448, 0.542\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.691\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0171\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.308\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e106.5 : 189.5, 93.3 : 258.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.360, 0.265\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0093\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.8 : 247.2, 58.7 : 293.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.165, 0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.7 : 289.3, 5.0 : 347.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023, 0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eHaplotype analysis revealed two risk haplotypes for women infertility. rs11675232, rs11692911, and rs4953361 constitute block 5, and the distribution of haplotype TGG and TGA between the two groups is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eIn the subgroup analysis of the different genotypes at rs4953361, we found that women with the AA and AG genotypes had a significantly higher BMI than that of women with the GG genotype (23.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16 and 23.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87 vs. 22.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09, p\u0026thinsp;=\u0026thinsp;0.0295). Women with the AA genotype had significantly higher plasma glucose levels at 120 min after OGTT than the levels of women with the GG genotype. Women with the AA genotype had significantly higher plasma insulin levels at 60 min and 120 min after OGTT than the levels of women with the GG genotype (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). These results indicated that the rs4953361 polymorphism was associated with obesity-induced impaired glucose and insulin tolerance.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we hypothesized that HIF2α polymorphisms might be involved in the pathogenesis of female infertility. We examined 29 HIF2α SNPs in women with and without infertility, and assessed the association of the allele, genotype and haplotype frequencies with the risk of female infertility and glucose metabolism. To our knowledge, this is the first study to report an association between HIF2α polymorphisms and female infertility and impaired glucose metabolism.\u003c/p\u003e \u003cp\u003eHere, we observed that elevated BMI, impaired glucose metabolism, and increased levels of plasma insulin were metabolic traits that were characteristic of infertile women. The increasing obesity epidemic, with its related metabolic disorders, is associated with an increased risk of many female reproductive conditions(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Obesity negatively affects female reproductive health, including increased risks of menstrual dysfunction, anovulation, and other fertility problems(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Reduced fertility in women who are overweight might be related to multiple endocrine, adipokine, and metabolic alterations that affect follicle growth, embryo development and implantation. Some previous studies have indicated that obese females had lower levels of estrogen(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Another recent study found that female obesity impaired in vitro fertilization outcome without affecting embryo quality(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The etiologies of obesity-related infertility remain unclear; however, one common underlying feature associated with obesity is hyperinsulinemia or insulin resistance (IR). Plasma levels of insulin are correlated with BMI, and insulin can regulate steroidogenesis in ovarian cells in vitro and in the stromal and follicular compartments of ovaries(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Xu et al. found elevated plasma insulin levels, along with increased glucose and insulin pathway dysfunction in overweight/obese women, which were associated with reduced follicle-stimulating hormone receptor (FSHR) expression, estrogen synthesis-related genes, and decreased estrogen production(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the analyzed HIF2α polymorphisms, among the 29 SNPs, the only significant association detected was between rs4953361 and female infertility. Not only were the allele and genotype distribution frequencies of rs4953361 significantly associated with female infertility, but the distribution of haplotypes containing this locus was also significantly associated with female infertility. These results suggested that rs4953361 of HIF2α was the most important polymorphism associated with female infertility. HIF has been postulated to be involved in follicle development due to the hypoxic microenvironment in follicles(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, it has become clear that expression of HIF1α is regulated by pituitary hormones instead of hypoxia in follicles(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). A previous study showed that the expression of HIF1α is primarily in the uterine luminal epithelium during the peri-implantation period. Interestingly, HIF2α is strongly expressed in the uterine stroma during peri-implantation and after embryo attachment(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). To explore the functional roles of HIF2α in embryo implantation, Matsumoto et al. generated mice with uterine tissue-specific deletion of HIF1α and HIF2α. They found that mice with HIF2α deletion in the entire uterus were infertile, whereas mice with uterine deletion of HIF1α showed subfertility, indicating the importance of HIF2α in fertility. Uterine HIF2α contributes to successful implantation regardless of decidualization and the position of embryo attachment(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). As an intron mutation, the mechanism of how the rs4953361 polymorphism affects female infertility still needs to be clarified in future studies.\u003c/p\u003e \u003cp\u003eAs an additional insight, we compared the BMI, glucose tolerance, and insulin release among different genotypes of rs4953361. We found that compared with women with the GG genotype, women with the AA genotype had a significantly higher BMI, two-hour postload plasma glucose, and one- to two-hour postload plasma insulin levels. These results indicated that the rs4953361 mutation on HIF2α might be associated with obesity and its related impaired glucose and insulin tolerance. The obesity-associated adipose tissue hypoxia response is largely mediated by HIFs. Adipocyte-specific HIF2α knockout exacerbated high-fat diet-induced inflammation and insulin resistance(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Adipocyte HIF2α is protective against maladaptation to obesity and metabolic dysregulation by promoting angiogenesis in both white adipose tissue and brown adipose tissue and by counteracting obesity-mediated brown adipose tissue dysfunction(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Macrophage HIF2α has been shown to counteract proinflammatory responses to relieve obesity-induced insulin resistance in adipose tissue(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Taken together, these studies indicate that HIF2α is an important regulator of obesity and insulin resistance.\u003c/p\u003e \u003cp\u003eThere are some limitations of this study that should be mentioned. First, we may not have enough power to detect potential association between the other 27 tag SNPs of HIF2α and female infertility for the limited sample size, or differences in genetic effect sizes. Second, the population included in this study is a single center in western China, the association between the rs4953361 polymorphism of HIF2α and female infertility and metabolic disorder still needs to be validated in other populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our data point to a possible association of the HIF2α polymorphism (rs4953361) with infertility in Chinese women. This finding clearly needs to be replicated in a larger and independent sample and in different populations. An association between HIF2α polymorphism and female infertility and obesity-associated glucose and insulin disorder would strongly support the hypothesis that HIF2α is an important regulator of obesity-related infertility. The mechanism of the effect of the HIF2α polymorphism rs4953361 on female infertility still needs to be clarified in future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Human Research Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. A signed informed consent form was obtained from each participant. Clinical data and peripheral blood samples were collected only after explaining the objectives of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consents for publication were obtained from all of the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the\u0026ensp;National\u0026ensp;Natural\u0026ensp;Science\u0026ensp;Foundation\u0026ensp;of\u0026ensp;China Youth Program (Grant\u0026ensp;No.81900733) in design of the study, the Chongqing municipal health and Family Planning Commission Fund (Number: 2021MSXM013) in collection of data, Bethune Charity Foundation (G-X-2020-1107-14) in interpretation of data, and\u0026nbsp;the National Key Clinical Specialties Construction Program of China (2011) in writing and publication the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaoya Zheng designed and wrote the manuscript; Jiani Ma and Min Hu collected the data; Jian Long and Qiang Wei analyzed the data; Wei Ren is the design consultant. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e Inhorn MC, Patrizio P. Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Hum Reprod Update. 2015;21(4):411-26.\u003c/li\u003e\n \u003cli\u003e Chu KY, Patel P, Ramasamy R. Consideration of gender differences in infertility evaluation. Curr Opin Urol. 2019;29(3):267-71.\u003c/li\u003e\n \u003cli\u003e Beke A. Genetic Causes of Female Infertility. Exp Suppl. 2019;111:367-83.\u003c/li\u003e\n \u003cli\u003e Rull K, Grigorova M, Ehrenberg A, Vaas P, Sekavin A, Nommemees D, et al. FSHB -211 G\u0026gt;T is a major genetic modulator of reproductive physiology and health in childbearing age women. Hum Reprod. 2018;33(5):954-66.\u003c/li\u003e\n \u003cli\u003e Andre GM, Barbosa CP, Teles JS, Vilarino FL, Christofolini DM, Bianco B. Analysis of FOXP3 polymorphisms in infertile women with and without endometriosis. Fertil Steril. 2011;95(7):2223-7.\u003c/li\u003e\n \u003cli\u003e Choudhry H, Harris AL. Advances in Hypoxia-Inducible Factor Biology. Cell Metab. 2018;27(2):281-98.\u003c/li\u003e\n \u003cli\u003e Majmundar AJ, Wong WJ, Simon MC. Hypoxia-inducible factors and the response to hypoxic stress. Mol Cell. 2010;40(2):294-309.\u003c/li\u003e\n \u003cli\u003e Daikoku T, Matsumoto H, Gupta RA, Das SK, Gassmann M, DuBois RN, et al. Expression of hypoxia-inducible factors in the peri-implantation mouse uterus is regulated in a cell-specific and ovarian steroid hormone-dependent manner. Evidence for differential function of HIFs during early pregnancy. J Biol Chem. 2003;278(9):7683-91.\u003c/li\u003e\n \u003cli\u003e Matsumoto L, Hirota Y, Saito-Fujita T, Takeda N, Tanaka T, Hiraoka T, et al. HIF2alpha in the uterine stroma permits embryo invasion and luminal epithelium detachment. J Clin Invest. 2018;128(7):3186-97.\u003c/li\u003e\n \u003cli\u003e Zheng X, Ren W, Zhang S, Liu J, Li S, Li J, et al. Serum levels of proamylin and amylin in normal subjects and patients with impaired glucose regulation and type 2 diabetes mellitus. Acta Diabetol. 2010;47(3):265-70.\u003c/li\u003e\n \u003cli\u003e Zheng X, Ren W, Zhang S, Liu J, Li S, Li J, et al. Association of type 2 diabetes susceptibility genes (TCF7L2, SLC30A8, PCSK1 and PCSK2) and proinsulin conversion in a Chinese population. Mol Biol Rep. 2012;39(1):17-23.\u003c/li\u003e\n \u003cli\u003e Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21(2):263-5.\u003c/li\u003e\n \u003cli\u003e Venkatesh SS, Ferreira T, Benonisdottir S, Rahmioglu N, Becker CM, Granne I, et al. Obesity and risk of female reproductive conditions: A Mendelian randomisation study. PLoS Med. 2022;19(2):e1003679.\u003c/li\u003e\n \u003cli\u003e van der Steeg JW, Steures P, Eijkemans MJ, Habbema JD, Hompes PG, Burggraaff JM, et al. Obesity affects spontaneous pregnancy chances in subfertile, ovulatory women. Hum Reprod. 2008;23(2):324-8.\u003c/li\u003e\n \u003cli\u003e Wise LA, Rothman KJ, Mikkelsen EM, Sorensen HT, Riis A, Hatch EE. An internet-based prospective study of body size and time-to-pregnancy. Hum Reprod. 2010;25(1):253-64.\u003c/li\u003e\n \u003cli\u003e Caillon H, Freour T, Bach-Ngohou K, Colombel A, Denis MG, Barriere P, et al. Effects of female increased body mass index on in vitro fertilization cycles outcome. Obes Res Clin Pract. 2015;9(4):382-8.\u003c/li\u003e\n \u003cli\u003e Bellver J, Ayllon Y, Ferrando M, Melo M, Goyri E, Pellicer A, et al. Female obesity impairs in vitro fertilization outcome without affecting embryo quality. Fertility and Sterility. 2010;93(2):447-54.\u003c/li\u003e\n \u003cli\u003e Mukherjee D, Majumder S, Roy Moulik S, Pal P, Gupta S, Guha P, et al. Membrane receptor cross talk in gonadotropin-, IGF-I-, and insulin-mediated steroidogenesis in fish ovary: An overview. Gen Comp Endocrinol. 2017;240:10-8.\u003c/li\u003e\n \u003cli\u003e Das D, Arur S. Conserved insulin signaling in the regulation of oocyte growth, development, and maturation. Mol Reprod Dev. 2017;84(6):444-59.\u003c/li\u003e\n \u003cli\u003e Xu P, Huang BY, Zhan JH, Liu MT, Fu Y, Su YQ, et al. Insulin Reduces Reaction of Follicular Granulosa Cells to FSH Stimulation in Women With Obesity-Related Infertility During IVF. J Clin Endocrinol Metab. 2019;104(7):2547-60.\u003c/li\u003e\n \u003cli\u003e Fadhillah, Yoshioka S, Nishimura R, Okuda K. Hypoxia promotes progesterone synthesis during luteinization in bovine granulosa cells. J Reprod Dev. 2014;60(3):194-201.\u003c/li\u003e\n \u003cli\u003e Lee HC, Tsai SJ. Endocrine targets of hypoxia-inducible factors. J Endocrinol. 2017;234(1):R53-R65.\u003c/li\u003e\n \u003cli\u003e Lee YS, Kim JW, Osborne O, Oh DY, Sasik R, Schenk S, et al. Increased adipocyte O2 consumption triggers HIF-1alpha, causing inflammation and insulin resistance in obesity. Cell. 2014;157(6):1339-52.\u003c/li\u003e\n \u003cli\u003e Garcia-Martin R, Alexaki VI, Qin N, Rubin de Celis MF, Economopoulou M, Ziogas A, et al. Adipocyte-Specific Hypoxia-Inducible Factor 2alpha Deficiency Exacerbates Obesity-Induced Brown Adipose Tissue Dysfunction and Metabolic Dysregulation. Mol Cell Biol. 2016;36(3):376-93.\u003c/li\u003e\n \u003cli\u003e Choe SS, Shin KC, Ka S, Lee YK, Chun JS, Kim JB. Macrophage HIF-2alpha ameliorates adipose tissue inflammation and insulin resistance in obesity. Diabetes. 2014;63(10):3359-71.\u003c/li\u003e\n \u003cli\u003e Aouadi M. HIF-2alpha blows out the flames of adipose tissue macrophages to keep obesity in a safe zone. Diabetes. 2014;63(10):3169-71.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Infertility, HIF2α gene, polymorphism, obesity, insulin","lastPublishedDoi":"10.21203/rs.3.rs-1467179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1467179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To evaluate HIF2α polymorphisms and glucose metabolism in a group of women with and without infertility. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDesign: \u003c/strong\u003eCase–control study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSetting: \u003c/strong\u003eFemale infertility patients who visited this reproductive center and age-matched females without infertility who underwent routine health examinations from January 2020 to December 2021.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePatients: \u003c/strong\u003eThe study group consisted of 148 women with infertility and 176 women without infertility as healthy controls.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIntervention: \u003c/strong\u003eWe\u003cstrong\u003e \u003c/strong\u003egenotyped\u003cstrong\u003e \u003c/strong\u003e29\u003cstrong\u003e \u003c/strong\u003esingle nucleotide\u003cstrong\u003e \u003c/strong\u003epolymorphisms (SNPs) of HIF2α by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based genotyping technology. The genetic associations were analyzed statistically.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMain Outcome Measures:\u003c/strong\u003e Allele frequency, genotype distribution and haplotype analysis of the HIF2α polymorphisms. BMI, glucose metabolism and insulin release were also measured. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eInfertile\u003cstrong\u003e \u003c/strong\u003ewomen\u003cstrong\u003e \u003c/strong\u003ehad\u003cstrong\u003e \u003c/strong\u003eelevated BMI, impaired glucose metabolism, and increased levels of plasma insulin compared to those of healthy women. SNP analysis of HIF2α revealed that the allele and genotype frequencies of rs4953361 were significantly associated with female infertility. Haplotype analysis of HIF2α polymorphism identified haplotypes TGG and TGA as being associated with female infertility. Women with the AA genotype of rs4953361 had a significantly higher BMI and postload plasma glucose and insulin levels than those of women with the GG genotype.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis is the first study to report an association between HIF2α polymorphisms and female infertility and glucose and insulin metabolic disorder. These results require replication in larger populations but suggest that the HIF2α polymorphisms may play an important role in female obesity-related infertility. \u003c/p\u003e\u003cp\u003e\tIn this observational study, we did not report the results of a health care intervention on human participants. The study was approved by the Human Research Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. Clinical data and peripheral blood samples were collected only after explaining the objectives of the study and obtaining a signed informed consent form.\u003c/p\u003e","manuscriptTitle":"The HIF2α polymorphism rs4953361 is associated with impaired glucose metabolism in Han Chinese women with infertility","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-29 13:57:47","doi":"10.21203/rs.3.rs-1467179/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b6e305e-6b54-4f44-b49c-79d07f47b0fa","owner":[],"postedDate":"March 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-03-29T14:02:48+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-29 13:57:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1467179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1467179","identity":"rs-1467179","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}