Association between hysterectomy status and hypertension: Results from NHANES 2007– 2018 and two-sample Mendelian randomization study

Association between hysterectomy status and hypertension: Results from NHANES 2007– 2018 and two-sample Mendelian randomization study | 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 Association between hysterectomy status and hypertension: Results from NHANES 2007– 2018 and two-sample Mendelian randomization study Weiren Yan, Jiahui Wang, Ke Xu, Xianglin Liu, Xinsheng Li, Haichen Lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4869562/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 Background Previous observational studies have shown an association between hysterectomy status and the risk of developing hypertension, but the exact relationship between the two is unclear. The aim of our study was to conduct an observational analysis of this relationship and to determine the causality of that relationship through a Mendelian randomization (MR) study. Methods This study included 12,628 participants from the 2007–2018 National Health and Nutrition Examination Survey and used weighted logistic regression to analyze the association between hysterectomy status and the risk of developing hypertension, followed by a subgroup analysis and restricted cubic splines (RCS) to further explore the associations. A two-sample MR study was conducted to determine the causal relationships between hysterectomy status and the risk of developing hypertension and increased systolic and diastolic blood pressure. Results Adjusted-weighted logistic regression revealed a significant association between hysterectomy status and the risk of developing hypertension (OR = 1.26, 95% CI: 1.04-1.52). Both subgroup and RCS analyses revealed that a younger age at hysterectomy was associated with a greater risk of hypertension. MR showed a causal association between genetically predicted hysterectomy status and the risk of developing hypertension (OR=1.205, 95% CI: 1.043-1.392), increased systolic blood pressure (beta =9.642, 95% CI: 2.125-17.159) and increased diastolic blood pressure (beta =6.695, 95% CI: 2.173-11.217). Conclusions Our study revealed that hysterectomy increases the risk of hypertension. Moreover, hysterectomy at an early age is associated with an increased prevalence of hypertension. Therefore, there is an urgent need to manage and monitor blood pressure in post hysterectomy patients. Hysterectomy Hypertension Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Introduction Hysterectomy accounts for a common surgical procedure performed on women, which ranks the second place among obstetric and gynecological surgeries with regard to its frequency, only second to cesarean section [1,2]. Although the increase in nonsurgical treatments for gynecologic conditions and uterus-preserving surgeries has gradually reduced the annual hysterectomy number carried out in recent years, there are still over 600,000 hysterectomy procedures being performed annually in the USA [3,4]. Most hysterectomy procedures are performed due to benign indications. The representative benign indications are menorrhagia, fibroids, endometriosis, and uterine prolapse [5]. Hysterectomy can lead to numerous complications including damage to the genitourinary and gastrointestinal systems, bleeding and rupture of the vaginal cuff [4,6]. In addition, many studies have found that post-hysterectomy patients are prone to ovarian hypoplasia and have a significantly higher colorectal cancer and cardiovascular disease risk [7,8]. Therefore, routine disease prevention for post-hysterectomy patients has begun to be emphasized in recent years. Hypertension shows the highest prevalence among chronic disorders worldwide. During 1990-2019, the number of hypertensive population with the age of 30-79 years increased by two folds from 317 million males and 331 million females in 1990 to 652 million males and 626 million females in 2019 [9]. As shown by National Health and Nutrition Examination Study (NHANES) data, hypertension prevalence in U.S. adults older than 18 years was 45.4% in 2017-2018, which elevated with age [10]. Similarly, among developing countries, such as China, where approximately 240 million people had inadequate blood pressure control in 2018, the hypertension treatment and control rates remain poor [11]. Hypertension serves as the major risk factor related to stroke, ischemic heart disease, chronic kidney disease and additional cardiovascular diseases (CVDs) [12]. Compared to that of men, women’s blood pressure increases more rapidly after the age of 30, resulting in a higher prevalence of hypertension in older females relative to age-matched males [13,14]. Pathogenesis of hypertension among women may be closely related to obesity, gynecologic disorders, reduced physiologic levels of estrogen, increased sodium sensitivity, and inflammatory diseases [15,16,17,18]. In recent years, many studies have shown a higher CVD risk among females undergoing hysterectomy. According to one retrospective study in Nurses’ Health Study II, hysterectomy status is related to the higher likelihood of CVD and coronary revascularization [19]. Michelsen et al. conducted one cohort study during the 18-year period and reported that hysterectomy status, but not bilateral salpingo-oophorectomy status, was linked with a higher all-cause and cardiovascular mortality risk [20]. It represents the main risk factor related to CVD, and has been suggested that a hypertension history probably explains relations between hysterectomy status and CVD risk [21]; however, not all studies have found an relation of hysterectomy status with hypertension risk, and relation of hysterectomy status with hypertension risk is still controversial. A prospective study revealed that after correcting for potential confounders, the hypertension risk elevates among women who had received hysterectomy previously [22]. Similarly, according to Ding et al., women undergoing hysterectomy showed an increased hypertension risk during follow-up [23]. However, in several other observational and prospective studies, hysterectomy is not related to the higher hypertension risk [24,25]. NHANES represents the persistent cross-sectional study performed by National Center for Health Statistics (NCHS) for assessing nutrition status among U.S. people and emerging public health conditions. Mendelian randomization (MR) accounts for the popular analytical technology for testing causality [26]. MR uses instrumental variables (IVs), including single nucleotide polymorphisms (SNPs), for assessing causal relationships of exposures with outcomes [27,28]. MR analysis results are not affected by reverse causality and residual confounding bias because of random assignment of genetic variation in meiosis, independent of environmental factors [29]. The present observational study was conducted for validating the relation of hysterectomy status with hypertension risk according to NHANES 2007-2018 data. In addition, the two-sample MR analysis was performed for assessing if causal relation of hysterectomy status with hypertension risk exists. Materials and methods Study population in NHANES This study included data from six continuous cycles of the NHANES database (2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, 2017–2018). We included 17,907 female participants aged ≥ 20 years with the following exclusion criteria: (1) Being pregnant (n=274), (2) Lack of informative data on diagnosis of hypertension (n=3), (3) insufficient hysterectomy data (n=2646), (4) lack of necessary demographic information and health-related data (n=2356). Finally, 12,628 participants with complete NHANES data were included in this work. Fig. 1 displays the participant selection procedure. Exposure and Outcomes Information about hysterectomy status was obtained in the form of a questionnaire. Hysterectomy status was defined using self-reported history by asking the following question in the reproductive health section:“Have you had a hysterectomy?”In line with American Heart Association/American College of Cardiology (AHA/ACC) 2017 guidelines and previous studies [30,31], The diagnosis of hypertension was determined by a questionnaire about hypertension and the average of four consecutive blood pressure measurements. Participants were diagnosed with hypertension if they had self-reported hypertension, DBP ≥ 90 mmHg, SBP ≥ 140 mmHg, or previous or current medication for hypertension. Covariates Covariates in the present work included demographic features (age, education, race, marital status, body mass index (BMI), poverty/income ratio (PIR), drinking and smoking), medical history (diabetes, oophorectomy, hormone therapy), laboratory tests (total cholesterol (TC), total glyceride (TG), high-density lipoprotein cholesterol (HDL), uric acid (UA), creatinine (Cr), glycosylated hemoglobin (GHb)) and dietary data (sodium intake). PIR was computed through the division of family (or individual) income by poverty criteria applicable to survey year, with the greater PIR indicating superior family income status. The PIR for the not poor was defined as ≥ 1, and for the poor, it was defined as < 1. The diagnosis of diabetes was made according to a self-reported diabetes history, glycosylated hemoglobin ≥ 6.5%, fasting blood glucose ≥ 126 mg/dl, insulin or antihyperglycemic agents medication [32]. Dietary sodium intake was defined as mean sodium intake of two 24-h recalls. Statistical analysis for NHANES analysis When performing the NHANES analyses, sampling weights provided by NHANES were utilized for weighting. Continuous variables were represented by mean±standard deviation (SD), whereas categorical variables were represented by numbers (percentages). Relationship of hysterectomy status with hypertension risk was evaluated using multivariate logistic regression models. Model 1 was not adjusted for all covariates. Model 2 was adjusted for age, race, educational level, marital status, PIR. In Model 3, BMI, smoking, alcohol, diabetes, oophorectomy, hormone therapy, levels of TC, HDL, TG, UA, Cr, GHb and sodium intake was adjusted based on Model 2. For determining whether the relation of hysterectomy status with hypertension risk was different among different subgroups, we carried out the independent stratification. Additionally, we utilized Wald test for calculating p -value for interaction. The restricted cubic splines (RCS) analysis was constructed for exploring association nonlinearity and depicting the overall trends. R version 4.1.3 was adopted in statistical analysis. p -value < 0.05 (two-sided) revealed significant difference. Assumptions and data sources of two‑sample MR Hysterectomy exposure data were obtained based on MRC Integrated Epidemiology Unit (MRC-IEU) Consortium, involving 462,933 Europeans (46,411 patients and 416,522 control participants) in total (ID:ukb-b-3700). Summary data of hypertension were acquired based on a large UK Biobank-based GWAS [33]. Researchers used ICD-10 codes, UK Biobank self-reported disease diagnosis to identify individuals with a history of hypertension. Through selection, this GWAS enrolled 129,909 cases and 354,689 controls in total (ID:ebi-a-GCST90038604). In addition, we also included data on SBP (ID:ieu-b-38) and DBP (ID:ieu-b-39) as outcomes, which were acquired based on the meta-analysis of GWAS involving 757,601 subjects from UK Biobank and International Council on Blood Pressure (ICBP) associations [34]. We obtained all the above GWAS data from Integrated Epidemiology Unit (IEU) OpenGWAS database (https://gwas. mrcieu. ac. uk/). Data download details can be observed from Additional file 1: Table S1. Selection of genetic instruments IVs used for MR analysis must satisfy three requirements: (1) the IVs should be convincingly correlated to hysterectomy; (2) IVs are not related to all confounders for exposure-outcome relation; and (3) are just related to outcomes via the interested exposure rather than via additional routes. To identify genetic variants for the causality of hysterectomy status with risk of developing several outcomes (hypertension, increased SBP, and DBP), we set genome-wide significance level as p < 5 × 10 -8 for screening for genetic variants closely related to exposure. Afterward, the linkage disequilibrium (LD) clumping test was conducted for identifying independent SNP (r 2 < 0.001; 10,000 kb). After excluding palindrome SNPs, effect alleles were coordinated in outcome and exposure datasets. We then examined SNPs via LDtrait Tool (https://ldlink.nih.gov/?tab=ldtrait) for removing those closely related to potential confounders such as BMI, waist circumference, diabetes, dyslipidemia [15, 35]. Subsequently, to further assess the strength of each IV, F-statistic was determined for the IVs in the exposure and excluded SNPs with F < 10 to ensure that the IVs had adequate validity and instrumental strength [36]. Statistical analysis for two‑sample MR In two-sample MR study, random-effects inverse-variance weighting (IVW) was used to be the primary method for assessing causal relationships of genetically predicted hysterectomy status with hypertension risk and increased SBP, and DBP. Fixed-effects estimates are not appropriate when there is excessive result heterogeneity; consequently, random-effects IVW model was adopted in our analysis, accounting for heterogeneity when assessing causality [37]. In addition, we used four complementary MR analysis approaches, such as simple mode, MR‒Egger, weighted mode, and weighted median approaches, to validate IVW results. MR‒Egger regression intercept was adopted for assessing potential horizontal multiplicity effects. Horizontal pleiotropy was considered not to be present at p - value >0.05 [36]. Cochrane's Q test was utilized for testing for possible SNP heterogeneity. Analyses with p -values > 0.05 did not reveal any obvious heterogeneity. Heterogeneity does not invalidate causality estimates in MR analyses because the random effects IVW approach balances potential total heterogeneity to some extent [38]. We also performed MR pleiotropy residual sum and outlier (MR-PRESSO) test in determining existence of outlier IVs. In addition, residual sum of squares accounts for a heterogeneity measure, which equals Cochran's Q statistic. In the presence of outlier IVs, we utilized the MR-PRESSO outlier-corrected test for obtaining corrected causal effects through removing outliers, and then assessed distortion of causal estimates prior to and following removing outliers with MR-PRESSO distortion test. Typically, MR-PRESSO test is useful if few genetic variants had heterogeneous ratio estimates, because they were excluded and therefore did not impact total estimate [39, 40]. Additionally, leave one-out analyses were carried out for assessing whether single-sensitive SNP affected IVW test. The results were also validated using funnel plots and scatter plots for further validation. Two-sample MR package (version 0.5.6) in R (version 4.1.3) was employed for analysis. Fig. 1 shows the research flowchart in the present study. Results Baseline characteristics of the study population Through selection, altogether 12628 subjects were enrolled into the present work. A total of 2858 participants underwent hysterectomy, and 5410 were diagnosed with hypertension. Table 1 presents study participant features according to hysterectomy category. Hysterectomy showed a higher prevalence in elderly non-Hispanic White females having decreased educational level and higher income. Additionally, compared to patients who did not undergo hysterectomy, these patients were less likely to drink alcohol but were more likely to have obesity, to smoke, to have diabetes, to undergo oophorectomy and to receive hormone therapy. Furthermore, as expected, they had significantly higher TC, TG, UA, Cr, and GHb values, while their sodium intake was lower. Relation of hysterectomy status with hypertension risk Table 2 displays the relation of hysterectomy status with hypertension risk. Hysterectomy status was positively related to hypertension risk in weighted logistic regression model following multivariate regression (OR = 1.26, 95% CI 1.04-1.52, p =0.017). Fig. 2 displays subgroup analysis with multivariate-controlled weighted logistic regression. Based on subgroup analysis, hysterectomy status was significantly positively associated with an increased hypertension risk among participants aged 20-40 years, non-Hispanic White individuals, individuals of other races, individuals with a BMI ≥ 23.9, individuals who consumed alcohol, individuals with diabetes, individuals without diabetes, individuals not undergoing oophorectomy and individuals receiving hormone therapy. An interaction of hysterectomy status with age could be obtained from subgroup analysis ( p for interaction = 0.043) related to the higher hypertension risk. RCS analysis suggested that age at hysterectomy was linearly and negatively related to hypertension when every covariate was adjusted for (Additional file 1: Fig. S1). Two ‑ sample MR analysis After comprehensive screening, 28 SNPs related to hysterectomy were enrolled for our MR study. F-statistic for every IV was > 10 (critical value), representing no weak instrumental bias. Additional file 1:Table S2 displays more details of such SNPs. Based on such findings, genetically predicted hysterectomy status showed causal relation with hypertension risk (IVW: OR=1.205, 95% CI: 1.043-1.392, p =0.011), and similarly, hysterectomy status has a potentially positive causal relationship with increased SBP (IVW: Beta=9.642, 95% CI: 2.125-17.159, p =0.011) and DBP (IVW: Beta=6.695, 95% CI: 2.173-11.217, p =0.003) (Fig. 3). MR-Egger intercept tests revealed the absence of pleiotropy (Additional file 1:Table S3). Obvious heterogeneity could be measured by Cochran's Q test ( p <0.001) for these above three causal relationships. Nonetheless, causal effect remained after eliminating outliers by the MR-PRESSO test (hypertension, outlier-corrected: p =0.004, distortion test: p =0.885; SBP, outlier-corrected: p =0.005, distortion test: p =0.662; DBP, outlier-corrected: p =0.002, distortion test: p =0.144) (Additional file 1: Table S3). Moreover, MR-Egger regression analysis and weighted medians of three causal associations directionally conformed to the IVW method, suggesting that these three associations were reliable. Sensitivity analyses on IVW results by leave one-out approach all revealed that eliminating one SNP each time made no difference to results for specific SNPs, and did not indicate potent SNPs (Additional file 1: Fig. S2). Scatter plots showing causal relationships and funnel plots can be observed from Additional file 1: Fig. S3-4. Discussion We investigated the relationship between hysterectomy status and hypertension risk. We selected 12,628 female participants from six consecutive 2-year cycles of the NHANES, and after adjusting for relevant confounders, hysterectomy status was related to a higher hypertension prevalence. Additionally, the two-sample MR analysis revealed potential causal associations of hysterectomy status with hypertension risk and increased SBP and DBP. As we know, the present study is the first that assesses causal relationship of hysterectomy status with hypertension risk. Relationship between hysterectomy status and hypertension risk is still inconsistent. According to one prospective study, there was no significant difference in SBP or DBP before or after surgery in middle-aged women who opted for hysterectomy [24]. Similarly, Appaih et al. reported that hysterectomy status was not related to other risk factors related to CVD, such as SBP, following a 25-year follow-up cycle [25]. However, according to one large cohort study that involved more than 50,000 subjects and one meta-analysis including 14 observational cohort studies, hysterectomy status was markedly related to the higher hypertension risk [22,42]. Based on our findings, hysterectomy status was positively related to the prevalence of hypertension, and this association persisted after adjusting for possible confounders. In addition, age was associated with hysterectomy, with a more significant relation of hysterectomy status with hypertension risk for younger participants. Similarly, the results of RCS showed the nonlinear negative relationship of age at hysterectomy with hypertension risk. According to the above results, hysterectomy may probably be the risk factor related to hypertension among younger women, which is not mentioned previously. Apart from exploring the relation of hysterectomy status with hypertension risk, two-sample MR method was adopted for investigating causality of hysterectomy status with hypertension risk, higher SBP, and DBP. Our results showed a potential positive causality of genetically predicted hysterectomy status with hypertension risk and SBP and DBP, suggesting that hysterectomy is the contributor for hypertension risk. Mechanisms underlying the relationship between hysterectomy status and the risk of developing hypertension are still unknown. One hypothesis may be that hysterectomy affects the blood flow of ovary to ovarian ligament, resulting in premature ovarian failure as well as the early onset of menopause, thereby causing the decrease of endogenous hormone level, and promoting atherosclerosis occurrence [43,44]. The findings of the present study verified this hypothesis to some extent. First, we found that hysterectomy status was markedly related to hypertension risk among female patients with the age of 20-40 years, whereas other age groups did not exhibit any obvious association. In addition, age at hysterectomy showed linear and negative relation to hypertension occurrence, possibly because hysterectomy leads to premature menopause, thus accelerating the onset of hypertension [45]. Similarly, we found that hysterectomy status was not significantly related to hypertension risk among participants on hormone therapy, whereas in contrast, hysterectomy status was found as the risk factor related to hypertension among women not on hormone therapy, which indirectly demonstrates the protective effect of sex hormones against hypertension. In addition, obesity has been suggested as another possible explanation for the relation of hysterectomy status with hypertension risk. Women undergoing hysterectomy are previously suggested to be associated with obesity and suffer from metabolic diseases [46, 47]. Through subgroup analyses, we also demonstrated that hysterectomy status was associated with hypertension risk among female participants who were overweight rather than in the normal weight range, indirectly validating this hypothesis. Notably, we found that hysterectomy was not associated with the development of hypertension in ovariectomized participants, suggesting that oophorectomy is not related to the higher hypertension risk, conforming to previous results [22, 23]. The main strength in the present study is the combination of observational research with MR analysis. Observational studies used alone are susceptible to unmeasured confounding and reverse causation. MR analysis alone, on the other hand, has a higher false-negative rate, although confounders can be controlled for. Noteworthily, the two methods yielded almost identical findings in this work, making our results reliable. Nonetheless, there are certain limitations in our work. Firstly, NHANES-based hysterectomy information was self-reported, and might induce reporting bias, affecting the study result accuracy. Second, despite including numerous factors in our analytic model, we were unable to control for confounders associated with stroke that were not included in the data sources. Third, for MR analysis, it remains not easy to totally eliminate influences of heterogeneity and possible directional pleiotropy. At last, the results were mainly obtained from American and European adults, which limited our result applicability to additional ethnic groups. More large-scale studies involving additional ethnic populations should be conducted for validating our results. Conclusion Taken together, this observational study suggested that hysterectomy status was strongly associated with hypertension risk. Younger hysterectomy patients may develop hypertension in comparison with older patients. Moreover, MR analysis revealed a potential causal relations of hysterectomy status with hypertension risk and increased SBP and DBP. The mechanisms underlying these associations must be further analyzed. Abbreviations MR Mendelian randomization NHANES National Health and Nutrition Examination Study CVD Cardiovascular disease IVs Instrumental variables SNP Single nucleotide polymorphism GWAS Genome-wide association study RCS Restricted cubic spline IVW Inverse-variance weighting MR-PRESSO MR pleiotropy residual sum and outlier Declarations Acknowledgements The study was based on summary statistics provided by the Integrated Epidemiology Unit (IEU) OpenGWAS database and NHANES database. We thank all investigators and consortium for sharing valuable summary data. Author contributions Weiren Yan contributed to conception and design of the study, acquisition, analysis, and interpretation of data, and drafted the manuscript; Jiahui Wang contributed to data analysis and manuscript revision; Ke Xu, Xianglin Liu, Xinsheng Li and Haichen Lv contributed to study design, data analysis, and drafted the manuscript. All authors gave their final approval and agree to be accountable for all aspects of the work. Funding This research was supported Dalian Science and Technology Innovation Fund [grant number 2023JJ13SN039]. Availability of data and materials The data that support the findings of this study were available from opensource database. Ethics approval Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Clayton RD. Hysterectomy. 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Front Endocrinol (Lausanne) . 2022;13:1035338. doi:10.3389/fendo.2022.1035338 Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol . 2017;46(6):1985-1998. doi:10.1093/ije/dyx102 Chen Y, Li C, Cheng S, et al. The Causal Relationships Between Sleep-related Phenotypes and Body Composition: A Mendelian Randomized Study. J Clin Endocrinol Metab . 2022;107(8):e3463-e3473. doi:10.1210/clinem/dgac234 Zou F, Hu Y, Xu M, Wang S, Wu Z, Deng F. Associations between sex hormones, receptors, binding proteins and inflammatory bowel disease: a Mendelian randomization study. Front Endocrinol . 2024;15:1272746. doi:10.3389/fendo.2024.1272746 Burgess S, Thompson SG. Mendelian randomization: methods for causal inference using genetic variants. Boca Raton: CRC Press (2015). Wang Z, Li X, Zhang D. Impact of hysterectomy on cardiovascular disease and different subtypes: a meta-analysis. Arch Gynecol Obstet . 2022;305(5):1255-1263. doi:10.1007/s00404-021-06240-2 Howard BV, Kuller L, Langer R, et al. Risk of cardiovascular disease by hysterectomy status, with and without oophorectomy: the Women’s Health Initiative Observational Study. Circulation . 2005;111(12):1462-1470. doi:10.1161/01.CIR.0000159344.21672.FD Farquhar CM, Sadler L, Harvey SA, Stewart AW. The association of hysterectomy and menopause: a prospective cohort study. BJOG . 2005;112(7):956-962. doi:10.1111/j.1471-0528.2005.00696.x Zhu D, Chung HF, Dobson AJ, et al. Age at natural menopause and risk of incident cardiovascular disease: a pooled analysis of individual patient data. Lancet Public Health . 2019;4(11):e553-e564. doi:10.1016/S2468-2667(19)30155-0 Mu F, Rich-Edwards J, Rimm EB, Spiegelman D, Forman JP, Missmer SA. Association Between Endometriosis and Hypercholesterolemia or Hypertension. Hypertension . 2017;70(1):59-65. doi:10.1161/HYPERTENSIONAHA.117.09056 Laughlin-Tommaso SK, Khan Z, Weaver AL, Schleck CD, Rocca WA, Stewart EA. Cardiovascular risk factors and diseases in women undergoing hysterectomy with ovarian conservation. Menopause . 2016;23(2):121-128. doi:10.1097/GME.0000000000000506 Tables Table 1 Baseline characteristics of study participants Characteristic Participants p -value Total (n=12,628) Non-hysterectomy (n=9,770) Hysterectomy (n=2,858) Age, years 48.46±16.97 44.87±16.30 61.43±12.43 <0.001 20-40 4280 (35.4%) 4152 (43.7%) 128 (5.1%) 60 3954 (26.6%) 2239 (18.9%) 1715 (54.6%) Race, n (%) <0.001 Mexican American 1828 (7.3%) 1522 (8.1%) 306 (4.2%) Non-Hispanic Black 2628 (11.0%) 1957 (10.9%) 671 (11.2%) Non-Hispanic White 5509 (69.5%) 4065 (67.6%) 1444 (76.2%) Other 2663 (12.2%) 2226 (13.3%) 437 (8.4%) Education, n (%) <0.001 Below high school 2746 (13.8%) 2066 (13.2%) 680 (15.9%) High school graduate 2759 (21.9%) 2024 (20.6%) 735 (26.8%) College or above 7123 (64.3%) 5680 (66.3%) 1443 (57.2%) Married, n (%) 0.534 Yes 6768 (59.7%) 5293 (59.5%) 1475 (60.5%) No 5860 (40.3%) 4477 (40.5%) 1383 (39.5%) PIR, n (%) <0.001 Not poor 9781 (84.9%) 7440 (84.1%) 2341 (88.0%) Poor 2847 (15.1%) 2330 (15.9%) 517 (12.0%) Smoking, n (%) <0.001 Yes 4675 (39.2%) 3439 (37.5%) 1236 (45.4%) No 7953 (60.8%) 6331 (62.5%) 1622 (54.6%) Alcohol, n (%) <0.001 Yes 7444(66.7%) 5923 (68.5%) 1521 (60.1%) No 5184(33.3%) 3847 (31.5%) 1337 (39.9%) Diabetes, n (%) <0.001 Yes 2203 (12.9%) 1396 (10.5%) 807 (21.6%) No 10425(87.1%) 8374 (89.5%) 2051 (78.4%) Oophorectomy, n (%) <0.001 Yes 1540 (12.0%) 36 (0.4%) 1504 (54.3%) No 11088 (88.0%) 9734 (99.6%) 1354 (45.7%) Hormone therapy, n (%) <0.001 Yes 2409 (21.6%) 1017 (12.7%) 1392 (54.2%) No 10219 (78.4%) 8753 (87.3%) 1466 (45.8%) Hypertension, n (%) <0.001 Yes 5410 (37.7%) 3466 (30.9%) 1944 (62.0%) No 7218 (62.3%) 6304 (69.1%) 914 (38.0%) BMI 29.37±7.67 29.12±7.80 30.29±7.07 <0.001 TC, mg/dL 196.45±41.33 193.80±39.87 206.04±44.95 <0.001 HDL, mg/dL 58.62±16.92 58.69±16.98 58.35±16.69 0.418 TG, mg/dL 137.29±133.45 129.11±107.42 166.89±198.48 <0.001 UA, umol/L 287.70±75.26 282.12±71.65 307.90±84.05 <0.001 Cr, umol/L 69.18±27.12 67.67±22.27 74.66±39.57 <0.001 GHb % 5.62±0.88 5.55±0.86 5.84±0.94 <0.001 Sodium intake, mg 3015.34±1391.59 3078.54±1415.45 2786.55±1275.88 <0.001 PIR, poverty impact ratio; BMI, body mass index; TC, total cholesterol; HDL, high-density lipoprotein cholesterol; TG, total glyceride; UA, uric acid; Cr, creatinine; GHb, glycosylated hemoglobin. Table 2 Multivariate regression analysis of the association between hysterectomy status and the risk of developing hypertension Items Hypertension Model 1 OR (95% CI) Model 2 OR (95% CI) Model 3 OR (95% CI) Non-hysterectomy Ref. Ref. Ref. Hysterectomy 3.64 (3.26-4.06) 1.47 (1.287-1.67) 1.26 (1.04-1.52) p value <0.001 <0.001 0.017 Model 1: Unadjusted model. Model 2: Adjusted for age, race, educational level, marital status, PIR. Model 3: Adjusted for BMI, smoking, alcohol, diabetes, oophorectomy, hormone therapy, TC, HDL, TG, UA, Cr, GHb, sodium intake based on model 2. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx STROBEMRchecklistfillable.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4869562","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339172747,"identity":"ba990fc2-c2f4-4b02-96c4-14bcd2c63e76","order_by":0,"name":"Weiren Yan","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weiren","middleName":"","lastName":"Yan","suffix":""},{"id":339172748,"identity":"e102d304-4c9c-43c5-bf5e-bc75f4c3149c","order_by":1,"name":"Jiahui Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Wang","suffix":""},{"id":339172749,"identity":"df40972b-7a28-403f-8d18-0fa7655de1f0","order_by":2,"name":"Ke Xu","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Xu","suffix":""},{"id":339172752,"identity":"444c6179-fdaf-40dc-8f0b-7a3253d3fb84","order_by":3,"name":"Xianglin Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianglin","middleName":"","lastName":"Liu","suffix":""},{"id":339172753,"identity":"a6cb6775-f94e-41dc-bedb-844591180f8e","order_by":4,"name":"Xinsheng Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinsheng","middleName":"","lastName":"Li","suffix":""},{"id":339172758,"identity":"58d3bdcf-d346-422c-bf6f-cbb21a205b59","order_by":5,"name":"Haichen Lv","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACAwbGBmYgLcPA3tj48AMpWngYeA43G0sQp4WBAaJFIr1NgIcYLebshxs/F1Tc4TG4+bCNQYLBTk63gYAWy57EZukZZ57xGNxObHtQwJBsbHaAkMMOJLYx87Yd5pGcndhuIMFwIHEbQS3nHwK1/ANqmXmwTYKHKC03QLY0HObhl2AkWsvDZmmeY0AtPInAQDYgxi/n0x9+5qk5LMfGfvzhww8VdnIEtaCbQJryUTAKRsEoGAU4AAAWNEDcHgYpqwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Haichen","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2024-08-06 15:23:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4869562/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4869562/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64568517,"identity":"080e9c6f-c21b-477f-9a70-fee956143f0f","added_by":"auto","created_at":"2024-09-16 00:41:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall study design based on NHANES analysis and MR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNHANES, National Health and Nutrition Examination Survey; MR, Mendelian randomization; RCS, restricted cubic spline; SNP, single-nucleotide polymorphism; IVs, instrumental variables.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4869562/v1/964148d4c0845129c875d9c2.png"},{"id":64569343,"identity":"14c589b4-0446-4ee3-896b-2659015422f0","added_by":"auto","created_at":"2024-09-16 00:49:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between hysterectomy status and the risk of developing hypertension according to subgroup.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStratification analyses were adjusted for age, race, educational level, marital status, PIR, BMI, smoking, alcohol, diabetes, oophorectomy, hormone therapy, TC, HDL, TG, UA, Cr, GHb, sodium intake except the stratification factor itself.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4869562/v1/0cda26ca28bd523c3d309558.png"},{"id":64568519,"identity":"aefdfa59-0106-4277-9330-3554787d0ced","added_by":"auto","created_at":"2024-09-16 00:41:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of two-sample Mendelian analysis for evaluating the causal associations between hysterectomy status and the risk of developing hypertension and increased SBP and DBP. (A) Forest plot of the effect of hysterectomy status on the risk of developing hypertension. (B) Forest plot of the effect of hysterectomy status on the risk of developing increased SBP. (C) Forest plot of the effect of hysterectomy status on the risk of developing increased DBP.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4869562/v1/52688792978f95be24f501ba.png"},{"id":65198545,"identity":"35e12f9d-de33-4861-9cd6-3c87daff0ff5","added_by":"auto","created_at":"2024-09-24 15:53:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1120609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4869562/v1/e607a887-a6f2-4b13-92bf-49092265ff8d.pdf"},{"id":64568520,"identity":"4258173a-e1f0-458a-bfdf-3e1ad6582309","added_by":"auto","created_at":"2024-09-16 00:41:03","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2065184,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4869562/v1/f133272c98b5a29691249a70.docx"},{"id":64569344,"identity":"f5018201-d899-486c-beca-cf394858d782","added_by":"auto","created_at":"2024-09-16 00:49:03","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":609054,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEMRchecklistfillable.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4869562/v1/5304a911cb5aa9c420aea0bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between hysterectomy status and hypertension: Results from NHANES 2007– 2018 and two-sample Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHysterectomy accounts for a common surgical procedure performed on women, which ranks the second place among obstetric and gynecological surgeries with regard to its frequency, only second to cesarean section [1,2]. Although the increase in nonsurgical treatments for gynecologic conditions and uterus-preserving surgeries has gradually reduced the annual hysterectomy number carried out in recent years, there are still over 600,000 hysterectomy procedures being performed annually in the USA [3,4]. Most hysterectomy procedures are performed due to benign indications. The representative benign indications are menorrhagia, fibroids, endometriosis, and uterine prolapse [5]. Hysterectomy can lead to numerous complications including damage to the genitourinary and gastrointestinal systems, bleeding and rupture of the vaginal cuff [4,6]. In addition, many studies have found that post-hysterectomy patients are prone to ovarian hypoplasia and have a significantly higher colorectal cancer and cardiovascular disease risk [7,8]. Therefore, routine disease prevention for post-hysterectomy patients has begun to be emphasized in recent years.\u003c/p\u003e\n\u003cp\u003eHypertension shows the highest prevalence among chronic disorders worldwide. During 1990-2019, the number of hypertensive population with the age of 30-79 years increased by two folds from 317 million males and 331 million females in 1990 to 652 million males and 626 million females in 2019 [9]. As shown by National Health and Nutrition Examination Study (NHANES) data, hypertension prevalence in U.S. adults older than 18 years was 45.4% in 2017-2018, which elevated with age [10]. Similarly, among developing countries, such as China, where approximately 240 million people had inadequate blood pressure control in 2018, the hypertension treatment and control rates remain poor [11].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypertension serves as the major risk factor related to stroke, ischemic heart disease, chronic kidney disease and additional cardiovascular diseases (CVDs)\u0026nbsp;[12]. Compared to that of men, women\u0026rsquo;s blood pressure increases more rapidly after the age of 30, resulting in a higher prevalence of hypertension in older females relative to age-matched males [13,14]. Pathogenesis of hypertension among women may be closely related to obesity, gynecologic disorders, reduced physiologic levels of estrogen, increased sodium sensitivity, and inflammatory diseases [15,16,17,18].\u003c/p\u003e\n\u003cp\u003eIn recent years, many studies have shown a higher CVD risk among females undergoing hysterectomy. According to one retrospective study in Nurses\u0026rsquo; Health Study II, hysterectomy status is related to the higher likelihood of CVD and coronary revascularization [19]. Michelsen et al. conducted one cohort study during the 18-year period and\u0026nbsp;reported\u0026nbsp;that hysterectomy status, but not bilateral salpingo-oophorectomy status, was linked with a higher all-cause and cardiovascular mortality risk [20]. It represents the main risk factor related to CVD, and has been suggested that a hypertension history probably explains relations between hysterectomy status and CVD risk [21]; however, not all studies have found an relation of hysterectomy status with hypertension risk, and relation of hysterectomy status with hypertension risk is still controversial. A prospective study\u0026nbsp;revealed\u0026nbsp;that after correcting for potential\u0026nbsp;confounders, the hypertension risk elevates among women who had received hysterectomy previously [22]. Similarly, according to Ding et al., women\u0026nbsp;undergoing\u0026nbsp;hysterectomy showed an increased hypertension risk during follow-up [23]. However, in several other observational and prospective studies, hysterectomy is not related to the higher hypertension risk [24,25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNHANES represents the persistent cross-sectional study performed by National Center for Health Statistics (NCHS) for assessing nutrition status among U.S. people and emerging public health conditions. Mendelian randomization (MR) accounts for the popular analytical technology for testing causality [26]. MR uses instrumental variables (IVs), including single nucleotide polymorphisms (SNPs), for assessing causal relationships of exposures with outcomes [27,28]. MR analysis results are not affected by reverse causality and residual confounding bias because of random assignment of genetic variation in meiosis, independent of environmental factors [29].\u003c/p\u003e\n\u003cp\u003eThe present observational study was conducted for validating the relation of hysterectomy status with hypertension risk according to NHANES 2007-2018 data. In addition, the two-sample MR analysis was performed for assessing if causal relation of hysterectomy status with hypertension risk exists.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population in NHANES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included data from six continuous cycles of the NHANES database (2007\u0026ndash;2008, 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014, 2015\u0026ndash;2016, 2017\u0026ndash;2018). We included 17,907 female participants aged \u0026ge; 20 years with the following exclusion criteria: (1) Being pregnant (n=274), (2) Lack of informative data on diagnosis of hypertension (n=3), (3) insufficient hysterectomy data (n=2646), (4) lack of necessary demographic information and health-related data (n=2356). Finally, 12,628 participants with complete NHANES data were included in this work.\u0026nbsp;Fig. 1\u0026nbsp;displays the participant selection procedure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure and Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformation about hysterectomy status was obtained in the form of a questionnaire. Hysterectomy status was defined using self-reported history by asking the following question in the reproductive health section:\u0026ldquo;Have you had a hysterectomy?\u0026rdquo;In line with American Heart Association/American College of Cardiology (AHA/ACC) 2017 guidelines and previous studies [30,31], The diagnosis of hypertension was determined by a questionnaire about hypertension and the average of four consecutive blood pressure measurements. Participants were diagnosed with hypertension if they had self-reported hypertension, DBP\u0026nbsp;\u0026ge;\u0026nbsp;90 mmHg, SBP\u0026nbsp;\u0026ge;\u0026nbsp;140 mmHg, or previous or current medication for hypertension. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCovariates in the present work included demographic features (age, education, race, marital status, body mass index (BMI), poverty/income ratio (PIR), drinking and smoking), medical history (diabetes, oophorectomy, hormone therapy), laboratory tests (total cholesterol (TC), total glyceride (TG), high-density lipoprotein cholesterol (HDL), uric acid (UA), creatinine (Cr), glycosylated hemoglobin (GHb)) and dietary data (sodium intake).\u003c/p\u003e\n\u003cp\u003ePIR was computed through the division of family (or individual) income by poverty criteria applicable to survey year, with the greater PIR indicating superior family income status. The PIR for the not poor was defined as\u0026nbsp;\u0026ge;\u0026nbsp;1, and for the poor, it was defined as \u0026lt; 1. The diagnosis of diabetes was made according to a self-reported diabetes history, glycosylated hemoglobin \u0026ge; 6.5%, fasting blood glucose \u0026ge; 126 mg/dl, insulin or antihyperglycemic agents medication [32]. Dietary sodium intake was defined as mean sodium intake of two 24-h recalls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis for NHANES analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen performing the NHANES analyses, sampling weights provided by NHANES were utilized for weighting. Continuous variables were represented by mean\u0026plusmn;standard deviation (SD), whereas categorical variables were represented by numbers (percentages). Relationship of hysterectomy status with hypertension risk was evaluated using multivariate logistic regression models. Model 1 was not adjusted for all covariates. Model 2 was adjusted for age, race, educational level, marital status, PIR. In Model 3, BMI, smoking, alcohol, diabetes, oophorectomy, hormone therapy, levels of TC, HDL, TG, UA, Cr, GHb and sodium intake was adjusted based on Model 2. For determining whether the relation of hysterectomy status with hypertension risk was different among different subgroups, we carried out the independent stratification. Additionally, we utilized Wald test for calculating\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value for interaction. The restricted cubic splines (RCS) analysis was constructed for exploring association nonlinearity and depicting the overall trends. R version 4.1.3 was adopted in statistical analysis.\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 (two-sided) revealed significant difference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssumptions and data sources of two‑sample MR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHysterectomy exposure data\u0026nbsp;were obtained\u0026nbsp;based on MRC Integrated Epidemiology Unit (MRC-IEU) Consortium, involving 462,933 Europeans (46,411 patients and 416,522 control participants) in total (ID:ukb-b-3700). Summary data of hypertension were acquired based on a large UK Biobank-based GWAS\u0026nbsp;[33]. Researchers used ICD-10 codes, UK Biobank self-reported disease diagnosis to identify individuals with a history of hypertension. Through selection, this GWAS enrolled 129,909 cases and 354,689 controls in total (ID:ebi-a-GCST90038604).\u003c/p\u003e\n\u003cp\u003eIn addition, we also included data on SBP (ID:ieu-b-38) and DBP (ID:ieu-b-39) as outcomes, which were acquired based on the meta-analysis of GWAS involving 757,601 subjects from UK Biobank and International Council on Blood Pressure (ICBP) associations\u0026nbsp;[34]. We obtained all the above GWAS data from Integrated Epidemiology Unit (IEU) OpenGWAS database (https://gwas. mrcieu. ac. uk/). Data download details can be observed from Additional file\u0026nbsp;1: Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of genetic instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIVs used for MR analysis must satisfy three requirements: (1) the IVs should be convincingly correlated to hysterectomy; (2) IVs are not related to all confounders for exposure-outcome relation; and (3) are just related to outcomes via the interested exposure rather than via additional routes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify genetic variants for the causality of hysterectomy status with risk of developing several outcomes (hypertension, increased SBP, and DBP), we set genome-wide significance level as\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 5 \u0026times; 10\u003csup\u003e-8\u0026nbsp;\u003c/sup\u003efor screening for genetic variants closely related to exposure. Afterward, the linkage disequilibrium (LD) clumping test was conducted for identifying independent SNP (r\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001; 10,000 kb). After excluding palindrome SNPs, effect alleles were coordinated in outcome and exposure datasets. We then examined SNPs via LDtrait Tool (https://ldlink.nih.gov/?tab=ldtrait) for removing those closely related to potential confounders such as BMI, waist circumference, diabetes, dyslipidemia [15, 35]. Subsequently, to further assess the strength of each IV, F-statistic was determined for the IVs in the exposure and excluded SNPs with F \u0026lt; 10 to ensure that the IVs had adequate validity and instrumental strength [36].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis for two‑sample MR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn two-sample MR study, random-effects inverse-variance weighting (IVW) was used to be the primary method for assessing causal relationships of genetically predicted hysterectomy status with hypertension risk and increased SBP, and DBP. Fixed-effects estimates are not appropriate when there is excessive result heterogeneity; consequently, random-effects IVW model was adopted in our analysis, accounting for heterogeneity when assessing causality [37]. In addition, we used four complementary MR analysis approaches, such as simple mode, MR‒Egger, weighted mode, and weighted median approaches,\u0026nbsp;to validate IVW\u0026nbsp;results. MR‒Egger regression intercept was adopted for assessing potential horizontal multiplicity effects. Horizontal pleiotropy was considered not\u0026nbsp;to be present\u0026nbsp;at \u003cem\u003ep\u003c/em\u003e\u003cem\u003e-\u003c/em\u003evalue \u0026gt;0.05 [36]. Cochrane\u0026apos;s Q test was utilized for testing for possible SNP heterogeneity. Analyses with\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-values \u0026gt; 0.05 did not reveal any obvious heterogeneity. Heterogeneity does not invalidate causality estimates in MR analyses because the random effects IVW approach balances potential total heterogeneity to some extent [38].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also performed MR pleiotropy residual sum and outlier (MR-PRESSO) test in determining existence of outlier IVs. In addition, residual sum of squares accounts for a heterogeneity measure, which equals Cochran\u0026apos;s Q statistic. In the presence of outlier IVs, we utilized the MR-PRESSO outlier-corrected test for obtaining corrected causal effects through removing outliers, and then assessed distortion of causal estimates prior to and following removing outliers with MR-PRESSO distortion test. Typically, MR-PRESSO test is useful if few genetic variants had heterogeneous ratio estimates, because they were excluded and therefore did not impact total estimate [39, 40]. Additionally, leave one-out analyses were carried out for assessing whether single-sensitive SNP affected IVW test. The results were also validated using funnel plots and scatter plots for further validation. Two-sample MR package (version 0.5.6) in R (version 4.1.3) was employed for analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 1 shows the research flowchart in the present study.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough selection, altogether 12628 subjects were enrolled into the present work. A total of 2858 participants underwent hysterectomy, and 5410 were diagnosed with hypertension. Table 1 presents study participant features according to hysterectomy category. Hysterectomy showed a higher prevalence in elderly non-Hispanic White females having decreased educational level and higher income. Additionally, compared to patients who did not undergo hysterectomy, these patients were less likely to drink alcohol but were more likely to have obesity, to smoke, to have diabetes, to undergo oophorectomy and to receive hormone therapy. Furthermore, as expected, they had significantly higher TC, TG, UA, Cr,\u0026nbsp;and GHb values, while\u0026nbsp;their sodium intake was lower.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelation of hysterectomy status with hypertension risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 displays the relation of hysterectomy status with hypertension risk. Hysterectomy status was positively related to hypertension risk in weighted logistic regression model following multivariate regression (OR = 1.26, 95% CI 1.04-1.52,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.017). Fig. 2 displays subgroup analysis with multivariate-controlled weighted logistic regression. Based on subgroup analysis, hysterectomy status was significantly positively associated with an increased hypertension risk among participants aged 20-40 years, non-Hispanic White individuals, individuals of other races, individuals with a BMI\u0026nbsp;≥\u0026nbsp;23.9, individuals who consumed alcohol,\u0026nbsp;individuals with\u0026nbsp;diabetes,\u0026nbsp;individuals without\u0026nbsp;diabetes,\u0026nbsp;individuals\u0026nbsp;not undergoing oophorectomy and\u0026nbsp;individuals receiving\u0026nbsp;hormone therapy.\u0026nbsp;An\u0026nbsp;interaction of hysterectomy status with age could be obtained from subgroup analysis (\u003cem\u003ep\u003c/em\u003e for interaction = 0.043) related to the higher hypertension risk. RCS analysis\u0026nbsp;suggested that\u0026nbsp;age at hysterectomy was linearly and negatively related to hypertension when every covariate was adjusted for (Additional file 1: Fig. S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTwo\u003c/strong\u003e\u003cstrong\u003e‑\u003c/strong\u003e\u003cstrong\u003esample MR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter comprehensive screening, 28 SNPs related to hysterectomy were enrolled for our MR study. F-statistic for every IV was \u0026gt; 10 (critical value), representing no weak instrumental bias. Additional file 1:Table S2 displays more details of such SNPs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on such findings, genetically predicted hysterectomy status showed causal relation with hypertension risk (IVW: OR=1.205, 95% CI: 1.043-1.392,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.011), and similarly, hysterectomy status has a potentially positive causal relationship with increased SBP (IVW: Beta=9.642, 95% CI: 2.125-17.159,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.011) and DBP (IVW: Beta=6.695, 95% CI: 2.173-11.217,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.003) (Fig. 3). MR-Egger intercept tests\u0026nbsp;revealed the absence of\u0026nbsp;pleiotropy (Additional file 1:Table S3). Obvious heterogeneity could be measured by Cochran's Q test (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) for these above three causal relationships. Nonetheless, causal effect remained after eliminating outliers by the MR-PRESSO test (hypertension,\u0026nbsp;outlier-corrected: \u003cem\u003ep\u003c/em\u003e=0.004,\u0026nbsp;distortion\u0026nbsp;test: \u003cem\u003ep\u003c/em\u003e=0.885; SBP,\u0026nbsp;outlier-corrected: \u003cem\u003ep\u003c/em\u003e=0.005,\u0026nbsp;distortion\u0026nbsp;test: \u003cem\u003ep\u003c/em\u003e=0.662; DBP,\u0026nbsp;outlier-corrected: \u003cem\u003ep\u003c/em\u003e=0.002,\u0026nbsp;distortion\u0026nbsp;test: \u003cem\u003ep\u003c/em\u003e=0.144) (Additional file 1: Table S3). Moreover, MR-Egger regression analysis and weighted medians of three causal associations directionally conformed to the IVW method, suggesting that these three associations were reliable. Sensitivity analyses on IVW results by leave one-out approach all revealed that eliminating one SNP each time made no difference to results for specific SNPs, and did not indicate potent SNPs (Additional file 1: Fig. S2). Scatter plots showing causal relationships and funnel plots can be observed from Additional file 1: Fig. S3-4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the relationship between hysterectomy status and hypertension risk. We selected 12,628 female participants from six consecutive 2-year cycles of\u0026nbsp;the\u0026nbsp;NHANES, and after adjusting for relevant confounders, hysterectomy status was related to a higher hypertension prevalence. Additionally, the two-sample MR analysis revealed potential causal associations of hysterectomy status with hypertension\u0026nbsp;risk and increased\u0026nbsp;SBP and DBP.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;As we know, the present study is the first that assesses causal relationship of\u0026nbsp;hysterectomy status with hypertension risk.\u003c/p\u003e\n\u003cp\u003eRelationship between hysterectomy status and hypertension risk is still inconsistent. According to one prospective study, there was no significant difference in SBP or DBP before or after surgery in middle-aged women who opted for hysterectomy [24]. Similarly, Appaih et al.\u0026nbsp;reported\u0026nbsp;that hysterectomy status was not related to other risk factors related to\u0026nbsp;CVD, such as SBP, following a 25-year follow-up cycle [25]. However, according to one large cohort study that involved more than 50,000 subjects and one meta-analysis including 14 observational cohort studies, hysterectomy status was markedly related to the higher hypertension risk [22,42]. Based on our findings, hysterectomy status was positively related to the prevalence of hypertension, and this association persisted after\u0026nbsp;adjusting for possible\u0026nbsp;confounders. In addition, age was associated with hysterectomy, with a more significant relation of hysterectomy status with hypertension risk for younger participants. Similarly, the results of RCS showed the nonlinear negative relationship of age at hysterectomy with hypertension risk. According to the above results, hysterectomy may probably be the risk factor related to hypertension among younger women, which is not mentioned previously.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApart from exploring the relation of hysterectomy status with hypertension risk, two-sample MR method was adopted for investigating causality of \u0026nbsp;hysterectomy status with hypertension risk, higher SBP, and DBP. Our results showed a potential positive causality of genetically predicted hysterectomy status with hypertension risk and SBP and DBP, suggesting that hysterectomy is the contributor for hypertension risk.\u003c/p\u003e\n\u003cp\u003eMechanisms underlying the relationship between hysterectomy status and the risk of developing hypertension are still unknown. One hypothesis may be that hysterectomy affects the blood flow of ovary to ovarian ligament, resulting in premature ovarian failure as well as the early onset of menopause, thereby causing the decrease of endogenous hormone level, and promoting atherosclerosis occurrence [43,44]. The findings of the present study verified this hypothesis to some extent. First, we found that hysterectomy status was markedly related to hypertension risk among female patients with the age of 20-40 years, whereas other age groups did not exhibit any obvious association. In addition, age at hysterectomy showed linear and negative relation to hypertension occurrence, possibly because hysterectomy leads to premature menopause, thus accelerating the onset of hypertension [45]. Similarly, we found that hysterectomy status was not significantly related to hypertension risk among participants on hormone therapy, whereas in contrast, hysterectomy status was found as the risk factor related to hypertension among women not on hormone therapy, which indirectly demonstrates the protective effect of sex hormones against hypertension. In addition, obesity has been suggested as another possible explanation for the relation of hysterectomy status with hypertension risk. Women undergoing hysterectomy are previously suggested to be associated with obesity and suffer from metabolic diseases [46, 47]. Through subgroup analyses, we also demonstrated that hysterectomy status was associated with hypertension risk among female participants who were overweight rather than in the normal weight range, indirectly validating this hypothesis. Notably, we found that hysterectomy was not associated with the development of hypertension in ovariectomized participants, suggesting that oophorectomy is not related to the higher hypertension risk, conforming to previous results [22, 23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe main strength in the present study is the combination of observational research with MR analysis. Observational studies used alone are susceptible to unmeasured confounding and reverse causation. MR analysis alone, on the other hand, has a higher false-negative rate, although confounders can be controlled for. Noteworthily, the two methods yielded almost identical findings in this work, making our results reliable.\u003c/p\u003e\n\u003cp\u003eNonetheless, there are certain limitations in our work. Firstly, NHANES-based hysterectomy information was self-reported, and might induce reporting bias, affecting the study result accuracy. Second, despite including numerous factors in our analytic model, we were unable to control for confounders associated with stroke that were not included in the data sources. Third, for MR analysis, it remains not easy to totally eliminate influences of heterogeneity and possible directional pleiotropy. At last, the results were mainly obtained from American and European adults, which limited our result applicability to additional ethnic groups. More large-scale studies involving additional ethnic populations should be conducted for validating our results.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTaken together, this observational study suggested that hysterectomy status was strongly associated with hypertension risk. Younger hysterectomy patients may develop hypertension in comparison with older patients. Moreover, MR analysis revealed a potential causal relations of hysterectomy status with hypertension risk and increased SBP and DBP. The mechanisms underlying these associations must be further analyzed.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mendelian randomization \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNHANES \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; National Health and Nutrition Examination Study\u003c/p\u003e\n\u003cp\u003eCVD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eIVs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Instrumental variables\u003c/p\u003e\n\u003cp\u003eSNP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eGWAS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Genome-wide association study\u003c/p\u003e\n\u003cp\u003eRCS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Restricted cubic spline\u003c/p\u003e\n\u003cp\u003eIVW \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Inverse-variance weighting \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMR-PRESSO \u0026nbsp; \u0026nbsp; \u0026nbsp; MR pleiotropy residual sum and outlier\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was based on summary statistics provided by\u0026nbsp;the Integrated Epidemiology Unit (IEU) OpenGWAS database and NHANES database. We thank all investigators and consortium for sharing valuable summary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWeiren Yan contributed to conception and design of the study, acquisition, analysis, and interpretation of data, and drafted the manuscript; Jiahui Wang contributed to data analysis and manuscript revision; Ke Xu, Xianglin Liu, Xinsheng Li and Haichen Lv contributed to study design, data analysis, and drafted the manuscript. All authors gave their final approval and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported Dalian Science and Technology Innovation Fund [grant number 2023JJ13SN039].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study were available from opensource database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eClayton RD. 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Mendelian Randomization as an Approach to Assess Causality Using Observational Data. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e. 2016;27(11):3253-3265. doi:10.1681/ASN.2016010098\u003c/li\u003e\n\u003cli\u003eEmdin CA, Khera AV, Kathiresan S. Mendelian Randomization. \u003cem\u003eJAMA\u003c/em\u003e. 2017;318(19):1925-1926. doi:10.1001/jama.2017.17219\u003c/li\u003e\n\u003cli\u003eBoehm FJ, Zhou X. Statistical methods for Mendelian randomization in genome-wide association studies: A review. \u003cem\u003eComput Struct Biotechnol J\u003c/em\u003e. 2022;20:2338-2351. doi:10.1016/j.csbj.2022.05.015\u003c/li\u003e\n\u003cli\u003eLiang X, Chou OHI, Cheung CL, Cheung BMY. Is hypertension associated with arthritis? The United States national health and nutrition examination survey 1999-2018. \u003cem\u003eAnn Med\u003c/em\u003e. 2022;54(1):1767-1775. doi:10.1080/07853890.2022.2089911\u003c/li\u003e\n\u003cli\u003eWhelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2018;71(19):e127-e248. doi:10.1016/j.jacc.2017.11.006\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022. \u003cem\u003eDiabetes Care\u003c/em\u003e. 2022;45(Suppl 1):S17-S38. doi:10.2337/dc22-S002\u003c/li\u003e\n\u003cli\u003eD\u0026ouml;nertaş HM, Fabian DK, Valenzuela MF, Partridge L, Thornton JM. Common genetic associations between age-related diseases. \u003cem\u003eNat Aging\u003c/em\u003e. 2021;1(4):400-412. doi:10.1038/s43587-021-00051-5\u003c/li\u003e\n\u003cli\u003eEvangelou E, Warren HR, Mosen-Ansorena D, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. \u003cem\u003eNat Genet\u003c/em\u003e. 2018;50(10):1412-1425. doi:10.1038/s41588-018-0205-x\u003c/li\u003e\n\u003cli\u003eRabi DM, McBrien KA, Sapir-Pichhadze R, et al. Hypertension Canada\u0026rsquo;s 2020 Comprehensive Guidelines for the Prevention, Diagnosis, Risk Assessment, and Treatment of Hypertension in Adults and Children. \u003cem\u003eCan J Cardiol\u003c/em\u003e. 2020;36(5):596-624. doi:10.1016/j.cjca.2020.02.086\u003c/li\u003e\n\u003cli\u003eSlatkin M. Linkage disequilibrium--understanding the evolutionary past and mapping the medical future. \u003cem\u003eNat Rev Genet\u003c/em\u003e. 2008;9(6):477-485. doi:10.1038/nrg2361\u003c/li\u003e\n\u003cli\u003eDai M, Guo W, Zhu S, et al. Type 2 diabetes mellitus and the risk of abnormal spermatozoa: A Mendelian randomization study. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e. 2022;13:1035338. doi:10.3389/fendo.2022.1035338\u003c/li\u003e\n\u003cli\u003eHartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. \u003cem\u003eInt J Epidemiol\u003c/em\u003e. 2017;46(6):1985-1998. doi:10.1093/ije/dyx102\u003c/li\u003e\n\u003cli\u003eChen Y, Li C, Cheng S, et al. The Causal Relationships Between Sleep-related Phenotypes and Body Composition: A Mendelian Randomized Study. \u003cem\u003eJ Clin Endocrinol Metab\u003c/em\u003e. 2022;107(8):e3463-e3473. doi:10.1210/clinem/dgac234\u003c/li\u003e\n\u003cli\u003eZou F, Hu Y, Xu M, Wang S, Wu Z, Deng F. Associations between sex hormones, receptors, binding proteins and inflammatory bowel disease: a Mendelian randomization study. \u003cem\u003eFront Endocrinol\u003c/em\u003e. 2024;15:1272746. doi:10.3389/fendo.2024.1272746\u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG. Mendelian randomization: methods for causal inference using genetic variants. Boca Raton: CRC Press (2015).\u003c/li\u003e\n\u003cli\u003eWang Z, Li X, Zhang D. Impact of hysterectomy on cardiovascular disease and different subtypes: a meta-analysis. \u003cem\u003eArch Gynecol Obstet\u003c/em\u003e. 2022;305(5):1255-1263. doi:10.1007/s00404-021-06240-2\u003c/li\u003e\n\u003cli\u003eHoward BV, Kuller L, Langer R, et al. Risk of cardiovascular disease by hysterectomy status, with and without oophorectomy: the Women\u0026rsquo;s Health Initiative Observational Study. \u003cem\u003eCirculation\u003c/em\u003e. 2005;111(12):1462-1470. doi:10.1161/01.CIR.0000159344.21672.FD\u003c/li\u003e\n\u003cli\u003eFarquhar CM, Sadler L, Harvey SA, Stewart AW. The association of hysterectomy and menopause: a prospective cohort study. \u003cem\u003eBJOG\u003c/em\u003e. 2005;112(7):956-962. doi:10.1111/j.1471-0528.2005.00696.x\u003c/li\u003e\n\u003cli\u003eZhu D, Chung HF, Dobson AJ, et al. Age at natural menopause and risk of incident cardiovascular disease: a pooled analysis of individual patient data. \u003cem\u003eLancet Public Health\u003c/em\u003e. 2019;4(11):e553-e564. doi:10.1016/S2468-2667(19)30155-0\u003c/li\u003e\n\u003cli\u003eMu F, Rich-Edwards J, Rimm EB, Spiegelman D, Forman JP, Missmer SA. Association Between Endometriosis and Hypercholesterolemia or Hypertension. \u003cem\u003eHypertension\u003c/em\u003e. 2017;70(1):59-65. doi:10.1161/HYPERTENSIONAHA.117.09056\u003c/li\u003e\n\u003cli\u003eLaughlin-Tommaso SK, Khan Z, Weaver AL, Schleck CD, Rocca WA, Stewart EA. Cardiovascular risk factors and diseases in women undergoing hysterectomy with ovarian conservation. \u003cem\u003eMenopause\u003c/em\u003e. 2016;23(2):121-128. doi:10.1097/GME.0000000000000506\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 \u0026nbsp;Baseline characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"63.3147113594041%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.35294117647059%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n=12,628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.470588235294116%\" valign=\"top\"\u003e\n \u003cp\u003eNon-hysterectomy\u003c/p\u003e\n \u003cp\u003e(n=9,770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eHysterectomy\u003c/p\u003e\n \u003cp\u003e(n=2,858)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e48.46\u0026plusmn;16.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e44.87\u0026plusmn;16.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e61.43\u0026plusmn;12.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003e20-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e4280 (35.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e4152\u0026nbsp;(43.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e128\u0026nbsp;(5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003e41-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e4394\u0026nbsp;(38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e3379\u0026nbsp;(37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1015\u0026nbsp;(40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e3954 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e2239\u0026nbsp;(18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1715\u0026nbsp;(54.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e1828\u0026nbsp;(7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e1522\u0026nbsp;(8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e306\u0026nbsp;(4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e2628\u0026nbsp;(11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e1957\u0026nbsp;(10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e671\u0026nbsp;(11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e5509\u0026nbsp;(69.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e4065\u0026nbsp;(67.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1444\u0026nbsp;(76.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e2663\u0026nbsp;(12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e2226\u0026nbsp;(13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e437\u0026nbsp;(8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eEducation, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e2746 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e2066 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e680 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eHigh school graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e2759 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e2024 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e735 (26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e7123 (64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e5680 (66.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1443 (57.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eMarried, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e6768 (59.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e5293 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1475 (60.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e5860 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e4477 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1383 (39.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003ePIR, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNot poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e9781 (84.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e7440 (84.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e2341 (88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e2847 (15.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e2330 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e517 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e4675 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e3439 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1236 (45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e7953 (60.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e6331 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1622 (54.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e7444(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e5923 (68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1521 (60.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e5184(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e3847 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1337 (39.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e2203 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e1396 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e807 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e10425(87.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e8374 (89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e2051 (78.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eOophorectomy, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e1540 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e36 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1504 (54.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e11088 (88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e9734 (99.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1354 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eHormone therapy, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e2409 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e1017 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1392 (54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e10219 (78.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e8753 (87.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1466 (45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e5410 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e3466 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e1944 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e7218 (62.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e6304 (69.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e914 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e29.37\u0026plusmn;7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e29.12\u0026plusmn;7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e30.29\u0026plusmn;7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eTC, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e196.45\u0026plusmn;41.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e193.80\u0026plusmn;39.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e206.04\u0026plusmn;44.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eHDL, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e58.62\u0026plusmn;16.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e58.69\u0026plusmn;16.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e58.35\u0026plusmn;16.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eTG, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e137.29\u0026plusmn;133.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e129.11\u0026plusmn;107.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e166.89\u0026plusmn;198.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eUA, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e287.70\u0026plusmn;75.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e282.12\u0026plusmn;71.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e307.90\u0026plusmn;84.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eCr, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e69.18\u0026plusmn;27.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e67.67\u0026plusmn;22.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e74.66\u0026plusmn;39.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eGHb %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e5.62\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e5.55\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e5.84\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.953445065176908%\" valign=\"top\"\u003e\n \u003cp\u003eSodium intake, mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.484171322160147%\" valign=\"top\"\u003e\n \u003cp\u003e3015.34\u0026plusmn;1391.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09124767225326%\" valign=\"top\"\u003e\n \u003cp\u003e3078.54\u0026plusmn;1415.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\" valign=\"top\"\u003e\n \u003cp\u003e2786.55\u0026plusmn;1275.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.731843575418994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePIR, poverty impact ratio; BMI, body mass index; TC, total cholesterol; HDL, high-density lipoprotein cholesterol; TG, total glyceride; UA, uric acid; Cr, creatinine; GHb, glycosylated hemoglobin.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMultivariate\u0026nbsp;regression analysis of the association between hysterectomy status and the risk of developing hypertension\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"485\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.185567010309278%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"73.81443298969072%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.357541899441344%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.28491620111732%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.357541899441344%\" valign=\"top\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.185567010309278%\" valign=\"top\"\u003e\n \u003cp\u003eNon-hysterectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.36082474226804%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRef.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09278350515464%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRef.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.36082474226804%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRef.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.185567010309278%\" valign=\"top\"\u003e\n \u003cp\u003eHysterectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.36082474226804%\" valign=\"top\"\u003e\n \u003cp\u003e3.64 (3.26-4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09278350515464%\" valign=\"top\"\u003e\n \u003cp\u003e1.47 (1.287-1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.36082474226804%\" valign=\"top\"\u003e\n \u003cp\u003e1.26 (1.04-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.185567010309278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.36082474226804%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09278350515464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.36082474226804%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: Unadjusted model.\u003c/p\u003e\n\u003cp\u003eModel 2: Adjusted for age, race, educational level, marital status, PIR.\u003c/p\u003e\n\u003cp\u003eModel 3: Adjusted for BMI, smoking, alcohol, diabetes, oophorectomy, hormone therapy, TC, HDL, TG, UA, Cr, GHb, sodium intake based on model 2.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\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":"Hysterectomy, Hypertension, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4869562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4869562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious observational studies have shown an association between hysterectomy status and the risk of developing hypertension, but the exact relationship between the two is unclear. The aim of our study was to conduct an observational analysis of this relationship and to determine the causality of that relationship through a Mendelian randomization (MR) study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 12,628 participants from the 2007–2018 National Health and Nutrition Examination Survey and used weighted logistic regression to analyze the association between hysterectomy status and the risk of developing hypertension, followed by a subgroup analysis and restricted cubic splines (RCS) to further explore the associations. A two-sample MR study was conducted to determine the causal relationships between hysterectomy status and the risk of developing hypertension and increased systolic and diastolic blood pressure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted-weighted logistic regression revealed a significant association between hysterectomy status and the risk of developing hypertension (OR = 1.26, 95% CI: 1.04-1.52). Both subgroup and RCS analyses revealed that a younger age at hysterectomy was associated with a greater risk of hypertension. MR showed a causal association between genetically predicted hysterectomy status and the risk of developing hypertension (OR=1.205, 95% CI: 1.043-1.392), increased systolic blood pressure (beta =9.642, 95% CI: 2.125-17.159) and increased diastolic blood pressure (beta =6.695, 95% CI: 2.173-11.217).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study revealed that hysterectomy increases the risk of hypertension. Moreover, hysterectomy at an early age is associated with an increased prevalence of hypertension. Therefore, there is an urgent need to manage and monitor blood pressure in post hysterectomy patients.\u003c/p\u003e","manuscriptTitle":"Association between hysterectomy status and hypertension: Results from NHANES 2007– 2018 and two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 00:40:58","doi":"10.21203/rs.3.rs-4869562/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":"082e14f8-fd9f-48d4-91b2-3675fd7937ae","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-24T15:53:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-16 00:40:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4869562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4869562","identity":"rs-4869562","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}