Age at Menarche is Inversely Related to the Prevalence of Common Gynecologic Cancers

Age at Menarche is Inversely Related to the Prevalence of Common Gynecologic Cancers | 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 Age at Menarche is Inversely Related to the Prevalence of Common Gynecologic Cancers Hao Sun, Xiaohui Pei, Yaoyun Zhang, mengmeng wang, Ziqian Song, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4796084/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2025 Read the published version in European Journal of Medical Research → Version 1 posted 9 You are reading this latest preprint version Abstract Objectives The objective of this study was to investigate the relationship between the age of menarche and the prevalence of gynecological cancer. Methods A total of 5540 women were screened from those who participated in the National Health And Nutrition Examination Survey (NHANES) questionnaire from 2007–2020, and their variable factors of age, race, education level, Poverty Impact Ratio (PIR), marital status, Body Mass Index (BMI), waist circumference, duration of moderate exercise, smoking habits, hypertension status, energy intake, diabetes and alcohol consumption habits were analysed statistically and by logistic regression. Results Univariate and multivariate logistic regression analysis of the relationship between age at menarche and gynaecological cancer (Uterus / Cervix / Ovary Cancer, the following gynecologic cancers in the article refer to having at least one of these three cancers) prevalence showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.82, 95%CI: 0.69–0.97), with a statistically significant difference (p = 0.02). Regression results of the association between age at menarche and different types of gynaecological cancers found a negative association between age at menarche and prevalence in uterine cancers (P = 0.03) and no association between age at menarche and prevalence in cervical and ovarian cancers (P = 0.17, P = 0.29). Those with a younger age at menarche were more likely to develop uterine cancer (OR: 0.72, 95%CI: 0.54–0.98). Conclusions There was a correlation between age at menarche and gynaecological cancer, with those who had menarche at an earlier age being at a higher risk of gynaecological cancer. More obviously, the younger the age of first menstruation, the higher the risk of uterine cancer. menarche gynaecological cancer NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Gynecological cancer was a cancer with high morbidity and mortality rates, and it was the most common cancer among women [1, 2] . Gynecological cancers posed a great threat to the physical and psychological health of women worldwide, and even caused significant economic pressure on societies and countries [3,4] . The most common type of gynaecological cancer was cervical cancer, followed by uterine and ovarian cancers [5] . Each of these cancers was unique in type, with different curative factors, risk factors, diagnosis and treatment, and prognostic outcomes [6] . However, through the control of risk factors, early screening of related factors, screening of high-risk groups and other means, reducing risk of the occurrence of gynecological cancer [7,8] . When a woman's hypothalamus-pituitary-gonad axis tended to mature, various parts of the woman's body would change, such as breast development and increased body hair. However, menarche was the most important manifestation [9] . There were many factors affecting the age of menarche, such as hormone level [10] , family economic condition [11,12] , birth weight [13] and so on. In addition, the age of menarche played a certain role in the occurrence of many diseases. A study on the relationship between age at menarche and cardiac function showed that there was a negative correlation between them [14] . The age of menarche had also been proved to be related to respiratory diseases [15] , multiple sclerosis [16] , myopia [17] , cardiovascular disease prognosis [18] and so on. As an important factor of women's physical index, the relationship between the age of menarche and gynecological cancer had aroused our interest. The important thing was that the relationship between the age of menarche and gynecological cancer was not clear. To investigate the relationship between age at menarche and gynaecological cancer prevalence, we conducted a statistical analysis of questionnaire data from the National Health and Nutrition Examination Survey (NHANES). The NHANES database was used as the data source for all data in this paper. In this study, a total of 13 factors that may have an impact on the results were considered and adjusted accordingly. These included a number of dimensions such as basic demographic information, personal fitness, lifestyle habits and disease status, with a number of factors such as age, marriage, body mass index and income having a significant effect on cancer prevalence. Several sensitivity analyses, subgroup analyses, and cross-validation were conducted to ensure the reliability and accuracy of the results based on these covariates that may have an impact on the results. In this study, we focused on the most common cancers among gynaecological cancers. We screened data from NHANES questionnaires from 2007 to 2020 and analyzed the data statistically. The relationship between age at menarche and the prevalence of gynecologic cancers was ultimately assessed. 2. Materials and methods 2.1 Study design and sample Health and nutrition data for the study population were obtained from the NHANES database. Some specific information on this survey is described elsewhere [19–20] . All the data of NHANES 2007–2020 were approved by the Ethics Review Committee, and all subjects had informed consent and written proof. The number of population initially enrolled in the study was 111797, and 47548 were excluded from the study because of incomplete cancer information. In addition, we excluded 36325 subjects with incomplete gynecological information. Of the remaining 27,924 study subjects, we further excluded a number of study subjects. These included study subjects with missing information on examination (n = 18,698), missing information on demographics (n = 2,505), missing information on diet (n = 1,073), and missing information on disease (hypertension and diabetes) (n = 108). A total of 5540 subjects were eligible for the experimental study. The screening process for the study population was shown in Fig. 1 . 2.2Assessments Population screening criteria were age > 18 years, complete and reliable data in all categories, and clear representation weights. The definition of the type of cancer was assessed according to the location of the cancer and the doctor's diagnosis. The statistics of the age of menarche were based on the results of a questionnaire. Considering that the study was on a female population, marital status was considered an important reference variable factor. Marital status was classified as: 1. married; 2. never married; 3. Widowed / divorced / separated. Some other relevant characteristics about the study population were based on the NHANES findings. Data were obtained on age (<65 / ≥ 65), race (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Hispanic, Other Race), Education level (Less than high school, High school ,More than high school). The poverty impact ratio (PIR) was used as a threshold for the variable at 2.8%. In addition, the inclusion criteria of waist circumference (< 94 cm / ≥ 94 cm), body mass index (BMI) (< 25 kg / m 2 / ≥ 25 kg / m 2 ), moderate exercise (< 45 minutes / ≥ 45 minutes) and energy intake (< 1700 kcal / ≥ 1700 kcal) were also evaluated according to the data in the questionnaire. Where the energy intake value was calculated as an average value based on the total energy intake over two days, the daily energy intake is derived from the total energy from food and beverages. Moderate exercise was defined as moderate intensity exercise, fitness or recreational activity that results in a small increase in breathing or heart rate and was performed continuously for at least 10 minutes. The smoking habits of the study population were assessed into the following three categories: 1, nexer (smoked less than 100 cigarettes in life); 2, former (smoked more than 100 cigarettes in life and smoke not at all now); 3, now (smoked more than 100 cigarettes in life and smoke some days or every day). The criteria for classifying drinking habits were based on the most recent assessment indicators (only female criteria have been used, as well as criteria for the shared component): 1, never (had < 12 drinks in lifetime); 2, former (had ≥ 12 drinks in 1 year and did not drink last year, or did not drink last year but drank ≥ 12 drinks in lifetime); 3, mild (≤ 1 drink per month); 4, moderate (≤ 2 drinks per month); 5, heavy (≥ 3 drinks per month). The diagnosis of the underlying disease reported in the investigation was assessed on the basis of the results of the examination and the doctor's diagnosis. Among them, the diagnostic criteria of hypertension: average blood pressure ≥ 140 / 90mmHg. Average blood pressure was calculated by the following protocol: 1, the diastolic reading with zero was not used to calculate the diastolic average; 2, if all diastolic reading were zero, then the average would be zero; 3, if only one blood pressure reading was obtained, that reading was the average; 4, if there was more than one blood pressure reading, the first reading was always exclude from the average. With regard to the inclusion criteria for diabetes, the assessment was classified according to the international customary criteria and related indicators: 1, doctor told you have diabetes; 2, glycohemoglobin HbA1c (%) > 6.5; 3, fasting glucose (mmol / L) ≥ 7.0; 4, random blood glucose (mmol / L) ≥ 11.1; 5, two-hour OGTT blood glucose (mmol / L) ≥ 11.1; 6, use of diabetes medication or insulin. 2.3Statistical analyses For random missing samples, we used multiple interpolation to fill in. For non-random missing data, we excluded them and the filtered data were analysed using R (v.4.2.1). For continuous variables, we used mean and 95% confidence intervals (x ̅ (95%CI) for statistical descriptions, and t-tests and ANOVA for between-group comparisons. For categorical variables, we used p-values and 95%CI for statistical descriptions and chi-square tests for comparisons between groups. For the adjustment analyses, we chose binary logistic regression for multifactor analysis and multiple sensitivity analysis. Twelve covariates were stratified into the model to ensure the reliability of the analyses. In addition, we further fitted separate models for the association between age at menarche and the prevalence of three common gynaecological cancers. Several subgroup analyses and interaction studies were also performed to obtain a more precise target population. We used three section restricted cubic spline (RCS) to explore the nonlinear relationship between menarche age and the prevalence of common gynecological cancers and uterine diseases. In this analysis, adjustments were made for all 12 covariates. In this study, P < 0.05 was considered statistically significant difference. The NHANES complex multi-stage sampling design was used for all analyses in this study. Appropriate weights were selected for calculations and weighted multivariate logistic regression to ensure a representative sample. In addition, we conducted several sensitivity analyses to verify the reliability of the results. We constructed multiple models adjusting for age, race, education level, marital status, BMI, PIR, waist circumference, moderate activity time, smoking, alcohol consumption, hypertension, and diabetes. 3. Results Our study included 5,540 NHANES participants, which could represent 38,016,087 US women, with an overall weighted prevalence of 1.91% for gynaecological cancers. The baseline characteristics of the study population were shown in Table 1 . The results of the study showed statistical differences in the prevalence of gynaecological cancers between age, education level, marital status, PIR, BMI, waist circumference, duration of moderate exercise, smoking habits, and hypertension status. The results showed that population with an earlier age of menarche were at higher risk of gynaecological cancers. Table.1. A table of baseline characteristics of the study population. Characteristics Gynecological Cancer P value no yes Total 98.09 1.91 Age ~ years 46.74(46.05,47.42) 51.25(48.42,54.07) < 0.01 Race~% 0.25 Non-Hispanic White 74.47(72.06,76.88) 73.05(63.16,82.94) Non-Hispanic Black 9.10(7.82,10.38) 5.59(2.05, 9.13) Mexican American 5.47(4.46,6.48) 4.11(1.66,6.56) Other Hispanic 4.39(3.64, 5.13) 6.84(2.44,11.23) Other Race 6.56(5.71, 7.41) 10.41(2.50,18.32) Education level~% < 0.01 Less than high school 6.30(5.44, 7.16) 15.54(7.67,23.40) High school 17.72(16.29,19.15) 18.44(11.50,25.38) More than high school 75.98(74.19,77.78) 66.03(55.51,76.55) Marital Status 0.01 married 63.01(61.06,64.96) 63.76(54.26,73.26) Never married 17.10(15.51,18.70) 6.03( 2.08, 9.99) Widowed/Divorced/Separated 19.89(18.62,21.15) 30.21(21.27,39.14) Family PIR~% 3.38(3.30,3.46) 2.95(2.57,3.33) 0.03 BMI ~ kg/m 2 28.27(28.00,28.54) 30.52(28.67,32.37) 0.01 Waist ~ cm 94.53(93.92, 95.15) 100.87(97.06,104.68) < 0.01 Moderate exercise ~ minutes 53.29(51.86,54.72) 63.92(50.01,77.84) 0.04 Smoking behavior~% < 0.01 never 66.02(64.03,68.00) 37.51(24.50,50.51) former 22.29(20.59,23.98) 29.64(17.42,41.86) now 11.70(10.57,12.82) 32.85(21.72,43.99) Alcohol consumption~% 0.55 never 10.96(9.47,12.45) 7.03(2.39,11.67) former 8.06(6.95, 9.17) 9.44(3.23,15.65) mild 36.61(34.49,38.74) 36.77(24.03,49.50) moderate 26.35(24.77,27.92) 26.12(14.27,37.97) heavy 18.02(16.53,19.50) 20.64(11.56,29.73) Hypertension~% < 0.01 yes 29.22(27.50,30.93) 48.49(36.87,60.11) no 70.78(69.07,72.50) 51.51(39.89,63.13) Diabetes~% 0.12 yes 9.64(8.78,10.51) 16.39(6.38,26.40) no 90.36(89.49,91.22) 83.61(73.60,93.62) First menstruation ~ years 12.20(11.78,12.62) 12.72(12.66,12.77) 0.02 Year cycle~% 0.16 2007–2008 13.26(11.05,14.48) 14.43(6.70,22.16) 2009–2010 13.34(11.76,14.91) 16.69(8.64,24.74) 2011–2012 15.46(13.14,17.78) 10.47(0.25,20.69) 2013–2014 15.11(11.86,31.45) 21.65(11.86,31.45) 2015–2016 15.59(13.01,30.39) 21.84(13.30,30.39) 2017–2018 13.58(11.78,15.38) 7.05(2.19,11.92) 2019–2020 13.65(11.92,15.38) 7.86(3.42,12.30) *OR : odds ratio; CI : confidence interval; PIR :property income ratio; BMI :body mass index. Data are shown as mean (x ̅) or n (%), combined with 95%CI. The results of the univariate and multivariate logistic regression analyses between age at menarche and gynaecological cancer prevalence were shown in Table 2 . Univariate logistic regression analysis showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.82, 95%CI: 0.69–0.97), with a statistically significant difference (P = 0.02). Model 1 was adjusted for age and ethnicity and showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.81, 95%CI: 0.68–0.96), with a statistically significant difference (P = 0.02). Model 2 was adjusted for age, race, education level, PIR, marital status, BMI, waist circumference and duration of moderate exercise. The results showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.82, 95%CI: 0.69–0.98), with a statistically significant difference (P = 0.03). Model 3 was adjusted for age, race, education level, PIR, marital status, BMI, waist circumference, duration of moderate exercise, smoking habits, alcohol consumption habits, hypertension status and diabetes. The results showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.84, 95%CI: 0.71-1.00), with a statistically significant difference (P = 0.05). In all three sensitivity analyses, age at menarche was significantly and negatively associated with the prevalence of gynaecological cancers. Even in model 3, where we adjusted for a total of 12 covariates, the results were still significantly different. This suggested that age at menarche was stably associated with the prevalence of gynaecological cancers in all cases. It was worth noting that as the number of covariates adjusted increases, the effect of age at menarche on the prevalence of gynaecological cancers in women decreases. Table.2. Univariate and multivariate logistic regression analysis between the age of menarche and the prevalence of gynecological cancer. Outcomes model OR (95%CI) P value Gynecological Cancer Crude 0.82(0.69,0.97) 0.02 Model1 0.81(0.68,0.96) 0.02 Model2 0.82(0.69,0.98) 0.03 Model3 0.84(0.71,1.00) 0.05 * Crude is an unadjusted model, Model1 is a model adjusted for age and race, Model2 is a model adjusted for age, race, education level, PIR, marital status, BMI, waist circumference and moderate exercise time, and Model3 is a model adjusted for age, race, education level, PIR, marital status, BMI, waist circumference, moderate exercise time, smoking habits, alcohol consumption, hypertension status, and diabetes. As shown in Fig. 2 , the relationship between age at menarche and the prevalence of different gynaecological cancers. We conducted regression analyses adjusted for basic demographic factors (age, race, education level, PIR, marital status). The results found a negative correlation between age at menarche and prevalence in uterine cancer (P = 0.03). No correlation between age at menarche and prevalence in cervical cancer (P = 0.17) and ovarian cancer (P = 0.29). Population with a young age of menarche were more likely to develop uterine cancer (OR: 0.72, 95%CI: 0.54–0.98). In addition, trends in the prevalence of gynaecological cancers and the distribution of uterine cancers among gynaecological cancers by age at menarche were studied according to the age of menarche (Fig. 3 ). The findings showed that there was an overall downward trend in the prevalence of gynaecological and uterine cancers as the age of menarche increases until the age of 18. The results of the non-linear analysis between age of menarche and gynecological cancer prevalence, as shown in Fig. 4 , revealed a significant negative linear relationship (P non-linear = 0.17). This relationship also appears in the prevalence of uterine cancer (P non-linear = 0.73) (Fig. 5 ). The results of the subgroup analysis were shown in Table 3 . No significant interactions with the albumin/globulin ratio were found for any of the subgroup variables. However, earlier age at menarche was associated with a greater risk of cancer among those with high school education or higher (P = 0.05), unmarried (P < 0.01), small BMI (P = 0.02), long duration of daily physical activity (P = 0.02), moderate alcohol consumption (P = 0.01), and no diabetes mellitus (P = 0.03). The results of the subgroup analysis on uterine cancer were shown in Table 4. Age at menarche increased the risk of uterine cancer in several subgroups, but only waist circumference and hypertension interacted with age at menarche. However, it did not show a significant association with the risk of uterine cancer in either of the two subgroups of waist circumference levels, whereas in those with hypertension. The younger the age at menarche, the higher the risk of uterine cancer (OR: 0.63, 95%CI: 0.40–0.99). Table.3. Results of subgroup analysis related to gynecological cancer. Subgroup Variable Gynecological Cancer OR (95%CI) P value P for interaction Age 0.47 <65 0.88(0.73,1.06) 0.17 ≥65 0.72(0.51, 1.02) 0.06 Race 0.52 Non-Hispanic White 0.82(0.64,1.05) 0.11 Non-Hispanic Black 0.87(0.65, 1.16) 0.34 Mexican American 1.46(0.96, 2.22) 0.08 Other Hispanic 0.70(0.30, 1.66) 0.42 Other Race 0.64(0.36,1.13) 0.12 Education level 0.72 Less than high school 0.77(0.51, 1.15) 0.20 High school 0.80(0.58, 1.11) 0.17 More than high school 0.81(0.66, 1.00) 0.05 Marital Status 0.50 Married/Living with partner 0.83(0.67, 1.02) 0.08 Never married 0.56(0.38,0.82) < 0.01 Widowed/Divorced/Separated 0.87(0.63, 1.19) 0.38 Family PIR 0.80 <2.8 0.80(0.63, 1.02) 0.08 ≥ 2.8 0.88(0.70, 1.11) 0.28 Waist 0.50 <94 0.76(0.55, 1.04) 0.09 ≥94 0.88(0.72,1.08) 0.23 BMI 0.21 <25 0.67(0.48, 0.93) 0.02 ≥25 0.89(0.72,1.09) 0.26 Moderate exercise 0.55 <45 0.89(0.68, 1.17) 0.39 ≥45 0.81(0.67, 0.97) 0.02 Smoking behavior 0.42 never 0.93(0.75,1.16) 0.53 former 0.76(0.57, 1.02) 0.07 now 0.85(0.61, 1.18) 0.32 Alcohol consumption 0.22 never 0.79(0.51,1.24) 0.31 former 1.14(0.81,1.61) 0.43 mild 0.80(0.57,1.11) 0.18 moderate 0.72(0.57,0.90) 0.01 heavy 0.94(0.63,1.41) 0.77 Hypertension 0.37 yes 0.79(0.60,1.04) 0.10 no 0.88(0.72, 1.07) 0.20 Diabetes 0.44 yes 0.89(0.62, 1.27) 0.52 no 0.81(0.67, 0.98) 0.03 * Models were adjusted for age, race, education level, PIR, marital status, BMI, waist circumference, moderate exercise time, smoking habits, alcohol consumption, hypertension status, and diabetes. (except subgroup variables); PIR, energy intake, waist circumference, and duration of moderate exercise were divided into groups based on median. Table.4. Results of subgroup analysis related to uterine cancer. Subgroup Variable Cancer = Uterine OR (95%CI) P value P for interaction Age 0.37 <65 0.88(0.63, 1.22) 0.43 ≥65 0.62(0.39, 0.97) 0.04 Race 0.20 Non-Hispanic White 0.77(0.50,1.18) 0.23 Non-Hispanic Black 0.46(0.21,0.98) 0.05 Mexican American 1.65(0.94,2.89) 0.08 Other Hispanic 0.10(0.01,1.09) 0.06 Other Race 0.83(0.66,1.05) 0.11 Education level 0.10 Less than high school 0.57(0.31, 1.05) 0.07 High school 0.65(0.20,2.10) 0.47 More than high school 0.69(0.47, 1.00) 0.05 Marital Status 0.32 Married/Living with partner 0.80(0.60, 1.06) 0.12 Never married 3.54(2.24, 5.58) 0.02 Widowed/Divorced/Separated 0.59(0.37, 0.92) < 0.01 Family PIR 0.23 <2.8 0.58(0.37, 0.90) 0.02 ≥ 2.8 0.91(0.61, 1.36) 0.65 Waist 0.01 <94 1.29(0.99,1.69) 0.06 ≥94 0.69(0.46, 1.04) 0.08 BMI 0.39 <25 0.94(0.58,1.53) 0.81 ≥25 0.73(0.49, 1.07) 0.11 Moderate exercise 0.79 <45 0.70(0.39, 1.27) 0.24 ≥45 0.77(0.62, 0.95) 0.02 Smoking behavior 0.48 never 0.79(0.58, 1.07) 0.12 former 0.69(0.39, 1.22) 0.20 now 0.83(0.50,1.36) 0.45 Alcohol consumption 0.36 never 0.37(0.12,1.16) 0.09 former 0.82(0.47, 1.43) 0.48 mild 0.69(0.40,1.16) 0.16 moderate 0.68(0.41, 1.12) 0.13 Hypertension 0.04 yes 0.63(0.40, 0.99) 0.05 no 1.00(0.81, 1.24) 0.99 * Models were adjusted for age, race, education level, PIR, marital status, BMI, waist circumference, moderate exercise time, smoking habits, alcohol consumption, hypertension status, and diabetes. (except subgroup variables); PIR, energy intake, waist circumference, and duration of moderate exercise were divided into groups based on median. 4. Discussion Menarche was a very important event in a woman's life, which was affected by life, family, body and other factors. Menarche also played a role in the occurrence of diseases [9–18] . According to the statistical analysis and logical regression analysis of the questionnaire data of NHANES 2007–2020, the results showed that there was a correlation between the age of menarche and the prevalence of gynecological cancer. We conducted a series of analyses on age, race, education, PIR, marital status, BMI, waistline, moderate exercise time, smoking habits, hypertension, energy intake, diabetes and drinking habits. Age was considered to be a highly correlated factor in cancer risk. At present, elderly patients account for the majority of all cancer patients, including cancer recurrence [21–23] . Patients who were younger at menarche and older at the time of the survey may experienced more times of menarche, which also had a certain impact on the occurrence of gynecological cancer. During menstruation, a woman's body would be altered in terms of trace elements and body status [24] . The process of menstruation was accompanied by metabolic changes, and the relationship between the occurrence and development of cancer and metabolism was very close [25–28] . When a woman's menstrual cycle increases, it also indicates frequent changes in the body's metabolic state. The younger the age of first menstruation, the greater the metabolic changes are likely to be and the risk of cancer gradually increases. The association between age at menarche and gallstone disease in women had been found to be mediated by obesity in several other correlation studies [29] . Moreover, obesity and birth weight are factors that influence the age of menarche [30,31] . The change of weight was closely related to the energy intake and living habits of the human body. An experiment by Moslshi et al. in 2021 found an association between intake of plant and animal protein in childhood and age at menarche [32] . Waist circumference, BMI, moderate exercise time, PIR, energy intake, smoking and alcohol consumption were all associated with obesity indices [33–35] . In a 2022 study on obesity among Japanese adolescents, a positive association between low household income and obesity was found [35] . In addition, many lifestyle habits played an important role in obesity in adolescents [36] . Obesity not only affected the age of first menstruation, but also played a role in the physical changes that occur after menarche. In our study, differences in the relationship between age at menarche and gynaecological cancer prevalence emerged in population of different weights. In the results of the subgroup analysis ( Tables 3 and 4 ), the prevalence of gynaecological cancers was higher in those with a young age at menarche than in those with an older age at menarche among those with a normal weight. Similar statistical results were found for the other variable factors of interest (marital status, length of moderate exercise, alcohol consumption, energy intake). Our study involved a variety of variable factors that are informative about the prevalence of gynaecological cancers. In addition, some of the variable factors had an impact on the age at menarche. More importantly, as age at menarche changed, age at menarche played a role in the variation of some of these variables. As a simple example, obesity itself was a factor that influences the onset of menstruation. After the onset of menstruation, obesity was a mediator between the onset of menstruation and the onset of disease. Multiple variable factors interacted and influenced each other, and the correlation between many of these variables needed to be verified by more experiments. This study has the following limitations: 1. this was a cross-sectional study in which there was no temporal relationship and causality cannot be determined; 2. there was a recall bias in the reporting of this questionnaire; 3. this experiment may have included some confounding factors for measurement purposes. Such as parents' education level, changes in family status, child's birth weight, genetic factors, etc; 4. spanning a long period of time, 16 years ago, individual education level and physical level may have changed. 5. Comparison with similar studies There are similarities between a previously published study and ours, but there are some significant differences between our studies. Compared with others [37–38] , our study aimed at the relationship between age at menarche and the prevalence of gynecological cancers. We found a negative correlation with the prevalence of uterine cancer, while the relationship with cervical and ovarian cancers was not significant, so we studied more on uterine cancer. On this basis, we conducted a large number of sensitivity analyses, subgroup analyses and cross-validation, which helped us to find the potential high-risk groups more accurately, and made our findings more meaningful in preventive screening. 6. Conclusion In conclusion, the analysis of the NHANES questionnaire data showed that the younger the age of menarche, the higher the risk of gynaecological cancer in the population. More obviously, the younger the age of first menstruation, the higher the risk of uterine cancer. Furthermore, according to our findings, the correlation between the prevalence of uterine cancer and age at menarche was even stronger. These results suggest that public health workers need to pay attention to the early age of menarche when developing screening programmes for gynaecological cancers in order to develop more appropriate screening programmes. Declarations The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The NHANES survey was approved by the National Center for Health Statistics Institutional Review Board. The study reported in this manuscript was exempt from ethical committee approval because it was based on publicly available data. Author Contributions Xiaohui Pei:Writing-Original Draft; Hao Sun: Methodology, Data Analysis; All authors contributed to the article and approved the submitted version. The authors declare no conflict of interest. Acknowledgments We thank all the participants and staff of NHANES 2007-2020 for their valuable contributions. Availability of data and materials The NHANES survey was approved by the National Center for Health Statistics Institutional Review Board. The study reported in this manuscript was exempt from ethical committee approval because it was based on publicly available data. Funding This study was not funded References Menon, U., Steps towards effective gynaecological cancer screening. Nat Rev Clin Oncol 2018, 15 (9), 538-540. Keyvani, V.; Kheradmand, N.; Navaei, Z. N.; Mollazadeh, S.; Esmaeili, S. A., Epidemiological trends and risk factors of gynecological cancers: an update. Med Oncol 2023, 40 (3), 93. Liontos, M.; Fiste, O.; Zagouri, F.; Dimopoulos, M. A., Advances in Gynecological Cancers. Int J Mol Sci 2022, 23 (11). Zhang, W.; Liu, Y.; Zhou, X.; Zhao, R.; Wang, H., Applications of CRISPR-Cas9 in gynecological cancer research. Clin Genet 2020, 97 (6), 827-834. Constantinou, P.; Tischkowitz, M., Genetics of gynaecological cancers. Best Pract Res Clin Obstet Gynaecol 2017, 42 , 114-124. Yetkin-Arik, B.; Kastelein, A. W.; Klaassen, I.; Jansen, C.; Latul, Y. P.; Vittori, M.; Biri, A.; Kahraman, K.; Griffioen, A. W.; Amant, F.; Lok, C. A. R.; Schlingemann, R. O.; van Noorden, C. J. F., Angiogenesis in gynecological cancers and the options for anti-angiogenesis therapy. Biochim Biophys Acta Rev Cancer 2021, 1875 (1), 188446. Tindale, L. C.; Zhantuyakova, A.; Lam, S.; Woo, M.; Kwon, J. S.; Hanley, G. E.; Knoppers, B.; Schrader, K. A.; Peacock, S. J.; Talhouk, A.; Dummer, T.; Metcalfe, K.; Pashayan, N.; Foulkes, W. D.; Manchanda, R.; Huntsman, D.; Stuart, G.; Simard, J.; Dawson, L., Gynecologic Cancer Risk and Genetics: Informing an Ideal Model of Gynecologic Cancer Prevention. Curr Oncol 2022, 29 (7), 4632-4646. Menon, U., Steps towards effective gynaecological cancer screening. Nat Rev Clin Oncol 2018, 15 (9), 538-540. Rivas Paz, M.; Torres Mendoza, B. M.; Torres Castillo, N., Age of the onset of menarche and its complications: a literature review. Int J Gynaecol Obstet 2023 . Gavela-Perez, T.; Navarro, P.; Soriano-Guillen, L.; Garces, C., High Prepubertal Leptin Levels Are Associated With Earlier Menarcheal Age. J Adolesc Health 2016, 59 (2), 177-81. Guo, S.; Lu, H. J.; Zhu, N.; Chang, L., Meta-Analysis of Direct and Indirect Effects of Father Absence on Menarcheal Timing. Front Psychol 2020, 11 , 1641. Ameade, E. P.; Garti, H. A., Age at Menarche and Factors that Influence It: A Study among Female University Students in Tamale, Northern Ghana. PLoS One 2016, 11 (5), e0155310. Aurino, E.; Schott, W.; Penny, M. E.; Behrman, J. R., Birth weight and prepubertal body size predict menarcheal age in India, Peru, and Vietnam. Ann N Y Acad Sci 2017 . Ryu, S.; Chang, Y.; Kang, J. G.; Sung, J.; Kim, J. Y.; Jung, H. S.; Yun, K. E.; Kim, C. W.; Cho, J.; Kwon, M. J.; Kim, K. H.; Shin, H.; Sung, K. C., Association of Age at Menarche With Left Ventricular Diastolic Dysfunction in Middle-Aged Women. Circ J 2018, 82 (3), 708-714. Zurawiecka, M.; Wronka, I., Age at Menarche and Risk of Respiratory Diseases. Adv Exp Med Biol 2019, 1222 , 9-16. Lulu, S.; Graves, J.; Waubant, E., Menarche increases relapse risk in pediatric multiple sclerosis. Mult Scler 2016, 22 (2), 193-200. Lee, J. J.; Cook-Wiens, G.; Johnson, B. D.; Braunstein, G. D.; Berga, S. L.; Stanczyk, F. Z.; Pepine, C. J.; Bairey Merz, C. N.; Shufelt, C. L., Age at Menarche and Risk of Cardiovascular Disease Outcomes: Findings From the National Heart Lung and Blood Institute-Sponsored Women's Ischemia Syndrome Evaluation. J Am Heart Assoc 2019, 8 (12), e012406. Lyu, I. J.; Kim, M. H.; Baek, S. Y.; Kim, J.; Park, K. A.; Oh, S. Y., The Association Between Menarche and Myopia: Findings From the Korean National Health and Nutrition Examination, 2008-2012. Invest Ophthalmol Vis Sci 2015, 56 (8), 4712-8. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/P_MCQ.htm#MCQ010. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/P_OSQ.htm#OSQ010A. Yoshida, Y., Current treatment of older patients with recurrent gynecologic cancer. Curr Opin Obstet Gynecol 2019, 31 (5), 340-344. Poiseuil, M.; Tron, L.; Woronoff, A. S.; Tretarre, B.; Dabakuyo-Yonli, T. S.; Fauvernier, M.; Roche, L.; Dejardin, O.; Molinie, F.; Launoy, G.; French Network of Cancer, R., How do age and social environment affect the dynamics of death hazard and survival in patients with breast or gynecological cancer in France? Int J Cancer 2022, 150 (2), 253-262. Pretzsch, E.; Niess, H.; Bosch, F.; Westphalen, C. B.; Jacob, S.; Neumann, J.; Werner, J.; Heinemann, V.; Angele, M. K., Age and metastasis - How age influences metastatic spread in cancer. Colorectal cancer as a model. Cancer Epidemiol 2022, 77 , 102112. Njoku, U. C.; Amadi, P. U.; Amadi, J. A., Nutritional modulation of blood pressure and vascular changes during severe menstrual cramps. J Taibah Univ Med Sci 2021, 16 (1), 93-101. Faubert, B.; Solmonson, A.; DeBerardinis, R. J., Metabolic reprogramming and cancer progression. Science 2020, 368 (6487). Gyamfi, J.; Kim, J.; Choi, J., Cancer as a Metabolic Disorder. Int J Mol Sci 2022, 23 (3). Sahoo, O. S.; Pethusamy, K.; Srivastava, T. P.; Talukdar, J.; Alqahtani, M. S.; Abbas, M.; Dhar, R.; Karmakar, S., The metabolic addiction of cancer stem cells. Frontiers in Oncology 2022, 12 . Yu, Y.; Gong, L.; Ye, J., The Role of Aberrant Metabolism in Cancer: Insights Into the Interplay Between Cell Metabolic Reprogramming, Metabolic Syndrome, and Cancer. Front Oncol 2020, 10 , 942. Ryu, S.; Chang, Y.; Choi, Y.; Kwon, M. J.; Yun, K. E.; Jung, H. S.; Kim, B. K.; Kim, Y. J.; Kim, K. H.; Cho, J.; Chung, E. C.; Shin, H.; Suh, B. S., Age at Menarche and Gallstone Disease in Middle-Aged Women. Reprod Sci 2016, 23 (10), 1304-13. Juul, F.; Chang, V. W.; Brar, P.; Parekh, N., Birth weight, early life weight gain and age at menarche: a systematic review of longitudinal studies. Obes Rev 2017, 18 (11), 1272-1288. Wang, L.; Xu, F.; Zhang, Q.; Chen, J.; Zhou, Q.; Sun, C., Causal relationships between birth weight, childhood obesity and age at menarche: A two-sample Mendelian randomization analysis. Clin Endocrinol (Oxf) 2023, 98 (2), 212-220. Moslehi, N.; Asghari, G.; Mirmiran, P.; Azizi, F., Longitudinal association of dietary sources of animal and plant protein throughout childhood with menarche. BMC Pediatr 2021, 21 (1), 206. Tayebi, N.; Yazdanpanahi, Z.; Yektatalab, S.; Pourahmad, S.; Akbarzadeh, M., The Relationship Between Body Mass Index (BMI) and Menstrual Disorders at Different Ages of Menarche and Sex Hormones. J Natl Med Assoc 2018, 110 (5), 440-447. Weaver, R. G.; Beets, M. W.; Brazendale, K.; Hunt, E., Disparities by household income and race/ethnicity: the utility of BMI for surveilling excess adiposity in children. Ethn Health 2021, 26 (8), 1180-1195. Doi, S.; Isumi, A.; Fujiwara, T., Association of Adverse Childhood Experiences Including Low Household Income and Peer Isolation With Obesity Among Japanese Adolescents: Results From A-CHILD Study. Front Public Health 2022, 10 , 754765. Carpena Lucas, P. J.; Sanchez-Cubo, F.; Vargas Vargas, M.; Mondejar Jimenez, J., Influence of Lifestyle Habits in the Development of Obesity during Adolescence. Int J Environ Res Public Health 2022, 19 (7). Cheng, G.; Wang, M.; Sun, H.; Lai, J.; Feng, Y.; Liu, H.; Shang, Y.; Zhao, Y.; Zuo, B.; Lu, Y., Age at menopause is inversely related to the prevalence of common gynecologic cancers: a study based on NHANES. Frontiers in Endocrinology 2023, 14 . Fuhrman BJ, Moore SC, Byrne C, Makhoul I, Kitahara CM, Berrington de González A, Linet MS, Weiderpass E, Adami HO, Freedman ND, Liao LM, Matthews CE, Stolzenberg-Solomon RZ, Gaudet MM, Patel AV, Lee IM, Buring JE, Wolk A, Larsson SC, Prizment AE, Robien K, Spriggs M, Check DP, Murphy N, Gunter MJ, Van Dusen HL Jr, Ziegler RG, Hoover RN. Association of the Age at Menarche with Site-Specific Cancer Risks in Pooled Data from Nine Cohorts. Cancer Res . 2021, 81(8),2246-2255. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Mar, 2025 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 29 Dec, 2024 Reviews received at journal 27 Dec, 2024 Reviewers agreed at journal 18 Dec, 2024 Reviews received at journal 14 Oct, 2024 Reviewers agreed at journal 02 Oct, 2024 Reviewers invited by journal 04 Sep, 2024 Editor assigned by journal 27 Jul, 2024 Submission checks completed at journal 26 Jul, 2024 First submitted to journal 24 Jul, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4796084","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335758039,"identity":"8134b206-d0db-4244-99c5-0094087948f5","order_by":0,"name":"Hao Sun","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Sun","suffix":""},{"id":335758040,"identity":"340c7206-e13a-46f2-aff5-0a64cd4b04ff","order_by":1,"name":"Xiaohui Pei","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Pei","suffix":""},{"id":335758041,"identity":"86df087f-ba3a-40fa-aaa9-48a50453963e","order_by":2,"name":"Yaoyun Zhang","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yaoyun","middleName":"","lastName":"Zhang","suffix":""},{"id":335758042,"identity":"1150a383-a45e-4acd-a8b7-a5d444a66873","order_by":3,"name":"mengmeng wang","email":"","orcid":"","institution":"Chinese Academy of traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"mengmeng","middleName":"","lastName":"wang","suffix":""},{"id":335758043,"identity":"38122bee-7db4-41a5-a0d6-c9a6024270b6","order_by":4,"name":"Ziqian Song","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ziqian","middleName":"","lastName":"Song","suffix":""},{"id":335758044,"identity":"16db86a1-6760-4157-827d-c8f4ec371b7f","order_by":5,"name":"Jialin Wang","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jialin","middleName":"","lastName":"Wang","suffix":""},{"id":335758045,"identity":"6f983a1e-8f55-41eb-a395-ecc8fc1087af","order_by":6,"name":"Yuantao 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14:21:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4796084/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4796084/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-025-02472-z","type":"published","date":"2025-03-26T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63026645,"identity":"3231647b-d46b-4c93-9e97-988186f2ccd5","added_by":"auto","created_at":"2024-08-22 08:35:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78946,"visible":true,"origin":"","legend":"\u003cp\u003eThe screening flow chart of the study population.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4796084/v1/a31dfc603d379ab879e9f7c2.png"},{"id":63026640,"identity":"e38b04f3-66d4-4e08-b3e9-8cc0c3c3611b","added_by":"auto","created_at":"2024-08-22 08:35:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60405,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of regression analysis between the age of menarche and the prevalence of different gynecological cancers were analyzed after adjustment according to the basic variables.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4796084/v1/cf4e0ed0fd42bb87f2b2d801.png"},{"id":63026638,"identity":"45741117-9f93-43bd-b706-d233eaf83191","added_by":"auto","created_at":"2024-08-22 08:35:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":269296,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of gynaecological cancers in women of different menarcheal ages and trends in the distribution of uterine cancers among gynaecological cancers.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4796084/v1/dfd149932242fbfc2562d899.png"},{"id":63026636,"identity":"f01231d7-bd22-46ad-822d-d642dc6818e2","added_by":"auto","created_at":"2024-08-22 08:35:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163644,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted Cubic Spline (RCS) Showing age of menarche and gynecological cancer prevalence.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4796084/v1/062db4082e27c268b292d35e.png"},{"id":63026639,"identity":"433eec7e-98f0-41c3-b82b-47219b4dce00","added_by":"auto","created_at":"2024-08-22 08:35:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":164462,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted Cubic Spline (RCS) Showing age of menarche and uterine cancer prevalence.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4796084/v1/3c2999522090a43f6a782bbf.png"},{"id":79605011,"identity":"d9ebafa7-78db-4c0f-812b-3c88b3921fdb","added_by":"auto","created_at":"2025-03-31 16:10:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2526026,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4796084/v1/a5ab2f52-347d-49a2-9b60-56bd747e748c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age at Menarche is Inversely Related to the Prevalence of Common Gynecologic Cancers","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGynecological cancer was a cancer with high morbidity and mortality rates, and it was the most common cancer among women \u003csup\u003e[1, 2]\u003c/sup\u003e. Gynecological cancers posed a great threat to the physical and psychological health of women worldwide, and even caused significant economic pressure on societies and countries \u003csup\u003e[3,4]\u003c/sup\u003e. The most common type of gynaecological cancer was cervical cancer, followed by uterine and ovarian cancers \u003csup\u003e[5]\u003c/sup\u003e. Each of these cancers was unique in type, with different curative factors, risk factors, diagnosis and treatment, and prognostic outcomes \u003csup\u003e[6]\u003c/sup\u003e. However, through the control of risk factors, early screening of related factors, screening of high-risk groups and other means, reducing risk of the occurrence of gynecological cancer \u003csup\u003e[7,8]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhen a woman's hypothalamus-pituitary-gonad axis tended to mature, various parts of the woman's body would change, such as breast development and increased body hair. However, menarche was the most important manifestation \u003csup\u003e[9]\u003c/sup\u003e. There were many factors affecting the age of menarche, such as hormone level \u003csup\u003e[10]\u003c/sup\u003e, family economic condition \u003csup\u003e[11,12]\u003c/sup\u003e, birth weight \u003csup\u003e[13]\u003c/sup\u003e and so on. In addition, the age of menarche played a certain role in the occurrence of many diseases. A study on the relationship between age at menarche and cardiac function showed that there was a negative correlation between them \u003csup\u003e[14]\u003c/sup\u003e. The age of menarche had also been proved to be related to respiratory diseases \u003csup\u003e[15]\u003c/sup\u003e, multiple sclerosis \u003csup\u003e[16]\u003c/sup\u003e, myopia \u003csup\u003e[17]\u003c/sup\u003e, cardiovascular disease prognosis \u003csup\u003e[18]\u003c/sup\u003e and so on. As an important factor of women's physical index, the relationship between the age of menarche and gynecological cancer had aroused our interest. The important thing was that the relationship between the age of menarche and gynecological cancer was not clear.\u003c/p\u003e \u003cp\u003eTo investigate the relationship between age at menarche and gynaecological cancer prevalence, we conducted a statistical analysis of questionnaire data from the National Health and Nutrition Examination Survey (NHANES). The NHANES database was used as the data source for all data in this paper. In this study, a total of 13 factors that may have an impact on the results were considered and adjusted accordingly. These included a number of dimensions such as basic demographic information, personal fitness, lifestyle habits and disease status, with a number of factors such as age, marriage, body mass index and income having a significant effect on cancer prevalence. Several sensitivity analyses, subgroup analyses, and cross-validation were conducted to ensure the reliability and accuracy of the results based on these covariates that may have an impact on the results. In this study, we focused on the most common cancers among gynaecological cancers. We screened data from NHANES questionnaires from 2007 to 2020 and analyzed the data statistically. The relationship between age at menarche and the prevalence of gynecologic cancers was ultimately assessed.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and sample\u003c/h2\u003e \u003cp\u003eHealth and nutrition data for the study population were obtained from the NHANES database. Some specific information on this survey is described elsewhere \u003csup\u003e[19\u0026ndash;20]\u003c/sup\u003e. All the data of NHANES 2007\u0026ndash;2020 were approved by the Ethics Review Committee, and all subjects had informed consent and written proof. The number of population initially enrolled in the study was 111797, and 47548 were excluded from the study because of incomplete cancer information. In addition, we excluded 36325 subjects with incomplete gynecological information. Of the remaining 27,924 study subjects, we further excluded a number of study subjects. These included study subjects with missing information on examination (n\u0026thinsp;=\u0026thinsp;18,698), missing information on demographics (n\u0026thinsp;=\u0026thinsp;2,505), missing information on diet (n\u0026thinsp;=\u0026thinsp;1,073), and missing information on disease (hypertension and diabetes) (n\u0026thinsp;=\u0026thinsp;108). A total of 5540 subjects were eligible for the experimental study. The screening process for the study population was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2Assessments\u003c/h2\u003e \u003cp\u003ePopulation screening criteria were age\u0026thinsp;\u0026gt;\u0026thinsp;18 years, complete and reliable data in all categories, and clear representation weights. The definition of the type of cancer was assessed according to the location of the cancer and the doctor's diagnosis. The statistics of the age of menarche were based on the results of a questionnaire. Considering that the study was on a female population, marital status was considered an important reference variable factor. Marital status was classified as: 1. married; 2. never married; 3. Widowed / divorced / separated. Some other relevant characteristics about the study population were based on the NHANES findings. Data were obtained on age (\u0026lt;65 / \u0026ge; 65), race (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Hispanic, Other Race), Education level (Less than high school, High school ,More than high school). The poverty impact ratio (PIR) was used as a threshold for the variable at 2.8%. In addition, the inclusion criteria of waist circumference (\u0026lt;\u0026thinsp;94 cm / \u0026ge; 94 cm), body mass index (BMI) (\u0026lt;\u0026thinsp;25 kg / m\u003csup\u003e2\u003c/sup\u003e / \u0026ge; 25 kg / m\u003csup\u003e2\u003c/sup\u003e), moderate exercise (\u0026lt;\u0026thinsp;45 minutes / \u0026ge; 45 minutes) and energy intake (\u0026lt;\u0026thinsp;1700 kcal / \u0026ge; 1700 kcal) were also evaluated according to the data in the questionnaire. Where the energy intake value was calculated as an average value based on the total energy intake over two days, the daily energy intake is derived from the total energy from food and beverages. Moderate exercise was defined as moderate intensity exercise, fitness or recreational activity that results in a small increase in breathing or heart rate and was performed continuously for at least 10 minutes.\u003c/p\u003e \u003cp\u003eThe smoking habits of the study population were assessed into the following three categories: 1, nexer (smoked less than 100 cigarettes in life); 2, former (smoked more than 100 cigarettes in life and smoke not at all now); 3, now (smoked more than 100 cigarettes in life and smoke some days or every day). The criteria for classifying drinking habits were based on the most recent assessment indicators (only female criteria have been used, as well as criteria for the shared component): 1, never (had\u0026thinsp;\u0026lt;\u0026thinsp;12 drinks in lifetime); 2, former (had\u0026thinsp;\u0026ge;\u0026thinsp;12 drinks in 1 year and did not drink last year, or did not drink last year but drank\u0026thinsp;\u0026ge;\u0026thinsp;12 drinks in lifetime); 3, mild (\u0026le;\u0026thinsp;1 drink per month); 4, moderate (\u0026le;\u0026thinsp;2 drinks per month); 5, heavy (\u0026ge;\u0026thinsp;3 drinks per month).\u003c/p\u003e \u003cp\u003eThe diagnosis of the underlying disease reported in the investigation was assessed on the basis of the results of the examination and the doctor's diagnosis. Among them, the diagnostic criteria of hypertension: average blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 / 90mmHg. Average blood pressure was calculated by the following protocol: 1, the diastolic reading with zero was not used to calculate the diastolic average; 2, if all diastolic reading were zero, then the average would be zero; 3, if only one blood pressure reading was obtained, that reading was the average; 4, if there was more than one blood pressure reading, the first reading was always exclude from the average. With regard to the inclusion criteria for diabetes, the assessment was classified according to the international customary criteria and related indicators: 1, doctor told you have diabetes; 2, glycohemoglobin HbA1c (%)\u0026thinsp;\u0026gt;\u0026thinsp;6.5; 3, fasting glucose (mmol / L)\u0026thinsp;\u0026ge;\u0026thinsp;7.0; 4, random blood glucose (mmol / L)\u0026thinsp;\u0026ge;\u0026thinsp;11.1; 5, two-hour OGTT blood glucose (mmol / L)\u0026thinsp;\u0026ge;\u0026thinsp;11.1; 6, use of diabetes medication or insulin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3Statistical analyses\u003c/h2\u003e \u003cp\u003eFor random missing samples, we used multiple interpolation to fill in. For non-random missing data, we excluded them and the filtered data were analysed using R (v.4.2.1). For continuous variables, we used mean and 95% confidence intervals (x ̅ (95%CI) for statistical descriptions, and t-tests and ANOVA for between-group comparisons. For categorical variables, we used p-values and 95%CI for statistical descriptions and chi-square tests for comparisons between groups. For the adjustment analyses, we chose binary logistic regression for multifactor analysis and multiple sensitivity analysis. Twelve covariates were stratified into the model to ensure the reliability of the analyses. In addition, we further fitted separate models for the association between age at menarche and the prevalence of three common gynaecological cancers. Several subgroup analyses and interaction studies were also performed to obtain a more precise target population. We used three section restricted cubic spline (RCS) to explore the nonlinear relationship between menarche age and the prevalence of common gynecological cancers and uterine diseases. In this analysis, adjustments were made for all 12 covariates. In this study, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant difference. The NHANES complex multi-stage sampling design was used for all analyses in this study. Appropriate weights were selected for calculations and weighted multivariate logistic regression to ensure a representative sample. In addition, we conducted several sensitivity analyses to verify the reliability of the results. We constructed multiple models adjusting for age, race, education level, marital status, BMI, PIR, waist circumference, moderate activity time, smoking, alcohol consumption, hypertension, and diabetes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOur study included 5,540 NHANES participants, which could represent 38,016,087 US women, with an overall weighted prevalence of 1.91% for gynaecological cancers. The baseline characteristics of the study population were shown in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. The results of the study showed statistical differences in the prevalence of gynaecological cancers between age, education level, marital status, PIR, BMI, waist circumference, duration of moderate exercise, smoking habits, and hypertension status. The results showed that population with an earlier age of menarche were at higher risk of gynaecological cancers.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1.\u003c/b\u003e A table of baseline characteristics of the study population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGynecological Cancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u0026thinsp;~\u0026thinsp;years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.74(46.05,47.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.25(48.42,54.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.47(72.06,76.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.05(63.16,82.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.10(7.82,10.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.59(2.05, 9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.47(4.46,6.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.11(1.66,6.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.39(3.64, 5.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.84(2.44,11.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.56(5.71, 7.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.41(2.50,18.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.30(5.44, 7.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.54(7.67,23.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.72(16.29,19.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.44(11.50,25.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.98(74.19,77.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.03(55.51,76.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.01(61.06,64.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.76(54.26,73.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.10(15.51,18.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.03( 2.08, 9.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.89(18.62,21.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.21(21.27,39.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily PIR~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.38(3.30,3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.95(2.57,3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u0026thinsp;~\u0026thinsp;kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.27(28.00,28.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.52(28.67,32.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist\u0026thinsp;~\u0026thinsp;cm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.53(93.92, 95.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.87(97.06,104.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate exercise\u0026thinsp;~\u0026thinsp;minutes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.29(51.86,54.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.92(50.01,77.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.02(64.03,68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.51(24.50,50.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.29(20.59,23.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.64(17.42,41.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.70(10.57,12.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.85(21.72,43.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.96(9.47,12.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.03(2.39,11.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.06(6.95, 9.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.44(3.23,15.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.61(34.49,38.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.77(24.03,49.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.35(24.77,27.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.12(14.27,37.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.02(16.53,19.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.64(11.56,29.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.22(27.50,30.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.49(36.87,60.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.78(69.07,72.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.51(39.89,63.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.64(8.78,10.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.39(6.38,26.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.36(89.49,91.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.61(73.60,93.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst menstruation\u0026thinsp;~\u0026thinsp;years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.20(11.78,12.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.72(12.66,12.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear cycle~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.26(11.05,14.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.43(6.70,22.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.34(11.76,14.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.69(8.64,24.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u0026ndash;2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.46(13.14,17.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.47(0.25,20.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.11(11.86,31.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.65(11.86,31.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.59(13.01,30.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.84(13.30,30.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.58(11.78,15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.05(2.19,11.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.65(11.92,15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.86(3.42,12.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e*OR\u003c/b\u003e: odds ratio; \u003cb\u003eCI\u003c/b\u003e: confidence interval; \u003cb\u003ePIR\u003c/b\u003e:property income ratio; \u003cb\u003eBMI\u003c/b\u003e:body mass index. \u003cem\u003eData are shown as mean (x ̅) or n (%), combined with 95%CI.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe results of the univariate and multivariate logistic regression analyses between age at menarche and gynaecological cancer prevalence were shown in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e. Univariate logistic regression analysis showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.82, 95%CI: 0.69\u0026ndash;0.97), with a statistically significant difference (P\u0026thinsp;=\u0026thinsp;0.02). Model 1 was adjusted for age and ethnicity and showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.81, 95%CI: 0.68\u0026ndash;0.96), with a statistically significant difference (P\u0026thinsp;=\u0026thinsp;0.02). Model 2 was adjusted for age, race, education level, PIR, marital status, BMI, waist circumference and duration of moderate exercise. The results showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.82, 95%CI: 0.69\u0026ndash;0.98), with a statistically significant difference (P\u0026thinsp;=\u0026thinsp;0.03). Model 3 was adjusted for age, race, education level, PIR, marital status, BMI, waist circumference, duration of moderate exercise, smoking habits, alcohol consumption habits, hypertension status and diabetes. The results showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.84, 95%CI: 0.71-1.00), with a statistically significant difference (P\u0026thinsp;=\u0026thinsp;0.05). In all three sensitivity analyses, age at menarche was significantly and negatively associated with the prevalence of gynaecological cancers. Even in model 3, where we adjusted for a total of 12 covariates, the results were still significantly different. This suggested that age at menarche was stably associated with the prevalence of gynaecological cancers in all cases. It was worth noting that as the number of covariates adjusted increases, the effect of age at menarche on the prevalence of gynaecological cancers in women decreases.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.2.\u003c/b\u003e Univariate and multivariate logistic regression analysis between the age of menarche and the prevalence of gynecological cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eGynecological Cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82(0.69,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81(0.68,0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82(0.69,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84(0.71,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e*\u003c/b\u003e \u003cem\u003eCrude is an unadjusted model, Model1 is a model adjusted for age and race, Model2 is a model adjusted for age, race, education level, PIR, marital status, BMI, waist circumference and moderate exercise time, and Model3 is a model adjusted for age, race, education level, PIR, marital status, BMI, waist circumference, moderate exercise time, smoking habits, alcohol consumption, hypertension status, and diabetes.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the relationship between age at menarche and the prevalence of different gynaecological cancers. We conducted regression analyses adjusted for basic demographic factors (age, race, education level, PIR, marital status). The results found a negative correlation between age at menarche and prevalence in uterine cancer (P\u0026thinsp;=\u0026thinsp;0.03). No correlation between age at menarche and prevalence in cervical cancer (P\u0026thinsp;=\u0026thinsp;0.17) and ovarian cancer (P\u0026thinsp;=\u0026thinsp;0.29). Population with a young age of menarche were more likely to develop uterine cancer (OR: 0.72, 95%CI: 0.54\u0026ndash;0.98). In addition, trends in the prevalence of gynaecological cancers and the distribution of uterine cancers among gynaecological cancers by age at menarche were studied according to the age of menarche (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The findings showed that there was an overall downward trend in the prevalence of gynaecological and uterine cancers as the age of menarche increases until the age of 18. The results of the non-linear analysis between age of menarche and gynecological cancer prevalence, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, revealed a significant negative linear relationship (P non-linear\u0026thinsp;=\u0026thinsp;0.17). This relationship also appears in the prevalence of uterine cancer (P non-linear\u0026thinsp;=\u0026thinsp;0.73) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the subgroup analysis were shown in \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e. No significant interactions with the albumin/globulin ratio were found for any of the subgroup variables. However, earlier age at menarche was associated with a greater risk of cancer among those with high school education or higher (P\u0026thinsp;=\u0026thinsp;0.05), unmarried (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), small BMI (P\u0026thinsp;=\u0026thinsp;0.02), long duration of daily physical activity (P\u0026thinsp;=\u0026thinsp;0.02), moderate alcohol consumption (P\u0026thinsp;=\u0026thinsp;0.01), and no diabetes mellitus (P\u0026thinsp;=\u0026thinsp;0.03). The results of the subgroup analysis on uterine cancer were shown in \u003cb\u003eTable\u0026nbsp;4.\u003c/b\u003e Age at menarche increased the risk of uterine cancer in several subgroups, but only waist circumference and hypertension interacted with age at menarche. However, it did not show a significant association with the risk of uterine cancer in either of the two subgroups of waist circumference levels, whereas in those with hypertension. The younger the age at menarche, the higher the risk of uterine cancer (OR: 0.63, 95%CI: 0.40\u0026ndash;0.99).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.3.\u003c/b\u003e Results of subgroup analysis related to gynecological cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGynecological Cancer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88(0.73,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72(0.51, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82(0.64,1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.65, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46(0.96, 2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70(0.30, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64(0.36,1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77(0.51, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80(0.58, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81(0.66, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.67, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56(0.38,0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.63, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80(0.63, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88(0.70, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76(0.55, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88(0.72,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67(0.48, 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89(0.72,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate exercise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89(0.68, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81(0.67, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93(0.75,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76(0.57, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85(0.61, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79(0.51,1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14(0.81,1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80(0.57,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72(0.57,0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94(0.63,1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79(0.60,1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88(0.72, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89(0.62, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81(0.67, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e* Models were adjusted for age, race, education level, PIR, marital status, BMI, waist circumference, moderate exercise time, smoking habits, alcohol consumption, hypertension status, and diabetes. (except subgroup variables); PIR, energy intake, waist circumference, and duration of moderate exercise were divided into groups based on median.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.4.\u003c/b\u003e Results of subgroup analysis related to uterine cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCancer\u0026thinsp;=\u0026thinsp;Uterine\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88(0.63, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62(0.39, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77(0.50,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46(0.21,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65(0.94,2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10(0.01,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.66,1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57(0.31, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65(0.20,2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69(0.47, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80(0.60, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54(2.24, 5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59(0.37, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58(0.37, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91(0.61, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29(0.99,1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69(0.46, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94(0.58,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73(0.49, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate exercise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70(0.39, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77(0.62, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79(0.58, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69(0.39, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.50,1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37(0.12,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82(0.47, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69(0.40,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68(0.41, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63(0.40, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(0.81, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e* Models were adjusted for age, race, education level, PIR, marital status, BMI, waist circumference, moderate exercise time, smoking habits, alcohol consumption, hypertension status, and diabetes. (except subgroup variables); PIR, energy intake, waist circumference, and duration of moderate exercise were divided into groups based on median.\u003c/em\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMenarche was a very important event in a woman's life, which was affected by life, family, body and other factors. Menarche also played a role in the occurrence of diseases \u003csup\u003e[9\u0026ndash;18]\u003c/sup\u003e. According to the statistical analysis and logical regression analysis of the questionnaire data of NHANES 2007\u0026ndash;2020, the results showed that there was a correlation between the age of menarche and the prevalence of gynecological cancer. We conducted a series of analyses on age, race, education, PIR, marital status, BMI, waistline, moderate exercise time, smoking habits, hypertension, energy intake, diabetes and drinking habits.\u003c/p\u003e \u003cp\u003eAge was considered to be a highly correlated factor in cancer risk. At present, elderly patients account for the majority of all cancer patients, including cancer recurrence \u003csup\u003e[21\u0026ndash;23]\u003c/sup\u003e. Patients who were younger at menarche and older at the time of the survey may experienced more times of menarche, which also had a certain impact on the occurrence of gynecological cancer. During menstruation, a woman's body would be altered in terms of trace elements and body status \u003csup\u003e[24]\u003c/sup\u003e. The process of menstruation was accompanied by metabolic changes, and the relationship between the occurrence and development of cancer and metabolism was very close \u003csup\u003e[25\u0026ndash;28]\u003c/sup\u003e. When a woman's menstrual cycle increases, it also indicates frequent changes in the body's metabolic state. The younger the age of first menstruation, the greater the metabolic changes are likely to be and the risk of cancer gradually increases.\u003c/p\u003e \u003cp\u003eThe association between age at menarche and gallstone disease in women had been found to be mediated by obesity in several other correlation studies \u003csup\u003e[29]\u003c/sup\u003e. Moreover, obesity and birth weight are factors that influence the age of menarche \u003csup\u003e[30,31]\u003c/sup\u003e. The change of weight was closely related to the energy intake and living habits of the human body. An experiment by Moslshi et al. in 2021 found an association between intake of plant and animal protein in childhood and age at menarche \u003csup\u003e[32]\u003c/sup\u003e. Waist circumference, BMI, moderate exercise time, PIR, energy intake, smoking and alcohol consumption were all associated with obesity indices \u003csup\u003e[33\u0026ndash;35]\u003c/sup\u003e. In a 2022 study on obesity among Japanese adolescents, a positive association between low household income and obesity was found \u003csup\u003e[35]\u003c/sup\u003e. In addition, many lifestyle habits played an important role in obesity in adolescents \u003csup\u003e[36]\u003c/sup\u003e. Obesity not only affected the age of first menstruation, but also played a role in the physical changes that occur after menarche. In our study, differences in the relationship between age at menarche and gynaecological cancer prevalence emerged in population of different weights. In the results of the subgroup analysis (\u003cb\u003eTables\u0026nbsp;3 and 4\u003c/b\u003e), the prevalence of gynaecological cancers was higher in those with a young age at menarche than in those with an older age at menarche among those with a normal weight. Similar statistical results were found for the other variable factors of interest (marital status, length of moderate exercise, alcohol consumption, energy intake).\u003c/p\u003e \u003cp\u003eOur study involved a variety of variable factors that are informative about the prevalence of gynaecological cancers. In addition, some of the variable factors had an impact on the age at menarche. More importantly, as age at menarche changed, age at menarche played a role in the variation of some of these variables. As a simple example, obesity itself was a factor that influences the onset of menstruation. After the onset of menstruation, obesity was a mediator between the onset of menstruation and the onset of disease. Multiple variable factors interacted and influenced each other, and the correlation between many of these variables needed to be verified by more experiments.\u003c/p\u003e \u003cp\u003eThis study has the following limitations: 1. this was a cross-sectional study in which there was no temporal relationship and causality cannot be determined; 2. there was a recall bias in the reporting of this questionnaire; 3. this experiment may have included some confounding factors for measurement purposes. Such as parents' education level, changes in family status, child's birth weight, genetic factors, etc; 4. spanning a long period of time, 16 years ago, individual education level and physical level may have changed.\u003c/p\u003e"},{"header":"5. Comparison with similar studies","content":"\u003cp\u003eThere are similarities between a previously published study and ours, but there are some significant differences between our studies. Compared with others \u003csup\u003e[37\u0026ndash;38]\u003c/sup\u003e, our study aimed at the relationship between age at menarche and the prevalence of gynecological cancers. We found a negative correlation with the prevalence of uterine cancer, while the relationship with cervical and ovarian cancers was not significant, so we studied more on uterine cancer. On this basis, we conducted a large number of sensitivity analyses, subgroup analyses and cross-validation, which helped us to find the potential high-risk groups more accurately, and made our findings more meaningful in preventive screening.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn conclusion, the analysis of the NHANES questionnaire data showed that the younger the age of menarche, the higher the risk of gynaecological cancer in the population. More obviously, the younger the age of first menstruation, the higher the risk of uterine cancer. Furthermore, according to our findings, the correlation between the prevalence of uterine cancer and age at menarche was even stronger. These results suggest that public health workers need to pay attention to the early age of menarche when developing screening programmes for gynaecological cancers in order to develop more appropriate screening programmes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;The NHANES survey was approved by the National Center for Health Statistics Institutional Review Board. The study reported in this manuscript was exempt from ethical committee approval because it was based on publicly available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaohui Pei:Writing-Original Draft;\u0026nbsp;Hao Sun:\u0026nbsp;Methodology,\u0026nbsp;Data Analysis;\u0026nbsp;All authors contributed to the article and approved the submitted version. The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the participants and staff of NHANES 2007-2020 for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES survey was approved by the National Center for Health Statistics Institutional Review Board. The study reported in this manuscript was exempt from ethical committee approval because it was based on publicly available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not funded\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMenon, U., Steps towards effective gynaecological cancer screening. \u003cem\u003eNat Rev Clin Oncol \u003c/em\u003e\u003cstrong\u003e2018,\u003c/strong\u003e \u003cem\u003e15\u003c/em\u003e (9), 538-540.\u003c/li\u003e\n\u003cli\u003eKeyvani, V.; Kheradmand, N.; Navaei, Z. N.; Mollazadeh, S.; Esmaeili, S. A., Epidemiological trends and risk factors of gynecological cancers: an update. \u003cem\u003eMed Oncol \u003c/em\u003e\u003cstrong\u003e2023,\u003c/strong\u003e \u003cem\u003e40\u003c/em\u003e (3), 93.\u003c/li\u003e\n\u003cli\u003eLiontos, M.; Fiste, O.; Zagouri, F.; Dimopoulos, M. 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Association of the Age at Menarche with Site-Specific Cancer Risks in Pooled Data from Nine Cohorts. \u003cem\u003eCancer Res\u003c/em\u003e. \u003cstrong\u003e2021,\u003c/strong\u003e\u003cem\u003e81(8),2246-2255.\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"menarche, gynaecological cancer, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4796084/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4796084/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe objective of this study was to investigate the relationship between the age of menarche and the prevalence of gynecological cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 5540 women were screened from those who participated in the National Health And Nutrition Examination Survey (NHANES) questionnaire from 2007\u0026ndash;2020, and their variable factors of age, race, education level, Poverty Impact Ratio (PIR), marital status, Body Mass Index (BMI), waist circumference, duration of moderate exercise, smoking habits, hypertension status, energy intake, diabetes and alcohol consumption habits were analysed statistically and by logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate logistic regression analysis of the relationship between age at menarche and gynaecological cancer (Uterus / Cervix / Ovary Cancer, the following gynecologic cancers in the article refer to having at least one of these three cancers) prevalence showed a negative association between age at menarche and gynaecological cancer prevalence (OR: 0.82, 95%CI: 0.69\u0026ndash;0.97), with a statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.02). Regression results of the association between age at menarche and different types of gynaecological cancers found a negative association between age at menarche and prevalence in uterine cancers (P\u0026thinsp;=\u0026thinsp;0.03) and no association between age at menarche and prevalence in cervical and ovarian cancers (P\u0026thinsp;=\u0026thinsp;0.17, P\u0026thinsp;=\u0026thinsp;0.29). Those with a younger age at menarche were more likely to develop uterine cancer (OR: 0.72, 95%CI: 0.54\u0026ndash;0.98).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThere was a correlation between age at menarche and gynaecological cancer, with those who had menarche at an earlier age being at a higher risk of gynaecological cancer. More obviously, the younger the age of first menstruation, the higher the risk of uterine cancer.\u003c/p\u003e","manuscriptTitle":"Age at Menarche is Inversely Related to the Prevalence of Common Gynecologic Cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 08:35:10","doi":"10.21203/rs.3.rs-4796084/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-29T07:53:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-27T14:28:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-12-18T10:21:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-15T00:13:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22694839295445261740898386991319425624","date":"2024-10-02T22:25:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-04T21:21:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-27T13:16:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-26T14:05:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2024-07-24T14:19:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"893be7b4-c8cc-4e5f-bc07-14985f4ed198","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T16:05:11+00:00","versionOfRecord":{"articleIdentity":"rs-4796084","link":"https://doi.org/10.1186/s40001-025-02472-z","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2025-03-26 15:57:26","publishedOnDateReadable":"March 26th, 2025"},"versionCreatedAt":"2024-08-22 08:35:10","video":"","vorDoi":"10.1186/s40001-025-02472-z","vorDoiUrl":"https://doi.org/10.1186/s40001-025-02472-z","workflowStages":[]},"version":"v1","identity":"rs-4796084","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4796084","identity":"rs-4796084","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}