Reproductive factors and risk of cardiovascular diseases and all-cause and cardiovascular mortality in American women: NHANES 2003-2018

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Aims To investigate the relationship of reproductive factors with the risk of CVDs, as well as all-cause and cardiovascular mortality. Methods This study included 16404 adults with reproductive factors from the National Health and Nutrition Examination Survey (NHANES) and followed up until 31 December 2019. Logistic models and restricted cubic spline models were used to assess the association of reproductive factors with CVDs. COX proportional hazards models and restricted cubic spline models, with adjustment for potential confounding, were employed to analyze the relation between reproductive factors and cardiovascular and all-cause death. Results There is a nonlinear relationship between age at menarche and CVDs. Age at menopause ≤ 11(OR 1.36, 95% CI 1.10–1.69) was associated with an increased risk of CVDs compared to ages 12–13 years. Age at Menopause ≤ 44 (OR 1.69, 95% CI 1.40–2.03) was associated with increased CVDs compared to age 35–49 years. Number of pregnancies ≥ 5(OR 1.26, 95% CI 1.02–1.55) was associated with an increased risk of CVDs compared to one pregnancy. In continuous variable COX regression models, a later age at menopause (HR 0.98, 95% CI 0.97–0.99) and a longer reproductive lifespan (HR 0.98, 95% CI 0.97–0.99) were associated with a decreased risk of all-cause death. A later age at menopause (HR 0.98, 95% CI 0.97–0.99) and a longer reproductive lifespan (HR 0.98, 95% CI 0.97–0.99) were associated with a decreased risk of cardiac death. Conclusions Female reproductive factors are significant risk factors for CVDs. Reproductive factors Cardiovascular diseases Nhanes Cardiovascular and all-cause death Figures Figure 1 Figure 2 Figure 3 Introduction Cardiovascular diseases(CVDs) are among the most serious diseases that harm health and cause death globally(1). Through interventions targeting high-risk factors, the incidence of CVDs has been effectively managed. Although the mortality and morbidity rates of CVDs have decreased, the CVD burden has increased in women(2). Sex has a profound impact on cardiovascular diseases(3). Before menopause, women generally exhibit a lower risk of cardiovascular disease than men. Changes in female sex hormones occur throughout the reproductive process, from menarche to pregnancy, childbirth, and menopause. These hormone levels affect a woman's blood lipid levels, blood pressure, blood sugar, and inflammation, subsequently influencing cardiovascular health (4). Reproductive health in women plays a vital role in the physiological and pathological processes of the cardiovascular system. Therefore, evaluating the influence of reproductive factors on cardiovascular diseases is crucial to reducing their incidence in women. By analyzing the impact of these factors on all-cause mortality and cardiovascular mortality, we can gain a deeper understanding of their effects on women’s overall health. Throughout a woman’s life, various reproductive factors play a significant role, encompassing the entire process from female reproductive system development to senescence. Some research has demonstrated a correlation between early menarche and an increased risk of all-cause mortality or cardiovascular mortality(5–8). However, contrasting findings from studies on European populations suggest that late menarche is associated with an elevated risk of cardiovascular death, while early menarche does not show the same association(9). Additionally, some studies have reported an association between an earlier age of menopause and higher mortality rates, particularly in relation to cardiovascular disease(10). Nevertheless, the relationship between female reproductive factors and the risk of all-cause and cardiovascular mortality death in women remains controversial. The development of treatment guidelines for cardiovascular diseases has predominantly focused on male patients, neglecting the importance of studying how female reproductive factors contribute to cardiovascular health. This study aimed to analyze the National Health and Nutrition Examination Survey (NHANES) database to explore the relationship between reproductive factors(such as age at menarche, age at menopause, reproductive span, age at first live birth, and number of live births) and cardiovascular disease, all-cause death, and cardiovascular death in women. The findings from this study offer valuable insights for the early identification and management of female-specific cardiovascular risk factors. Methods Study population We downloaded data from the 2003–2018 NHANES to explore the associations between reproductive factors and CVDs. The cohort study sample comprised individuals who participated in the NHANES survey from 2003–2018, a population-based survey designed to collect health and nutritional statistics of adults and children in the United States(11). All participants provided written informed consent. A total of 28,063 participants included in the NHANES from 2003–2014. Of these, participants had mortality follow-up data available through December 31, 2019 from the US National Death Index(NDI). We excluded individuals with missing data and included 16,404 participants in the final analysis. Outcome Outcome were obtained from the National Death Index through 31 December 2019 to identify mortality status. The primary cause of death was determined according to ICD-10 codes. Outcomes were be defined as all-cause and cardiovascular mortality. Cardiovascular diseases include conditions such as coronary heart disease, congestive heart failure, heart attack, stroke, and angina. Study covariates Demographic information (sex, age, family income-poverty ratio, race/ethnicity and educational level) and lifestyles(smoking, drinking alcohol) were collected during in-home interviews. Body mass index (BMI) was measured at mobile examination centers. BMI is calculated by dividing an individual's weight by the square of their height. Race/ethnicity was categorized as Mexican American, non-Hispanic white, non-Hispanic black, other Hispanic, and others. Education levels were categorized as follows: less than 9th grade, 9-11th grade (includes 12th grade with no diploma), high school graduate/GED or equivalent, some college or AA degree, and college graduate or above. Hypertension was defined as either a self-reported medical history or a blood pressure reading of 130/80 mmHg or higher. Diabetes was determined through a combination of self-reported medical history, glycosylated hemoglobin A1c level of 6.5% or higher, or fasting plasma glucose level of 126 mg/dL or higher. Statistical analyses Sampling weights were considered in our study due to the complex sampling design of the NHANES. Means ± standard deviation are used for continuous variables, and percentages are utilized for categorical variables. We used a logistic model to assess the correlation between reproductive factors and CVDs events. Multivariable Cox proportional hazards models were used to assess the association of reproductive factors with cardiovascular death and all-cause death. All reproductive factors were assessed as both continuous and categorical variables. The non-linear relationship between reproductive factors and cardiovascular events, all-cause death, and cardiovascular death was evaluated using restricted cube plots (4 knots). Categorical variables were used to evaluate the relationship between reproductive factors and cardiovascular events, all-cause death, and cardiovascular death. Two models were used to analyze confounding factors. Model 1 makes adjustments for age. Model 2 was adjusted for race, BMI, alcohol consumption, smoking status, hypertension, diabetes mellitus, and education, Statistical analyses were conducted using R software version 4.1.0, P-value < 0.05 was considered statistically significant. Results Baseline characteristics of the study population The baseline information is presented in Table 1 . During a median follow-up of 105(IQR: 59–148) months, 1693 participants died, and 459 individuals experienced cardiovascular mortality.1572 participants was recorded as having CVDs. Table 1 Baseline Characteristics of the Study Population variable total At age Menarche ,y 12.70 ± 0.02 Age, y 48.22 ± 0.25 Poverty-income ratio 2.95 ± 0.03 Body mass index, kg/m2 29.09 ± 0.10 Alcohol consumption missing 12102175.08(13.37) no 24670017.57(27.26) yes 53719548.35(59.36) Race Mexican American 6419253.04( 7.09) Non-Hispanic Black 10197540.29(11.27) Non-Hispanic White 63250421.66(69.90) Other Hispanic 4484634.05( 4.96) Other Race - Including Multi-Racial 6139891.94( 6.79) Education 9-11th grade (Includes 12th grade with no diploma) 9008261.61( 9.95) College graduate or above 26411609.41(29.19) High school graduate/GED or equivalent 20523026.66(22.68) Less than 9th grade 4045007.76( 4.47) Some college or AA degree 30503835.55(33.71) Smoke former 19206204.48(21.22) missing 31429.73( 0.03) never 54391361.42(60.11) now 16862745.36(18.63) Diabetes Mellitus DM 11857004.74(13.10) IFG 3060212.57( 3.38) IGT 2989888.69( 3.30) no 72584634.99(80.21) Hypertension no 56544122.58(62.49) yes 33947618.41(37.51) CVD no 83449764.35(92.22) yes 7041976.64( 7.78) Incidence of CVD according to reproductive factors The reproductive factors showed a relationship with CVDs in adjusted models. A nonlinear relationship was observed between age at menarche with CVDs, as indicated in the cube plot (Fig. 1 ). Linear relationships were established between age at menopause, reproductive lifespan, and number of pregnancies with CVDs in the multivariable model. In continuous variable logistic regression models, a later age at menopause (OR 0.96, 95% CI 0.96–0.97), a longer reproductive lifespan(OR 0.97, 95% CI 0.96–0.97),and a greater number of pregnancies(OR 1.06,95% CI 1.02–1.09) were associated with a decreased risk of CVDs(Table 2 ). A greater number of pregnancies (OR 1.06, 95% CI 1.02–1.09) was associated with an increased risk of CVDs. After categorizing the variables, in the multivariate model(model 2), an age at menarche ≤ 11 was associated with increased CVDs, while ages 14–15 and ≥ 16 were not significantly associated with increased CVDs compared to the reference. Age at menopause ≤ 44(OR 1.69, 95% CI 1.40–2.03) was associated with increased CVDs, whereas ages 50–54 and ≥ 55 were not. A reproductive lifespan of ≤ 32 was associated with increased CVDs, while spans of 36–38, 39–41, and ≥ 42 were not. Maternal age at first live birth of ≤ 19, 24–26, and ≥ 27 and maternal age at last live birth of ≤ 26, 30–34, 35–39, and ≥ 40 were not associated with increased cardiovascular disease compared to the reference. A number of pregnancies ≥ 5(OR 1.26, 95% CI 1.02–1.55) was associated with an increase in cardiovascular disease, whereas numbers 1,3,4 were not. Table 2 Assocaition of reproductive factors with incident CVDs Reproductive Factors Age-Adjusted Model Multivariable Model OR (95% CI) P Value OR (95% CI) P Value Age at menarche ≤ 11 1.55(0.03–1.89) 0.000 1.36(1.10–1.69) 0.005 12–13 ref 14–15 1.07(0.16–1.27) 0.447 1.06(0.89–1.27) 0.497 ≥ 16 1.35(0.10–1.71) 0.016 1.26(1.00-1.59) 0.054 Age at menopause ≤ 44 1.84(0.90–2.22) 0.000 1.69(1.40–2.03) 0.000 35–49 ref 50–54 0.83(0.87–1.04) 0.102 0.93(0.73–1.18) 0.538 ≥ 55 0.87(0.11–1.14) 0.316 0.93(0.71–1.22) 0.600 Reproductive lifespan ≤ 32 1.79(0.08–2.21) 0.000 1.63(1.33-2.00) 0.000 33–35 ref 36–38 0.92(0.79–1.21) 0.565 0.94(0.70–1.26) 0.660 39–41 0.80(0.03–1.10) 0.174 0.90(0.65–1.26) 0.540 ≥ 42 0.97(0.11–1.26) 0.814 1.02(0.77–1.35) 0.904 Maternal age at first live birth ≤ 19 1.37(0.23–1.72) 0.006 1.04(0.81–1.33) 0.742 21–23 ref 24–26 0.67(0.97 − 0.94) 0.021 0.77(0.53–1.11) 0.159 ≥ 27 0.50(0.11–0.69) 0.000 0.75(0.52–1.08) 0.123 Maternal age at last live birth ≤ 26 1.26(0.98–1.62) 0.066 1.08(0.85–1.37) 0.545 27–29 ref 30–34 0.90(0.70–1.16) 0.427 1.00(0.77–1.30) 0.979 35–39 0.95(0.72–1.26) 0.716 1.09(0.82–1.44) 0.561 ≥ 40 1.39(0.87–2.22) 0.166 1.44(0.89–2.32) 0.135 Number of pregnancies 1 0.96(0.03–1.28) 0.793 1.00(0.74–1.34) 0.984 2 ref 3 0.89(0.17–1.13) 0.328 0.92(0.71–1.20) 0.551 4 1.21(0.21–1.58) 0.165 1.13(0.85–1.50) 0.393 ≥ 5 1.52(0.10–1.84) 0.000 1.26(1.02–1.55) 0.034 Age at menarche 0.95(0.10–0.99) 0.017 0.95(0.91–1.01) 0.105 Age at menopause 0.95(0.11–0.96) 0.000 0.96(0.96–0.97) 0.000 Reproductive lifespan 0.96(0.11–0.96) 0.957 0.97(0.96–0.97) 0.000 Maternal age at first live birth 0.92(0.11–0.95) 0.000 0.97(0.95-1.00) 0.057 Maternal age at last live birth 0.99(0.11-1.00) 0.105 1.01(0.99–1.03) 0.520 Number of pregnancies 1.10(0.10–1.13) 0.000 1.06(1.02–1.09) 0.001 Cardiac death according to reproductive factors A non-linear relationship between reproductive factors and cardiovascular death was indicated in the cube plot (Fig. 2 ). Linear relationships were observed between age at menopause, reproductive lifespan, and number of pregnancies with cardiac death in the multivariable model. In continuous variable COX regression models, a later age at menopause (HR 0.98, 95% CI 0.97–0.99), a longer reproductive lifespan (HR 0.98, 95% CI 0.97–0.99) were associated with a decreased risk of cardiac death (Table 3 ). After categorizing the variables, in the multivariate model(model 2), ages at menarche of ≤ 11, 14–15, and ≥ 16 were not significantly associated with increased cardiovascular death compared to the reference. Ages at menopause of ≤ 44, 50–54, and ≥ 55 were not associated with increased risk. A reproductive lifespan of ≤ 32 (HR 1.62, 95% CI 1.10–2.39) was associated with increased risk, while spans of 36–38, 39–41, and ≥ 42 were not. Maternal age at first live birth of ≤ 19(HR 1.63, 95% CI 1.10–2.41) was associated with increased cardiovascular death risk, while ages 24–26 and ≥ 27 were not. Maternal ages at last live birth of ≤ 26, 30–34, 35–39, and ≥ 40 and number of pregnancies of ≥ 5, 1, 3, 4 were not associated with increased risk compared to the reference. Table 3 Assocaition of reproductive factors with incident cardic death Reproductive Factors Age-Adjusted Model Multivariable Model HR (95% CI) P Value HR (95% CI) P Value Age at menarche ≤ 11 1.06(0.78–1.44) 0.696 0.97(0.71–1.31) 0.828 12–13 ref 14–15 1.11(0.82–1.51) 0.488 1.00(0.75–1.34) 0.989 ≥ 16 1.31(0.91–1.88) 0.141 1.14(0.81–1.60) 0.463 Age at menopause ≤ 44 1.34(0.96–1.87) 0.085 1.21(0.87–1.68) 0.256 35–49 ref 50–54 0.91(0.67–1.24) 0.562 0.94(0.68–1.28) 0.677 ≥ 55 0.80(0.54–1.18) 0.260 0.81(0.53–1.22) 0.309 Reproductive lifespan ≤ 32 1.73(1.21–2.49) 0.003 1.62(1.10–2.39) 0.014 33–35 ref 36–38 1.18(0.83–1.69) 0.356 1.19(0.81–1.76) 0.367 39–41 0.97(0.65–1.45) 0.886 1.07(0.71–1.63) 0.739 ≥ 42 0.89(0.56–1.41) 0.614 0.91(0.55–1.51) 0.719 Maternal age at first live birth ≤ 19 2.19(1.55–3.09) 0.000 1.63(1.10–2.41) 0.015 21–23 ref 24–26 1.30(0.80–2.12) 0.294 1.45(1.37–2.46) 0.172 ≥ 27 1.15(0.69–1.90) 0.595 1.36(1.18–2.44) 0.309 Maternal age at last live birth ≤ 26 1.21(0.69–2.10) 0.504 1.11(0.64–1.92) 0.709 27–29 ref 30–34 1.09(0.60–2.01) 0.774 1.10(0.58–2.07) 0.776 35–39 1.12(0.62-2.00) 0.714 1.09(0.61–1.96) 0.764 ≥ 40 1.31(0.77–2.21) 0.315 1.06(0.63–1.78) 0.838 Number of fetation 1 1.44(0.89–2.32) 0.134 1.60(0.97–2.63) 0.067 2 ref 3 1.09(0.75–1.61) 0.644 1.19(0.80–1.76) 0.398 4 1.41(0.94–2.12) 0.096 1.35(0.89–2.05) 0.155 ≥ 5 1.40(1.00-1.96) 0.052 1.08(0.75–1.55) 0.666 Age at menarche 1.05(0.98–1.12) 0.188 1.04(0.97–1.11) 0.279 Age at menopause 0.97(0.96–0.99) 0.000 0.98(0.97–0.99) 0.001 Reproductive lifespan 0.97(0.96–0.98) 0.000 0.98(0.97–0.99) 0.000 Maternal age at first live birth 0.95(0.91-1.00) 0.031 1.00(0.95–1.05) 0.976 Maternal age at last live birth 0.98(0.95–1.02) 0.363 0.99(0.96–1.02) 0.470 Number of pregnancies 1.02(0.98–1.07) 0.328 0.96(0.91–1.02) 0.209 All-cause mortality according to reproductive factors A non-linear relationship between reproductive factors and all-cause death was indicated in the cubic plot (Fig. 3 ). A linear relationship were established between age at menopause, reproductive lifespan, and number of pregnancies with cardiac death in the multivariable model. In continuous variable COX regression models, a later age at menopause (HR 0.98, 95% CI 0.97–0.99), a longer reproductive lifespan (HR 0.98, 95% CI 0.97–0.99) were associated with a decreased risk of all-cause death (Table 4 ). After categorizing the variables, in the multivariate model(model 2), ages at menarche of ≤ 11, 14–15, and ≥ 16 were not significantly associated with increased all-cause death compared to the reference. Ages at menopause of ≤ 44, 50–54, and ≥ 55 were not associated with increased risk. The reproductive lifespans of ≤ 32, 36–38, 39–41, and ≥ 42 were not associated with increased risk compared to the reference. Maternal age at first live birth of 24–26(HR 1.37, 95% CI 1.06–1.76) was associated with increased all-cause death, while ages ≤ 19 and ≥ 27 were not. Maternal ages at last live birth of ≤ 26, 30–34, 35–39, and ≥ 40 and numbers of pregnancies of ≥ 5, 1, 3, 4 were not associated with increased all-cause death compared to the reference. Table 4 Assocaition of reproductive factors with incident death Reproductive Factors Age-Adjusted Model Multivariable Model HR (95% CI) P Value HR (95% CI) P Value Age at menarche ≤ 11 1.13(0.97–1.31) 0.116 1.05(0.91–1.22) 0.477 12–13 ref 14–15 1.03(0.88–1.21) 0.731 0.95(0.81–1.12) 0.562 ≥ 16 1.16(0.92–1.46) 0.209 1.05(0.83–1.32) 0.687 Age at menopause ≤ 44 1.22(1.03–1.44) 0.021 1.12(0.94–1.34) 0.191 35–49 ref 50–54 0.90(0.76–1.07) 0.247 0.96(0.81–1.15) 0.663 ≥ 55 0.87(0.70–1.09) 0.235 0.93(0.72–1.19) 0.542 Reproductive lifespan ≤ 32 1.32(1.07–1.63) 0.010 1.24(0.99–1.57) 0.065 33–35 ref 36–38 1.08(0.84–1.39) 0.525 1.14(0.86–1.51) 0.350 39–41 0.79(0.62–1.02) 0.073 0.88(0.67–1.16) 0.354 ≥ 42 1.03(0.80–1.32) 0.840 1.10(0.83–1.46) 0.494 Maternal age at first live birth ≤ 19 1.44(1.17–1.77) 0.000 1.10(0.87–1.39) 0.428 21–23 ref 24–26 1.23(0.96–1.57) 0.100 1.37(1.06–1.76) 0.015 ≥ 27 0.97(0.73–1.29) 0.840 1.18(0.85–1.62) 0.316 Maternal age at last live birth ≤ 26 1.09(0.87–1.36) 0.462 0.98(0.80–1.22) 0.890 27–29 ref 30–34 0.95(0.73–1.23) 0.703 0.96(0.73–1.26) 0.754 35–39 1.22(0.92–1.63) 0.163 1.30(0.99–1.72) 0.061 ≥ 40 1.33(0.92–1.91) 0.131 1.18(0.81–1.73) 0.380 Number of pregnancies 1 1.11(0.89–1.39) 0.354 1.16(0.93–1.45) 0.190 2 ref 3 1.03(0.85–1.27) 0.741 1.12(0.91–1.38) 0.271 4 1.17(0.96–1.44) 0.129 1.13(0.92–1.40) 0.242 ≥ 5 1.21(1.04–1.42) 0.017 1.00(0.84–1.19) 0.990 Age at menarche 1.00(0.96–1.04) 0.976 0.99(0.96–1.03) 0.672 Age at menopause 0.98(0.97–0.99) 0.000 0.99(0.98-1.00) 0.006 Reproductive lifespan 0.98(0.98–0.99) 0.000 0.99(0.98-1.00) 0.016 Maternal age at first live birth 0.98(0.95-1.00) 0.021 1.02(1.00-1.04) 0.118 Maternal age at last live birth 1.00(0.98–1.01) 0.966 1.01(0.99–1.02) 0.290 Number of pregnancies 1.02(0.99–1.05) 0.160 0.98(0.95–1.01) 0.145 Discussion This is the first comprehensive assessment of the association between female reproductive factors and all-cause mortality, cardiogenic death, and all-cause death using the NHANES database. We found that, after controlling for known CVDs risk factors, a later age at menopause, a longer reproductive lifespan, and fewer pregnancies were associated with a significantly lower risk of CVDs. Our study also found that a shorter reproductive lifespan and earlier age at menopause were associated with an increased risk of both all-cause and cardiovascular death. We found that there is a nonlinear relationship between age at menarche and CVDs in our study. Previous studies have identified various patterns of association between age at menarche and CVDs risk in women. Some studies have shown an increased risk of CVDs with early age at menarche(12), while others have indicated an increased risk with later age at menarche(13). In a retrospective study, earlier age at menarche was associated with a higher risk of heart failure in women(14). Some studies have suggested that early menarche is associated with an increased risk of CVDs in women (15–17). However, a study from Korea determined that late menarche was associated with an increased risk of MI, but early age at menarche was not (13). Another study of 1,088,992 premenopausal women found that late menarche was associated with an increased risk of all-cause disease, while early menarche was not(18). Both later and earlier menarche are influenced by female adolescent BMI, hormones, and renal hormones. Beyond the relationship between age at menarche (AAM) and CVDs, we also explored its association with all-cause mortality. We found no significant relationship between AAM and all-cause mortality. Yet, some researchers have suggested that AAM is linked to all-cause mortality(15). Additionally, a recent systematic review showed an inverse association between age at menarche and both all-cause mortality and mortality due to ischemic heart disease(19). Our study confirms that early age at menopause significantly increases the risk of all-cause diseases. This conclusion is consistent with numerous other studies that have found premature menopause, whether natural or surgical, to be a risk factor for various all-cause diseases(20). The decrease in endogenous estrogen secretion in postmenopausal women significantly affects low-density lipoprotein control, which may explain why postmenopausal women have a lower incidence of coronary heart disease compared to men(21). In our study, even after adjusting for common risk factors, premature menopause was associated with a heightened risk of all-cause diseases. By identifying women with premature menopause, targeted prevention could reduce the incidence of all-cause diseases. Our research also determined that a shorter reproductive lifespan was linked to increased all-cause risk and cardiac death. This might result from the combined effects of irregular estrogen production timing and insufficient estrogen exposure. The Women’s Ischemia Syndrome Evaluation study noted a connection between disrupted ovulatory cycles characterized by hypoestrogenemia of hypothalamic origin and angiographic CAD (Coronary Artery Disease)(22). We found no significant association between the first or last live birth and the occurrence of all-cause diseases (CVDs) or all-cause mortality. However, a study using data from the UK Biobank indicated that a younger maternal age at the time of the first or last live birth is a risk factor for heart failure (HF) events (14). Previous studies have suggested that a younger maternal age at conception might be associated with CVD(23, 24). A systematic review of observational studies from 1980 to 2016 found the relationship between early age at first live birth and all-cause disease risk to be uncertain, though there is some evidence suggesting a potential increase in risk. Of the studies reviewed, ten found that women with an early age at first live birth had a higher risk of developing CVDs, while two found that women with a later age of first birth had a greater risk of developing all-cause disease(25). Studies have shown a U-shaped association between age at first birth and death from all-cause disease and all-cause mortality(23). While our study did not replicate these findings, we did determine that an early age at first birth was associated with an increased risk of all-cause death. The study identified a linear relationship between the number of pregnancies and the increased risk of all-cause disease. Some research has indicated that the higher the number of live births correlates with a greater risk of HF(14). This relationship might be due to hormonal changes during pregnancy, increased cardiac workload, abnormal blood sugar levels, dyslipidemia, and the long-term effects of pregnancy on all-cause health. Additionally, multiple pregnancies can lead to future weight gain and the onset of metabolic syndrome(26). In a large prospective cohort, researchers found that a higher number of pregnancies correlated with a greater risk of subsequent atrial fibrillation(27). Repeated exposure to metabolic, physiological, and hormonal changes during pregnancy, combined with increased cardiac workload, might contribute to the onset of atrial fibrillation later in life. During pregnancy, there are notable changes in hormone levels, especially increases in estrogen and progesterone. These shifts can cause a metabolic slowdown, potentially leading to fat accumulation in the abdominal area(28). However, more research is needed to fully understand this connection. Importantly, our study did not find a significant relationship between the number of pregnancies and all-cause mortality. Some research has identified a nonlinear relationship between number of pregnancies and all-cause death (29), while other studies have found no such connection(30). The relationship between the number of pregnancies and mortality is intricate, influenced by various factors like chronic diseases, race, and socioeconomic status. More investigation is essential to decipher the specific mechanisms behind these associations and to analyze the effects of repeated pregnancies on long-term health outcomes. Study strengths and limitations This study has several advantages over previous studies. The study benefits from an extended follow-up period, enabling a thorough assessment of the relationship between reproductive factors and all-cause disease, as well as all-cause mortality. This research examines a broad spectrum of reproductive factors, offering a comprehensive understanding of their effects on all-cause health outcomes. Utilizing the NHANES database ensures national representativeness, which enhances the generalizability of the results to other populations. The analysis considers the influence of race, allowing for a more precise evaluation of the relationships between reproductive factors and health outcomes across various racial groups. However, our study has some limitations. Firstly, relying on self-reported questionnaire data might lead to recall bias, as participants may not remember specific reproductive factors accurately, potentially affecting the study's conclusions. Secondly, while the study addresses six particular reproductive factors, it doesn't consider other possibly relevant aspects such as hormone use, breastfeeding, or history of reproductive system surgeries. Their impact on overall health outcomes warrants further investigation. Thirdly, as patient baseline data can change over time, this variability might introduce bias into the study's findings. Lastly, the research doesn't comprehensively compare the effects of reproductive factors on distinct categories of all-cause diseases. Conclusion In summary, we discovered a significant association between female reproductive factors and the risk of all-cause disease in women. Our findings emphasize the importance of including an evaluation of reproductive factors in the assessment of women's overall health risk. Declarations Acknowledgments The data used in this work were obtained from Nhanes. We are grateful to Nhanes researchers for providing the data. Contributors This study was conducted in collaboration with the authors listed below: YZ and SL contributed to the conception, design, and interpretation of data. YY analyzed the data and drafted the manuscript. HJ edited the manuscript. Ethics approval The analysis utilized summary data from NHANES, which had obtained written informed consent and ethics approval. Conflict of interest: The authors declare no conflict of interest. Funding The study was supported by the National Natural Science Foundation of China (Grant No: 81900248). Data availability Data for this study were sourced from National Health and Nutrition Examination Survey (NHANES) and available here: https://www.cdc.gov/nchs/nhanes/index.htm. References Mensah GA, Roth GA, Fuster V. The Global Burden of Cardiovascular Diseases and Risk Factors: 2020 and Beyond. J Am Coll Cardiol. 2019;74(20):2529-32.doi:10.1016/j.jacc.2019.10.009. Vogel B, Acevedo M, Appelman Y, Bairey Merz CN, Chieffo A, Figtree GA, et al. The Lancet women and cardiovascular disease Commission: reducing the global burden by 2030. Lancet. 2021;397(10292):2385-438.doi:10.1016/s0140-6736(21)00684-x. Lam CSP, Arnott C, Beale AL, Chandramouli C, Hilfiker-Kleiner D, Kaye DM, et al. Sex differences in heart failure. Eur Heart J. 2019;40(47):3859-68c.doi:10.1093/eurheartj/ehz835. Subramanya V, Zhao D, Ouyang P, Lima JA, Vaidya D, Ndumele CE, et al. Sex hormone levels and change in left ventricular structure among men and post-menopausal women: The Multi-Ethnic Study of Atherosclerosis (MESA). Maturitas. 2018;108:37-44.doi:10.1016/j.maturitas.2017.11.006. Charalampopoulos D, McLoughlin A, Elks CE, Ong KK. Age at menarche and risks of all-cause and cardiovascular death: a systematic review and meta-analysis. Am J Epidemiol. 2014;180(1):29-40.doi:10.1093/aje/kwu113. Wu X, Cai H, Kallianpur A, Gao YT, Yang G, Chow WH, et al. Age at menarche and natural menopause and number of reproductive years in association with mortality: results from a median follow-up of 11.2 years among 31,955 naturally menopausal Chinese women. PLoS One. 2014;9(8):e103673.doi:10.1371/journal.pone.0103673. Jacobsen BK, Oda K, Knutsen SF, Fraser GE. Age at menarche, total mortality and mortality from ischaemic heart disease and stroke: the Adventist Health Study, 1976-88. Int J Epidemiol. 2009;38(1):245-52.doi:10.1093/ije/dyn251. Gallagher LG, Davis LB, Ray RM, Psaty BM, Gao DL, Checkoway H, et al. Reproductive history and mortality from cardiovascular disease among women textile workers in Shanghai, China. Int J Epidemiol. 2011;40(6):1510-8.doi:10.1093/ije/dyr134. . !!! INVALID CITATION !!! [9]. Ossewaarde ME, Bots ML, Verbeek AL, Peeters PH, van der Graaf Y, Grobbee DE, et al. Age at menopause, cause-specific mortality and total life expectancy. Epidemiology. 2005;16(4):556-62.doi:10.1097/01.ede.0000165392.35273.d4. Ruhl CE, Everhart JE. Fatty liver indices in the multiethnic United States National Health and Nutrition Examination Survey. Aliment Pharmacol Ther. 2015;41(1):65-76.doi:10.1111/apt.13012. Canoy D, Beral V, Balkwill A, Wright FL, Kroll ME, Reeves GK, et al. Age at menarche and risks of coronary heart and other vascular diseases in a large UK cohort. Circulation. 2015;131(3):237-44.doi:10.1161/circulationaha.114.010070. Jeong SM, Yoo JE, Jeon KH, Han K, Lee H, Lee DY, et al. Associations of reproductive factors with incidence of myocardial infarction and ischemic stroke in postmenopausal women: a cohort study. BMC Med. 2023;21(1):64.doi:10.1186/s12916-023-02757-2. Zhu F, Qi H, Bos M, Boersma E, Kavousi M. Female Reproductive Factors and Risk of New-Onset Heart Failure: Findings From UK Biobank. JACC Heart Fail. 2023.doi:10.1016/j.jchf.2023.02.019. Lakshman R, Forouhi NG, Sharp SJ, Luben R, Bingham SA, Khaw KT, et al. Early age at menarche associated with cardiovascular disease and mortality. J Clin Endocrinol Metab. 2009;94(12):4953-60.doi:10.1210/jc.2009-1789. Ley SH, Li Y, Tobias DK, Manson JE, Rosner B, Hu FB, et al. Duration of Reproductive Life Span, Age at Menarche, and Age at Menopause Are Associated With Risk of Cardiovascular Disease in Women. J Am Heart Assoc. 2017;6(11).doi:10.1161/jaha.117.006713. Peters SA, Woodward M. Women's reproductive factors and incident cardiovascular disease in the UK Biobank. Heart. 2018;104(13):1069-75.doi:10.1136/heartjnl-2017-312289. Jeong SM, Jeon KH, Jung W, Yoo JE, Yoo J, Han K, et al. Association of reproductive factors with cardiovascular disease risk in pre-menopausal women: nationwide population-based cohort study. Eur J Prev Cardiol. 2023;30(3):264-73.doi:10.1093/eurjpc/zwac265. Chen X, Liu Y, Sun X, Yin Z, Li H, Liu X, et al. Age at menarche and risk of all-cause and cardiovascular mortality: a systematic review and dose–response meta-analysis. Menopause. 2019;26(6):670-6.doi:10.1097/gme.0000000000001289. Honigberg MC, Zekavat SM, Aragam K, Finneran P, Klarin D, Bhatt DL, et al. Association of Premature Natural and Surgical Menopause With Incident Cardiovascular Disease. JAMA. 2019;322(24):2411-21.doi:10.1001/jama.2019.19191. Barrett-Connor E, Bush TL. Estrogen and coronary heart disease in women. Jama. 1991;265(14):1861-7. Bairey Merz CN, Johnson BD, Sharaf BL, Bittner V, Berga SL, Braunstein GD, et al. Hypoestrogenemia of hypothalamic origin and coronary artery disease in premenopausal women: a report from the NHLBI-sponsored WISE study. J Am Coll Cardiol. 2003;41(3):413-9.doi:10.1016/s0735-1097(02)02763-8. Woo D, Jae S, Park S. U-shaped association between age at first childbirth and mortality: A prospective cohort study. Maturitas. 2022;161:33-9.doi:10.1016/j.maturitas.2022.01.015. Täufer Cederlöf E, Lundgren M, Lindahl B, Christersson C. Pregnancy Complications and Risk of Cardiovascular Disease Later in Life: A Nationwide Cohort Study. J Am Heart Assoc. 2022;11(2):e023079.doi:10.1161/jaha.121.023079. Rosendaal NTA, Pirkle CM. Age at first birth and risk of later-life cardiovascular disease: a systematic review of the literature, its limitation, and recommendations for future research. BMC Public Health. 2017;17(1):627.doi:10.1186/s12889-017-4519-x. Gunderson EP, Jacobs DR, Jr., Chiang V, Lewis CE, Tsai A, Quesenberry CP, Jr., et al. Childbearing is associated with higher incidence of the metabolic syndrome among women of reproductive age controlling for measurements before pregnancy: the CARDIA study. Am J Obstet Gynecol. 2009;201(2):177.e1-9.doi:10.1016/j.ajog.2009.03.031. Wong JA, Rexrode KM, Sandhu RK, Conen D, Albert CM. Number of Pregnancies and Atrial Fibrillation Risk: The Women's Health Study. Circulation. 2017;135(6):622-4.doi:10.1161/circulationaha.116.026629. Li W, Wang Y, Shen L, Song L, Li H, Liu B, et al. Association between parity and obesity patterns in a middle-aged and older Chinese population: a cross-sectional analysis in the Tongji-Dongfeng cohort study. Nutr Metab (Lond). 2016;13:72.doi:10.1186/s12986-016-0133-7. Jaffe DH, Neumark YD, Eisenbach Z, Manor O. Parity-related mortality: shape of association among middle-aged and elderly men and women. Eur J Epidemiol. 2009;24(1):9-16.doi:10.1007/s10654-008-9310-y. Spence NJ, Eberstein IW. Age at first birth, parity, and post-reproductive mortality among white and black women in the US, 1982-2002. Soc Sci Med. 2009;68(9):1625-32.doi:10.1016/j.socscimed.2009.02.018. Additional Declarations No competing interests reported. Supplementary Files graphicabstract.tif rawdata.csv Cite Share Download PDF Status: Published Journal Publication published 05 Apr, 2024 Read the published version in BMC Women's Health → Version 1 posted Editorial decision: Revision requested 21 Nov, 2023 Reviews received at journal 18 Nov, 2023 Reviewers agreed at journal 16 Nov, 2023 Reviews received at journal 22 Oct, 2023 Reviewers agreed at journal 19 Oct, 2023 Reviewers agreed at journal 12 Oct, 2023 Reviewers invited by journal 05 Oct, 2023 Editor assigned by journal 05 Oct, 2023 Editor invited by journal 28 Sep, 2023 Submission checks completed at journal 28 Sep, 2023 First submitted to journal 17 Sep, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-3362485","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":236783429,"identity":"badc86c2-1685-4f4e-a1ea-9d430c89706d","order_by":0,"name":"Yufeng Yan","email":"","orcid":"","institution":"Department of Cardiology, Nanjing First Hospital, Nanjing Medical University,","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Yan","suffix":""},{"id":236783430,"identity":"81a25c96-1238-4f49-a387-158f0f6ffacb","order_by":1,"name":"Hongjing Lu","email":"","orcid":"","institution":"Department of Cardiology, Nanjing First Hospital, Nanjing Medical University,","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongjing","middleName":"","lastName":"Lu","suffix":""},{"id":236783431,"identity":"7f206e8c-d315-4354-9900-b2692ca901bd","order_by":2,"name":"Song Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie2PMUvEMBTHUwK95bV1TFDsV+hRKDcU/CAulYPrdKt0qCXHQRxdeyD4FW50TAjUJTi7iqtDDhdHe9hJaLMKl9/y58H/x3sPIYfjHxLi34QofmfCVOQSYYviD4ULypYb2epFaleGzBNRblXAqxtmO8yfBcocnu8gEZL1Cimf7gNpUJ1fjx8WruhOvwCVGyYfX8l6r8IlQd1qPbbOx5DhgHcQKo+Jz9tewZAQj6kpJf06KqjrFfBJGW8h/bYoyXnAazjTHpPASYEUZLYtGd1xAbTtlVaT+b5XFsXEL1GkU3PgzVVEZh/GVE0cP+j0zdT5qDKg/szFdP1IY684HA7H6fIDn6ZX5gw/nf4AAAAASUVORK5CYII=","orcid":"","institution":"Department of Cardiology, Nanjing First Hospital, Nanjing Medical 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mortality\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3EffectPlotsforNonlinearAssociationsofReproductiveFactorsWithIncidentAllcausemortality.png","url":"https://assets-eu.researchsquare.com/files/rs-3362485/v1/287654566677e9cabcf959c6.png"},{"id":54303938,"identity":"075311ba-4bb0-45a9-8c00-bd78e72c8e20","added_by":"auto","created_at":"2024-04-08 15:12:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1152810,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3362485/v1/925ed5a9-74b2-44d7-a529-f0d1af2eee73.pdf"},{"id":44206881,"identity":"c02a75b5-0854-445b-8921-6b3c682dddce","added_by":"auto","created_at":"2023-10-06 20:22:18","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6312276,"visible":true,"origin":"","legend":"","description":"","filename":"graphicabstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-3362485/v1/c414d9d1bdd530eb2d954780.tif"},{"id":44206880,"identity":"d7160ec0-2a1f-4503-ac7e-8de5373af676","added_by":"auto","created_at":"2023-10-06 20:22:18","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5251730,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.csv","url":"https://assets-eu.researchsquare.com/files/rs-3362485/v1/a1c55d309441bc6a5ec0f323.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reproductive factors and risk of cardiovascular diseases and all-cause and cardiovascular mortality in American women: NHANES 2003-2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases(CVDs) are among the most serious diseases that harm health and cause death globally(1). Through interventions targeting high-risk factors, the incidence of CVDs has been effectively managed. Although the mortality and morbidity rates of CVDs have decreased, the CVD burden has increased in women(2). Sex has a profound impact on cardiovascular diseases(3). Before menopause, women generally exhibit a lower risk of cardiovascular disease than men. Changes in female sex hormones occur throughout the reproductive process, from menarche to pregnancy, childbirth, and menopause. These hormone levels affect a woman's blood lipid levels, blood pressure, blood sugar, and inflammation, subsequently influencing cardiovascular health (4). Reproductive health in women plays a vital role in the physiological and pathological processes of the cardiovascular system. Therefore, evaluating the influence of reproductive factors on cardiovascular diseases is crucial to reducing their incidence in women.\u003c/p\u003e \u003cp\u003eBy analyzing the impact of these factors on all-cause mortality and cardiovascular mortality, we can gain a deeper understanding of their effects on women\u0026rsquo;s overall health. Throughout a woman\u0026rsquo;s life, various reproductive factors play a significant role, encompassing the entire process from female reproductive system development to senescence. Some research has demonstrated a correlation between early menarche and an increased risk of all-cause mortality or cardiovascular mortality(5\u0026ndash;8). However, contrasting findings from studies on European populations suggest that late menarche is associated with an elevated risk of cardiovascular death, while early menarche does not show the same association(9). Additionally, some studies have reported an association between an earlier age of menopause and higher mortality rates, particularly in relation to cardiovascular disease(10). Nevertheless, the relationship between female reproductive factors and the risk of all-cause and cardiovascular mortality death in women remains controversial.\u003c/p\u003e \u003cp\u003e The development of treatment guidelines for cardiovascular diseases has predominantly focused on male patients, neglecting the importance of studying how female reproductive factors contribute to cardiovascular health. This study aimed to analyze the National Health and Nutrition Examination Survey (NHANES) database to explore the relationship between reproductive factors(such as age at menarche, age at menopause, reproductive span, age at first live birth, and number of live births) and cardiovascular disease, all-cause death, and cardiovascular death in women. The findings from this study offer valuable insights for the early identification and management of female-specific cardiovascular risk factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eWe downloaded data from the 2003\u0026ndash;2018 NHANES to explore the associations between reproductive factors and CVDs. The cohort study sample comprised individuals who participated in the NHANES survey from 2003\u0026ndash;2018, a population-based survey designed to collect health and nutritional statistics of adults and children in the United States(11). All participants provided written informed consent. A total of 28,063 participants included in the NHANES from 2003\u0026ndash;2014. Of these, participants had mortality follow-up data available through December 31, 2019 from the US National Death Index(NDI). We excluded individuals with missing data and included 16,404 participants in the final analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eOutcome were obtained from the National Death Index through 31 December 2019 to identify mortality status. The primary cause of death was determined according to ICD-10 codes. Outcomes were be defined as all-cause and cardiovascular mortality. Cardiovascular diseases include conditions such as coronary heart disease, congestive heart failure, heart attack, stroke, and angina.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy covariates\u003c/h2\u003e \u003cp\u003eDemographic information (sex, age, family income-poverty ratio, race/ethnicity and educational level) and lifestyles(smoking, drinking alcohol) were collected during in-home interviews. Body mass index (BMI) was measured at mobile examination centers. BMI is calculated by dividing an individual's weight by the square of their height. Race/ethnicity was categorized as Mexican American, non-Hispanic white, non-Hispanic black, other Hispanic, and others. Education levels were categorized as follows: less than 9th grade, 9-11th grade (includes 12th grade with no diploma), high school graduate/GED or equivalent, some college or AA degree, and college graduate or above. Hypertension was defined as either a self-reported medical history or a blood pressure reading of 130/80 mmHg or higher. Diabetes was determined through a combination of self-reported medical history, glycosylated hemoglobin A1c level of 6.5% or higher, or fasting plasma glucose level of 126 mg/dL or higher.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eSampling weights were considered in our study due to the complex sampling design of the NHANES. Means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation are used for continuous variables, and percentages are utilized for categorical variables. We used a logistic model to assess the correlation between reproductive factors and CVDs events. Multivariable Cox proportional hazards models were used to assess the association of reproductive factors with cardiovascular death and all-cause death. All reproductive factors were assessed as both continuous and categorical variables. The non-linear relationship between reproductive factors and cardiovascular events, all-cause death, and cardiovascular death was evaluated using restricted cube plots (4 knots). Categorical variables were used to evaluate the relationship between reproductive factors and cardiovascular events, all-cause death, and cardiovascular death. Two models were used to analyze confounding factors. Model 1 makes adjustments for age. Model 2 was adjusted for race, BMI, alcohol consumption, smoking status, hypertension, diabetes mellitus, and education, Statistical analyses were conducted using R software version 4.1.0, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study population\u003c/h2\u003e \u003cp\u003eThe baseline information is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. During a median follow-up of 105(IQR: 59\u0026ndash;148) months, 1693 participants died, and 459 individuals experienced cardiovascular mortality.1572 participants was recorded as having CVDs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Study Population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003etotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt age Menarche ,y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty-income ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12102175.08(13.37)\u003c/p\u003e \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\u003e24670017.57(27.26)\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\u003e53719548.35(59.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\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\u003e6419253.04( 7.09)\u003c/p\u003e \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\u003e10197540.29(11.27)\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\u003e63250421.66(69.90)\u003c/p\u003e \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\u003e4484634.05( 4.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6139891.94( 6.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade (Includes 12th grade with no diploma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9008261.61( 9.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26411609.41(29.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20523026.66(22.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4045007.76( 4.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30503835.55(33.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\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\u003e19206204.48(21.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31429.73( 0.03)\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\u003e54391361.42(60.11)\u003c/p\u003e \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\u003e16862745.36(18.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11857004.74(13.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3060212.57( 3.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2989888.69( 3.30)\u003c/p\u003e \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\u003e72584634.99(80.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\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\u003e56544122.58(62.49)\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\u003e33947618.41(37.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\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\u003e83449764.35(92.22)\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\u003e7041976.64( 7.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIncidence of CVD according to reproductive factors\u003c/h2\u003e \u003cp\u003eThe reproductive factors showed a relationship with CVDs in adjusted models. A nonlinear relationship was observed between age at menarche with CVDs, as indicated in the cube plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Linear relationships were established between age at menopause, reproductive lifespan, and number of pregnancies with CVDs in the multivariable model. In continuous variable logistic regression models, a later age at menopause (OR 0.96, 95% CI 0.96\u0026ndash;0.97), a longer reproductive lifespan(OR 0.97, 95% CI 0.96\u0026ndash;0.97),and a greater number of pregnancies(OR 1.06,95% CI 1.02\u0026ndash;1.09) were associated with a decreased risk of CVDs(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A greater number of pregnancies (OR 1.06, 95% CI 1.02\u0026ndash;1.09) was associated with an increased risk of CVDs. After categorizing the variables, in the multivariate model(model 2), an age at menarche\u0026thinsp;\u0026le;\u0026thinsp;11 was associated with increased CVDs, while ages 14\u0026ndash;15 and \u0026ge;\u0026thinsp;16 were not significantly associated with increased CVDs compared to the reference. Age at menopause\u0026thinsp;\u0026le;\u0026thinsp;44(OR 1.69, 95% CI 1.40\u0026ndash;2.03) was associated with increased CVDs, whereas ages 50\u0026ndash;54 and \u0026ge;\u0026thinsp;55 were not. A reproductive lifespan of \u0026le;\u0026thinsp;32 was associated with increased CVDs, while spans of 36\u0026ndash;38, 39\u0026ndash;41, and \u0026ge;\u0026thinsp;42 were not. Maternal age at first live birth of \u0026le;\u0026thinsp;19, 24\u0026ndash;26, and \u0026ge;\u0026thinsp;27 and maternal age at last live birth of \u0026le;\u0026thinsp;26, 30\u0026ndash;34, 35\u0026ndash;39, and \u0026ge;\u0026thinsp;40 were not associated with increased cardiovascular disease compared to the reference. A number of pregnancies\u0026thinsp;\u0026ge;\u0026thinsp;5(OR 1.26, 95% CI 1.02\u0026ndash;1.55) was associated with an increase in cardiovascular disease, whereas numbers 1,3,4 were not.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssocaition of reproductive factors with incident CVDs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eReproductive Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAge-Adjusted Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariable Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55(0.03\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36(1.10\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07(0.16\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06(0.89\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35(0.10\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26(1.00-1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84(0.90\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.69(1.40\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83(0.87\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93(0.73\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87(0.11\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93(0.71\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79(0.08\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.63(1.33-2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92(0.79\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94(0.70\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u0026ndash;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80(0.03\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90(0.65\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97(0.11\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02(0.77\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at first live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37(0.23\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04(0.81\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67(0.97\u0026thinsp;\u0026minus;\u0026thinsp;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77(0.53\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50(0.11\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75(0.52\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at last live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26(0.98\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08(0.85\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90(0.70\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(0.77\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95(0.72\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09(0.82\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39(0.87\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.44(0.89\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96(0.03\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(0.74\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89(0.17\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92(0.71\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21(0.21\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13(0.85\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52(0.10\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26(1.02\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.95(0.10\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95(0.91\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.95(0.11\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96(0.96\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.96(0.11\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97(0.96\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at first live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.92(0.11\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97(0.95-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at last live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.99(0.11-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01(0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.10(0.10\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06(1.02\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCardiac death according to reproductive factors\u003c/h2\u003e \u003cp\u003eA non-linear relationship between reproductive factors and cardiovascular death was indicated in the cube plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Linear relationships were observed between age at menopause, reproductive lifespan, and number of pregnancies with cardiac death in the multivariable model. In continuous variable COX regression models, a later age at menopause (HR 0.98, 95% CI 0.97\u0026ndash;0.99), a longer reproductive lifespan (HR 0.98, 95% CI 0.97\u0026ndash;0.99) were associated with a decreased risk of cardiac death (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After categorizing the variables, in the multivariate model(model 2), ages at menarche of \u0026le;\u0026thinsp;11, 14\u0026ndash;15, and \u0026ge;\u0026thinsp;16 were not significantly associated with increased cardiovascular death compared to the reference. Ages at menopause of \u0026le;\u0026thinsp;44, 50\u0026ndash;54, and \u0026ge;\u0026thinsp;55 were not associated with increased risk. A reproductive lifespan of \u0026le;\u0026thinsp;32 (HR 1.62, 95% CI 1.10\u0026ndash;2.39) was associated with increased risk, while spans of 36\u0026ndash;38, 39\u0026ndash;41, and \u0026ge;\u0026thinsp;42 were not. Maternal age at first live birth of \u0026le;\u0026thinsp;19(HR 1.63, 95% CI 1.10\u0026ndash;2.41) was associated with increased cardiovascular death risk, while ages 24\u0026ndash;26 and \u0026ge;\u0026thinsp;27 were not. Maternal ages at last live birth of \u0026le;\u0026thinsp;26, 30\u0026ndash;34, 35\u0026ndash;39, and \u0026ge;\u0026thinsp;40 and number of pregnancies of \u0026ge;\u0026thinsp;5, 1, 3, 4 were not associated with increased risk compared to the reference.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssocaition of reproductive factors with incident cardic death\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eReproductive Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAge-Adjusted Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariable Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06(0.78\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97(0.71\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11(0.82\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00(0.75\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31(0.91\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14(0.81\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34(0.96\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21(0.87\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91(0.67\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94(0.68\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80(0.54\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81(0.53\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73(1.21\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62(1.10\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18(0.83\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19(0.81\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u0026ndash;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97(0.65\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07(0.71\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89(0.56\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91(0.55\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at first live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19(1.55\u0026ndash;3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63(1.10\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30(0.80\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45(1.37\u0026ndash;2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15(0.69\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36(1.18\u0026ndash;2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at last live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21(0.69\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11(0.64\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09(0.60\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10(0.58\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12(0.62-2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09(0.61\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31(0.77\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06(0.63\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of fetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44(0.89\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60(0.97\u0026ndash;2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09(0.75\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19(0.80\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41(0.94\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35(0.89\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40(1.00-1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08(0.75\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05(0.98\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04(0.97\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97(0.96\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97(0.96\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at first live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95(0.91-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00(0.95\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at last live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98(0.95\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(0.96\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02(0.98\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.91\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAll-cause mortality according to reproductive factors\u003c/h2\u003e \u003cp\u003eA non-linear relationship between reproductive factors and all-cause death was indicated in the cubic plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A linear relationship were established between age at menopause, reproductive lifespan, and number of pregnancies with cardiac death in the multivariable model. In continuous variable COX regression models, a later age at menopause (HR 0.98, 95% CI 0.97\u0026ndash;0.99), a longer reproductive lifespan (HR 0.98, 95% CI 0.97\u0026ndash;0.99) were associated with a decreased risk of all-cause death (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). After categorizing the variables, in the multivariate model(model 2), ages at menarche of \u0026le;\u0026thinsp;11, 14\u0026ndash;15, and \u0026ge;\u0026thinsp;16 were not significantly associated with increased all-cause death compared to the reference. Ages at menopause of \u0026le;\u0026thinsp;44, 50\u0026ndash;54, and \u0026ge;\u0026thinsp;55 were not associated with increased risk. The reproductive lifespans of \u0026le;\u0026thinsp;32, 36\u0026ndash;38, 39\u0026ndash;41, and \u0026ge;\u0026thinsp;42 were not associated with increased risk compared to the reference. Maternal age at first live birth of 24\u0026ndash;26(HR 1.37, 95% CI 1.06\u0026ndash;1.76) was associated with increased all-cause death, while ages\u0026thinsp;\u0026le;\u0026thinsp;19 and \u0026ge;\u0026thinsp;27 were not. Maternal ages at last live birth of \u0026le;\u0026thinsp;26, 30\u0026ndash;34, 35\u0026ndash;39, and \u0026ge;\u0026thinsp;40 and numbers of pregnancies of \u0026ge;\u0026thinsp;5, 1, 3, 4 were not associated with increased all-cause death compared to the reference.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssocaition of reproductive factors with incident death\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReproductive Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAge-Adjusted Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariable Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13(0.97\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05(0.91\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03(0.88\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.81\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16(0.92\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05(0.83\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22(1.03\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12(0.94\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90(0.76\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.81\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87(0.70\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93(0.72\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32(1.07\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24(0.99\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08(0.84\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14(0.86\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u0026ndash;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79(0.62\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88(0.67\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03(0.80\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10(0.83\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at first live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44(1.17\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10(0.87\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23(0.96\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37(1.06\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97(0.73\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18(0.85\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMaternal age at last live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09(0.87\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.80\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95(0.73\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.73\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22(0.92\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30(0.99\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33(0.92\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18(0.81\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11(0.89\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16(0.93\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03(0.85\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12(0.91\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17(0.96\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13(0.92\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21(1.04\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00(0.84\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.00(0.96\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(0.96\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.98(0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.98(0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age at first live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.98(0.95-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02(1.00-1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age at last live birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.00(0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01(0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.02(0.99\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.95\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first comprehensive assessment of the association between female reproductive factors and all-cause mortality, cardiogenic death, and all-cause death using the NHANES database. We found that, after controlling for known CVDs risk factors, a later age at menopause, a longer reproductive lifespan, and fewer pregnancies were associated with a significantly lower risk of CVDs. Our study also found that a shorter reproductive lifespan and earlier age at menopause were associated with an increased risk of both all-cause and cardiovascular death.\u003c/p\u003e \u003cp\u003eWe found that there is a nonlinear relationship between age at menarche and CVDs in our study. Previous studies have identified various patterns of association between age at menarche and CVDs risk in women. Some studies have shown an increased risk of CVDs with early age at menarche(12), while others have indicated an increased risk with later age at menarche(13). In a retrospective study, earlier age at menarche was associated with a higher risk of heart failure in women(14). Some studies have suggested that early menarche is associated with an increased risk of CVDs in women (15\u0026ndash;17). However, a study from Korea determined that late menarche was associated with an increased risk of MI, but early age at menarche was not (13). Another study of 1,088,992 premenopausal women found that late menarche was associated with an increased risk of all-cause disease, while early menarche was not(18). Both later and earlier menarche are influenced by female adolescent BMI, hormones, and renal hormones. Beyond the relationship between age at menarche (AAM) and CVDs, we also explored its association with all-cause mortality. We found no significant relationship between AAM and all-cause mortality. Yet, some researchers have suggested that AAM is linked to all-cause mortality(15). Additionally, a recent systematic review showed an inverse association between age at menarche and both all-cause mortality and mortality due to ischemic heart disease(19).\u003c/p\u003e \u003cp\u003eOur study confirms that early age at menopause significantly increases the risk of all-cause diseases. This conclusion is consistent with numerous other studies that have found premature menopause, whether natural or surgical, to be a risk factor for various all-cause diseases(20). The decrease in endogenous estrogen secretion in postmenopausal women significantly affects low-density lipoprotein control, which may explain why postmenopausal women have a lower incidence of coronary heart disease compared to men(21). In our study, even after adjusting for common risk factors, premature menopause was associated with a heightened risk of all-cause diseases. By identifying women with premature menopause, targeted prevention could reduce the incidence of all-cause diseases. Our research also determined that a shorter reproductive lifespan was linked to increased all-cause risk and cardiac death. This might result from the combined effects of irregular estrogen production timing and insufficient estrogen exposure. The Women\u0026rsquo;s Ischemia Syndrome Evaluation study noted a connection between disrupted ovulatory cycles characterized by hypoestrogenemia of hypothalamic origin and angiographic CAD (Coronary Artery Disease)(22).\u003c/p\u003e \u003cp\u003eWe found no significant association between the first or last live birth and the occurrence of all-cause diseases (CVDs) or all-cause mortality. However, a study using data from the UK Biobank indicated that a younger maternal age at the time of the first or last live birth is a risk factor for heart failure (HF) events (14). Previous studies have suggested that a younger maternal age at conception might be associated with CVD(23, 24). A systematic review of observational studies from 1980 to 2016 found the relationship between early age at first live birth and all-cause disease risk to be uncertain, though there is some evidence suggesting a potential increase in risk. Of the studies reviewed, ten found that women with an early age at first live birth had a higher risk of developing CVDs, while two found that women with a later age of first birth had a greater risk of developing all-cause disease(25). Studies have shown a U-shaped association between age at first birth and death from all-cause disease and all-cause mortality(23). While our study did not replicate these findings, we did determine that an early age at first birth was associated with an increased risk of all-cause death.\u003c/p\u003e \u003cp\u003eThe study identified a linear relationship between the number of pregnancies and the increased risk of all-cause disease. Some research has indicated that the higher the number of live births correlates with a greater risk of HF(14). This relationship might be due to hormonal changes during pregnancy, increased cardiac workload, abnormal blood sugar levels, dyslipidemia, and the long-term effects of pregnancy on all-cause health. Additionally, multiple pregnancies can lead to future weight gain and the onset of metabolic syndrome(26). In a large prospective cohort, researchers found that a higher number of pregnancies correlated with a greater risk of subsequent atrial fibrillation(27). Repeated exposure to metabolic, physiological, and hormonal changes during pregnancy, combined with increased cardiac workload, might contribute to the onset of atrial fibrillation later in life. During pregnancy, there are notable changes in hormone levels, especially increases in estrogen and progesterone. These shifts can cause a metabolic slowdown, potentially leading to fat accumulation in the abdominal area(28). However, more research is needed to fully understand this connection.\u003c/p\u003e \u003cp\u003eImportantly, our study did not find a significant relationship between the number of pregnancies and all-cause mortality. Some research has identified a nonlinear relationship between number of pregnancies and all-cause death (29), while other studies have found no such connection(30). The relationship between the number of pregnancies and mortality is intricate, influenced by various factors like chronic diseases, race, and socioeconomic status. More investigation is essential to decipher the specific mechanisms behind these associations and to analyze the effects of repeated pregnancies on long-term health outcomes.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy strengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several advantages over previous studies. The study benefits from an extended follow-up period, enabling a thorough assessment of the relationship between reproductive factors and all-cause disease, as well as all-cause mortality. This research examines a broad spectrum of reproductive factors, offering a comprehensive understanding of their effects on all-cause health outcomes. Utilizing the NHANES database ensures national representativeness, which enhances the generalizability of the results to other populations. The analysis considers the influence of race, allowing for a more precise evaluation of the relationships between reproductive factors and health outcomes across various racial groups. However, our study has some limitations. Firstly, relying on self-reported questionnaire data might lead to recall bias, as participants may not remember specific reproductive factors accurately, potentially affecting the study's conclusions. Secondly, while the study addresses six particular reproductive factors, it doesn't consider other possibly relevant aspects such as hormone use, breastfeeding, or history of reproductive system surgeries. Their impact on overall health outcomes warrants further investigation. Thirdly, as patient baseline data can change over time, this variability might introduce bias into the study's findings. Lastly, the research doesn't comprehensively compare the effects of reproductive factors on distinct categories of all-cause diseases.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we discovered a significant association between female reproductive factors and the risk of all-cause disease in women. Our findings emphasize the importance of including an evaluation of reproductive factors in the assessment of women's overall health risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this work were obtained from Nhanes. We are grateful to Nhanes researchers for providing the data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in collaboration with the authors listed below: YZ and SL contributed to the conception, design, and interpretation of data. YY analyzed the data and drafted the manuscript. HJ edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis utilized summary data from NHANES, which had obtained written informed consent and ethics approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the National Natural Science Foundation of China (Grant No: 81900248).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study were sourced from National Health and Nutrition Examination Survey (NHANES) and available here: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMensah GA, Roth GA, Fuster V. The Global Burden of Cardiovascular Diseases and Risk Factors: 2020 and Beyond. J Am Coll Cardiol. 2019;74(20):2529-32.doi:10.1016/j.jacc.2019.10.009.\u003c/li\u003e\n\u003cli\u003eVogel B, Acevedo M, Appelman Y, Bairey Merz CN, Chieffo A, Figtree GA, et al. The Lancet women and cardiovascular disease Commission: reducing the global burden by 2030. Lancet. 2021;397(10292):2385-438.doi:10.1016/s0140-6736(21)00684-x.\u003c/li\u003e\n\u003cli\u003eLam CSP, Arnott C, Beale AL, Chandramouli C, Hilfiker-Kleiner D, Kaye DM, et al. Sex differences in heart failure. Eur Heart J. 2019;40(47):3859-68c.doi:10.1093/eurheartj/ehz835.\u003c/li\u003e\n\u003cli\u003eSubramanya V, Zhao D, Ouyang P, Lima JA, Vaidya D, Ndumele CE, et al. Sex hormone levels and change in left ventricular structure among men and post-menopausal women: The Multi-Ethnic Study of Atherosclerosis (MESA). Maturitas. 2018;108:37-44.doi:10.1016/j.maturitas.2017.11.006.\u003c/li\u003e\n\u003cli\u003eCharalampopoulos D, McLoughlin A, Elks CE, Ong KK. Age at menarche and risks of all-cause and cardiovascular death: a systematic review and meta-analysis. Am J Epidemiol. 2014;180(1):29-40.doi:10.1093/aje/kwu113.\u003c/li\u003e\n\u003cli\u003eWu X, Cai H, Kallianpur A, Gao YT, Yang G, Chow WH, et al. Age at menarche and natural menopause and number of reproductive years in association with mortality: results from a median follow-up of 11.2 years among 31,955 naturally menopausal Chinese women. PLoS One. 2014;9(8):e103673.doi:10.1371/journal.pone.0103673.\u003c/li\u003e\n\u003cli\u003eJacobsen BK, Oda K, Knutsen SF, Fraser GE. Age at menarche, total mortality and mortality from ischaemic heart disease and stroke: the Adventist Health Study, 1976-88. Int J Epidemiol. 2009;38(1):245-52.doi:10.1093/ije/dyn251.\u003c/li\u003e\n\u003cli\u003eGallagher LG, Davis LB, Ray RM, Psaty BM, Gao DL, Checkoway H, et al. Reproductive history and mortality from cardiovascular disease among women textile workers in Shanghai, China. Int J Epidemiol. 2011;40(6):1510-8.doi:10.1093/ije/dyr134.\u003c/li\u003e\n\u003cli\u003e. !!! INVALID CITATION !!! [9].\u003c/li\u003e\n\u003cli\u003eOssewaarde ME, Bots ML, Verbeek AL, Peeters PH, van der Graaf Y, Grobbee DE, et al. Age at menopause, cause-specific mortality and total life expectancy. Epidemiology. 2005;16(4):556-62.doi:10.1097/01.ede.0000165392.35273.d4.\u003c/li\u003e\n\u003cli\u003eRuhl CE, Everhart JE. Fatty liver indices in the multiethnic United States National Health and Nutrition Examination Survey. Aliment Pharmacol Ther. 2015;41(1):65-76.doi:10.1111/apt.13012.\u003c/li\u003e\n\u003cli\u003eCanoy D, Beral V, Balkwill A, Wright FL, Kroll ME, Reeves GK, et al. Age at menarche and risks of coronary heart and other vascular diseases in a large UK cohort. Circulation. 2015;131(3):237-44.doi:10.1161/circulationaha.114.010070.\u003c/li\u003e\n\u003cli\u003eJeong SM, Yoo JE, Jeon KH, Han K, Lee H, Lee DY, et al. Associations of reproductive factors with incidence of myocardial infarction and ischemic stroke in postmenopausal women: a cohort study. BMC Med. 2023;21(1):64.doi:10.1186/s12916-023-02757-2.\u003c/li\u003e\n\u003cli\u003eZhu F, Qi H, Bos M, Boersma E, Kavousi M. Female Reproductive Factors and Risk of New-Onset Heart Failure: Findings From UK Biobank. JACC Heart Fail. 2023.doi:10.1016/j.jchf.2023.02.019.\u003c/li\u003e\n\u003cli\u003eLakshman R, Forouhi NG, Sharp SJ, Luben R, Bingham SA, Khaw KT, et al. Early age at menarche associated with cardiovascular disease and mortality. J Clin Endocrinol Metab. 2009;94(12):4953-60.doi:10.1210/jc.2009-1789.\u003c/li\u003e\n\u003cli\u003eLey SH, Li Y, Tobias DK, Manson JE, Rosner B, Hu FB, et al. Duration of Reproductive Life Span, Age at Menarche, and Age at Menopause Are Associated With Risk of Cardiovascular Disease in Women. J Am Heart Assoc. 2017;6(11).doi:10.1161/jaha.117.006713.\u003c/li\u003e\n\u003cli\u003ePeters SA, Woodward M. Women\u0026apos;s reproductive factors and incident cardiovascular disease in the UK Biobank. Heart. 2018;104(13):1069-75.doi:10.1136/heartjnl-2017-312289.\u003c/li\u003e\n\u003cli\u003eJeong SM, Jeon KH, Jung W, Yoo JE, Yoo J, Han K, et al. Association of reproductive factors with cardiovascular disease risk in pre-menopausal women: nationwide population-based cohort study. Eur J Prev Cardiol. 2023;30(3):264-73.doi:10.1093/eurjpc/zwac265.\u003c/li\u003e\n\u003cli\u003eChen X, Liu Y, Sun X, Yin Z, Li H, Liu X, et al. Age at menarche and risk of all-cause and cardiovascular mortality: a systematic review and dose\u0026ndash;response meta-analysis. Menopause. 2019;26(6):670-6.doi:10.1097/gme.0000000000001289.\u003c/li\u003e\n\u003cli\u003eHonigberg MC, Zekavat SM, Aragam K, Finneran P, Klarin D, Bhatt DL, et al. Association of Premature Natural and Surgical Menopause With Incident Cardiovascular Disease. JAMA. 2019;322(24):2411-21.doi:10.1001/jama.2019.19191.\u003c/li\u003e\n\u003cli\u003eBarrett-Connor E, Bush TL. Estrogen and coronary heart disease in women. Jama. 1991;265(14):1861-7.\u003c/li\u003e\n\u003cli\u003eBairey Merz CN, Johnson BD, Sharaf BL, Bittner V, Berga SL, Braunstein GD, et al. Hypoestrogenemia of hypothalamic origin and coronary artery disease in premenopausal women: a report from the NHLBI-sponsored WISE study. J Am Coll Cardiol. 2003;41(3):413-9.doi:10.1016/s0735-1097(02)02763-8.\u003c/li\u003e\n\u003cli\u003eWoo D, Jae S, Park S. U-shaped association between age at first childbirth and mortality: A prospective cohort study. Maturitas. 2022;161:33-9.doi:10.1016/j.maturitas.2022.01.015.\u003c/li\u003e\n\u003cli\u003eT\u0026auml;ufer Cederl\u0026ouml;f E, Lundgren M, Lindahl B, Christersson C. Pregnancy Complications and Risk of Cardiovascular Disease Later in Life: A Nationwide Cohort Study. J Am Heart Assoc. 2022;11(2):e023079.doi:10.1161/jaha.121.023079.\u003c/li\u003e\n\u003cli\u003eRosendaal NTA, Pirkle CM. Age at first birth and risk of later-life cardiovascular disease: a systematic review of the literature, its limitation, and recommendations for future research. BMC Public Health. 2017;17(1):627.doi:10.1186/s12889-017-4519-x.\u003c/li\u003e\n\u003cli\u003eGunderson EP, Jacobs DR, Jr., Chiang V, Lewis CE, Tsai A, Quesenberry CP, Jr., et al. Childbearing is associated with higher incidence of the metabolic syndrome among women of reproductive age controlling for measurements before pregnancy: the CARDIA study. Am J Obstet Gynecol. 2009;201(2):177.e1-9.doi:10.1016/j.ajog.2009.03.031.\u003c/li\u003e\n\u003cli\u003eWong JA, Rexrode KM, Sandhu RK, Conen D, Albert CM. Number of Pregnancies and Atrial Fibrillation Risk: The Women\u0026apos;s Health Study. Circulation. 2017;135(6):622-4.doi:10.1161/circulationaha.116.026629.\u003c/li\u003e\n\u003cli\u003eLi W, Wang Y, Shen L, Song L, Li H, Liu B, et al. Association between parity and obesity patterns in a middle-aged and older Chinese population: a cross-sectional analysis in the Tongji-Dongfeng cohort study. Nutr Metab (Lond). 2016;13:72.doi:10.1186/s12986-016-0133-7.\u003c/li\u003e\n\u003cli\u003eJaffe DH, Neumark YD, Eisenbach Z, Manor O. Parity-related mortality: shape of association among middle-aged and elderly men and women. Eur J Epidemiol. 2009;24(1):9-16.doi:10.1007/s10654-008-9310-y.\u003c/li\u003e\n\u003cli\u003eSpence NJ, Eberstein IW. Age at first birth, parity, and post-reproductive mortality among white and black women in the US, 1982-2002. Soc Sci Med. 2009;68(9):1625-32.doi:10.1016/j.socscimed.2009.02.018.\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":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Reproductive factors, Cardiovascular diseases, Nhanes, Cardiovascular and all-cause death","lastPublishedDoi":"10.21203/rs.3.rs-3362485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3362485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe evidence regarding the association of reproductive factors with cardiovascular diseases (CVDs) is limited.\u003c/p\u003e\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eTo investigate the relationship of reproductive factors with the risk of CVDs, as well as all-cause and cardiovascular mortality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study included 16404 adults with reproductive factors from the National Health and Nutrition Examination Survey (NHANES) and followed up until 31 December 2019. Logistic models and restricted cubic spline models were used to assess the association of reproductive factors with CVDs. COX proportional hazards models and restricted cubic spline models, with adjustment for potential confounding, were employed to analyze the relation between reproductive factors and cardiovascular and all-cause death.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere is a nonlinear relationship between age at menarche and CVDs. Age at menopause\u0026thinsp;\u0026le;\u0026thinsp;11(OR 1.36, 95% CI 1.10\u0026ndash;1.69) was associated with an increased risk of CVDs compared to ages 12\u0026ndash;13 years. Age at Menopause\u0026thinsp;\u0026le;\u0026thinsp;44 (OR 1.69, 95% CI 1.40\u0026ndash;2.03) was associated with increased CVDs compared to age 35\u0026ndash;49 years. Number of pregnancies\u0026thinsp;\u0026ge;\u0026thinsp;5(OR 1.26, 95% CI 1.02\u0026ndash;1.55) was associated with an increased risk of CVDs compared to one pregnancy. In continuous variable COX regression models, a later age at menopause (HR 0.98, 95% CI 0.97\u0026ndash;0.99) and a longer reproductive lifespan (HR 0.98, 95% CI 0.97\u0026ndash;0.99) were associated with a decreased risk of all-cause death. A later age at menopause (HR 0.98, 95% CI 0.97\u0026ndash;0.99) and a longer reproductive lifespan (HR 0.98, 95% CI 0.97\u0026ndash;0.99) were associated with a decreased risk of cardiac death.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFemale reproductive factors are significant risk factors for CVDs.\u003c/p\u003e","manuscriptTitle":"Reproductive factors and risk of cardiovascular diseases and all-cause and cardiovascular mortality in American women: NHANES 2003-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-06 20:22:13","doi":"10.21203/rs.3.rs-3362485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2023-11-21T05:12:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-11-18T17:01:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e99079b1-573d-4696-bb99-8d7acf632fa7","date":"2023-11-16T14:13:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-10-22T18:42:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2fd8a91a-166e-4122-8805-39ab4c7b201a","date":"2023-10-19T19:03:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35bc2405-4d52-466f-8d7b-9ab9d55731c3","date":"2023-10-12T04:10:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-10-05T14:31:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-10-05T14:26:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-09-29T01:15:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-09-29T01:13:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2023-09-17T05:49:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ee56786-e76d-4239-ba57-93b4921ed202","owner":[],"postedDate":"October 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-08T15:06:30+00:00","versionOfRecord":{"articleIdentity":"rs-3362485","link":"https://doi.org/10.1186/s12905-024-03055-6","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2024-04-05 15:01:40","publishedOnDateReadable":"April 5th, 2024"},"versionCreatedAt":"2023-10-06 20:22:13","video":"","vorDoi":"10.1186/s12905-024-03055-6","vorDoiUrl":"https://doi.org/10.1186/s12905-024-03055-6","workflowStages":[]},"version":"v1","identity":"rs-3362485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3362485","identity":"rs-3362485","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}