Reproductive life span and diabetes mellitus, and metabolic syndrome in the general US population

Reproductive life span and diabetes mellitus, and metabolic syndrome in the general US population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Reproductive life span and diabetes mellitus, and metabolic syndrome in the general US population AJin Cho, Yun Soo Hong, Hoon Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5035398/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims The associations of a short reproductive life span (RLS) with diabetes and metabolic syndrome in menopausal women are not clearly understood. Therefore, we investigated whether a short RLS is associated with diabetes and metabolic syndrome using a representative population-based survey data in the US. Methods We evaluated the cross-sectional associations of RLS with diabetes and metabolic syndrome in menopausal women in the US National Health and Nutrition Examination Survey (NHANES) from 2007 through 2018. We used weighted logistic regression using complex survey design to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of diabetes and metabolic syndrome by quartile groups of RLS. Results A total of 888 postmenopausal women with a mean age of 53.9 were included in the study. The mean age at menarche and menopause of the participants were 12.7 and 50.9 years, respectively. The mean RLS was 38.2 years and was categorized into quartiles (11–35, 36–39, 40–42 (reference group), and 43–50 years. Compared to the reference group, the OR for diabetes was 2.24 (95% CI, 1.02–4.94) in women in the 2nd quartile group and had never received hormone replacement therapy (HRT) after adjusting for age, demographics, behavioral factors, and age at menarche. Women in the 2nd quartile group were more likely to have metabolic syndrome irrespective of HRT (OR, 1.86; 95% CI, 1.06–3.26). Conclusion Women with a relatively short RLS were more likely to have diabetes and metabolic syndrome. The findings suggest that estrogen deficiency may increase the likelihood of diabetes and metabolic syndrome, which are major cardiovascular risk factors. The findings need to be further evaluated in a longitudinal study. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Reproductive life span menopause menarche diabetes metabolic syndrome Figures Figure 1 Figure 2 Introduction Menopause, a significant transition in women’s reproductive life, is accompanied by dramatic hormonal fluctuations that influence changes in body composition and metabolism, leading to various health outcomes 1 . Early menopause is a known risk factor for atherosclerosis, cardiovascular disease (CVD), and cardiovascular death, likely due to the protective role of endogenous estrogen on glucose homeostasis and vaso-relaxation 2 – 7 . In a prospective cohort study of 73,814 women in the Nurses’ Health Study, for instance, early menopause was associated with incident coronary heart disease or stroke 8 . The relationship between early age at menopause and risk factors of CVD, including type 2 diabetes and metabolic syndrome, however, has not been consistent in previous studies 9 – 16 . Similarly, the associations of age at menarche with diabetes and metabolic syndrome are unclear, with some studies suggesting an inverse association between age at menarche and diabetes in two meta-analyses 17 – 22 . To assess the impact of the duration of endogenous estrogen exposure on health outcomes, reproductive life span (RLS), defined as the interval between age at menarche and age at menopause, has been introduced to represent the length of the entire reproductive period 9 , 23 , 24 . In a pooled analysis of 12 prospective cohort studies, a shorter RLS was associated with an increased risk of CVD, particularly for those who had menarche at a young age 25 . However, in a meta-analysis of 17 studies, there was no clear association of a shorter RLS with an increased risk of CVD mortality 26 . Moreover, the association between RLS and diabetes 9 , 10 , 23 , 27 – 30 or metabolic syndrome 24 , 31 , 32 remains inconclusive. For instance, the InterAct study of postmenopausal women found that shorter RLS was associated with an increased risk of diabetes, but the risk was not significant when comparing the first quartile of RLS (< 33 years of age) to the highest quartile of RSL (≥ 40 years of age) 9 . In a cross-sectional study of 1,536 postmenopausal women in China, a longer RLS and time since menopause were associated with a higher prevalence of metabolic syndrome 24 . On the other hand, a longer RLS was significantly associated with a lower prevalence of metabolic syndrome in a study using the Korean National Health and Nutrition Examination Survey 32 . In this study, we aimed to investigate the relationship of RLS with diabetes and metabolic syndrome in the general US population using the National Health and Nutritional Examination Survey (NHANES) data from 2007 to 2018. Methods Study population The NHANES is a nationally representative survey designed to assess the health of noninstitutionalized, civilian US population using a complex, stratified, multistage probability-cluster sampling design. Participants underwent at-home interviews and a health examination at a mobile examination center, and provided information on demographic, socioeconomic, dietary, medical history, and medication use as well as blood pressure measurements and blood samples. Of the 17,075 women between 19 and 79 years of age who participated in the NHANES from 2007 to 2018, we conducted a cross-sectional study of women who experienced natural menopause or surgical menopause (bilateral salpingo-oophorectomy [BSO]) before 60 years of age and had been menopausal for 5 years or less (Fig. 1 ). We excluded individuals who were premenopausal at the time of participation or did not answer the question about menstruation (n = 10,173), who had hysterectomy without BSO (n = 1,592), and who did not have information on age at menarche or menopause (n = 218). In addition, to reduce potential selection bias by including healthier older individuals with a long interval between menopause and the study visit and to compare individuals with a similar interval since menopause, women who had been menopause for more than 5 years (n = 4,204) were excluded. The final population for the main analysis included 888 postmenopausal women. The National Center for Health Statistics (NCHS) Research Ethics Review Board approved the protocol and data collection methods of the NHANES (Protocol#2005-06, Protocol#2011-17, and Protocol#2018-01). Written informed consent was obtained from all participants. The study was approved by the Institutional Review Board (IRB) of National Center for Health Statistics and Kangnam Sacred Heart Hospital (IRB No: HKS 2022-09-021). Data collection Race/ethnicity was self-reported and categorized as Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other. Education was defined by the highest degree or level of school completed and categorized as less than high school, high school graduate, some college or Associate of Arts (AA) degree, and college graduate or above. The family income-to-poverty ratio was determined by dividing family income to the poverty level threshold specific to family size and survey year. The ratio followed the US Department of Health and Human Services federal poverty guidelines which are issued each year in the Federal Register ( http://aspe.hhs.gov/POVERTY/index.shtml ). The cut-off values for the family income-to-poverty ratio were < 1.3, 1.3–2.9, and ≥ 3.0. Self-reported smoking status was classified as never, former, or current. High-risk alcohol intake was defined as 4 or more drinks per day. Physical activity was reported using the International Physical Activity Questionnaire (IPAQ) 33 . For variables with missing data, we included missing as a category if they were missing in more than 3% and excluded from the study population otherwise. Blood pressure was measured 4 times on the same arm with a mercury sphygmomanometer in a seated position after resting for 5 minutes. We used the maximum value of the 4 measurements. Waist circumference was measured just above the iliac crest with a measuring tape. Body mass index (BMI) was calculated as weight divided by height (kg/m 2 ). Fasting glucose, triglyceride, and high-density lipoprotein (HDL) cholesterol levels were measured enzymatically using an autoanalyzer. Glycated hemoglobin (HbA1c) was measured using high-performance liquid chromatography methods. Assessment of reproductive factors Menopause was defined as cessation of menstrual bleeding for at least 12 months, and age at menopause was defined as age at last menstruation. RLS was calculated by subtracting age at menarche from age at menopause and was categorized into quartiles (11–35, 36–39, 40–42 (reference group), and 43–50 years). The use of hormone therapy was identified as on ever being on hormone therapy by self-report. Parity was categorized as 0, 1–3 times, 4 or more times using number of livebirths. Outcome definitions Diabetes was defined as either self-reported physician diagnosis of diabetes or fasting glucose ≥ 126 mg/dl (≥ 7.0 mmol/l) and HbA1c ≥ 6.5% in those who had not been diagnosed with diabetes 34 . Metabolic syndrome was defined using the National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III criteria as the presence of three or more of the following conditions: 1) increased waist circumference (> 102 cm for men, > 88 cm for women); 2) elevated triglycerides (≥ 150 mg/dl); 3) low HDL cholesterol (< 40 mg/dl in men, < 50 mg/dl in women); 4) hypertension (≥ 130/85 mmHg); and 5) impaired fasting glucose (≥ 100 mg/dl) 35 . Statistical analyses Participant characteristics are presented as the number of participants (weighted proportion) for categorical variables and the mean (standard error [SE]) for continuous variables. We used multivariable logistic regression models to estimate the odds ratios (ORs) and corresponding 95% confidence intervals (CIs) of the associations of RLS with diabetes and metabolic syndrome. We provide unadjusted estimates (Model 1), estimates adjusted for age, self-reported race/ethnicity, education, BMI, marital status, parity, and the ratio of family income-to-poverty (Model 2), and estimates further adjusted for smoking, high-risk alcohol intake, physical activity, ever use of hormone therapy, and age at menarche (Model 3). P for trend across categories of RLS was evaluated using the categories as a continuous variable. In addition, we evaluated a potential non-linear association between RLS and the outcomes (diabetes and metabolic syndrome) using restricted cubic splines for RLS with 3 knots located at 30, 40, and 50 years. To account for confounding due to hormone replacement therapy (HRT), we repeated the analysis restricted to women who were never treated with HRT (HRT-naïve). Analyses were performed using appropriate survey weights, which account for complex survey design, to provide nationally representative estimates of the general, noninstitutionalized US population. All statistical analyses were performed using Stata 17.0 (StataCorp LLC, College Station, TX). Results Characteristics of the participants by RLS quartiles The mean (SE) age of study participants was 53.9 (0.2) years (Table 1 ). The age at menarche and menopause of the participants were 12.7 (0.1) and 50.9 (0.2) years, respectively. Individuals with a longer RLS, on average, had younger age at menarche and older age at menopause. Non-Hispanic Whites were more likely to have a longer RLS, and non-Hispanic Blacks and Hispanics were more likely to have a shorter RLS. In addition, women with a longer RLS were more likely to have higher education, and to have higher family income, and less likely to be current smokers. The proportion of never using hormone therapy was the highest in the 2nd quartile (36–39 years). Table 1 Baseline characteristics of study participants. All Reproductive lifespan (RLS) Characteristic 11–35 36–39 40–42 43–50 No. of participants 888 232 347 171 138 No. of participants, weighted 7,094,780 1,528,941 2,881,737 1,515,206 1,168,896 Age, years 53.9 ± 0.2 48.2 ± 0.4 53.5 ± 0.2 56.4 ± 0.3 58.9 ± 0.2 Age at menarche 12.7 ± 0.1 13.5 ± 0.2 12.8 ± 0.1 12.6 ± 0.2 11.8 ± 0.2 Age at menopause 50.9 ± 0.2 45.3 ± 0.5 50.5 ± 0.1 53.5 ± 0.2 56.1 ± 0.2 RLS 38.2 ± 0.2 31.8 ± 0.4 37.7 ± 0.1 41.0 ± 0.1 44.3 ± 0.2 Race/Ethnicity, weighted % Hispanic 246 (10.7%) 67 (15.7%) 107 (11.7%) 38 (7.3%) 34 (6.4%) Non-Hispanic White 346 (72.0%) 85 (63.0%) 130 (73.0%) 72 (75.4%) 59 (76.8%) Non-Hispanic Black 177 (9.1%) 47 (11.9%) 59 (7.7%) 40 (9.5%) 31 (8.4%) Other races 119 (8.2%) 33 (9.4%) 51 (7.7%) 21 (7.8%) 14 (8.3%) Education, weighted % Less than high school 188 (12.2%) 57 (17.4%) 73 (12.4%) 34 (9.3%) 24 (8.7%) High school graduate 199 (20.5%) 55 (19.1%) 81 (22.6%) 33 (19.9%) 30 (17.7%) Some college or AA degree 280 (32.3%) 76 (33.4%) 94 (26.2%) 63 (42.3%) 47 (32.8%) College graduate or above 221 (35.1%) 44 (30.1%) 99 (38.8%) 41 (28.6%) 37 (40.8%) BMI, kg/m 2 29.7 ± 0.3 28.6 ± 0.5 29.5 ± 0.6 29.7 ± 0.8 31.6 ± 1.0 Family income-to-poverty ratio, weighted % < 1.3 226 (14.5%) 68 (20.7%) 85 (13.7%) 41 (12.1%) 32 (11.2%) 1.3 ≤ < 3.0 266 (24.7%) 76 (27.1%) 109 (28.1%) 45 (19.5%) 36 (19.8%) ≥ 3.0 307 (53.5%) 69 (43.7%) 115 (51.0%) 65 (60.6%) 58 (63.1%) Missing 89 (7.4%) 19 (8.5%) 38 (7.2%) 20 (7.9%) 12 (5.8%) Marital status, weighted % Married/ living with partners 516 (66.6%) 137 (62.0%) 203 (69.9%) 105 (68.5%) 71 (62.2%) Widowed/ divorced/ separated 269 (25.0%) 62 (24.7%) 107 (23.2%) 48 (24.4%) 52 (30.8%) Never married 103 (8.4%) 33 (13.3%) 37 (7.0%) 18 (7.1%) 15 (7.0%) Number of vaginal/Cesarean deliveries, weighted % 0 34 (4.8%) 9 (7.2%) 9 (2.3%) 6 (3.4%) 10 (9.7%) 1–3 times 598 (71.9%) 152 (67.9%) 243 (77.0%) 114 (69.1%) 89 (68.5%) ≥ 4 times 170 (13.0%) 46 (14.3%) 66 (12.8%) 35 (14.6%) 23 (10.0%) Missing 86 (10.2%) 25 (10.6%) 29 (7.9%) 16 (13.0%) 16 (11.8%) High-risk alcohol intake, weighted % 76 (7.7%) 23 (9.7%) 26 (4.8%) 15 (6.3%) 12 (13.9%) Smoking status, weighted % Never smoker 549 (61.5%) 140 (58.7%) 213 (62.0%) 112 (66.8%) 84 (57.0%) Ex-smoker 178 (22.5%) 41 (19.7%) 69 (20.4%) 33 (21.7%) 35 (32.6%) Current smoker 161 (16.0%) 51 (21.6%) 65 (17.6%) 26 (11.6%) 19 (10.5%) Physical activity Low 532 (56.8%) 127 (55.2%) 205 (54.5%) 112 (62.4%) 88 (57.1%) Moderate 153 (19.0%) 40 (18.0%) 63 (19.7%) 29 (20.8%) 21 (16.5%) Vigorous 202 (24.1%) 65 (26.8%) 78 (25.7%) 30 (16.8%) 29 (26.3%) Missing 1 (0.04%) 0 1 (0.1%) 0 0 Use of postmenopausal hormone therapy Never user 747 (78.3%) 195 (72.3%) 300 (85.7%) 142 (75.0%) 110 (72.4%) Previous user 105 (15.7%) 26 (18.6%) 33 (9.0%) 23 (20.8%) 23 (21.8%) Current user 32 (5.8%) 10 (8.8%) 11 (5.0%) 6 (4.2%) 5 (5.8%) Missing 4 (0.2%) 1 (0.3%) 3 (0.3%) 0 0 Abbreviations: AA, Associate of Arts; BMI, body mass index; and RLS, reproductive lifespan. Numbers represent mean ± standard error or number of individuals (proportion). Association of RLS with diabetes and metabolic syndrome Compared to women with RLS of 40–42 years (3rd quartile), the unadjusted ORs (95% CI) for diabetes in the 1st, 2nd, and 4th quartiles were 0.92 (0.41–2.05), 1.91 (0.97–3.75), and 1.43 (0.62–3.28), respectively (Table 2 ). After adjusting for potential confounders, the adjusted ORs (95% CI) for diabetes in the 1st, 2nd, and 4th quartiles were 0.69 (0.27–1.76), 2.17 (0.97–4.89), and 1.45 (0.48–4.34), respectively. There was no linear trend across quartiles of RLS and diabetes (P for trend in Model 3 = 0.70). For metabolic syndrome, compared to women in the 3rd quartile, the fully adjusted ORs (95% CI) in the 1st, 2nd, and 4th quartiles were 1.59 (0.74–3.40), 1.86 (1.06–3.26), and 1.32 (0.56–3.12), respectively (Table 2 ). When RLS was used as a continuous variable, there was a decreasing trend of ORs for diabetes and metabolic syndrome with longer RLS, however, the confidence intervals were wide (Fig. 2 ) . Table 2 The odd ratios (95% confidence intervals) of diabetes mellitus and metabolic syndrome by reproductive lifespan. Variable Reproductive lifespan P for trend 11–35 36–39 40–42 43–50 Diabetes mellitus Model 1 0.92 (0.41–2.05) 1.91 (0.97–3.75) Ref 1.43 (0.62–3.28) 0.88 Model 2 0.72 (0.32–1.65) 1.91 (0.86–4.23) Ref 1.28 (0.46–3.52) 0.63 Model 3 0.69 (0.27–1.76) 2.17 (0.97–4.89) Ref 1.45 (0.48–4.34) 0.70 Metabolic syndrome Model 1 1.36 (0.74–2.50) 1.45 (0.91–2.29) Ref 1.83 (0.90–3.71) 0.72 Model 2 1.46 (0.77–2.74) 1.59 (0.96–2.64) Ref 1.17 (0.48–2.82) 0.26 Model 3 1.59 (0.74–3.40) 1.86 (1.06–3.26) Ref 1.32 (0.56–3.12) 0.28 Model 1: no adjustment. Model 2: adjusted for age, body mass index, race/ethnicity, education level, marital status, parity and ratio of family income-to-poverty. Model 3: further adjusted for high-risk alcohol intake, smoking, physical activity, ever use of hormone therapy, and age at menarche. Association of RLS with diabetes and metabolic syndrome in HRT naïve group We further evaluated the associations of RLS with diabetes and metabolic syndrome in women who had never received HRT (Table 3 ). Women with shorter RLS (36–39 years, 2nd quartile) had a higher OR for diabetes compared to the reference group (40–42 years, 3rd quartile) (OR 2.24; 95% CI 1.02–4.94) in the fully adjusted model. Similarly, for metabolic syndrome, women in the 2nd quartile had a higher OR compared to the reference group in the fully adjusted model (OR 1.68; 95% CI 0.86–3.29), although it was not significant. Table 3 The odd ratios (95% confidence intervals) of diabetes mellitus and metabolic syndrome by reproductive lifespan in women never treated with hormone replacement therapy. Variable Reproductive lifespan P for trend 11–35 36–39 40–42 43–50 Diabetes mellitus Model 1 1.09 (0.47–2.50) 1.81 (0.88–3.71) Ref 1.13 (0.50–2.53) 0.55 Model 2 0.86 (0.37–2.03) 1.97 (0.90–4.30) Ref 1.18 (0.41–3.40) 0.94 Model 3 0.70 (0.27–1.87) 2.24 (1.02–4.94) Ref 1.54 (0.53–4.54) 0.52 Metabolic syndrome Model 1 1.15 (0.62–2.13) 1.14 (0.66–1.97) Ref 1.77 (0.86–3.64) 0.35 Model 2 1.15 (0.60–2.22) 1.41 (0.76–2.64) Ref 0.96 (0.37–2.44) 0.45 Model 3 1.06 (0.49–2.30) 1.68 (0.86–3.29) Ref 1.20 (0.50–2.87) 0.77 Model 1: no adjustment Model 2: adjusted for age, body mass index, race/ethnicity, education level, marital status, parity and ratio of family income-to-poverty Model 3: further adjusted for high-risk alcohol intake, smoking, physical activity, and age at menarche Discussion In this study using the nationally representative survey, we found that women with a relatively short RLS (36–39 years) had an increased odds for diabetes and metabolic syndrome compared to those with 40–42 years of RLS. These findings suggest that there may be a window of RLS with a lower risk of diabetes or metabolic syndrome. These results are in line with previous studies that showed the protective role of endogenous and exogenous estrogen in glucose metabolism and a long-term benefit for insulin secretion and glucose homeostasis 2 . Furthermore, based on experimental studies, estrogen may increase insulin sensitivity. For instance, ovariectomized mice had poorer pancreatic β-cell survival and function compared to non-ovariectomized mice 36 , 37 , and insulin resistance induced by ovariectomy was reversed by estrogen treatment in mice 38 . Low estrogen also has a negative impact on body fat distribution and fat accumulation. Adiponectin, an adipocyte-derived cytokine that is inversely correlated with estradiol levels, is known to be associated with improved glycemic control and reduced inflammation in diabetics 39 , 40 . As a results, low adiponectin levels are associated with insulin resistance and the development of diabetes 41 . The observed associations of shorter RLS with diabetes and metabolic syndrome are consistent with the hypothesis that decreased estrogen exposure contributes to the development of metabolic complications. This is further supported by the findings that postmenopausal hormone therapy was associated with a reduced risk of diabetes in a large clinical trial 42 . However, the association between RLS and diabetes has been inconsistent in epidemiologic studies. In a recent prospective study of 6357 post-menopausal women, the risk of type 2 diabetes was higher in women at the lowest quartile of RLS (< 35years) compared with women who had a longer RLS (38–40 years) and there was a linear increase in the risk of diabetes with a shorter RLS 28 . Similarly, in another cohort study of 7768 women, those with RLS < 33 years had a 17% (hazard ratio [HR] 1.17; 95% CI 0.98–1.39) higher risk of diabetes compared with those with RLS ≥ 40 years with an inverse association between RLS and incident diabetes 9 . In addition, in a cross-sectional study of Japanese women, individuals with RLS of ≥ 29 years were 19–26% less likely to have diabetes than those with RLS ≤ 28 years, although the associations were not statistically significant 10 . On the contrary, in the Women’s Health Initiative observational study, a large cohort study of postmenopausal women, women with RLS of 36–40 years had the lowest risk of incident diabetes with a U-shaped association between RLS and diabetes 30 . In a cross-sectional study of an occupational cohort of Chinese women, there was also a U-shaped association between RLS and diabetes, with the lowest prevalence in women with 30–35 years of RLS 29 , whereas in another study of Chinese women showed no association between RLS and prevalent diabetes 23 . In our study, we found that short RLS was associated with diabetes particularly in postmenopausal women who never received hormone therapy, providing evidence for an increased risk of diabetes with estrogen deficiency. While women with the shortest RLS group did not have an increased odds of diabetes, we observed a potential inverse association between RLS and diabetes. The difference in the results may be explained by the relatively young age of women included in the lowest RLS group at the time of the study (mean age 48.2 years). Because they are still young, diabetes or metabolic syndrome might have not yet developed, leading to an imprecise estimate. Indeed, CDC reported that the incidence of diabetes in women younger than 45 years was one-third of the incidence in women of 45–64 years (National Diabetes Statistics Report, https://www.cdc.gov/diabetes/php/data-research/ ). A longitudinal study with detailed information on the age of menarche, menopause, and diabetes diagnosis, and confounders measured at multiple time points are needed to further elucidate the relationship. There are limited number of studies investigating whether RLS is associated with metabolic syndrome, with inconsistent results 24 , 31 , 32 . RLS was inversely associated with metabolic syndrome in a nationally representative study of Korean postmenopausal women 32 . However, in two cross-sectional studies from China, RLS was positively associated with metabolic syndrome 24 , 31 . In this study, we found a significant relationship between RLS and metabolic syndrome, but the relationship was less pronounced when we included only women who had never taken hormone therapy. Due to the multiple components that define metabolic syndrome, there appear to be mixed results for the relationship between metabolic syndrome and RLS. Longitudinal studies are needed to better understand whether RLS is a risk factor for metabolic syndrome and for each component of metabolic syndrome. We performed a cross-sectional analysis evaluating the associations of RLS with diabetes and metabolic syndrome in a nationally representative sample. However, there are some limitations to this study. As a cross-sectional study, there is potential for a reverse causation, where the presence of diabetes or metabolic syndrome before menopause affects the duration of RLS. We did not have information on when the participants developed diabetes or metabolic syndrome (before or after menopause) and could not evaluate whether RLS is associated with the risk of developing the disease. Therefore, the findings need to be interpreted with caution. Prospective cohort studies are needed to better evaluate the risk of diabetes and metabolic syndrome incidence by RLS. In addition, the data on reproductive history including age at menarche, age at menopause, and parity were based on self-report and may be measured with error. However, a previous study showed that self-reported menopause status and age at menarche were highly reproducible in a subsample of the Nurses’ Health Study participants 43 . Almost 80% of women were able to recall their age at menarche within one year on the 2 follow-up questionnaires. Furthermore, although we accounted for multiple potential confounders, as an observational study, there may be potential residual or unmeasured confounding. While childhood exposures, including the family income level at the time of menarche, may have an impact on their reproductive lifespan, health conditions, and their susceptivity to diabetes and metabolism syndrome at later age, we were not able to account for childhood exposures as they were not available in the NHANES. In conclusion, we found that a short RLS may be associated with diabetes and metabolic syndrome. However, due to the limitations of a cross-sectional study, the findings need to be further evaluated in a longitudinal study to better understand the relationship between endogenous estrogen exposure and risk factors for cardiovascular disease. Declarations Funding Information This research received no external funding. Informed Consent Statement We did not obtain informed consent from the participants because we used deidentified and retrospective data. This issue was also confirmed by the hospital’s Institutional Review Board. Data Availability Statement The data presented in this study are publicly available from the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm) . Acknowledgments None Conflicts of Interest Hoon Kim has received honoraria for participation on the advisory board of Bayer, consulting for Merck and LG Chemical, and lectures for Roche Diagnostics and Organon, which are unrelated to the subjects addressed in this paper. Other authors report no financial or commercial conflicts of interest. Author contributions AJin Cho contributed to the study conceptualization, design, supervision, analysis and interpretation of data, literature review, and manuscript writing. Yun Soo Hong contributed to the design, analysis and interpretation of data, literature review, and manuscript writing. Hoon Kim contributed to the study conceptualization, design, supervision, materials, analysis and interpretation of data, literature review, and manuscript writing. All authors reviewed and approved the final version; no other person contributed substantially to the paper. Ethics approval : The National Center for Health Statistics (NCHS) Research Ethics Review Board approved the protocol and data collection methods of the NHANES (Protocol#2005- 06, Protocol#2011-17, and Protocol#2018-01). Written informed consent was obtained from all participants. The study was approved by the Institutional Review Board (IRB) of National Center for Health Statistics and Kangnam Sacred Heart Hospital (IRB No: HKS 2022-09- 021) References Goodman, N. F. et al. American Association of Clinical Endocrinologists Medical Guidelines for Clinical Practice for the diagnosis and treatment of menopause. Endocr. Pract. 17 (Suppl 6), 1–25. https://doi.org:10.4158/ep.17.s6.1 (2011). Godsland, I. F. Oestrogens and insulin secretion. Diabetologia 48 , 2213–2220. https://doi.org:10.1007/s00125-005-1930-0 (2005). Reckelhoff, J. F. & Fortepiani, L. A. 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Age at Menarche and Cardiometabolic Risk in Adulthood: The Coronary Artery Risk Development in Young Adults Study. J Pediatr 167, 344–352 e341 (2015). https://doi.org:10.1016/j.jpeds.2015.04.032 Janghorbani, M., Mansourian, M. & Hosseini, E. Systematic review and meta-analysis of age at menarche and risk of type 2 diabetes. Acta Diabetol. 51 , 519–528. https://doi.org:10.1007/s00592-014-0579-x (2014). Ren, Y., Zou, H., Zhang, D., Han, C. & Hu, D. Relationship between age at menarche and risk of glucose metabolism disorder: a systematic review and dose-response meta-analysis. Menopause 27 , 818–826. https://doi.org:10.1097/GME.0000000000001529 (2020). Heys, M. et al. Age of menarche and the metabolic syndrome in China. Epidemiology 18 , 740–746. https://doi.org:10.1097/EDE.0b013e3181567faf (2007). Chang, C. J. et al. Age at menarche and its association with the metabolic syndrome in Taiwan. Obes. Res. Clin. Pract. 10 (Suppl 1). https://doi.org:10.1016/j.orcp.2015.10.003 (2016). S26-S34. Remsberg, K. E. et al. Early menarche and the development of cardiovascular disease risk factors in adolescent girls: the Fels Longitudinal Study. J. Clin. Endocrinol. Metab. 90 , 2718–2724. https://doi.org:10.1210/jc.2004-1991 (2005). Yu, Y. et al. Association between Reproductive Factors and Type 2 Diabetes: A Cross-Sectional Study. Int. J. Environ. Res. Public. Health . 19 https://doi.org:10.3390/ijerph19021019 (2022). Yu, W. et al. Duration of reproductive years and time since menopause were associated with metabolic syndrome in postmenopausal parous women of Chinese ancestry. Menopause 27 , 216–222. https://doi.org:10.1097/GME.0000000000001445 (2020). Mishra, S. R. et al. Association Between Reproductive Life Span and Incident Nonfatal Cardiovascular Disease: A Pooled Analysis of Individual Patient Data From 12 Studies. JAMA Cardiol. 5 , 1410–1418. https://doi.org:10.1001/jamacardio.2020.4105 (2020). Mishra, S. R., Chung, H. F., Waller, M. & Mishra, G. D. Duration of estrogen exposure during reproductive years, age at menarche and age at menopause, and risk of cardiovascular disease events, all-cause and cardiovascular mortality: a systematic review and meta-analysis. Bjog 128 , 809–821. https://doi.org:10.1111/1471-0528.16524 (2021). Mishra, S. R., Waller, M., Chung, H. F. & Mishra, G. D. Epidemiological studies of the association between reproductive lifespan characteristics and risk of Type 2 diabetes and hypertension: A systematic review. Maturitas 155 , 14–23. https://doi.org:10.1016/j.maturitas.2021.09.009 (2022). Mishra, S. R., Waller, M., Chung, H. F. & Mishra, G. D. Association between reproductive lifespan and risk of incident type 2 diabetes and hypertension in postmenopausal women: Findings from a 20-year prospective study. Maturitas 159 , 52–61. https://doi.org:10.1016/j.maturitas.2022.01.001 (2022). Yang, A. et al. Reproductive factors and risk of type 2 diabetes in an occupational cohort of Chinese women. J. Diabetes Complications . 30 , 1217–1222. https://doi.org:10.1016/j.jdiacomp.2016.06.011 (2016). LeBlanc, E. S. et al. Reproductive history and risk of type 2 diabetes mellitus in postmenopausal women: findings from the Women's Health Initiative. Menopause 24 , 64–72. https://doi.org:10.1097/gme.0000000000000714 (2017). Cao, X., Zhou, J., Yuan, H. & Chen, Z. Duration of reproductive lifespan and age at menarche in relation to metabolic syndrome in postmenopausal Chinese women. J. Obstet. Gynaecol. Res. 42 , 1581–1587. https://doi.org:10.1111/jog.13093 (2016). Park, Y. C. et al. Association Between Duration of Reproductive Years and Metabolic Syndrome. J. Womens Health (Larchmt) . 27 , 271–277. https://doi.org:10.1089/jwh.2017.6364 (2018). Lee, P. H., Macfarlane, D. J., Lam, T. H. & Stewart, S. M. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int. J. Behav. Nutr. Phys. Act. 8 , 115. https://doi.org:10.1186/1479-5868-8-115 (2011). Selvin, E., Wang, D., Lee, A. K., Bergenstal, R. M. & Coresh, J. Identifying Trends in Undiagnosed Diabetes in U.S. Adults by Using a Confirmatory Definition: A Cross-sectional Study. Ann. Intern. Med. 167 , 769–776. https://doi.org:10.7326/M17-1272 (2017). Expert Panel on Detection. Treatment of High Blood Cholesterol in, A. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 285 , 2486–2497. https://doi.org:10.1001/jama.285.19.2486 (2001). Tiano, J. P. & Mauvais-Jarvis, F. Importance of oestrogen receptors to preserve functional beta-cell mass in diabetes. Nat. Rev. Endocrinol. 8 , 342–351. https://doi.org:10.1038/nrendo.2011.242 (2012). Mauvais-Jarvis, F. Role of Sex Steroids in beta Cell Function, Growth, and Survival. Trends Endocrinol. Metab. 27 , 844–855. https://doi.org:10.1016/j.tem.2016.08.008 (2016). Zhu, L. et al. Estrogen treatment after ovariectomy protects against fatty liver and may improve pathway-selective insulin resistance. Diabetes 62 , 424–434. https://doi.org:10.2337/db11-1718 (2013). Karim, R. et al. Association of endogenous sex hormones with adipokines and ghrelin in postmenopausal women. J. Clin. Endocrinol. Metab. 100 , 508–515. https://doi.org:10.1210/jc.2014-2834 (2015). Mantzoros, C. S., Li, T., Manson, J. E., Meigs, J. B. & Hu, F. B. Circulating adiponectin levels are associated with better glycemic control, more favorable lipid profile, and reduced inflammation in women with type 2 diabetes. J. Clin. Endocrinol. Metab. 90 , 4542–4548. https://doi.org:10.1210/jc.2005-0372 (2005). Li, S., Shin, H. J., Ding, E. L. & van Dam, R. M. Adiponectin levels and risk of type 2 diabetes: a systematic review and meta-analysis. JAMA 302 , 179–188. https://doi.org:10.1001/jama.2009.976 (2009). Margolis, K. L. et al. Effect of oestrogen plus progestin on the incidence of diabetes in postmenopausal women: results from the Women's Health Initiative Hormone Trial. Diabetologia 47 , 1175–1187. https://doi.org:10.1007/s00125-004-1448-x (2004). Colditz, G. A. et al. Reproducibility and validity of self-reported menopausal status in a prospective cohort study. Am. J. Epidemiol. 126 , 319–325. https://doi.org:10.1093/aje/126.2.319 (1987). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5035398","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471097471,"identity":"7c9dac3c-b7b8-4a91-90fd-d43235f02b53","order_by":0,"name":"AJin Cho","email":"","orcid":"","institution":"Konkuk University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"AJin","middleName":"","lastName":"Cho","suffix":""},{"id":471097472,"identity":"945fc120-644b-4d30-b711-620450dfe277","order_by":1,"name":"Yun Soo Hong","email":"","orcid":"","institution":"McKusick-Nathans Institute, Johns Hopkins University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"Soo","lastName":"Hong","suffix":""},{"id":471097473,"identity":"5d5806b9-9b3c-412a-b617-93b892cc8235","order_by":2,"name":"Hoon Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACCQYGZhDNDyISCkjRItkA0mJAihaDA2CSCB380u3XpAsqbPKMz69O/PDAgEGeX+wAfi2Sc86USc84k1ZsduPtZgmgwwxnzk7Ar8XgRk6aNG/b4cRtN85uAGlJMLhNrJbNM85u/kGklvRjYC0b+Hu3EWcL0C/M1jxn0hJn3ODdZpFgIEHYL8AQe3ibp8Imsb//7OabPyps5PmlCWhhYOAxkQDTEmCVEoSUgwD74w8Q+w4Qo3oUjIJRMApGIgAAk2BFuz0xGkQAAAAASUVORK5CYII=","orcid":"","institution":"Seoul National University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hoon","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2024-09-05 05:33:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5035398/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5035398/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86783119,"identity":"d1888640-b572-4035-a3e2-7cd3287c935f","added_by":"auto","created_at":"2025-07-15 13:50:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":502034,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study population.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5035398/v1/7d2a67318227ccc950c8d4d7.png"},{"id":86785223,"identity":"657cdcaf-a444-4072-b25d-267578f1c1cb","added_by":"auto","created_at":"2025-07-15 14:06:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1032568,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between RLS and diabetes (A) and metabolic syndrome (B). Odds ratios (95% confidence intervals) were derived from logistic regression models that include RLS as restricted cubic splines with knots at 30, 40 (reference), and 50 years, and adjusted for age, body mass index, race/ethnicity, education level, marital status, parity, family income, high-risk alcohol intake, smoking, physical activity, ever use of hormone therapy, and age at menarche.\u003c/p\u003e\n\u003cp\u003eAbbreviation: OR, odds ratio; and RLS, reproductive lifespan.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5035398/v1/957faaac655c45cf6ae64353.png"},{"id":99798266,"identity":"98d43a89-9587-4809-90f2-82f0fde47b8c","added_by":"auto","created_at":"2026-01-08 13:47:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3075588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5035398/v1/9d7dd45b-b883-4ae6-b38b-56a7b447ace2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reproductive life span and diabetes mellitus, and metabolic syndrome in the general US population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMenopause, a significant transition in women\u0026rsquo;s reproductive life, is accompanied by dramatic hormonal fluctuations that influence changes in body composition and metabolism, leading to various health outcomes \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Early menopause is a known risk factor for atherosclerosis, cardiovascular disease (CVD), and cardiovascular death, likely due to the protective role of endogenous estrogen on glucose homeostasis and vaso-relaxation \u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In a prospective cohort study of 73,814 women in the Nurses\u0026rsquo; Health Study, for instance, early menopause was associated with incident coronary heart disease or stroke \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe relationship between early age at menopause and risk factors of CVD, including type 2 diabetes and metabolic syndrome, however, has not been consistent in previous studies \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Similarly, the associations of age at menarche with diabetes and metabolic syndrome are unclear, with some studies suggesting an inverse association between age at menarche and diabetes in two meta-analyses \u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo assess the impact of the duration of endogenous estrogen exposure on health outcomes, reproductive life span (RLS), defined as the interval between age at menarche and age at menopause, has been introduced to represent the length of the entire reproductive period \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In a pooled analysis of 12 prospective cohort studies, a shorter RLS was associated with an increased risk of CVD, particularly for those who had menarche at a young age \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, in a meta-analysis of 17 studies, there was no clear association of a shorter RLS with an increased risk of CVD mortality \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, the association between RLS and diabetes \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e or metabolic syndrome \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e remains inconclusive. For instance, the InterAct study of postmenopausal women found that shorter RLS was associated with an increased risk of diabetes, but the risk was not significant when comparing the first quartile of RLS (\u0026lt;\u0026thinsp;33 years of age) to the highest quartile of RSL (\u0026ge;\u0026thinsp;40 years of age) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In a cross-sectional study of 1,536 postmenopausal women in China, a longer RLS and time since menopause were associated with a higher prevalence of metabolic syndrome \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. On the other hand, a longer RLS was significantly associated with a lower prevalence of metabolic syndrome in a study using the Korean National Health and Nutrition Examination Survey \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to investigate the relationship of RLS with diabetes and metabolic syndrome in the general US population using the National Health and Nutritional Examination Survey (NHANES) data from 2007 to 2018.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe NHANES is a nationally representative survey designed to assess the health of noninstitutionalized, civilian US population using a complex, stratified, multistage probability-cluster sampling design. Participants underwent at-home interviews and a health examination at a mobile examination center, and provided information on demographic, socioeconomic, dietary, medical history, and medication use as well as blood pressure measurements and blood samples.\u003c/p\u003e \u003cp\u003eOf the 17,075 women between 19 and 79 years of age who participated in the NHANES from 2007 to 2018, we conducted a cross-sectional study of women who experienced natural menopause or surgical menopause (bilateral salpingo-oophorectomy [BSO]) before 60 years of age and had been menopausal for 5 years or less (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We excluded individuals who were premenopausal at the time of participation or did not answer the question about menstruation (n\u0026thinsp;=\u0026thinsp;10,173), who had hysterectomy without BSO (n\u0026thinsp;=\u0026thinsp;1,592), and who did not have information on age at menarche or menopause (n\u0026thinsp;=\u0026thinsp;218). In addition, to reduce potential selection bias by including healthier older individuals with a long interval between menopause and the study visit and to compare individuals with a similar interval since menopause, women who had been menopause for more than 5 years (n\u0026thinsp;=\u0026thinsp;4,204) were excluded. The final population for the main analysis included 888 postmenopausal women.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e The National Center for Health Statistics (NCHS) Research Ethics Review Board approved the protocol and data collection methods of the NHANES (Protocol#2005-06, Protocol#2011-17, and Protocol#2018-01). Written informed consent was obtained from all participants. The study was approved by the Institutional Review Board (IRB) of National Center for Health Statistics and Kangnam Sacred Heart Hospital (IRB No: HKS 2022-09-021).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eRace/ethnicity was self-reported and categorized as Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other. Education was defined by the highest degree or level of school completed and categorized as less than high school, high school graduate, some college or Associate of Arts (AA) degree, and college graduate or above. The family income-to-poverty ratio was determined by dividing family income to the poverty level threshold specific to family size and survey year. The ratio followed the US Department of Health and Human Services federal poverty guidelines which are issued each year in the Federal Register (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://aspe.hhs.gov/POVERTY/index.shtml\u003c/span\u003e\u003cspan address=\"http://aspe.hhs.gov/POVERTY/index.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The cut-off values for the family income-to-poverty ratio were \u0026lt;\u0026thinsp;1.3, 1.3\u0026ndash;2.9, and \u0026ge;\u0026thinsp;3.0. Self-reported smoking status was classified as never, former, or current. High-risk alcohol intake was defined as 4 or more drinks per day. Physical activity was reported using the International Physical Activity Questionnaire (IPAQ) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. For variables with missing data, we included missing as a category if they were missing in more than 3% and excluded from the study population otherwise.\u003c/p\u003e \u003cp\u003eBlood pressure was measured 4 times on the same arm with a mercury sphygmomanometer in a seated position after resting for 5 minutes. We used the maximum value of the 4 measurements. Waist circumference was measured just above the iliac crest with a measuring tape. Body mass index (BMI) was calculated as weight divided by height (kg/m\u003csup\u003e2\u003c/sup\u003e). Fasting glucose, triglyceride, and high-density lipoprotein (HDL) cholesterol levels were measured enzymatically using an autoanalyzer. Glycated hemoglobin (HbA1c) was measured using high-performance liquid chromatography methods.\u003c/p\u003e\n\u003ch3\u003eAssessment of reproductive factors\u003c/h3\u003e\n\u003cp\u003eMenopause was defined as cessation of menstrual bleeding for at least 12 months, and age at menopause was defined as age at last menstruation. RLS was calculated by subtracting age at menarche from age at menopause and was categorized into quartiles (11\u0026ndash;35, 36\u0026ndash;39, 40\u0026ndash;42 (reference group), and 43\u0026ndash;50 years). The use of hormone therapy was identified as on ever being on hormone therapy by self-report. Parity was categorized as 0, 1\u0026ndash;3 times, 4 or more times using number of livebirths.\u003c/p\u003e\n\u003ch3\u003eOutcome definitions\u003c/h3\u003e\n\u003cp\u003eDiabetes was defined as either self-reported physician diagnosis of diabetes or fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dl (\u0026ge;\u0026thinsp;7.0 mmol/l) and HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5% in those who had not been diagnosed with diabetes \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Metabolic syndrome was defined using the National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III criteria as the presence of three or more of the following conditions: 1) increased waist circumference (\u0026gt;\u0026thinsp;102 cm for men, \u0026gt;\u0026thinsp;88 cm for women); 2) elevated triglycerides (\u0026ge;\u0026thinsp;150 mg/dl); 3) low HDL cholesterol (\u0026lt;\u0026thinsp;40 mg/dl in men, \u0026lt;\u0026thinsp;50 mg/dl in women); 4) hypertension (\u0026ge;\u0026thinsp;130/85 mmHg); and 5) impaired fasting glucose (\u0026ge;\u0026thinsp;100 mg/dl) \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eParticipant characteristics are presented as the number of participants (weighted proportion) for categorical variables and the mean (standard error [SE]) for continuous variables. We used multivariable logistic regression models to estimate the odds ratios (ORs) and corresponding 95% confidence intervals (CIs) of the associations of RLS with diabetes and metabolic syndrome. We provide unadjusted estimates (Model 1), estimates adjusted for age, self-reported race/ethnicity, education, BMI, marital status, parity, and the ratio of family income-to-poverty (Model 2), and estimates further adjusted for smoking, high-risk alcohol intake, physical activity, ever use of hormone therapy, and age at menarche (Model 3). P for trend across categories of RLS was evaluated using the categories as a continuous variable. In addition, we evaluated a potential non-linear association between RLS and the outcomes (diabetes and metabolic syndrome) using restricted cubic splines for RLS with 3 knots located at 30, 40, and 50 years. To account for confounding due to hormone replacement therapy (HRT), we repeated the analysis restricted to women who were never treated with HRT (HRT-na\u0026iuml;ve).\u003c/p\u003e \u003cp\u003eAnalyses were performed using appropriate survey weights, which account for complex survey design, to provide nationally representative estimates of the general, noninstitutionalized US population. All statistical analyses were performed using Stata 17.0 (StataCorp LLC, College Station, TX).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the participants by RLS quartiles\u003c/h2\u003e \u003cp\u003eThe mean (SE) age of study participants was 53.9 (0.2) years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The age at menarche and menopause of the participants were 12.7 (0.1) and 50.9 (0.2) years, respectively. Individuals with a longer RLS, on average, had younger age at menarche and older age at menopause. Non-Hispanic Whites were more likely to have a longer RLS, and non-Hispanic Blacks and Hispanics were more likely to have a shorter RLS. In addition, women with a longer RLS were more likely to have higher education, and to have higher family income, and less likely to be current smokers. The proportion of never using hormone therapy was the highest in the 2nd quartile (36\u0026ndash;39 years).\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 study participants.\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eReproductive lifespan (RLS)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026ndash;35\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u0026ndash;39\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u0026ndash;42\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026ndash;50\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of participants, weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,094,780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,528,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,881,737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,515,206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,168,896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\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\" colname=\"c2\"\u003e \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\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\" colname=\"c2\"\u003e \u003cp\u003e50.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity, weighted %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (6.4%)\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\u003e346 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (63.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (73.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72 (75.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (76.8%)\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\u003e177 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, weighted %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (17.7%)\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\u003e280 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (32.8%)\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\u003e221 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (40.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income-to-poverty ratio,\u003c/p\u003e \u003cp\u003eweighted %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u0026lt; 1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3 \u0026le; \u0026lt; 3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e266 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58 (63.1%)\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\u003e89 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, weighted %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003eMarried/ living with partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e516 (66.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203 (69.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71 (62.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/ divorced/ separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of vaginal/Cesarean deliveries, weighted %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e598 (71.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243 (77.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114 (69.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 4 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (10.0%)\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\u003e86 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-risk alcohol intake, weighted %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, weighted %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e549 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (58.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112 (66.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84 (57.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e532 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (26.3%)\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\u003e1 (0.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of postmenopausal hormone therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003eNever user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747 (78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (5.8%)\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\u003e4 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: AA, Associate of Arts; BMI, body mass index; and RLS, reproductive lifespan.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNumbers represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error or number of individuals (proportion).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation of RLS with diabetes and metabolic syndrome\u003c/h3\u003e\n\u003cp\u003eCompared to women with RLS of 40\u0026ndash;42 years (3rd quartile), the unadjusted ORs (95% CI) for diabetes in the 1st, 2nd, and 4th quartiles were 0.92 (0.41\u0026ndash;2.05), 1.91 (0.97\u0026ndash;3.75), and 1.43 (0.62\u0026ndash;3.28), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjusting for potential confounders, the adjusted ORs (95% CI) for diabetes in the 1st, 2nd, and 4th quartiles were 0.69 (0.27\u0026ndash;1.76), 2.17 (0.97\u0026ndash;4.89), and 1.45 (0.48\u0026ndash;4.34), respectively. There was no linear trend across quartiles of RLS and diabetes (P for trend in Model 3\u0026thinsp;=\u0026thinsp;0.70). For metabolic syndrome, compared to women in the 3rd quartile, the fully adjusted ORs (95% CI) in the 1st, 2nd, and 4th quartiles were 1.59 (0.74\u0026ndash;3.40), 1.86 (1.06\u0026ndash;3.26), and 1.32 (0.56\u0026ndash;3.12), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When RLS was used as a continuous variable, there was a decreasing trend of ORs for diabetes and metabolic syndrome with longer RLS, however, the confidence intervals were wide (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\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\u003eThe odd ratios (95% confidence intervals) of diabetes mellitus and metabolic syndrome by reproductive lifespan.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;35\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u0026ndash;39\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026ndash;42\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u0026ndash;50\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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 \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\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.41\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.91 (0.97\u0026ndash;3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43 (0.62\u0026ndash;3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.32\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.91 (0.86\u0026ndash;4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.28 (0.46\u0026ndash;3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.27\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.17 (0.97\u0026ndash;4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.45 (0.48\u0026ndash;4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.36 (0.74\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.45 (0.91\u0026ndash;2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.83 (0.90\u0026ndash;3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46 (0.77\u0026ndash;2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.59 (0.96\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.17 (0.48\u0026ndash;2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59 (0.74\u0026ndash;3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.86 (1.06\u0026ndash;3.26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32 (0.56\u0026ndash;3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1: no adjustment.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 2: adjusted for age, body mass index, race/ethnicity, education level, marital status, parity and ratio of family income-to-poverty.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 3: further adjusted for high-risk alcohol intake, smoking, physical activity, ever use of hormone therapy, and age at menarche.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of RLS with diabetes and metabolic syndrome in HRT na\u0026iuml;ve group\u003c/h2\u003e \u003cp\u003eWe further evaluated the associations of RLS with diabetes and metabolic syndrome in women who had never received HRT (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Women with shorter RLS (36\u0026ndash;39 years, 2nd quartile) had a higher OR for diabetes compared to the reference group (40\u0026ndash;42 years, 3rd quartile) (OR 2.24; 95% CI 1.02\u0026ndash;4.94) in the fully adjusted model. Similarly, for metabolic syndrome, women in the 2nd quartile had a higher OR compared to the reference group in the fully adjusted model (OR 1.68; 95% CI 0.86\u0026ndash;3.29), although it was not significant.\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\u003eThe odd ratios (95% confidence intervals) of diabetes mellitus and metabolic syndrome by reproductive lifespan in women never treated with hormone replacement therapy.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;35\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u0026ndash;39\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026ndash;42\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u0026ndash;50\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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 \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\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.47\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.81 (0.88\u0026ndash;3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.13 (0.50\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.37\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.97 (0.90\u0026ndash;4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18 (0.41\u0026ndash;3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.27\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.24 (1.02\u0026ndash;4.94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54 (0.53\u0026ndash;4.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.62\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.66\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.77 (0.86\u0026ndash;3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.60\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41 (0.76\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96 (0.37\u0026ndash;2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.49\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.68 (0.86\u0026ndash;3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20 (0.50\u0026ndash;2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1: no adjustment\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 2: adjusted for age, body mass index, race/ethnicity, education level, marital status, parity and ratio of family income-to-poverty\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 3: further adjusted for high-risk alcohol intake, smoking, physical activity, and age at menarche\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study using the nationally representative survey, we found that women with a relatively short RLS (36\u0026ndash;39 years) had an increased odds for diabetes and metabolic syndrome compared to those with 40\u0026ndash;42 years of RLS. These findings suggest that there may be a window of RLS with a lower risk of diabetes or metabolic syndrome.\u003c/p\u003e \u003cp\u003eThese results are in line with previous studies that showed the protective role of endogenous and exogenous estrogen in glucose metabolism and a long-term benefit for insulin secretion and glucose homeostasis \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Furthermore, based on experimental studies, estrogen may increase insulin sensitivity. For instance, ovariectomized mice had poorer pancreatic β-cell survival and function compared to non-ovariectomized mice \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, and insulin resistance induced by ovariectomy was reversed by estrogen treatment in mice \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Low estrogen also has a negative impact on body fat distribution and fat accumulation. Adiponectin, an adipocyte-derived cytokine that is inversely correlated with estradiol levels, is known to be associated with improved glycemic control and reduced inflammation in diabetics \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. As a results, low adiponectin levels are associated with insulin resistance and the development of diabetes \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The observed associations of shorter RLS with diabetes and metabolic syndrome are consistent with the hypothesis that decreased estrogen exposure contributes to the development of metabolic complications. This is further supported by the findings that postmenopausal hormone therapy was associated with a reduced risk of diabetes in a large clinical trial \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the association between RLS and diabetes has been inconsistent in epidemiologic studies. In a recent prospective study of 6357 post-menopausal women, the risk of type 2 diabetes was higher in women at the lowest quartile of RLS (\u0026lt;\u0026thinsp;35years) compared with women who had a longer RLS (38\u0026ndash;40 years) and there was a linear increase in the risk of diabetes with a shorter RLS \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Similarly, in another cohort study of 7768 women, those with RLS\u0026thinsp;\u0026lt;\u0026thinsp;33 years had a 17% (hazard ratio [HR] 1.17; 95% CI 0.98\u0026ndash;1.39) higher risk of diabetes compared with those with RLS\u0026thinsp;\u0026ge;\u0026thinsp;40 years with an inverse association between RLS and incident diabetes \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In addition, in a cross-sectional study of Japanese women, individuals with RLS of \u0026ge;\u0026thinsp;29 years were 19\u0026ndash;26% less likely to have diabetes than those with RLS\u0026thinsp;\u0026le;\u0026thinsp;28 years, although the associations were not statistically significant \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. On the contrary, in the Women\u0026rsquo;s Health Initiative observational study, a large cohort study of postmenopausal women, women with RLS of 36\u0026ndash;40 years had the lowest risk of incident diabetes with a U-shaped association between RLS and diabetes \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In a cross-sectional study of an occupational cohort of Chinese women, there was also a U-shaped association between RLS and diabetes, with the lowest prevalence in women with 30\u0026ndash;35 years of RLS \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, whereas in another study of Chinese women showed no association between RLS and prevalent diabetes \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, we found that short RLS was associated with diabetes particularly in postmenopausal women who never received hormone therapy, providing evidence for an increased risk of diabetes with estrogen deficiency. While women with the shortest RLS group did not have an increased odds of diabetes, we observed a potential inverse association between RLS and diabetes. The difference in the results may be explained by the relatively young age of women included in the lowest RLS group at the time of the study (mean age 48.2 years). Because they are still young, diabetes or metabolic syndrome might have not yet developed, leading to an imprecise estimate. Indeed, CDC reported that the incidence of diabetes in women younger than 45 years was one-third of the incidence in women of 45\u0026ndash;64 years (National Diabetes Statistics Report, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/diabetes/php/data-research/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/diabetes/php/data-research/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A longitudinal study with detailed information on the age of menarche, menopause, and diabetes diagnosis, and confounders measured at multiple time points are needed to further elucidate the relationship.\u003c/p\u003e \u003cp\u003eThere are limited number of studies investigating whether RLS is associated with metabolic syndrome, with inconsistent results \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. RLS was inversely associated with metabolic syndrome in a nationally representative study of Korean postmenopausal women \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. However, in two cross-sectional studies from China, RLS was positively associated with metabolic syndrome \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In this study, we found a significant relationship between RLS and metabolic syndrome, but the relationship was less pronounced when we included only women who had never taken hormone therapy. Due to the multiple components that define metabolic syndrome, there appear to be mixed results for the relationship between metabolic syndrome and RLS. Longitudinal studies are needed to better understand whether RLS is a risk factor for metabolic syndrome and for each component of metabolic syndrome.\u003c/p\u003e \u003cp\u003eWe performed a cross-sectional analysis evaluating the associations of RLS with diabetes and metabolic syndrome in a nationally representative sample. However, there are some limitations to this study. As a cross-sectional study, there is potential for a reverse causation, where the presence of diabetes or metabolic syndrome before menopause affects the duration of RLS. We did not have information on when the participants developed diabetes or metabolic syndrome (before or after menopause) and could not evaluate whether RLS is associated with the risk of developing the disease. Therefore, the findings need to be interpreted with caution. Prospective cohort studies are needed to better evaluate the risk of diabetes and metabolic syndrome incidence by RLS. In addition, the data on reproductive history including age at menarche, age at menopause, and parity were based on self-report and may be measured with error. However, a previous study showed that self-reported menopause status and age at menarche were highly reproducible in a subsample of the Nurses\u0026rsquo; Health Study participants \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Almost 80% of women were able to recall their age at menarche within one year on the 2 follow-up questionnaires. Furthermore, although we accounted for multiple potential confounders, as an observational study, there may be potential residual or unmeasured confounding. While childhood exposures, including the family income level at the time of menarche, may have an impact on their reproductive lifespan, health conditions, and their susceptivity to diabetes and metabolism syndrome at later age, we were not able to account for childhood exposures as they were not available in the NHANES.\u003c/p\u003e \u003cp\u003eIn conclusion, we found that a short RLS may be associated with diabetes and metabolic syndrome. However, due to the limitations of a cross-sectional study, the findings need to be further evaluated in a longitudinal study to better understand the relationship between endogenous estrogen exposure and risk factors for cardiovascular disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe did not obtain informed consent from the participants because we used deidentified and\u003c/p\u003e\n\u003cp\u003eretrospective data. This issue was also confirmed by the hospital\u0026rsquo;s Institutional Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are publicly available from the NHANES website\u003c/p\u003e\n\u003cp\u003e(https://www.cdc.gov/nchs/nhanes/index.htm) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHoon Kim has received honoraria for participation on the advisory board of Bayer, consulting for Merck\u003c/p\u003e\n\u003cp\u003eand LG Chemical, and lectures for Roche Diagnostics and Organon, which are unrelated to the subjects\u003c/p\u003e\n\u003cp\u003eaddressed in this paper. Other authors report no financial or commercial conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAJin Cho contributed to the study conceptualization, design, supervision, analysis and interpretation of\u003c/p\u003e\n\u003cp\u003edata, literature review, and manuscript writing.\u003c/p\u003e\n\u003cp\u003eYun Soo Hong contributed to the design, analysis and interpretation of data, literature review, and\u003c/p\u003e\n\u003cp\u003emanuscript writing.\u003c/p\u003e\n\u003cp\u003eHoon Kim contributed to the study conceptualization, design, supervision, materials, analysis and\u003c/p\u003e\n\u003cp\u003einterpretation of data, literature review, and manuscript writing.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version; no other person contributed substantially to the\u003c/p\u003e\n\u003cp\u003epaper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e: The National Center for Health Statistics (NCHS) Research Ethics Review\u003c/p\u003e\n\u003cp\u003eBoard approved the protocol and data collection methods of the NHANES (Protocol#2005-\u003c/p\u003e\n\u003cp\u003e06, Protocol#2011-17, and Protocol#2018-01). Written informed consent was obtained from\u003c/p\u003e\n\u003cp\u003eall participants. The study was approved by the Institutional Review Board (IRB) of National\u003c/p\u003e\n\u003cp\u003eCenter for Health Statistics and Kangnam Sacred Heart Hospital (IRB No: HKS 2022-09- 021)\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGoodman, N. F. et al. American Association of Clinical Endocrinologists Medical Guidelines for Clinical Practice for the diagnosis and treatment of menopause. \u003cem\u003eEndocr. Pract.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (Suppl 6), 1\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.4158/ep.17.s6.1\u003c/span\u003e\u003cspan address=\"https://doi.org:10.4158/ep.17.s6.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodsland, I. F. 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Epidemiol.\u003c/em\u003e \u003cb\u003e126\u003c/b\u003e, 319\u0026ndash;325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/aje/126.2.319\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/aje/126.2.319\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1987).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Reproductive life span, menopause, menarche, diabetes, metabolic syndrome","lastPublishedDoi":"10.21203/rs.3.rs-5035398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5035398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eThe associations of a short reproductive life span (RLS) with diabetes and metabolic syndrome in menopausal women are not clearly understood. Therefore, we investigated whether a short RLS is associated with diabetes and metabolic syndrome using a representative population-based survey data in the US.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe evaluated the cross-sectional associations of RLS with diabetes and metabolic syndrome in menopausal women in the US National Health and Nutrition Examination Survey (NHANES) from 2007 through 2018. We used weighted logistic regression using complex survey design to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of diabetes and metabolic syndrome by quartile groups of RLS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 888 postmenopausal women with a mean age of 53.9 were included in the study. The mean age at menarche and menopause of the participants were 12.7 and 50.9 years, respectively. The mean RLS was 38.2 years and was categorized into quartiles (11\u0026ndash;35, 36\u0026ndash;39, 40\u0026ndash;42 (reference group), and 43\u0026ndash;50 years. Compared to the reference group, the OR for diabetes was 2.24 (95% CI, 1.02\u0026ndash;4.94) in women in the 2nd quartile group and had never received hormone replacement therapy (HRT) after adjusting for age, demographics, behavioral factors, and age at menarche. Women in the 2nd quartile group were more likely to have metabolic syndrome irrespective of HRT (OR, 1.86; 95% CI, 1.06\u0026ndash;3.26).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWomen with a relatively short RLS were more likely to have diabetes and metabolic syndrome. The findings suggest that estrogen deficiency may increase the likelihood of diabetes and metabolic syndrome, which are major cardiovascular risk factors. The findings need to be further evaluated in a longitudinal study.\u003c/p\u003e","manuscriptTitle":"Reproductive life span and diabetes mellitus, and metabolic syndrome in the general US population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 13:50:39","doi":"10.21203/rs.3.rs-5035398/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd5e6b26-2c57-4345-8f47-43aff158fc3d","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50033741,"name":"Health sciences/Diseases"},{"id":50033742,"name":"Health sciences/Endocrinology"},{"id":50033743,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-01-08T11:09:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 13:50:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5035398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5035398","identity":"rs-5035398","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}