Association of composite dietary antioxidant index with age of menopause and reproductive lifespan: A cross-sectional study of NHANES Data, 1999-2018

Association of composite dietary antioxidant index with age of menopause and reproductive lifespan: A cross-sectional study of NHANES Data, 1999-2018 | 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 Association of composite dietary antioxidant index with age of menopause and reproductive lifespan: A cross-sectional study of NHANES Data, 1999-2018 Xiaoxuan Zhao, Zanche Huang, Nan Shi, Fangxuan Lin, Qin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5219594/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Objectives Accumulated evidence has shown that the antioxidant diet exhibits protective effects on women’s reproductive health. The Composite Dietary Antioxidant Index (CDAI) serves as a crucial indicator for assessing antioxidant-rich diets. However, the relationship between CDAI and menopause age as well as reproductive lifespan remains undefined, thus motivating this cross-sectional study to investigate these connections. Methods This study analyzed post-menopausal women participating in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. Information on age at menopause and reproductive lifespan was derived from questionnaire data. The CDAI was calculated based on the intake of selenium, zinc, carotenoid, Vitamin A, C and E. Multiple linear regression, smooth curve fitting, threshold effect analysis, and subgroup analysis were used to investigate the association between the CDAI and age at menopause as well as reproductive lifespan. Results A total of 4514 patients were enrolled in the study. After adjusting for confounding factors, the results revealed that individuals with a higher CDAI typically had a later age at menopause (β = 0.08, 95% CI = 0.03–0.13, P < 0.01) and longer reproductive lifespan (β = 0.06, 95% CI = 0.01–0.11, P = 0.01). And each standard deviation increase in CDAI was associated with a 4% decrease in early menopause risk (OR = 0.96, 95% CI = 0.94–0.99, P = 0.01). Besides, the use of oral contraceptives and female hormones could impact these relationships. Conclusions Our research highlighted a positive non-linear association between CDAI and age at menopause, as well as reproductive lifespan. Health sciences/Health care/Nutrition Health sciences/Risk factors composite dietary antioxidant index menopause reproductive lifespan cross-sectional study Figures Figure 1 Figure 2 Figure 3 1. Introduction Menopause is an irreversible stage that women will eventually face in their lifetime, which is defined as the permanent cessation of menstruation due to the loss of ovarian follicular activity [1], marking the end of reproductive capability. Early menopause (before 45 years) is associated with an increased risk of overall mortality, cardiovascular diseases, neurological disorders, mental illnesses, osteoporosis, and other related health sequelae in women, and the risk intensifies as the age at menopause advances [2]. Reproductive lifespan is defined as the duration from the age at menarche to the age at menopause. Research indicates that a shorter reproductive lifespan is significantly associated with an increased risk of cardiovascular diseases [3], mood disorders [4], chronic kidney disease [5], and pulmonary dysfunction [6].The menopause age and reproductive lifespan can be influenced by various factors including race, geographical region, genetics and levels of oxidative stress [7, 8]. Oxidative stress is defined as the condition arising from an imbalance between free radicals and antioxidants in the body, which can lead to cellular damage, accelerated aging [9], and a decline in female reproductive capacity. A direct link between oxidative stress and ovarian function has been firmly established in substantial animal models [10]. Furthermore, clinical studies have also demonstrated that women with decrease ovarian reserve have increased ROS level and decreased antioxidant status [11], indicating the decisive impact of oxidative stress on ovarian function. In addition, adjusting the diet structure has been regarded as an effective way to influence reproductive lifespan [12]. More importantly, daily dietary intake of antioxidants can serve as an effective means to combat oxidative stress [13, 14]. Furthermore, the antioxidant capacity of food is now being evaluated more objectively. The Composite Dietary Antioxidant Index (CDAI) is an effective and robust nutritional tool to measure of dietary antioxidant characteristics, which is calculated based on several dietary antioxidants including selenium, zinc, carotenoid, Vitamin A, C and E [15]. This definition is first proposed by Wright in 2003, and has been progressively and extensively used as an indicator to measure an individual's antioxidant capacity. Now various disease has been found to be closely related to CDAI [16]. For instance, Liu et al. found that higher CDAI levels in postmenopausal women are associated with a lower risk of atherosclerotic cardiovascular disease [17]. Similarly, Wang et al. found that the CDAI was positively associated with a lower prevalence of chronic kidney disease in adults in the United States [18]. However, the relationship between CDAI and menopause age as well as reproductive lifespan remains unknown. The National Health and Nutrition Examination Survey (NHANES) is a population-based cross-sectional survey designed to gather data about the health and nutrition of American households. This database uses a complex stratified, multistage probability cluster sampling design to represent the entire US population. To date, researchers have used the NHANES database to unveil the negative association between CDAI and a likelihood of hypertension [19], stroke [20], and depression [21]. However, the relationship between the CDAI, age at menopause, and reproductive lifespan has not been evaluated. To address this gap, we utilized data involving 4514 participants with natural post menopause from the NHANES database spanning 1999–2018 to investigate these associations. 2. Materials and Methods 2.1. Survey description The Centers for Disease Control and Prevention (CDC) conducts the NHANES to explore the US population's data on health, nutritional, and social status. The survey uses a stratified multistage probability sampling method to ensure that the included samples are representative [22, 23]. The study was approved by the National Center for Health Statistics Research Ethics Review Board, and written informed consent was obtained from each participant [24]. 2.2. Study Population From the participants of ten cycles from 1999 to 2018 (N = 101,316), we selected women who reached menopause due to natural causes or hysterectomy (N = 10,598). After excluding participants with hysterectomy or oophorectomy (N = 3,907), 6691 individuals were identified. We further eliminated cases missing CDAI data and information on age at menarche or menopause (N = 1,386), as well as individuals lacking other covariates (N = 791). Finally, 4,514 participants were enrolled in our analyses. The screening process is shown in Fig. 1 . 2.3. Exposure and outcomes In the NHANES dataset, each participant's food and nutrient intake was recorded using a 24-hour dietary recall interview. The initial dietary recall was conducted in person, followed by a telephone interview three to ten days later. The intake of antioxidants, micronutrients, and total energy was calculated using the Food and Nutrient Database for Dietary Studies provided by the United States Department of Agriculture [25]. From the questionnaire interview, we assessed the intake of dietary supplements over the past month, detailing dosage, frequency, and duration of consumption [26]. CDAI was calculated as the sum of the daily average intakes of zinc, selenium, carotenoids, vitamin A, vitamin C, and vitamin E. Each nutrient was first normalized by subtracting the mean and then dividing by its standard deviation (SD) [16, 17]. Reproductive lifespan was defined as the duration from the age at menarche to the age at menopause. 2.4. Covariates In our study, we identified several potential factors that could influence the relationship between the CDAI and both age at menopause and reproductive lifespan, including race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Races), BMI, educational status (less than high school, high school or GED, above high school), poverty-income ratio (PIR), marital status (married/living with partner, never married, divorced/separated/widowed), alcohol consumption, smoking habits, and the use of oral contraceptives or female hormones. All these factors were incorporated into our multivariate adjustment model. BMI was calculated by dividing weight in kilograms by the square of height in meters. Following WHO recommendations, we used BMI cutoffs of 25 kg/m² and 30 kg/m² to classify overweight and obesity [27]. Alcohol consumption was classified as none, moderate (1 drink/day), heavy (2–3 drinks/day) or binge (≥ 4 drinks/day) according to definitions from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) in the National Institute of Health. The smoking habits involved in the questionnaire data was ascribed as “never/former” (100 cigarettes in lifetime) or “now” (lifetime smoking). The detailed processes for measuring all variables in the study can be found at www.cdc.gov/nchs/nhanes/ . 2.5. Statistical analysis Continuous variables are represented by mean ± standard deviation based on their distribution. The associations between different reproductive lifespans or groups due to age at menopause are analyzed using either the Chi-square test or the Kruskal-Wallis H test. Multivariate linear regression analysis is employed to investigate the relationships between CDAI and age at menarche, age at menopause, and reproductive age range. Model 1 remains unadjusted; Model 2 is adjusted for age and race; and Model 3 additionally adjusts for BMI, PIR, educational level, marital status, alcohol consumption, smoking status, and the use of oral contraceptives or female hormones. Subgroup analyses were conducted based on age, race, BMI, educational level, smoking habits, and use of oral contraceptives or female hormones to identify potential variations within these specific populations. Our study utilizes smooth curve fitting to thoroughly examine the potential nonlinear relationships between CDAI and both age at menopause and reproductive lifespan. Threshold effect analysis is utilized to examine the saturation effect of CDAI. Additionally, multivariate linear regression equations were applied to analyze the relationships between the components of CDAI and both age at menopause and reproductive lifespan. All analyses were performed using Empower software ( www.empowerstats.com ; X&Y Solutions, Inc., Boston, MA, USA) and R version 3.4.3 ( http://www.R-project.org , The R Foundation), P < 0.05 was considered statistically significant. 3. Results 3.1. Baseline characteristics of Participants according to menarche age, menopause age and reproduction lifespan tertile The study included 4514 participants, with a mean CDAI of 0.0 ± 3.4. Participants' ethnic backgrounds included 16.7% Mexican American, 10.5% other Hispanic, 45.9% non-Hispanic white, 18.5% non-Hispanic black, and 8.3% other races. The median age at menarche was 12.9 ± 1.8 years, and the median reproductive lifespan was 36.2 ± 5.8 years. Then, participants were stratified into tertiles based on their age at menarche, age at menopause and reproductive lifespan, as shown in Table 1 . Accordingly, age at menarche was categorized into three groups: earlier than 11 years, 12–13 years, and later than 14 years. There were significant differences among different menarche age groups in the following indicators, including race ( P < 0.001), BMI ( P < 0.001), educational level ( P < 0.001), PIR ( P < 0.001), smoking habits ( P < 0.001), use of oral contraceptives (OC) ( P < 0.001) or female hormones ( P = 0.005), reproductive lifespan ( P < 0.001) and CDAI ( P = 0.049). Based on age at menopause, T1 represented participants under 47 years old, T2 represented those aged 48 to 51 years, and T3 represented those aged 52 and above. Age at menopause was significantly associated with age ( P < 0.001), race ( P < 0.001), educational level ( P < 0.001), PIR ( P < 0.001), marital status ( P < 0.001), alcohol consumption ( P < 0.001), smoking habits ( P = 0.006), and use of female hormones ( P = 0.001) across tertiles. In a separate column, participants were categorized based on reproductive lifespan, with T1 representing below 33 years, T2 representing 34 to 38 years, and T3 representing 39 years and above. Reproductive lifespan was significantly associated with race ( P < 0.001), BMI ( P = 0.006), educational level ( P < 0.001), PIR ( P < 0.001), marital status ( P = 0.002), alcohol consumption ( P < 0.001), and use of OC or female hormones ( P < 0.001), as well as the CDAI ( P < 0.001). We found that CDAI showed an overall upward trend as both age at menopause and reproductive lifespan increased. Table 1 The baseline characteristics according to age of menarche, age of menopause and reproductive lifespan tertiles. Total Age at menarche, years Age at menopause, years Reproductive lifespan, years T1(≤ 11) T2(12 ~ 13) T3(≥ 14) P -value T1(≤ 47) T2(48 ~ 51) T3(≥ 52) P -value T1(≤ 33) T2(34 ~ 38) T3(≥ 39) P -value Age 64.6 ± 9.7 63.0 ± 9.3 64.5 ± 9.7 65.5 ± 9.8 < 0.001 63.4 ± 10.9 63.7 ± 9.5 66.5 ± 8.4 < 0.001 64.1 ± 11.0 63.6 ± 9.6 65.9 ± 8.5 < 0.001 Race, n (%) < 0.001 < 0.001 < 0.001 Mexican American 755 (16.7) 158 (19.1) 323 (14.8) 274 (18.2) 269 (18.9) 297 (18.9) 189 (12.4) 230 (18.5) 308 (18.5) 217 (13.5) Other Hispanic 476 (10.5) 108 (13.1) 199 (9.1) 169 (11.2) 167 (11.7) 166 (10.6) 143 (9.4) 143 (11.5) 172 (10.3) 161 (10.0) Non-Hispanic White 2072 (45.9) 370 (44.8) 1099 (50.4) 603 (40.0) 598 (42.0) 697 (44.5) 777 (51.0) 507 (40.7) 752 (45.2) 813 (50.7) Non-Hispanic Black 837 (18.5) 158 (19.1) 389 (17.8) 290 (19.2) 293 (20.6) 263 (16.8) 281 (18.5) 260 (20.9) 286 (17.2) 291 (18.2) Other Races 374 (8.3) 32 (3.9) 171 (7.8) 171 (11.3) 96 (6.7) 145 (9.2) 133 (8.7) 106 (8.5) 147 (8.8) 121 (7.5) BMI category, n (%) < 0.001 0.969 0.006 Normal weight 1216 (26.9) 160 (19.4) 580 (26.6) 476 (31.6) 385 (27.1) 419 (26.7) 412 (27.1) 352 (28.3) 461 (27.7) 403 (25.1) Overweight 1394 (30.9) 238 (28.8) 643 (29.5) 513 (34.0) 446 (31.3) 487 (31.1) 461 (30.3) 399 (32.0) 531 (31.9) 464 (28.9) Obese 1904 (42.2) 428 (51.8) 958 (43.9) 518 (34.4) 592 (41.6) 662 (42.2) 650 (42.7) 495 (39.7) 673 (40.4) 736 (45.9) Education level, n (%) < 0.001 < 0.001 < 0.001 Less than high school 1381 (30.6) 213 (25.8) 603 (27.6) 565 (37.5) 505 (35.5) 483 (30.8) 393 (25.8) 468 (37.6) 515 (30.9) 398 (24.8) High school or GED 1102 (24.4) 190 (23.0) 543 (24.9) 369 (24.5) 382 (26.8) 358 (22.8) 362 (23.8) 324 (26.0) 410 (24.6) 368 (23.0) Above high school 2031 (45.0) 423 (51.2) 1035 (47.5) 573 (38.0) 536 (37.7) 727 (46.4) 768 (50.4) 454 (36.4) 740 (44.4) 837 (52.2) Income to poverty ratio (%) < 0.001 < 0.001 < 0.001 < 1.5 1475 (32.7) 270 (32.7) 655 (30.0) 550 (36.5) 556 (39.1) 517 (33.0) 402 (26.4) 507 (40.7) 545 (32.7) 423 (26.4) 1.5 ~ 3.5 1827 (40.5) 314 (38.0) 908 (41.6) 605 (40.1) 567 (39.8) 623 (39.7) 637 (41.8) 494 (39.6) 669 (40.2) 664 (41.4) ≥ 3.5 1212 (26.8) 242 (29.3) 618 (28.3) 352 (23.4) 300 (21.1) 428 (27.3) 484 (31.8) 245 (19.7) 451 (27.1) 516 (32.2) Marital status, n (%) 0.351 < 0.001 0.002 Married/living with partner 2308 (51.1) 403 (48.8) 1137 (52.1) 768 (51.0) 668 (46.9) 820 (52.3) 820 (53.8) 586 (47.0) 860 (51.7) 862 (53.8) Never married 219 (4.9) 49 (5.9) 98 (4.5) 72 (4.8) 87 (6.1) 77 (4.9) 55 (3.6) 78 (6.3) 76 (4.6) 65 (4.1) Divorced/separated/widowed 1987 (44.0) 374 (45.3) 946 (43.4) 667 (44.3) 668 (46.9) 671 (42.8) 648 (42.5) 582 (46.7) 729 (43.8) 676 (42.2) Alcohol consumption, n (%) 0.072 < 0.001 < 0.001 None 1276 (28.3) 220 (26.6) 589 (27.0) 467 (31.0) 421 (29.6) 424 (27.0) 431 (28.3) 386 (31.0) 462 (27.7) 428 (26.7) Moderate 1898 (42.0) 346 (41.9) 934 (42.8) 618 (41.0) 541 (38.0) 674 (43.0) 683 (44.8) 464 (37.2) 717 (43.1) 717 (44.7) Heavy 1134 (25.1) 225 (27.2) 561 (25.7) 348 (23.1) 366 (25.7) 404 (25.8) 364 (23.9) 314 (25.2) 415 (24.9) 405 (25.3) Binge 206 (4.6) 35 (4.2) 97 (4.4) 74 (4.9) 95 (6.7) 66 (4.2) 45 (3.0) 82 (6.6) 71 (4.3) 53 (3.3) Smoking habits, n (%) < 0.001 0.006 0.130 Never/former 2727 (60.4) 467 (56.5) 1280 (58.7) 980 (65.0) 817 (57.4) 948 (60.5) 962 (63.2) 754 (60.5) 977 (58.7) 996 (62.1) Now 1787 (39.6) 359 (43.5) 901 (41.3) 527 (35.0) 606 (42.6) 620 (39.5) 561 (36.8) 492 (39.5) 688 (41.3) 607 (37.9) Ever OC use, n (%) 2747 (60.9) 558 (67.6) 1365 (62.6) 824 (54.7) < 0.001 836 (58.7) 976 (62.2) 935 (61.4) 0.128 677 (54.3) 1060 (63.7) 1010 (63.0) < 0.001 Ever Female Hormones use, n (%) 1168 (25.9) 224 (27.1) 599 (27.5) 345 (22.9) 0.005 337 (23.7) 386 (24.6) 445 (29.2) 0.001 276 (22.2) 426 (25.6) 466 (29.1) < 0.001 Age of menarche 12.9 ± 1.8 10.5 ± 0.8 12.5 ± 0.5 14.9 ± 1.1 < 0.001 12.9 ± 1.9 12.9 ± 1.8 13.0 ± 1.7 0.381 13.6 ± 2.0 13.0 ± 1.6 12.4 ± 1.7 < 0.001 Age of menopause 49.1 ± 5.5 48.9 ± 5.5 49.1 ± 5.4 49.3 ± 5.7 0.153 42.7 ± 4.1 49.6 ± 1.0 54.6 ± 2.5 < 0.001 42.5 ± 4.4 49.3 ± 1.9 54.1 ± 2.9 < 0.001 Reproductive lifespan 36.2 ± 5.8 38.4 ± 5.5 36.5 ± 5.4 34.4 ± 5.9 < 0.001 29.8 ± 4.5 36.7 ± 2.0 41.6 ± 2.9 < 0.001 28.9 ± 4.1 36.3 ± 1.4 41.7 ± 2.6 < 0.001 CDAI 0.0 ± 3.4 0.1 ± 3.6 0.1 ± 3.4 -0.2 ± 3.3 0.049 -0.3 ± 3.4 0.1 ± 3.4 0.2 ± 3.3 < 0.001 -0.4 ± 3.4 0.0 ± 3.4 0.3 ± 3.4 < 0.001 3.2. Baseline characteristics according to the CDAI tertile To better illustrate the impact of the CDAI, Table 2 trisected the CDAI values into three distinct groups: T1 ranged from − 7.06 to -1.80, T2 from − 1.80 to 0.96, and T3 from 0.96 to 12.32. Among different CDAI groups, there were significant differences in race ( P < 0.001), BMI ( P < 0.001), PIR ( P < 0.001), educational level ( P < 0.001), marital status ( P < 0.001), drinking status ( P < 0.001), and use of female hormones ( P < 0.001). Similar to Table 1 , participants with higher CDAI tended to experience later ages at menopause and longer reproductive lifespans. Table 2 The baseline characteristics according to the CDAI tertiles. Tertile 1(-7.06~-1.80) Tertile 2(-1.80 ~ 0.96) Tertile 3(0.96 ~ 12.32) P -value Age 64.9 ± 9.7 64.5 ± 9.8 64.3 ± 9.5 0.209 Race, n (%) < 0.001 Mexican American 280 (18.6) 268 (17.8) 207 (13.8) Other Hispanic 159 (10.6) 181 (12.0) 136 (9.0) Non-Hispanic White 612 (40.7) 698 (46.3) 762 (50.6) Non-Hispanic Black 337 (22.4) 255 (16.9) 245 (16.3) Other Races 115 (7.7) 104 (6.9) 155 (10.3) BMI category, n (%) < 0.001 Normal weight 373 (24.8) 357 (23.7) 486 (32.3) Overweight 462 (30.7) 484 (32.1) 448 (29.8) Obese 668 (44.4) 665 (44.2) 571 (37.9) Education level, n (%) < 0.001 Less than high school 579 (38.5) 442 (29.3) 360 (23.9) High school or GED 375 (25.0) 402 (26.7) 325 (21.6) Above high school 549 (36.5) 662 (44.0) 820 (54.5) Income to poverty ratio, n (%) < 0.001 < 1.5 607 (40.4) 461 (30.6) 407 (27.0) 1.5 ~ 3.5 596 (39.7) 646 (42.9) 585 (38.9) ≥ 3.5 300 (20.0) 399 (26.5) 513 (34.1) Marital status, n (%) < 0.001 Married/living with partner 697 (46.4) 777 (51.6) 834 (55.4) Never married 88 (5.9) 69 (4.6) 62 (4.1) Divorced/separated/widowed 718 (47.8) 660 (43.8) 609 (40.5) Alcohol consumption, n (%) < 0.001 None 490 (32.6) 397 (26.4) 389 (25.8) Moderate 579 (38.5) 658 (43.7) 661 (43.9) Heavy 368 (24.5) 376 (25.0) 390 (25.9) Binge 66 (4.4) 75 (5.0) 65 (4.3) Smoking habits, n (%) 0.664 Never/former 917 (61.0) 896 (59.5) 914 (60.7) Now 586 (39.0) 610 (40.5) 591 (39.3) Ever OC use, n (%) 895 (59.5) 922 (61.2) 930 (61.8) 0.423 Ever Female Hormones use, n (%) 315 (21.0) 387 (25.7) 466 (31.0) < 0.001 Age of menarche 13.0 ± 1.8 13.0 ± 1.8 12.9 ± 1.7 0.154 Age of menopause 48.6 ± 5.9 49.2 ± 5.3 49.5 ± 5.4 < 0.001 Reproductive lifespan 35.6 ± 6.1 36.3 ± 5.5 36.7 ± 5.6 < 0.001 CDAI -3.3 ± 1.1 -0.5 ± 0.8 3.9 ± 2.5 < 0.001 3.3. Association between CDAI and the age at menopause as well as reproductive lifespan Table 3 displayed the results of multivariate linear regression analyzing the association of CDAI with age at menarche, age at menopause and reproductive lifespan. In the unadjusted model, there is a significant positive correlation between CDAI and both age at menopause (β = 0.12, 95%CI = 0.07–0.17, P < 0.01) and reproductive lifespan (β = 0.10, 95% CI = 0.06–0.15, P < 0.01). After adjusting for age and race in model 2, the significant positive effects of age at menopause (β = 0.10, 95% CI = 0.05–0.15, P < 0.01) and reproductive lifespan (β = 0.10, 95%CI = 0.05–0.15, P < 0.01) persist. In model 3, adjustments were incorporated for additional variables including BMI, educational status, PIR, marital status, alcohol consumption, smoking habits, use of oral contraceptives or female hormones. The fully adjusted model revealed that increased CDAI was correlated with increased age at menopause (β = 0.08, 95%CI = 0.03–0.13, P < 0.01) and increased reproductive lifespan (β = 0.06, 95%CI = 0.01–0.11, P = 0.01). Additionally, after full adjustment for confounding factors, the analysis showed that T3, compared to T1, had a significant positive correlation with age at menopause (β = 0.65, 95%CI = 0.26–1.05, P < 0.01) and reproductive lifespan (β = 0.72, 95%CI = 0.31–1.13, P < 0.01). Furthermore, in the analysis without categorization, we observed a significant negative correlation between CDAI and age at menarche in the fully adjusted model (β=-0.02, 95%CI=-0.03-0, P = 0.04). However, after categorization into tertiles, no significant correlation was found. Next, multivariate regression was employed to evaluate the impact of CDAI on early (under 45 years) and late (over 55 years) menopause. According to Table 4 , across all models, an increased CDAI significantly reduced the risk of early menopause. In Model 1, without adjusting for variables, each standard deviation increase in CDAI was associated with a 5% reduction in the risk of early menopause (OR = 0.95, P < 0.01) and a 2% increase in the risk of late menopause, though this was not statistically significant (OR = 1.02, P = 0.06). In Model 2, after adjusting for age and race, each standard deviation increase in CDAI was associated with a 5% reduction in the risk of early menopause (OR = 0.95, P < 0.01) and a 2% increase in the risk of late menopause, though this was not statistically significant (OR = 1.02, P = 0.05). In Model 3, after adjusting for age, race, BMI, educational level, PIR, marital status, alcohol consumption, smoking habits, and use of oral contraceptives or female hormones, CDAI remained negatively associated with the risk of early menopause (OR = 0.96, P = 0.01) and was negatively associated with the risk of late menopause (OR = 1.02, P = 0.19). After dividing CDAI into tertiles, in Model 1, the risk of early menopause in T3 compared to T1 is reduced by 35% (OR = 0.65, P < 0.01); in Model 2, it was reduced by 34% (OR = 0.66, P < 0.01); and in Model 3, it was reduced by 26% (OR = 0.74, P < 0.01). Conversely, in Model 1, the risk of late menopause in T3 compared to T1 increased by 36% (OR = 1.36, P < 0.01); in Model 2, it increased by 39% (OR = 1.39, P < 0.01); and in Model 3, it increased by 31% (OR = 1.31, P = 0.01). To investigate whether the effects of the CDAI on menopause age and reproductive lifespan continue to persist with increasing levels, we used CDAI to create smooth curves for age at menopause and reproductive lifespan. Figure 2 revealed an inverted U-shaped curve between CDAI and age at menopause, and an L-shaped curve for CDAI and reproductive lifespan. Therefore, we further conducted a threshold effect analysis as presented in Table 5 , which indicated that below an inflection point of 2.4, there was a positive correlation between CDAI and menopause age (β = 0.16, 95% CI = 0.09–0.24, P < 0.01) as well as reproductive lifespan (β = 0.16, 95% CI = 0.08–0.24, P = 0.01), with log-likelihood ratios of 0.001 and 0.016, respectively. Above this point, the influence of CDAI ceased to be statistically significant. This may indicate that a reasonable increase in the CDAI up to 2.4 could be a clinical strategy to mitigate the risk of early menopause and prolong the reproductive age interval. Table 3 The association of CDAI with the age of menopause and reproductive lifespan. β (95%CI), P -value Model 1 Model 2 Model 3 Age of menarche CDAI -0.02 (-0.03, -0.00) 0.01 -0.02 (-0.03, -0.00) 0.01 -0.02 (-0.03, -0.00) 0.04 CDAI group Tertile 1 Reference Reference Reference Tertile 2 -0.03 (-0.16, 0.10) 0.67 -0.00 (-0.13, 0.13) 0.98 0.05 (-0.08, 0.17) 0.44 Tertile 3 -0.12 (-0.25, 0.01) 0.07 -0.11 (-0.24, 0.01) 0.08 -0.07 (-0.19, 0.06) 0.29 P for trend -0.02 (-0.04, 0.00) 0.06 -0.02 (-0.04, 0.00) 0.07 -0.01 (-0.03, 0.01) 0.23 Age of menopause CDAI 0.12 (0.07, 0.17) < 0.01 0.10 (0.05, 0.15) < 0.01 0.08 (0.03, 0.13) < 0.01 CDAI group Tertile 1 Reference Reference Reference Tertile 2 0.69 (0.29, 1.08) < 0.01 0.69 (0.30, 1.08) < 0.01 0.52 (0.13, 0.91) < 0.01 Tertile 3 0.97 (0.58, 1.37) < 0.01 0.92 (0.53, 1.32) < 0.01 0.65 (0.26, 1.05) < 0.01 P for trend 0.15 (0.09, 0.21) < 0.01 0.14 (0.08, 0.20) < 0.01 0.10 (0.04, 0.16) < 0.01 Reproductive lifespan CDAI 0.10 (0.06, 0.15) < 0.001 0.10 (0.05, 0.15) < 0.001 0.06 (0.01, 0.11) 0.01 CDAI group Tertile 1 Reference Reference Reference Tertile 2 0.71 (0.30, 1.13) < 0.01 0.69 (0.28, 1.10) < 0.01 0.48 (0.07, 0.88) 0.02 Tertile 3 1.09 (0.68, 1.51) < 0.01 1.03 (0.62, 1.45) < 0.01 0.72 (0.31, 1.13) < 0.01 P for trend 0.17 (0.10, 0.23) < 0.01 0.16 (0.09, 0.22) < 0.01 0.11 (0.05, 0.17) < 0.01 Model 1: No covariates were adjusted. Model 2: Adjusted for age and race. Model 3: Adjusted for age, race, BMI, education level, PIR, marital status, alcohol consumption, smoking habits, use of oral contraceptives or female hormones. Table 4 Associations between CDAI and age at menopause. OR (95%CI) P -value Early menopause (< 45 years) Late menopause (≥ 55 years) Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 CDAI 0.95 (0.92, 0.97) < 0.01 0.95 (0.93, 0.97) < 0.01 0.96 (0.94, 0.99) 0.01 1.02 (1.00, 1.05) 0.06 1.02 (1.00, 1.05) 0.05 1.02 (0.99, 1.04) 0.19 Tertile 1 Reference Reference Reference Reference Reference Reference Tertile 2 0.73 (0.60, 0.88) < 0.01 0.72 (0.60, 0.88) < 0.01 0.77 (0.64, 0.94) < 0.01 1.19 (0.97, 1.45) 0.09 1.21 (0.99, 1.49) 0.06 1.18 (0.96, 1.45) 0.11 Tertile 3 0.65 (0.54, 0.79) < 0.01 0.66 (0.54, 0.81) < 0.01 0.74 (0.60, 0.90) < 0.01 1.36 (1.12, 1.65) < 0.01 1.39 (1.14, 1.69) < 0.01 1.31 (1.07, 1.61) 0.01 P for trend 0.94 (0.91, 0.97) < 0.01 0.94 (0.91, 0.97) < 0.01 0.95 (0.92, 0.98) < 0.01 1.05 (1.02, 1.08) < 0.01 1.05 (1.02, 1.08) < 0.01 1.04 (1.01, 1.08) 0.01 Model 1: No covariates were adjusted. Model 2: Adjusted for age and race. Model 3: Adjusted for age, race, BMI, education level, PIR, marital status, alcohol consumption, smoking habits, use of oral contraceptives or female hormones. Table 5 Threshold effect analysis of CDAI on age at menopause and reproductive lifespan by the two-piecewise linear regression. Inflection point Adjusted β (95% CI), P -value Age at menopause Reproductive lifespan ≤ 2.4 0.16 (0.09, 0.24) 2.4 -0.12 (-0.23, 0.00) 0.05 -0.06 (-0.18, 0.06) 0.34 Log-likelihood ratio 0.01 0.02 3.4. Subgroup analysis As shown in Fig. 3 , a subgroup analysis was conducted to assess the robustness of the correlation between CDAI and age at menopause as well as reproductive lifespan. Age was categorized into two groups based on threshold of 60. The variables considered included age, race, BMI, educational level, smoking status, marital status, and use of oral contraceptives and female hormones. It was observed that in populations who had used oral contraceptives and female hormones, the correlations between CDAI and age at menopause as well as reproductive lifespan were more pronounced. Specifically, in the subgroup using oral contraceptives, each standard deviation increase in CDAI was associated with a delay in age at menopause by 0.11 years (β = 0.11, 95%CI = 0.04–0.17, P < 0.01) and an increase in the reproductive age range by 0.16 years (β = 0.16, 95%CI = 0.04–0.17, P < 0.01). Similarly, in the subgroup using female hormones, each standard deviation increasing in CDAI resulted in a delay in age at menopause by 0.12 years (β = 0.12, 95% CI = 0.06–0.19, P < 0.01) and an increase in the reproductive lifespan by 0.17 years (β = 0.17, 95% CI = 0.07–0.26, P < 0.01). In participants that did not use oral contraceptives or female hormones, no significant correlation was observed between CDAI and age at menopause as well as reproductive lifespan. Interaction tests within subgroups for age, race, BMI, educational level, smoking status, and marital status were all not significant, indicating that the relationships between CDAI and age at menopause as well as reproductive lifespan were consistent across these factors. 3.5. Association between components of CDAI and the age at menopause and reproductive lifespan In Table 6 , a multivariate linear regression equation was used to explore the relationship between the six components of CDAI and age at menopause as well as reproductive lifespan. In the fully adjusted Model 3, only Vitamin C and carotenoids showed a significant positive correlation. Table 6 Association of six components of CDAI (mg) with age at menopause and reproductive lifespan. Age at Menopause Reproductive lifespan Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Vitamin A 0.001 (0, 0.001) 0.03 0 (0, 0.001) 0.06 0 (0, 0.001) 0.28 0.001 (0, 0.001) < 0.01 0.001 (0, 0.001) 0.01 0(0, 0.001) 0.08 Vitamin C 0.005 (0.002, 0.007) < 0.01 0.004 (0.002, 0.007) < 0.01 0.003 (0.001, 0.006) < 0.01 0.004 (0.002, 0.006) < 0.01 0.004 (0.002, 0.006) < 0.01 0.003 (0.001, 0.005) 0.01 Vitamin E 0.049 (0.013, 0.085) 0.01 0.050 (0.014, 0.086) < 0.01 0.021 (-0.014, 0.057) 0.24 0.069 (0.031, 0.106) < 0.01 0.0667 (0.029, 0.104) < 0.01 0.034 (-0.004, 0.071) 0.08 Selenium 0.002 (-0.002, 0.006) 0.27 0.003 (-0.001, 0.007) 0.09 0.002(-0.002, 0.005) 0.39 0.003(0, 0.007) 0.08 0.004 (0.001, 0.008) 0.03 0.002(-0.001, 0.006) 0.22 Zinc 0.026 (-0.007, 0.060) 0.13 0.028 (-0.006, 0.062) 0.10 0.018 (-0.015, 0.052) 0.28 0.031 (-0.004, 0.066) 0.08 0.029 (-0.007, 0.064) 0.11 0.017(-0.018, 0.052) 0.34 Carotenoid 0.033 (0.017, 0.049) < 0.01 0.029 (0.013, 0.046) < 0.01 0.022 (0.005, 0.038) < 0.01 0.039 (0.022, 0.056) < 0.01 0.036 (0.019, 0.053) < 0.01 0.028 (0.011, 0.045) 0.01 Model 1: No covariates were adjusted. Model 2: Adjusted for age and race. Model 3: Adjusted for age, race, BMI, education level, PIR, marital status, alcohol consumption, smoking habits, use of oral contraceptives or female hormones. 4. Discussion Our study found that an increase in the CDAI was positively correlated with an increase in the age at menopause and reproductive lifespan. Higher CDAI significantly reduced the risk of early menopause (under 45 years) and there was a threshold effect on menopausal age and reproductive interval. Subgroup analysis indicated that the use of oral contraceptives or female hormones impacted the relationship between CDAI and reproductive metrics. Further, Vitamin C and carotenoids, key components of CDAI, appear to extend reproductive lifespan significantly. This study is the first to explore these relationships in naturally menopausal women, suggesting that dietary antioxidants may delay menopause onset and extend reproductive lifespan, with significant implications for improving women's reproductive health. Currently, the use of dietary antioxidant properties to regulate ovarian function has been a hot topic of research today [28]. For instance, dietary flaxseed, due to its antioxidant components, has been proven to potentially improve menopausal symptoms such as hot flashes and sweating [29, 30]. Additionally, a cross-sectional study conducted by Alina et al. using NHANES data from 2001–2018 also found that an increased intake of Vitamin D, a promoter of gene expression for antioxidant effects by binding to Vitamin D response elements (VDRE), could reduce the risk of early menopause and shortened reproductive lifespan [31]. In addition, calculating based on the intake of various antioxidant micronutrients (selenium, zinc, carotenoids, and vitamins A, C, E), CDAI is frequently employed to assess dietary antioxidant levels and has been linked to various health outcomes. Liu et al. reported that postmenopausal women with high CDAI scores often exhibit a lower incidence of atherosclerotic cardiovascular disease [17]. Moreover, research by He et al. has shown a significant negative correlation between CDAI and biological phenotypic age [32], which aligns with our findings that a higher CDAI is associated with a delayed onset of menopause in women. To date, no study has considered the impact of CDAI on reproductive lifespan. Our results revealed that CDAI, especially the components of Vitamin C and carotenoids, contribute to menopause delay. Vitamin C is a hydrophilic compound that acts as a cofactor for the hydroxylation of proline and lysine during collagen formation. Available results indicate its multi-directional cellular effect, especially its role in scavenging free radicals. A randomized, triple-blind placebo-controlled clinical trial revealed that Vitamin C combined with Vitamin E could significantly reduce the level of MDA and ROS in patients with endometriosis and improved dyspareunia and severity of pelvic pain [33]. In female mice, a diet rich in vitamin C can prevent age-related declines in both the quantity and quality of oocytes [34].Our findings may further advance clinical studies exploring the antioxidant stress role of vitamin C in ovarian hypofunction. In addition, Carotenoids, another compound in CDAI are 40-carbon isoprenoid molecules that produce the red, yellow, and orange pigmentation found in nature. Various plants, microalgae, bacteria, and fungi are natural sources of carotenoids. Among the 50 kinds of carotenes present in nature, the best known are α-carotene and β-carotene. These natural antioxidants could aid in quenching free radicals produced by complex physiological reactions and, consequently, protect the tissue from oxidative stress, apoptosis, mitochondrial dysfunction, and inflammation [35]. Clinical studies suggest that carotenoid consumption is associated with lower risk of cardiovascular disease, cancer, and eye disease. This substantial evidence supports its protective effect on female reproductive function due to oxidative stress damage. However, clinical evidence of a direct link between CDAI composition and reproductive lifespan is relatively scarce. Our findings may suggest that for people with risk factors for early menopause, an adequate intake of a diet with a high CDAI, especially within the threshold range we have provided, may be beneficial in prolonging reproductive life and reducing other health problems. Of course, prospective large cohort studies are needed to further verify the scientific nature of this hypothesis. Although the mechanism by which antioxidant foods prolong reproductive life is still being explored, it may be partly related to improving decreased ovarian function caused by oxidative stress. Oxidative stress occurs when the body’s antioxidant system is depleted owing to an excess of reactive oxygen species (ROS). Exuberant ROS in the ovary can dysregulate the dynamics of the ovarian reserve and/or impair the survival and competence of the oocytes [36], thus potentially leading to an earlier onset of menopause and shorten the reproductive lifespan [8]. Besides, oxidative stress can mediate the onset of inflammation, which could facilitate apoptosis of granulosa cells and oocytes, resulting in the early loss of the ovaries' role in maintaining menstruation [37–39]. On the contrary, antioxidant enzymes serve to neutralize ROS production, thereby protecting ovary function. For instance, quercetin, a dietary antioxidant, can enhance ARE binding activity and Nrf-2-mediated transcriptional activity, thus inducing the expression of antioxidant enzymes [40], thereby preventing oxidative stress by inhibiting the NF-κB pathway [41]. Besides, oocytes cultured in media supplemented with quercetin exhibit improved capabilities of maturation and early embryonic development [42]. Similarly, dietary trace element zinc, which act as a transcriptional regulator of Nrf2, can upregulate downstream antioxidants through nuclear translocation, thereby responding to ROS-induced damage [43]. Zinc is also a cofactor for one of the most important antioxidant enzymes, Zn-SOD/SOD1, and play a crucial role in female reproduction by scavenging ROS [44, 45]. Collectively, dietary antioxidants can improve ovarian function by mitigating oxidative stress through various mechanisms. In addition, our research introduces a novel observation that the use of oral contraceptives or female hormones may influence the relationship between the CDAI and reproductive age. The primary components of oral contraceptives and female hormones include estrogens and progestogens. Existing research has demonstrated their antioxidant properties [46]. Estrogen replacement therapy, for instance, has been confirmed to induce antioxidant and protect various tissues from oxidative stress, including brain, bone and myocardial tissue [47, 48]. Unlike estrogens, progesterone does not possess the characteristic chemical structure of antioxidants, but high levels of progesterone appear to reduce oxidative damage [49]. Adler et al. confirmed through real-time quantitative PCR that in vitro progesterone treatment significantly increased myeloperoxidase expression in isolated human neutrophils, while significantly reducing NADPH oxidase gene expression [50]. Progesterone exerts protective effects in various diseases by upregulating γ-aminobutyric acid inhibition, reducing lipid peroxidation and oxidative stress, decreasing the release of inflammatory cytokines, and minimizing apoptosis-induced cell death, thereby promoting cell survival and proliferation [49]. These findings suggested that hormone supplementation may synergize with antioxidant diets in protecting ovarian function. Notably, hormone replacement therapy is subject to strict indications and contra-indications, and how to combine it with high-CDAI foods to properly avoid premature menopause needs further research and discussion. Taken together, our study is the first to explore the relationship between CDAI and both the age at menopause and reproductive lifespan, highlighting potential dietary strategies to improve women’s reproductive health. However, its cross-sectional nature introduces several limitations. Firstly, the simultaneous assessment of exposure and outcome does not confirm a temporal relationship, underscoring the need for future longitudinal studies to establish causality. Secondly, selection bias may be present as the study only includes participants who meet specific criteria. Lastly, not all variables related to menopause and reproductive age span were accounted for, potentially overlooking unknown confounding factors. 5. Conclusions In conclusion, our study indicates that within a certain range, higher CDAI levels are associated with reduced risk of early menopause, delayed menopause, and prolonged reproductive lifespan. These findings highlight the role of dietary guidance in improving reproductive health. However, the inherent limitations of cross-sectional studies and the complex influences on menopause necessitate further in-depth research. Declarations Conflicts of Interest: The authors declare no conflicts of interest. Funding: This research was funded by Major project of Hangzhou Health Commission (NO. Z20230103) Author Contribution Conceptualization, Q.Z. and X.Z.; methodology, X.Z.; software, X.Z.; validation, Z.H., N.S. and F.L.; formal analysis, X.Z.; investigation, X.Z.; resources, Z.H.; data curation, Z.H.; writing—original draft preparation, Z.H.; writing—review and editing, X.Z.; visualization, Z.H.; supervision, X.Z.; project administration, X.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets analyzed during the current study are available in the NHANES repository at the following links: www.cdc.gov/nchs/nhanes/. References Research on the menopause in the 1990s. Report of a WHO Scientific Group. World Health Organ Tech Rep Ser, 1996. 866 : p. 1-107. Shuster, L.T., et al., Premature menopause or early menopause: long-term health consequences. Maturitas, 2010. 65 (2): p. 161-6. Chen, L., et al., Age at Menarche and Menopause, Reproductive Lifespan, and Risk of Cardiovascular Events Among Chinese Postmenopausal Women: Results From a Large National Representative Cohort Study. Front Cardiovasc Med, 2022. 9 : p. 870360. Wu, Q., et al., Association of reproductive lifespan and age at menopause with depression: Data from NHANES 2005–2018. J Affect Disord, 2024. Kang, S.C., et al., Association of Reproductive Lifespan Duration and Chronic Kidney Disease in Postmenopausal Women. Mayo Clin Proc, 2020. 95 (12): p. 2621–2632. Lim, J.H., et al., Association between reproductive lifespan and lung function among postmenopausal women. J Thorac Dis, 2020. 12 (8): p. 4243–4252. Harlow, S.D., et al., Executive summary of the Stages of Reproductive Aging Workshop + 10: addressing the unfinished agenda of staging reproductive aging. J Clin Endocrinol Metab, 2012. 97 (4): p. 1159-68. Agarwal, A., S. Gupta, and R.K. Sharma, Role of oxidative stress in female reproduction. Reprod Biol Endocrinol, 2005. 3 : p. 28. Monti, D.M., et al., Role of Antioxidants in the Protection from Aging-Related Diseases. Oxid Med Cell Longev, 2019. 2019 : p. 7450693. He, X., et al., Secoisolariciresinol Diglucoside Improves Ovarian Reserve in Aging Mouse by Inhibiting Oxidative Stress. Front Mol Biosci, 2021. 8 : p. 806412. Smits, M.A.J., et al., Human ovarian aging is characterized by oxidative damage and mitochondrial dysfunction. Hum Reprod, 2023. 38 (11): p. 2208–2220. Silva, T.R., et al., Nutrition in Menopausal Women: A Narrative Review. Nutrients, 2021. 13 (7). Lindsay, D.G., Diet and ageing: the possible relation to reactive oxygen species. J Nutr Health Aging, 1999. 3 (2): p. 84–91. Rondanelli, M., et al., Food pyramid for subjects with chronic pain: foods and dietary constituents as anti-inflammatory and antioxidant agents. Nutr Res Rev, 2018. 31 (1): p. 131–151. Yu, Y.C., et al., Composite dietary antioxidant index and the risk of colorectal cancer: Findings from the Singapore Chinese Health Study. Int J Cancer, 2022. 150 (10): p. 1599–1608. Wright, M.E., et al., Development of a comprehensive dietary antioxidant index and application to lung cancer risk in a cohort of male smokers. Am J Epidemiol, 2004. 160 (1): p. 68–76. Liu, C., et al., Association between the Composite Dietary Antioxidant Index and Atherosclerotic Cardiovascular Disease in Postmenopausal Women: A Cross-Sectional Study of NHANES Data, 2013–2018. Antioxidants (Basel), 2023. 12 (9). Wang, M., et al., Association between the composite dietary antioxidant index and chronic kidney disease: evidence from NHANES 2011–2018. Food Funct, 2023. 14 (20): p. 9279–9286. Wu, M., et al., Association between composite dietary antioxidant index and hypertension: insights from NHANES. Clin Exp Hypertens, 2023. 45 (1): p. 2233712. Teng, T.Q., et al., Association of composite dietary antioxidant index with prevalence of stroke: insights from NHANES 1999–2018. Front Immunol, 2024. 15 : p. 1306059. Zhao, L., et al., Relationship between composite dietary antioxidant index and depression among overweight and obese adults. J Affect Disord, 2023. 341 : p. 358–365. Curtin, L.R., et al., National Health and Nutrition Examination Survey: sample design, 2007–2010. Vital Health Stat 2, 2013(160): p. 1–23. Johnson, C.L., et al., National health and nutrition examination survey: sample design, 2011–2014. Vital Health Stat 2, 2014(162): p. 1–33. Skrivankova, V.W., et al., Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. Jama, 2021. 326 (16): p. 1614–1621. Ahuja, J.K., et al., USDA food and nutrient databases provide the infrastructure for food and nutrition research, policy, and practice. J Nutr, 2013. 143 (2): p. 241s-9s. Kantor, E.D., et al., Trends in Dietary Supplement Use Among US Adults From 1999–2012. Jama, 2016. 316 (14): p. 1464–1474. Kurisu, S., et al., Frontal QRS-T angle and World Health Organization classification for body mass index. Int J Cardiol, 2018. 272 : p. 185–188. Liu, Z., et al., Role of ROS and Nutritional Antioxidants in Human Diseases. Front Physiol, 2018. 9 : p. 477. Cetisli, N.E., A. Saruhan, and B. Kivcak, The effects of flaxseed on menopausal symptoms and quality of life. Holist Nurs Pract, 2015. 29 (3): p. 151-7. Landete, J.M., et al., Bioactivation of Phytoestrogens: Intestinal Bacteria and Health. Crit Rev Food Sci Nutr, 2016. 56 (11): p. 1826-43. Alinia, T., et al., Relationship between vitamin D levels and age of menopause and reproductive lifespan: Analysis based on the National health and nutrition examination survey (NHANES) 2001–2018. Eur J Obstet Gynecol Reprod Biol, 2023. 289 : p. 183–189. He, H., et al., Composite dietary antioxidant index associated with delayed biological aging: a population-based study. Aging (Albany NY), 2024. 16 (1): p. 15–27. Amini, L., et al., The Effect of Combined Vitamin C and Vitamin E Supplementation on Oxidative Stress Markers in Women with Endometriosis: A Randomized, Triple-Blind Placebo-Controlled Clinical Trial. Pain Res Manag, 2021. 2021 : p. 5529741. Tarín, J.J., S. Pérez-Albalá, and A. Cano, Oral antioxidants counteract the negative effects of female aging on oocyte quantity and quality in the mouse. Mol Reprod Dev, 2002. 61 (3): p. 385 − 97. Johra, F.T., et al., A Mechanistic Review of β-Carotene, Lutein, and Zeaxanthin in Eye Health and Disease. Antioxidants (Basel), 2020. 9 (11). Dri, M., F.G. Klinger, and M. De Felici, The ovarian reserve as target of insulin/IGF and ROS in metabolic disorder-dependent ovarian dysfunctions. Reprod Fertil, 2021. 2 (3): p. R103-r112. Reuter, S., et al., Oxidative stress, inflammation, and cancer: how are they linked? Free Radic Biol Med, 2010. 49 (11): p. 1603-16. Arulselvan, P., et al., Role of Antioxidants and Natural Products in Inflammation. Oxid Med Cell Longev, 2016. 2016 : p. 5276130. Liguori, I., et al., Oxidative stress, aging, and diseases. Clin Interv Aging, 2018. 13 : p. 757–772. Sun, L., et al., Quercetin Protects Against Lipopolysaccharide-Induced Intestinal Oxidative Stress in Broiler Chickens through Activation of Nrf2 Pathway. Molecules, 2020. 25 (5). Ferraz, C.R., et al., Therapeutic Potential of Flavonoids in Pain and Inflammation: Mechanisms of Action, Pre-Clinical and Clinical Data, and Pharmaceutical Development. Molecules, 2020. 25 (3). Vašková, J., et al., The Importance of Natural Antioxidants in Female Reproduction. Antioxidants (Basel), 2023. 12 (4). Lu, X., et al., Zinc is essential for the transcription function of the PGC-1α/Nrf2 signaling pathway in human primary endometrial stromal cells. Am J Physiol Cell Physiol, 2020. 318 (3): p. C640-c648. Lewandowski, Ł., M. Kepinska, and H. Milnerowicz, The copper-zinc superoxide dismutase activity in selected diseases. Eur J Clin Invest, 2019. 49 (1): p. e13036. Wang, S., et al., The Role of Antioxidant Enzymes in the Ovaries. Oxid Med Cell Longev, 2017. 2017 : p. 4371714. Chainy, G.B.N. and D.K. Sahoo, Hormones and oxidative stress: an overview. Free Radic Res, 2020. 54 (1): p. 1–26. Shafin, N., et al., Association of oxidative stress and memory performance in postmenopausal women receiving estrogen-progestin therapy. Menopause, 2013. 20 (6): p. 661-6. Mohamad, N.V., S. Ima-Nirwana, and K.Y. Chin, Are Oxidative Stress and Inflammation Mediators of Bone Loss Due to Estrogen Deficiency? A Review of Current Evidence. Endocr Metab Immune Disord Drug Targets, 2020. 20 (9): p. 1478–1487. Hernández-Rabaza, V., R. López-Pedrajas, and I. Almansa, Progesterone, Lipoic Acid, and Sulforaphane as Promising Antioxidants for Retinal Diseases: A Review. Antioxidants (Basel), 2019. 8 (3). Adler, I., et al., The effect of certain steroid hormones on the expression of genes involved in the metabolism of free radicals. Gynecol Endocrinol, 2012. 28 (11): p. 912-6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Mar, 2025 Reviews received at journal 26 Feb, 2025 Reviews received at journal 25 Feb, 2025 Reviewers agreed at journal 14 Feb, 2025 Reviewers agreed at journal 14 Feb, 2025 Reviews received at journal 12 Feb, 2025 Reviewers agreed at journal 12 Feb, 2025 Reviewers agreed at journal 05 Dec, 2024 Reviewers agreed at journal 03 Dec, 2024 Reviewers invited by journal 25 Nov, 2024 Editor assigned by journal 17 Nov, 2024 Editor invited by journal 30 Oct, 2024 Submission checks completed at journal 29 Oct, 2024 First submitted to journal 07 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5219594","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":374859173,"identity":"6878af5b-61b7-4369-9c84-bafc23d89e95","order_by":0,"name":"Xiaoxuan Zhao","email":"","orcid":"","institution":"Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoxuan","middleName":"","lastName":"Zhao","suffix":""},{"id":374859175,"identity":"3bed9566-1fcb-4b2e-81c1-4cd8cf82c159","order_by":1,"name":"Zanche Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYJACZgaGAyCa8UEFmMvcQLQWZoMzYIqReC1sEmAtDAS0yLefPfi5oOJOYv/s9msVByoOR/O3A7X8qNiGUwtjT16y9IwzzxJn3DlTduPAmbTcGYcZGxh7ztzG46gcM2betsOJDTdy0m5/bLPJbQBqYWZsw62Fjf8NUMu/w4nzgVoKDrZJ5M4npIVHAmRLw+HEDTfSjzEcBNqygZAWCYk3xtI8xw4bb7yRwywB8stGoJaD+Pwi359j+Jmn5rDsvBvpDz8AQyx33vnDBx/8qMCtBdmNBnDmAWLUAwH7AyIVjoJRMApGwUgDAK3KYbbsOexmAAAAAElFTkSuQmCC","orcid":"","institution":"Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zanche","middleName":"","lastName":"Huang","suffix":""},{"id":374859178,"identity":"92d62d47-fbee-47c5-9b2f-d8c859b02696","order_by":2,"name":"Nan Shi","email":"","orcid":"","institution":"Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Shi","suffix":""},{"id":374859180,"identity":"d88fb609-eb4f-4dbc-8393-c40b1af24cd8","order_by":3,"name":"Fangxuan Lin","email":"","orcid":"","institution":"Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fangxuan","middleName":"","lastName":"Lin","suffix":""},{"id":374859182,"identity":"d7fd2da1-023a-4210-ab63-ac624d5c4c19","order_by":4,"name":"Qin Zhang","email":"","orcid":"","institution":"Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-10-07 16:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5219594/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5219594/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-23666-9","type":"published","date":"2025-11-14T15:58:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69435800,"identity":"106b0e0e-9b2f-4ba8-999e-dd8c1ed3e467","added_by":"auto","created_at":"2024-11-20 10:32:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":705406,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the participant selection from NHANES 1999–2018.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5219594/v1/0103f159867c2842f9132452.png"},{"id":69435799,"identity":"e78d96f7-ec15-49a7-bfac-97406dccccdf","added_by":"auto","created_at":"2024-11-20 10:32:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245653,"visible":true,"origin":"","legend":"\u003cp\u003eSmooth curve fitting for the association between CDAI and the age at menopause (a) and reproductive lifespan (b).\u003c/p\u003e","description":"","filename":"Figure2a1.png","url":"https://assets-eu.researchsquare.com/files/rs-5219594/v1/17eca5e196a4498db0c35652.png"},{"id":69435801,"identity":"d14d653a-0220-435c-838e-65fc0580b3b0","added_by":"auto","created_at":"2024-11-20 10:32:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":330807,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between CDAI and age at menopause as well as reproductive lifespan stratified by age, race, BMI, smoking status, marital status, ever use OC or female hormones.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5219594/v1/73fec2e9ff52ba8bdffdc435.png"},{"id":96105133,"identity":"d503f789-9199-4d33-b872-92a94aa9e5a3","added_by":"auto","created_at":"2025-11-17 16:09:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2892484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5219594/v1/27434277-eb2b-4e2a-b0ff-23cf0c7a610a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of composite dietary antioxidant index with age of menopause and reproductive lifespan: A cross-sectional study of NHANES Data, 1999-2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMenopause is an irreversible stage that women will eventually face in their lifetime, which is defined as the permanent cessation of menstruation due to the loss of ovarian follicular activity [1], marking the end of reproductive capability. Early menopause (before 45 years) is associated with an increased risk of overall mortality, cardiovascular diseases, neurological disorders, mental illnesses, osteoporosis, and other related health sequelae in women, and the risk intensifies as the age at menopause advances [2]. Reproductive lifespan is defined as the duration from the age at menarche to the age at menopause. Research indicates that a shorter reproductive lifespan is significantly associated with an increased risk of cardiovascular diseases [3], mood disorders [4], chronic kidney disease [5], and pulmonary dysfunction [6].The menopause age and reproductive lifespan can be influenced by various factors including race, geographical region, genetics and levels of oxidative stress [7, 8]. Oxidative stress is defined as the condition arising from an imbalance between free radicals and antioxidants in the body, which can lead to cellular damage, accelerated aging [9], and a decline in female reproductive capacity. A direct link between oxidative stress and ovarian function has been firmly established in substantial animal models [10]. Furthermore, clinical studies have also demonstrated that women with decrease ovarian reserve have increased ROS level and decreased antioxidant status [11], indicating the decisive impact of oxidative stress on ovarian function.\u003c/p\u003e \u003cp\u003eIn addition, adjusting the diet structure has been regarded as an effective way to influence reproductive lifespan [12]. More importantly, daily dietary intake of antioxidants can serve as an effective means to combat oxidative stress [13, 14]. Furthermore, the antioxidant capacity of food is now being evaluated more objectively. The Composite Dietary Antioxidant Index (CDAI) is an effective and robust nutritional tool to measure of dietary antioxidant characteristics, which is calculated based on several dietary antioxidants including selenium, zinc, carotenoid, Vitamin A, C and E [15]. This definition is first proposed by Wright in 2003, and has been progressively and extensively used as an indicator to measure an individual's antioxidant capacity. Now various disease has been found to be closely related to CDAI [16]. For instance, Liu et al. found that higher CDAI levels in postmenopausal women are associated with a lower risk of atherosclerotic cardiovascular disease [17]. Similarly, Wang et al. found that the CDAI was positively associated with a lower prevalence of chronic kidney disease in adults in the United States [18]. However, the relationship between CDAI and menopause age as well as reproductive lifespan remains unknown.\u003c/p\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a population-based cross-sectional survey designed to gather data about the health and nutrition of American households. This database uses a complex stratified, multistage probability cluster sampling design to represent the entire US population. To date, researchers have used the NHANES database to unveil the negative association between CDAI and a likelihood of hypertension [19], stroke [20], and depression [21]. However, the relationship between the CDAI, age at menopause, and reproductive lifespan has not been evaluated. To address this gap, we utilized data involving 4514 participants with natural post menopause from the NHANES database spanning 1999\u0026ndash;2018 to investigate these associations.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Survey description\u003c/h2\u003e \u003cp\u003eThe Centers for Disease Control and Prevention (CDC) conducts the NHANES to explore the US population's data on health, nutritional, and social status. The survey uses a stratified multistage probability sampling method to ensure that the included samples are representative [22, 23]. The study was approved by the National Center for Health Statistics Research Ethics Review Board, and written informed consent was obtained from each participant [24].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study Population\u003c/h2\u003e \u003cp\u003eFrom the participants of ten cycles from 1999 to 2018 (N\u0026thinsp;=\u0026thinsp;101,316), we selected women who reached menopause due to natural causes or hysterectomy (N\u0026thinsp;=\u0026thinsp;10,598). After excluding participants with hysterectomy or oophorectomy (N\u0026thinsp;=\u0026thinsp;3,907), 6691 individuals were identified. We further eliminated cases missing CDAI data and information on age at menarche or menopause (N\u0026thinsp;=\u0026thinsp;1,386), as well as individuals lacking other covariates (N\u0026thinsp;=\u0026thinsp;791). Finally, 4,514 participants were enrolled in our analyses. The screening process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Exposure and outcomes\u003c/h2\u003e \u003cp\u003eIn the NHANES dataset, each participant's food and nutrient intake was recorded using a 24-hour dietary recall interview. The initial dietary recall was conducted in person, followed by a telephone interview three to ten days later. The intake of antioxidants, micronutrients, and total energy was calculated using the Food and Nutrient Database for Dietary Studies provided by the United States Department of Agriculture [25]. From the questionnaire interview, we assessed the intake of dietary supplements over the past month, detailing dosage, frequency, and duration of consumption [26]. CDAI was calculated as the sum of the daily average intakes of zinc, selenium, carotenoids, vitamin A, vitamin C, and vitamin E. Each nutrient was first normalized by subtracting the mean and then dividing by its standard deviation (SD) [16, 17]. Reproductive lifespan was defined as the duration from the age at menarche to the age at menopause.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Covariates\u003c/h2\u003e \u003cp\u003eIn our study, we identified several potential factors that could influence the relationship between the CDAI and both age at menopause and reproductive lifespan, including race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Races), BMI, educational status (less than high school, high school or GED, above high school), poverty-income ratio (PIR), marital status (married/living with partner, never married, divorced/separated/widowed), alcohol consumption, smoking habits, and the use of oral contraceptives or female hormones. All these factors were incorporated into our multivariate adjustment model. BMI was calculated by dividing weight in kilograms by the square of height in meters. Following WHO recommendations, we used BMI cutoffs of 25 kg/m\u0026sup2; and 30 kg/m\u0026sup2; to classify overweight and obesity [27]. Alcohol consumption was classified as none, moderate (1 drink/day), heavy (2\u0026ndash;3 drinks/day) or binge (\u0026ge;\u0026thinsp;4 drinks/day) according to definitions from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) in the National Institute of Health. The smoking habits involved in the questionnaire data was ascribed as \u0026ldquo;never/former\u0026rdquo; (100 cigarettes in lifetime) or \u0026ldquo;now\u0026rdquo; (lifetime smoking). The detailed processes for measuring all variables in the study can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\nwww.cdc.gov/nchs/nhanes/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are represented by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation based on their distribution. The associations between different reproductive lifespans or groups due to age at menopause are analyzed using either the Chi-square test or the Kruskal-Wallis H test. Multivariate linear regression analysis is employed to investigate the relationships between CDAI and age at menarche, age at menopause, and reproductive age range. Model 1 remains unadjusted; Model 2 is adjusted for age and race; and Model 3 additionally adjusts for BMI, PIR, educational level, marital status, alcohol consumption, smoking status, and the use of oral contraceptives or female hormones. Subgroup analyses were conducted based on age, race, BMI, educational level, smoking habits, and use of oral contraceptives or female hormones to identify potential variations within these specific populations. Our study utilizes smooth curve fitting to thoroughly examine the potential nonlinear relationships between CDAI and both age at menopause and reproductive lifespan. Threshold effect analysis is utilized to examine the saturation effect of CDAI. Additionally, multivariate linear regression equations were applied to analyze the relationships between the components of CDAI and both age at menopause and reproductive lifespan.\u003c/p\u003e \u003cp\u003eAll analyses were performed using Empower software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\nwww.empowerstats.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; X\u0026amp;Y Solutions, Inc., Boston, MA, USA) and R version 3.4.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline characteristics of Participants according to menarche age, menopause age and reproduction lifespan tertile\u003c/h2\u003e \u003cp\u003eThe study included 4514 participants, with a mean CDAI of 0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4. Participants' ethnic backgrounds included 16.7% Mexican American, 10.5% other Hispanic, 45.9% non-Hispanic white, 18.5% non-Hispanic black, and 8.3% other races. The median age at menarche was 12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 years, and the median reproductive lifespan was 36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 years.\u003c/p\u003e \u003cp\u003eThen, participants were stratified into tertiles based on their age at menarche, age at menopause and reproductive lifespan, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Accordingly, age at menarche was categorized into three groups: earlier than 11 years, 12\u0026ndash;13 years, and later than 14 years. There were significant differences among different menarche age groups in the following indicators, including race (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), educational level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PIR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smoking habits (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), use of oral contraceptives (OC) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or female hormones (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), reproductive lifespan (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CDAI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). Based on age at menopause, T1 represented participants under 47 years old, T2 represented those aged 48 to 51 years, and T3 represented those aged 52 and above. Age at menopause was significantly associated with age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), race (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), educational level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PIR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), marital status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alcohol consumption (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smoking habits (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), and use of female hormones (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) across tertiles. In a separate column, participants were categorized based on reproductive lifespan, with T1 representing below 33 years, T2 representing 34 to 38 years, and T3 representing 39 years and above. Reproductive lifespan was significantly associated with race (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), educational level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PIR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), marital status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), alcohol consumption (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and use of OC or female hormones (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as the CDAI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). We found that CDAI showed an overall upward trend as both age at menopause and reproductive lifespan increased.\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\u003eThe baseline characteristics according to age of menarche, age of menopause and reproductive lifespan tertiles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\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\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eAge at menarche, years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eAge at menopause, years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c16\" namest=\"c13\"\u003e \u003cp\u003eReproductive lifespan, years\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1(\u0026le;\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT2(12\u0026thinsp;~\u0026thinsp;13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT3(\u0026ge;\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT1(\u0026le;\u0026thinsp;47)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT2(48\u0026thinsp;~\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eT3(\u0026ge;\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eT1(\u0026le;\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eT2(34\u0026thinsp;~\u0026thinsp;38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eT3(\u0026ge;\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e64.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e63.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e65.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e755 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e274 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e269 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e297 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e189 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e230 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e308 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e217 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e476 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e167 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e166 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e143 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e143 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e172 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e161 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\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\u003e2072 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e370 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1099 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e603 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e598 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e697 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e777 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e507 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e752 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e813 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e837 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e389 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e290 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e293 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e263 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e281 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e260 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e286 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e291 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\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\u003e374 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e145 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e133 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e106 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e147 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e121 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1216 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e580 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e476 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e385 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e419 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e412 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e352 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e461 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e403 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1394 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e643 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e513 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e446 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e487 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e461 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e399 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e531 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e464 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1904 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e958 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e518 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e592 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e662 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e650 (42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e495 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e673 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e736 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1381 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e603 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e565 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e505 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e483 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e393 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e468 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e515 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e398 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1102 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e543 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e369 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e382 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e358 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e362 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e324 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e410 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e368 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2031 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e423 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1035 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e573 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e536 (37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e727 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e768 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e454 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e740 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e837 (52.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome to poverty ratio (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1475 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e655 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e550 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e556 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e517 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e402 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e507 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e545 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e423 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5\u0026thinsp;~\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1827 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e908 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e605 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e567 (39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e623 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e637 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e494 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e669 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e664 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1212 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e618 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e352 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e300 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e428 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e484 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e245 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e451 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e516 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2308 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1137 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e768 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e668 (46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e820 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e820 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e586 (47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e860 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e862 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e77 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e78 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e76 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e65 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/separated/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1987 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e374 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e946 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e667 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e668 (46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e671 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e648 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e582 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e729 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e676 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1276 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e589 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e467 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e421 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e424 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e431 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e386 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e462 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e428 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1898 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e934 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e618 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e541 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e674 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e683 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e464 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e717 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e717 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1134 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e561 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e348 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e366 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e404 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e364 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e314 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e415 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e405 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e66 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e82 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e71 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e53 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking habits, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever/former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2727 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e467 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1280 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e980 (65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e817 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e948 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e962 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e754 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e977 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e996 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1787 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e901 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e527 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e606 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e620 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e561 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e492 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e688 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e607 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver OC use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2747 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e558 (67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1365 (62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e824 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e836 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e976 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e935 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e677 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1060 (63.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1010 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver Female Hormones use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1168 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e345 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e337 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e386 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e445 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e276 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e426 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e466 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e54.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e54.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e41.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Baseline characteristics according to the CDAI tertile\u003c/h2\u003e \u003cp\u003eTo better illustrate the impact of the CDAI, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e trisected the CDAI values into three distinct groups: T1 ranged from \u0026minus;\u0026thinsp;7.06 to -1.80, T2 from \u0026minus;\u0026thinsp;1.80 to 0.96, and T3 from 0.96 to 12.32. Among different CDAI groups, there were significant differences in race (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PIR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), educational level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), marital status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), drinking status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and use of female hormones (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, participants with higher CDAI tended to experience later ages at menopause and longer reproductive lifespans.\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 baseline characteristics according to the CDAI tertiles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertile 1(-7.06~-1.80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTertile 2(-1.80\u0026thinsp;~\u0026thinsp;0.96)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTertile 3(0.96\u0026thinsp;~\u0026thinsp;12.32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003e612 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e698 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e762 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003e115 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e357 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e486 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e462 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e484 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e448 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e668 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e665 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e571 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e402 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e549 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e662 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e820 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome to poverty ratio, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e607 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e407 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5\u0026thinsp;~\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e596 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e646 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e585 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e399 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e513 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e697 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e777 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e834 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/separated/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e718 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e660 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e609 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e490 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e397 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e389 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e658 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e661 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e368 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e376 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e390 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking habits, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever/former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e917 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e896 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e914 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e586 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e610 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e591 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver OC use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e895 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e922 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e930 (61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver Female Hormones use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e466 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Association between CDAI and the age at menopause as well as reproductive lifespan\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displayed the results of multivariate linear regression analyzing the association of CDAI with age at menarche, age at menopause and reproductive lifespan. In the unadjusted model, there is a significant positive correlation between CDAI and both age at menopause (β\u0026thinsp;=\u0026thinsp;0.12, 95%CI\u0026thinsp;=\u0026thinsp;0.07\u0026ndash;0.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and reproductive lifespan (β\u0026thinsp;=\u0026thinsp;0.10, 95% CI\u0026thinsp;=\u0026thinsp;0.06\u0026ndash;0.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). After adjusting for age and race in model 2, the significant positive effects of age at menopause (β\u0026thinsp;=\u0026thinsp;0.10, 95% CI\u0026thinsp;=\u0026thinsp;0.05\u0026ndash;0.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and reproductive lifespan (β\u0026thinsp;=\u0026thinsp;0.10, 95%CI\u0026thinsp;=\u0026thinsp;0.05\u0026ndash;0.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) persist. In model 3, adjustments were incorporated for additional variables including BMI, educational status, PIR, marital status, alcohol consumption, smoking habits, use of oral contraceptives or female hormones. The fully adjusted model revealed that increased CDAI was correlated with increased age at menopause (β\u0026thinsp;=\u0026thinsp;0.08, 95%CI\u0026thinsp;=\u0026thinsp;0.03\u0026ndash;0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and increased reproductive lifespan (β\u0026thinsp;=\u0026thinsp;0.06, 95%CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Additionally, after full adjustment for confounding factors, the analysis showed that T3, compared to T1, had a significant positive correlation with age at menopause (β\u0026thinsp;=\u0026thinsp;0.65, 95%CI\u0026thinsp;=\u0026thinsp;0.26\u0026ndash;1.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and reproductive lifespan (β\u0026thinsp;=\u0026thinsp;0.72, 95%CI\u0026thinsp;=\u0026thinsp;0.31\u0026ndash;1.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, in the analysis without categorization, we observed a significant negative correlation between CDAI and age at menarche in the fully adjusted model (β=-0.02, 95%CI=-0.03-0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). However, after categorization into tertiles, no significant correlation was found.\u003c/p\u003e \u003cp\u003eNext, multivariate regression was employed to evaluate the impact of CDAI on early (under 45 years) and late (over 55 years) menopause. According to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, across all models, an increased CDAI significantly reduced the risk of early menopause. In Model 1, without adjusting for variables, each standard deviation increase in CDAI was associated with a 5% reduction in the risk of early menopause (OR\u0026thinsp;=\u0026thinsp;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a 2% increase in the risk of late menopause, though this was not statistically significant (OR\u0026thinsp;=\u0026thinsp;1.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06). In Model 2, after adjusting for age and race, each standard deviation increase in CDAI was associated with a 5% reduction in the risk of early menopause (OR\u0026thinsp;=\u0026thinsp;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a 2% increase in the risk of late menopause, though this was not statistically significant (OR\u0026thinsp;=\u0026thinsp;1.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05). In Model 3, after adjusting for age, race, BMI, educational level, PIR, marital status, alcohol consumption, smoking habits, and use of oral contraceptives or female hormones, CDAI remained negatively associated with the risk of early menopause (OR\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and was negatively associated with the risk of late menopause (OR\u0026thinsp;=\u0026thinsp;1.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19). After dividing CDAI into tertiles, in Model 1, the risk of early menopause in T3 compared to T1 is reduced by 35% (OR\u0026thinsp;=\u0026thinsp;0.65, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); in Model 2, it was reduced by 34% (OR\u0026thinsp;=\u0026thinsp;0.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); and in Model 3, it was reduced by 26% (OR\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, in Model 1, the risk of late menopause in T3 compared to T1 increased by 36% (OR\u0026thinsp;=\u0026thinsp;1.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); in Model 2, it increased by 39% (OR\u0026thinsp;=\u0026thinsp;1.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); and in Model 3, it increased by 31% (OR\u0026thinsp;=\u0026thinsp;1.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eTo investigate whether the effects of the CDAI on menopause age and reproductive lifespan continue to persist with increasing levels, we used CDAI to create smooth curves for age at menopause and reproductive lifespan. Figure\u0026nbsp;2 revealed an inverted U-shaped curve between CDAI and age at menopause, and an L-shaped curve for CDAI and reproductive lifespan. Therefore, we further conducted a threshold effect analysis as presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which indicated that below an inflection point of 2.4, there was a positive correlation between CDAI and menopause age (β\u0026thinsp;=\u0026thinsp;0.16, 95% CI\u0026thinsp;=\u0026thinsp;0.09\u0026ndash;0.24, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) as well as reproductive lifespan (β\u0026thinsp;=\u0026thinsp;0.16, 95% CI\u0026thinsp;=\u0026thinsp;0.08\u0026ndash;0.24, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), with log-likelihood ratios of 0.001 and 0.016, respectively. Above this point, the influence of CDAI ceased to be statistically significant. This may indicate that a reasonable increase in the CDAI up to 2.4 could be a clinical strategy to mitigate the risk of early menopause and prolong the reproductive age interval.\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 association of CDAI with the age of menopause and reproductive lifespan.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eβ (95%CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of menarche\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02 (-0.03, -0.00) 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.03, -0.00) 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02 (-0.03, -0.00) 0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI group\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03 (-0.16, 0.10) 0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00 (-0.13, 0.13) 0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05 (-0.08, 0.17) 0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12 (-0.25, 0.01) 0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.11 (-0.24, 0.01) 0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07 (-0.19, 0.06) 0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02 (-0.04, 0.00) 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.04, 0.00) 0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01 (-0.03, 0.01) 0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of menopause\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12 (0.07, 0.17)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.05, 0.15)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08 (0.03, 0.13)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI group\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.29, 1.08)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 (0.30, 1.08)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.13, 0.91)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.58, 1.37)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.53, 1.32)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.26, 1.05)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15 (0.09, 0.21)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14 (0.08, 0.20)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10 (0.04, 0.16)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReproductive lifespan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10 (0.06, 0.15)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.05, 0.15)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06 (0.01, 0.11) 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI group\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.30, 1.13)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 (0.28, 1.10)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 (0.07, 0.88) 0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.68, 1.51)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.62, 1.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.31, 1.13)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17 (0.10, 0.23)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (0.09, 0.22)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11 (0.05, 0.17)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eModel 1: No covariates were adjusted.\u003c/p\u003e \u003cp\u003eModel 2: Adjusted for age and race.\u003c/p\u003e \u003cp\u003eModel 3: Adjusted for age, race, BMI, education level, PIR, marital status, alcohol consumption, smoking habits, use of oral contraceptives or female hormones.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between CDAI and age at menopause.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eOR (95%CI) \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eEarly menopause (\u0026lt;\u0026thinsp;45 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eLate menopause (\u0026ge;\u0026thinsp;55 years)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.95 (0.92, 0.97)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.93, 0.97)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 (0.94, 0.99) 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02 (1.00, 1.05) 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02 (1.00, 1.05) 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.02 (0.99, 1.04) 0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.73 (0.60, 0.88)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.60, 0.88)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77 (0.64, 0.94)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19 (0.97, 1.45) 0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21 (0.99, 1.49) 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.18 (0.96, 1.45) 0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.65 (0.54, 0.79)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.54, 0.81)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74 (0.60, 0.90)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.36 (1.12, 1.65)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39 (1.14, 1.69)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.31 (1.07, 1.61) 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.94 (0.91, 0.97)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.91, 0.97)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.92, 0.98)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05 (1.02, 1.08)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05 (1.02, 1.08)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.04 (1.01, 1.08) 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eModel 1: No covariates were adjusted.\u003c/p\u003e \u003cp\u003eModel 2: Adjusted for age and race.\u003c/p\u003e \u003cp\u003eModel 3: Adjusted for age, race, BMI, education level, PIR, marital status, alcohol consumption, smoking habits, use of oral contraceptives or female hormones.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of CDAI on age at menopause and reproductive lifespan by the two-piecewise linear regression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAdjusted β (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge at menopause\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16 (0.09, 0.24)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (0.08, 0.24) 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12 (-0.23, 0.00) 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06 (-0.18, 0.06) 0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Subgroup analysis\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a subgroup analysis was conducted to assess the robustness of the correlation between CDAI and age at menopause as well as reproductive lifespan. Age was categorized into two groups based on threshold of 60. The variables considered included age, race, BMI, educational level, smoking status, marital status, and use of oral contraceptives and female hormones. It was observed that in populations who had used oral contraceptives and female hormones, the correlations between CDAI and age at menopause as well as reproductive lifespan were more pronounced. Specifically, in the subgroup using oral contraceptives, each standard deviation increase in CDAI was associated with a delay in age at menopause by 0.11 years (β\u0026thinsp;=\u0026thinsp;0.11, 95%CI\u0026thinsp;=\u0026thinsp;0.04\u0026ndash;0.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and an increase in the reproductive age range by 0.16 years (β\u0026thinsp;=\u0026thinsp;0.16, 95%CI\u0026thinsp;=\u0026thinsp;0.04\u0026ndash;0.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, in the subgroup using female hormones, each standard deviation increasing in CDAI resulted in a delay in age at menopause by 0.12 years (β\u0026thinsp;=\u0026thinsp;0.12, 95% CI\u0026thinsp;=\u0026thinsp;0.06\u0026ndash;0.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and an increase in the reproductive lifespan by 0.17 years (β\u0026thinsp;=\u0026thinsp;0.17, 95% CI\u0026thinsp;=\u0026thinsp;0.07\u0026ndash;0.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In participants that did not use oral contraceptives or female hormones, no significant correlation was observed between CDAI and age at menopause as well as reproductive lifespan. Interaction tests within subgroups for age, race, BMI, educational level, smoking status, and marital status were all not significant, indicating that the relationships between CDAI and age at menopause as well as reproductive lifespan were consistent across these factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Association between components of CDAI and the age at menopause and reproductive lifespan\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, a multivariate linear regression equation was used to explore the relationship between the six components of CDAI and age at menopause as well as reproductive lifespan. In the fully adjusted Model 3, only Vitamin C and carotenoids showed a significant positive correlation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of six components of CDAI (mg) with age at menopause and reproductive lifespan.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAge at Menopause\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReproductive lifespan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001 (0, 0.001) 0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 0.001) 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0, 0.001) 0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001 (0, 0.001)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001 (0, 0.001) 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0(0, 0.001) 0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005 (0.002, 0.007)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004 (0.002, 0.007)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003 (0.001, 0.006)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004 (0.002, 0.006)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004 (0.002, 0.006)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003 (0.001, 0.005) 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.049 (0.013, 0.085) 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050 (0.014, 0.086)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021 (-0.014, 0.057) 0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.069 (0.031, 0.106)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0667 (0.029, 0.104)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.034 (-0.004, 0.071) 0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelenium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002 (-0.002, 0.006) 0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003 (-0.001, 0.007) 0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002(-0.002, 0.005) 0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003(0, 0.007) 0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004 (0.001, 0.008) 0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002(-0.001, 0.006) 0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026 (-0.007, 0.060) 0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028 (-0.006, 0.062) 0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018 (-0.015, 0.052) 0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031 (-0.004, 0.066) 0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029 (-0.007, 0.064) 0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.017(-0.018, 0.052) 0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotenoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033 (0.017, 0.049)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029 (0.013, 0.046)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022 (0.005, 0.038)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039 (0.022, 0.056)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036 (0.019, 0.053)\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.028 (0.011, 0.045) 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eModel 1: No covariates were adjusted.\u003c/p\u003e \u003cp\u003eModel 2: Adjusted for age and race.\u003c/p\u003e \u003cp\u003eModel 3: Adjusted for age, race, BMI, education level, PIR, marital status, alcohol consumption, smoking habits, use of oral contraceptives or female hormones.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study found that an increase in the CDAI was positively correlated with an increase in the age at menopause and reproductive lifespan. Higher CDAI significantly reduced the risk of early menopause (under 45 years) and there was a threshold effect on menopausal age and reproductive interval. Subgroup analysis indicated that the use of oral contraceptives or female hormones impacted the relationship between CDAI and reproductive metrics. Further, Vitamin C and carotenoids, key components of CDAI, appear to extend reproductive lifespan significantly. This study is the first to explore these relationships in naturally menopausal women, suggesting that dietary antioxidants may delay menopause onset and extend reproductive lifespan, with significant implications for improving women's reproductive health.\u003c/p\u003e \u003cp\u003eCurrently, the use of dietary antioxidant properties to regulate ovarian function has been a hot topic of research today [28]. For instance, dietary flaxseed, due to its antioxidant components, has been proven to potentially improve menopausal symptoms such as hot flashes and sweating [29, 30]. Additionally, a cross-sectional study conducted by Alina et al. using NHANES data from 2001\u0026ndash;2018 also found that an increased intake of Vitamin D, a promoter of gene expression for antioxidant effects by binding to Vitamin D response elements (VDRE), could reduce the risk of early menopause and shortened reproductive lifespan [31]. In addition, calculating based on the intake of various antioxidant micronutrients (selenium, zinc, carotenoids, and vitamins A, C, E), CDAI is frequently employed to assess dietary antioxidant levels and has been linked to various health outcomes. Liu et al. reported that postmenopausal women with high CDAI scores often exhibit a lower incidence of atherosclerotic cardiovascular disease [17]. Moreover, research by He et al. has shown a significant negative correlation between CDAI and biological phenotypic age [32], which aligns with our findings that a higher CDAI is associated with a delayed onset of menopause in women.\u003c/p\u003e \u003cp\u003eTo date, no study has considered the impact of CDAI on reproductive lifespan. Our results revealed that CDAI, especially the components of Vitamin C and carotenoids, contribute to menopause delay. Vitamin C is a hydrophilic compound that acts as a cofactor for the hydroxylation of proline and lysine during collagen formation.\u003c/p\u003e \u003cp\u003eAvailable results indicate its multi-directional cellular effect, especially its role in scavenging free radicals. A randomized, triple-blind placebo-controlled clinical trial revealed that Vitamin C combined with Vitamin E could significantly reduce the level of MDA and ROS in patients with endometriosis and improved dyspareunia and severity of pelvic pain [33]. In female mice, a diet rich in vitamin C can prevent age-related declines in both the quantity and quality of oocytes [34].Our findings may further advance clinical studies exploring the antioxidant stress role of vitamin C in ovarian hypofunction. In addition, Carotenoids, another compound in CDAI are 40-carbon isoprenoid molecules that produce the red, yellow, and orange pigmentation found in nature. Various plants, microalgae, bacteria, and fungi are natural sources of carotenoids. Among the 50 kinds of carotenes present in nature, the best known are α-carotene and β-carotene. These natural antioxidants could aid in quenching free radicals produced by complex physiological reactions and, consequently, protect the tissue from oxidative stress, apoptosis, mitochondrial dysfunction, and inflammation [35]. Clinical studies suggest that carotenoid consumption is associated with lower risk of cardiovascular disease, cancer, and eye disease. This substantial evidence supports its protective effect on female reproductive function due to oxidative stress damage. However, clinical evidence of a direct link between CDAI composition and reproductive lifespan is relatively scarce. Our findings may suggest that for people with risk factors for early menopause, an adequate intake of a diet with a high CDAI, especially within the threshold range we have provided, may be beneficial in prolonging reproductive life and reducing other health problems. Of course, prospective large cohort studies are needed to further verify the scientific nature of this hypothesis.\u003c/p\u003e \u003cp\u003eAlthough the mechanism by which antioxidant foods prolong reproductive life is still being explored, it may be partly related to improving decreased ovarian function caused by oxidative stress. Oxidative stress occurs when the body\u0026rsquo;s antioxidant system is depleted owing to an excess of reactive oxygen species (ROS). Exuberant ROS in the ovary can dysregulate the dynamics of the ovarian reserve and/or impair the survival and competence of the oocytes [36], thus potentially leading to an earlier onset of menopause and shorten the reproductive lifespan [8]. Besides, oxidative stress can mediate the onset of inflammation, which could facilitate apoptosis of granulosa cells and oocytes, resulting in the early loss of the ovaries' role in maintaining menstruation [37\u0026ndash;39]. On the contrary, antioxidant enzymes serve to neutralize ROS production, thereby protecting ovary function. For instance, quercetin, a dietary antioxidant, can enhance ARE binding activity and Nrf-2-mediated transcriptional activity, thus inducing the expression of antioxidant enzymes [40], thereby preventing oxidative stress by inhibiting the NF-κB pathway [41]. Besides, oocytes cultured in media supplemented with quercetin exhibit improved capabilities of maturation and early embryonic development [42]. Similarly, dietary trace element zinc, which act as a transcriptional regulator of Nrf2, can upregulate downstream antioxidants through nuclear translocation, thereby responding to ROS-induced damage [43]. Zinc is also a cofactor for one of the most important antioxidant enzymes, Zn-SOD/SOD1, and play a crucial role in female reproduction by scavenging ROS [44, 45]. Collectively, dietary antioxidants can improve ovarian function by mitigating oxidative stress through various mechanisms.\u003c/p\u003e \u003cp\u003eIn addition, our research introduces a novel observation that the use of oral contraceptives or female hormones may influence the relationship between the CDAI and reproductive age. The primary components of oral contraceptives and female hormones include estrogens and progestogens. Existing research has demonstrated their antioxidant properties [46]. Estrogen replacement therapy, for instance, has been confirmed to induce antioxidant and protect various tissues from oxidative stress, including brain, bone and myocardial tissue [47, 48]. Unlike estrogens, progesterone does not possess the characteristic chemical structure of antioxidants, but high levels of progesterone appear to reduce oxidative damage [49]. Adler et al. confirmed through real-time quantitative PCR that in vitro progesterone treatment significantly increased myeloperoxidase expression in isolated human neutrophils, while significantly reducing NADPH oxidase gene expression [50]. Progesterone exerts protective effects in various diseases by upregulating γ-aminobutyric acid inhibition, reducing lipid peroxidation and oxidative stress, decreasing the release of inflammatory cytokines, and minimizing apoptosis-induced cell death, thereby promoting cell survival and proliferation [49]. These findings suggested that hormone supplementation may synergize with antioxidant diets in protecting ovarian function. Notably, hormone replacement therapy is subject to strict indications and contra-indications, and how to combine it with high-CDAI foods to properly avoid premature menopause needs further research and discussion.\u003c/p\u003e \u003cp\u003eTaken together, our study is the first to explore the relationship between CDAI and both the age at menopause and reproductive lifespan, highlighting potential dietary strategies to improve women\u0026rsquo;s reproductive health. However, its cross-sectional nature introduces several limitations. Firstly, the simultaneous assessment of exposure and outcome does not confirm a temporal relationship, underscoring the need for future longitudinal studies to establish causality. Secondly, selection bias may be present as the study only includes participants who meet specific criteria. Lastly, not all variables related to menopause and reproductive age span were accounted for, potentially overlooking unknown confounding factors.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, our study indicates that within a certain range, higher CDAI levels are associated with reduced risk of early menopause, delayed menopause, and prolonged reproductive lifespan. These findings highlight the role of dietary guidance in improving reproductive health. However, the inherent limitations of cross-sectional studies and the complex influences on menopause necessitate further in-depth research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by Major project of Hangzhou Health Commission (NO. Z20230103)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Q.Z. and X.Z.; methodology, X.Z.; software, X.Z.; validation, Z.H., N.S. and F.L.; formal analysis, X.Z.; investigation, X.Z.; resources, Z.H.; data curation, Z.H.; writing\u0026mdash;original draft preparation, Z.H.; writing\u0026mdash;review and editing, X.Z.; visualization, Z.H.; supervision, X.Z.; project administration, X.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are available in the NHANES repository at the following links: www.cdc.gov/nchs/nhanes/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cem\u003eResearch on the menopause in the 1990s. Report of a WHO Scientific Group.\u003c/em\u003e World Health Organ Tech Rep Ser, 1996. \u003cstrong\u003e866\u003c/strong\u003e: p. 1-107.\u003c/li\u003e\n \u003cli\u003eShuster, L.T., et al., \u003cem\u003ePremature menopause or early menopause: long-term health consequences.\u003c/em\u003e Maturitas, 2010. \u003cstrong\u003e65\u003c/strong\u003e(2): p. 161-6.\u003c/li\u003e\n \u003cli\u003eChen, L., et al., \u003cem\u003eAge at Menarche and Menopause, Reproductive Lifespan, and Risk of Cardiovascular Events Among Chinese Postmenopausal Women: Results From a Large National Representative Cohort Study.\u003c/em\u003e Front Cardiovasc Med, 2022. \u003cstrong\u003e9\u003c/strong\u003e: p. 870360.\u003c/li\u003e\n \u003cli\u003eWu, Q., et al., \u003cem\u003eAssociation of reproductive lifespan and age at menopause with depression: Data from NHANES 2005\u0026ndash;2018.\u003c/em\u003e J Affect Disord, 2024.\u003c/li\u003e\n \u003cli\u003eKang, S.C., et al., \u003cem\u003eAssociation of Reproductive Lifespan Duration and Chronic Kidney Disease in Postmenopausal Women.\u003c/em\u003e Mayo Clin Proc, 2020. \u003cstrong\u003e95\u003c/strong\u003e(12): p. 2621\u0026ndash;2632.\u003c/li\u003e\n \u003cli\u003eLim, J.H., et al., \u003cem\u003eAssociation between reproductive lifespan and lung function among postmenopausal women.\u003c/em\u003e J Thorac Dis, 2020. \u003cstrong\u003e12\u003c/strong\u003e(8): p. 4243\u0026ndash;4252.\u003c/li\u003e\n \u003cli\u003eHarlow, S.D., et al., \u003cem\u003eExecutive summary of the Stages of Reproductive Aging Workshop\u0026thinsp;+\u0026thinsp;10: addressing the unfinished agenda of staging reproductive aging.\u003c/em\u003e J Clin Endocrinol Metab, 2012. \u003cstrong\u003e97\u003c/strong\u003e(4): p. 1159-68.\u003c/li\u003e\n \u003cli\u003eAgarwal, A., S. Gupta, and R.K. Sharma, \u003cem\u003eRole of oxidative stress in female reproduction.\u003c/em\u003e Reprod Biol Endocrinol, 2005. \u003cstrong\u003e3\u003c/strong\u003e: p. 28.\u003c/li\u003e\n \u003cli\u003eMonti, D.M., et al., \u003cem\u003eRole of Antioxidants in the Protection from Aging-Related Diseases.\u003c/em\u003e Oxid Med Cell Longev, 2019. \u003cstrong\u003e2019\u003c/strong\u003e: p. 7450693.\u003c/li\u003e\n \u003cli\u003eHe, X., et al., \u003cem\u003eSecoisolariciresinol Diglucoside Improves Ovarian Reserve in Aging Mouse by Inhibiting Oxidative Stress.\u003c/em\u003e Front Mol Biosci, 2021. \u003cstrong\u003e8\u003c/strong\u003e: p. 806412.\u003c/li\u003e\n \u003cli\u003eSmits, M.A.J., et al., \u003cem\u003eHuman ovarian aging is characterized by oxidative damage and mitochondrial dysfunction.\u003c/em\u003e Hum Reprod, 2023. \u003cstrong\u003e38\u003c/strong\u003e(11): p. 2208\u0026ndash;2220.\u003c/li\u003e\n \u003cli\u003eSilva, T.R., et al., \u003cem\u003eNutrition in Menopausal Women: A Narrative Review.\u003c/em\u003e Nutrients, 2021. \u003cstrong\u003e13\u003c/strong\u003e(7).\u003c/li\u003e\n \u003cli\u003eLindsay, D.G., \u003cem\u003eDiet and ageing: the possible relation to reactive oxygen species.\u003c/em\u003e J Nutr Health Aging, 1999. \u003cstrong\u003e3\u003c/strong\u003e(2): p. 84\u0026ndash;91.\u003c/li\u003e\n \u003cli\u003eRondanelli, M., et al., \u003cem\u003eFood pyramid for subjects with chronic pain: foods and dietary constituents as anti-inflammatory and antioxidant agents.\u003c/em\u003e Nutr Res Rev, 2018. \u003cstrong\u003e31\u003c/strong\u003e(1): p. 131\u0026ndash;151.\u003c/li\u003e\n \u003cli\u003eYu, Y.C., et al., \u003cem\u003eComposite dietary antioxidant index and the risk of colorectal cancer: Findings from the Singapore Chinese Health Study.\u003c/em\u003e Int J Cancer, 2022. \u003cstrong\u003e150\u003c/strong\u003e(10): p. 1599\u0026ndash;1608.\u003c/li\u003e\n \u003cli\u003eWright, M.E., et al., \u003cem\u003eDevelopment of a comprehensive dietary antioxidant index and application to lung cancer risk in a cohort of male smokers.\u003c/em\u003e Am J Epidemiol, 2004. \u003cstrong\u003e160\u003c/strong\u003e(1): p. 68\u0026ndash;76.\u003c/li\u003e\n \u003cli\u003eLiu, C., et al., \u003cem\u003eAssociation between the Composite Dietary Antioxidant Index and Atherosclerotic Cardiovascular Disease in Postmenopausal Women: A Cross-Sectional Study of NHANES Data, 2013\u0026ndash;2018.\u003c/em\u003e Antioxidants (Basel), 2023. \u003cstrong\u003e12\u003c/strong\u003e(9).\u003c/li\u003e\n \u003cli\u003eWang, M., et al., \u003cem\u003eAssociation between the composite dietary antioxidant index and chronic kidney disease: evidence from NHANES 2011\u0026ndash;2018.\u003c/em\u003e Food Funct, 2023. \u003cstrong\u003e14\u003c/strong\u003e(20): p. 9279\u0026ndash;9286.\u003c/li\u003e\n \u003cli\u003eWu, M., et al., \u003cem\u003eAssociation between composite dietary antioxidant index and hypertension: insights from NHANES.\u003c/em\u003e Clin Exp Hypertens, 2023. \u003cstrong\u003e45\u003c/strong\u003e(1): p. 2233712.\u003c/li\u003e\n \u003cli\u003eTeng, T.Q., et al., \u003cem\u003eAssociation of composite dietary antioxidant index with prevalence of stroke: insights from NHANES 1999\u0026ndash;2018.\u003c/em\u003e Front Immunol, 2024. \u003cstrong\u003e15\u003c/strong\u003e: p. 1306059.\u003c/li\u003e\n \u003cli\u003eZhao, L., et al., \u003cem\u003eRelationship between composite dietary antioxidant index and depression among overweight and obese adults.\u003c/em\u003e J Affect Disord, 2023. \u003cstrong\u003e341\u003c/strong\u003e: p. 358\u0026ndash;365.\u003c/li\u003e\n \u003cli\u003eCurtin, L.R., et al., \u003cem\u003eNational Health and Nutrition Examination Survey: sample design, 2007\u0026ndash;2010.\u003c/em\u003e Vital Health Stat 2, 2013(160): p. 1\u0026ndash;23.\u003c/li\u003e\n \u003cli\u003eJohnson, C.L., et al., \u003cem\u003eNational health and nutrition examination survey: sample design, 2011\u0026ndash;2014.\u003c/em\u003e Vital Health Stat 2, 2014(162): p. 1\u0026ndash;33.\u003c/li\u003e\n \u003cli\u003eSkrivankova, V.W., et al., \u003cem\u003eStrengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement.\u003c/em\u003e Jama, 2021. \u003cstrong\u003e326\u003c/strong\u003e(16): p. 1614\u0026ndash;1621.\u003c/li\u003e\n \u003cli\u003eAhuja, J.K., et al., \u003cem\u003eUSDA food and nutrient databases provide the infrastructure for food and nutrition research, policy, and practice.\u003c/em\u003e J Nutr, 2013. \u003cstrong\u003e143\u003c/strong\u003e(2): p. 241s-9s.\u003c/li\u003e\n \u003cli\u003eKantor, E.D., et al., \u003cem\u003eTrends in Dietary Supplement Use Among US Adults From 1999\u0026ndash;2012.\u003c/em\u003e Jama, 2016. \u003cstrong\u003e316\u003c/strong\u003e(14): p. 1464\u0026ndash;1474.\u003c/li\u003e\n \u003cli\u003eKurisu, S., et al., \u003cem\u003eFrontal QRS-T angle and World Health Organization classification for body mass index.\u003c/em\u003e Int J Cardiol, 2018. \u003cstrong\u003e272\u003c/strong\u003e: p. 185\u0026ndash;188.\u003c/li\u003e\n \u003cli\u003eLiu, Z., et al., \u003cem\u003eRole of ROS and Nutritional Antioxidants in Human Diseases.\u003c/em\u003e Front Physiol, 2018. \u003cstrong\u003e9\u003c/strong\u003e: p. 477.\u003c/li\u003e\n \u003cli\u003eCetisli, N.E., A. Saruhan, and B. Kivcak, \u003cem\u003eThe effects of flaxseed on menopausal symptoms and quality of life.\u003c/em\u003e Holist Nurs Pract, 2015. \u003cstrong\u003e29\u003c/strong\u003e(3): p. 151-7.\u003c/li\u003e\n \u003cli\u003eLandete, J.M., et al., \u003cem\u003eBioactivation of Phytoestrogens: Intestinal Bacteria and Health.\u003c/em\u003e Crit Rev Food Sci Nutr, 2016. \u003cstrong\u003e56\u003c/strong\u003e(11): p. 1826-43.\u003c/li\u003e\n \u003cli\u003eAlinia, T., et al., \u003cem\u003eRelationship between vitamin D levels and age of menopause and reproductive lifespan: Analysis based on the National health and nutrition examination survey (NHANES) 2001\u0026ndash;2018.\u003c/em\u003e Eur J Obstet Gynecol Reprod Biol, 2023. \u003cstrong\u003e289\u003c/strong\u003e: p. 183\u0026ndash;189.\u003c/li\u003e\n \u003cli\u003eHe, H., et al., \u003cem\u003eComposite dietary antioxidant index associated with delayed biological aging: a population-based study.\u003c/em\u003e Aging (Albany NY), 2024. \u003cstrong\u003e16\u003c/strong\u003e(1): p. 15\u0026ndash;27.\u003c/li\u003e\n \u003cli\u003eAmini, L., et al., \u003cem\u003eThe Effect of Combined Vitamin C and Vitamin E Supplementation on Oxidative Stress Markers in Women with Endometriosis: A Randomized, Triple-Blind Placebo-Controlled Clinical Trial.\u003c/em\u003e Pain Res Manag, 2021. \u003cstrong\u003e2021\u003c/strong\u003e: p. 5529741.\u003c/li\u003e\n \u003cli\u003eTar\u0026iacute;n, J.J., S. P\u0026eacute;rez-Albal\u0026aacute;, and A. Cano, \u003cem\u003eOral antioxidants counteract the negative effects of female aging on oocyte quantity and quality in the mouse.\u003c/em\u003e Mol Reprod Dev, 2002. \u003cstrong\u003e61\u003c/strong\u003e(3): p. 385\u0026thinsp;\u0026minus;\u0026thinsp;97.\u003c/li\u003e\n \u003cli\u003eJohra, F.T., et al., \u003cem\u003eA Mechanistic Review of \u0026beta;-Carotene, Lutein, and Zeaxanthin in Eye Health and Disease.\u003c/em\u003e Antioxidants (Basel), 2020. \u003cstrong\u003e9\u003c/strong\u003e(11).\u003c/li\u003e\n \u003cli\u003eDri, M., F.G. Klinger, and M. De Felici, \u003cem\u003eThe ovarian reserve as target of insulin/IGF and ROS in metabolic disorder-dependent ovarian dysfunctions.\u003c/em\u003e Reprod Fertil, 2021. \u003cstrong\u003e2\u003c/strong\u003e(3): p. R103-r112.\u003c/li\u003e\n \u003cli\u003eReuter, S., et al., \u003cem\u003eOxidative stress, inflammation, and cancer: how are they linked?\u003c/em\u003e Free Radic Biol Med, 2010. \u003cstrong\u003e49\u003c/strong\u003e(11): p. 1603-16.\u003c/li\u003e\n \u003cli\u003eArulselvan, P., et al., \u003cem\u003eRole of Antioxidants and Natural Products in Inflammation.\u003c/em\u003e Oxid Med Cell Longev, 2016. \u003cstrong\u003e2016\u003c/strong\u003e: p. 5276130.\u003c/li\u003e\n \u003cli\u003eLiguori, I., et al., \u003cem\u003eOxidative stress, aging, and diseases.\u003c/em\u003e Clin Interv Aging, 2018. \u003cstrong\u003e13\u003c/strong\u003e: p. 757\u0026ndash;772.\u003c/li\u003e\n \u003cli\u003eSun, L., et al., \u003cem\u003eQuercetin Protects Against Lipopolysaccharide-Induced Intestinal Oxidative Stress in Broiler Chickens through Activation of Nrf2 Pathway.\u003c/em\u003e Molecules, 2020. \u003cstrong\u003e25\u003c/strong\u003e(5).\u003c/li\u003e\n \u003cli\u003eFerraz, C.R., et al., \u003cem\u003eTherapeutic Potential of Flavonoids in Pain and Inflammation: Mechanisms of Action, Pre-Clinical and Clinical Data, and Pharmaceutical Development.\u003c/em\u003e Molecules, 2020. \u003cstrong\u003e25\u003c/strong\u003e(3).\u003c/li\u003e\n \u003cli\u003eVa\u0026scaron;kov\u0026aacute;, J., et al., \u003cem\u003eThe Importance of Natural Antioxidants in Female Reproduction.\u003c/em\u003e Antioxidants (Basel), 2023. \u003cstrong\u003e12\u003c/strong\u003e(4).\u003c/li\u003e\n \u003cli\u003eLu, X., et al., \u003cem\u003eZinc is essential for the transcription function of the PGC-1\u0026alpha;/Nrf2 signaling pathway in human primary endometrial stromal cells.\u003c/em\u003e Am J Physiol Cell Physiol, 2020. \u003cstrong\u003e318\u003c/strong\u003e(3): p. C640-c648.\u003c/li\u003e\n \u003cli\u003eLewandowski, Ł., M. Kepinska, and H. Milnerowicz, \u003cem\u003eThe copper-zinc superoxide dismutase activity in selected diseases.\u003c/em\u003e Eur J Clin Invest, 2019. \u003cstrong\u003e49\u003c/strong\u003e(1): p. e13036.\u003c/li\u003e\n \u003cli\u003eWang, S., et al., \u003cem\u003eThe Role of Antioxidant Enzymes in the Ovaries.\u003c/em\u003e Oxid Med Cell Longev, 2017. \u003cstrong\u003e2017\u003c/strong\u003e: p. 4371714.\u003c/li\u003e\n \u003cli\u003eChainy, G.B.N. and D.K. Sahoo, \u003cem\u003eHormones and oxidative stress: an overview.\u003c/em\u003e Free Radic Res, 2020. \u003cstrong\u003e54\u003c/strong\u003e(1): p. 1\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eShafin, N., et al., \u003cem\u003eAssociation of oxidative stress and memory performance in postmenopausal women receiving estrogen-progestin therapy.\u003c/em\u003e Menopause, 2013. \u003cstrong\u003e20\u003c/strong\u003e(6): p. 661-6.\u003c/li\u003e\n \u003cli\u003eMohamad, N.V., S. Ima-Nirwana, and K.Y. Chin, \u003cem\u003eAre Oxidative Stress and Inflammation Mediators of Bone Loss Due to Estrogen Deficiency? A Review of Current Evidence.\u003c/em\u003e Endocr Metab Immune Disord Drug Targets, 2020. \u003cstrong\u003e20\u003c/strong\u003e(9): p. 1478\u0026ndash;1487.\u003c/li\u003e\n \u003cli\u003eHern\u0026aacute;ndez-Rabaza, V., R. L\u0026oacute;pez-Pedrajas, and I. Almansa, \u003cem\u003eProgesterone, Lipoic Acid, and Sulforaphane as Promising Antioxidants for Retinal Diseases: A Review.\u003c/em\u003e Antioxidants (Basel), 2019. \u003cstrong\u003e8\u003c/strong\u003e(3).\u003c/li\u003e\n \u003cli\u003eAdler, I., et al., \u003cem\u003eThe effect of certain steroid hormones on the expression of genes involved in the metabolism of free radicals.\u003c/em\u003e Gynecol Endocrinol, 2012. \u003cstrong\u003e28\u003c/strong\u003e(11): p. 912-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"composite dietary antioxidant index, menopause, reproductive lifespan, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-5219594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5219594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eAccumulated evidence has shown that the antioxidant diet exhibits protective effects on women\u0026rsquo;s reproductive health. The Composite Dietary Antioxidant Index (CDAI) serves as a crucial indicator for assessing antioxidant-rich diets. However, the relationship between CDAI and menopause age as well as reproductive lifespan remains undefined, thus motivating this cross-sectional study to investigate these connections.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study analyzed post-menopausal women participating in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. Information on age at menopause and reproductive lifespan was derived from questionnaire data. The CDAI was calculated based on the intake of selenium, zinc, carotenoid, Vitamin A, C and E. Multiple linear regression, smooth curve fitting, threshold effect analysis, and subgroup analysis were used to investigate the association between the CDAI and age at menopause as well as reproductive lifespan.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 4514 patients were enrolled in the study. After adjusting for confounding factors, the results revealed that individuals with a higher CDAI typically had a later age at menopause (β\u0026thinsp;=\u0026thinsp;0.08, 95% CI\u0026thinsp;=\u0026thinsp;0.03\u0026ndash;0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and longer reproductive lifespan (β\u0026thinsp;=\u0026thinsp;0.06, 95% CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). And each standard deviation increase in CDAI was associated with a 4% decrease in early menopause risk (OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI\u0026thinsp;=\u0026thinsp;0.94\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Besides, the use of oral contraceptives and female hormones could impact these relationships.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur research highlighted a positive non-linear association between CDAI and age at menopause, as well as reproductive lifespan.\u003c/p\u003e","manuscriptTitle":"Association of composite dietary antioxidant index with age of menopause and reproductive lifespan: A cross-sectional study of NHANES Data, 1999-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 10:32:07","doi":"10.21203/rs.3.rs-5219594/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-03T07:21:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-27T02:57:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-25T19:02:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52345104051033032043536469189779902042","date":"2025-02-14T12:48:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158777808958077583459022983988726586900","date":"2025-02-14T11:43:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-12T17:18:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265005381667209614358870370607537205355","date":"2025-02-12T17:12:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219795245806145653685193343132298784079","date":"2024-12-05T13:29:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74653469929993160974975061319945151015","date":"2024-12-03T13:02:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-26T00:34:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-17T05:44:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-30T17:16:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-29T09:01:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-07T16:08:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e8ef354-ce81-496a-89d8-ae16b25795aa","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39901776,"name":"Health sciences/Health care/Nutrition"},{"id":39901777,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-17T16:03:35+00:00","versionOfRecord":{"articleIdentity":"rs-5219594","link":"https://doi.org/10.1038/s41598-025-23666-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-14 15:58:28","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2024-11-20 10:32:07","video":"","vorDoi":"10.1038/s41598-025-23666-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-23666-9","workflowStages":[]},"version":"v1","identity":"rs-5219594","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5219594","identity":"rs-5219594","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}