{"paper_id":"38c82749-4850-4a5e-be2a-5550541f4475","body_text":"Association between cardiometabolic index and female infertility: a cross-sectional analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between cardiometabolic index and female infertility: a cross-sectional analysis Yiran Zhao, Weihui Shi, Yang Liu, Ningxin Qin, Hefeng Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4865845/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2024 Read the published version in Reproductive Biology and Endocrinology → Version 1 posted 7 You are reading this latest preprint version Abstract Background Adverse lipid profile leads to female infertility. The correlation between the cardiometabolic index (CMI) and female infertility merits further investigation. Methods The data for this study were acquired from the 2013–2020 National Health and Nutrition Examination Survey (NHANES), with 2333 women enrolled. The cardiometabolic index (CMI) of each participant was calculated as the ratio of triglycerides and high-density lipoprotein cholesterol multiplied by waist-to-height ratio. Weighted multivariate logistic regression models were used to assess the independent association between the log-transformed CMI and infertility. Subgroup analyses were carried out to assess the reliability of the findings. Interaction tests were employed to find out if variables affected infertility by interacting with log CMI. Results A total of 2333 participants aged 18–45 years were enrolled, 274 of whom were infertile. Log CMI of the infertility group was significantly higher than that of the non-infertility group ( P < 0.001). After adjustment for potential confounders, women with higher CMI were more prevalent of infertility (OR = 2.411, 95% CI: 1.416–4.112), and this association was still consistent in subgroups aged under 35 years ( P < 0.001). Furthermore, restricted cubic spline analysis showed a positive non-linear relationship between log CMI and infertility. Conclusions Cardiometabolic index levels were positively associated with increased risk of infertility in American females. Our study demonstrated the predictive capacity of CMI for female infertility; nevertheless, additional clarification is required to establish the direct relationship between CMI and female infertility, which will serve as a foundation for future efforts to prevent female infertility. Female infertility cardiometabolic index obesity NHANES Figures Figure 1 Figure 2 Introduction Infertility is characterized by the inability to conceive after 12 months or more of consistent, unprotected sexual intercourse [ 1 ], which affects a significant portion of the population in their lifetime. Between 12.6% and 17.5% of couples in reproductive age have suffered from infertility globally [ 2 ]. As a worldwide public health concern, infertility profoundly affects individuals' mental and emotional well-being, often leading to severe psychological distress, social stigma, and considerable financial burdens. Infertility can be caused by a number of various factors, such as age, diet, psychological stress, and environmental pollution, among which, obesity has received a considerable attention due to its alarming rise worldwide. A great deal of research has been conducted on the negative association between obesity and reproductive outcomes [ 3 – 5 ]. Typical dyslipidemia in obesity is characterized by elevated triglycerides (TG), free fatty acids (FFA), decreased high-density lipoprotein cholesterol (HDL-C) with impaired HDL function, and slightly elevated low-density lipoprotein cholesterol (LDL-C) with increased small dense LDL [ 6 – 9 ]. Evidence from human studies indicated that irregular female lipid profiles such as elevated total cholesterol, triglycerides, LDL-C and reduced HDL-C could lead to diminished fecundability, poorer oocyte quality and ovarian function [ 10 , 11 ]. The cardiometabolic index (CMI), calculated by multiplying TG/HDL-C ratio by waist-to-height ratio (WHtR), was initially introduced by Wakabayashi et al. in 2015 [ 12 ]. It was devised as a novel diagnostic tool for the identification of diabetes, and can be used to assess the distribution and functional impairments of visceral adipose tissue. A growing number of studies have established a robust correlation between CMI and cardiovascular disease, renal dysfunction, acute pancreatitis as well as adverse metabolic profiles, and it might be a potentially valuable predictor of metabolism-linked disorders [ 13 – 18 ]. Considering the promising diagnostic potential of the CMI for various metabolic disorders, and given the positive association between irregular lipid profiles and female infertility, the potential role of CMI in diagnosing female infertility merits further investigation. Hence, this study aimed to systematically examine the relationship between CMI and female infertility, as well as to assess the predictive efficacy of CMI for female infertility. Materials and methods Data source NHANES is a nationwide representative cross-sectional survey to assess and evaluate Americans' health and nutritional status. Administered by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC), NHANES is based on questionnaires, physical examinations, household interviews, and laboratory testing. Multistage stratified probability sampling is used in the study to ensure a highly representative sample. All participants have provided written informed consent in accordance with the NHANES protocols, which have been approved by the NCHS Research Ethics Review Board. Data used in this study is publicly available at https://www.cdc.gov/nchs/nhanes . Study population The present study incorporated NHANES data from 2013–2014, 2015–2016, 2017–2018 and 2019–2020. Women aged 18–45 years were enrolled (n = 6502); after the exclusion of missing data of CMI (n = 3191) or diagnosis of infertility (n = 978), 2333 participants were finally included in the analysis (Fig. 1 ). Assessment of infertility The dependent variable of infertility was computed using the responses to the Reproductive Health Questionnaire (variable name in the questionnaire: RHQ074). Participants were assumed to be infertile if they responded positively to the survey question, \"Have you ever attempted to become pregnant for at least a year without becoming pregnant?\" [ 19 ]. Assessment of cardiometabolic index The TG/HDL-C ratio was calculated by dividing the serum concentration of TG (mg/dL) by HDL-C (mg/dL), which was obtained from the database. Additional information about laboratory examinations is available at https://www.cdc.gov/nchs/nhanes . WHtR was obtained by dividing waist circumference (WC, cm) by height (cm). CMI was computed as TG/HDL-C×WHtR using the formula in previously published research [ 12 ]. Assessment of covariates of interest Our study considered the following variables that may influence the relationship between CMI and infertility: age (years), race (non-Hispanic White/non-Hispanic Black/Mexican-American/other race), marital status (married or cohabiting/widowed or divorced or separated/other), education level (below high school/ high school/above high school), smoking status (never/former/current), drinking status (never/former/mild/moderate/heavy), hypertension (no/yes) and diabetes (no/IGT/yes). The complete measurement procedures for these variables are available at https://www.cdc.gov/nchs/nhanes . Statistical analysis All statistical analyses were carried out in accordance with the recommendations of the CDC using the appropriate NHANES sampling weights. The participants were divided into two groups based on their infertility status. Mean ± standard deviation are used to represent continuous variables in descriptive studies, while percentages are used to represent categorical variables. To lessen data skewness, control outlier effects, and enhance the interpretation of association results, the CMI was log-transformed. A weighted chi-squared test was used for categorical data, and a weighted Student's t-test or Mann-Whitney U-test for continuous variables were used to evaluate differences between the two groups. Using the log-transformed CMI data as continuous variables and quartiles, respectively, weighted multivariate logistic regression models were used to evaluate the independent connection between infertility and the log-transformed CMI. No covariate adjustment was made to the crude model. In Model 1, age, race, marital status and education were adjusted. In addition to the covariates in Model 1, Model 2 was further adjusted for smoking status, drinking status, hypertension and diabetes. The study employed restricted cubic spline analysis to examine the potential linear correlation between the log-transformed CMI and infertility. Subgroup analyses were carried out to evaluate the reliability of the findings. To find out if variables affected infertility by interacting with the log-transformed CMI, interaction tests were employed. All statistical analyses were conducted using R Version 4.3.1 ( http://www.R-project.org , The R Foundation). P < 0.05 (two-tailed) was considered to indicate statistical significance. Results Basic characteristics of the included participants Table 1 presents the baseline characteristics of participants selected from NHANES 2013 to 2020, stratified by their fertility status. The analysis included 274 participants with infertility, which accounted for 11.74% of women aged 18–45 years. The average age of infertile women was 33.91 ± 7.15 years, while the non-infertility group consisted of 2059 participants with a mean age of 31.03 ± 8.28 years, indicating that self-reported infertility was more prevalent in women who were over 35 years old. In addition, women who were not Hispanic white, were married or in a partnership, had hypertension, smoked or drank before were more likely to be affected by infertility (all P < 0.05). Furthermore, self-reported infertility was also significantly more prevalent among women who had higher log-transformed CMI with an average of -0.90 ± 0.79 ( P < 0.05). Table 1 Baseline characteristics of participants Variables Total (n = 2333) Non-Infertility (n = 2059) Infertility (n = 274) P Age, years, mean (SD) 31.37 (8.21) 31.03 (8.28) 33.91 (7.15) < 0.001 * Age < 0.001 * < 35 years 1426 (61.12) 1289 (62.60) 137 (50.00) ≥ 35 years 907 (38.88) 770 (37.40) 137 (50.00) Race 0.033 * Non-Hispanic White 748 (32.06) 639 (31.03) 109 (39.78) Non-Hispanic Black 551 (23.62) 492 (23.90) 59 (21.53) Mexican-American 378 (16.20) 337 (16.37) 41 (14.96) Other Race 656 (28.12) 591 (28.70) 65 (23.72) Marital status < 0.001 * Married/Cohabiting 1228 (52.64) 1024 (49.73) 204 (74.45) Widowed/Divorced/Separated 218 (9.34) 190 (9.23) 28 (10.22) Other 887 (38.02) 845 (41.04) 42 (15.33) Education level 0.128 Below high school 444 (19.03) 396 (19.23) 48 (17.52) High school 482 (20.66) 436 (21.18) 46 (16.79) Above high school 1407 (60.31) 1227 (59.59) 180 (65.69) Smoking status 0.002 * Never 1689 (72.40) 1515 (73.58) 174 (63.50) Former 267 (11.44) 223 (10.83) 44 (16.06) Current 377 (16.16) 321 (15.59) 56 (20.44) Drinking status 0.021 * Never 530 (22.72) 483 (23.46) 47 (17.15) Former 79 (3.39) 66 (3.21) 13 (4.74) Mild 580 (24.86) 507 (24.62) 73 (26.64) Moderate 568 (24.35) 510 (24.77) 58 (21.17) Heavy 576 (24.69) 493 (23.94) 83 (30.29) Hypertension < 0.001 * No 1967 (84.31) 1764 (85.67) 203 (74.09) Yes 366 (15.69) 295 (14.33) 71 (25.91) Diabetes 0.201 No 1929 (82.68) 1705 (82.81) 224 (81.75) IGT 215 (9.22) 194 (9.42) 21 (7.66) Yes 189 (8.10) 160 (7.77) 29 (10.58) Log CMI, mean (SD) –0.96 (0.27) –0.97 (0.27) –0.90 (0.26) < 0.001 * Log CMI < 0.001 * Q1 586 (25.12) 546 (26.52) 40 (14.60) Q2 584 (25.03) 513 (24.92) 71 (25.91) Q3 580 (24.86) 499 (24.24) 81 (29.56) Q4 583 (24.99) 501 (24.33) 82 (29.93) Data are shown as number (%) unless otherwise indicated. * Statistically significant ( P < 0.05). Log CMI quartile range: Q1, − 1.799, − 1.153; Q2, − 1.154, − 0.984; Q3, − 0.985, − 0.785; Q4, − 0.786, 0.318. SD, standard deviation; IGT, impaired glucose tolerance; CMI, cardiometabolic index; Q, quartile. Association between CMI and prevalence of infertility Table 2 shows the association between log CMI and the risk of infertility. In the crude model, the odds ratio (OR) was 2.859 (95% CI: 1.806–4.528), indicating a significant positive correlation between log CMI and infertility. Model 1, which adjusted for age, race, marital status, and education, also showed a positive association (OR = 2.680, 95% CI: 1.647–4.365). In addition, in Model 2, after further adjustment, a positive association between the log-transformed CMI and infertility was still observed (OR = 2.411, 95% CI: 1.416–4.112). In order to obtain additional understanding regarding the correlation between CMI and infertility, log CMI was categorized into quartiles. Based on Model 2, the OR between the highest quartile (Q4) and the lowest quartile (Q1) was 1.843 (95% CI: 1.205–2.848), suggesting there is a positive association between higher CMI levels and infertility. As demonstrated by the results of CMI and restricted cubic spline analysis presented in Fig. 2 , we observed a positive non-linear relationship between infertility and log CMI ( P for trend < 0.05). Table 2 Logistic regression analysis on the association between Log CMI and infertility. Log CMI Crude model Model 1 a Model 2 b OR (95% CI) P OR (95% CI) P OR (95% CI) P Continuous 2.859 (1.806–4.528) < 0.001 * 2.680 (1.647–4.365) < 0.001 * 2.411 (1.416–4.112) 0.001 * Categories Q1 Ref. Ref. Ref. Q2 1.889 (1.265–2.857) 0.002 * 1.820 (1.217–2.756) 0.004 * 1.790 (1.197–2.707) 0.005 * Q3 2.216 (1.498–3.327) < 0.001 * 2.045 (1.375–3.083) < 0.001 * 1.937 (1.298–2.929) 0.001 * Q4 2.234 (1.511–3.353) < 0.001 * 2.053 (1.371–3.117) < 0.001 * 1.843 (1.205–2.848) 0.005 * P for trend < 0.001 * < 0.001 * 0.008 * * Statistically significant ( P < 0.05). a Model 1was adjusted for age, race, marital status, and education. b Model 2 was adjusted for age, race, marital status, education, smoking status, drinking status, hypertension and diabetes. CMI, cardiometabolic index; OR, odds ratio; CI, confidence interval; Q, quartile. Subgroup analysis To further determine the robustness of the association between CMI and infertility, subgroup analysis was conducted. As presented in Table 3 , different subgroups of study participants showed consistent positive correlations between log CMI and infertility, indicating the robustness of the association. Notably, there were no significant interactions between hypertension and diabetes, suggesting that neither variable had an effect on the association (all P for interaction > 0.05). Nevertheless, age was found to significantly influence the strength of the CMI-infertility association ( P for interaction < 0.05). A higher risk was identified among participants younger than 35 years old as compared to the older participants, with an odds ratio of 6.847 (95% CI: 3.115–15.255). In light of these findings, it is evident that age influences the association between CMI and infertility; individuals under 35 years old demonstrate a stronger association. Table 3 The results of subgroup analyses and interaction analyses. Variables OR 95% CI P P for interaction Age < 0.001 * < 35 years 6.847 3.115–15.255 < 0.001 * ≥ 35 years 0.857 0.388–1.878 0.700 Hypertension 0.402 No 2.634 1.416–4.909 0.002 * Yes 2.116 0.634–7.196 0.225 Diabetes 0.351 No 2.799 1.523–5.157 0.001 * IGT 0.288 0.029–2.542 0.270 Yes 2.684 0.397–19.365 0.316 * Statistically significant ( P < 0.05). Adjusted for age, hypertension and diabetes. Stratified variables were not adjusted in the subgroup analysis. Discussion This research conducted a thorough investigation into the association between CMI and infertility in non-institutionalized American women, while also evaluating the predictive power of CMI for female infertility. We found that participants with higher CMI had an increasing risk of infertility in our cross-sectional analysis, which included 2333 females. Subgroup analysis and interaction tests have confirmed the strength of the correlation between CMI and infertility. Furthermore, we discovered a significant association between log CMI and infertility among females who were under 35 years old, indicating younger females with higher CMI levels were more susceptible to infertility. Based on our research, CMI has the potential to serve as a valuable predictor of infertility prevalence. Additionally, effectively managing obesity based on CMI assessment may contribute to reducing the risk of infertility. This study represents the initial attempt to directly evaluate the association between CMI and female infertility. Obesity poses a substantial worldwide public health concern, leading to a multitude of detrimental health consequences. According to the 2016 population statistics from the World Health Organization (WHO), 40% of women were classified as overweight and 15% were obese, and the pronounced adverse outcomes associated with obesity among women require special attention [ 20 ]. Obesity has been linked to numerous negative impacts on female fertility [ 4 , 21 ]. Ovulatory dysfunction is more common in obese women due to disruptions in the hypothalamic-pituitary-ovarian (HPO) axis [ 22 ]. Additionally, obese women with polycystic ovarian syndrome (PCOS) tend to experience more severe metabolic and reproductive symptoms [ 23 , 24 ]. Obesity can also affect the development of oocyte and preimplantation embryo [ 25 , 26 ]. Excess free fatty acids may cause negative impacts on reproductive tissues, resulting in chronic inflammation and cellular dysfunction [ 27 ]. Additionally, the endometrium is vulnerable to obesity, as manifested by instances of compromised stromal decidualization processes among obese female individuals [ 5 ]. Most studies on the relationship between female infertility and obesity are based on body mass index (BMI, in kg/m 2 ) and use the ranges of 18.5–24.9 for normal weight, 25-29.9 for overweight, and ≥ 30 for obesity. Although BMI is a conventional and economical way to assess obesity, it fails to correctly reflect fat mass and lacks insight into obesity distribution. Hence, relying solely on BMI for obesity assessment is inadequate [ 28 ]. Recently, researchers have proposed various indicators for the scientific evaluation of obesity, which can provide a more precise depiction of fat distribution and are deemed to be more scientifically rigorous when compared to BMI. Numerous studies have shown that visceral adiposity index (VAI) [ 29 ], weight-adjusted waist circumference index (WWI) [ 30 ], WC [ 31 , 32 ] and waist-hip ratio (WHR) [ 33 ] were associated with an increased prevalence of infertility. As a novel indicator, CMI involves anthropometric and biochemical parameters, and shows a robust correlation with abnormal lipid profiles as well as metabolism-linked disorders. Metabolic diseases and infertility are complicated processes, with lipid metabolism abnormalities potentially having a major impact on follicular growth, maturation of eggs, and hormone release [ 34 , 35 ]. Studies have shown that CMI is higher in PCOS patients than control subjects, and is significantly and positively associated with insulin resistance in PCOS patients [ 36 ], which may lead to ovulation disorders and result in infertility [ 37 , 38 ]. Numerous investigations conducted on animals have verified that dyslipidemia can result in a reduction in the capacity of female reproduction [ 39 – 42 ]. Combined with the findings of our study, CMI offers not only an early warning of poor metabolism, but can also predict the risk of infertility and further encourage us to maintain good health on a daily basis. Based on the NHANES database, our study for the first time elucidated the direct association and investigated the non-linear link between CMI and prevalence of female infertility through a cross-sectional study. However, there are some limitations to our study. First, our study is aimed on women aged 18–45 years in the United States; whether the findings extend to studies conducted outside of this age and location is uncertain. Secondly, database data may ignore family history of infertility and other reproductive diseases, such as tubal obstruction or PCOS, which also contribute to infertility. Furthermore, other additional data and information, such as circulating immune cells and antibodies, as well as sex hormones, need to be included to better clarify the predictive potential of CMI for infertility. Conclusion According to our research, a high level of CMI is positively correlated to the prevalence of female infertility. CMI plays a predictive role in assess metabolic and reproductive problems in women. Nevertheless, further extensive prospective investigations are required to support the findings of this study. Declarations Data availability Data used in this study is publicly available at https://www.cdc.gov/nchs/nhanes. Ethical approval All data obtained from NHANES were reviewed and approved by National Center for Health Statistics (NCHS) Ethics Review Board and all participants agreed on the survey and signed written consent. The NHANES was conducted in compliance with local laws and institutional guidelines. Since NHANES is a publicly accessible database, no additional ethical approvals are required. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China (82088102, 82301814, 82201882), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-064), Collaborative Innovation Program of Shanghai Municipal Health Commission (2020CXJQ01), Key Discipline Construction Project (2023–2025) of Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai (GWVI-11.1-35), Shanghai Clinical Research Center for Gynecological Diseases (22MC1940200), Shanghai Urogenital System Diseases Research Center (2022ZZ01012) and Shanghai Frontiers Science Research Center of Reproduction and Development. Author Contribution YZ performed the statistical analysis and drafted the manuscript. WS collected data and performed further methodology. YL reviewed and revised the manuscript. NQ conceptualized and organized the research. HH conceptualized and supervised the research; reviewed and revised the manuscript. 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Supplementary Files Table1.docx Table2.docx Table3.docx Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2024 Read the published version in Reproductive Biology and Endocrinology → Version 1 posted Editorial decision: Revision requested 26 Oct, 2024 Reviews received at journal 23 Sep, 2024 Reviewers agreed at journal 18 Sep, 2024 Reviewers invited by journal 07 Aug, 2024 Editor assigned by journal 07 Aug, 2024 Submission checks completed at journal 07 Aug, 2024 First submitted to journal 06 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4865845\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":347742338,\"identity\":\"596e9ba0-da1b-4522-b14a-925b023cd0af\",\"order_by\":0,\"name\":\"Yiran Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Obstetrics and Gynecology Hospital of Fudan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yiran\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":347742339,\"identity\":\"23178997-8779-4a2b-ba2f-846ff15ddce0\",\"order_by\":1,\"name\":\"Weihui Shi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Obstetrics and Gynecology Hospital of Fudan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Weihui\",\"middleName\":\"\",\"lastName\":\"Shi\",\"suffix\":\"\"},{\"id\":347742340,\"identity\":\"27b2606a-de14-4fe8-8933-737c580a3a42\",\"order_by\":2,\"name\":\"Yang Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Obstetrics and Gynecology Hospital of Fudan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yang\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":347742341,\"identity\":\"6d140e5a-ea55-45b5-bcc8-0c31e3bfdc74\",\"order_by\":3,\"name\":\"Ningxin Qin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai First Maternity and Infant Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ningxin\",\"middleName\":\"\",\"lastName\":\"Qin\",\"suffix\":\"\"},{\"id\":347742343,\"identity\":\"d4f25764-3ed3-4c17-a3de-3679bbc22465\",\"order_by\":4,\"name\":\"Hefeng Huang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACAxDB2MDAz8DeAxbg4SNWi2QDzxkGhgNALWzEa5HIAWthIKjFXCL52cOvO2wk5CPfHnz8McdOho2B+eGjG3i0WM5IMzeWPZMmYXg7L9ng4LZkoMPYjI1z8DnsRoKZtGTb4TrD2TlmEge3MQO18LBJ49eS/g2o5b+E4cwzIC31xGjJMZP82HZAQl6CB6TlMBFazrwpk2ZsS5Yw4MkxNji77TgPGzMhvxxP3yb5s81OQr79jOGDym3V9vzszQ8f49MCAsw8IL0H4FwCykGA8QeQkG8gQuUoGAWjYBSMTAAAi1xHwhconygAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Obstetrics and Gynecology Hospital of Fudan University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Hefeng\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-08-06 06:01:13\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4865845/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4865845/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12958-024-01312-9\",\"type\":\"published\",\"date\":\"2024-11-14T15:57:28+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":64712883,\"identity\":\"5cee6dbc-83bc-457d-9aa2-dd46dc197918\",\"added_by\":\"auto\",\"created_at\":\"2024-09-18 02:08:42\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":103249,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe flow chart of study participants.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4865845/v1/646bef8f439396e5f4b79474.jpg\"},{\"id\":64711815,\"identity\":\"e76e6722-3422-47ac-a649-e70f752dc700\",\"added_by\":\"auto\",\"created_at\":\"2024-09-18 02:00:42\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":417175,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRestricted cubic spline of odds ratio and 95% confidence interval (shaded area) for the association between log cardiometabolic index and infertility.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4865845/v1/342218740efb7cbec85d3d6e.jpg\"},{\"id\":69285496,\"identity\":\"320a8ed2-f412-4e6b-b97c-8e2fc54093a4\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 19:26:15\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1175979,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4865845/v1/8f2530e8-3db2-405c-bdd1-ac2f99ffff2d.pdf\"},{\"id\":64711818,\"identity\":\"b0fcfbf9-274f-4c45-aeb7-887db994c055\",\"added_by\":\"auto\",\"created_at\":\"2024-09-18 02:00:42\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":24183,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4865845/v1/50160857328c7b1e2ed1c065.docx\"},{\"id\":64711817,\"identity\":\"6d9d095c-d71e-44a3-bbdc-bab55f842145\",\"added_by\":\"auto\",\"created_at\":\"2024-09-18 02:00:42\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":19984,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4865845/v1/85357d9c3bed7a9ad727f708.docx\"},{\"id\":64711819,\"identity\":\"c23fff61-b565-4536-ae87-faa60caa5404\",\"added_by\":\"auto\",\"created_at\":\"2024-09-18 02:00:42\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":19148,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table3.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4865845/v1/cb00d83ce66cdf3e09df318e.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Association between cardiometabolic index and female infertility: a cross-sectional analysis\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eInfertility is characterized by the inability to conceive after 12 months or more of consistent, unprotected sexual intercourse [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e], which affects a significant portion of the population in their lifetime. Between 12.6% and 17.5% of couples in reproductive age have suffered from infertility globally [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. As a worldwide public health concern, infertility profoundly affects individuals' mental and emotional well-being, often leading to severe psychological distress, social stigma, and considerable financial burdens.\\u003c/p\\u003e \\u003cp\\u003eInfertility can be caused by a number of various factors, such as age, diet, psychological stress, and environmental pollution, among which, obesity has received a considerable attention due to its alarming rise worldwide. A great deal of research has been conducted on the negative association between obesity and reproductive outcomes [\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Typical dyslipidemia in obesity is characterized by elevated triglycerides (TG), free fatty acids (FFA), decreased high-density lipoprotein cholesterol (HDL-C) with impaired HDL function, and slightly elevated low-density lipoprotein cholesterol (LDL-C) with increased small dense LDL [\\u003cspan additionalcitationids=\\\"CR7 CR8\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Evidence from human studies indicated that irregular female lipid profiles such as elevated total cholesterol, triglycerides, LDL-C and reduced HDL-C could lead to diminished fecundability, poorer oocyte quality and ovarian function [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe cardiometabolic index (CMI), calculated by multiplying TG/HDL-C ratio by waist-to-height ratio (WHtR), was initially introduced by Wakabayashi \\u003cem\\u003eet al.\\u003c/em\\u003e in 2015 [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. It was devised as a novel diagnostic tool for the identification of diabetes, and can be used to assess the distribution and functional impairments of visceral adipose tissue. A growing number of studies have established a robust correlation between CMI and cardiovascular disease, renal dysfunction, acute pancreatitis as well as adverse metabolic profiles, and it might be a potentially valuable predictor of metabolism-linked disorders [\\u003cspan additionalcitationids=\\\"CR14 CR15 CR16 CR17\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Considering the promising diagnostic potential of the CMI for various metabolic disorders, and given the positive association between irregular lipid profiles and female infertility, the potential role of CMI in diagnosing female infertility merits further investigation.\\u003c/p\\u003e \\u003cp\\u003eHence, this study aimed to systematically examine the relationship between CMI and female infertility, as well as to assess the predictive efficacy of CMI for female infertility.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData source\\u003c/h2\\u003e \\u003cp\\u003eNHANES is a nationwide representative cross-sectional survey to assess and evaluate Americans' health and nutritional status. Administered by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC), NHANES is based on questionnaires, physical examinations, household interviews, and laboratory testing. Multistage stratified probability sampling is used in the study to ensure a highly representative sample. All participants have provided written informed consent in accordance with the NHANES protocols, which have been approved by the NCHS Research Ethics Review Board. Data used in this study is publicly available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.cdc.gov/nchs/nhanes\\u003c/span\\u003e\\u003cspan address=\\\"https://www.cdc.gov/nchs/nhanes\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy population\\u003c/h2\\u003e \\u003cp\\u003eThe present study incorporated NHANES data from 2013\\u0026ndash;2014, 2015\\u0026ndash;2016, 2017\\u0026ndash;2018 and 2019\\u0026ndash;2020. Women aged 18\\u0026ndash;45 years were enrolled (n\\u0026thinsp;=\\u0026thinsp;6502); after the exclusion of missing data of CMI (n\\u0026thinsp;=\\u0026thinsp;3191) or diagnosis of infertility (n\\u0026thinsp;=\\u0026thinsp;978), 2333 participants were finally included in the analysis (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\\u003eAssessment of infertility\\u003c/h2\\u003e \\u003cp\\u003eThe dependent variable of infertility was computed using the responses to the Reproductive Health Questionnaire (variable name in the questionnaire: RHQ074). Participants were assumed to be infertile if they responded positively to the survey question, \\\"Have you ever attempted to become pregnant for at least a year without becoming pregnant?\\\" [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssessment of cardiometabolic index\\u003c/h2\\u003e \\u003cp\\u003eThe TG/HDL-C ratio was calculated by dividing the serum concentration of TG (mg/dL) by HDL-C (mg/dL), which was obtained from the database. Additional information about laboratory examinations is available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.cdc.gov/nchs/nhanes\\u003c/span\\u003e\\u003cspan address=\\\"https://www.cdc.gov/nchs/nhanes\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. WHtR was obtained by dividing waist circumference (WC, cm) by height (cm). CMI was computed as TG/HDL-C\\u0026times;WHtR using the formula in previously published research [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssessment of covariates of interest\\u003c/h2\\u003e \\u003cp\\u003eOur study considered the following variables that may influence the relationship between CMI and infertility: age (years), race (non-Hispanic White/non-Hispanic Black/Mexican-American/other race), marital status (married or cohabiting/widowed or divorced or separated/other), education level (below high school/ high school/above high school), smoking status (never/former/current), drinking status (never/former/mild/moderate/heavy), hypertension (no/yes) and diabetes (no/IGT/yes). The complete measurement procedures for these variables are available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.cdc.gov/nchs/nhanes\\u003c/span\\u003e\\u003cspan address=\\\"https://www.cdc.gov/nchs/nhanes\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eAll statistical analyses were carried out in accordance with the recommendations of the CDC using the appropriate NHANES sampling weights.\\u003c/p\\u003e \\u003cp\\u003eThe participants were divided into two groups based on their infertility status. Mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation are used to represent continuous variables in descriptive studies, while percentages are used to represent categorical variables. To lessen data skewness, control outlier effects, and enhance the interpretation of association results, the CMI was log-transformed. A weighted chi-squared test was used for categorical data, and a weighted Student's t-test or Mann-Whitney U-test for continuous variables were used to evaluate differences between the two groups. Using the log-transformed CMI data as continuous variables and quartiles, respectively, weighted multivariate logistic regression models were used to evaluate the independent connection between infertility and the log-transformed CMI. No covariate adjustment was made to the crude model. In Model 1, age, race, marital status and education were adjusted. In addition to the covariates in Model 1, Model 2 was further adjusted for smoking status, drinking status, hypertension and diabetes. The study employed restricted cubic spline analysis to examine the potential linear correlation between the log-transformed CMI and infertility. Subgroup analyses were carried out to evaluate the reliability of the findings. To find out if variables affected infertility by interacting with the log-transformed CMI, interaction tests were employed.\\u003c/p\\u003e \\u003cp\\u003eAll statistical analyses were conducted using R Version 4.3.1 (\\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 (two-tailed) was considered to indicate statistical significance.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBasic characteristics of the included participants\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents the baseline characteristics of participants selected from NHANES 2013 to 2020, stratified by their fertility status. The analysis included 274 participants with infertility, which accounted for 11.74% of women aged 18\\u0026ndash;45 years. The average age of infertile women was 33.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.15 years, while the non-infertility group consisted of 2059 participants with a mean age of 31.03\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.28 years, indicating that self-reported infertility was more prevalent in women who were over 35 years old. In addition, women who were not Hispanic white, were married or in a partnership, had hypertension, smoked or drank before were more likely to be affected by infertility (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Furthermore, self-reported infertility was also significantly more prevalent among women who had higher log-transformed CMI with an average of -0.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.79 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline characteristics of participants\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" 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 \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2333)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNon-Infertility\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2059)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eInfertility\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;274)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, years, mean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31.37 (8.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.03 (8.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33.91 (7.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\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\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;35 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1426 (61.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1289 (62.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137 (50.00)\\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;35 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e907 (38.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e770 (37.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137 (50.00)\\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\\u003eRace\\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\\u003e0.033\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-Hispanic White\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e748 (32.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e639 (31.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e109 (39.78)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e551 (23.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e492 (23.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e59 (21.53)\\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\\u003eMexican-American\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e378 (16.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e337 (16.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e41 (14.96)\\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 Race\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e656 (28.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e591 (28.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e65 (23.72)\\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\\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\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried/Cohabiting\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1228 (52.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1024 (49.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e204 (74.45)\\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\\u003eWidowed/Divorced/Separated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e218 (9.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e190 (9.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28 (10.22)\\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\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e887 (38.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e845 (41.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e42 (15.33)\\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\\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\\u003e0.128\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBelow high school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e444 (19.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e396 (19.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e48 (17.52)\\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\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e482 (20.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e436 (21.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e46 (16.79)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1407 (60.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1227 (59.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e180 (65.69)\\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 status\\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\\u003e0.002\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNever\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1689 (72.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1515 (73.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e174 (63.50)\\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\\u003eFormer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e267 (11.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e223 (10.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e44 (16.06)\\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\\u003eCurrent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e377 (16.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e321 (15.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56 (20.44)\\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\\u003eDrinking status\\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\\u003e0.021\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNever\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e530 (22.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e483 (23.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e47 (17.15)\\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\\u003eFormer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e79 (3.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66 (3.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13 (4.74)\\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\\u003eMild\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e580 (24.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e507 (24.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e73 (26.64)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e568 (24.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e510 (24.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e58 (21.17)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e576 (24.69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e493 (23.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e83 (30.29)\\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\\u003eHypertension\\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\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1967 (84.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1764 (85.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e203 (74.09)\\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\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e366 (15.69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e295 (14.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e71 (25.91)\\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\\u003eDiabetes\\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\\u003e0.201\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1929 (82.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1705 (82.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e224 (81.75)\\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\\u003eIGT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e215 (9.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e194 (9.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21 (7.66)\\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\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e189 (8.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e160 (7.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e29 (10.58)\\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\\u003eLog CMI, mean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ndash;0.96 (0.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026ndash;0.97 (0.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026ndash;0.90 (0.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLog CMI\\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\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e586 (25.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e546 (26.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e40 (14.60)\\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\\u003eQ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e584 (25.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e513 (24.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e71 (25.91)\\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\\u003eQ3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e580 (24.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e499 (24.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e81 (29.56)\\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\\u003eQ4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e583 (24.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e501 (24.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e82 (29.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eData are shown as number (%) unless otherwise indicated.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003e*\\u003c/sup\\u003eStatistically significant (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eLog CMI quartile range: Q1, \\u0026minus;\\u0026thinsp;1.799, \\u0026minus;\\u0026thinsp;1.153; Q2, \\u0026minus;\\u0026thinsp;1.154, \\u0026minus;\\u0026thinsp;0.984; Q3, \\u0026minus;\\u0026thinsp;0.985, \\u0026minus;\\u0026thinsp;0.785; Q4, \\u0026minus;\\u0026thinsp;0.786, 0.318.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eSD, standard deviation; IGT, impaired glucose tolerance; CMI, cardiometabolic index; Q, quartile.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation between CMI and prevalence of infertility\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows the association between log CMI and the risk of infertility. In the crude model, the odds ratio (OR) was 2.859 (95% CI: 1.806\\u0026ndash;4.528), indicating a significant positive correlation between log CMI and infertility. Model 1, which adjusted for age, race, marital status, and education, also showed a positive association (OR\\u0026thinsp;=\\u0026thinsp;2.680, 95% CI: 1.647\\u0026ndash;4.365). In addition, in Model 2, after further adjustment, a positive association between the log-transformed CMI and infertility was still observed (OR\\u0026thinsp;=\\u0026thinsp;2.411, 95% CI: 1.416\\u0026ndash;4.112). In order to obtain additional understanding regarding the correlation between CMI and infertility, log CMI was categorized into quartiles. Based on Model 2, the OR between the highest quartile (Q4) and the lowest quartile (Q1) was 1.843 (95% CI: 1.205\\u0026ndash;2.848), suggesting there is a positive association between higher CMI levels and infertility. As demonstrated by the results of CMI and restricted cubic spline analysis presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, we observed a positive non-linear relationship between infertility and log CMI (\\u003cem\\u003eP\\u003c/em\\u003e for trend\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\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\\u003eLogistic regression analysis on the association between Log CMI and infertility.\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eLog CMI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eCrude model\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eModel 1\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003eModel 2\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eContinuous\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.859 (1.806\\u0026ndash;4.528)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.680 (1.647\\u0026ndash;4.365)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.411 (1.416\\u0026ndash;4.112)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCategories\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eRef.\\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\\u003eRef.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.889 (1.265\\u0026ndash;2.857)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.002\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.820 (1.217\\u0026ndash;2.756)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.004\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.790 (1.197\\u0026ndash;2.707)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.005\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.216 (1.498\\u0026ndash;3.327)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.045 (1.375\\u0026ndash;3.083)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.937 (1.298\\u0026ndash;2.929)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.234 (1.511\\u0026ndash;3.353)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.053 (1.371\\u0026ndash;3.117)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.843 (1.205\\u0026ndash;2.848)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.005\\u003csup\\u003e*\\u003c/sup\\u003e\\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\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\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\\u003e0.008\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003e\\u003csup\\u003e*\\u003c/sup\\u003e Statistically significant (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003e\\u003csup\\u003ea\\u003c/sup\\u003eModel 1was adjusted for age, race, marital status, and education.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003e\\u003csup\\u003eb\\u003c/sup\\u003eModel 2 was adjusted for age, race, marital status, education, smoking status, drinking status, hypertension and diabetes.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003eCMI, cardiometabolic index; OR, odds ratio; CI, confidence interval; Q, quartile.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSubgroup analysis\\u003c/h2\\u003e \\u003cp\\u003eTo further determine the robustness of the association between CMI and infertility, subgroup analysis was conducted. As presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, different subgroups of study participants showed consistent positive correlations between log CMI and infertility, indicating the robustness of the association. Notably, there were no significant interactions between hypertension and diabetes, suggesting that neither variable had an effect on the association (all \\u003cem\\u003eP\\u003c/em\\u003e for interaction\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). Nevertheless, age was found to significantly influence the strength of the CMI-infertility association (\\u003cem\\u003eP\\u003c/em\\u003e for interaction\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). A higher risk was identified among participants younger than 35 years old as compared to the older participants, with an odds ratio of 6.847 (95% CI: 3.115\\u0026ndash;15.255). In light of these findings, it is evident that age influences the association between CMI and infertility; individuals under 35 years old demonstrate a stronger association.\\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 results of subgroup analyses and interaction analyses.\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" 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 \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOR\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e95% CI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e for interaction\\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\\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\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;35 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.847\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.115\\u0026ndash;15.255\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\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;35 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.857\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.388\\u0026ndash;1.878\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.700\\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\\u003eHypertension\\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\\u003e0.402\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.634\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.416\\u0026ndash;4.909\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.002\\u003csup\\u003e*\\u003c/sup\\u003e\\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\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.116\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.634\\u0026ndash;7.196\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.225\\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\\u003eDiabetes\\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\\u003e0.351\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.799\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.523\\u0026ndash;5.157\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\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\\u003eIGT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.288\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.029\\u0026ndash;2.542\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.270\\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\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.684\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.397\\u0026ndash;19.365\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.316\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003e*\\u003c/sup\\u003eStatistically significant (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eAdjusted for age, hypertension and diabetes. Stratified variables were not adjusted in the subgroup analysis.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis research conducted a thorough investigation into the association between CMI and infertility in non-institutionalized American women, while also evaluating the predictive power of CMI for female infertility. We found that participants with higher CMI had an increasing risk of infertility in our cross-sectional analysis, which included 2333 females. Subgroup analysis and interaction tests have confirmed the strength of the correlation between CMI and infertility. Furthermore, we discovered a significant association between log CMI and infertility among females who were under 35 years old, indicating younger females with higher CMI levels were more susceptible to infertility. Based on our research, CMI has the potential to serve as a valuable predictor of infertility prevalence. Additionally, effectively managing obesity based on CMI assessment may contribute to reducing the risk of infertility.\\u003c/p\\u003e \\u003cp\\u003eThis study represents the initial attempt to directly evaluate the association between CMI and female infertility. Obesity poses a substantial worldwide public health concern, leading to a multitude of detrimental health consequences. According to the 2016 population statistics from the World Health Organization (WHO), 40% of women were classified as overweight and 15% were obese, and the pronounced adverse outcomes associated with obesity among women require special attention [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Obesity has been linked to numerous negative impacts on female fertility [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Ovulatory dysfunction is more common in obese women due to disruptions in the hypothalamic-pituitary-ovarian (HPO) axis [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Additionally, obese women with polycystic ovarian syndrome (PCOS) tend to experience more severe metabolic and reproductive symptoms [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Obesity can also affect the development of oocyte and preimplantation embryo [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Excess free fatty acids may cause negative impacts on reproductive tissues, resulting in chronic inflammation and cellular dysfunction [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Additionally, the endometrium is vulnerable to obesity, as manifested by instances of compromised stromal decidualization processes among obese female individuals [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eMost studies on the relationship between female infertility and obesity are based on body mass index (BMI, in kg/m\\u003csup\\u003e2\\u003c/sup\\u003e) and use the ranges of 18.5\\u0026ndash;24.9 for normal weight, 25-29.9 for overweight, and \\u0026ge;\\u0026thinsp;30 for obesity. Although BMI is a conventional and economical way to assess obesity, it fails to correctly reflect fat mass and lacks insight into obesity distribution. Hence, relying solely on BMI for obesity assessment is inadequate [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Recently, researchers have proposed various indicators for the scientific evaluation of obesity, which can provide a more precise depiction of fat distribution and are deemed to be more scientifically rigorous when compared to BMI. Numerous studies have shown that visceral adiposity index (VAI) [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], weight-adjusted waist circumference index (WWI) [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e], WC [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e] and waist-hip ratio (WHR) [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e] were associated with an increased prevalence of infertility. As a novel indicator, CMI involves anthropometric and biochemical parameters, and shows a robust correlation with abnormal lipid profiles as well as metabolism-linked disorders. Metabolic diseases and infertility are complicated processes, with lipid metabolism abnormalities potentially having a major impact on follicular growth, maturation of eggs, and hormone release [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. Studies have shown that CMI is higher in PCOS patients than control subjects, and is significantly and positively associated with insulin resistance in PCOS patients [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e], which may lead to ovulation disorders and result in infertility [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Numerous investigations conducted on animals have verified that dyslipidemia can result in a reduction in the capacity of female reproduction [\\u003cspan additionalcitationids=\\\"CR40 CR41\\\" citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. Combined with the findings of our study, CMI offers not only an early warning of poor metabolism, but can also predict the risk of infertility and further encourage us to maintain good health on a daily basis.\\u003c/p\\u003e \\u003cp\\u003eBased on the NHANES database, our study for the first time elucidated the direct association and investigated the non-linear link between CMI and prevalence of female infertility through a cross-sectional study. However, there are some limitations to our study. First, our study is aimed on women aged 18\\u0026ndash;45 years in the United States; whether the findings extend to studies conducted outside of this age and location is uncertain. Secondly, database data may ignore family history of infertility and other reproductive diseases, such as tubal obstruction or PCOS, which also contribute to infertility. Furthermore, other additional data and information, such as circulating immune cells and antibodies, as well as sex hormones, need to be included to better clarify the predictive potential of CMI for infertility.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eAccording to our research, a high level of CMI is positively correlated to the prevalence of female infertility. CMI plays a predictive role in assess metabolic and reproductive problems in women. Nevertheless, further extensive prospective investigations are required to support the findings of this study.\\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData used in this study is publicly available at https://www.cdc.gov/nchs/nhanes.\\u003c/p\\u003e\\u003cp\\u003e \\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e \\u003cp\\u003eAll data obtained from NHANES were reviewed and approved by National Center for Health Statistics (NCHS) Ethics Review Board and all participants agreed on the survey and signed written consent. The NHANES was conducted in compliance with local laws and institutional guidelines. Since NHANES is a publicly accessible database, no additional ethical approvals are required.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e \\u003cp\\u003eNot applicable.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e \\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis work was supported by the National Natural Science Foundation of China (82088102, 82301814, 82201882), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-064), Collaborative Innovation Program of Shanghai Municipal Health Commission (2020CXJQ01), Key Discipline Construction Project (2023\\u0026ndash;2025) of Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai (GWVI-11.1-35), Shanghai Clinical Research Center for Gynecological Diseases (22MC1940200), Shanghai Urogenital System Diseases Research Center (2022ZZ01012) and Shanghai Frontiers Science Research Center of Reproduction and Development.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eYZ performed the statistical analysis and drafted the manuscript. WS collected data and performed further methodology. YL reviewed and revised the manuscript. NQ conceptualized and organized the research. HH conceptualized and supervised the research; reviewed and revised the manuscript. All authors reviewed the manuscript and approved the submitted version.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eCarson SA, Kallen AN. Diagnosis and Management of Infertility. JAMA. 2021;326(1):65.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCox CM, Thoma ME, Tchangalova N, Mburu G, Bornstein MJ, Johnson CL, Kiarie J. Infertility prevalence and the methods of estimation from 1990 to 2021: a systematic review and meta-analysis. Hum Reprod Open 2022, 2022(4).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eArmstrong A, Berger M, Al-Safi Z. Obesity and reproduction. Curr Opin Obstet Gynecol. 2022;34(4):184\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBroughton DE, Moley KH. Obesity and female infertility: potential mediators of obesity's impact. Fertil Steril. 2017;107(4):840\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang T, Zhao J, Liu F, Li Y. Lipid metabolism and endometrial receptivity. Hum Reprod Update. 2022;28(6):858\\u0026ndash;89.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhuang J, Wang S, Wang Y, Hu R, Wu Y. Association Between Triglyceride Glucose Index and Infertility in Reproductive-Aged Women: A Cross-Sectional Study. Int J Women's Health. 2024;16:937\\u0026ndash;46.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStadler JT, Marsche G. Obesity-Related Changes in High-Density Lipoprotein Metabolism and Function. Int J Mol Sci. 2020;21(23):8985.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKlop B, Elte J, Cabezas M. Dyslipidemia in Obesity: Mechanisms and Potential Targets. Nutrients. 2013;5(4):1218\\u0026ndash;40.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVekic J, Stefanovic A, Zeljkovic A. Obesity and Dyslipidemia: A Review of Current Evidence. Curr Obes Rep. 2023;12(3):207\\u0026ndash;22.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePugh SJ, Schisterman EF, Browne RW, Lynch AM, Mumford SL, Perkins NJ, Silver R, Sjaarda L, Stanford JB, Wactawski-Wende J, et al. Preconception maternal lipoprotein levels in relation to fecundability. Hum Reprod. 2017;32(5):1055\\u0026ndash;63.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMiller WL, Auchus RJ. The Molecular Biology, Biochemistry, and Physiology of Human Steroidogenesis and Its Disorders. Endocr Rev. 2011;32(1):81\\u0026ndash;151.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWakabayashi I, Daimon T. The cardiometabolic index as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta. 2015;438:274\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLazzer S, D\\u0026rsquo;Alleva M, Isola M, De Martino M, Caroli D, Bondesan A, Marra A, Sartorio A. Cardiometabolic Index (CMI) and Visceral Adiposity Index (VAI) Highlight a Higher Risk of Metabolic Syndrome in Women with Severe Obesity. J Clin Med. 2023;12(9):3055.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSun Q, Ren Q, Du L, Chen S, Wu S, Zhang B, Wang B. Cardiometabolic Index (CMI), Lipid Accumulation Products (LAP), Waist Triglyceride Index (WTI) and the risk of acute pancreatitis: a prospective study in adults of North China. Lipids Health Dis 2023, 22(1).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMiao M, Deng X, Wang Z, Jiang D, Lai S, Yu S, Yan L. Cardiometabolic index is associated with urinary albumin excretion and renal function in aged person over 60: Data from NHANES 2011\\u0026ndash;2018. Int J Cardiol. 2023;384:76\\u0026ndash;81.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYe R, Zhang X, Zhang Z, Wang S, Liu L, Jia S, Yang X, Liu X, Chen X. Association of cardiometabolic and triglyceride-glucose index with left ventricular diastolic function in asymptomatic individuals. Nutr Metabolism Cardiovasc Dis 2024.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShi W-R, Wang H-Y, Chen S, Guo X-F, Li Z, Sun Y-X. Estimate of prevalent diabetes from cardiometabolic index in general Chinese population: a community-based study. Lipids Health Dis 2018, 17(1).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSong J, Li Y, Zhu J, Liang J, Xue S, Zhu Z. Non-linear associations of cardiometabolic index with insulin resistance, impaired fasting glucose, and type 2 diabetes among US adults: a cross-sectional study. Front Endocrinol 2024, 15.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDick MLB. Self-reported difficulty in conceiving as a measure of infertility. Hum Reprod. 2003;18(12):2711\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOrganization WH. WHO consultation to adapt influenza sentinel surveillance systems to include COVID-19 virological surveillance: virtual meeting, 6\\u0026ndash;8 October 2020. World Health Organization; 2022.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKlenov VE, Jungheim ES. Obesity and reproductive function. Curr Opin Obst Gynecol. 2014;26(6):455\\u0026ndash;60.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJungheim ES, Moley KH. Current knowledge of obesity's effects in the pre- and periconceptional periods and avenues for future research. Am J Obstet Gynecol. 2010;203(6):525\\u0026ndash;30.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMoran LJ, Norman RJ, Teede HJ. Metabolic risk in PCOS: phenotype and adiposity impact. Trends Endocrinol Metab. 2015;26(3):136\\u0026ndash;43.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHan Y, Wu H, Sun S, Zhao R, Deng Y, Zeng S, Chen J. Effect of High Fat Diet on Disease Development of Polycystic Ovary Syndrome and Lifestyle Intervention Strategies. Nutrients. 2023;15(9):2230.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGhasemi-Tehrani H, Askari G, Allameh FZ, Vajdi M, Amiri Khosroshahi R, Talebi S, Ziaei R, Ghavami A, Askari F. 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Characteristics of Different Obesity Metabolic Indexes and their Correlation with Insulin Resistance in Patients with Polycystic Ovary Syndrome. Reprod Sci 2024.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLei R, Chen S, Li W. Advances in the study of the correlation between insulin resistance and infertility. Front Endocrinol (Lausanne). 2024;15:1288326.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFica S, Albu A, Constantin M, Dobri GA. Insulin resistance and fertility in polycystic ovary syndrome. J Med Life. 2008;1(4):415\\u0026ndash;22.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMiettinen HE, Rayburn H, Krieger M. Abnormal lipoprotein metabolism and reversible female infertility in HDL receptor (SR-BI)-deficient mice. J Clin Invest. 2001;108(11):1717\\u0026ndash;22.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYesilaltay A, Dokshin GA, Busso D, Wang L, Galiani D, Chavarria T, Vasile E, Quilaqueo L, Orellana JA, Walzer D, et al. Excess cholesterol induces mouse egg activation and may cause female infertility. Proc Natl Acad Sci U S A. 2014;111(46):E4972\\u0026ndash;4980.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDi Berardino C, Peserico A, Capacchietti G, Zappacosta A, Bernab\\u0026ograve; N, Russo V, Mauro A, El Khatib M, Gonnella F, Konstantinidou F et al. High-Fat Diet and Female Fertility across Lifespan: A Comparative Lesson from Mammal Models. Nutrients 2022, 14(20).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHou YJ, Zhu CC, Duan X, Liu HL, Wang Q, Sun SC. Both diet and gene mutation induced obesity affect oocyte quality in mice. Sci Rep. 2016;6:18858.\\u003c/span\\u003e\\u003c/li\\u003e\\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\":\"info@researchsquare.com\",\"identity\":\"reproductive-biology-and-endocrinology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"rbej\",\"sideBox\":\"Learn more about [Reproductive Biology and Endocrinology](http://rbej.biomedcentral.com)\",\"snPcode\":\"12958\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12958/3\",\"title\":\"Reproductive Biology and Endocrinology\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Female infertility, cardiometabolic index, obesity, NHANES\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4865845/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4865845/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eAdverse lipid profile leads to female infertility. The correlation between the cardiometabolic index (CMI) and female infertility merits further investigation.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThe data for this study were acquired from the 2013\\u0026ndash;2020 National Health and Nutrition Examination Survey (NHANES), with 2333 women enrolled. The cardiometabolic index (CMI) of each participant was calculated as the ratio of triglycerides and high-density lipoprotein cholesterol multiplied by waist-to-height ratio. Weighted multivariate logistic regression models were used to assess the independent association between the log-transformed CMI and infertility. Subgroup analyses were carried out to assess the reliability of the findings. Interaction tests were employed to find out if variables affected infertility by interacting with log CMI.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eA total of 2333 participants aged 18\\u0026ndash;45 years were enrolled, 274 of whom were infertile. Log CMI of the infertility group was significantly higher than that of the non-infertility group (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). After adjustment for potential confounders, women with higher CMI were more prevalent of infertility (OR\\u0026thinsp;=\\u0026thinsp;2.411, 95% CI: 1.416\\u0026ndash;4.112), and this association was still consistent in subgroups aged under 35 years (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Furthermore, restricted cubic spline analysis showed a positive non-linear relationship between log CMI and infertility.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eCardiometabolic index levels were positively associated with increased risk of infertility in American females. Our study demonstrated the predictive capacity of CMI for female infertility; nevertheless, additional clarification is required to establish the direct relationship between CMI and female infertility, which will serve as a foundation for future efforts to prevent female infertility.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Association between cardiometabolic index and female infertility: a cross-sectional analysis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-09-18 02:00:37\",\"doi\":\"10.21203/rs.3.rs-4865845/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-10-26T20:37:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-09-23T08:28:46+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"169046332844042800612206829281126389696\",\"date\":\"2024-09-18T06:51:29+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-08-08T00:33:22+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-08-07T15:47:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-08-07T15:47:03+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Reproductive Biology and Endocrinology\",\"date\":\"2024-08-06T05:59:55+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"reproductive-biology-and-endocrinology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"rbej\",\"sideBox\":\"Learn more about [Reproductive Biology and Endocrinology](http://rbej.biomedcentral.com)\",\"snPcode\":\"12958\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12958/3\",\"title\":\"Reproductive Biology and Endocrinology\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"8c842d58-b41d-4b9a-bb8b-dbf6808c0d17\",\"owner\":[],\"postedDate\":\"September 18th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-11-18T19:21:01+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4865845\",\"link\":\"https://doi.org/10.1186/s12958-024-01312-9\",\"journal\":{\"identity\":\"reproductive-biology-and-endocrinology\",\"isVorOnly\":false,\"title\":\"Reproductive Biology and Endocrinology\"},\"publishedOn\":\"2024-11-14 15:57:28\",\"publishedOnDateReadable\":\"November 14th, 2024\"},\"versionCreatedAt\":\"2024-09-18 02:00:37\",\"video\":\"\",\"vorDoi\":\"10.1186/s12958-024-01312-9\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12958-024-01312-9\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4865845\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4865845\",\"identity\":\"rs-4865845\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}