Correlation of Sex Hormone Binding Globulin with Metabolic Syndrome in US Adults: Insights from National Health and Nutrition Examination Survey (NHANES) 2013–2016

Correlation of Sex Hormone Binding Globulin with Metabolic Syndrome in US Adults: Insights from National Health and Nutrition Examination Survey (NHANES) 2013–2016 | 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 Correlation of Sex Hormone Binding Globulin with Metabolic Syndrome in US Adults: Insights from National Health and Nutrition Examination Survey (NHANES) 2013–2016 Yang Yang, Jie Wang, Yuhang Liu, Shuwan Liu, Huabao Liu, Meiao Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4128989/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Metabolic syndrome (MetS) poses a significant public health challenge worldwide, significantly impacting the health and quality of life of individuals. Increasing evidence suggests a strong correlation between MetS and sex hormone levels. The objective of this study is to explore the possible relationship between sex hormone binding globulin (SHBG) and Mets, aiming to furnish evidence that could inform the development of effective prevention strategies for Mets. Methods The data for this cross-sectional investigation were collected during the 2013–2016 cycle of the National Health and Nutrition Examination Survey (NHANES), from which 5,499 adults were sampled. The criteria established by the Adult Treatment Program III of the National Cholesterol Education Program were utilized to define MetS. SHBG were measured using a standardized technique. Multivariable-adjusted Logistic regression analysis, curve fitting, and threshold effects analysis were utilized to investigate the association between SHBG levels and Mets. Moreover, the stratified analyses and interaction tests of covariables were presented in the forest plot. Finally, sensitivity analysis was utilized to ensure the the robustness of the results. Results Among the participants, 1822 those had Mets. After adjusting for possible confounders, the SHBG level was associated with Mets (Odds ratio [OR], 0.984; 95% confidence interval [CI], 0.981–0.986; P < 0.01). The multivariable restricted cubic spline demonstrated a non-linear association between SHBG and Mets (P < 0.001). With two piecewise regression models, the adjusted OR of developing Mets was 0.964 (95% CI, 0.959–0.969; P < 0.001) among people with SHBG < 76.653nmol/L, but there was no correlation between SHBG and Mets in participants with SHBG ≥ 76.653nmol/L. The stability of the association between SHBG and MetS was confirmed through subgroup analysis and sensitivity analysis. Conclusions Our results suggest that reduced SHBG levels are associated with an increased prevalence of MetS in adults, particularly when SHBG levels are below 76.653 nmol/L. More investigation is required to comprehend the mechanisms underlying these results and to delve into their clinical implications. Sex hormone binding globulin NHANES Metabolic syndrome Cross-sectional study Figures Figure 1 Figure 2 Figure 3 Introduction Metabolic syndrome (MetS) is the concurrent presence of multiple risk factors of metabolic and cardiovascular origin that have shared underlying causal processes[ 1 ]. Numerous studies have been conducted to investigate the definition, prevalence, and related features of MetS, as well as to explore its connection with cardiovascular disease (CVD)[ 2 ], diabetes mellitus (DM)[ 3 ], and dementia[ 4 ]. MetS is widely widespread in several nations such as the United States[ 5 ], China[ 6 ], all-cause mortality[ 7 ], despite variations in the definitions of MetS, it is widely acknowledged as a significant health-related condition. Due to the significant health risks associated with MetS, there is considerable scholarly focus on identifying methods for early detection and intervention in related fields.. Human sex hormone-binding globulin (SHBG) is a serum protein that has a strong and specific ability to bind to androgens and estrogens[ 8 ]. Research revealed that the prevalence of low SHBG in the American population is 3.3% among males and 2.7% among females. Risk factors associated with low SHBG levels include elevated body mass index, diabetes, race (Hispanic, non-Hispanic black, or non-Hispanic white), chronic obstructive pulmonary disease, coronary heart disease, and smoking[ 9 ]. SHBG is associated independently with the risk of diabetes, dementia, nonalcoholic fatty liver disease, hypertension, CHD, ischemic stroke[ 10 – 17 ], all of which are associated with a higher risk of metabolic syndrome. Furthermore, a study proposed that SHBG might serve as a promising therapeutic option for liver metabolic disorders[ 18 ]. Other studies have indicated that SHBG can improve lipid metabolism[ 19 ], and enhance insulin sensitivity[ 20 ]. While some researches has explored the relationship between metabolic syndrome and SHBG levels[ 21 – 25 ], the association between SHBG and metabolic syndrome remains a topic of debate. The consensus on this matter is based on low-quality evidence and lacks definitive data, failing to explore the dose-response relationship between sex hormone binding globulin levels and metabolic syndrome. Hence, our hypothesis was that sex hormones might underlie the observed alterations in insulin resistance and shed light on the factors influencing Mets. Therefore, the main objective of our study was to explore the association between SHBG and Meta, utilizing data from the National Health and Nutrition Examination Survey (NHANES). Through this extensive cross-sectional survey, our research aimed to offer fresh perspectives on the link between SHBG and MetS among American adults. Materials and methods Study design and population Our study relied on data from the National Health and Nutrition Examination Survey (NHANES), conducted by the National Center for Health Statistics (NCHS). NHANES is an extensive survey aimed at gathering detailed information regarding the health and nutritional status of the non-institutionalized civilian population across the United States. NHANES employs a stratified, multistage probability sampling approach to ensure a varied representation, recruiting participants from various regions throughout the nation. Data collection for the survey involves standardized in-home interviews, physical examinations, and laboratory tests conducted at mobile examination facilities[ 26 ]. For this specific study, we concentrated on adults who participated in the NHANES 2013–2016 cycle. The original sample comprised 20,146 participants. We excluded individuals under 18 years of age, pregnant women, those taking sex hormone medication, and individuals with missing data on sex hormone binding globulin and certain covariates (such as marital status, poverty income ratio (PIR), body mass index (BMI), alcohol consumption, smoking, energy intake, and physical activity). Ultimately, the study included 5499 participants. The flowchart depicting the sample selection is displayed in Fig. 1 . Figure 1 . Inclusion and exclusion process for the final analysis was based on the 2013–2016 National Health and Nutrition Examination Survey. Assessment of sex hormone binding globulin In this study, SHBG, a glycoprotein that binds to testosterone and estradiol, was measured in blood samples using immuno-antibodies and chemiluminescence. The limit of detection for SHBG was determined to be 0.80 nmol/L[ 27 ]. Metabolic syndrome definition Mets diagnosis follows the criteria specified in the Adult Treatment Program III of the National Cholesterol Education Program. The criteria consist of the following: (1) Triglyceride (TG) levels ≥ 1.69 mmol/L (150 mg/dL); (2) Reduced levels of high-density lipoprotein cholesterol (HDL-C): < 1.03 mmol/L (40 mg/dL) in men and 102 cm in men and > 88 cm in women; (5) ystolic blood pressure (BP) equal to or greater than 130 mmHg and/or diastolic BP equal to or greater than 85 mmHg. Blood samples were obtained in the morning after a 9-hour fast, and blood pressure (BP) was measured three times by the physician to establish the average value[ 28 ]. Covariates Drawing from prior research [ 28 ], we incorporated covariates for Mets such as low socioeconomic status, smoking habits, alcohol consumption, physical activity level, energy intake, and family history of diabetes. We accounted for various demographic and lifestyle factors in our analysis, including sex (male or female), age (categorized as 18–39, 40–59, or ≥ 60 years), race/ethnicity (non-Hispanic black, Mexican American, non-Hispanic white, or other races), marital status (married, single, or separated), educational attainment (less than high school, high school, or more than high school), poverty income ratio (categorized as < 1.3 or ≥ 3.5), smoking status (never smoked, ever smoked but quit before the survey, or current smoker), body mass index (BMI) and self-reported family history of diabetes. According to the definition of alcohol consumption in previous literature[ 31 ], participants were categorized into four groups: a. Never drinking: individuals with no history of alcohol consumption or former drinkers, b. Current heavy drinking (≥ 3 drinks/day for women, ≥ 4 drinks/day for men), c. current moderate drinking (≥ 2 drinks/day for women, ≥ 3 drinks/day for men), d. current light drinking: individuals who do not meet the criteria for the above categories. Additionally, we incorporated covariates representing factors associated with adverse cardiometabolic health risks[ 32 ], such as physical activity(PA), energy intake. According to the literature[ 33 ], PA was transformed into metabolic equivalent (MET) minutes of moderate to vigorous PA per week. Based on this literature[ 34 ], dietary intake was calculated. Due to the fact that sex hormone binding globulin changes over time, we included the time of blood draw as a covariate[ 27 ]. A comprehensive categorization of these factors may be found in Table 1 . Table 1 Baseline characteristics according to Mets Total (n = 5499) No Mets(n = 3677) Mets(n = 1822) p Age, year 47.8 ± 17.0 45.0 ± 17.1 53.4 ± 15.3 < 0.001 Gender, n (%) 0.002 Male 2966 (53.9) 2038 (55.4) 928 (50.9) Female 2533 (46.1) 1639 (44.6) 894 (49.1) Race, n (%) < 0.001 Non-Hispanic White 2264 (41.2) 1508 (41) 756 (41.5) Non-Hispanic Black 1072 (19.5) 742 (20.2) 330 (18.1) Mexican American 812 (14.8) 491 (13.4) 321 (17.6) Others 1351 (24.6) 936 (25.5) 415 (22.8) Marital status, n (%) 0.002 Married 3340 (60.7) 2181 (59.3) 1159 (63.6) Single or separated 2159 (39.3) 1496 (40.7) 663 (36.4) PIR (%) < 0.001 = 3.5 1766 (32.1) 1243 (33.8) 523 (28.7) Educational level, n (%) < 0.001 Less than high school 971 (17.7) 596 (16.2) 375 (20.6) High school diploma 1244 (22.6) 797 (21.7) 447 (24.5) More than high school 3284 (59.7) 2284 (62.1) 1000 (54.9) Smoke, n (%) < 0.001 Never 3083 (56.1) 2107 (57.3) 976 (53.6) Former 1099 (20.0) 752 (20.5) 347 (19) Now 1317 (23.9) 818 (22.2) 499 (27.4) Alcohol consumption < 0.001 Former 814 (14.8) 432 (11.7) 382 (21) Heavy 1130 (20.5) 807 (21.9) 323 (17.7) Mild 1917 (34.9) 1315 (35.8) 602 (33) Moderate 898 (16.3) 653 (17.8) 245 (13.4) Never 740 (13.5) 470 (12.8) 270 (14.8) BMI (kg/m 2) 29.2 ± 6.9 27.3 ± 6.2 33.1 ± 6.7 < 0.001 Everrage Energy intake (kcal) 2018.0 (1492.5, 2665.0) 2059.0 (1512.0, 2698.0) 1963.0 (1461.0, 2587.5) < 0.001 Total PA MET (minutes/week) 2160.0 (840.0, 6000.0) 2400.0 (960.0, 6720.0) 1680.0 (610.0, 4800.0) < 0.001 SHBG 46.3 (31.4, 68.9) 50.2 (34.0, 75.5) 39.2 (27.5, 57.0) < 0.001 Time of blood draw 0.133 Morning 2618 (47.6) 1718 (46.7) 900 (49.4) Afternoon 886 (16.1) 611 (16.6) 275 (15.1) Evening 1995 (36.3) 1348 (36.7) 647 (35.5) Family history of diabetes 0.07 No 4360 (79.3) 2941 (80) 1419 (77.9) Yes 1139 (20.7) 736 (20) 403 (22.1) PIR: poverty income ratio; BMI: body mass index; PA MET: physical activity metabolic equivalent; SHBG: sex hormone binding globulin (this table should be presented in 186line) . Statistical analysis All participants were categorized into two groups based on the presence or absence of Mets. Continuous variables were reported as either mean ± standard deviation for normally distributed data, or as medians and interquartile range (IQR, 25–75%) for non-normally distributed data, while categorical variables were presented as frequency (percentages). To assess the linear correlation between levels of SHBG and Mets,, we utilized restricted cubic spline analysis. Additionally, we conducted multivariate logistic regression analysis using different models to explore the relationship between levels of SHBG and the risk of developing Mets. In Model 1, we adjusted for age and sex. In Model 2, we extended our adjustments to incorporate additional variables including educational level, poverty income ratio, and marital status. In Model 3, we included additional variables beyond those in Model 2 to enhance the scope of our analysis, including BMI, alcohol consumption, smoking, physical activity, family history of diabetes and energy intake. Additionally, subgroup analyses were conducted based on age (< 40 years, ≥ 60 years), sex (male, female), poverty income ratio ( 3.5), BMI (< 25, ≥ 30), smoking status (never, former, current), and alcohol consumption (former, current, mild, moderate, heavy), as well as family history of diabetes to explore the association between sex hormone binding globulin levels and the risk of Mets. Multivariate logistic regression was employed to evaluate diversity within subgroups, with interactions between subgroups scrutinized via likelihood ratio testing. To fortify the reliability of our results, we conducted sensitivity analyses by employing multiple imputation to address missing values in covariates. Furthermore, we analyzed the association between SHBG and Mets according to the 2009 criteria outlined by the International Diabetes Federation for defining metabolic syndrome[ 35 ]. Since the sample size was determined solely based on the available data, no preliminary statistical power estimates were carried out. All analyses were performed using the statistical software packages R 4.2.2 ( http://www.R-project.org , The R Foundation) and Free Statistics analysis platform (Version 1.9, Beijing, China, http://www.clinicalscientists.cn/freestatistics ) [ 36 ]. A study was done to describe all participants, and it was found that the p-value was < 0.05, indicating significance in a two-tailed test. Result Baseline characteristics of participants Following a thorough screening procedure adhering to predefined inclusion and exclusion criteria, the study enrolled a total of 5499 patients. Among these subjects, 1822 had a documented history of Mets. The baseline characteristics of the patients were categorized according to the presence or absence of Mets, can be found in Table 1 . Participants with reduced levels of sex hormone binding globulin were noted to have a higher percentage of individuals diagnosed with Mets (P < 0.01). The group of patients with Mets exhibited distinctive features compared to those without Mets. Specifically, the Mets group had a higher proportion of male participants, smokers, and alcohol consumers (P < 0.01), as well as lower educational attainment levels and PIR (P 0.05). Association between Sex Hormone Binding Globulin and Metabolic Syndrome In the multiple logistic regression analyses, there was an inverse relationship between SHBG, analyzed as a continuous variable, and the probability of Mets (OR = 0.988, 95% CI = 0.986–0.990, P < 0.01). This association remained statistically significant even after adjusting for age, sex, race, educational level, PIR, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes (OR = 0.984, 95% CI = 0.981–0.986, P < 0.01, Table 2 ). Analysis of restricted cubic spline regression After accounting for several covariates, we identified a significant nonlinear relationship between SHBG and MetS in the RCS regression analysis (P < 0.001, Fig. 2 ), characterized by an L-shaped dose–response curve. As depicted in Fig. 3 , when Sex Hormone Binding Globulin levels were below 76.653nmol/L, the risk of Mets decreased as SHBG increased (OR = 0.964, 95% CI = 0.959–0.969, P < 0.001). Conversely, beyond the turning point of 76.653nmol/L, the estimated dose-response curve indicated a flat line, suggesting a non-significant association between SHBG levels and the risk of Mets (Table 3 ). Figure 2 Association between SHBG and metabolic syndrome. Solid and dashed lines indicate the predicted value and 95% CI. The restricted cubic spline model was adjusted for age, sex, race, educational level, poverty income ratio, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes. SHBG, sex hormone binding globulin. BMI, Body mass index. Table 3 Association between SHBG and Mets using two-piecewise regression models SHBG Adjusted Model* OR (95%CI) P < 76.653 0.964 (0.959 ~ 0.969) =76.653 1.001 (0.996 ~ 1.006) 0.763 Likelihood Ratio test < 0.001 Adjusted for age, sex, race, educational level, poverty income ratio, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes. Stratified Analyses Based on Additional Variables Upon adjusting for multiple covariates, we discovered a statistically significant non-linear correlation between SHBG and MetS in the RCS regression analysis (P < 0.001, Fig. 3 ), which was delineated by an L-shaped dose–response curve. Illustrated in Fig. 3 , when SHBG levels were below 76.653nmol/L, the risk of Mets decreased as SHBG increased (OR = 0.964, 95% CI = 0.959–0.969, P < 0.001). Conversely, beyond the turning point of 76.653nmol/L, the estimated dose-response curve indicated a flat line, suggesting a non-significant association between the levels of SHBG and the the likelihood of developing metabolic syndrome. Figure 3 Association between sex hormone binding globulin and metabolic syndrome. Except for the stratification factor itself, the stratifications were adjusted for all variables (age, sex, race, educational level, poverty income ratio, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes). BMI, body mass index. Sensitivity Analysis In the sensitivity analysis, sex hormone binding globulin was converted from a continuous variable to categorical variables, grouped into quartiles (Q1-Q4). The results of the sensitivity analysis suggested that individuals in the Q2, Q3, and Q4 groups had a lower risk of developing metabolic syndrome compared to those in the Q1 group ( Additional file 1: Table S1 ). Furthermore, we reanalyzed the link between SHBG and Mets using the Mets criteria defined by the 2009 International Diabetes Federation, and the results confirmed a stable association between SHBG and Mets. ( Additional file 1: Table S2 ). Because of a significant amount of missing data in covariates, we conducted multiple imputation on the covariates and performed multiple model logistic regression, curve fitting, and inflection point analysis on a dataset ( Additional file 1: Table S3 , Table S4 , Fig. S1 ). The results revealed no significant changes. Discussion Our research utilized data from the NHANES in the United States to assess the correlation between SHBG and the prevalence of Mets among adults. Even after accounting for potential confounding factors, high levels of SHBG showed a linked reduction in the risk of developing Mets. Notably, our findings revealed a non-linear relationship in the form of an "L" shaped curve (P for nonlinearity < 0.001), indicating that the relationship between SHBG and Mets was notably accentuated at certain thresholds. Specifically, the relationship appeared to plateau at SHBG levels of 76.653 nmol/L. Furthermore, through exploratory subgroup analyses and sensitivity analysis, we observed that the association between SHBG and Mets remained consistent in adults. Numerous studies have indicated an association between SHBG and the occurrence of Mets[ 37 – 40 ]. However, the association between SHBG and Mets remains unclear. The results of previous studies, however, presented conflicting findings, highlighting the need for further investigation in this area. In the context of male, a study revealed a statistically significant link between decreased levels of SHBG and a higher incidence rate of Mets[ 21 ]. Meanwhile, in studies involving women, independent inverse correlations of SHBG with MetS were identified[ 22 ]. Nevertheless, research indicated that the association between SHBG and MetS was not statistically significant among postmenopausal women[ 23 ]. Similarly, another study revealed that SHBG is not connected to Mets[ 24 ]. Notably, a meta-analysis showed a negative association between SHBG and Mets, with no gender difference[ 25 ]. In conclusion, total SHBG has been found to be inversely associated with incident Mets in men. However, none of these studies indicated the critical value of the correlation between SHBG and Mets, whereas our study used statistical methods such as restricted cubic splines and logistic regression inflection point analysis to observe that there is no linear association between SHBG levels and the occurrence of Mets. SHBG is secreted by liver cells and can bind to steroid hormones such as testosterone and estradiol, regulating their bioavailability and delivery to target organs and tissues[ 37 ].The Free active testosterone concentrations in plasma are substantially impacted by SHBG concentrations, since 65% of SHBG is bound to SHBG, consequently adults with low SHBG can have elevated bioavailable and free testosterone levels[ 38 ]. Elevated testosterone levels may reduce the incidence rate of metabolic syndrome[ 39 , 40 ]. Furthermore, lower SHBG levels was associated with higher daily alcohol intake levels, higher BMI, and the higher risk of diabetes, CHD, and non-alcoholic fatty liver disease[ 41 – 47 ], - all of which contributed to a elevated risk of Mets. The investigation carried out by our team has numerous notable strengths. Firstly, we utilized a large representative sample from NHANES, which enhances the generalizability and applicability of our findings to non-institutionalized civilian populations. Moreover, we established stringent participant selection criteria, which specifically excluded pregnant individuals, those undergoing sex hormone therapy, and those with missing information on SHBG and MetS, thereby bolstering the study's reliability. Furthermore, The extensive sample size enabled us to perform subgroup analyses, thereby allowing us to evaluate the potential impact of additional variables on the association between SHBG and MetS. Nevertheless, this study had notable limitations. Primarily, the cross-sectional design hindered the ability to establish causality. Additionally, the assessment of SHBG may have been impacted by multiple factors, such as laboratory protocols. Furthermore, although we controlled for numerous potential confounding variables, we were unable to fully mitigate the influence of unmeasured confounders. As a result, it is important to be cautious in drawing conclusions, and additional research in various disease groups is necessary to bolster our findings. For future studies, it is advisable to utilize longitudinal study designs to investigate the possible causal connection between SHBG levels and the development of MetS. Moreover, delving into the genetic correlations between SHBG levels and the occurrence of MetS would further advance our comprehension of this relationship. Conclusions This study assessed the connection between SHBG levels and Mets. It was found that higher levels of SHBG were inversely related to MetS in adults, even after adjustment for other potential confounding factors. There was observed non-linear L-shaped association between SHBG levels and MetS. A non-linear "L-shaped" relationship between SHBG and Mets was observed, with a threshold value of 76.653 nmol/L. These results are noteworthy and could have implications for healthcare providers treating Mets. However, due to the potential for confounding, further research is necessary to confirm these findings. Declarations Author Contributions Yang yang(First author): Conceptualization, Data curation, Methodology, Software, Funding acquisition, Writing - original draft. Wang jie (Co-first author): Conceptualization, Methodology, Software, Validation, Writing - original draft. Liu Yuhang: Data curation, Visualization, Supervision, Investigation. Liu Shuwan: Data curation, Visualization, Supervision, Investigation. Liu Huabao(Corresponding author): Project administration, Conceptualization, Supervision, Methodology, Funding acquisition, Writing - review & editing. Tan Meiao: Supervision, Investigation, Software, Validation, Writing - review & editing. Author 4: Data curation, Visualization, Supervision, Investigation. Funding This work was supported by National Famous Traditional Chinese Medicine Inheritance Studio . The funder did not contribute to the study’s design, collection, analysis, and interpretation of data. This study was supported by , Traditional Chinese Medicine Prevention and Treatment of Liver Fibrosis Inheritance and Innovation Team . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Institutional Review Board Statement : Ethical review and approval were waived for this study because no additional institutional review board approval was required for the secondary analysis. Informed Consent Statement: The NHANES was authorized by the National Center for Health Statistics (NCHS) Ethics Review Committee, and all participants completed written informed consent forms before participation. Data availability statement The authors confirm that all data underlying the findings are fully available without restriction.The repository/repositories name and accession numbers are available online at http://www.cdc.gov/nchs/nhanes.htm Acknowledgment We appreciatively thank Dr. Jie Liu (Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital) for his consultation on language polishing, proofreading, and comments regarding the manuscript. We also thanks to Zhang Jing (Second Department of Infectious Disease, Shanghai Fifth People's Hospital, Fudan University) for his work on the NHANES database. Conflicts of Interest : The authors declare no conflict of interest. References Silveira Rossi JL, Barbalho SM, Reverete de Araujo R, Bechara MD, Sloan KP, Sloan LA. Metabolic syndrome and cardiovascular diseases: Going beyond traditional risk factors. Diabetes Metab Res Rev. 2022;38:e3502. Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. 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Associations of starchy and non-starchy vegetables with risk of metabolic syndrome: evidence from the NHANES 1999-2018. Nutr Metab. 2023;20:36. Shakya S, Shrestha V, Neupane D. Social determinants of health and cardiometabolic risk factors in Nepal: A scoping review. Nutr Metab Cardiovasc Dis. 2023;33(12):2308-2316. Liang J, Huang S, Jiang N, Kakaer A, Chen Y, Liu M, et al. Association between joint physical activity and dietary quality and lower risk of depression symptoms in US adults: cross-sectional NHANES study. JMIR Public Health Surveillance. 2023;9:e45776. Xiang L, Wu M, Wang Y, Liu S, Lin Q, Luo G, et al. Inverse J-shaped relationship of dietary carbohydrate intake with serum klotho in NHANES 2007-2016. Nutrients. 2023;15:3956. Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; american heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation. 2009;120:1640–5. Liu H, Wang L, Chen C, Dong Z, Yu S. Association between dietary niacin intake and migraine among american adults: national health and nutrition examination survey. Nutrients. 2022;14:3052. Yeap BB, Marriott RJ, Antonio L, Raj S, Dwivedi G, Reid CM, et al. Associations of Serum Testosterone and Sex Hormone-Binding Globulin With Incident Cardiovascular Events in Middle-Aged to Older Men. Ann Intern Med. 2022;175:159–70. Zhao D, Guallar E, Ouyang P, Subramanya V, Vaidya D, Ndumele CE, et al. Endogenous sex hormones and incident cardiovascular disease in post-menopausal women. J Am Coll Cardiol. 2018;71:2555–66. Li J, Zheng L, Chan KHK, Zou X, Zhang J, Liu J, et al. Sex Hormone-Binding Globulin and Risk of Coronary Heart Disease in Men and Women. Clin Chem. 2023;69:374–85. Li C, Ford ES, Li B, Giles WH, Liu S. Association of testosterone and sex hormone-binding globulin with metabolic syndrome and insulin resistance in men. Diabetes Care. 2010;33:1618–24. Fenske B, Kische H, Gross S, Wallaschofski H, Völzke H, Dörr M, et al. Endogenous Androgens and Sex Hormone-Binding Globulin in Women and Risk of Metabolic Syndrome and Type 2 Diabetes. J Clin Endocrinol Metab. 2015;100:4595–603. Hajamor S, Després J-P, Couillard C, Lemieux S, Tremblay A, Prud’homme D, et al. Relationship between sex hormone-binding globulin levels and features of the metabolic syndrome. Metabolism. 2003;52:724–30. Alinezhad A, Jafari F. The relationship between components of metabolic syndrome and plasma level of sex hormone-binding globulin. Eur j transl myol. 2019;29:8196. Brand JS, van der Tweel I, Grobbee DE, Emmelot-Vonk MH, van der Schouw YT. Testosterone, sex hormone-binding globulin and the metabolic syndrome: a systematic review and meta-analysis of observational studies. Int J Epidemiol. 2011;40:189–207. Bourebaba N, Ngo T, Śmieszek A, etal. Sex hormone binding globulin as a potential drug candidate for liver-related metabolic disorders treatment. Biomed Pharmacother. 2022;153:11326146. Ahmad IH, Mohamed Mostafa ER, Mohammed SA, etal. Correlations between serum testosterone and irisin levels in a sample of Egyptian men with metabolic syndrome; (case-control study). Arch Physiol Biochem. 2023;129(1):180-185. Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files figureS1.doc TableS1.xls tableS2.xls tableS3.xls table2.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4128989","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283758881,"identity":"c4532a67-fb95-44b1-82d3-5b4bd989614c","order_by":0,"name":"Yang Yang","email":"","orcid":"","institution":"Chongqing Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":283758882,"identity":"a35fa697-f35e-4d03-9d5d-af2409c03d8f","order_by":1,"name":"Jie Wang","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Wang","suffix":""},{"id":283758883,"identity":"a38398ab-2af9-4fba-90e6-8fe3034d5f1c","order_by":2,"name":"Yuhang Liu","email":"","orcid":"","institution":"Chongqing Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuhang","middleName":"","lastName":"Liu","suffix":""},{"id":283758884,"identity":"d101bdc0-71b6-440c-936d-59bc9f027fd4","order_by":3,"name":"Shuwan Liu","email":"","orcid":"","institution":"Chongqing Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuwan","middleName":"","lastName":"Liu","suffix":""},{"id":283758885,"identity":"7b9d6b84-f445-4beb-960a-0bfdd2ce08a5","order_by":4,"name":"Huabao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACNvnDBw58+GEjxy9/+OCDhIoawlr4JNgSH87sSTOWnMGWbPDgzDHCWuQkeIyNedgOJxrM4DGTfNjCTITDpHvMpHl40hIMpNvSKhIb2Bj427sT8GuROVYmOcfCJs9c5vCxG4k7ZBgkzpzdgF8LQ/I2iTc8acWWDWlpNxLPsDEYSOQS0pJgJgHyy4YDOWYFiW3MRGiRSDE2BGu5kWPGQJwWnmPQQO45liyRcOYYD0G/yLc3Q6OSvfngxx8VNXL87b34tWAAHtKUj4JRMApGwSjACgAQEk2dH6IKHAAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Traditional Chinese Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"Huabao","middleName":"","lastName":"Liu","suffix":""},{"id":283758886,"identity":"35664643-0cb4-4957-bc4a-d10446587bf1","order_by":5,"name":"Meiao Tan","email":"","orcid":"","institution":"Chongqing Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meiao","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2024-03-19 09:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4128989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4128989/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53669010,"identity":"bfec4093-6f85-4e38-98a9-a588e1bfd788","added_by":"auto","created_at":"2024-03-28 17:33:37","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165858,"visible":true,"origin":"","legend":"\u003cp\u003eInclusion and exclusion process for the final analysis was based on the 2013-2016 National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4128989/v1/5067bd0387d060098f4957b5.jpeg"},{"id":53669015,"identity":"ced25202-173c-49e6-b656-06279b00591b","added_by":"auto","created_at":"2024-03-28 17:33:38","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93429,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between SHBG and metabolic syndrome. Solid and dashed lines indicate the predicted value and 95% CI. The restricted cubic spline model was adjusted for age, sex, race, educational level, poverty income ratio, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes. SHBG, sex hormone binding globulin. BMI, Body mass index.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4128989/v1/c3b3d919689136e4934e4c0e.jpeg"},{"id":53669018,"identity":"3b6b3146-9cf9-4a04-b9c6-fff43a39cb92","added_by":"auto","created_at":"2024-03-28 17:33:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2654293,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between sex hormone binding globulin and metabolic syndrome. Except for the stratification factor itself, the stratifications were adjusted for all variables (age, sex, race, educational level, poverty income ratio, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes). BMI, body mass index.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4128989/v1/d0620e719d72f41a6a3802ce.png"},{"id":59052609,"identity":"eae6043c-2ed3-48d2-815e-cac696744381","added_by":"auto","created_at":"2024-06-25 20:19:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1025644,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4128989/v1/78d235e3-baab-45eb-b9c5-0deb3ec36b86.pdf"},{"id":53669012,"identity":"1df02e72-7b02-4dff-b69f-749c37165b03","added_by":"auto","created_at":"2024-03-28 17:33:38","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1845248,"visible":true,"origin":"","legend":"","description":"","filename":"figureS1.doc","url":"https://assets-eu.researchsquare.com/files/rs-4128989/v1/626e18726ff00ad98f85f2cf.doc"},{"id":53669013,"identity":"b56c06c9-916a-458b-b1af-217d2bde0da7","added_by":"auto","created_at":"2024-03-28 17:33:38","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28672,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xls","url":"https://assets-eu.researchsquare.com/files/rs-4128989/v1/872792122363447875c23329.xls"},{"id":53669016,"identity":"1daf7ddc-86cd-4a7b-9e95-bbd29e40121b","added_by":"auto","created_at":"2024-03-28 17:33:38","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":28160,"visible":true,"origin":"","legend":"","description":"","filename":"tableS2.xls","url":"https://assets-eu.researchsquare.com/files/rs-4128989/v1/383c41445619ad09692c56af.xls"},{"id":53669017,"identity":"a59dbcc5-7994-4c30-a51f-431e05f0b8d0","added_by":"auto","created_at":"2024-03-28 17:33:38","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":28160,"visible":true,"origin":"","legend":"","description":"","filename":"tableS3.xls","url":"https://assets-eu.researchsquare.com/files/rs-4128989/v1/e59f3cff8599aee4e55ebe0e.xls"},{"id":53669020,"identity":"30caca7a-ab96-41da-8dae-0a774cda5eb2","added_by":"auto","created_at":"2024-03-28 17:33:38","extension":"xls","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":28160,"visible":true,"origin":"","legend":"","description":"","filename":"table2.xls","url":"https://assets-eu.researchsquare.com/files/rs-4128989/v1/f1c54e07ca872ac24aa5e26d.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation of Sex Hormone Binding Globulin with Metabolic Syndrome in US Adults: Insights from National Health and Nutrition Examination Survey (NHANES) 2013–2016","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS) is the concurrent presence of multiple risk factors of metabolic and cardiovascular origin that have shared underlying causal processes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Numerous studies have been conducted to investigate the definition, prevalence, and related features of MetS, as well as to explore its connection with cardiovascular disease (CVD)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], diabetes mellitus (DM)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and dementia[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. MetS is widely widespread in several nations such as the United States[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], China[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], all-cause mortality[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], despite variations in the definitions of MetS, it is widely acknowledged as a significant health-related condition. Due to the significant health risks associated with MetS, there is considerable scholarly focus on identifying methods for early detection and intervention in related fields..\u003c/p\u003e \u003cp\u003eHuman sex hormone-binding globulin (SHBG) is a serum protein that has a strong and specific ability to bind to androgens and estrogens[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research revealed that the prevalence of low SHBG in the American population is 3.3% among males and 2.7% among females. Risk factors associated with low SHBG levels include elevated body mass index, diabetes, race (Hispanic, non-Hispanic black, or non-Hispanic white), chronic obstructive pulmonary disease, coronary heart disease, and smoking[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. SHBG is associated independently with the risk of diabetes, dementia, nonalcoholic fatty liver disease, hypertension, CHD, ischemic stroke[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], all of which are associated with a higher risk of metabolic syndrome.\u003c/p\u003e \u003cp\u003eFurthermore, a study proposed that SHBG might serve as a promising therapeutic option for liver metabolic disorders[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Other studies have indicated that SHBG can improve lipid metabolism[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and enhance insulin sensitivity[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While some researches has explored the relationship between metabolic syndrome and SHBG levels[\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], the association between SHBG and metabolic syndrome remains a topic of debate. The consensus on this matter is based on low-quality evidence and lacks definitive data, failing to explore the dose-response relationship between sex hormone binding globulin levels and metabolic syndrome. Hence, our hypothesis was that sex hormones might underlie the observed alterations in insulin resistance and shed light on the factors influencing Mets. Therefore, the main objective of our study was to explore the association between SHBG and Meta, utilizing data from the National Health and Nutrition Examination Survey (NHANES). Through this extensive cross-sectional survey, our research aimed to offer fresh perspectives on the link between SHBG and MetS among American adults.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eOur study relied on data from the National Health and Nutrition Examination Survey (NHANES), conducted by the National Center for Health Statistics (NCHS). NHANES is an extensive survey aimed at gathering detailed information regarding the health and nutritional status of the non-institutionalized civilian population across the United States. NHANES employs a stratified, multistage probability sampling approach to ensure a varied representation, recruiting participants from various regions throughout the nation. Data collection for the survey involves standardized in-home interviews, physical examinations, and laboratory tests conducted at mobile examination facilities[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor this specific study, we concentrated on adults who participated in the NHANES 2013\u0026ndash;2016 cycle. The original sample comprised 20,146 participants. We excluded individuals under 18 years of age, pregnant women, those taking sex hormone medication, and individuals with missing data on sex hormone binding globulin and certain covariates (such as marital status, poverty income ratio (PIR), body mass index (BMI), alcohol consumption, smoking, energy intake, and physical activity). Ultimately, the study included 5499 participants. The flowchart depicting the sample selection is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Inclusion and exclusion process for the final analysis was based on the 2013\u0026ndash;2016 National Health and Nutrition Examination Survey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of sex hormone binding globulin\u003c/h2\u003e \u003cp\u003eIn this study, SHBG, a glycoprotein that binds to testosterone and estradiol, was measured in blood samples using immuno-antibodies and chemiluminescence. The limit of detection for SHBG was determined to be 0.80 nmol/L[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic syndrome definition\u003c/h2\u003e \u003cp\u003eMets diagnosis follows the criteria specified in the Adult Treatment Program III of the National Cholesterol Education Program. The criteria consist of the following: (1) Triglyceride (TG) levels\u0026thinsp;\u0026ge;\u0026thinsp;1.69 mmol/L (150 mg/dL); (2) Reduced levels of high-density lipoprotein cholesterol (HDL-C): \u0026lt; 1.03 mmol/L (40 mg/dL) in men and \u0026lt;\u0026thinsp;1.29 mmol/L (50 mg/dL) in women; (3) Elevated fasting plasma glucose (FPG) levels\u0026thinsp;\u0026ge;\u0026thinsp;6.1 mmol/L (110 mg/dL); (4) Increased waist circumference (WC): \u0026gt; 102 cm in men and \u0026gt;\u0026thinsp;88 cm in women; (5) ystolic blood pressure (BP) equal to or greater than 130 mmHg and/or diastolic BP equal to or greater than 85 mmHg. Blood samples were obtained in the morning after a 9-hour fast, and blood pressure (BP) was measured three times by the physician to establish the average value[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eDrawing from prior research [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], we incorporated covariates for Mets such as low socioeconomic status, smoking habits, alcohol consumption, physical activity level, energy intake, and family history of diabetes. We accounted for various demographic and lifestyle factors in our analysis, including sex (male or female), age (categorized as 18\u0026ndash;39, 40\u0026ndash;59, or \u0026ge;\u0026thinsp;60 years), race/ethnicity (non-Hispanic black, Mexican American, non-Hispanic white, or other races), marital status (married, single, or separated), educational attainment (less than high school, high school, or more than high school), poverty income ratio (categorized as \u0026lt;\u0026thinsp;1.3 or \u0026ge;\u0026thinsp;3.5), smoking status (never smoked, ever smoked but quit before the survey, or current smoker), body mass index (BMI) and self-reported family history of diabetes. According to the definition of alcohol consumption in previous literature[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], participants were categorized into four groups: a. Never drinking: individuals with no history of alcohol consumption or former drinkers, b. Current heavy drinking (\u0026ge;\u0026thinsp;3 drinks/day for women, \u0026ge; 4 drinks/day for men), c. current moderate drinking (\u0026ge;\u0026thinsp;2 drinks/day for women, \u0026ge; 3 drinks/day for men), d. current light drinking: individuals who do not meet the criteria for the above categories. Additionally, we incorporated covariates representing factors associated with adverse cardiometabolic health risks[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], such as physical activity(PA), energy intake. According to the literature[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], PA was transformed into metabolic equivalent (MET) minutes of moderate to vigorous PA per week. Based on this literature[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], dietary intake was calculated. Due to the fact that sex hormone binding globulin changes over time, we included the time of blood draw as a covariate[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A comprehensive categorization of these factors may be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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 according to Mets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;5499)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Mets(n\u0026thinsp;=\u0026thinsp;3677)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMets(n\u0026thinsp;=\u0026thinsp;1822)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2966 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2038 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e928 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2533 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1639 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e894 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2264 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1508 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e756 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1072 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e742 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e812 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e491 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e321 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1351 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e936 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e415 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3340 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2181 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1159 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle or separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2159 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1496 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e663 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1680 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1078 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602 (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\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2053 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1356 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e697 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1766 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1243 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e523 (28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e971 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e596 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e375 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1244 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e797 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e447 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3284 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2284 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1000 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3083 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2107 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e976 (53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1099 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e752 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1317 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e818 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e499 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e814 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e432 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1130 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e807 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1917 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1315 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602 (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\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e898 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e653 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e740 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e470 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e270 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEverrage Energy intake (kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018.0 (1492.5, 2665.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2059.0 (1512.0, 2698.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1963.0 (1461.0, 2587.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal PA MET (minutes/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2160.0 (840.0, 6000.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2400.0 (960.0, 6720.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1680.0 (610.0, 4800.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHBG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.3 (31.4, 68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.2 (34.0, 75.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.2 (27.5, 57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of blood draw\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\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2618 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1718 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e900 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfternoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e886 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e611 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e275 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1348 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e647 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of diabetes\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\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4360 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2941 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1419 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1139 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e736 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e403 (22.1)\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\"\u003ePIR: poverty income ratio; BMI: body mass index; PA MET: physical activity metabolic equivalent; SHBG: sex hormone binding globulin (this table should be presented in 186line)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll participants were categorized into two groups based on the presence or absence of Mets. Continuous variables were reported as either mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed data, or as medians and interquartile range (IQR, 25\u0026ndash;75%) for non-normally distributed data, while categorical variables were presented as frequency (percentages).\u003c/p\u003e \u003cp\u003eTo assess the linear correlation between levels of SHBG and Mets,, we utilized restricted cubic spline analysis. Additionally, we conducted multivariate logistic regression analysis using different models to explore the relationship between levels of SHBG and the risk of developing Mets. In Model 1, we adjusted for age and sex. In Model 2, we extended our adjustments to incorporate additional variables including educational level, poverty income ratio, and marital status. In Model 3, we included additional variables beyond those in Model 2 to enhance the scope of our analysis, including BMI, alcohol consumption, smoking, physical activity, family history of diabetes and energy intake.\u003c/p\u003e \u003cp\u003eAdditionally, subgroup analyses were conducted based on age (\u0026lt;\u0026thinsp;40 years, \u0026ge; 60 years), sex (male, female), poverty income ratio (\u0026lt;\u0026thinsp;1.3, \u0026gt;\u0026thinsp;3.5), BMI (\u0026lt;\u0026thinsp;25, \u0026ge; 30), smoking status (never, former, current), and alcohol consumption (former, current, mild, moderate, heavy), as well as family history of diabetes to explore the association between sex hormone binding globulin levels and the risk of Mets.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression was employed to evaluate diversity within subgroups, with interactions between subgroups scrutinized via likelihood ratio testing. To fortify the reliability of our results, we conducted sensitivity analyses by employing multiple imputation to address missing values in covariates. Furthermore, we analyzed the association between SHBG and Mets according to the 2009 criteria outlined by the International Diabetes Federation for defining metabolic syndrome[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the sample size was determined solely based on the available data, no preliminary statistical power estimates were carried out. All analyses were performed using the statistical software packages R 4.2.2 (\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) and Free Statistics analysis platform (Version 1.9, Beijing, China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.clinicalscientists.cn/freestatistics\u003c/span\u003e\u003cspan address=\"http://www.clinicalscientists.cn/freestatistics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A study was done to describe all participants, and it was found that the p-value was \u0026lt;\u0026thinsp;0.05, indicating significance in a two-tailed test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e \u003cp\u003eFollowing a thorough screening procedure adhering to predefined inclusion and exclusion criteria, the study enrolled a total of 5499 patients. Among these subjects, 1822 had a documented history of Mets. The baseline characteristics of the patients were categorized according to the presence or absence of Mets, can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants with reduced levels of sex hormone binding globulin were noted to have a higher percentage of individuals diagnosed with Mets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The group of patients with Mets exhibited distinctive features compared to those without Mets. Specifically, the Mets group had a higher proportion of male participants, smokers, and alcohol consumers (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as well as lower educational attainment levels and PIR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), compared to the non-Mets group. However, no statistically significant variances detected among the groups with regards to a family history of diabetes (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Sex Hormone Binding Globulin and Metabolic Syndrome\u003c/h2\u003e \u003cp\u003eIn the multiple logistic regression analyses, there was an inverse relationship between SHBG, analyzed as a continuous variable, and the probability of Mets (OR\u0026thinsp;=\u0026thinsp;0.988, 95% CI\u0026thinsp;=\u0026thinsp;0.986\u0026ndash;0.990, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This association remained statistically significant even after adjusting for age, sex, race, educational level, PIR, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes (OR\u0026thinsp;=\u0026thinsp;0.984, 95% CI\u0026thinsp;=\u0026thinsp;0.981\u0026ndash;0.986, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of restricted cubic spline regression\u003c/h2\u003e \u003cp\u003eAfter accounting for several covariates, we identified a significant nonlinear relationship between SHBG and MetS in the RCS regression analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), characterized by an L-shaped dose\u0026ndash;response curve. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, when Sex Hormone Binding Globulin levels were below 76.653nmol/L, the risk of Mets decreased as SHBG increased (OR\u0026thinsp;=\u0026thinsp;0.964, 95% CI\u0026thinsp;=\u0026thinsp;0.959\u0026ndash;0.969, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, beyond the turning point of 76.653nmol/L, the estimated dose-response curve indicated a flat line, suggesting a non-significant association between SHBG levels and the risk of Mets (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Association between SHBG and metabolic syndrome. Solid and dashed lines indicate the predicted value and 95% CI. The restricted cubic spline model was adjusted for age, sex, race, educational level, poverty income ratio, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes. SHBG, sex hormone binding globulin. BMI, Body mass index.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between SHBG and Mets using two-piecewise regression models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHBG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAdjusted Model*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;76.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.964 (0.959\u0026thinsp;~\u0026thinsp;0.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=76.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.001 (0.996\u0026thinsp;~\u0026thinsp;1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikelihood Ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdjusted for age, sex, race, educational level, poverty income ratio, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStratified Analyses Based on Additional Variables\u003c/h2\u003e \u003cp\u003eUpon adjusting for multiple covariates, we discovered a statistically significant non-linear correlation between SHBG and MetS in the RCS regression analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which was delineated by an L-shaped dose\u0026ndash;response curve. Illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, when SHBG levels were below 76.653nmol/L, the risk of Mets decreased as SHBG increased (OR\u0026thinsp;=\u0026thinsp;0.964, 95% CI\u0026thinsp;=\u0026thinsp;0.959\u0026ndash;0.969, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, beyond the turning point of 76.653nmol/L, the estimated dose-response curve indicated a flat line, suggesting a non-significant association between the levels of SHBG and the the likelihood of developing metabolic syndrome.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Association between sex hormone binding globulin and metabolic syndrome. Except for the stratification factor itself, the stratifications were adjusted for all variables (age, sex, race, educational level, poverty income ratio, marital status, BMI, alcohol consumption, smoke, physical activity, energy intake, family history of diabetes). BMI, body mass index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eIn the sensitivity analysis, sex hormone binding globulin was converted from a continuous variable to categorical variables, grouped into quartiles (Q1-Q4). The results of the sensitivity analysis suggested that individuals in the Q2, Q3, and Q4 groups had a lower risk of developing metabolic syndrome compared to those in the Q1 group (\u003cb\u003eAdditional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Furthermore, we reanalyzed the link between SHBG and Mets using the Mets criteria defined by the 2009 International Diabetes Federation, and the results confirmed a stable association between SHBG and Mets. (\u003cb\u003eAdditional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Because of a significant amount of missing data in covariates, we conducted multiple imputation on the covariates and performed multiple model logistic regression, curve fitting, and inflection point analysis on a dataset (\u003cb\u003eAdditional file 1: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The results revealed no significant changes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur research utilized data from the NHANES in the United States to assess the correlation between SHBG and the prevalence of Mets among adults. Even after accounting for potential confounding factors, high levels of SHBG showed a linked reduction in the risk of developing Mets. Notably, our findings revealed a non-linear relationship in the form of an \"L\" shaped curve (P for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the relationship between SHBG and Mets was notably accentuated at certain thresholds. Specifically, the relationship appeared to plateau at SHBG levels of 76.653 nmol/L. Furthermore, through exploratory subgroup analyses and sensitivity analysis, we observed that the association between SHBG and Mets remained consistent in adults.\u003c/p\u003e \u003cp\u003eNumerous studies have indicated an association between SHBG and the occurrence of Mets[\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, the association between SHBG and Mets remains unclear. The results of previous studies, however, presented conflicting findings, highlighting the need for further investigation in this area. In the context of male, a study revealed a statistically significant link between decreased levels of SHBG and a higher incidence rate of Mets[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Meanwhile, in studies involving women, independent inverse correlations of SHBG with MetS were identified[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nevertheless, research indicated that the association between SHBG and MetS was not statistically significant among postmenopausal women[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, another study revealed that SHBG is not connected to Mets[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Notably, a meta-analysis showed a negative association between SHBG and Mets, with no gender difference[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In conclusion, total SHBG has been found to be inversely associated with incident Mets in men. However, none of these studies indicated the critical value of the correlation between SHBG and Mets, whereas our study used statistical methods such as restricted cubic splines and logistic regression inflection point analysis to observe that there is no linear association between SHBG levels and the occurrence of Mets.\u003c/p\u003e \u003cp\u003eSHBG is secreted by liver cells and can bind to steroid hormones such as testosterone and estradiol, regulating their bioavailability and delivery to target organs and tissues[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].The Free active testosterone concentrations in plasma are substantially impacted by SHBG concentrations, since 65% of SHBG is bound to SHBG, consequently adults with low SHBG can have elevated bioavailable and free testosterone levels[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Elevated testosterone levels may reduce the incidence rate of metabolic syndrome[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, lower SHBG levels was associated with higher daily alcohol intake levels, higher BMI, and the higher risk of diabetes, CHD, and non-alcoholic fatty liver disease[\u003cspan additionalcitationids=\"CR42 CR43 CR44 CR45 CR46\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e47\u003c/span\u003e], - all of which contributed to a elevated risk of Mets.\u003c/p\u003e \u003cp\u003eThe investigation carried out by our team has numerous notable strengths. Firstly, we utilized a large representative sample from NHANES, which enhances the generalizability and applicability of our findings to non-institutionalized civilian populations. Moreover, we established stringent participant selection criteria, which specifically excluded pregnant individuals, those undergoing sex hormone therapy, and those with missing information on SHBG and MetS, thereby bolstering the study's reliability. Furthermore, The extensive sample size enabled us to perform subgroup analyses, thereby allowing us to evaluate the potential impact of additional variables on the association between SHBG and MetS.\u003c/p\u003e \u003cp\u003eNevertheless, this study had notable limitations. Primarily, the cross-sectional design hindered the ability to establish causality. Additionally, the assessment of SHBG may have been impacted by multiple factors, such as laboratory protocols. Furthermore, although we controlled for numerous potential confounding variables, we were unable to fully mitigate the influence of unmeasured confounders. As a result, it is important to be cautious in drawing conclusions, and additional research in various disease groups is necessary to bolster our findings. For future studies, it is advisable to utilize longitudinal study designs to investigate the possible causal connection between SHBG levels and the development of MetS. Moreover, delving into the genetic correlations between SHBG levels and the occurrence of MetS would further advance our comprehension of this relationship.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study assessed the connection between SHBG levels and Mets. It was found that higher levels of SHBG were inversely related to MetS in adults, even after adjustment for other potential confounding factors. There was observed non-linear L-shaped association between SHBG levels and MetS. A non-linear \"L-shaped\" relationship between SHBG and Mets was observed, with a threshold value of 76.653 nmol/L. These results are noteworthy and could have implications for healthcare providers treating Mets. However, due to the potential for confounding, further research is necessary to confirm these findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang yang(First author): Conceptualization, Data curation, Methodology, Software, Funding acquisition, Writing - original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWang jie (Co-first author): Conceptualization, Methodology, Software, Validation, Writing - original draft.\u003c/p\u003e\n\u003cp\u003eLiu Yuhang: Data curation, Visualization, Supervision, Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLiu Shuwan: Data curation, Visualization, Supervision, Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLiu Huabao(Corresponding author): Project administration, Conceptualization, Supervision, Methodology, Funding acquisition, Writing - review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTan Meiao: Supervision, Investigation, Software, Validation, Writing - review \u0026amp; editing. Author 4: Data curation, Visualization, Supervision, Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Famous Traditional Chinese Medicine Inheritance Studio\u0026nbsp;.\u0026nbsp;The funder did not contribute to the study\u0026rsquo;s design, collection, analysis, and interpretation of data. This study was supported by , Traditional Chinese Medicine Prevention and Treatment of Liver Fibrosis Inheritance and Innovation Team\u0026nbsp;. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e: Ethical review and approval were waived for this study because no additional institutional review board approval was required for the secondary analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e The NHANES was authorized by the National Center for Health Statistics (NCHS) Ethics Review Committee, and all participants completed written informed consent forms before participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that all data underlying the findings are fully available without restriction.The repository/repositories name and accession numbers are available online at http://www.cdc.gov/nchs/nhanes.htm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciatively thank Dr. Jie Liu (Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital) for his consultation on language polishing, proofreading, and comments regarding the manuscript. We also thanks to Zhang Jing (Second Department of Infectious Disease, Shanghai Fifth People\u0026apos;s Hospital, Fudan University) for his work on the NHANES database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSilveira Rossi JL, Barbalho SM, Reverete de Araujo R, Bechara MD, Sloan KP, Sloan LA. Metabolic syndrome and cardiovascular diseases: Going beyond traditional risk factors. Diabetes Metab Res Rev. 2022;38:e3502. \u003c/li\u003e\n\u003cli\u003eMottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56:1113\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eLi W, Wang D, Wang X, Gong Y, Cao S, Yin X, et al. 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Associations of Serum Testosterone and Sex Hormone-Binding Globulin With Incident Cardiovascular Events in Middle-Aged to Older Men. Ann Intern Med. 2022;175:159\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eZhao D, Guallar E, Ouyang P, Subramanya V, Vaidya D, Ndumele CE, et al. Endogenous sex hormones and incident cardiovascular disease in post-menopausal women. J Am Coll Cardiol. 2018;71:2555\u0026ndash;66. \u003c/li\u003e\n\u003cli\u003eLi J, Zheng L, Chan KHK, Zou X, Zhang J, Liu J, et al. Sex Hormone-Binding Globulin and Risk of Coronary Heart Disease in Men and Women. Clin Chem. 2023;69:374\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eLi C, Ford ES, Li B, Giles WH, Liu S. Association of testosterone and sex hormone-binding globulin with metabolic syndrome and insulin resistance in men. Diabetes Care. 2010;33:1618\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eFenske B, Kische H, Gross S, Wallaschofski H, V\u0026ouml;lzke H, D\u0026ouml;rr M, et al. 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Arch Physiol Biochem. 2023;129(1):180-185.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sex hormone binding globulin, NHANES, Metabolic syndrome, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-4128989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4128989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetabolic syndrome (MetS) poses a significant public health challenge worldwide, significantly impacting the health and quality of life of individuals. Increasing evidence suggests a strong correlation between MetS and sex hormone levels. The objective of this study is to explore the possible relationship between sex hormone binding globulin (SHBG) and Mets, aiming to furnish evidence that could inform the development of effective prevention strategies for Mets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe data for this cross-sectional investigation were collected during the 2013\u0026ndash;2016 cycle of the National Health and Nutrition Examination Survey (NHANES), from which 5,499 adults were sampled. The criteria established by the Adult Treatment Program III of the National Cholesterol Education Program were utilized to define MetS. SHBG were measured using a standardized technique. Multivariable-adjusted Logistic regression analysis, curve fitting, and threshold effects analysis were utilized to investigate the association between SHBG levels and Mets. Moreover, the stratified analyses and interaction tests of covariables were presented in the forest plot. Finally, sensitivity analysis was utilized to ensure the the robustness of the results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the participants, 1822 those had Mets. After adjusting for possible confounders, the SHBG level was associated with Mets (Odds ratio [OR], 0.984; 95% confidence interval [CI], 0.981\u0026ndash;0.986; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The multivariable restricted cubic spline demonstrated a non-linear association between SHBG and Mets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). With two piecewise regression models, the adjusted OR of developing Mets was 0.964 (95% CI, 0.959\u0026ndash;0.969; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among people with SHBG\u0026thinsp;\u0026lt;\u0026thinsp;76.653nmol/L, but there was no correlation between SHBG and Mets in participants with SHBG\u0026thinsp;\u0026ge;\u0026thinsp;76.653nmol/L. The stability of the association between SHBG and MetS was confirmed through subgroup analysis and sensitivity analysis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results suggest that reduced SHBG levels are associated with an increased prevalence of MetS in adults, particularly when SHBG levels are below 76.653 nmol/L. More investigation is required to comprehend the mechanisms underlying these results and to delve into their clinical implications.\u003c/p\u003e","manuscriptTitle":"Correlation of Sex Hormone Binding Globulin with Metabolic Syndrome in US Adults: Insights from National Health and Nutrition Examination Survey (NHANES) 2013–2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 17:33:33","doi":"10.21203/rs.3.rs-4128989/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb779dc7-0645-4555-9fe0-09ac95eb4ad3","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-25T20:10:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 17:33:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4128989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4128989","identity":"rs-4128989","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}