Association between Oxidative Balance Scores (OBS) and Gamma- Glutamyltransferase (GGT) among US adults in NHANES 2013– 2018

Association between Oxidative Balance Scores (OBS) and Gamma- Glutamyltransferase (GGT) among US adults in NHANES 2013– 2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Oxidative Balance Scores (OBS) and Gamma- Glutamyltransferase (GGT) among US adults in NHANES 2013– 2018 Xinli Gan, Xiaowen Li, Haibin Wen^, Zhonglin Wang, Ning Tan, Zhongqi Mao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5413780/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 The Oxidative Balance Score (OBS) quantifies the balance between antioxidants and pro-oxidants, which is influenced by diet and lifestyle, and is used to evaluate the overall oxidative stress status. Elevated levels of γ-glutamyl transferase (GGT) are considered a primary indicator of oxidative stress. This study aims to explore the association between OBS and GGT using data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2013 and 2018. A total of 7,998 people were included in the study. Research has revealed a significant linear negative correlation was found between OBS scores and GGT. Fully adjusted weighted logistic regression analysis showed that each unit increase in OBS was associated with a 3% decrease in the odds of abnormal GGT [OR = 0.97, 95%CI: 0.95, 0.99, P < 0.001]. By exploring this link, this could provide valuable insights into developing preventive strategies and interventions for GGT abnormalities. Figures Figure 1 Figure 2 1.Introduction Liver disease is a significant problem worldwide and is common as the liver is the primary organ involved in the biotransformation of food and drugs 1 . Many enzymes are produced in the liver and are usually distributed in liver cells. Elevated serum enzymes are considered sensitive biomarkers of liver damage 2 . Determination of various liver enzymes in serum, such as ALT, AST, GGT, etc., to evaluate the functional status of the liver and detect liver damage 3 . It is worth noting that the elevation of GGT is often associated with increased liver damage and oxidative stress 4 . The pathogenesis of liver damage is complex and is influenced by genetic, dietary, nutritional, environmental and biological factors 5 – 7 . In addition, the oxidative stress mechanism of liver injury indicates that the liver is continuously exposed to different toxic and reactive metabolites, including reactive oxygen species (ROS) and reactive nitrogen species (RNS), and the shift of redox balance toward oxidative stress can be considered the first step in the pathogenesis of liver diseases 8 . This process is affected by comorbidities such as metabolic syndrome and various exogenous factors such as alcohol abuse, viral infections, drug overdose, high-calorie diets, and exposure to environmental toxins, UV rays, or heavy metals 9 . The surge in ROS and RNS levels plays an important role in the occurrence of liver inflammation, fibrosis, necrosis, apoptosis or malignant transformation 10 . In the past, the main cause of liver disease was hepatitis virus, but improvements in the prevention and treatment of hepatitis viruses have led to an improvement in the trend of chronic liver disease. At the same time, obesity and alcohol consumption have become major risk factors for liver disease, both of which are common and increasing in many parts of the world. They are expected to drive the epidemiology of chronic liver diseases and cause increasing numbers of deaths in the future 11 . Therefore, it is crucial to prevent liver disease by controlling diet. The Oxidative Balance Score (OBS) is a comprehensive measure of an individual's oxidative balance, derived by quantifying the antioxidant and pro-oxidant components present in dietary and lifestyle factors 12 . In general, higher OBS indicates that antioxidants are superior to prooxidants 13 . Several epidemiological studies have found a negative correlation between OBS and various diseases, including cardiovascular disease 14 、type 2 diabetes mellitus 15 、nonalcoholic fatty liver disease 16 、Chronic kidney disease 17 、depression 18 、osteoarthritis 19 and cancer 20 . However, the association between OBS and liver injury or GGT levels is unclear. Some preliminary studies have suggested a potential association between higher oxidative balance scores (OBS) and lower liver enzyme levels. For example, an analysis based on the Korean Genomic and Epidemiological Study found that higher OBS, indicating an advantage of antioxidant over prooxidant exposure, was strongly negatively correlated with GGT levels. However, these preliminary observations were primarily from single populations or small samples, highlighting the need for more comprehensive epidemiological evidence from larger, multiethnic populations. Based on this background, our study hypothesized that higher oxidative balance scores would be associated with lower liver enzyme levels. This hypothesis prompted us to evaluate the association between OBS and GGT levels in a nationally representative sample of the U.S. population using data from the National Health and Nutrition Examination Survey (NHANES). 2. Materials and methods 2.1 Study design and participants NHANES is a national cross-sectional survey sponsored by the Centers for Disease Control and Prevention that collects information on participants' demographics, lifestyle, and health and dietary status 21 . The survey integrates demographic, socioeconomic, dietary and health-related data collected through face-to-face interviews, physical examinations and extensive laboratory testing. In our cross-sectional analysis of National Health and Nutrition Examination Survey (NHANES) data from 2013 to 2018, we initially included 29 400 participants. Participants outside the age range of 18 to 80 years (12,343 people) were first excluded. We also excluded pregnant subjects (191) and subjects with incomplete data for key variables including: oxidative balance score (4195). We further excluded individuals with a history of cancer (1350), individuals with unreliable energy intake data (757), and patients with hepatitis B or C (268). In the final step, we removed subjects with missing data on basic covariates (2298), bringing the study population to 7998 participants (Figure 1). 2.2 Ethical considerations National Health and Nutrition Examination Survey (NHANES) was conducted in accordance with the ethical standards laid down by the National Centre for Health Statistics’ Ethical Review Committee. Prior to participation, all participants were provided with comprehensive information regarding the study’s aims, procedures, potential risks, and benefits. Written informed consent was obtained from each participant, ensuring their voluntary participation and the confidentiality of their personal and health information. 2.3 Data collection 2.3.1 Exposure variable: oxidative balance score The Oxidative Balance Score (OBS) is a composite measure designed to quantify the balance between pro-oxidant and antioxidant exposures in the diet and lifestyle of individuals. The methodology for constructing and calculating OBS has been previously described in detail 22 .OBS construction incorporates a comprehensive array of 20 factors, encompassing both dietary nutrients and lifestyle behaviors. These factors are categorized into pro-oxidants, which include total fat, iron, alcohol intake, body mass index (BMI), and cotinine levels, and antioxidants, which comprise dietary fiber, b-carotene, vitamins B2, niacin, B6, total folate, B12, C, E, and minerals such as calcium, magnesium, zinc, copper, selenium, alongside physical activity. The assignment of scores to each component is based on its oxidative property (either antioxidant or prooxidant) and differentiated by gender, adhering to a predefined OBS component assignment scheme. Specifically, the OBS component assignment scheme incorporates alcohol consumption levels, categorizing participants into non-drinkers, non-heavy drinkers (0 to 15 g/day for women and 0 to 30 g/day for men), and heavy drinkers (≥15 g/day for women and ≥30 g/day for men). Scores of 2, 1, and 0 points are allocated to these categories, respectively 23 . The scoring for other components is determined through tertiles based on gender, with antioxidant groups receiving scores from 0 to 2 points from the lowest to the highest tertile, whereas prooxidant groups are scored inversely, with the highest tertile receiving 0 points and the lowest tertile 2 points. 2.3.2 Outcome variable The outcome variable of interest in this study was liver damage, assessed by major markers of liver enzymes，Liver injury was defined as the abnormal GGT, in which male: GGT > 68 U/L, female: GGT > 40 U/L 24,25 . 2.3.3 Covariate definitions The covariates were selected based on their potential impact on liver health and included sociodemographic factors, lifestyle characteristics, and comorbid conditions. Participants’ demographic information was collected through a self-administered questionnaire, which included details on age, gender, and race/ethnicity. Race/ethnicity was classified into four categories: Mexican American, Non-Hispanic White, Non-Hispanic Black, and Other. Body Mass Index (BMI) was calculated using the formula: weight (kg)/height squared (m²), and categorized into three groups: underweight/normal (<25 kg/ m²), overweight (25-30 kg/m²), and obese (≥30 kg/m²) 26 . The poverty-to-income ratio, a measure of socioeconomic status, was determined by dividing the household income by the poverty threshold for the survey year and state, providing insight into the participants’ economic conditions. Dietary intake was assessed using a 24-hour dietary recall interview, which captured detailed information on all food and beverage consumption from the previous day. This method provided a snapshot of the participant’s daily caloric intake, essential for evaluating dietary habits and their potential impact on health outcomes. Total energy intake was calculated in kilocalories per day (kcal/day) based on the data from the first dietary recall 26 . Hypertension was defined as having a systolic blood pressure (BP) ≥140 mmHg, diastolic BP ≥90 mmHg, or current use of antihypertensive medication 27 . Diabetes and Prediabetes classifications adhered to the American Diabetes Association (ADA) criteria, utilizing fasting blood glucose (FBG) and Hemoglobin A1c (HbA1c) thresholds 28 . Hyperlipidemia was identified through specific lipid profile levels or the use of lipid-lowering medications 29 . Chronic kidney disease(CKD) is defined according to the current clinical guidelines as an eGFR of less than 60 mL/min/1.73 m², albuminuria of 30 mg/g or greater, or both conditions 30 . The cardiovascular disease is identified through self-reporting 31 . 2.4 Statistical analysis According to the oxidation balance score (OBS) level of the study population, it was divided into quartiles defined as 2-11, 12-16, 17-21 and 22-31 for comparative analysis. The continuous variables are expressed as mean ± standard error (SE). Categorical variables are expressed as frequency (%). In order to assess the difference between the OBS quartiles of continuous variables, we used analysis of variance (ANOVA). For categorical variables, we applied the chi-square test or Fisher's exact test to examine differences between OBS quartiles based on expected frequencies in the contingency table. OBS levels were analyzed both as continuous variables (per unit increase) and in categorical format (quartiles), and GGT levels were divided into two modes: as continuous variables and as categorical variables (abnormal GGT) according to the above-mentioned definition of liver injury. The relationship between OBS levels and GGT levels was examined by weighted logistic regression analysis, adjusting for various covariates. The analyses adopted a hierarchical structure, starting with a crude model and then sequentially adjusting for sociodemographic factors, lifestyle characteristics, and comorbidities. To investigate the potential nonlinear associations between OBS and GGT, restricted cubic spline (RCS) regression was used to verify the complex relationships between OBS and GGT. Weighted logistic regression was used for subgroup analysis and interaction effect evaluation of different demographic and clinical categories. Sensitivity analysis was performed on diet and lifestyle. All analyses were performed using R version 4.2.0 (http: / / www.R-project.org ), FreeStatistics software version 1.9 and EmpowerStats ( http : / / www.powerstats.com ). 3. Results Table 1 Baseline Characteristics of participants based on OBS quartiles Variable Total Q1[2,11] Q2[12,16] Q3[17,21] Q4[22,31] P value Year 0.7 2013-2014 3086(35.76) 783(34.45) 691(35.20) 908(38.01) 704(34.89) 2015-2016 2772(34.44) 784(35.36) 642(34.41) 727(32.36) 619(36.03) 2017-2018 2140(29.80) 617(30.19) 488(30.39) 582(29.63) 453(29.08) Age （ years ） 46.41(0.38) 46.67(0.53) 47.02(0.62) 45.97(0.55) 46.12(0.59) 0.48 PIR 3.11(0.06) 2.59(0.07) 3.08(0.07) 3.20(0.06) 3.54(0.07) < 0.0001 Sedentary Activity 397.74(4.23) 391.53(6.84) 403.91(6.37) 398.93(6.46) 396.55(6.67) 0.45 height 168.55(0.20) 167.90(0.28) 168.49(0.36) 168.43(0.30) 169.36(0.32) < 0.001 weight 84.21(0.46) 87.36(0.58) 85.94(0.78) 83.81(0.67) 80.21(0.76) < 0.0001 Waist 100.22(0.38) 103.62(0.51) 102.01(0.60) 99.69(0.56) 96.11(0.61) < 0.0001 UA 5.41(0.02) 5.64(0.04) 5.53(0.05) 5.31(0.04) 5.18(0.05) < 0.0001 HDL 54.27(0.37) 51.64(0.48) 52.94(0.55) 54.67(0.53) 57.43(0.63) < 0.0001 BUN 14.07(0.12) 13.34(0.15) 14.04(0.15) 14.19(0.16) 14.64(0.17) < 0.0001 Hba1c 5.62(0.02) 5.71(0.03) 5.68(0.03) 5.61(0.02) 5.51(0.03) < 0.0001 GGT 27.91(0.47) 32.09(1.04) 30.96(1.27) 26.79(0.85) 22.58(0.74) < 0.0001 Gender,n(%) 0.08 Female 4251(52.45) 1099(49.91) 946(50.64) 1200(53.96) 1006(54.70) Male 3747(47.55) 1085(50.09) 875(49.36) 1017(46.04) 770(45.30) Race/ethnicity,n(%) < 0.0001 Non-Hispanic White 3077(66.60) 781(61.32) 715(67.40) 876(67.29) 705(69.98) Non-Hispanic Black 1675(10.51) 685(17.57) 389(10.95) 382(8.56) 219(5.85) Mexican American 1187(8.56) 250(7.22) 286(8.84) 349(9.28) 302(8.71) Other Race 2059(14.32) 468(13.89) 431(12.82) 610(14.86) 550(15.46) Education ,n(%) < 0.0001 Less than high school 1418(10.88) 527(17.37) 350(11.96) 335( 8.87) 206( 6.23) High school 1811(23.11) 607(29.99) 457(26.94) 455(20.95) 292(15.79) More than high school 4767(66.00) 1049(52.64) 1013(61.10) 1427(70.18) 1278(77.97) Marital status , n (%) < 0.001 Cohabitation 4899(65.47) 1200(58.94) 1107(64.97) 1396(66.79) 1196(70.43) Solitude 3097(34.52) 982(41.02) 714(35.03) 821(33.21) 580(29.57) DM,n(%) < 0.0001 no 6520(86.14) 1653(81.74) 1462(84.05) 1854(87.15) 1551(90.90) yes 1478(13.86) 531(18.26) 359(15.95) 363(12.85) 225( 9.10) Hypertension,n(%) < 0.0001 no 4713(63.66) 1116(56.60) 1053(61.61) 1356(65.26) 1188(70.18) yes 3285(36.34) 1068(43.40) 768(38.39) 861(34.74) 588(29.82) Hyperlipidemia,n(%) < 0.0001 no 2494(32.76) 593(27.76) 534(30.04) 690(31.81) 677(40.92) yes 5504(67.24) 1591(72.24) 1287(69.96) 1527(68.19) 1099(59.08) CVD,n(%) < 0.0001 no 7266(92.84) 1891(89.60) 1656(92.85) 2039(93.54) 1680(95.03) yes 732( 7.16) 293(10.40) 165( 7.15) 178( 6.46) 96( 4.97) CKD,n(%) < 0.0001 no 6736(86.82) 1723(82.65) 1524(85.99) 1930(88.76) 1559(89.19) yes 1262(13.18) 461(17.35) 297(14.01) 287(11.24) 217(10.81) Abbreviations:PIR: Poverty Income Ratio; DM: Diabetes Mellitus; CVD:Cardiovascular Disease; CKD:Chronic Kidney Disease. Continuous Variables: Presented as means with standard errors (SE); Categorical Variables: Displayed as counts (n) and percentages (%). Baseline characteristics In this study, a total of 7998 participants were divided into quartiles based on oxidative balance score (OBS). Demographic and health characteristics showed significant differences between quartiles. There were significant racial differences, with non-Hispanic whites dominating the highest quartile of OBS (P < 0.0001). Socioeconomic status as indicated by poverty-income ratio (PIR) and dietary habits as reflected by energy intake were significantly different among different OBS levels (P<0. 0001 for both). Liver enzymes, key markers of liver injury, including ALT and GGT, showed significant differences (P < 0. 001, P < 0. 0001), while AST did not show significant differences (P = 0. 41). In the lower OBS quartiles, the prevalence of diabetes, hypertension, chronic kidney disease, cardiovascular disease (CVD), and hyperlipidemia was significantly higher (P<0.0001), and the key serological indicators corresponding to these diseases: Hba1c, UA, HDL, BUN also showed corresponding trends (P<0.0001), which was consistent with the increase in the level of liver enzyme GGT in these groups (P<0.0001)(Table 1). Table 2: Relationship between OBS and abnormal GGT in different models. Exposure Variable Non-adjusted model OR (95%CI) P value Adjust I model OR (95%CI) P value Adjust II model OR (95%CI) P value Adjust III model OR (95%CI) P value OBS (per unit change) 0.96(0.94,0.97)，<0.0001 0.97(0.95,0.98)，<0.001 0.97(0.96,0.99)，<0.001 0.97(0.95,0.99)，<0.001 OBS quartile Q1 ref ref ref ref Q2 0.85(0.65,1.12) 0.25 0.88(0.65,1.19) 0.40 0.91(0.67,1.23) 0.53 0.92(0.67,1.26) 0.59 Q3 0.63(0.46,0.85) 0.003 0.68(0.50,0.92) 0.01 0.72(0.53,0.99) 0.04 0.73(0.53,1.01) 0.06 Q4 0.48(0.32,0.71) <0.001 0.57(0.38,0.86) 0.01 0.62(0.41,0.92) 0.02 0.64(0.42,0.97) 0.04 P for trend 0.44 0.55 0.68 0.72 Note: Non-adjusted model: adjusted for no covariates. Model I: Adjustments are made for Age, Gender, Race.ethnicity, Education, PIR, Marital.status, SedentaryActivity, height, weight, Waist. Model II: This model includes all adjustments from Model I, with additional adjustments for SUA, HDL, BUN, Hba1c. Model III: Extends the adjustments of Model II to include comorbidities: DM, Hypertension, Hyperlipidemia, CVD, coronary.heart.disease, CKD. Association between OBS and GGT As shown in Table 2, the relationship between OBS and the odds of abnormal GGT was examined using weighted logistic regression analysis, and in the unadjusted model, each unit increase in OBS was associated with a 4% decrease in the odds of abnormal GGT [OR=0.96, 95%CI: 0.94, 0.97, P<0.0001]. This association remained significant in the fully adjusted model, which showed that for every one unit increase in OBS, the odds of the abnormal GGT would decrease by 3% [OR=0.97, 95% CI: 0.95, 0.99, P<0.001]. In the fully adjusted model, as shown in Tables S1 and S2 of the supplementary material, a per unit increase in lifestyle OBS was associated with a 15% decrease in the odds of abnormal GGT [OR=0.85, 95% CI: 0.77, 0.93, P=0.002]. Similarly, one unit increase in dietary OBS was linked to a 2% decrease in the odds of abnormal GGT [OR=0.98, 95% CI: 0.96, 0.99, P=0.01]. When OBS was divided into quartiles, the highest quartile (Q4) showed the strongest association with lower liver enzyme GGT levels compared with the reference first quartile (Q1), and the odds ratios (ORs) of each quartile showed a gradually decreasing trend. Specifically, in the fully adjusted model, compared with Q1, the OR of Q4 was 0.64 (95 % CI:0.42-0.97, P =0.04), but whether there was a significant trend between the OBS quartiles was uncertain (P indicates a trend=0.72). Analysis of restricted cubic spline regression Through RCS analysis of weighted multivariate logistic regression adjusted for covariates, we identified a linear relationship between OBS, dietary OBS, lifestyle OBS and the incidence of the abnormal GGT respectively (P-nonlinear=0.231, 0.063, 0.848) in Fig. 2A-C. Our findings indicate a negative correlation between OBS and dietary OBS with the incidence of the abnormal GGT, while lifestyle OBS showed no significant correlation. Table 3 Stratified Analysis and Interaction Tests for the Association Between Oxidative Balance Score and GGT level in U.S. Adults: NHANES 2013–2018 Character Q1 Q2 Q3 Q4 p for trend (character2integer) p for interaction Age 0.326 20-45 years ref 1.044(0.635,1.715) 0.582(0.355,0.955) 0.380(0.198,0.729) <0.001 45-65 years ref 0.694(0.397,1.212) 0.597(0.333,1.068) 0.556(0.236,1.312) 0.102 65-85 years ref 1.392(0.552,3.507) 1.047(0.406,2.695) 0.626(0.199,1.963) 0.274 Gender 0.842 Male ref 0.882(0.489,1.589) 0.510(0.253,1.026) 0.459(0.182,1.160) 0.899 Female ref 0.909(0.593,1.394) 0.698(0.396,1.230) 0.538(0.281,1.030) 0.048 Race.ethnicity 0.512 Non-Hispanic White ref 0.723(0.423,1.234) 0.471(0.261,0.851) 0.399(0.195,0.815) 0.007 Non-Hispanic Black ref 1.155(0.608,2.191) 1.017(0.531,1.948) 0.688(0.333,1.422) 0.867 Mexican American ref 2.301(1.184,4.472) 1.694(0.571,5.025) 1.369(0.545,3.438) 0.673 Other Race ref 0.889(0.451,1.751) 0.729(0.408,1.303) 0.613(0.261,1.439) 0.3 PIR.group 0.338 Low (3.5) ref 0.551(0.306,0.990) 0.413(0.208,0.819) 0.331(0.138,0.793) 0.024 PA.group 0.924 No.PA(8000) ref 0.715(0.227,2.255) 0.700(0.232,2.114) 0.887(0.231,3.398) 0.838 BMI.group 0.76 Normal(<25) ref 0.646(0.230,1.817) 0.546(0.251,1.190) 0.632(0.204,1.960) 0.54 Overweight(25 to <30) ref 0.711(0.428,1.182) 0.649(0.379,1.110) 0.530(0.240,1.170) 0.172 Obese(30 or greater) ref 1.102(0.674,1.801) 0.676(0.353,1.296) 0.527(0.246,1.129) 0.629 DM 0.591 no ref 0.826(0.539,1.268) 0.562(0.358,0.884) 0.429(0.235,0.781) 0.003 yes ref 1.078(0.692,1.680) 0.822(0.416,1.624) 1.023(0.484,2.162) 0.639 Hypertension 0.351 no ref 1.179(0.712,1.952) 0.689(0.430,1.103) 0.525(0.281,0.981) 0.007 yes ref 0.690(0.451,1.054) 0.572(0.323,1.014) 0.518(0.243,1.104) 0.131 Hyperlipidemia 0.833 no ref 0.766(0.346,1.697) 0.540(0.202,1.443) 0.530(0.179,1.567) 0.608 yes ref 0.911(0.601,1.380) 0.639(0.375,1.088) 0.496(0.264,0.932) 0.019 CKD 0.812 no ref 0.846(0.576,1.243) 0.608(0.374,0.988) 0.460(0.260,0.814) 0.543 yes ref 0.915(0.488,1.718) 0.541(0.278,1.051) 0.667(0.244,1.818) 0.577 CVD 0.382 no ref 0.854(0.602,1.211) 0.586(0.373,0.921) 0.468(0.264,0.829) 0.006 yes ref 1.002(0.333,3.013) 0.922(0.356,2.389) 1.107(0.365,3.359) 0.857 Note: The Oxidative Balance Score was categorized into four quartiles, designated as Q1, Q2, Q3, and Q4. Q1 ref denotes the lowest quartile of the OBS as the reference category.P for interaction was calculated using weighted multivariable logistic regression analysis adjusted for age, gender, race, poverty-to-income ratio (PIR), energy intake, and comorbidities including hypertension, diabetes, and hyperlipidemia, excluding the specific subgroup variable. In Table 3, the oxidative balance scores are divided into four quartiles, designated as Q1, Q2, Q3, and Q4. Specifically, Q1 represents the lowest 25% of the observations, Q2 represents the 25th to 50th percentile of the observations, Q3 represents the 50th to 75th percentile of the observations, and Q4 represents the highest 25% of the observations. We performed stratified analyses by age, sex, race, poverty-income ratio, BMI, diabetes status, hypertension, chronic renal failure, and hyperlipidemia and tested potential interactions of these factors with OBS on the risk of the abnormal GGT. Overall, the inverse association between higher OBS and lower risk of abnormal GGT levels was consistently observed across most strata, except for a positive association in those with CVD, although the strength of this association appeared to vary depending on certain demographic and clinical factors. For example, the inverse relationship between OBS and GGT was most pronounced in adults aged 20-45 years, men, non-Hispanic whites, those with higher income levels, and overweight individuals. This negative association was attenuated in obese participants and those with diabetes. However, most tests of interaction between OBS and risk stratification factors for abnormal GGT levels were not statistically significant, suggesting that higher OBS has a generally consistent protective effect across the population. Sensitivity analyses were conducted independently for dietary and lifestyle Oxidative Balance Scores (OBS) to evaluate their respective associations with The abnormal GGT risk, utilizing weighted logistic regression models adjusted for Age, Gender, Race.ethnicity, Education, PIR, Marital.status, SedentaryActivity, height, weight, Waist, SUA, HDL, BUN, Hba1c, DM, Hypertension, Hyperlipidemia, CVD, coronary.heart.disease, CKD. Fully adjusted weighted logistic regression analysis indicated that each unit increase in dietary OBS was associated with a 2% reduction in the abnormal GGT prevalence [OR: 0.98 (95% CI: 0.96–0.99), P = 0.01]. Similarly, for lifestyle OBS, each unit increase was linked to a 15% reduction in the abnormal GGT prevalence [OR: 0.85 (95% CI: 0.77–0.93), P = 0.002] (Supplementary Table S1). 4. Discussion This study used data from the 2013-2018 National Health and Nutrition Examination Survey (NHANES) in the United States, involving a total of 7,998 people, to explore in detail the relationship between oxidative balance score (OBS) and liver damage and liver enzyme levels. After adjusting for multiple covariates, our analysis showed a significant inverse association between OBS and GGT levels and liver injury characterized by abnormal GGT levels. The negative correlation between OBS and GGT levels emphasizes the importance of antioxidant and prooxidant balance in liver injury marked by GGT levels. It opens the door to innovative preventive strategies focused on enhancing oxidative balance, possibly through dietary and lifestyle changes. This method can provide a new way for the risk management of liver injury with GGT level as a marker, emphasizing the need for further research to elucidate the mechanism of OBS affecting the development and progression of GGT level. The redox state constitutes an important background for many liver diseases. The redox state is involved in the progression of inflammatory, metabolic and proliferative liver diseases. Reactive oxygen species (ROS) are primarily generated in mitochondria and the endoplasmic reticulum of hepatocytes by cytochrome P450 enzymes 32 . Under appropriate conditions, cells are equipped with special molecular strategies that can control oxidative stress levels and maintain a balance between oxidants and antioxidant particles 33 . Oxidative stress represents an imbalance between oxidants and antioxidants. Hepatocyte proteins, lipids, and DNA are the main cellular structures affected by reactive oxygen species (ROS) and reactive nitrogen species (RNS), a process that leads to abnormal liver structure and function 34 . The OBS used in our study is a comprehensive measure consisting of 20 factors representing diet and lifestyle prooxidants and antioxidants, which has been widely validated in several studies 35-37 . By integrating these 20 factors, the OBS allows for a comprehensive assessment of an individual's oxidative status, providing a biologically plausible basis for interpreting our findings and warranting further validation in larger cohorts. At present, many literatures have proved that there is a significant association between OBS and liver disease. A study based on Korean genomic and epidemiological studies has found that higher OBS scores are significantly associated with lower risk of nonalcoholic fatty liver disease ( NAFLD ) 16 . At the same time, a study based on the National Health and Nutrition Examination Survey found that higher OBS was associated with a lower incidence of metabolic dysfunction-associated fatty liver disease (MASLD) 38,39 . These results are consistent with the findings of our study, in which higher OBS scores were associated with a lower risk of liver injury. However, the focus of these studies is not on liver enzyme levels, which are markers of liver damage. A study based on the Korean Genomic and Epidemiological Study found that higher OBS in Korean adults, indicating the advantage of antioxidant over prooxidant exposure, was strongly negatively correlated with GGT levels 40 . This is consistent with our findings, but our study extends these insights to the US population, confirming the importance of OBS in reducing GGT levels in a diverse population. Our study is unique in analyzing the direct effects of OBS on GGT levels in the U.S. context, in which different ethnic and socioeconomic factors may have unique effects on oxidative stress. Furthermore, our analysis includes a wider age range, enhancing the national representativeness of our findings. Furthermore, the differences in OBS calculation methods between studies highlight the need for standardization to more effectively compare and replicate study findings. Our approach includes a broader range of biochemical markers and could serve as a step toward standardization, lay the foundation for future studies, and lead to more consistent and reliable measurements of oxidative balance. The main strengths of our study are the use of a large, nationally representative data set and a rigorous methodological framework, which included validated data collection methods, a comprehensive analysis strategy, and the innovative use of RCS diagrams to elucidate the relationship between oxidative balance score (OBS) and GGT levels. In addition, our subgroup and sensitivity analyses further validated the robustness of our conclusions. RCS results showed a linear negative correlation between OBS and GGT. Compared with the general population, the greater the OBS, the lower the risk of liver injury. Although our study did not delve into the mechanisms linking OBS with GGT, oxidative stress offers a plausible explanation. Oxidative stress refers to the imbalance between pro-oxidation and anti-oxidation processes in the body. It is closely related to the occurrence of various diseases including liver damage, chronic kidney disease, lung damage, heart failure, and aging 41-45 . Gamma-glutamyl transferase (GGT) is an intracellular enzyme located on the plasma membrane of a variety of cells and tissues, especially hepatocytes, and is considered a marker of hepatobiliary disease and drug consumption. Alcohol may increase GGT levels by promoting the induction of microsomal synthesis or by direct extraction from hepatocytes, and therefore, GGT is a sensitive indicator of liver disease associated with alcohol consumption. GGT maintains intracellular glutathione levels by participating in the γ-glutamyl cycle, and glutathione is the most important endogenous antioxidant produced by cells. It protects cells from oxidative stress (OS), neutralizes the effects of reactive oxygen species, and maintains the levels of exogenous antioxidants such as vitamins C and E. Previous studies have reported that serum GGT activity is an oxidative stress biomarker 46 ，Plasma GGT levels may reflect the development of liver damage involving oxidative stress as a pathophysiological mechanism. Although the relationship between cells and serum GGT is still unclear, many studies have shown that serum GGT within the normal range may be an early and sensitive enzyme in oxidative stress 47,48 . However, the cross-sectional design of our study, which relied on National Health and Nutrition Examination Survey (NHANES) data, limits our ability to infer a causal relationship between OBS levels and GGT levels. This limitation highlights the need for longitudinal studies to more clearly determine the dynamics of this relationship 49 . Furthermore, limitations of this study include the use of self-reported dietary data, which are susceptible to recall and reporting biases, thereby affecting the accuracy of oxidative balance score (OBS) calculations and observed associations. To reduce this measurement error and improve estimates of daily intake, we applied methods from the National Cancer Institute 49 . Furthermore, although we adjusted for various known confounders, there is still the possibility of residual confounding due to unmeasured or inadequately measured variables. These factors may have influenced the observed relationship between OBS and GGT levels, thus requiring caution in interpreting our findings. 5. Conclusion In conclusion, our examination of the NHANES data showed a significant inverse association between the oxidative balance score (OBS) and GGT levels or the risk of liver injury with abnormal GGT levels. This relationship highlights the importance of oxidative balance in the context of liver injury and suggests that dietary and lifestyle modifications may influence liver injury risk, suggesting that serum GGT may be an early and sensitive enzyme for oxidative stress. The cross-sectional nature of our study calls for further longitudinal investigations to explore these associations in depth and to assess the effectiveness of specific interventions. Declarations Author Contributions Statement XL. G and XW. L contributed equally to this work. They were involved in conceptualization, methodology, data curation, performing the analysis and writing the original draft. HB. W and ZL. W was involved in the investigation process and contributed to the review and editing of the manuscript. ZQ. M as one of the corresponding authors, contributed to project administration, supervision, and securing funding. He also contributed to the review and editing of the manuscript. N. T also a corresponding author, played a crucial role in the conceptualization, methodology, resources, and writing, review and editing of the manuscript. All authors reviewed the manuscript. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 82160699), the Guangxi Natural Science Foundation of China (Grant Nos. 2023GXNSFAA026477 and 2023GXNSFAA026282), the Guangxi Department of Education 2021 Graduate Education Innovation Program Project of China (Grant No. JGY2021138), and the Guangxi Health and Wellness Commission 2020 Science and Technology Project (Approval No. Z20201104). Ethics statement The studies involving human participants were reviewed and approved by National Center for Health Statistics Ethics Review Board Approval. The patients/participants provided their written informed consent to participate in this study. Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm. Conflict of interest The authors declare that this study was conducted in the absence of any commercial or financial relationships that could serve as a potential conflict of interest. References Ingawale, D.K., Mandlik, S.K., and Naik, S.R. (2014). Models of hepatotoxicity and the underlying cellular, biochemical and immunological mechanism (s): a critical discussion. Environmental toxicology and pharmacology 37 , 118–133. Zhou, S., Cheng, K., Peng, Y., Liu, Y., Hu, Q., Zeng, S., Qi, X., and Yu, L. (2024). 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(2022). Association between the Oxidative Balance Score and Telomere Length from the National Health and Nutrition Examination Survey 1999-2002. Oxidative medicine and cellular longevity 2022 , 1345071. Clark, and J. (2003). The prevalence and etiology of elevated aminotransferase levels in the United States. The American Journal of Gastroenterology 98 , 960–967. 10.1111/j.1572-0241.2003.07486.x . Ceriotti, F., Henny, J., Queralto, J., Ziyu, S., Ozarda, Y., Chen, B., Boyd, J.C., Panteghini, M., Intervals, I.C.o.R., Decision, L., and Committee on Reference Systems for, E. (2010). Common reference intervals for aspartate aminotransferase (AST), alanine aminotransferase (ALT) and gamma-glutamyl transferase (GGT) in serum: results from an IFCC multicenter study. Clin Chem Lab Med 48 , 1593–1601. 10.1515/CCLM.2010.315 . Shan, Z., Rehm, C.D., Rogers, G., Ruan, M., Wang, D.D., Hu, F.B., Mozaffarian, D., Zhang, F.F., and Bhupathiraju, S.N. (2019). Trends in dietary carbohydrate, protein, and fat intake and diet quality among US adults, 1999–2016. Jama 322 , 1178–1187. Unger, T., Borghi, C., Charchar, F., Khan, N.A., Poulter, N.R., Prabhakaran, D., Ramirez, A., Schlaich, M., Stergiou, G.S., and Tomaszewski, M. (2020). 2020 International Society of Hypertension global hypertension practice guidelines. Hypertension 75 , 1334–1357. Zou, X., Zhou, X., Zhu, Z., and Ji, L. (2019). Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. The Lancet Diabetes & Endocrinology 7 , 9–11. Aggarwal, R., Bhatt, D.L., Rodriguez, F., Yeh, R.W., and Wadhera, R.K. (2022). Trends in lipid concentrations and lipid control among US adults, 2007–2018. Jama 328 , 737–745. Beck Jr, L.H., Ayoub, I., Caster, D., Choi, M.J., Cobb, J., Geetha, D., Rheault, M.N., Wadhwani, S., Yau, T., and Whittier, W.L. (2023). KDOQI US commentary on the 2021 KDIGO clinical practice guideline for the management of glomerular diseases. American Journal of Kidney Diseases 82 , 121–175. Zhang, Y., Wang, F., Tang, J., Shen, L., He, J., and Chen, Y. (2024). Association of triglyceride glucose-related parameters with all-cause mortality and cardiovascular disease in NAFLD patients: NHANES 1999–2018. Cardiovascular Diabetology 23 , 262. Poljsak, B., Šuput, D., and Milisav, I. (2013). Achieving the balance between ROS and antioxidants: when to use the synthetic antioxidants. Oxidative medicine and cellular longevity 2013 , 956792. Birben, E., Sahiner, U.M., Sackesen, C., Erzurum, S., and Kalayci, O. (2012). Oxidative stress and antioxidant defense. World allergy organization journal 5 , 9–19. Cichoż-Lach, H., and Michalak, A. (2014). Oxidative stress as a crucial factor in liver diseases. World journal of gastroenterology: WJG 20 , 8082. Kong, X., Gao, X., and Wang, W. (2024). Oxidative balance score and associations with dyslipidemia and mortality among US adults: A mortality follow-up study of a cross‐sectional cohort. Journal of Parenteral and Enteral Nutrition. Zhu, Z., Bai, H., Li, Z., Fan, M., Li, G., and Chen, L. (2024). Association of the oxidative balance score with obesity and body composition among young and middle-aged adults. Frontiers in Nutrition 11 , 1373709. Jia, S., Huo, X., Sun, L., and Chen, X. (2024). Association of oxidative balance score with all-cause mortality among older adults in the United States: evidence from NHANES 2007–2018. The Journal of nutrition, health and aging 28 , 100297. Tan, Z., Wu, Y., Meng, Y., Liu, C., Deng, B., Zhen, J., and Dong, W. (2023). Trends in Oxidative Balance Score and Prevalence of Metabolic Dysfunction-Associated Steatotic Liver Disease in the United States: National Health and Nutrition Examination Survey 2001 to 2018. Nutrients 15 , 4931. Peng, L., Li, L., Liu, J., and Li, Y. (2024). New insights into metabolic dysfunction-associated steatotic liver disease and oxidative balance score. Frontiers in Nutrition 10 , 1320238. Cho, A.-R., Kwon, Y.-J., Lim, H.-J., Lee, H.S., Kim, S., Shim, J.-Y., Lee, H.-R., and Lee, Y.-J. (2018). Oxidative balance score and serum γ-glutamyltransferase level among Korean adults: a nationwide population-based study. European Journal of Nutrition 57 , 1237–1244. Liguori, I., Russo, G., Curcio, F., Bulli, G., Aran, L., Della-Morte, D., Gargiulo, G., Testa, G., Cacciatore, F., and Bonaduce, D. (2018). Oxidative stress, aging, and diseases. Clinical interventions in aging, 757–772. Ezhilarasan, D. (2018). Oxidative stress is bane in chronic liver diseases: Clinical and experimental perspective. Arab Journal of Gastroenterology 19 , 56–64. Daenen, K., Andries, A., Mekahli, D., Van Schepdael, A., Jouret, F., and Bammens, B. (2019). Oxidative stress in chronic kidney disease. Pediatric nephrology 34 , 975–991. Chow, C.-W., Herrera Abreu, M.T., Suzuki, T., and Downey, G.P. (2003). Oxidative stress and acute lung injury. American journal of respiratory cell and molecular biology 29 , 427–431. Tsutsui, H., Kinugawa, S., and Matsushima, S. (2011). Oxidative stress and heart failure. American journal of physiology-Heart and circulatory physiology 301 , H2181-H2190. Lee, D.-H., Gross, M.D., and Jacobs Jr, D.R. (2004). Association of serum carotenoids and tocopherols with γ-glutamyltransferase: the Cardiovascular Risk Development in Young Adults (CARDIA) study. Clinical chemistry 50 , 582–588. Lee, D.S., Evans, J.C., Robins, S.J., Wilson, P.W., Albano, I., Fox, C.S., Wang, T.J., Benjamin, E.J., D’Agostino, R.B., and Vasan, R.S. (2007). Gamma glutamyl transferase and metabolic syndrome, cardiovascular disease, and mortality risk: the Framingham Heart Study. Arteriosclerosis, thrombosis, and vascular biology 27 , 127–133. Lim, J.-S., Yang, J.-H., Chun, B.-Y., Kam, S., Jacobs Jr, D.R., and Lee, D.-H. (2004). Is serum γ-glutamyltransferase inversely associated with serum antioxidants as a marker of oxidative stress? Free Radical Biology and Medicine 37 , 1018–1023. Shan, Z., Guo, Y., Hu, F.B., Liu, L., and Qi, Q. (2020). Association of low-carbohydrate and low-fat diets with mortality among US adults. JAMA internal medicine 180 , 513–523. Additional Declarations No competing interests reported. Supplementary Files tableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5413780","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376945346,"identity":"cc8ac33c-369d-41d0-a96c-8fbfce6f241b","order_by":0,"name":"Xinli Gan","email":"data:image/png;base64,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","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Xinli","middleName":"","lastName":"Gan","suffix":""},{"id":376945347,"identity":"9a0c993a-537d-4e66-8b66-ceaeb3dc4a90","order_by":1,"name":"Xiaowen Li","email":"","orcid":"","institution":"Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaowen","middleName":"","lastName":"Li","suffix":""},{"id":376945348,"identity":"c47be746-09cb-434e-96a0-7757ef9d0774","order_by":2,"name":"Haibin Wen^","email":"","orcid":"","institution":"Jiangbin Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Wen^","suffix":""},{"id":376945349,"identity":"560a2b74-9ab9-4fdb-af7b-e7fbfbbe136f","order_by":3,"name":"Zhonglin Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Zhonglin","middleName":"","lastName":"Wang","suffix":""},{"id":376945350,"identity":"d69076a7-0a98-4e68-b078-fd17ee233f30","order_by":4,"name":"Ning Tan","email":"","orcid":"","institution":"Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Tan","suffix":""},{"id":376945351,"identity":"a3def704-3db9-4d7f-92f6-3d56ee76bff7","order_by":5,"name":"Zhongqi Mao","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Zhongqi","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2024-11-08 05:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5413780/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5413780/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69994614,"identity":"b30bcfbb-5364-4fdb-b708-f2e75e3ae5ad","added_by":"auto","created_at":"2024-11-27 09:56:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":269317,"visible":true,"origin":"","legend":"\u003cp\u003eBaseline characteristics of participants based on OBS quartiles.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5413780/v1/56b42b0a55c6aadb24a8a6ee.jpg"},{"id":69994615,"identity":"02178b05-6a88-4b83-9e46-c8378634b38a","added_by":"auto","created_at":"2024-11-27 09:56:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66419,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between the abnormal GGT and OBS, OBS.dietary, and OBS. lifestyle. (A) Restricted cubic spline analysis of OBS. lifestyle for estimating the risk of the abnormal GGT; (B) Restricted cubic spline analysis of OBS.dietary for estimating the risk of the abnormal GGT. (C) Restricted cubic spline analysis of OBS for estimating the risk of the abnormal GGT. The restricted cubic spline model was adjusted for demographic factors (age, sex, race, and poverty-income ratio PIR), lifestyle factors (energy intake), and existing comorbidities (hypertension, diabetes, chronic kidney disease, hyperlipidemia).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5413780/v1/c543a5374d4d2f06f85e473e.jpg"},{"id":71300825,"identity":"141b361e-23dd-40d8-b853-5471b915bddf","added_by":"auto","created_at":"2024-12-13 05:25:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1193865,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5413780/v1/502aa63d-abee-4360-ab56-3c4b1cb7606d.pdf"},{"id":69994616,"identity":"1605bec4-8d7d-432e-ba74-5fc72db3ba37","added_by":"auto","created_at":"2024-11-27 09:56:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21851,"visible":true,"origin":"","legend":"","description":"","filename":"tableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5413780/v1/a6abfb3d4173e0a4e698d08c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Oxidative Balance Scores (OBS) and Gamma- Glutamyltransferase (GGT) among US adults in NHANES 2013– 2018","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eLiver disease is a significant problem worldwide and is common as the liver is the primary organ involved in the biotransformation of food and drugs\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Many enzymes are produced in the liver and are usually distributed in liver cells. Elevated serum enzymes are considered sensitive biomarkers of liver damage\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Determination of various liver enzymes in serum, such as ALT, AST, GGT, etc., to evaluate the functional status of the liver and detect liver damage\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. It is worth noting that the elevation of GGT is often associated with increased liver damage and oxidative stress\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe pathogenesis of liver damage is complex and is influenced by genetic, dietary, nutritional, environmental and biological factors\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In addition, the oxidative stress mechanism of liver injury indicates that the liver is continuously exposed to different toxic and reactive metabolites, including reactive oxygen species (ROS) and reactive nitrogen species (RNS), and the shift of redox balance toward oxidative stress can be considered the first step in the pathogenesis of liver diseases \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This process is affected by comorbidities such as metabolic syndrome and various exogenous factors such as alcohol abuse, viral infections, drug overdose, high-calorie diets, and exposure to environmental toxins, UV rays, or heavy metals\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The surge in ROS and RNS levels plays an important role in the occurrence of liver inflammation, fibrosis, necrosis, apoptosis or malignant transformation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the past, the main cause of liver disease was hepatitis virus, but improvements in the prevention and treatment of hepatitis viruses have led to an improvement in the trend of chronic liver disease. At the same time, obesity and alcohol consumption have become major risk factors for liver disease, both of which are common and increasing in many parts of the world. They are expected to drive the epidemiology of chronic liver diseases and cause increasing numbers of deaths in the future\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Therefore, it is crucial to prevent liver disease by controlling diet.\u003c/p\u003e \u003cp\u003eThe Oxidative Balance Score (OBS) is a comprehensive measure of an individual's oxidative balance, derived by quantifying the antioxidant and pro-oxidant components present in dietary and lifestyle factors\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In general, higher OBS indicates that antioxidants are superior to prooxidants\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Several epidemiological studies have found a negative correlation between OBS and various diseases, including cardiovascular disease \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e、type 2 diabetes mellitus \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e、nonalcoholic fatty liver disease \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e、Chronic kidney disease \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e、depression \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e、osteoarthritis \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003eand cancer\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, the association between OBS and liver injury or GGT levels is unclear.\u003c/p\u003e \u003cp\u003eSome preliminary studies have suggested a potential association between higher oxidative balance scores (OBS) and lower liver enzyme levels. For example, an analysis based on the Korean Genomic and Epidemiological Study found that higher OBS, indicating an advantage of antioxidant over prooxidant exposure, was strongly negatively correlated with GGT levels. However, these preliminary observations were primarily from single populations or small samples, highlighting the need for more comprehensive epidemiological evidence from larger, multiethnic populations. Based on this background, our study hypothesized that higher oxidative balance scores would be associated with lower liver enzyme levels. This hypothesis prompted us to evaluate the association between OBS and GGT levels in a nationally representative sample of the U.S. population using data from the National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003ch3\u003e2.1 Study design and participants\u003c/h3\u003e\n\u003cp\u003eNHANES is a national cross-sectional survey sponsored by the Centers for Disease Control and Prevention that collects information on participants\u0026apos; demographics, lifestyle, and health and dietary status\u003csup\u003e21\u003c/sup\u003e. The survey integrates demographic, socioeconomic, dietary and health-related data collected through face-to-face interviews, physical examinations and extensive laboratory testing.\u003c/p\u003e\n\u003cp\u003eIn our cross-sectional analysis of National Health and Nutrition Examination Survey (NHANES) data from 2013 to 2018, we initially included 29 400 participants. Participants outside the age range of 18 to 80 years (12,343 people) were first excluded. We also excluded pregnant subjects (191) and subjects with incomplete data for key variables including: oxidative balance score (4195). We further excluded individuals with a history of cancer (1350), individuals with unreliable energy intake data (757), and patients with hepatitis B or C (268). In the final step, we removed subjects with missing data on basic covariates (2298), bringing the study population to 7998 participants (Figure 1).\u003c/p\u003e\n\u003ch3\u003e2.2 Ethical considerations\u003c/h3\u003e\n\u003cp\u003eNational Health and Nutrition Examination Survey (NHANES) was conducted in accordance with the ethical standards laid down by the National Centre for Health Statistics\u0026rsquo; Ethical Review Committee. Prior to participation, all participants were provided with comprehensive information regarding the study\u0026rsquo;s aims, procedures, potential risks, and benefits. Written informed consent was obtained from each participant, ensuring their voluntary participation and the confidentiality of their personal and health information.\u003c/p\u003e\n\u003ch3\u003e2.3 Data collection\u0026nbsp;\u003c/h3\u003e\n\u003ch4\u003e2.3.1 Exposure variable: oxidative balance score\u003c/h4\u003e\n\u003cp\u003eThe Oxidative Balance Score (OBS) is a composite measure designed to quantify the balance between pro-oxidant and antioxidant exposures in the diet and lifestyle of individuals. The methodology for constructing and calculating OBS has been previously described in detail \u003csup\u003e22\u003c/sup\u003e.OBS construction incorporates a comprehensive array of 20 factors, encompassing both dietary nutrients and lifestyle behaviors.\u003c/p\u003e\n\u003cp\u003eThese factors are categorized into pro-oxidants, which include total fat, iron, alcohol intake, body mass index (BMI), and cotinine levels, and antioxidants, which comprise dietary fiber, b-carotene, vitamins B2, niacin, B6, total folate, B12, C, E, and minerals such as calcium, magnesium, zinc, copper, selenium, alongside physical activity. The assignment of scores to each component is based on its oxidative property (either antioxidant or prooxidant) and differentiated by gender, adhering to a predefined OBS component assignment scheme. Specifically, the OBS component assignment scheme incorporates alcohol consumption levels, categorizing participants into non-drinkers, non-heavy drinkers (0 to 15 g/day for women and 0 to 30 g/day for men), and heavy drinkers (\u0026ge;15 g/day for women and \u0026ge;30 g/day for men). Scores of 2, 1, and 0 points are allocated to these categories, respectively\u003csup\u003e23\u003c/sup\u003e. The scoring for other components is determined through tertiles based on gender, with antioxidant groups receiving scores from 0 to 2 points from the lowest to the highest tertile, whereas prooxidant groups are scored inversely, with the highest tertile receiving 0 points and the lowest tertile 2 points.\u003c/p\u003e\n\u003ch4\u003e2.3.2 Outcome variable\u003c/h4\u003e\n\u003cp\u003eThe outcome variable of interest in this study was liver damage, assessed by major markers of liver enzymes，Liver injury was defined as the abnormal GGT, in which male: GGT \u0026gt; 68 U/L, female: GGT \u0026gt; 40 U/L \u003csup\u003e24,25\u003c/sup\u003e.\u003c/p\u003e\n\u003ch4\u003e2.3.3 Covariate definitions\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eThe covariates were selected based on their potential impact on liver health and included sociodemographic factors, lifestyle characteristics, and comorbid conditions.\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; demographic information was collected through a self-administered questionnaire, which included details on age, gender, and race/ethnicity. Race/ethnicity was classified into four categories: Mexican American, Non-Hispanic White, Non-Hispanic Black, and Other. Body Mass Index (BMI) was calculated using the formula: weight (kg)/height squared (m\u0026sup2;), and categorized into three groups: underweight/normal (\u0026lt;25 kg/ m\u0026sup2;), overweight (25-30 kg/m\u0026sup2;), and obese (\u0026ge;30 kg/m\u0026sup2;)\u003csup\u003e26\u003c/sup\u003e. The poverty-to-income ratio, a measure of socioeconomic status, was determined by dividing the household income by the poverty threshold for the survey year and state, providing insight into the participants\u0026rsquo; economic conditions.\u003c/p\u003e\n\u003cp\u003eDietary intake was assessed using a 24-hour dietary recall interview, which captured detailed information on all food and beverage consumption from the previous day. This method provided a snapshot of the participant\u0026rsquo;s daily caloric intake, essential for evaluating dietary habits and their potential impact on health outcomes. Total energy intake was calculated in kilocalories per day (kcal/day) based on the data from the first dietary recall \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHypertension was defined as having a systolic blood pressure (BP) \u0026ge;140 mmHg, diastolic BP \u0026ge;90 mmHg, or current use of antihypertensive medication\u003csup\u003e27\u003c/sup\u003e . Diabetes and Prediabetes classifications adhered to the American Diabetes Association (ADA) criteria, utilizing fasting blood glucose (FBG) and Hemoglobin A1c (HbA1c) thresholds \u003csup\u003e28\u003c/sup\u003e. Hyperlipidemia was identified through specific lipid profile levels or the use of lipid-lowering medications \u003csup\u003e29\u003c/sup\u003e. Chronic kidney disease(CKD) is defined according to the current clinical guidelines as an eGFR of less than 60 mL/min/1.73 m\u0026sup2;, albuminuria of 30 mg/g or greater, or both conditions\u003csup\u003e30\u003c/sup\u003e. The cardiovascular disease is identified through self-reporting\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e2.4 Statistical analysis\u003c/h3\u003e\n\u003cp\u003eAccording to the oxidation balance score (OBS) level of the study population, it was divided into quartiles defined as 2-11, 12-16, 17-21 and 22-31 for comparative analysis. The continuous variables are expressed as mean \u0026plusmn; standard error (SE). Categorical variables are expressed as frequency (%). In order to assess the difference between the OBS quartiles of continuous variables, we used analysis of variance (ANOVA). For categorical variables, we applied the chi-square test or Fisher\u0026apos;s exact test to examine differences between OBS quartiles based on expected frequencies in the contingency table.\u003c/p\u003e\n\u003cp\u003eOBS levels were analyzed both as continuous variables (per unit increase) and in categorical format (quartiles), and GGT levels were divided into two modes: as continuous variables and as categorical variables (abnormal GGT) according to the above-mentioned definition of liver injury. The relationship between OBS levels and GGT levels was examined by weighted logistic regression analysis, adjusting for various covariates. The analyses adopted a hierarchical structure, starting with a crude model and then sequentially adjusting for sociodemographic factors, lifestyle characteristics, and comorbidities. To investigate the potential nonlinear associations between OBS and GGT, restricted cubic spline (RCS) regression was used to verify the complex relationships between OBS and GGT. Weighted logistic regression was used for subgroup analysis and interaction effect evaluation of different demographic and clinical categories. Sensitivity analysis was performed on diet and lifestyle.\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R version 4.2.0 (http: / / www.R-project.org ), FreeStatistics software version 1.9 and EmpowerStats ( http : / / www.powerstats.com ).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eTable 1 Baseline Characteristics of participants based on OBS quartiles\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"735\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003eQ1[2,11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003eQ2[12,16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003eQ3[17,21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003eQ4[22,31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e2013-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e3086(35.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e783(34.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e691(35.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e908(38.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e704(34.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e2015-2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e2772(34.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e784(35.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e642(34.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e727(32.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e619(36.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e2017-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e2140(29.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e617(30.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e488(30.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e582(29.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e453(29.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eyears\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e46.41(0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e46.67(0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e47.02(0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e45.97(0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e46.12(0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e3.11(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e2.59(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e3.08(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e3.20(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e3.54(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eSedentary Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e397.74(4.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e391.53(6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e403.91(6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e398.93(6.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e396.55(6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eheight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e168.55(0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e167.90(0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e168.49(0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e168.43(0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e169.36(0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e84.21(0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e87.36(0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e85.94(0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e83.81(0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e80.21(0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eWaist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e100.22(0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e103.62(0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e102.01(0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e99.69(0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e96.11(0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.41(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.64(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.53(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.31(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.18(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e54.27(0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e51.64(0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e52.94(0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e54.67(0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e57.43(0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e14.07(0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e13.34(0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e14.04(0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e14.19(0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e14.64(0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eHba1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.62(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.71(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.68(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.61(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5.51(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e27.91(0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e32.09(1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e30.96(1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e26.79(0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e22.58(0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender,n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e4251(52.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1099(49.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e946(50.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1200(53.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1006(54.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e3747(47.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1085(50.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e875(49.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1017(46.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e770(45.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/ethnicity,n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e3077(66.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e781(61.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e715(67.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e876(67.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e705(69.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1675(10.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e685(17.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e389(10.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e382(8.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e219(5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1187(8.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e250(7.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e286(8.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e349(9.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e302(8.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e2059(14.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e468(13.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e431(12.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e610(14.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e550(15.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1418(10.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e527(17.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e350(11.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e335( 8.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e206( 6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1811(23.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e607(29.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e457(26.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e455(20.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e292(15.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eMore than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e4767(66.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1049(52.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1013(61.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1427(70.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1278(77.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eCohabitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e4899(65.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1200(58.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1107(64.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1396(66.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1196(70.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eSolitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e3097(34.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e982(41.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e714(35.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e821(33.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e580(29.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM,n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e6520(86.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1653(81.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1462(84.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1854(87.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1551(90.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1478(13.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e531(18.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e359(15.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e363(12.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e225( 9.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension,n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e4713(63.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1116(56.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1053(61.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1356(65.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1188(70.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e3285(36.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1068(43.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e768(38.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e861(34.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e588(29.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHyperlipidemia,n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e2494(32.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e593(27.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e534(30.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e690(31.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e677(40.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e5504(67.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1591(72.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1287(69.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1527(68.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1099(59.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD,n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e7266(92.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1891(89.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1656(92.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e2039(93.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1680(95.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e732( 7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e293(10.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e165( 7.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e178( 6.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e96( 4.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD,n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e6736(86.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1723(82.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1524(85.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1930(88.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1559(89.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.7211%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e1262(13.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e461(17.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e297(14.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e287(11.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1973%;\"\u003e\n \u003cp\u003e217(10.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.2925%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations:PIR: Poverty Income Ratio; DM: Diabetes Mellitus; CVD:Cardiovascular Disease;\u0026nbsp;CKD:Chronic Kidney Disease.\u003c/p\u003e\n\u003cp\u003eContinuous Variables: Presented as means with standard errors (SE); Categorical Variables: Displayed as counts (n) and percentages (%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 7998 participants were divided into quartiles based on oxidative balance score (OBS). Demographic and health characteristics showed significant differences between quartiles. There were significant racial differences, with non-Hispanic whites dominating the highest quartile of OBS (P \u0026lt; 0.0001). Socioeconomic status as indicated by poverty-income ratio (PIR) and dietary habits as reflected by energy intake were significantly different among different OBS levels (P\u0026lt;0. 0001 for both). Liver enzymes, key markers of liver injury, including ALT and GGT, showed significant differences (P \u0026lt; 0. 001, P \u0026lt; 0. 0001), while AST did not show significant differences (P = 0. 41). In the lower OBS quartiles, the prevalence of diabetes, hypertension, chronic kidney disease, cardiovascular disease (CVD), and hyperlipidemia was significantly higher (P\u0026lt;0.0001), and the key serological indicators corresponding to these diseases: Hba1c, UA, HDL, BUN also showed corresponding trends (P\u0026lt;0.0001), which was consistent with the increase in the level of liver enzyme GGT in these groups (P\u0026lt;0.0001)(Table 1).\u003c/p\u003e\n\u003cp\u003eTable 2: Relationship between OBS and abnormal GGT in different models.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6944%;\"\u003e\n \u003cp\u003eExposure Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7882%;\"\u003e\n \u003cp\u003eNon-adjusted model\u003c/p\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4477%;\"\u003e\n \u003cp\u003eAdjust I model\u003c/p\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003eAdjust II model\u003c/p\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003eAdjust III model\u003c/p\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6944%;\"\u003e\n \u003cp\u003eOBS (per unit change)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7882%;\"\u003e\n \u003cp\u003e0.96(0.94,0.97)，\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4477%;\"\u003e\n \u003cp\u003e0.97(0.95,0.98)，\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.97(0.96,0.99)，\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.97(0.95,0.99)，\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6944%;\"\u003e\n \u003cp\u003eOBS quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7882%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6944%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7882%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4477%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6944%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7882%;\"\u003e\n \u003cp\u003e0.85(0.65,1.12)\u003c/p\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4477%;\"\u003e\n \u003cp\u003e0.88(0.65,1.19)\u003c/p\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.91(0.67,1.23)\u0026nbsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.92(0.67,1.26)\u0026nbsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6944%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7882%;\"\u003e\n \u003cp\u003e0.63(0.46,0.85)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4477%;\"\u003e\n \u003cp\u003e0.68(0.50,0.92)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.72(0.53,0.99)\u0026nbsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.73(0.53,1.01)\u0026nbsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6944%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7882%;\"\u003e\n \u003cp\u003e0.48(0.32,0.71)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4477%;\"\u003e\n \u003cp\u003e0.57(0.38,0.86)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.62(0.41,0.92)\u0026nbsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.64(0.42,0.97)\u0026nbsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6944%;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7882%;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4477%;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003eNon-adjusted model:\u0026nbsp;adjusted for no covariates.\u003c/p\u003e\n\u003cp\u003eModel I: Adjustments are made for Age, Gender, Race.ethnicity, Education, PIR, Marital.status, SedentaryActivity, height, weight, Waist.\u003c/p\u003e\n\u003cp\u003eModel II: This model includes all adjustments from Model I, with additional adjustments for SUA, HDL, BUN, Hba1c.\u003c/p\u003e\n\u003cp\u003eModel III: Extends the adjustments of Model II to include comorbidities: DM, Hypertension, Hyperlipidemia, CVD, coronary.heart.disease, CKD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between OBS and GGT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, the relationship between OBS and the odds of\u0026nbsp;abnormal\u0026nbsp;GGT was examined using weighted logistic regression analysis, and in the unadjusted model, each unit increase in OBS was associated with\u0026nbsp;a 4% decrease in the odds of\u0026nbsp;abnormal GGT\u0026nbsp;[OR=0.96, 95%CI: 0.94, 0.97, P\u0026lt;0.0001].\u0026nbsp;This association remained significant in the fully adjusted model, which showed that for every one unit increase in OBS,\u0026nbsp;the odds of\u0026nbsp;the abnormal GGT\u0026nbsp;would decrease by 3% [OR=0.97, 95% CI: 0.95, 0.99, P\u0026lt;0.001].\u0026nbsp;In the fully adjusted model, as shown in Tables S1 and S2 of the supplementary material, a per unit increase in lifestyle OBS was associated with a 15% decrease in the odds of\u0026nbsp;abnormal GGT\u0026nbsp;[OR=0.85, 95% CI: 0.77, 0.93, P=0.002]. Similarly, one unit increase in dietary OBS was linked to a 2% decrease in the odds of\u0026nbsp;abnormal GGT\u0026nbsp;[OR=0.98, 95% CI: 0.96, 0.99, P=0.01].\u003c/p\u003e\n\u003cp\u003eWhen OBS was divided into quartiles, the highest quartile (Q4) showed the strongest association with lower liver enzyme GGT levels compared with the reference first quartile (Q1), and the odds ratios (ORs) of each quartile showed a gradually decreasing trend. Specifically, in the fully adjusted model, compared with Q1, the OR of Q4 was 0.64 (95 % CI:0.42-0.97, P =0.04), but whether there was a significant trend between the OBS quartiles was uncertain (P indicates a trend=0.72).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of restricted cubic spline regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough RCS analysis of weighted multivariate logistic regression adjusted for covariates, we identified a linear relationship between OBS, dietary OBS, lifestyle OBS and the incidence of the abnormal GGT respectively (P-nonlinear=0.231, 0.063, 0.848) in Fig. 2A-C. Our findings indicate a negative correlation between OBS and dietary OBS with the incidence of the abnormal GGT, while lifestyle OBS showed no significant correlation.\u003c/p\u003e\n\u003cp\u003eTable 3 Stratified Analysis and Interaction Tests for the Association Between Oxidative Balance Score and GGT level in U.S. Adults: NHANES 2013\u0026ndash;2018\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"746\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eCharacter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003cp\u003e(character2integer)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003ep for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003e20-45 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.044(0.635,1.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.582(0.355,0.955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.380(0.198,0.729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003e45-65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.694(0.397,1.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.597(0.333,1.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.556(0.236,1.312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003e65-85 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.392(0.552,3.507)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.047(0.406,2.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.626(0.199,1.963)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.882(0.489,1.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.510(0.253,1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.459(0.182,1.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.909(0.593,1.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.698(0.396,1.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.538(0.281,1.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eRace.ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.723(0.423,1.234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.471(0.261,0.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.399(0.195,0.815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.155(0.608,2.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.017(0.531,1.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.688(0.333,1.422)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e2.301(1.184,4.472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.694(0.571,5.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e1.369(0.545,3.438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.889(0.451,1.751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.729(0.408,1.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.613(0.261,1.439)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003ePIR.group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eLow (\u0026lt;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.533(0.823,2.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.196(0.657,2.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.960(0.459,2.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eMedium (1.5-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.016(0.567,1.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.597(0.330,1.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.530(0.280,1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eHigh (\u0026gt;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.551(0.306,0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.413(0.208,0.819)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.331(0.138,0.793)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003ePA.group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eNo.PA(\u0026lt;600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.882(0.554,1.405)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.740(0.392,1.396)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.532(0.217,1.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eLow.PA(600-8000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.970(0.573,1.643)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.544(0.306,0.965)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.448(0.217,0.925)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eHigh.PA (\u0026gt;8000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.715(0.227,2.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.700(0.232,2.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.887(0.231,3.398)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eBMI.group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eNormal(\u0026lt;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.646(0.230,1.817)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.546(0.251,1.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.632(0.204,1.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eOverweight(25 to \u0026lt;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.711(0.428,1.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.649(0.379,1.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.530(0.240,1.170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eObese(30 or greater)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.102(0.674,1.801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.676(0.353,1.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.527(0.246,1.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.826(0.539,1.268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.562(0.358,0.884)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.429(0.235,0.781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.078(0.692,1.680)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.822(0.416,1.624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e1.023(0.484,2.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.179(0.712,1.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.689(0.430,1.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.525(0.281,0.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.690(0.451,1.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.572(0.323,1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.518(0.243,1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.766(0.346,1.697)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.540(0.202,1.443)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.530(0.179,1.567)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.911(0.601,1.380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.639(0.375,1.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.496(0.264,0.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.846(0.576,1.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.608(0.374,0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.460(0.260,0.814)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.915(0.488,1.718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.541(0.278,1.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.667(0.244,1.818)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.854(0.602,1.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.586(0.373,0.921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e0.468(0.264,0.829)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4155%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.03217%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e1.002(0.333,3.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3646%;\"\u003e\n \u003cp\u003e0.922(0.356,2.389)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.0349%;\"\u003e\n \u003cp\u003e1.107(0.365,3.359)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3941%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003eThe Oxidative Balance Score was categorized into four quartiles, designated as Q1, Q2, Q3, and Q4. Q1 ref denotes the lowest quartile of the OBS as the reference category.P for interaction was calculated using weighted multivariable logistic regression analysis adjusted for age, gender, race, poverty-to-income ratio (PIR), energy intake, and comorbidities including hypertension, diabetes, and hyperlipidemia, excluding the specific subgroup variable.\u003c/p\u003e\n\u003cp\u003eIn Table 3, the oxidative balance scores are divided into four quartiles, designated as Q1, Q2, Q3, and Q4. Specifically, Q1 represents the lowest 25% of the observations, Q2 represents the 25th to 50th percentile of the observations, Q3 represents the 50th to 75th percentile of the observations, and Q4 represents the highest 25% of the observations. We performed stratified analyses by age, sex, race, poverty-income ratio, BMI, diabetes status, hypertension, chronic renal failure, and hyperlipidemia and tested potential interactions of these factors with OBS on the risk of\u0026nbsp;the abnormal GGT.\u003c/p\u003e\n\u003cp\u003eOverall, the inverse association between higher OBS and lower risk of abnormal GGT levels was consistently observed across most strata, except for a positive association in those with CVD, although the strength of this association appeared to vary depending on certain demographic and clinical factors. For example, the inverse relationship between OBS and GGT was most pronounced in adults aged 20-45 years, men, non-Hispanic whites, those with higher income levels, and overweight individuals. This negative association was attenuated in obese participants and those with diabetes. However, most tests of interaction between OBS and risk stratification factors for abnormal GGT levels were not statistically significant, suggesting that higher OBS has a generally consistent protective effect across the population.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses were conducted independently for dietary and lifestyle Oxidative Balance Scores (OBS) to evaluate their respective associations with The abnormal GGT risk, utilizing weighted logistic regression models adjusted for Age, Gender, Race.ethnicity, Education, PIR, Marital.status, SedentaryActivity, height, weight, Waist, SUA, HDL, BUN, Hba1c, DM, Hypertension, Hyperlipidemia, CVD, coronary.heart.disease, CKD. Fully adjusted weighted logistic regression analysis indicated that each unit increase in dietary OBS was associated with a 2% reduction in the abnormal GGT prevalence [OR: 0.98 (95% CI: 0.96\u0026ndash;0.99), P = 0.01]. Similarly, for lifestyle OBS, each unit increase was linked to a 15% reduction in the abnormal GGT prevalence [OR: 0.85 (95% CI: 0.77\u0026ndash;0.93), P = 0.002] (Supplementary Table S1).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study used data from the 2013-2018 National Health and Nutrition Examination Survey (NHANES) in the United States, involving a total of 7,998 people, to explore in detail the relationship between oxidative balance score (OBS) and liver damage and liver enzyme levels.\u0026nbsp;After adjusting for multiple covariates, our analysis showed a significant inverse association between OBS and GGT levels and liver injury characterized by abnormal GGT levels. The negative correlation between OBS and GGT levels emphasizes the importance of antioxidant and prooxidant balance in liver injury marked by GGT levels. It opens the door to innovative preventive strategies focused on enhancing oxidative balance, possibly through dietary and lifestyle changes. This method can provide a new way for the risk management of liver injury with GGT level as a marker, emphasizing the need for further research to elucidate the mechanism of OBS affecting the development and progression of GGT level.\u003c/p\u003e\n\u003cp\u003eThe redox state constitutes an important background for many liver diseases. The redox state is involved in the progression of inflammatory, metabolic and proliferative liver diseases. Reactive oxygen species (ROS) are primarily generated in mitochondria and the endoplasmic reticulum of hepatocytes by cytochrome P450 enzymes\u003csup\u003e32\u003c/sup\u003e. Under appropriate conditions, cells are equipped with special molecular strategies that can control oxidative stress levels and maintain a balance between oxidants and antioxidant particles\u003csup\u003e33\u003c/sup\u003e. Oxidative stress represents an imbalance between oxidants and antioxidants. Hepatocyte proteins, lipids, and DNA are the main cellular structures affected by reactive oxygen species (ROS) and reactive nitrogen species (RNS), a process that leads to abnormal liver structure and function\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe OBS used in our study is a comprehensive measure consisting of 20 factors representing diet and lifestyle prooxidants and antioxidants, which has been widely validated in several studies\u0026nbsp;\u003csup\u003e35-37\u003c/sup\u003e. By integrating these 20 factors, the OBS allows for a comprehensive assessment of an individual\u0026apos;s oxidative status, providing a biologically plausible basis for interpreting our findings and warranting further validation in larger cohorts.\u003c/p\u003e\n\u003cp\u003eAt present, many literatures have proved that there is a significant association between OBS and liver disease. A study based on Korean genomic and epidemiological studies has found that higher OBS scores are significantly associated with lower risk of nonalcoholic fatty liver disease ( NAFLD )\u003csup\u003e16\u003c/sup\u003e. At the same time, a study based on the National Health and Nutrition Examination Survey found that higher OBS was associated with a lower incidence of metabolic dysfunction-associated fatty liver disease (MASLD)\u003csup\u003e38,39\u003c/sup\u003e. These results are consistent with the findings of our study, in which higher OBS scores were associated with a lower risk of liver injury. However, the focus of these studies is not on liver enzyme levels, which are markers of liver damage. A study based on the Korean Genomic and Epidemiological Study found that higher OBS in Korean adults, indicating the advantage of antioxidant over prooxidant exposure, was strongly negatively correlated with GGT levels\u003csup\u003e40\u003c/sup\u003e. This is consistent with our findings, but our study extends these insights to the US population, confirming the importance of OBS in reducing GGT levels in a diverse population. Our study is unique in analyzing the direct effects of OBS on GGT levels in the U.S. context, in which different ethnic and socioeconomic factors may have unique effects on oxidative stress. Furthermore, our analysis includes a wider age range, enhancing the national representativeness of our findings. Furthermore, the differences in OBS calculation methods between studies highlight the need for standardization to more effectively compare and replicate study findings. Our approach includes a broader range of biochemical markers and could serve as a step toward standardization, lay the foundation for future studies, and lead to more consistent and reliable measurements of oxidative balance.\u003c/p\u003e\n\u003cp\u003eThe main strengths of our study are the use of a large, nationally representative data set and a rigorous methodological framework, which included validated data collection methods, a comprehensive analysis strategy, and the innovative use of RCS diagrams to elucidate the relationship between oxidative balance score (OBS) and GGT levels. In addition, our subgroup and sensitivity analyses further validated the robustness of our conclusions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRCS results showed a linear negative correlation between OBS and GGT. Compared with the general population, the greater the OBS, the lower the risk of liver injury. Although our study did not delve into the mechanisms linking OBS with GGT, oxidative stress offers a plausible explanation. Oxidative stress refers to the imbalance between pro-oxidation and anti-oxidation processes in the body. It is closely related to the occurrence of various diseases including liver damage, chronic kidney disease, lung damage, heart failure, and aging\u0026nbsp;\u003csup\u003e41-45\u003c/sup\u003e. Gamma-glutamyl transferase (GGT) is an intracellular enzyme located on the plasma membrane of a variety of cells and tissues, especially hepatocytes, and is considered a marker of hepatobiliary disease and drug consumption. Alcohol may increase GGT levels by promoting the induction of microsomal synthesis or by direct extraction from hepatocytes, and therefore, GGT is a sensitive indicator of liver disease associated with alcohol consumption. GGT maintains intracellular glutathione levels by participating in the \u0026gamma;-glutamyl cycle, and glutathione is the most important endogenous antioxidant produced by cells. It protects cells from oxidative stress (OS), neutralizes the effects of reactive oxygen species, and maintains the levels of exogenous antioxidants such as vitamins C and E. Previous studies have reported that serum GGT activity is an oxidative stress biomarker\u0026nbsp;\u003csup\u003e46\u003c/sup\u003e，Plasma GGT levels may reflect the development of liver damage involving oxidative stress as a pathophysiological mechanism. Although the relationship between cells and serum GGT is still unclear, many studies have shown that serum GGT within the normal range may be an early and sensitive enzyme in oxidative stress\u003csup\u003e47,48\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, the cross-sectional design of our study, which relied on National Health and Nutrition Examination Survey (NHANES) data, limits our ability to infer a causal relationship between OBS levels and GGT levels. This limitation highlights the need for longitudinal studies to more clearly determine the dynamics of this relationship\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, limitations of this study include the use of self-reported dietary data, which are susceptible to recall and reporting biases, thereby affecting the accuracy of oxidative balance score (OBS) calculations and observed associations. To reduce this measurement error and improve estimates of daily intake, we applied methods from the National Cancer Institute\u003csup\u003e49\u003c/sup\u003e. Furthermore, although we adjusted for various known confounders, there is still the possibility of residual confounding due to unmeasured or inadequately measured variables. These factors may have influenced the observed relationship between OBS and GGT levels, thus requiring caution in interpreting our findings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our examination of the NHANES data showed a significant inverse association between the oxidative balance score (OBS) and GGT levels or the risk of liver injury with abnormal GGT levels. This relationship highlights the importance of oxidative balance in the context of liver injury and suggests that dietary and lifestyle modifications may influence liver injury risk, suggesting that serum GGT may be an early and sensitive enzyme for oxidative stress. The cross-sectional nature of our study calls for further longitudinal investigations to explore these associations in depth and to assess the effectiveness of specific interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions Statement\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eXL. G\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eXW. L\u003c/strong\u003e contributed equally to this work. They were involved in conceptualization, methodology, data curation, performing the analysis and writing the original draft. \u003cstrong\u003eHB. W\u003c/strong\u003e and \u003cstrong\u003eZL. W\u0026nbsp;\u003c/strong\u003ewas involved in the investigation process and contributed to the review and editing of the manuscript. \u003cstrong\u003eZQ. M\u003c/strong\u003e as one of the corresponding authors, contributed to project administration, supervision, and securing funding. He also contributed to the review and editing of the manuscript. \u003cstrong\u003eN. T\u003c/strong\u003e also a corresponding author, played a crucial role in the conceptualization, methodology, resources, and writing, review and editing of the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82160699), the Guangxi Natural Science Foundation of China (Grant Nos. 2023GXNSFAA026477 and 2023GXNSFAA026282), the Guangxi Department of Education 2021 Graduate Education Innovation Program Project of China (Grant No. JGY2021138), and the Guangxi Health and Wellness Commission 2020 Science and Technology Project (Approval No. Z20201104).\u003c/p\u003e\n\u003ch2\u003eEthics statement\u003c/h2\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by National Center for Health Statistics Ethics Review Board Approval. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that this study was conducted in the absence of any commercial or financial relationships that could serve as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIngawale, D.K., Mandlik, S.K., and Naik, S.R. (2014). Models of hepatotoxicity and the underlying cellular, biochemical and immunological mechanism (s): a critical discussion. Environmental toxicology and pharmacology \u003cem\u003e37\u003c/em\u003e, 118\u0026ndash;133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, S., Cheng, K., Peng, Y., Liu, Y., Hu, Q., Zeng, S., Qi, X., and Yu, L. (2024). Regulation mechanism of endoplasmic reticulum stress on metabolic enzymes in liver diseases. 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JAMA internal medicine \u003cem\u003e180\u003c/em\u003e, 513\u0026ndash;523.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5413780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5413780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Oxidative Balance Score (OBS) quantifies the balance between antioxidants and pro-oxidants, which is influenced by diet and lifestyle, and is used to evaluate the overall oxidative stress status. Elevated levels of γ-glutamyl transferase (GGT) are considered a primary indicator of oxidative stress. This study aims to explore the association between OBS and GGT using data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2013 and 2018. A total of 7,998 people were included in the study. Research has revealed a significant linear negative correlation was found between OBS scores and GGT. Fully adjusted weighted logistic regression analysis showed that each unit increase in OBS was associated with a 3% decrease in the odds of abnormal GGT [OR\u0026thinsp;=\u0026thinsp;0.97, 95%CI: 0.95, 0.99, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. By exploring this link, this could provide valuable insights into developing preventive strategies and interventions for GGT abnormalities.\u003c/p\u003e","manuscriptTitle":"Association between Oxidative Balance Scores (OBS) and Gamma- Glutamyltransferase (GGT) among US adults in NHANES 2013– 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 09:56:44","doi":"10.21203/rs.3.rs-5413780/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":"0e9498f6-8d8e-435a-9a00-b7fca8fba774","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T07:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-27 09:56:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5413780","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5413780","identity":"rs-5413780","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}