Association of ethylene oxide with nonalcoholic fatty liver disease among US adults.

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Shiwe Yan, Haolong Pei, Qian Li, Wenzhe Cao, Yan Dou, Shihan Zhen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3300124/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background Growing evidence suggest that ethylene oxide (EO) may have deleterious effects on health conditions, but the relationship between EO and adulthood nonalcoholic fatty liver disease (NAFLD) remains vague. Our objective is to evaluate whether EO exposure would influence the prevalence of NAFLD in a nationally cross-sectional study. Materials and methods In this cross-sectional study, We analyzed 2,394 participants from the National Health and Nutrition Examination Survey (NHANES) 2013-2018. Blood concentrations of EO were measured using high-performance liquid chromatography coupled with tandem mass spectrometry. US fatty liver index (USFLI) and FLI were applied to define NAFLD. Logistic regression analysis was adopted to investigate the relationship of Hemoglobin adducts of EO (HbEO) exposure with the prevalence of NAFLD. Mediation analysis was performed to assess the effect of inflammatory biomarkers on the association between HbEO levels and USFLI, FLI, and NAFLD. Results After adjustment for age, gender, race/ethnicity, education, income, smoking, drinking, hypertension, diabetes, and TC, logistic regression analysis showed that HbEO in the highest quartile was negatively associated with the prevalence of NAFLD than those in the lowest quartile (OR: 0.50, 95% CI: 0.33-0.92, P for trend = 0.008 for USFLI and OR: 0.42, 95% CI: 0.29-0.61, P for trend <0.001 for FLI). In addition, inflammation significantly mediated the relationships between HbEO and NAFLD. Conclusions Our study demonstrated that higher EO levels were negatively associated with the prevalence of NAFLD. The underlying mechanisms were required to be identify in the future study. Epidemiology EO NHANES NAFLD Inflammation Figures Figure 1 Figure 2 Introduction Non-alcoholic fatty liver disease (NAFLD) has been currently the most widespread chronic liver disease, affecting approximately 25% general population worldwide [ 1 , 2 ]. One large systematic review meta-analysis pointed out an overall prevalence in the United State reached 35.3% [ 3 ], which is continuing to increase in the future years. In view of its high prevalence, NAFLD had a great likelihood of worsening into liver fbrosis, cirrhosis, and even hepatocellular carcinoma, further placing a huge public health concern and economic burden [ 4 , 5 ]. Although a complex interaction of dietary habit, lifestyle, and genetic factors can sometimes explain for the epidemic, the potential effect of environmental pollutants on risk of NAFLD should not be ignored. Ethylene oxide (EO), a highly reactive organic compound in the environment, is mainly utilized to manufacture detergents, solvents, textiles, and plastics [ 6 , 7 ]. Due to its excellent disinfection and sterilization effects, EO is used in large quantities in medical devices and supplies during the COVID-19 pandemic [ 8 ]. The global production of EO reached 20 million metric tons in 2009, and the requirement for EO is estimated to raise its annual production rate by 2% by 2025 [ 9 ]. There exist the close link between the risk of EO exposure and sectional occupations, such as doctors, nurses, and industrial workers. The major route of EO exposure in the general population may be via air, tobacco smoke, and vehicle exhaust. Hemoglobin adducts of EO (HbEO) produced by the reaction between EO and hemoglobin (Hb) is a sensitive and effective hematological marker to assess EO exposure [ 10 , 11 ]. Current evidence which suggested, as a potential exogenous toxicant, EO can pose deteriorated effects in human health is not extensive. Although some epidemiological studies reported that exposure to EO was associated with increased risk of asthma, serum lipid, hypertension, diabetes, and cardiovascular diseases (CVD) [ 12 – 16 ], one recent cross-sectional study have found an inverse association of blood EO levels with obesity in adults [ 17 ]. Considering that obesity and hypertension, diabetes, and CVD are all important risk factors in contributing to the development of NAFLD, it is required to further evaluate the uncertain association between EO and NAFLD. In addition, some rodent studies demonstrated that chronic exposure to EO caused hepatic lipid peroxidation and inflammatory reactions in systemic organs that are both closely involved in the pathogenesis of NAFLD [ 18 , 19 ]. Hence, in the current study, we aimed to evaluate whether EO exposure was closely associated with the prevalence of NAFLD based on nationally representative data from the National Health and Nutrition Examination Survey (NHANES) 2013–2018 and to assess the effect of HbEO on inflammatory markers and NAFLD. Methods Study population We used public data across three three-year cycles (2013-2018) of NHANES, an ongoing nationwide cross-sectional survey of the non-institutionalized civilians living in the US using complex, multi-stage probability sampling designs. All participants have signed informed consent forms during the period of recruitment. This study participants were restricted to adults (aged ≥ 18 years) who had complete data of blood EO concentrations, inflammatory markers, glycohemoglobin, blood pressure, and information on the definition of NAFLD. Additionally we excluded individuals who were pregnant and had viral hepatitis (hepatitis B surface antigen or hepatitis C RNA). Finally, 2,394 adults were left for the analysis ( Figure S1 ). Measurement of EO Considering that HbEO has a longer half-life in contrast to EO in the body, it was used to assess EO exposure in this study. Following NHANES Laboratory/Medical Technologists Procedures Manual (https:// wwwn.cdc.gov/Nchs/Nhanes/2017-2018/ETHOX_J.htm), HbEO levels were determined. The red blood cell samples by washing and packing were stored at -30°C up to ship National Center for Environmental Health for examination. Subsequently, HbEO levels were detected based on a modified Edman reaction using high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). More details are described in the NHANES Laboratory/Medical Technician Procedures Manual. Measurements below the limit of detection (12.9 pmol/g Hb) were imputed as the LOD divided by √2. Additionally, the assays accord with the quality control and quality assurance performance criteria of the NCEH Division of Laboratory Science for accuracy. Assessment of NAFLD We defined NAFLD using US fatty liver index (US FLI) and FLI, highly effective diagnostic indices, which was used to monitor and screen NAFLD cases via a non-invasive method and has been validated in the numerous epidemiological studies [20-22]. Compared with traditional ultrasonography and CT tests, USFLI and FLI were calculated by formulas that included information on age, race, waist circumference, body mass index (BMI), fasting insulin, fasting glucose, gamma glutamyl transferase (GGT), and triglyceride (TG). The cut-off of 30 for USFLI and 60 for FLI were considered to define NAFLD in this study. Covariates Demographic information was obtained by questionnaires examinations, anthropometric assessment, and laboratory testing. We considered some variables as potential confounders: age, gender, race, education level, annual household income, BMI, smoking, drinking, hypertension, diabetes, and total cholesterol (TC). Of which, race/ethnicity was classified as Mexican American, Non-Hispanic white, Non-Hispanic black, and others. Education level was classified as lower than 9th grade, 9-11th grade, high school/ GED or equivalent, some college or Associate in Arts degree, and college graduate or above. Annual household income was classified as $75,000. Current smoking and drinking were categorized into yes and no. Diabetes is assessed by self-reported diagnosis, use of insulin or oral hypoglycemic medication, FBG ≥ 126 mg/dL or HbA1c level ≥ 6.5%. Hypertension was assessed as self-reported hypertension systolic blood pressure (SBP) ≥ 140mmHg or diastolic blood pressure (DBP) ≥ 90mmHg or use of anti-hypertensive medication. Inflammatory markers, including ALP, monocyte count, and lymphocyte count in blood samples were obtained from the laboratory data. Statistical analysis The baseline characteristics including means (SD) and frequency (%) were compared by the quartile of HbEO with using ANOVA and χ 2 tests. HbEO levels were classified into four groups based on quartiles. Since levels of blood HbEO were in skewed distributions, it was naturally ln-transformed. General linear regression analysis was adopted to assess the effect of HbEO on liver function indices and USFLI, and FLI. The relationship between HbEO and NAFLD was investigated using logistic regression models. Coefficient or odd ratio (OR) and 95% confidence intervals (CI) were shown in three model. Model 1 was adjusted for age and gender. Model 2 was adjusted for covariates in model 1 plus race, education, income, smoking, and drinking. Model 3 was adjusted for covariates in model 2 plus hypertension, diabetes, and TC. In order to investigate the susceptibility of demographic-related differences, we adopted subgroup analyses to evaluate whether the relationship between HbEO and NAFLD were affected by sex. Restricted cubic spline (RCS) was used to model the trend of USFLI and FLI and prevalence of NAFLD across ln-transformed HbEO range. Mediation models were subsequently conducted to explore the role of inflammatory markers as potential mediators of the relationship between ln-transformed HbEO and the prevalence of NAFLD. Considering that CVD and diabetes might influence the association between HbEO and NAFLD, their family history was further adjusted in the sensitivity analyse. All data were analyzed using R 4.3.2, and a two-tailed P <0.05 was regarded as statistical significance. Results Baseline Characteristics Table 1 showed the demographic characteristics by the quartile of HbEO levels. Of the 2,394 participants, There were significant differences in age, gender, race, education level, annual household income, smoking, drinking, BMI, waist circumference, fasting glucose, fasting insulin, glycohemoglobin, diabetes, hypertension, TG, ALT (alanine aminotransferase), AST (aspartate aminotransferase), GGT, TBIL (total bilirubin), USFLI, and FLI between the four groups. In addition, participants’ characteristics by NAFLD status were presented in Table S1. For USFLI and FLI, 792 (33.1%) and 1,005 (42.0%) of participants were considered as the current NAFLD group and the remaining 1,602 (66.9%), and 1,389 (58.0%) with non-NAFLD acted as the control group, respectively. In general, compared with individuals without NAFLD, those with NAFLD assessed by USFLI and FLI were more likely to be older, Non-Hispanic white, smoker, drinker, diabetic, and hypertensive and had lower education and income levels. Also, NAFLD participants had higher BMI, waist circumference, SBP, and DBP and levels of fasting glucose, fasting insulin, glycohemoglobin, TC (total cholesterol), TG, ALT, AST, and GGT and lower levels of TBIL and ALB (albumin). Table 1 Baseline characteristics of study variables by NAFLD status in the NHANES 2013–2018. Variables HbEO (pmol/g Hb) P-value Q1 ≤ 16.7 Q2 1.67 < to ≤ 23.6 Q3 23.6 89.0 Number of participants 603 598 596 597 Age, years 50.2 (18.4) 48.8 (18.9) 48.1 (18.3) 45.7 (15.4) < 0.001 Gender (female), % 321 (53.2) 309 (51.7) 292 (49.0) 254 (42.5) < 0.001 Non-Hispanic white, % 282 (46.8) 205 (34.3) 168 (28.2) 281 (47.1) < 0.001 College graduate or above, % 168 (27.9) 176 (29.4) 150 (25.2) 47 (7.9) < 0.001 Over $ 75,000, % 184 (30.5) 180 (30.1) 184 (30.9) 62 (10.4) < 0.001 Current smoking, % 16 (2.7) 19 (3.2) 125 (21.0) 524 (87.8) < 0.001 Current drinking, % 378 (62.7) 296 (49.5) 344 (57.7) 439 (73.5) < 0.001 BMI, kg/m 2 30.2 (7.0) 29.5 (7.4) 28.2 (6.3) 28.0 (7.2) < 0.001 Waist circumference, cm 101.7 (16.5) 99.6 (16.8) 97.2 (16.0) 97.3 (17.3) < 0.001 SBP, mmHg 124.3 (17.5) 123.7 (18.8) 123.8 (19.0) 124.2 (18.9) 0.95 DBP, mmHg 69.9 (11.8) 69.9 (11.2) 70.1 (11.8) 69.3 (13.4) 0.73 Hypertension, % 261 (43.3) 222 (37.1) 251 (42.1) 269 (45.1) 0.036 Fasting glucose, mg/dL 110.2 (34.4) 109.7 (34.7) 113.5 (42.3) 106.1 (30.6) 0.003 Fasting insulin, pmol/L 79.4 (77.0) 87.8 (122.1) 77.9 (89.8) 68.4 (65.6) 0.002 Glycohemoglobin, % 5.64 (1.01) 5.77 (1.01) 5.95 (1.30) 5.70 (0.93) < 0.001 Diabetes, % 111 (18.4) 117 (19.6) 141 (23.7) 89 (14.9) 0.002 TG, mg/dL 116.1 (106.8) 107.1 (88.6) 117.6 (82.7) 123.6 (107.9) < 0.001 TC, mg/dL 189.5 (42.2) 187.4 (41.5) 187.6 (41.4) 187.5 (41.6) 0.78 ALT, U/L 24.4 (15.1) 24.5 (18.0) 25.7 (24.1) 22.9 (16.5) 0.003 AST, U/L 24.5 (11.3) 24.8 (34.3) 24.8 (17.9) 23.4 (13.6) 0.018 GGT, U/L 31.9 (47.7) 26.2 (26.6) 27.7 (30.9) 31.8 (39.0) 0.007 TBIL, umol/L 10.8 (5.2) 10.1 (5.1) 10.1 (4.8) 9.29 (4.39) < 0.001 ALB, g/dL 4.23 (0.33) 4.21 (0.33) 4.21 (0.36) 4.19 (0.35) 0.24 USFLI 28.6 (24.0) 27.3 (24.2) 25.4 (22.6) 21.6 (21.6) < 0.001 FLI 54.4 (32.9) 49.3 (33.4) 47.4 (32.2) 48.0 (33.0) < 0.001 BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; TC, total cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma glutamyl transaminase; TBIL, total bilirubin; ALB, albumin; USFLI, US fatty liver index; NAFLD, non-alcoholic fatty liver disease. Continuous variables are presented as mean and SD. Categorical variables are presented as numbers and frequency. Association of HbEO levels with liver function, USFLI, and FLI Association of HbEO levels with liver function indices, USFLI, and FLI in both continuous and categorical analyses are shown in Table 2 . After adjusting for age, gender, race, education, income, smoking, drinking, hypertension, diabetes, and TC, the continuous analyse manifested that ALT (β: -0.04, 95% CI: -0.07–0.01), AST (β: -0.03, 95% CI: -0.05–0.01), TBIL (β: -0.53, 95% CI: -0.80–0.26), USFLI (β: -2.53, 95% CI: -3.66–1.39), and FLI (β: -4.07, 95% CI: -5.76–2.38) were negatively associated with ln-transformed HbEO levels. Similar associations were observed when HbEO levels were modeled as quartiles. By referring the lowest quartile of HbEO, the highest quartiles were related to higher ALT (β: -0.09, 95% CI: -0.17–0.01, P for trend = 0.006) and AST (β: -0.07, 95% CI: -0.13–0.02, P for trend = 0.017). For TBIL and FLI, there existed the negative association in the second, third, and fourth quartiles of HbEO ( β: -0.71, 95% CI: -1.25-0.17, β: -0.65, 95% CI: -1.21–0.10, and β: -1.08, 95% CI: -0.89–0.28; P -trend = 0.076 and β: -3.48, 95% CI: -6.85–0.10, β: -7.73, 95% CI: -11.21–4.24, and β: -11.48, 95% CI: -16.53–6.44; P -trend< 0.01). Also, relationships of HbEO with USFLI (β: -3.94, 95% CI: -6.27–1.60, P for trend < 0.001 and β: -7.02, 95% CI: -10.39–3.64, P for trend < 0.001) were shown in the third and fourth quartile in contrast to the first quartile. Table 2 Association of concentrations of HbEO with liver function, USFLI and FLI in adults. Outcomes Model Ln-transformed continuation Q1 Q2 Q3 Q4 P for trend β (95% CI) β β (95% CI) β (95% CI) β (95% CI) ALT a Model 1 −0.04 (−0.06–0.03) 0.00 (Ref) −0.02 (−0.07−0.04) 0.00 (−0.06−0.06) −0.12 (−0.17–0.07) < 0.001 Model 2 −0.04 (−0.07–1.20) 0.00 (Ref) −0.01 (−0.06−4.75) 0.01 (−0.06−6.59) −0.10 (−0.18–1.61) 0.005 Model 3 −0.04 (−0.07–0.01) 0.00 (Ref) 0.00 (−0.05−0.05) 0.01 (−0.05−0.06) −0.09 (−0.17–0.01) 0.006 AST a Model 1 −0.03 (−0.04–0.01) 0.00 (Ref) −0.03 (−0.07−0.01) −0.02 (−0.06−0.02) −0.08 (−0.11–0.04) < 0.001 Model 2 −0.03 (−5.27–0.01) 0.00 (Ref) −0.02 (−6.09−0.02) −0.02 (−5.89−0.02) −0.08 (−1.36–0.02) 0.016 Model 3 −0.03 (−0.05–0.01) 0.00 (Ref) −0.02 (−0.06−0.02) −0.02 (−0.06−0.03) −0.07 (−0.13–0.02) 0.017 GGT a Model 1 0.03 (0.01–0.05) 0.00 (Ref) −0.06 (−0.13−0.01) −0.02 (−0.09−0.06) 0.06 (−0.01−0.13) 0.004 Model 2 −0.02 (−0.06−0.01) 0.00 (Ref) −0.06 (−0.13−0.01) −0.06 (−0.13−0.02) −0.05 (−0.16−0.05) 0.74 Model 3 −0.02 (−0.06−0.01) 0.00 (Ref) −0.05 (−0.12−0.02) −0.06 (−0.13−0.01) −0.05 (−0.15−0.06) 0.85 TBIL Model 1 −0.60 (−0.77–0.43) 0.00 (Ref) −0.76 (−1.30–0.23) −0.81 (−1.34–0.27) −1.77 (−1.34–1.24) < 0.001 Model 2 −0.53 (−0.80–0.26) 0.00 (Ref) −0.69 (−1.23–0.16) −0.65 (−1.21–0.10) −1.08 (−1.88–0.27) 0.078 Model 3 −0.53 (−0.80–0.26) 0.00 (Ref) −0.71 (−1.25–0.17) −0.65 (−1.21–0.10 ) −1.08 (−0.89–0.28) 0.076 ALB Model 1 −0.03 (−0.04–0.02) 0.00 (Ref) −0.03 (−0.06−0.01) −0.03 (−0.06−0.01) −0.08 (−0.12–0.05) < 0.001 Model 2 −0.01 (−0.03−0.01) 0.00 (Ref) −0.02 (−0.05−0.02) −0.01 (−0.05−0.03) −0.03 (−0.08−0.03) 0.44 Model 3 −0.01 (−0.03−0.01) 0.00 (Ref) −0.02 (−0.05−0.02) −0.01 (−0.05−0.04) −0.03 (−0.08−0.03) 0.42 USFLI Model 1 −2.01 (−2.81–1.21) 0.00 (Ref) −0.97 (−3.53−1.60) −2.76 (−5.32−0.19) −6.20 (−8.78–3.62) < 0.001 Model 2 −2.42 (−3.67–1.17) 0.00 (Ref) −1.11 (−3.61−1.39) −2.78 (−5.36–0.19) −6.99 (−10.73–3.25) < 0.001 Model 3 −2.53 (−3.66–1.39) 0.00 (Ref) −0.94 (−3.20−1.32) −3.94 (−6.27–1.60) −7.02 (−10.39–3.64) < 0.001 Continued FLI Model 1 −1.15 (−2.29–0.01) 0.00 (Ref) −4.66 (−8.32–1.01) −6.38 (−10.04–2.72) −5.11 (−8.79–1.43) < 0.001 Model 2 −4.08 (−5.90–2.26) 0.00 (Ref) −4.165 (−7.80–0.53) −6.96 (−10.71–3.21) −11.74 (−17.18–6.31) < 0.01 Model 3 −4.07 (−5.76–2.38) 0.00 (Ref) −3.48 (−6.85–0.10) −7.727 (−11.21–4.24) −11.48 (−16.53–6.44) < 0.01 HbEO: hemoglobin adduct of ethylene oxide; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma glutamyl transaminase; TBIL, total bilirubin; ALB, albumin; USFLI, US fatty liver index; Model 1 was adjusted for age and gender. Model 2 was adjusted for covariates in model 1 plus race, education, income, smoking, and drinking. Model 3 was adjusted for covariates in model 2 plus hypertension, diabetes, and TC. Association of HbEO exposure with NAFLD assessed by USFLI and FLI As shown in Table 3 , in logistic regression analyses, ln-transformed HbEO levels were found to be associated with an increased prevalence of NAFLD (OR: 0.87, 95% CI: 0.80–0.95 for USFLI and OR: 0.90 95% CI: 0.83–0.97 for FLI), after adjusting for age, gender, race, education, income, smoking, drinking, hypertension, diabetes, and TC. Compared with participants in the first quartile, those in the third and fourth quartiles presented a higher prevalence of NAFLD (OR: 0.70, 95% CI: 0.53–0.92 and OR: 0.50, 95% CI: 0.33–0.75, P for trend = 0.008 for USFLI and OR: 0.58, 95% CI: 0.45–0.76 and OR: 0.42, 95% CI: 0.29–0.61, P for trend < 0.001 for FLI). In the RCS, HbEO levels were not nonlinearly associated with USFLI ( P for nonlinearity = 0.43, Fig. 1A ) and FLI ( P for nonlinearity = 0.21, Fig. 1B ). Similar association was found in NAFLD assessed by USFLI ( P for nonlinearity = 0.21, Fig. 1C ) and FLI ( P for nonlinearity = 0.093, Fig. 1D ). Subgroup analyses of gender were used to verify independent relationship between HbEO and NAFLD, which was shown in Fig. 2 . Exposure to HbEO was still associated with the lower prevalence of NAFLD for USFLI and FLI in both male and female participants. Table 3 Association of HbEO exposure with risk of NAFLD in adults. Case/N Model 1 Model 2 Model 3 OR (95% CI) OR (95% CI) OR (95% CI) NAFLD USFLI Ln-transformed continuation 0.84 (0.78–0.91) 0.90 (0.83–0.98) 0.87 (0.80–0.95) Q1 235/603 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Q2 207/598 0.84 (0.66–1.07) 0.82 (0.64–1.05) 0.82 (0.63–1.06) Q3 199/596 0.80 (0.63–1.02) 0.80 (0.61–1.03) 0.70 (0.53–0.92) Q4 151/597 0.56 (0.44–0.72) 0.54 (0.37–0.81) 0.50 (0.33–0.75) P for trend < 0.001 0.009 0.008 NAFLD FLI Ln-transformed continuation 0.93 (0.86–0.996) 0.93 (0.86–0.999) 0.90 (0.83–0.97) Q1 292/603 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Q2 248/598 0.76 (0.61–0.96) 0.78 (0.61–0.98) 0.78 (0.61−1.00) Q3 233/596 0.70 (0.55–0.88) 0.65 (0.51–0.83) 0.58 (0.45–0.76) Q4 232/597 0.71 (0.56–0.89) 0.45 (0.32–0.65) 0.42 (0.29–0.61) P for trend 0.11 < 0.001 < 0.001 Model 1 was adjusted for age and gender. Model 2 was adjusted for covariates in model 1 plus race, education, income, smoking, and drinking. Model 3 was adjusted for covariates in model 2 plus hypertension, diabetes, and TC. Mediation analyse We observed that exposure to HbEO was associated with inflammatory markers and USFLI, FLI, and NAFLD, thus conducting the mediation analyse to further explore the potential association. As shown in Figure S2 , the associations between HbEO levels and USFLI, FLI, and NAFLD assessed by USFLI and FLI were significantly mediated by monocyte ( Figure S2B, 2E, 2H and 2K ) and lymphocyte ( Figure S2C, 2F, 2I and 2L ), the proportions were − 13.1%, -12.7%, -17.2%, and − 10.1% and − 19.3%, -19.1%, -25.6%, and − 16.3% (all P < 0.001), respectively. Nevertheless, there was no prominent mediation effect of ALP on the association between HbEO and USFLI, FLI, and NAFLD ( Figure S2A, 2D, 2G and 2J ). Sensitivity analyses Sensitivity analyses were performed in Table S2 . After additional adjustment for the family history of CVD, the results remained largely unchanged, indicating the associations between HbEO with the prevalence of NAFLD were relatively robust. Discussion To the best of our knowledge, this is the first study to evaluate the association between HbEO exposure and NAFLD among a large sample of US adults. In this cross-sectional study from NHANES database 2013–2018, we observed that higher levels of HbEO were negatively associated with liver function indices, after adjusting for age, gender, race, education, income, smoking, drinking, hypertension, diabetes, and TC. This association remained pronounced in male and female participants. Additionally, we demonstrated that systemic inflammation partially mediated the influence of HbEO exposure on the prevalence of NAFLD, which might provide critical theoretical basis on the potential mechanisms. Mounting evidence suggest exposure to EO can pose a variety of irreversible effects to human health. EO, as an alkylating agent, may directly react with nucleophilic macromolecules to form the DNA adducts, and the resulting genetic damage was viewed to act a key role in inducing the mutation and chromosomal aberration in humans and animals [ 10 ]. In considering of its genotoxic and mutagenic abilities, the International Agency for Research on Cancer has classified EO as a group 1 human carcinogen [ 23 ]. Although one systematic review based on extensive studies in epidemiology, animals, and mechanism thought no prominent association between EO exposure and risk of breast and stomach cancer as well lymphohematopoietic malignancies [ 24 ], animal experiments suggested the carcinogenicity of EO exposure [ 25 , 26 ]. In a large-scale study of 173,670 postmenopausal women, airborne EO emissions were found to be related with an elevated risk of breast cancer in situ [ 27 ]. Recent studies reported there exist the link between EO exposure and some metabolic disorders, of which abdominal obesity was demonstrated to be significant determinants of hepatic lipid metabolism and independently predict the development of NAFLD [ 28 – 30 ]. In the present study, high EO levels were found to negatively associate with the risk of NAFLD, which was similar with one study result of evaluating the effect of EO on obesity [ 17 ]. Compared with the other studies with NAFLD as the outcome, we adopt two widely confirmed liver indices to assess NAFLD, thereby ensuring the stability of the results. Research into rodent showed that long-term exposure to EO induced inflammatory lesions in several organs. Also, growing epidemiological data demonstrated that EO exposure could induce systemic inflammation. It is well known that inflammation and oxidative stress (OS), as critical pathogenetic factors, might contribute to numerous forms of liver diseases, regardless of etiology. When the status of redox balance in vivo are broke up and have tendency to oxidation, abundant reactive oxygen species (ROS) fail to be catabolized [ 31 ], which lead to hepatocellular injury and apoptosis, thus influencing lipid metabolism and causing excessive accumulation of fat in the liver. Study conducted by Bian et al. showed that inflammation can interfere with insulin signaling through activation of NF-kB pathway, further leading to the development of NAFLD [ 32 ]. Nevertheless, the present study manifested that high levels of HbEO was negatively associated with risk of NAFLD in the general population, and this association was partially mediated by inflammatory markers (monocyte count, and lymphocyte count). This suggested that control of EO-driven inflammation is a single mechanism and insufficient to explain this phenomenon alone. Since suppression effects of EO exposure on NAFLD are relatively sparse in terms of biology, the underlying mechanisms are required to be further explored. Smoking was a main pathway of EO exposure in the general population, and adult smokers were reported to have higher levels of EO in contrast to non smokers. In line with our baseline characteristics, the proportion of smoking presented a prominent upward trend with the increase of EO quartiles. Noteworthy is that tobacco consist of numerous chemicals that promoted the abdominal lipolysis via accelerating internal metabolism, which possibly reduced the formation of fatty liver [ 33 ]. Certainly, this explanation should be cautiously adopt, given the lack of quantitative assessment of tobacco consumption. Additionally, subgroup analyses showed that there still existed negative associations between high HbEO levels and the prevalence of NAFLD, in both male and female participants. The parallel findings in rodent experiments provide strong evidence to explain this similar phenomenon. Mori et al, conducted chronic exposure to EO at a concentration of 250 ppm to observe the potential effects of EO on the liver in terms of sex difference [ 34 ]. Of note, EO significantly increased the activity of glutathione-S-transferase in the male and female EO-exposed groups, which might prevent metabolic disorders and decreased immune function caused by lipid peroxidation to hepatic damage. There are several strengths in this study, including the first study linking EO exposure and the risk of NAFLD, large-scale general population representative, multi-ethnic characteristics, and adjustment for sufficient cofounders. Besides, sensitivity analyses were conducted to assess the robustness and stability of our results. Of course, some limitations should be highlighted. First, in consider of the cross-sectional nature of data, the results can not ascertain the potential causality, hence further prospective studies are needed to probe the relationship between EO exposure the and prevalence of NAFLD. Second, this study merely adopted the data about single measurement of blood EO rather than repeated measurements that was more applicable to reflect the cumulative effect of long-term exposure. Third, due to lack of data on EO-related metabolites, we are unable to design mixed exposure studies. Fourth, it was hard for this study to offer effective proposals to reduce the exposure risk because there were lack of detailed information on the source and route of EO exposure. Fifth, although we used USFLI and FLI to define NAFLD in this study, which were likely to overestimate or underestimate the true prevalence of NAFLD, in contrast to traditional liver biopsy, this noninvasive test is more economical and convenient to screen the cases of NAFLD and its accuracy have been confirmed in prior large-scales epidemiological studies. Finally, the sample data from NHANES was merely limited in US population, so our findings may not be generalized to other population groups with EO exposure. Hence, studies in a large sample size were warrant to demonstrate the link between EO and NAFLD. Conclusions In summary, the current study demonstrated that higher EO levels were negatively associated with the prevalence of NAFLD. Additionally, systemic inflammation involved in the mediation effect of EO-NAFLD association. Further research are required to identify a potential biological mechanism by which EO-induced NAFLD. Declarations Acknowledgements Not applicable. Authors contributions YS organized all data and wrote the manuscript. LF conceived the study design. PH, CW, LQ, ZS, DY and WQ contributed to the interpretation of the results and revision, and finalization of the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China (42107465) and Talents enlisted in major talent programs of Guangdong Province (20210N020921). Availability of data and materials The datasets used and/or analyzed during the current study are available from the NHANES database, https://wwwn.cdc.gov/nchs/nhanes/ Declarations Ethics approval and consent to participate The requirement of ethical approval for this was waived by the Institutional Review Board of Hainan Affiliated Hospital of Hainan Medical University, because the data was accessed from NHANES (a publicly available database). All methods were carried out in accordance with relevant guidelines and regulations (declaration of Helsinki). All individuals provided written informed consent before participating in the study. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests References Byrne CD, Targher G (2015) NAFLD: a multisystem disease. J Hepatol 62(1 Suppl):S47–64 Castera L (2018) Diagnosis of non-alcoholic fatty liver disease/non-alcoholic steatohepatitis: non-invasive tests are enough. Liver Int 38(Suppl 1):67–70 Le MH, Yeo YH, Li X, Li J, Zou B, Wu Y et al (2022) 2019 Global NAFLD Prevalence: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol 20(12):2809–2817 Estes C, Razavi H, Loomba R, Younossi Z, Sanyal AJ (2018) Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology 67:123–133 Wong RJ, Cheung R, Ahmed A, Ahmed A (2014) Nonalcoholic steatohepatitis is the most rapidly growing indication for liver transplantation in patients with hepatocellular carcinoma in the US. Hepatology 59:2188–2195 Kolman A, Chovanec M, Osterman-Golkar S (2002) Genotoxic effects of ethylene oxide, propylene oxide and epichlorohydrin in humans: update review (1990–2001). Mutat Res 512:173–194 Nagy M, Szollosi L, Keki S, Zsuga M (2007) Self-assembly study of polydisperse ethylene oxide-based nonionic surfactants. Langmuir 23:1014–1017 Kirman CR, Li AA, Sheehan PJ, Bus JS, Lewis RC, Hays SM (2021) Ethylene oxide review: characterization of total exposure via endogenous and exogenous pathways and their implications to risk assessment and risk management. 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Environ Sci Pollut Res Int 30(9):24154–24167 Wu N, Cao W, Wang Y, Liu X (2022) Association between blood ethylene oxide levels and the prevalence of hypertension. Environ Sci Pollut Res Int 29(51):76937–76943 Zeng G, Zhang Q, Wang X, Wu KH (2021) Association between blood ethylene oxide levels and the risk of cardiovascular diseases in the general population. Environ Sci Pollut Res Int 28(45):64921–64928 Zhu X, Kong X, Chen M, Shi S, Cheang I, Zhu Q et al (2022) Blood ethylene oxide, systemic inflammation, and serum lipid profiles: Results from NHANES 2013–2016. ;Chemosphere. 299:134336 Cheang I, Zhu X, Zhu Q, Li M, Liao S, Zuo Z et al (2022) Inverse association between blood ethylene oxide levels and obesity in the general population: NHANES 2013–2016. Front Endocrinol (Lausanne) 13:926971 Katoh T, Higashi K, Inoue N, Tanaka I (1989) Lipid peroxidation and the metabolism of glutathione in rat liver and brain following ethylene oxide inhalation. Toxicology 58:1–9 Rusyn I, Asakura S, Li Y, Kosyk O, Koc H, Nakamura J et al (2005) Effects of ethylene oxide and ethylene inhalation on DNA adducts, apurinic/apyrimidinic sites and expression of base excision DNA repair genes in rat brain, spleen, and liver. DNA Repair (Amst) 4(10):1099–1110 Chen C, Zhou Q, Yang R, Wu Z, Yuan H, Zhang N et al (2021) Copper exposure association with prevalence of non-alcoholic fatty liver disease and insulin resistance among US adults (NHANES 2011–2014). Ecotoxicol Environ Saf 218:112295 Moon MK, Lee I, Lee A, Park H, Kim MJ, Kim S et al (2022) Lead, mercury, and cadmium exposures are associated with obesity but not with diabetes mellitus: Korean national environmental health survey (KoNEHS) 2015–2017. Environ Res. ;204(Pt A):111888. Lee JH, Kim D, Kim HJ, Lee CH, Yang JI, Kim W et al (2010) Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis 42(7):503–508 Yang M, Frame T, Tse C, Vesper HW (2018) High-throughput, simultaneous quantitation of hemoglobin adducts of acrylamide, glycidamide, and ethylene oxide using UHPLC-MS/MS. J Chromatogr B Analyt Technol Biomed Life Sci 1086:197–205 Lynch HN, Kozal JS, Russell AJ, Thompson WJ, Divis HR, Freid R et al (2022) Systematic review of the scientific evidence on ethylene oxide as a human carcinogen. Chem Biol Interact 364:110031 Garman RH, Snellings WM, Maronpot RR (1986) Frequency, size and location of brain tumours in F-344 rats chronically exposed to ethylene oxide. Food Chem Toxicol 24:145–153 Snellings WM, Weil CS, Maronpot RR (1984) A two-year inhalation study of the carcinogenic potential of ethylene oxide in Fischer 344 rats. Toxicol Appl Pharmacol 75:105–117 Hodson L, Banerjee R, Rial B, Arlt W, Adiels M, Boren J et al (2015) Menopausal Status and Abdominal Obesity Are Significant Determinants of Hepatic Lipid Metabolism in Women. J Am Heart Assoc 4(10):e002258 Tantanavipas S, Vallibhakara O, Sobhonslidsuk A, Phongkitkarun S, Vallibhakara SA, Promson K et al (2019) Abdominal Obesity as a Predictive Factor of Nonalcoholic Fatty Liver Disease Assessed by Ultrasonography and Transient Elastography in Polycystic Ovary Syndrome and Healthy Women. Biomed Res Int 2019:9047324 Chen X, Shi F, Xiao J, Huang F, Cheng F, Wang L et al (2022) Associations Between Abdominal Obesity Indices and Nonalcoholic Fatty Liver Disease: Chinese Visceral Adiposity Index. Front Endocrinol (Lausanne) 13:831960 Jones RR, Fisher JA, Medgyesi DN, Buller ID, Liao LM, Gierach G et al (2023) Ethylene oxide emissions and incident breast cancer and non-Hodgkin lymphoma in a US cohort. J Natl Cancer Inst 115(4):405–412 Reed DJ (1986) Regulation of reductive processes by glutathione. Biochem Pharmacol 35(1):7–13 Bian F, Yang XY, Xu G, Zheng T, Jin S (2019) Crp-Induced NLRP3 Inflammasome Activation Increases LDL Transcytosis Across Endothelial Cells. Front Pharmacol 10:40 Handali S, Rezaei M (2021) Arsenic and weight loss: At a crossroad between lipogenesis and lipolysis. J Trace Elem Med Biol 68:126836 Mori K, Fujishiro K, Inoue N, Kohriyama K, Hori H (1990) Effects of sexual difference on the toxicity of ethylene oxide. II. Glutathione metabolism and lipid peroxidation in the liver. J UOEH 12(2):183–189 Supplementary Files SupplementalMaterial.docx Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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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-3300124","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301456936,"identity":"af8ebcd5-a490-4e40-9a55-b0a6f6f34d6c","order_by":0,"name":"Shiwe Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACdgglx8befoBILcwQypiP50wCaVoS50k4GBCng7+Z+Zg0z5/D6W0SDAkMPyq2EdYicZgt2Zi37XBum3TjAcaeM7eJsOYwj+Fj3obbuW0yBxKYGduI0CJ/mP/DYZ4/t9PZJBIMiNNicJiH8TEP2+0E4rUYHmYzNpzb9t+wDRjIB4nyi9zx5mcSb/6kycu3tx988KOCGO8DARMPlHGAOPVAwPiDaKWjYBSMglEwIgEAs8o51SG0kE4AAAAASUVORK5CYII=","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Shiwe","middleName":"","lastName":"Yan","suffix":""},{"id":301457132,"identity":"35bc98c8-033f-4aaf-a19f-2a5d3710715f","order_by":1,"name":"Haolong Pei","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Haolong","middleName":"","lastName":"Pei","suffix":""},{"id":301457133,"identity":"670a5145-4541-429f-b362-6026facf03e7","order_by":2,"name":"Qian Li","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Li","suffix":""},{"id":301457134,"identity":"56714441-9c81-4a42-aa21-6ec3f5edc67e","order_by":3,"name":"Wenzhe Cao","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenzhe","middleName":"","lastName":"Cao","suffix":""},{"id":301457135,"identity":"a3e6bfb3-a954-412e-865a-23029936efee","order_by":4,"name":"Yan Dou","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Dou","suffix":""},{"id":301457136,"identity":"7dfda368-5bc9-4e03-8b23-95f57803402b","order_by":5,"name":"Shihan Zhen","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shihan","middleName":"","lastName":"Zhen","suffix":""},{"id":301457137,"identity":"caa556e4-fb5f-4475-a9ce-db0087480788","order_by":6,"name":"Qingyao Wu","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qingyao","middleName":"","lastName":"Wu","suffix":""},{"id":301457138,"identity":"1f62efa0-84d4-4ba9-9d06-5546a29ebbac","order_by":7,"name":"Fengchao Liang","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fengchao","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2023-08-27 09:44:10","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3300124/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3300124/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56618459,"identity":"b0cec904-7af8-4b47-bd0d-d363465ed593","added_by":"auto","created_at":"2024-05-16 17:34:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":532523,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS) plots of the association of HbEO levels with USFLI (A) and FLI (B)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eand prevalence of NAFLDUSFLI (C) and NAFLDFLI (D). Adjusted for age, sex, race, education, income, smoking, drinking, hypertension, diabetes, and TC.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3300124/v2/fa3306731f25ceaa2a64f2f2.jpg"},{"id":56618807,"identity":"793bcdae-f5b8-4fc7-b19d-3cdab24cba5a","added_by":"auto","created_at":"2024-05-16 17:42:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1019372,"visible":true,"origin":"","legend":"\u003cp\u003eThe risk of NAFLD by USFLI and FLI stratified by gender. Model 1 was adjusted for age. Model 2 was adjusted for covariates in model 1 plus race,\u003c/p\u003e\n\u003cp\u003eeducation, income, smoking, and drinking. Model 3 was adjusted for covariates in model 2 plus hypertension, diabetes, and TC.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3300124/v2/a38c0ae9ff8c6d12b0c381ca.jpg"},{"id":56619807,"identity":"d77690c1-8ad6-4c1d-88ea-37a047e208a7","added_by":"auto","created_at":"2024-05-16 17:50:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2454699,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3300124/v2/8da1e8ba-3607-4d12-8b14-80f56b91a349.pdf"},{"id":56618458,"identity":"e99bf00d-0f36-4221-97ca-97bbeeb0718a","added_by":"auto","created_at":"2024-05-16 17:34:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":426598,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3300124/v2/24e81f2c0af30e5629c0e1ca.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAssociation of ethylene oxide with nonalcoholic fatty liver disease among US adults.\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) has been currently the most widespread chronic liver disease, affecting approximately 25% general population worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. One large systematic review meta-analysis pointed out an overall prevalence in the United State reached 35.3% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which is continuing to increase in the future years. In view of its high prevalence, NAFLD had a great likelihood of worsening into liver fbrosis, cirrhosis, and even hepatocellular carcinoma, further placing a huge public health concern and economic burden [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although a complex interaction of dietary habit, lifestyle, and genetic factors can sometimes explain for the epidemic, the potential effect of environmental pollutants on risk of NAFLD should not be ignored.\u003c/p\u003e \u003cp\u003eEthylene oxide (EO), a highly reactive organic compound in the environment, is mainly utilized to manufacture detergents, solvents, textiles, and plastics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Due to its excellent disinfection and sterilization effects, EO is used in large quantities in medical devices and supplies during the COVID-19 pandemic [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The global production of EO reached 20\u0026nbsp;million metric tons in 2009, and the requirement for EO is estimated to raise its annual production rate by 2% by 2025 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. There exist the close link between the risk of EO exposure and sectional occupations, such as doctors, nurses, and industrial workers. The major route of EO exposure in the general population may be via air, tobacco smoke, and vehicle exhaust. Hemoglobin adducts of EO (HbEO) produced by the reaction between EO and hemoglobin (Hb) is a sensitive and effective hematological marker to assess EO exposure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent evidence which suggested, as a potential exogenous toxicant, EO can pose deteriorated effects in human health is not extensive. Although some epidemiological studies reported that exposure to EO was associated with increased risk of asthma, serum lipid, hypertension, diabetes, and cardiovascular diseases (CVD) [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], one recent cross-sectional study have found an inverse association of blood EO levels with obesity in adults [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Considering that obesity and hypertension, diabetes, and CVD are all important risk factors in contributing to the development of NAFLD, it is required to further evaluate the uncertain association between EO and NAFLD. In addition, some rodent studies demonstrated that chronic exposure to EO caused hepatic lipid peroxidation and inflammatory reactions in systemic organs that are both closely involved in the pathogenesis of NAFLD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHence, in the current study, we aimed to evaluate whether EO exposure was closely associated with the prevalence of NAFLD based on nationally representative data from the National Health and Nutrition Examination Survey (NHANES) 2013\u0026ndash;2018 and to assess the effect of HbEO on inflammatory markers and NAFLD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used public data across three three-year cycles (2013-2018) of NHANES, an ongoing nationwide cross-sectional survey of the non-institutionalized civilians living in the US using complex, multi-stage probability sampling designs. All participants have signed informed consent forms during the period of recruitment. This study participants were restricted to adults (aged \u0026ge; 18 years) who had complete data of blood EO concentrations, inflammatory markers, glycohemoglobin, blood pressure, \u0026nbsp;and information on the definition of NAFLD. Additionally we excluded individuals who were pregnant and had viral hepatitis (hepatitis B surface antigen or hepatitis C RNA). Finally, 2,394 adults were left for the analysis (\u003cstrong\u003eFigure S1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of EO\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering that HbEO has a longer half-life in contrast to EO in the body, it was used to assess EO exposure in this study. Following NHANES Laboratory/Medical Technologists Procedures Manual (https:// wwwn.cdc.gov/Nchs/Nhanes/2017-2018/ETHOX_J.htm), HbEO levels were determined. The red blood cell samples by washing and packing were stored at -30\u0026deg;C up to ship National Center for Environmental Health for examination. Subsequently, HbEO levels were detected based on a modified Edman reaction using high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). More details are described in the NHANES Laboratory/Medical Technician Procedures Manual. Measurements below the limit of detection (12.9 pmol/g Hb) were imputed as the LOD divided by \u0026radic;2. Additionally, the assays accord with the quality control and quality assurance performance criteria of the NCEH Division of Laboratory Science for accuracy.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of NAFLD\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe defined NAFLD using\u0026nbsp;US fatty liver index (US FLI) and FLI, highly effective diagnostic indices, which was used to monitor and screen NAFLD cases via a non-invasive method and has been validated in the numerous epidemiological studies [20-22]. Compared with traditional ultrasonography and CT tests, USFLI and FLI were calculated by formulas that included information on age, race, waist circumference, body mass index (BMI), fasting insulin, fasting glucose, gamma glutamyl transferase (GGT), and triglyceride (TG). The cut-off of 30 for USFLI and 60 for FLI were considered to define NAFLD in this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic information was obtained by questionnaires examinations, anthropometric assessment, and laboratory testing. We considered some variables as potential confounders: age,\u0026nbsp;gender, race, education level, annual household income, BMI, smoking, drinking, hypertension, diabetes, and total cholesterol (TC). Of which, race/ethnicity was classified as Mexican American, Non-Hispanic white, Non-Hispanic black, and others. Education level was classified as lower than 9th grade, 9-11th grade, high school/ GED or equivalent, some college or Associate in Arts degree, and college graduate or above. Annual household income was classified as \u0026lt;$20,000, $20,000\u0026ndash;$45,000, $45,000\u0026ndash;$75,000, and \u0026gt;$75,000. Current smoking and drinking were categorized into yes and no. Diabetes is assessed by self-reported diagnosis, use of insulin or oral hypoglycemic medication, FBG \u0026ge; 126 mg/dL or HbA1c level \u0026ge; 6.5%. Hypertension was assessed as self-reported hypertension systolic blood pressure (SBP) \u0026ge; 140mmHg or diastolic blood pressure (DBP) \u0026ge; 90mmHg or use of anti-hypertensive medication. Inflammatory markers, including \u0026nbsp; ALP,\u0026nbsp;monocyte count, and\u0026nbsp;lymphocyte\u0026nbsp;count in blood samples were obtained from the laboratory data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics including means (SD) and frequency (%) were compared by the quartile of HbEO with using ANOVA and \u0026chi;\u003csup\u003e2\u003c/sup\u003e tests. HbEO levels were classified into four groups based on quartiles. Since levels of blood HbEO were in skewed distributions, it was naturally ln-transformed. General linear regression analysis was adopted to assess the effect of HbEO on liver function indices and USFLI, and FLI. The relationship between HbEO and NAFLD was investigated using logistic regression models. Coefficient or odd ratio (OR) and 95% confidence intervals (CI) were shown in three model. Model 1 was adjusted for age and gender. Model 2 was adjusted for covariates in model 1 plus race, education, income, smoking, and drinking. Model 3 was adjusted for covariates in model 2 plus hypertension, diabetes, and TC. In order to investigate the susceptibility of demographic-related differences, we adopted subgroup analyses to evaluate whether the relationship between HbEO and NAFLD were affected by sex. Restricted cubic spline (RCS) was used to model the trend of USFLI and FLI and\u0026nbsp;prevalence\u0026nbsp;of NAFLD across ln-transformed HbEO range. Mediation models were subsequently conducted to explore the role of inflammatory markers as potential mediators of the relationship between ln-transformed HbEO and the prevalence of NAFLD. Considering that CVD and diabetes might influence the association between HbEO and NAFLD, their family history was further adjusted in the sensitivity analyse. All data were analyzed using R 4.3.2, and a two-tailed \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05 was regarded as statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the demographic characteristics by the quartile of HbEO levels. Of the 2,394 participants, There were significant differences in age, gender, race, education level, annual household income, smoking, drinking, BMI, waist circumference, fasting glucose, fasting insulin, glycohemoglobin, diabetes, hypertension, TG, ALT (alanine aminotransferase), AST (aspartate aminotransferase), GGT, TBIL (total bilirubin), USFLI, and FLI between the four groups. In addition, participants\u0026rsquo; characteristics by NAFLD status were presented in \u003cb\u003eTable S1.\u003c/b\u003e For USFLI and FLI, 792 (33.1%) and 1,005 (42.0%) of participants were considered as the current NAFLD group and the remaining 1,602 (66.9%), and 1,389 (58.0%) with non-NAFLD acted as the control group, respectively. In general, compared with individuals without NAFLD, those with NAFLD assessed by USFLI and FLI were more likely to be older, Non-Hispanic white, smoker, drinker, diabetic, and hypertensive and had lower education and income levels. Also, NAFLD participants had higher BMI, waist circumference, SBP, and DBP and levels of fasting glucose, fasting insulin, glycohemoglobin, TC (total cholesterol), TG, ALT, AST, and GGT and lower levels of TBIL and ALB (albumin).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study variables by NAFLD status in the NHANES 2013\u0026ndash;2018.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHbEO (pmol/g Hb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e\u0026le;\u0026thinsp;16.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e1.67\u0026thinsp;\u0026lt;\u0026thinsp;to \u0026le;\u0026thinsp;23.6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e23.6\u0026thinsp;\u0026lt;\u0026thinsp;to \u0026le;\u0026thinsp;89.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;89.0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.2 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.8 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.1 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.7 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female), %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e321 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e309 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e254 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic white, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e281 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOver \u003cspan\u003e$\u003c/span\u003e75,000, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e524 (87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinking, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e296 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e439 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.2 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.5 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.2 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.0 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.7 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.6 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.2 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.3 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.3 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.7 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.8 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.2 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.9 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.9 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.1 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.3 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e261 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e269 (45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.2 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.7 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.5 (42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.1 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting insulin, pmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.4 (77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.8 (122.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.9 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.4 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycohemoglobin, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.64 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.77 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.95 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.70 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.1 (106.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.1 (88.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.6 (82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123.6 (107.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189.5 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187.4 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187.6 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187.5 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.4 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.7 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.9 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.5 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.4 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.9 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.2 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.7 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.8 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.29 (4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23 (0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.21 (0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.19 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.6 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.4 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.6 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.4 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.3 (33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.4 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.0 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; TC, total cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma glutamyl transaminase; TBIL, total bilirubin; ALB, albumin; USFLI, US fatty liver index; NAFLD, non-alcoholic fatty liver disease.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eContinuous variables are presented as mean and SD. Categorical variables are presented as numbers and frequency.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of HbEO levels with liver function, USFLI, and FLI\u003c/h2\u003e \u003cp\u003eAssociation of HbEO levels with liver function indices, USFLI, and FLI in both continuous and categorical analyses are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After adjusting for age, gender, race, education, income, smoking, drinking, hypertension, diabetes, and TC, the continuous analyse manifested that ALT (β: -0.04, 95% CI: -0.07\u0026ndash;0.01), AST (β: -0.03, 95% CI: -0.05\u0026ndash;0.01), TBIL (β: -0.53, 95% CI: -0.80\u0026ndash;0.26), USFLI (β: -2.53, 95% CI: -3.66\u0026ndash;1.39), and FLI (β: -4.07, 95% CI: -5.76\u0026ndash;2.38) were negatively associated with ln-transformed HbEO levels. Similar associations were observed when HbEO levels were modeled as quartiles. By referring the lowest quartile of HbEO, the highest quartiles were related to higher ALT (β: -0.09, 95% CI: -0.17\u0026ndash;0.01, \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.006) and AST (β: -0.07, 95% CI: -0.13\u0026ndash;0.02, \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.017). For TBIL and FLI, there existed the negative association in the second, third, and fourth quartiles of HbEO ( β: -0.71, 95% CI: -1.25-0.17, β: -0.65, 95% CI: -1.21\u0026ndash;0.10, and β: -1.08, 95% CI: -0.89\u0026ndash;0.28; \u003cem\u003eP\u003c/em\u003e-trend\u0026thinsp;=\u0026thinsp;0.076 and β: -3.48, 95% CI: -6.85\u0026ndash;0.10, β: -7.73, 95% CI: -11.21\u0026ndash;4.24, and β: -11.48, 95% CI: -16.53\u0026ndash;6.44; \u003cem\u003eP\u003c/em\u003e-trend\u0026lt;\u0026thinsp;0.01). Also, relationships of HbEO with USFLI (β: -3.94, 95% CI: -6.27\u0026ndash;1.60, \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and β: -7.02, 95% CI: -10.39\u0026ndash;3.64, \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were shown in the third and fourth quartile in contrast to the first quartile.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of concentrations of HbEO with liver function, USFLI and FLI in adults.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLn-transformed continuation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.04 (\u0026minus;0.06\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;0.07\u0026minus;0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.00 (\u0026minus;0.06\u0026minus;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.12 (\u0026minus;0.17\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.04 (\u0026minus;0.07\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.01 (\u0026minus;0.06\u0026minus;4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.01 (\u0026minus;0.06\u0026minus;6.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.10 (\u0026minus;0.18\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.04 (\u0026minus;0.07\u0026ndash;0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e0.00 (\u0026minus;0.05\u0026minus;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.01 (\u0026minus;0.05\u0026minus;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.09 (\u0026minus;0.17\u0026ndash;0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.03 (\u0026minus;0.04\u0026ndash;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.03 (\u0026minus;0.07\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;0.06\u0026minus;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.08 (\u0026minus;0.11\u0026ndash;0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.03 (\u0026minus;5.27\u0026ndash;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;6.09\u0026minus;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;5.89\u0026minus;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.08 (\u0026minus;1.36\u0026ndash;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.03 (\u0026minus;0.05\u0026ndash;0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;0.06\u0026minus;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;0.06\u0026minus;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.07 (\u0026minus;0.13\u0026ndash;0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (0.01\u0026ndash;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.06 (\u0026minus;0.13\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;0.09\u0026minus;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.06 (\u0026minus;0.01\u0026minus;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;0.06\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.06 (\u0026minus;0.13\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.06 (\u0026minus;0.13\u0026minus;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.05 (\u0026minus;0.16\u0026minus;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;0.06\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.05 (\u0026minus;0.12\u0026minus;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.06 (\u0026minus;0.13\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.05 (\u0026minus;0.15\u0026minus;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.60 (\u0026minus;0.77\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.76 (\u0026minus;1.30\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.81 (\u0026minus;1.34\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;1.77 (\u0026minus;1.34\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.53 (\u0026minus;0.80\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.69 (\u0026minus;1.23\u0026ndash;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.65 (\u0026minus;1.21\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;1.08 (\u0026minus;1.88\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.53 (\u0026minus;0.80\u0026ndash;0.26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.71 (\u0026minus;1.25\u0026ndash;0.17)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.65 (\u0026minus;1.21\u0026ndash;0.10\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;1.08 (\u0026minus;0.89\u0026ndash;0.28)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.03 (\u0026minus;0.04\u0026ndash;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.03 (\u0026minus;0.06\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.03 (\u0026minus;0.06\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.08 (\u0026minus;0.12\u0026ndash;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.01 (\u0026minus;0.03\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;0.05\u0026minus;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.01 (\u0026minus;0.05\u0026minus;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.03 (\u0026minus;0.08\u0026minus;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.01 (\u0026minus;0.03\u0026minus;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.02 (\u0026minus;0.05\u0026minus;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.01 (\u0026minus;0.05\u0026minus;0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.03 (\u0026minus;0.08\u0026minus;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.01 (\u0026minus;2.81\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.97 (\u0026minus;3.53\u0026minus;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;2.76 (\u0026minus;5.32\u0026minus;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;6.20 (\u0026minus;8.78\u0026ndash;3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.42 (\u0026minus;3.67\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.11 (\u0026minus;3.61\u0026minus;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;2.78 (\u0026minus;5.36\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;6.99 (\u0026minus;10.73\u0026ndash;3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;2.53 (\u0026minus;3.66\u0026ndash;1.39)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.94 (\u0026minus;3.20\u0026minus;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;3.94 (\u0026minus;6.27\u0026ndash;1.60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;7.02 (\u0026minus;10.39\u0026ndash;3.64)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinued\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.15 (\u0026minus;2.29\u0026ndash;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;4.66 (\u0026minus;8.32\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;6.38 (\u0026minus;10.04\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;5.11 (\u0026minus;8.79\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;4.08 (\u0026minus;5.90\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;4.165 (\u0026minus;7.80\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;6.96 (\u0026minus;10.71\u0026ndash;3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;11.74 (\u0026minus;17.18\u0026ndash;6.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;4.07 (\u0026minus;5.76\u0026ndash;2.38)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;3.48 (\u0026minus;6.85\u0026ndash;0.10)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;7.727 (\u0026minus;11.21\u0026ndash;4.24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;11.48 (\u0026minus;16.53\u0026ndash;6.44)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eHbEO: hemoglobin adduct of ethylene oxide; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma glutamyl transaminase; TBIL, total bilirubin; ALB, albumin; USFLI, US fatty liver index; Model 1 was adjusted for age and gender. Model 2 was adjusted for covariates in model 1 plus race, education, income, smoking, and drinking. Model 3 was adjusted for covariates in model 2 plus hypertension, diabetes, and TC.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of HbEO exposure with NAFLD assessed by USFLI and FLI\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in logistic regression analyses, ln-transformed HbEO levels were found to be associated with an increased prevalence of NAFLD (OR: 0.87, 95% CI: 0.80\u0026ndash;0.95 for USFLI and OR: 0.90 95% CI: 0.83\u0026ndash;0.97 for FLI), after adjusting for age, gender, race, education, income, smoking, drinking, hypertension, diabetes, and TC. Compared with participants in the first quartile, those in the third and fourth quartiles presented a higher prevalence of NAFLD (OR: 0.70, 95% CI: 0.53\u0026ndash;0.92 and OR: 0.50, 95% CI: 0.33\u0026ndash;0.75, \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.008 for USFLI and OR: 0.58, 95% CI: 0.45\u0026ndash;0.76 and OR: 0.42, 95% CI: 0.29\u0026ndash;0.61, \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for FLI). In the RCS, HbEO levels were not nonlinearly associated with USFLI (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.43, \u003cb\u003eFig.\u0026nbsp;1A\u003c/b\u003e) and FLI (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.21, \u003cb\u003eFig.\u0026nbsp;1B\u003c/b\u003e). Similar association was found in NAFLD assessed by USFLI (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.21, \u003cb\u003eFig.\u0026nbsp;1C\u003c/b\u003e) and FLI (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.093, \u003cb\u003eFig.\u0026nbsp;1D\u003c/b\u003e). Subgroup analyses of gender were used to verify independent relationship between HbEO and NAFLD, which was shown in \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e. Exposure to HbEO was still associated with the lower prevalence of NAFLD for USFLI and FLI in both male and female participants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of HbEO exposure with risk of NAFLD in adults.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCase/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAFLD\u003csub\u003eUSFLI\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn-transformed continuation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.78\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.83\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.87 (0.80\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235/603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207/598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.66\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.64\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82 (0.63\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199/596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.63\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.61\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.70 (0.53\u0026ndash;0.92)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151/597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56 (0.44\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.37\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.50 (0.33\u0026ndash;0.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAFLD\u003csub\u003eFLI\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn-transformed continuation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.86\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.86\u0026ndash;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.90 (0.83\u0026ndash;0.97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292/603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248/598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.61\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78 (0.61\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78 (0.61\u0026minus;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233/596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.55\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.51\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.58 (0.45\u0026ndash;0.76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232/597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.56\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45 (0.32\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.42 (0.29\u0026ndash;0.61)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1 was adjusted for age and gender. Model 2 was adjusted for covariates in model 1 plus race, education, income, smoking, and drinking. Model 3 was adjusted for covariates in model 2 plus hypertension, diabetes, and TC.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMediation analyse\u003c/h2\u003e \u003cp\u003eWe observed that exposure to HbEO was associated with inflammatory markers and USFLI, FLI, and NAFLD, thus conducting the mediation analyse to further explore the potential association. As shown in \u003cb\u003eFigure S2\u003c/b\u003e, the associations between HbEO levels and USFLI, FLI, and NAFLD assessed by USFLI and FLI were significantly mediated by monocyte (\u003cb\u003eFigure S2B, 2E, 2H and 2K\u003c/b\u003e) and lymphocyte (\u003cb\u003eFigure S2C, 2F, 2I and 2L\u003c/b\u003e), the proportions were \u0026minus;\u0026thinsp;13.1%, -12.7%, -17.2%, and \u0026minus;\u0026thinsp;10.1% and \u0026minus;\u0026thinsp;19.3%, -19.1%, -25.6%, and \u0026minus;\u0026thinsp;16.3% (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. Nevertheless, there was no prominent mediation effect of ALP on the association between HbEO and USFLI, FLI, and NAFLD (\u003cb\u003eFigure S2A, 2D, 2G and 2J\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eSensitivity analyses were performed in \u003cb\u003eTable S2\u003c/b\u003e. After additional adjustment for the family history of CVD, the results remained largely unchanged, indicating the associations between HbEO with the prevalence of NAFLD were relatively robust.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cp\u003eTo the best of our knowledge, this is the first study to evaluate the association between HbEO exposure and NAFLD among a large sample of US adults. In this cross-sectional study from NHANES database 2013\u0026ndash;2018, we observed that higher levels of HbEO were negatively associated with liver function indices, after adjusting for age, gender, race, education, income, smoking, drinking, hypertension, diabetes, and TC. This association remained pronounced in male and female participants. Additionally, we demonstrated that systemic inflammation partially mediated the influence of HbEO exposure on the prevalence of NAFLD, which might provide critical theoretical basis on the potential mechanisms.\u003c/p\u003e\u003c/p\u003e \u003cp\u003eMounting evidence suggest exposure to EO can pose a variety of irreversible effects to human health. EO, as an alkylating agent, may directly react with nucleophilic macromolecules to form the DNA adducts, and the resulting genetic damage was viewed to act a key role in inducing the mutation and chromosomal aberration in humans and animals [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In considering of its genotoxic and mutagenic abilities, the International Agency for Research on Cancer has classified EO as a group 1 human carcinogen [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Although one systematic review based on extensive studies in epidemiology, animals, and mechanism thought no prominent association between EO exposure and risk of breast and stomach cancer as well lymphohematopoietic malignancies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], animal experiments suggested the carcinogenicity of EO exposure [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In a large-scale study of 173,670 postmenopausal women, airborne EO emissions were found to be related with an elevated risk of breast cancer in situ [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Recent studies reported there exist the link between EO exposure and some metabolic disorders, of which abdominal obesity was demonstrated to be significant determinants of hepatic lipid metabolism and independently predict the development of NAFLD [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the present study, high EO levels were found to negatively associate with the risk of NAFLD, which was similar with one study result of evaluating the effect of EO on obesity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Compared with the other studies with NAFLD as the outcome, we adopt two widely confirmed liver indices to assess NAFLD, thereby ensuring the stability of the results.\u003c/p\u003e \u003cp\u003eResearch into rodent showed that long-term exposure to EO induced inflammatory lesions in several organs. Also, growing epidemiological data demonstrated that EO exposure could induce systemic inflammation. It is well known that inflammation and oxidative stress (OS), as critical pathogenetic factors, might contribute to numerous forms of liver diseases, regardless of etiology. When the status of redox balance in vivo are broke up and have tendency to oxidation, abundant reactive oxygen species (ROS) fail to be catabolized [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which lead to hepatocellular injury and apoptosis, thus influencing lipid metabolism and causing excessive accumulation of fat in the liver. Study conducted by Bian et al. showed that inflammation can interfere with insulin signaling through activation of NF-kB pathway, further leading to the development of NAFLD [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Nevertheless, the present study manifested that high levels of HbEO was negatively associated with risk of NAFLD in the general population, and this association was partially mediated by inflammatory markers (monocyte count, and lymphocyte count). This suggested that control of EO-driven inflammation is a single mechanism and insufficient to explain this phenomenon alone. Since suppression effects of EO exposure on NAFLD are relatively sparse in terms of biology, the underlying mechanisms are required to be further explored.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSmoking was a main pathway of EO exposure in the general population, and adult smokers were reported to have higher levels of EO in contrast to non smokers. In line with our baseline characteristics, the proportion of smoking presented a prominent upward trend with the increase of EO quartiles. Noteworthy is that tobacco consist of numerous chemicals that promoted the abdominal lipolysis via accelerating internal metabolism, which possibly reduced the formation of fatty liver [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Certainly, this explanation should be cautiously adopt, given the lack of quantitative assessment of tobacco consumption. Additionally, subgroup analyses showed that there still existed negative associations between high HbEO levels and the prevalence of NAFLD, in both male and female participants. The parallel findings in rodent experiments provide strong evidence to explain this similar phenomenon. Mori et al, conducted chronic exposure to EO at a concentration of 250 ppm to observe the potential effects of EO on the liver in terms of sex difference [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Of note, EO significantly increased the activity of glutathione-S-transferase in the male and female EO-exposed groups, which might prevent metabolic disorders and decreased immune function caused by lipid peroxidation to hepatic damage.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThere are several strengths in this study, including the first study linking EO exposure and the risk of NAFLD, large-scale general population representative, multi-ethnic characteristics, and adjustment for sufficient cofounders. Besides, sensitivity analyses were conducted to assess the robustness and stability of our results. Of course, some limitations should be highlighted. First, in consider of the cross-sectional nature of data, the results can not ascertain the potential causality, hence further prospective studies are needed to probe the relationship between EO exposure the and prevalence of NAFLD. Second, this study merely adopted the data about single measurement of blood EO rather than repeated measurements that was more applicable to reflect the cumulative effect of long-term exposure. Third, due to lack of data on EO-related metabolites, we are unable to design mixed exposure studies. Fourth, it was hard for this study to offer effective proposals to reduce the exposure risk because there were lack of detailed information on the source and route of EO exposure. Fifth, although we used USFLI and FLI to define NAFLD in this study, which were likely to overestimate or underestimate the true prevalence of NAFLD, in contrast to traditional liver biopsy, this noninvasive test is more economical and convenient to screen the cases of NAFLD and its accuracy have been confirmed in prior large-scales epidemiological studies. Finally, the sample data from NHANES was merely limited in US population, so our findings may not be generalized to other population groups with EO exposure. Hence, studies in a large sample size were warrant to demonstrate the link between EO and NAFLD.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn summary, the current study demonstrated that higher EO levels were negatively associated with the prevalence of NAFLD. Additionally, systemic inflammation involved in the mediation effect of EO-NAFLD association. Further research are required to identify a potential biological mechanism by which EO-induced NAFLD.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYS\u0026nbsp;organized all data and wrote the manuscript. LF conceived the study design. PH, CW, LQ, ZS, DY and WQ contributed to the interpretation of the results and revision, and finalization of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (42107465) and Talents enlisted in major talent programs of Guangdong Province (20210N020921).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the NHANES database, https://wwwn.cdc.gov/nchs/nhanes/\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe requirement of ethical approval for this was waived by the Institutional Review Board of Hainan Affiliated Hospital of Hainan Medical University, because the data was accessed from NHANES (a publicly available database). All methods were carried out in accordance with relevant guidelines and regulations (declaration of Helsinki). All individuals provided written informed consent before participating in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eByrne CD, Targher G (2015) NAFLD: a multisystem disease. 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Environ Sci Pollut Res Int 28(45):64921\u0026ndash;64928\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu X, Kong X, Chen M, Shi S, Cheang I, Zhu Q et al (2022) Blood ethylene oxide, systemic inflammation, and serum lipid profiles: Results from NHANES 2013\u0026ndash;2016. ;Chemosphere. 299:134336\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheang I, Zhu X, Zhu Q, Li M, Liao S, Zuo Z et al (2022) Inverse association between blood ethylene oxide levels and obesity in the general population: NHANES 2013\u0026ndash;2016. Front Endocrinol (Lausanne) 13:926971\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatoh T, Higashi K, Inoue N, Tanaka I (1989) Lipid peroxidation and the metabolism of glutathione in rat liver and brain following ethylene oxide inhalation. Toxicology 58:1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusyn I, Asakura S, Li Y, Kosyk O, Koc H, Nakamura J et al (2005) Effects of ethylene oxide and ethylene inhalation on DNA adducts, apurinic/apyrimidinic sites and expression of base excision DNA repair genes in rat brain, spleen, and liver. DNA Repair (Amst) 4(10):1099\u0026ndash;1110\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen C, Zhou Q, Yang R, Wu Z, Yuan H, Zhang N et al (2021) Copper exposure association with prevalence of non-alcoholic fatty liver disease and insulin resistance among US adults (NHANES 2011\u0026ndash;2014). Ecotoxicol Environ Saf 218:112295\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon MK, Lee I, Lee A, Park H, Kim MJ, Kim S et al (2022) Lead, mercury, and cadmium exposures are associated with obesity but not with diabetes mellitus: Korean national environmental health survey (KoNEHS) 2015\u0026ndash;2017. Environ Res. ;204(Pt A):111888.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JH, Kim D, Kim HJ, Lee CH, Yang JI, Kim W et al (2010) Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis 42(7):503\u0026ndash;508\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Frame T, Tse C, Vesper HW (2018) High-throughput, simultaneous quantitation of hemoglobin adducts of acrylamide, glycidamide, and ethylene oxide using UHPLC-MS/MS. J Chromatogr B Analyt Technol Biomed Life Sci 1086:197\u0026ndash;205\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynch HN, Kozal JS, Russell AJ, Thompson WJ, Divis HR, Freid R et al (2022) Systematic review of the scientific evidence on ethylene oxide as a human carcinogen. Chem Biol Interact 364:110031\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarman RH, Snellings WM, Maronpot RR (1986) Frequency, size and location of brain tumours in F-344 rats chronically exposed to ethylene oxide. Food Chem Toxicol 24:145\u0026ndash;153\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnellings WM, Weil CS, Maronpot RR (1984) A two-year inhalation study of the carcinogenic potential of ethylene oxide in Fischer 344 rats. Toxicol Appl Pharmacol 75:105\u0026ndash;117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHodson L, Banerjee R, Rial B, Arlt W, Adiels M, Boren J et al (2015) Menopausal Status and Abdominal Obesity Are Significant Determinants of Hepatic Lipid Metabolism in Women. 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J Natl Cancer Inst 115(4):405\u0026ndash;412\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReed DJ (1986) Regulation of reductive processes by glutathione. Biochem Pharmacol 35(1):7\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBian F, Yang XY, Xu G, Zheng T, Jin S (2019) Crp-Induced NLRP3 Inflammasome Activation Increases LDL Transcytosis Across Endothelial Cells. Front Pharmacol 10:40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHandali S, Rezaei M (2021) Arsenic and weight loss: At a crossroad between lipogenesis and lipolysis. J Trace Elem Med Biol 68:126836\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMori K, Fujishiro K, Inoue N, Kohriyama K, Hori H (1990) Effects of sexual difference on the toxicity of ethylene oxide. II. Glutathione metabolism and lipid peroxidation in the liver. J UOEH 12(2):183\u0026ndash;189\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":"EO, NHANES, NAFLD, Inflammation","lastPublishedDoi":"10.21203/rs.3.rs-3300124/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3300124/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Growing evidence suggest that ethylene oxide (EO) may have deleterious effects on health conditions, but the relationship between EO and adulthood nonalcoholic fatty liver disease (NAFLD) remains vague. Our objective is to evaluate whether EO exposure would influence the prevalence of NAFLD in a nationally cross-sectional study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods \u003c/strong\u003eIn this cross-sectional study, We analyzed 2,394 participants from the National Health and Nutrition Examination Survey (NHANES) 2013-2018. Blood concentrations of EO were measured using high-performance liquid chromatography coupled with tandem mass spectrometry. US fatty liver index (USFLI) and FLI were applied to define NAFLD. Logistic regression analysis was adopted to investigate the relationship of Hemoglobin adducts of EO (HbEO) exposure with the prevalence of NAFLD. Mediation analysis was performed to assess the effect of inflammatory biomarkers on the association between HbEO levels and USFLI, FLI, and NAFLD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e After adjustment for age, gender, race/ethnicity, education, income, smoking, drinking, hypertension, diabetes, and TC, logistic regression analysis showed that HbEO in the highest quartile was negatively associated with the prevalence of NAFLD than those in the lowest quartile (OR: 0.50, 95% CI: 0.33-0.92, \u003cem\u003eP\u003c/em\u003e for trend = 0.008 for USFLI and OR: 0.42, 95% CI: 0.29-0.61, \u003cem\u003eP\u003c/em\u003e for trend \u0026lt;0.001 for FLI). In addition, inflammation significantly mediated the relationships between HbEO and NAFLD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eOur study demonstrated that higher EO levels were negatively associated with the prevalence of NAFLD. The underlying mechanisms were required to be identify in the future study.\u003c/p\u003e","manuscriptTitle":"Association of ethylene oxide with nonalcoholic fatty liver disease among US adults.","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-05-16 17:33:55","doi":"10.21203/rs.3.rs-3300124/v2","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}},{"code":1,"date":"2023-09-29 22:45:20","doi":"10.21203/rs.3.rs-3300124/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":"f01ee52d-8fb1-4bd5-abdc-e3c05525d493","owner":[],"postedDate":"May 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31807601,"name":"Epidemiology"}],"tags":[],"updatedAt":"2024-04-17T06:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-16 17:33:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-3300124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3300124","identity":"rs-3300124","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}