Triiodothyronine is associated with incidence and resolution of fatty liver disease: a longitudinal study in euthyroid Korean adults

Triiodothyronine is associated with incidence and resolution of fatty liver disease: a longitudinal study in euthyroid Korean adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Triiodothyronine is associated with incidence and resolution of fatty liver disease: a longitudinal study in euthyroid Korean adults Hye In Kim, Jun Young Kim, Jung Hwan Cho, Ji Min Han, Sunghwan Suh, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3790646/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The positive relationship between triiodothyronine (T3) and fatty liver demonstrated only in cross-sectional study. In this longitudinal cohort study, we aimed to evaluated whether total T3 (TT3) is associated with the development/resolution of fatty liver. We included 1665 South Korean euthyroid adults with ≥ 4 thyroid function tests. We explored the impact of TT3 average on development/resolution of either fatty liver (diagnosed by ultrasound) or modified metabolic dysfunction-associated fatty liver (MAFLD) using Cox proportional hazards regression models. During median 5 years follow-up, 891 (66.9%) participants among participants without fatty liver at baseline developed fatty liver, and 265 (79.6%) participants among participants with fatty liver at baseline resolved fatty liver. Compared with low TT3 average group, high TT3 average group was positively associated with development of fatty liver [adjusted HR 1.17 (1.03–1.34); P = 0.016] and inversely associated with resolution of fatty liver [adjusted HR 0.64 (0.50–0.82); P < 0.001]. The statistical significance was remained for development [adjusted HR 1.24 (1.07–1.44); P = 0.004] and resolution [adjusted HR 0.72 (0.54–0.95); P = 0.024] of modified MAFLD. Our finding provides longitudinal evidence that TT3 level was associated with development and resolution of either fatty liver or modified MAFLD. Health sciences/Endocrinology Health sciences/Gastroenterology Health sciences/Risk factors Figures Figure 1 Figure 2 Figure 3 Introduction Fatty liver is the leading cause of chronic liver disease. The global prevalence of fatty liver is assumed about 24% 1 , representing a substantial clinical burden and a public health concern. Most of fatty liver is considered to reflect the hepatic component of the metabolic syndrome, since it has strong association with insulin resistance, obesity, and dyslipidemia. Recently, a novel definition of metabolic dysfunction-associated fatty liver disease (MAFLD) replacing non-alcoholic fatty liver disease (NAFLD) was proposed in a conception that fatty liver and metabolic syndromes share a common pathway of pathogenesis. MAFLD can be diagnosed by positive diagnostic criteria of hepatic steatosis and metabolic dysfunctions regardless of alcohol consumption or other concomitant liver disease, and it can identify more patients at risk for cirrhosis and liver cancer 2 . Thyroid hormone has important role in lipid metabolism and energy homeostasis. After thyroxine is converted to the active hormone, triiodothyronine (T3), by deiodinase, it enters target cells such as hypothalamus, adipose tissue, muscle, and hepatocyte via thyroid hormone receptor (THR). In hepatocyte, T3 affects regulation of hepatic fat accumulation through multiple pathways, including stimulation of free fatty acid delivery to the liver for re-esterification to triglyceride (TG), or increasing fatty acid β-oxidation 3 – 5 . The relationship between thyroid hormone marker, especially T3, and metabolic disease factor such as high BMI, insulin resistance, diabetes also has been suggested in recent decades 6 – 8 . Considering fatty liver is recognized as a hepatic presentation of metabolic syndrome, it is reasonable that T3 is associated with fatty liver. The large cohort study (The Lifelines Cohort Study) demonstrated that NAFLD was independently associated with a high-normal FT3 level 9 . Also, a study in the middle-aged and elderly euthyroid subjects showed that high-normal FT3 are independently associated with a higher risk of NAFLD 10 . However, the cause-consequence effect may not be established due to cross-sectional design or lack of survival analysis with adjustment. Furthermore, the association between MAFLD and T3 has not been researched in longitudinal design though a novel concept of MAFLD replacing NAFLD in clinical practice 2 . Therefore, in this long term follow up longitudinal data, we aimed to explore whether the thyroid hormone maker, especially total T3 (TT3) is associated with either the development of fatty liver or the resolution of fatty liver. In addition, we speculated the relationship between TT3 and modified MAFLD presenting the emerging concept of MAFLD. Materials and Methods Study design and study population In this retrospective longitudinal study, we assessed 2372 participants who completed annual or biennial examinations between 2007 and 2014 at Gyeongsang National University Hospital (GNUH) healthcare center in South Korea for eligibility. Among them, the participants who aged ≥ 18 years old and underwent medical examinations with serial thyroid hormone measurements ≥ 4 times were included (N = 2246). The participants with abnormal initial results of the thyroid function test (N = 294), missing data (N = 272), and a history of thyroid disease or recent medication affecting thyroid function (N = 15) were excluded. Finally, 1665 participants were included (baseline cohort). The baseline cohort was used for investigating the association between prevalent fatty liver or modified MAFLD and thyroid hormone markers. Among them, the participants with initial fatty liver were used for fatty liver resolution cohort (N = 333). After excluding the participants who already have fatty liver disease at baseline, we generated fatty liver risk cohort (N = 1332) to figure out whether the thyroid hormone marker related to prevalence of fatty liver was also associated with incident fatty liver. To investigate the relationship between the thyroid hormone markers and modified MAFLD, we used 249 participants with modified MAFLD at baseline (MAFLD resolution cohort), and 1416 participants without initial modified MAFLD (MAFLD risk cohort) (Fig. 1 ). The Institutional Review Board (IRB) of GNUH approved this study (No. 2022-02-003) and the requirement for informed consent was waived by the IRB. All methods were performed in compliance with the relevant guidelines or regulations. Data collection Demographic and anthropometric characteristics were obtained. Body mass index (BMI) was calculated as the body weight (kg) divided by the height squared (m 2 ) that were measured by trained nurses. All participants underwent an overnight fasting and the following laboratory data were measured by blood samples: the levels of thyroid-stimulating hormone (TSH), free thyroxine (FT4), TT3, TG, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST), alanine transaminase (ALT), and HbA1c. The thyroid hormone including TSH, FT4, TT3 levels were determined by an electro-chemiluminescent immunoassay (Roche Diagnostics Ltd., Mannheim, Germany) (reference ranges of 0.27–4.2 mU/L for TSH, 0.93–1.70 ng/dL for FT4, and 80–200 ng/dL for TT3). Well trained radiologist performed liver ultrasonography (USG) using an ultrasound system (APLIO I70, Canon, Japan). Definitions of study outcome and subgroups Fatty liver was defined as USG diagnosed fatty liver according to the Asia-Pacific Guidelines (the presence of at least two of the followings: 1) diffusely increased echogenicity liver with liver echogenicity greater than kidney or spleen; 2) vascular blurring; 3) deep attenuation of ultrasound signal) 11 , 12 . For sensitivity analysis, we used modified MAFLD which defined the participants with fatty liver in USG in addition to one of the following three criteria: 1) BMI ≥ 23 kg/m 2 ; 2) presence of type 2 diabetes mellitus (T2DM); 3) evidence of metabolic dysregulation defined by the presence of at least 2 metabolic risks among TG ≥ 150mg/dL, HDL-C ≤ 40 mg/dL, and prediabetes. The definition was adopted from diagnostic criteria of MAFLD excluding hypertension (HTN), insulin resistance, and c-reactive protein (CRP) criteria in metabolic dysregulation part 2 . To investigate the association between prevalence of fatty liver and thyroid hormone makers, TSH, FT4, and TT3 at baseline were used. When we explore the association between development or resolution of fatty liver and thyroid hormone makers, average of thyroid hormone makers (TSH average, FT4 average, TT3 average) which defined as average of hormone levels during follow-up were adopted for quantitative evaluation of thyroid hormone exposure during observation instead of one spot value of baseline. After determining that TT3 average as a continuous variable is associated with fatty liver, we further defined TT3 average into categorical variable: 1) high TT3 average group - the participants who recorded TT3 average higher than median TT3 average (109.45 ng/dL); 2) low TT3 average group - the participants who recorded TT3 average same or lower than median TT3 average (109.45 ng/dL). BMI was classified into 3 subgroups [< 23 (normal), 23–25 (overweight), and ≥ 25 kg/m 2 (obese)] using Asia-Pacific BMI classification 13 . Bodyweight gain group was defined as bodyweight increment over 5% and bodyweight loss group was defined as bodyweight decrement over 5% from the baseline during follow-up. Stable bodyweight group included participants whose bodyweight changed less than 5% from the baseline 14 , 15 . Statistical analysis All continuous variables are presented as the median and interquartile range (IQR), and all categorical variables are presented as the number and percentage. To investigate the effect of thyroid hormone marker on prevalence of fatty liver and modified MAFLD, binary logistic regression models were generated in baseline cohort. The odds ratio (OR) with 95% confidence interval (CI) were reported. To investigate the effect of thyroid hormone marker on incidence of fatty liver or modified MAFLD, Kaplan–Meier curves by the log-rank test and Cox proportional hazards regression models were generated in fatty liver risk cohort and modified MAFLD risk cohort, respectively. To figure out the relationship between the thyroid hormone markers and resolution of fatty liver or modified MAFLD, Kaplan–Meier curves by the log-rank test and Cox proportional hazards regression models were also used in fatty liver resolution cohort and modified MAFLD resolution cohort, respectively. Age, gender, BMI, HbA1c, TG, HDL-C, LDL-C, AST, and ALT were adjusted in multivariable analysis. The hazard ratio (HR) with 95% confidence interval (CI) were reported. IBM SPSS Statistics for Windows (version 22.0, Armonk, NY, USA) was used for the statistical analysis and P -values of < 0.05 with two-sided were considered statistically significant. Results Baseline characteristics The baseline characteristics of the baseline cohort, fatty liver risk cohort, and fatty liver resolution cohort are presented in Table 1 . Of the 1665 participants in baseline cohort, women comprised 35.0%, median age was 43.0 years (IQR 37.0–50.0 years), and median BMI was 23.7 kg/m 2 (21.8–25.7 kg/m 2 ). The median serum TSH level, FT4 level, and TT3 levels were 1.69 mU/L (1.19–2.34 mU/L), 1.31 ng/dL (1.20–1.43 ng/dL), and 109.00 ng/dL (98.13–119.90 ng/dL), respectively. In fatty liver risk cohort, median age at baseline was 43.0 years (37.0–50.0 years), and there were 481 women (35.9%). The median BMI was 23.6 kg/m 2 (21.6–25.6 kg/m 2 ), and the median serum TSH level, FT4 level, and TT3 levels were 1.70 mU/L (1.19–2.35 mU/L), 1.32 ng/dL (1.20–1.43 ng/dL), and 108.60 ng/dL (97.66–119.17 ng/dL), respectively. In fatty liver resolution cohort, median age at baseline was 44.0 years (38.0–50.0 years), and 32.1% were women. The median BMI was 24.2 kg/m2 (22.3–26.2 kg/m2), and the median serum TSH level, FT4 level, and TT3 levels were 1.65 mU/L (1.19–2.33 mU/L), 1.30 ng/dL (1.21–1.44 ng/dL), and 111.70 ng/dL (100.26–123.15 ng/dL), respectively. Table 1 Baseline characteristics of the participants for fatty liver analysis Characteristics Baseline cohort Without fatty liver With fatty liver (N = 1665) (N = 1332) (N = 333) Age, years (IQR) 43.0 (37.0–50.0) 43.0 (37.0–50.0) 44.0 (38.0–50.0) Women, n (%) 583 (35.0%) 481 (35.9%) 107 (32.1%) BMI (kg/m 2 ) (IQR) 23.7 (21.8–25.7) 23.6 (21.6–25.6) 24.2 (22.3–26.2) Obesity, n (%) Normal (BMI < 23 kg/m 2 ) 671 (40.3%) 563 (42.0%) 114 (34.2%) Overweight (BMI 23–25 kg/m 2 ) 430 (25.8%) 338 (25.2%) 92 (27.6%) Obese (BMI ≥ 25 kg/m 2 ) 564 (33.9%) 440 (32.8%) 127 (38.1%) TSH level (mU/L) 1.69 (1.19–2.34) 1.70 (1.19–2.35) 1.65 (1.19–2.33) FT4 level (ng/dL) 1.31 (1.20–1.43) 1.32 (1.20–1.43) 1.30 (1.21–1.44) TT3 level (ng/dL) 109.00 (98.13–119.90) 108.60 (97.66–119.17) 111.70 (100.26–123.15) AST level (IU/L) 21.0 (17.0–26.0) 21.0 (17.0–26.0) 21.0 (17.5–26.5) ALT level (IU/L) 21.0 (15.0–30.0) 21.0 (15.0–29.0) 22.0 (16.0–30.0) HbA1c level (%) 5.1 (5.0–5.7) 5.1 (5.0–5.2) 5.6 (5.1–6.1) Triglyceride level 108.0 (77.0–159.0) 107.0 (77.0–156.7) 113.0 (81.0–170.0) HDL-C level 51.0 (43.0–60.0) 51.0 (43.0–61.0) 51.0 (42.5–59.0) LDL-C level 115.5 (96.0–136.7) 116.0 (96.0–137.0) 115.0 (94.0–136.0) Median follow up (months) (IQR) 61.0 (51.0–71.0) 63.0 (52.0–71.0) 60.0 (48.0–70.0) a TSH, Thyroid-stimulating hormone; TT3, Total triiodothyronine; FT4, Free thyroxine; BMI, Body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; IQR, Interquartile range. Association between TT3 and prevalence of fatty liver In baseline cohort, 20.0% (333/1665) of the participants had fatty liver. Binary logistic regression analysis showed that high TT3 level was independently associated with fatty liver [adjusted OR 1.01 (1.00–1.01); P = 0.012] even after adjusting other factors. However, TSH level [adjusted OR 1.05 (0.91–1.22); P = 0.468] or FT4 level [adjusted OR 1.28 (0.58–2.80); P = 0.533] had no association with prevalent fatty liver. High BMI [adjusted OR 1.05 (1.00–1.11); P = 0.021] and high HbA1c [adjusted OR 1.98 (1.71–2.30); P < 0.001] were also independent factors associated with the prevalence of fatty liver (Table 2 ). Table 2 Binary regression models for prevalent fatty liver Variables Model 1* Model 2* Model 3* OR (95% CI) P -value OR (95% CI) P -value OR (95% CI) P -value Age (years) 1.01 (0.99–1.02) 0.152 1.01 (0.99–1.02) 0.142 1.01 (0.99–1.02) 0.136 Sex 0.95 (0.71–1.26) 0.727 0.95 (0.71–1.26) 0.733 0.96 (0.71–1.28) 0.782 BMI (kg/m 2 ) 1.05 (1.00-1.11) 0.021 1.05 (1.00-1.11) 0.021 1.05 (1.00-1.11) 0.021 AST level (IU/L) 1.00 (0.98–1.01) 0.705 1.00 (0.98–1.01) 0.719 1.00 (0.98–1.01) 0.696 ALT level (IU/L) 0.99 (0.98-1.00) 0.696 0.99 (0.98–1.01) 0.714 0.99 (0.98-1.00) 0.674 HbA1c (%) 2.01 (1.73–2.34) < 0.001 2.01 (1.73–2.34) < 0.001 1.98 (1.71–2.30) < 0.001 TG (mg/dL) 1.00 (0.99-1.00) 0.924 1.00 (0.99-1.00) 0.901 1.00 (0.99-1.00) 0.941 HDL-C (mg/dL) 0.99 (0.98–1.01) 0.885 0.99 (0.98–1.01) 0.898 1.00 (0.98–1.01) 0.962 LDL-C (mg/dL) 0.99 (0.99-1.00) 0.473 0.99 (0.99-1.00) 0.492 0.99 (0.99-1.00) 0.503 TSH (mU/L) 1.05 (0.91–1.22) 0.468 - - - - FT4 (ng/dL) - - 1.28 (0.58–2.80) 0.533 - - TT3 (ng/dL) - - - - 1.01 (1.00-1.01) 0.012 a HR, Hazard ratio; CI, Confidence interval; BMI, Body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; TG, Triglyceride; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; TSH, Thyroid-stimulating hormone; FT4, Free thyroxine; TT3, Total triiodothyronine. *Model: The models were adjusted for age, sex, BMI (kg/m 2 ), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, and thyroid hormones (TSH for Model 1, FT4 for Model 2, and TT3 for Model 3). Association between TT3 average and the development of fatty liver In fatty liver risk cohort, 66.9% (891/1332) of the participants developed fatty liver with median time to onset as 38.0 months during the follow-up period of a median of 63.0 months (IQR 52.0–71.0 months). Among the three thyroid hormone makers, TT3 average was positively associated with occurrence of fatty liver in the multivariable analysis [adjusted HR 1.01 (1.00–1.02); P = 0.002; Model 3]. Participants with high TT3 average had a significantly higher risk to develop fatty liver than those of low TT3 average [adjusted HR 1.17 (1.03–1.34); P = 0.016; Model 3]. The statistical significance was absent for TSH average [adjusted HR 0.93 (0.86–1.01); P = 0.099; Model 1] and for FT4 average [adjusted HR 1.17 (0.54–2.53); P = 0.677; Model 2]. Other factors associated with fatty liver occurrence were old age [adjusted HR 1.00 (1.00–1.01); P = 0.048; Model 4], high HbA1c [adjusted HR 1.09 (1.00–1.20); P = 0.042; Model 4] and female gender [adjusted HR 0.82 (0.70–0.95); P = 0.011; Model 4] (Table 3 ). Table 3 Multivariate cox proportional hazards models for fatty liver risk Variables Model 1* Model 2* Model 3* Model 4** HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value Age (years) 1.00 (1.00-1.01) 0.053 1.00 (1.00-1.01) 0.055 1.00 (1.00-1.01) 0.038 1.00 (1.00-1.01) 0.048 Sex 0.81 (0.70–0.95) 0.009 0.82 (0.70–0.95) 0.012 0.82 (0.71–0.96) 0.014 0.82 (0.70–0.95) 0.011 BMI (kg/m 2 ) 1.01 (0.98–1.04) 0.281 1.01 (0.98–1.04) 0.247 1.01 (0.98–1.04) 0.264 1.01 (0.99–1.04) 0.242 AST level (IU/L) 0.99 (0.99-1.00) 0.713 0.99 (0.99-1.00) 0.744 0.99 (0.99-1.00) 0.769 0.99 (0.99-1.00) 0.796 ALT level (IU/L) 1.00 (0.99-1.00) 0.512 1.00 (0.99-1.00) 0.533 1.00 (0.99-1.00) 0.574 1.00 (0.99-1.00) 0.601 HbA1c (%) 1.10 (1.00-1.20) 0.032 1.10 (1.00-1.20) 0.032 1.09 (1.00-1.20) 0.044 1.09 (1.00-1.20) 0.042 TG (mg/dL) 1.00 (0.99-1.00) 0.880 1.00 (0.99-1.00) 0.804 1.00 (0.99-1.00) 0.806 1.00 (0.99-1.00) 0.808 HDL-C (mg/dL) 0.99 (0.99-1.00) 0.516 0.99 (0.99-1.00) 0.570 0.99 (0.99-1.00) 0.556 0.99 (0.99-1.00) 0.579 LDL-C (mg/dL) 1.00 (0.99-1.00) 0.851 1.00 (0.99-1.00) 0.869 1.00 (0.99-1.00) 0.900 1.00 (0.99-1.00) 0.865 TSH average (mU/L) 0.93 (0.86–1.01) 0.099 - - - - - - FT4 average (ng/dL) - - 1.17 (0.54–2.53) 0.677 - - - - TT3 average (ng/dL) - - - - 1.01 (1.00-1.02) 0.002 - - High TT3 average - - - - - - 1.17 (1.03–1.34) 0.016 a HR, Hazard ratio; CI, Confidence interval; BMI, body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; TG, Triglyceride; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; TSH, Thyroid-stimulating hormone; FT4, Free thyroxine; TT3, Total triiodothyronine. * Models 1, 2, and 3 were adjusted for age, sex, BMI (kg/m 2 ), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, and thyroid hormone (TSH average for Model 1, FT4 average for Model 2, TT3 average for Model 3). ** Model 4 was adjusted for age, sex, BMI (kg/m 2 ), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, and high TT3 average. Association between TT3 average and the resolution of fatty liver In fatty liver resolution cohort, fatty liver resolved in 79.6% (265/333) of the participants with median time to onset as 23.0 months during the follow-up period of a median of 60.0 months (IQR 48.0–70.0 months). Among the three thyroid hormone makers, TT3 average was inversely associated with resolution of fatty liver in the multivariable analysis [adjusted HR 0.97 (0.96–0.99); P = 0.002; Model 3]. Participants with High TT3 average had a significantly lower chance to resolve fatty liver than those of low TT3 average [adjusted HR 0.64 (0.50–0.82); P < 0.001; Model 4]. The statistical significance was absent for TSH average [adjusted HR 1.08 (0.91–1.28); P = 0.340; Model 1] and for FT4 average [adjusted HR 0.40 (0.10–1.49); P = 0.174; Model 2]. Younger age was also significantly associated with the resolution of fatty liver [adjusted HR 0.98 (0.97–0.99); P = 0.039; Model 4] (Table 4 ). Table 4 Multivariate cox proportional hazards models for fatty liver resolution Variables Model 1* Model 2* Model 3* Model 4** HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value Age (years) 0.98 (0.97–0.99) 0.032 0.98 (0.97–0.99) 0.040 0.99 (0.98–1.01) 0.044 0.98 (0.97–0.99) 0.039 Sex 0.95 (0.72–1.26) 0.755 0.95 (0.72–1.26) 0.750 0.82 (0.63–1.05) 0.792 0.96 (0.72–1.27) 0.793 BMI (kg/m 2 ) 0.99 (0.94–1.04) 0.738 0.99 (0.94–1.04) 0.773 0.98 (0.94–1.03) 0.713 0.99 (0.94–1.04) 0.719 AST level (IU/L) 0.98 (0.96-1.00) 0.125 0.98 (0.96-1.00) 0.144 0.99 (0.99-1.00) 0.097 0.98 (0.96-1.00) 0.114 ALT level (IU/L) 1.00 (0.99–1.01) 0.214 1.00 (0.99–1.01) 0.225 1.00 (0.99-1.00) 0.175 1.00 (0.99–1.02) 0.190 HbA1c (%) 0.90 (0.81-1.00) 0.072 0.90 (0.81–1.01) 0.080 1.09 (1.00-1.19) 0.131 0.90 (0.81-1.00) 0.064 TG (mg/dL) 1.00 (0.99-1.00) 0.514 1.00 (0.99-1.00) 0.470 1.00 (0.99-1.00) 0.572 1.00 (0.99-1.00) 0.702 HDL-C (mg/dL) 0.99 (0.98-1.00) 0.765 0.99 (0.98-1.00) 0.692 0.99 (0.99-1.00) 0.706 0.99 (0.98-1.00) 0.553 LDL-C (mg/dL) 1.00 (0.99-1.00) 0.544 1.00 (0.99-1.00) 0.690 1.00 (0.99-1.00) 0.503 1.00 (0.99-1.00) 0.458 TSH average (mU/L) 1.08 (0.91–1.28) 0.340 - - - - - - FT4 average (ng/dL) - - 0.40 (0.10–1.49) 0.174 - - - - TT3 average (ng/dL) - - - - 0.97 (0.96–0.99) 0.002 - - High TT3 average - - - - - - 0.64 (0.50–0.82) < 0.001 a HR, Hazard ratio; CI, Confidence interval; BMI, Body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; TG, Triglyceride; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; TSH, Thyroid-stimulating hormone; FT4, Free thyroxine; TT3, Total triiodothyronine. * Models 1, 2, and 3 were adjusted for age, sex, BMI (kg/m 2 ), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, and thyroid hormone (TSH average for Model 1, FT4 average for Model 2, TT3 average for Model 3). **Model 4 was adjusted for age, sex, BMI (kg/m 2 ), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, High TT3 average. Sensitivity analysis using the modified MAFLD The baseline characteristics of the baseline cohort, modified MAFLD risk cohort, and modified MAFLD resolution cohort are shown in Supplementary Table 1. In sensitivity analysis using the modified MAFLD instead of simple fatty liver, similar associations between TT3 and modified MAFLD were observed. In baseline cohort, 249 (14.9%) participants have prevalent modified MAFLD. High TT3 level was also positively associated with modified MAFLD [adjusted OR 1.01 (1.00-1.01); P = 0.031, not shown in table] in binary logistic regression analysis with adjusting other factors. In modified MAFLD risk cohort, 705 out of 1416 participants (49.8%) developed modified MAFLD during the follow-up period of a median of 48.0 months (IQR 24.0–64.0 months). In the multivariable analysis, the statistical significance was sustained for modified MAFLD [adjusted HR 1.01 (1.00-1.02); P = 0.002; Model 1] [adjusted HR 1.24 (1.07–1.44); P = 0.004; Model 2]. In modified MAFLD resolution cohort, modified MAFLD resolved in 204 out of 249 participants (81.9%). Multivariate cox hazard models showed that TT3 average is negatively associated with resolution of modified MAFLD [adjusted HR 0.97 (0.95–0.99); P = 0.003; Model 1]. Compared with participants with low TT3 average, those in high TT3 average were significantly inversely associated with the resolution of modified MAFLD [adjusted HR 0.72 (0.54–0.95); P = 0.024; Model 2] (Table 5 ). Table 5 Sensitivity analysis: Relationship between TT3 average and modified MAFLD Variables Modified MAFLD risk cohort for incident MAFLD evaluation (N = 1416) Modified MAFLD resolution cohort for MAFLD resolution evaluation (N = 249) Model 1* Model 2** Model 1* Model 2** HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value Age (years) 1.01 (1.00-1.02) < 0.001 1.01 (1.00-1.02) < 0.001 1.00 (0.98–1.01) 0.635 1.00 (0.98–1.01) 0.750 Sex 0.75 (0.63–0.90) 0.002 0.75 (0.63–0.89) 0.002 0.76 (0.54–1.06) 0.109 0.74 (0.52–1.03) 0.079 BMI (kg/m 2 ) 1.14 (1.11–1.17) < 0.001 1.14 (1.11–1.17) < 0.001 0.92 (0.86–0.98) 0.014 0.93 (0.87–0.99) 0.026 AST level (IU/L) 1.00 (0.99–1.01) 0.076 1.00 (1.00-1.01) 0.060 0.99 (0.97-1.00) 0.346 0.99 (0.97–1.01) 0.453 ALT level (IU/L) 0.99 (0.98-1.00) 0.148 0.99 (0.98-1.00) 0.118 1.00 (0.99–1.01) 0.430 1.00 (0.99–1.01) 0.516 HbA1c (%) 1.11 (1.00-1.24) 0.043 1.11 (1.00-1.24) 0.044 0.99 (0.89–1.10) 0.883 0.97 (0.87–1.08) 0.658 TG (mg/dL) 1.00 (1.00–1.00) 0.484 1.00 (1.00–1.00) 0.478 1.00 (0.99-1.00) 0.827 1.00 (0.99-1.00) 0.960 HDL-C (mg/dL) 0.98 (0.98–0.99) 0.002 0.98 (0.98–0.99) 0.003 0.99 (0.98–1.01) 0.658 0.99 (0.98-1.00) 0.572 LDL-C (mg/dL) 1.00 (0.99-1.00) 0.105 1.00 (1.00–1.00) 0.093 1.00 (0.99-1.00) 0.549 1.00 (0.99-1.00) 0.810 TT3 average (ng/dL) 1.01 (1.00-1.02) 0.002 - - 0.97 (0.95–0.99) 0.003 - High TT3 average - - 1.24 (1.07–1.44) 0.004 - - 0.72 (0.54–0.95) 0.024 a HR, Hazard ratio; CI, Confidence interval; MAFLD, Metabolic dysfunction-associated fatty liver; BMI, Body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; TG, Triglyceride; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; TSH, Thyroid-stimulating hormone; FT4, Free thyroxine; TT3, Total triiodothyronine. *Model 1 was adjusted for age, sex, BMI (kg/m 2 ), AST (IU/L), ALT (IU/L), HbA1c, TG, HDL-C, LDL-C, and TT3 average. **Model 2 was adjusted for age, sex, BMI (kg/m 2 ), AST level (IU/L), ALT level (IU/L), HbA1c, TG, HDL-C, LDL-C, High TT3 average. Subgroup analysis according to baseline BMI or bodyweight change After dividing the participants in the fatty liver risk cohort into 3 subgroups according to baseline BMI (< 23, 23–25, and ≥ 25 kg/m 2 ), Kaplan–Meier curves for fatty liver risk according to the TT3 average status in normal (log-rank P = 0.983) (Fig. 2 A), overweight (log-rank P = 0.252) (Fig. 2 B), and obese participants (log-rank P = 0.001) (Fig. 2 C) were generated, respectively. In the multivariate analysis, the adjusted HRs of high TT3 average on incident fatty liver risk were 1.00 (0.81–1.24; P = 0.934), 1.14 (0.87–1.48; P = 0.319), and 1.46 (1.15–1.84; P = 0.001), respectively. The effect of high TT3 average level on incident fatty liver risk was evident in obese subgroups (Fig. 2 ) (Supplementary Table 2). When the participants in the fatty liver risk cohort divided into 3 subgroups according to bodyweight change during follow up (bodyweight loss group, stable bodyweight group, bodyweight gain group), Kaplan–Meier curves for incident fatty liver according to the TT3 average status showed that the participants with high TT3 average were more likely to develop fatty liver than those with low TT3 average in bodyweight loss group (log-rank P = 0.020) (Fig. 3 A) and stable bodyweight group (log-rank P = 0.004) (Fig. 3 B), whereas not in bodyweight gain group (log-rank P = 0.126) (Fig. 3 C). Similar results were shown in multivariable analysis as follows according to bodyweight change: bodyweight loss group- adjusted HR 1.35 (1.00–1.81, P = 0.045), stable bodyweight group- adjusted HR 1.13 (1.09–1.57, P = 0.003), and bodyweight gain group- adjusted HR 0.81 (0.61–1.06, P = 0.149) (Supplementary Table 3). Discussion In this large and long-term followed longitudinal cohort study, we firstly demonstrated that TT3 was associated with development and resolution of fatty liver. We also showed that TT3 was also related to development and resolution of modified MAFLD. The association of high TT3 average with development of fatty liver was most prominent in obese participants (BMI of ≥ 25 kg/m 2 ), and the association was sustained in bodyweight stable or bodyweight loss group. Several previous cross-sectional studies in euthyroid subjects showed a positive correlation between free triiodothyronine (FT3) and prevalent NAFLD 9 , 10 , 16 . In line with the study, we also found that higher TT3 level rather than TSH or FT4 was associated with increased prevalence of fatty liver. However, studying relationship between TT3 and development of fatty liver was an unmet need thus far - this is the first longitudinal study using survival analysis to demonstrate that TT3 is associated with development of fatty liver. The participants with high TT3 level during follow up were more likely to develop fatty liver comparing those with low TT3 level during follow up (HR 1.17). A previous study with longitudinal design showed that increased FT3 was indicated to be independently associated with increased hepatic steatosis 16 . However, the study did not apply survival analysis and average follow-up duration was short (2.2 years). About 2 years of follow-up duration might be insufficient considering that median onset duration for the fatty liver occurrence was over 2 years in current study. Comparing the previous study, we traced the participants over 5 years (median 61.0 months). Notably, we also showed that TT3 level was also associated with the resolution of fatty liver when maintained in the lower normal level. It might assume an even stronger association between TT3 level and fatty liver. There are a few studies reporting that T3 level is not associated with the fatty liver 17 , 18 , and it could be explained by the fact that the difference of baseline demographics such as BMI or ethnicity. A novel concept of MAFLD is now replacing the NAFLD which needs excluding excessive alcohol intake or other chronic liver disease 2 . For sensitivity analysis, we assessed modified MAFLD as an outcome replacing simple steatosis, and the result was similar to the main analysis. The results indicate that the previous association with the T3 and NAFLD can be postulated in the context of the MAFLD. Also, the fact that the sensitivity analysis including modified MAFLD was concordant with the main analysis supports the validity of our findings. Although lack of baseline data about HTN, insulin resistance, CRP limited the definite diagnosis of MAFLD, in our knowledge, this is the first study which evaluated the correlation between TT3 and development & resolution of fatty liver using the novel concept of MAFLD. The possible pathophysiology linking TT3 and fatty liver are as follows. At first, high TT3 could be a result of the compensatory mechanism. Obesity is proven cause of fatty liver, and obese patients tend to have an increased deiodinase activity which conversion of FT4 to FT3 6,19 as a compensatory mechanism to prevent further fat accumulation. In line with the concept, the impact of high TT3 average on development of fatty liver is prominent in obese participants in subgroup analysis of this study. To completely explain, nevertheless, it is insufficient because the impact of high TT3 average on development of fatty liver was remained despite steady or even declining body weight group when we performed a subgroup analysis according to bodyweight change during follow up. Second, it might be the manifestation of resistance of hepatic T3 uptake caused by repeated fat accumulation. High-fat diet and low levels of physical exercise accumulate excessive fat in hepatocytes and causes a production of proinflammatory factors such as IL-6 and TNF-α and reduction of antioxidants. These conditions decreased the activity of type 1 deiodinase, responsible for converting FT4 to T3, increased the activity of type 3 deiodinase, responsible for inactivating T3 20–23 . In addition, serum T3 signals gene expression through THR-β isoform 24 – 26 , and obese patients showed decreased the THR in peripheral cells 27 . These conditions indicate a T3 resistance state, which means decreased availability of T3 in hepatocyte despite of increased T3 production. This lower availability of T3 leads to a reduction in free fatty acid uptake by hepatocytes, lower mitochondrial β-oxidation and results in fatty liver disease, and further advanced stage. Recently, liver-directed selective THR-β agonist (resmetirom), a spotlighting possible medicine of MAFLD, showed the improved mitochondrial capacity and reduced liver fat that prevents progression to further stages of MAFLD in phase 2 trial 28 – 30 . Considering together the hopeful result of the trial and the association between TT3 average and development/resolution of fatty liver in current study, hepatic T3 uptake resistance needs to be considered as an important role of pathology and drug target for fatty liver. The current study has several strengths. First, we comprehensively demonstrated the association between TT3 and fatty liver through prevalence, incidence, or resolution. To our knowledge, this is the first study evaluating the relationship between TT3 and development and resolution of fatty liver using survival analysis in longitudinal data. Second, the sensitivity analysis adopting concept of MAFLD not only confirms the reliability of our study but also shed light on future research of relationship between MAFLD and thyroid hormone. Third, we proposed the possible mechanism (the concept of hepatic T3 resistance) that TT3 is positively associated with incidence of fatty liver through subgroup analysis and review of other articles. However, the present study also has limitations. First, we used USG to define fatty liver, which may not be objective. Despite this shortage, USG is the most widely accepted and recommended as first diagnostic modality for hepatic steatosis, and well-trained radiologist performed USG in current study. Also, there was only 2 pathological obese patients (BMI ≥ 35 kg/m 2 ) who have possibility of prominent low sensitivity/specificity of USG to diagnosis fatty liver 31 in current study. The further study using more objective and quantifiable modality such as CT or MRI 32 , 33 is needed. Second, we could not use accurate definition of MAFLD because information of HTN, insulin resistance, and CRP was absent in row data. So, a future study with accurate definition of MAFLD is warranted to determine whether TT3 is associated with development and resolution of MAFLD. At last, this was a retrospective study involving the participants of a single ethnicity. Therefore, the results should be verified in additional large, and prospective studies. In conclusion, TT3 level in normal range was associated with development and resolution of either fatty liver or modified MAFLD in Korean euthyroid adults. Declarations Authorship Contribution Statement Hye In Kim : Conceptualization, Formal analysis, Writing – original draft. Jun Young Kim: Conceptualization, Methodology, Writing – original draft. Jung Hwan Cho: Writing - review & editing. Ji Min Han: Writing - review & editing. Sunghwan Suh: Writing - review & editing. Ji Cheol Bae: Conceptualization. Tae Hyuk Kim : Investigation. Sun Wook Kim : Investigation. Jong Ryeal Hahm : Data curation, Supervision, Writing – review & editing. Jae Hoon Chung : Data curation, Supervision, Writing – review & editing. Funding Statement This work was supported by the fund of research promotion program, Gyeongsang National University, 2023. Competing interests The authors declare no competing interests. Data availability statement Data described in the manuscript, code book, and analytic code will be made available upon reasonable request from the corresponding author. References Younossi, Z. M. et al. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology (Baltimore, Md.) 64, 73–84, doi: 10.1002/hep.28431 (2016). Eslam, M. et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. Journal of hepatology 73, 202–209, doi: 10.1016/j.jhep.2020.03.039 (2020). Cordeiro, A., Souza, L. L., Einicker-Lamas, M. & Pazos-Moura, C. C. Non-classic thyroid hormone signalling involved in hepatic lipid metabolism. The Journal of endocrinology 216, R47-57, doi: 10.1530/joe-12-0542 (2013). Heimberg, M., Olubadewo, J. O. & Wilcox, H. G. Plasma lipoproteins and regulation of hepatic metabolism of fatty acids in altered thyroid states. Endocrine reviews 6, 590–607, doi: 10.1210/edrv-6-4-590 (1985). van Tienhoven-Wind, L. & Dullaart, R. P. Low normal thyroid function as a determinant of increased large very low density lipoprotein particles. Clinical biochemistry 48, 489–494, doi: 10.1016/j.clinbiochem.2015.01.015 (2015). De Pergola, G., Ciampolillo, A., Paolotti, S., Trerotoli, P. & Giorgino, R. Free triiodothyronine and thyroid stimulating hormone are directly associated with waist circumference, independently of insulin resistance, metabolic parameters and blood pressure in overweight and obese women. Clinical endocrinology 67, 265–269, doi: 10.1111/j.1365-2265.2007.02874.x (2007). Reinehr, T. Obesity and thyroid function. Molecular and cellular endocrinology 316, 165–171, doi: 10.1016/j.mce.2009.06.005 (2010). Roef, G. et al. Body composition and metabolic parameters are associated with variation in thyroid hormone levels among euthyroid young men. European journal of endocrinology 167, 719–726, doi: 10.1530/eje-12-0447 (2012). van den Berg, E. H. et al. Higher free triiodothyronine is associated with non-alcoholic fatty liver disease in euthyroid subjects: the Lifelines Cohort Study. Metabolism: clinical and experimental 67, 62–71, doi: 10.1016/j.metabol.2016.11.002 (2017). Liu, G. et al. Free triiodothyronine levels are positively associated with non-alcoholic fatty liver disease in euthyroid middle-aged subjects. Endocrine research 40, 188–193, doi: 10.3109/07435800.2014.987399 (2015). Farrell, G. C., Chitturi, S., Lau, G. K. & Sollano, J. D. Guidelines for the assessment and management of non-alcoholic fatty liver disease in the Asia-Pacific region: executive summary. Journal of gastroenterology and hepatology 22, 775–777, doi: 10.1111/j.1440-1746.2007.05002.x (2007). Yajima, Y. et al. Ultrasonographical diagnosis of fatty liver: significance of the liver-kidney contrast. The Tohoku journal of experimental medicine 139, 43–50, doi: 10.1620/tjem.139.43 (1983). Pan, W. H. & Yeh, W. T. How to define obesity? Evidence-based multiple action points for public awareness, screening, and treatment: an extension of Asian-Pacific recommendations. Asia Pacific journal of clinical nutrition 17, 370–374 (2008). Williamson, D. A., Bray, G. A. & Ryan, D. H. Is 5% weight loss a satisfactory criterion to define clinically significant weight loss? Obesity (Silver Spring, Md.) 23, 2319–2320, doi: 10.1002/oby.21358 (2015). Kushner, R. F. & Ryan, D. H. Assessment and lifestyle management of patients with obesity: clinical recommendations from systematic reviews. Jama 312, 943–952, doi: 10.1001/jama.2014.10432 (2014). Chen, P. et al. Free triiodothyronine is associated with the occurrence and remission of nonalcoholic fatty liver disease in euthyroid women. European journal of clinical investigation 49, e13070, doi: 10.1111/eci.13070 (2019). Ittermann, T. et al. Inverse association between serum free thyroxine levels and hepatic steatosis: results from the Study of Health in Pomerania. Thyroid: official journal of the American Thyroid Association 22, 568–574, doi: 10.1089/thy.2011.0279 (2012). Tao, Y., Gu, H., Wu, J. & Sui, J. Thyroid function is associated with non-alcoholic fatty liver disease in euthyroid subjects. Endocrine research 40, 74–78, doi: 10.3109/07435800.2014.952014 (2015). Dall'Asta, C. et al. Weight loss through gastric banding: effects on TSH and thyroid hormones in obese subjects with normal thyroid function. Obesity (Silver Spring, Md.) 18, 854–857, doi: 10.1038/oby.2009.320 (2010). Nozaki, S. et al. Stimulation of the activity and mRNA level of hepatic triacylglycerol lipase by triiodothyronine in HepG2 cells. Biochimica et biophysica acta 1127, 298–302, doi: 10.1016/0005-2760(92)90235-n (1992). Zhang, Y. et al. Peroxisomal proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1 alpha) enhances the thyroid hormone induction of carnitine palmitoyltransferase I (CPT-I alpha). The Journal of biological chemistry 279, 53963–53971, doi: 10.1074/jbc.M406028200 (2004). Xia, S. F. et al. Myricetin alleviated hepatic steatosis by acting on microRNA-146b/thyroid hormone receptor b pathway in high-fat diet fed C57BL/6J mice. Food & function 10, 1465–1477, doi: 10.1039/c8fo01452c (2019). Alonso-Merino, E. et al. Thyroid hormones inhibit TGF-β signaling and attenuate fibrotic responses. Proceedings of the National Academy of Sciences of the United States of America 113, E3451-3460, doi: 10.1073/pnas.1506113113 (2016). Ritter, M. J., Amano, I. & Hollenberg, A. N. Thyroid Hormone Signaling and the Liver. Hepatology (Baltimore, Md.) 72, 742–752, doi: 10.1002/hep.31296 (2020). Mendoza, A. & Hollenberg, A. N. New insights into thyroid hormone action. Pharmacology & therapeutics 173, 135–145, doi: 10.1016/j.pharmthera.2017.02.012 (2017). Ishizuka, T. & Lazar, M. A. The nuclear receptor corepressor deacetylase activating domain is essential for repression by thyroid hormone receptor. Molecular endocrinology (Baltimore, Md.) 19, 1443–1451, doi: 10.1210/me.2005-0009 (2005). Nannipieri, M. et al. Expression of thyrotropin and thyroid hormone receptors in adipose tissue of patients with morbid obesity and/or type 2 diabetes: effects of weight loss. International journal of obesity (2005) 33, 1001–1006, doi: 10.1038/ijo.2009.140 (2009). Negi, C. K., Babica, P., Bajard, L., Bienertova-Vasku, J. & Tarantino, G. Insights into the molecular targets and emerging pharmacotherapeutic interventions for nonalcoholic fatty liver disease. Metabolism: clinical and experimental 126, 154925, doi: 10.1016/j.metabol.2021.154925 (2022). Harrison, S. A. et al. Effects of Resmetirom on Noninvasive Endpoints in a 36-Week Phase 2 Active Treatment Extension Study in Patients With NASH. Hepatology communications 5, 573–588, doi: 10.1002/hep4.1657 (2021). Harrison, S. A. et al. Resmetirom (MGL-3196) for the treatment of non-alcoholic steatohepatitis: a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial. Lancet (London, England) 394, 2012–2024, doi: 10.1016/s0140-6736(19)32517-6 (2019). Mottin, C. C. et al. The role of ultrasound in the diagnosis of hepatic steatosis in morbidly obese patients. Obesity surgery 14, 635–637, doi: 10.1381/096089204323093408 (2004). Park, S. H. et al. Macrovesicular hepatic steatosis in living liver donors: use of CT for quantitative and qualitative assessment. Radiology 239, 105–112, doi: 10.1148/radiol.2391050361 (2006). McPherson, S. et al. Magnetic resonance imaging and spectroscopy accurately estimate the severity of steatosis provided the stage of fibrosis is considered. Journal of hepatology 51, 389–397, doi: 10.1016/j.jhep.2009.04.012 (2009). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3790646","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":263703047,"identity":"2cd39747-23d0-4361-945b-a6ba1740fa41","order_by":0,"name":"Hye In Kim","email":"","orcid":"","institution":"Samsung Changwon Hospital, Sungkyunkwan University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hye","middleName":"In","lastName":"Kim","suffix":""},{"id":263703048,"identity":"20eddaa2-1b1b-4b2e-a349-209c1efa0dcf","order_by":1,"name":"Jun Young Kim","email":"","orcid":"","institution":"Samsung Changwon Hospital, 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18:36:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177388,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier graphs for the development of fatty liver in participants with BMI \u0026lt;23 (N = 557) (A), 23–25 (N = 338) (B), and ≥25 kg/m\u003csup\u003e2\u003c/sup\u003e (N = 437) (C) according to TT3 average status.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3790646/v1/d978752927e03ab5e159886b.png"},{"id":49076046,"identity":"2026eb04-590e-4fbf-a5ae-d33dbf6e26ae","added_by":"auto","created_at":"2024-01-02 18:36:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":177087,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier graphs for the development of fatty liver in the body weight loss group (N = 267)(A), bodyweight stable group (N = 745) (B), and body weight gain group(N = 320) (C) according to TT3 average status.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3790646/v1/23e620257b496bf677f7c9d3.png"},{"id":56220466,"identity":"5cc1b4f4-0ab4-4332-876a-df4174686352","added_by":"auto","created_at":"2024-05-10 04:23:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1852857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3790646/v1/ac1ab530-4e95-4ec7-b11a-3d54d041e508.pdf"},{"id":49076047,"identity":"a18c33ba-09b6-4f97-bcc7-204ceabf5099","added_by":"auto","created_at":"2024-01-02 18:36:41","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":35800,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3790646/v1/136a56d3e26ac08b441db755.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Triiodothyronine is associated with incidence and resolution of fatty liver disease: a longitudinal study in euthyroid Korean adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFatty liver is the leading cause of chronic liver disease. The global prevalence of fatty liver is assumed about 24%\u003csup\u003e1\u003c/sup\u003e, representing a substantial clinical burden and a public health concern. Most of fatty liver is considered to reflect the hepatic component of the metabolic syndrome, since it has strong association with insulin resistance, obesity, and dyslipidemia. Recently, a novel definition of metabolic dysfunction-associated fatty liver disease (MAFLD) replacing non-alcoholic fatty liver disease (NAFLD) was proposed in a conception that fatty liver and metabolic syndromes share a common pathway of pathogenesis. MAFLD can be diagnosed by positive diagnostic criteria of hepatic steatosis and metabolic dysfunctions regardless of alcohol consumption or other concomitant liver disease, and it can identify more patients at risk for cirrhosis and liver cancer\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThyroid hormone has important role in lipid metabolism and energy homeostasis. After thyroxine is converted to the active hormone, triiodothyronine (T3), by deiodinase, it enters target cells such as hypothalamus, adipose tissue, muscle, and hepatocyte via thyroid hormone receptor (THR). In hepatocyte, T3 affects regulation of hepatic fat accumulation through multiple pathways, including stimulation of free fatty acid delivery to the liver for re-esterification to triglyceride (TG), or increasing fatty acid β-oxidation\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe relationship between thyroid hormone marker, especially T3, and metabolic disease factor such as high BMI, insulin resistance, diabetes also has been suggested in recent decades\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Considering fatty liver is recognized as a hepatic presentation of metabolic syndrome, it is reasonable that T3 is associated with fatty liver. The large cohort study (The Lifelines Cohort Study) demonstrated that NAFLD was independently associated with a high-normal FT3 level\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Also, a study in the middle-aged and elderly euthyroid subjects showed that high-normal FT3 are independently associated with a higher risk of NAFLD\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, the cause-consequence effect may not be established due to cross-sectional design or lack of survival analysis with adjustment. Furthermore, the association between MAFLD and T3 has not been researched in longitudinal design though a novel concept of MAFLD replacing NAFLD in clinical practice\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, in this long term follow up longitudinal data, we aimed to explore whether the thyroid hormone maker, especially total T3 (TT3) is associated with either the development of fatty liver or the resolution of fatty liver. In addition, we speculated the relationship between TT3 and modified MAFLD presenting the emerging concept of MAFLD.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and study population\u003c/h2\u003e \u003cp\u003eIn this retrospective longitudinal study, we assessed 2372 participants who completed annual or biennial examinations between 2007 and 2014 at Gyeongsang National University Hospital (GNUH) healthcare center in South Korea for eligibility. Among them, the participants who aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years old and underwent medical examinations with serial thyroid hormone measurements\u0026thinsp;\u0026ge;\u0026thinsp;4 times were included (N\u0026thinsp;=\u0026thinsp;2246). The participants with abnormal initial results of the thyroid function test (N\u0026thinsp;=\u0026thinsp;294), missing data (N\u0026thinsp;=\u0026thinsp;272), and a history of thyroid disease or recent medication affecting thyroid function (N\u0026thinsp;=\u0026thinsp;15) were excluded. Finally, 1665 participants were included (baseline cohort). The baseline cohort was used for investigating the association between prevalent fatty liver or modified MAFLD and thyroid hormone markers. Among them, the participants with initial fatty liver were used for fatty liver resolution cohort (N\u0026thinsp;=\u0026thinsp;333). After excluding the participants who already have fatty liver disease at baseline, we generated fatty liver risk cohort (N\u0026thinsp;=\u0026thinsp;1332) to figure out whether the thyroid hormone marker related to prevalence of fatty liver was also associated with incident fatty liver. To investigate the relationship between the thyroid hormone markers and modified MAFLD, we used 249 participants with modified MAFLD at baseline (MAFLD resolution cohort), and 1416 participants without initial modified MAFLD (MAFLD risk cohort) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Institutional Review Board (IRB) of GNUH approved this study (No. 2022-02-003) and the requirement for informed consent was waived by the IRB. All methods were performed in compliance with the relevant guidelines or regulations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eDemographic and anthropometric characteristics were obtained. Body mass index (BMI) was calculated as the body weight (kg) divided by the height squared (m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) that were measured by trained nurses. All participants underwent an overnight fasting and the following laboratory data were measured by blood samples: the levels of thyroid-stimulating hormone (TSH), free thyroxine (FT4), TT3, TG, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST), alanine transaminase (ALT), and HbA1c. The thyroid hormone including TSH, FT4, TT3 levels were determined by an electro-chemiluminescent immunoassay (Roche Diagnostics Ltd., Mannheim, Germany) (reference ranges of 0.27\u0026ndash;4.2 mU/L for TSH, 0.93\u0026ndash;1.70 ng/dL for FT4, and 80\u0026ndash;200 ng/dL for TT3). Well trained radiologist performed liver ultrasonography (USG) using an ultrasound system (APLIO I70, Canon, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions of study outcome and subgroups\u003c/h2\u003e \u003cp\u003eFatty liver was defined as USG diagnosed fatty liver according to the Asia-Pacific Guidelines (the presence of at least two of the followings: 1) diffusely increased echogenicity liver with liver echogenicity greater than kidney or spleen; 2) vascular blurring; 3) deep attenuation of ultrasound signal)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. For sensitivity analysis, we used modified MAFLD which defined the participants with fatty liver in USG in addition to one of the following three criteria: 1) BMI\u0026thinsp;\u0026ge;\u0026thinsp;23 kg/m\u003csup\u003e2\u003c/sup\u003e; 2) presence of type 2 diabetes mellitus (T2DM); 3) evidence of metabolic dysregulation defined by the presence of at least 2 metabolic risks among TG\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL, HDL-C\u0026thinsp;\u0026le;\u0026thinsp;40 mg/dL, and prediabetes. The definition was adopted from diagnostic criteria of MAFLD excluding hypertension (HTN), insulin resistance, and c-reactive protein (CRP) criteria in metabolic dysregulation part\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo investigate the association between prevalence of fatty liver and thyroid hormone makers, TSH, FT4, and TT3 at baseline were used. When we explore the association between development or resolution of fatty liver and thyroid hormone makers, average of thyroid hormone makers (TSH average, FT4 average, TT3 average) which defined as average of hormone levels during follow-up were adopted for quantitative evaluation of thyroid hormone exposure during observation instead of one spot value of baseline. After determining that TT3 average as a continuous variable is associated with fatty liver, we further defined TT3 average into categorical variable: 1) high TT3 average group - the participants who recorded TT3 average higher than median TT3 average (109.45 ng/dL); 2) low TT3 average group - the participants who recorded TT3 average same or lower than median TT3 average (109.45 ng/dL).\u003c/p\u003e \u003cp\u003eBMI was classified into 3 subgroups [\u0026lt;\u0026thinsp;23 (normal), 23\u0026ndash;25 (overweight), and \u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e (obese)] using Asia-Pacific BMI classification\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Bodyweight gain group was defined as bodyweight increment over 5% and bodyweight loss group was defined as bodyweight decrement over 5% from the baseline during follow-up. Stable bodyweight group included participants whose bodyweight changed less than 5% from the baseline\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll continuous variables are presented as the median and interquartile range (IQR), and all categorical variables are presented as the number and percentage. To investigate the effect of thyroid hormone marker on prevalence of fatty liver and modified MAFLD, binary logistic regression models were generated in baseline cohort. The odds ratio (OR) with 95% confidence interval (CI) were reported. To investigate the effect of thyroid hormone marker on incidence of fatty liver or modified MAFLD, Kaplan\u0026ndash;Meier curves by the log-rank test and Cox proportional hazards regression models were generated in fatty liver risk cohort and modified MAFLD risk cohort, respectively. To figure out the relationship between the thyroid hormone markers and resolution of fatty liver or modified MAFLD, Kaplan\u0026ndash;Meier curves by the log-rank test and Cox proportional hazards regression models were also used in fatty liver resolution cohort and modified MAFLD resolution cohort, respectively. Age, gender, BMI, HbA1c, TG, HDL-C, LDL-C, AST, and ALT were adjusted in multivariable analysis. The hazard ratio (HR) with 95% confidence interval (CI) were reported. IBM SPSS Statistics for Windows (version 22.0, Armonk, NY, USA) was used for the statistical analysis and \u003cem\u003eP\u003c/em\u003e-values of \u0026lt;\u0026thinsp;0.05 with two-sided were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the baseline cohort, fatty liver risk cohort, and fatty liver resolution cohort are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the 1665 participants in baseline cohort, women comprised 35.0%, median age was 43.0 years (IQR 37.0\u0026ndash;50.0 years), and median BMI was 23.7 kg/m\u003csup\u003e2\u003c/sup\u003e (21.8\u0026ndash;25.7 kg/m\u003csup\u003e2\u003c/sup\u003e). The median serum TSH level, FT4 level, and TT3 levels were 1.69 mU/L (1.19\u0026ndash;2.34 mU/L), 1.31 ng/dL (1.20\u0026ndash;1.43 ng/dL), and 109.00 ng/dL (98.13\u0026ndash;119.90 ng/dL), respectively. In fatty liver risk cohort, median age at baseline was 43.0 years (37.0\u0026ndash;50.0 years), and there were 481 women (35.9%). The median BMI was 23.6 kg/m\u003csup\u003e2\u003c/sup\u003e (21.6\u0026ndash;25.6 kg/m\u003csup\u003e2\u003c/sup\u003e), and the median serum TSH level, FT4 level, and TT3 levels were 1.70 mU/L (1.19\u0026ndash;2.35 mU/L), 1.32 ng/dL (1.20\u0026ndash;1.43 ng/dL), and 108.60 ng/dL (97.66\u0026ndash;119.17 ng/dL), respectively. In fatty liver resolution cohort, median age at baseline was 44.0 years (38.0\u0026ndash;50.0 years), and 32.1% were women. The median BMI was 24.2 kg/m2 (22.3\u0026ndash;26.2 kg/m2), and the median serum TSH level, FT4 level, and TT3 levels were 1.65 mU/L (1.19\u0026ndash;2.33 mU/L), 1.30 ng/dL (1.21\u0026ndash;1.44 ng/dL), and 111.70 ng/dL (100.26\u0026ndash;123.15 ng/dL), respectively.\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 the participants for fatty liver analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout fatty liver\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith fatty liver\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1665)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1332)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;333)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.0 (37.0\u0026ndash;50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.0 (37.0\u0026ndash;50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.0 (38.0\u0026ndash;50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e583 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e481 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107 (32.1%)\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) (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.7 (21.8\u0026ndash;25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.6 (21.6\u0026ndash;25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.2 (22.3\u0026ndash;26.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (BMI\u0026thinsp;\u0026lt;\u0026thinsp;23 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e671 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e563 (42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (BMI 23\u0026ndash;25 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e430 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e338 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e564 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e440 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH level (mU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.69 (1.19\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.70 (1.19\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.65 (1.19\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4 level (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31 (1.20\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.20\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.21\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT3 level (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109.00 (98.13\u0026ndash;119.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.60 (97.66\u0026ndash;119.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.70 (100.26\u0026ndash;123.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.0 (17.0\u0026ndash;26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.0 (17.0\u0026ndash;26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.0 (17.5\u0026ndash;26.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.0 (15.0\u0026ndash;30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.0 (15.0\u0026ndash;29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.0 (16.0\u0026ndash;30.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c level (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.1 (5.0\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1 (5.0\u0026ndash;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.6 (5.1\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.0 (77.0\u0026ndash;159.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107.0 (77.0\u0026ndash;156.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.0 (81.0\u0026ndash;170.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.0 (43.0\u0026ndash;60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.0 (43.0\u0026ndash;61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.0 (42.5\u0026ndash;59.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115.5 (96.0\u0026ndash;136.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116.0 (96.0\u0026ndash;137.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115.0 (94.0\u0026ndash;136.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian follow up (months) (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.0 (51.0\u0026ndash;71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.0 (52.0\u0026ndash;71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.0 (48.0\u0026ndash;70.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eTSH, Thyroid-stimulating hormone; TT3, Total triiodothyronine; FT4, Free thyroxine; BMI, Body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; IQR, Interquartile range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between TT3 and prevalence of fatty liver\u003c/h2\u003e \u003cp\u003eIn baseline cohort, 20.0% (333/1665) of the participants had fatty liver. Binary logistic regression analysis showed that high TT3 level was independently associated with fatty liver [adjusted OR 1.01 (1.00\u0026ndash;1.01); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012] even after adjusting other factors. However, TSH level [adjusted OR 1.05 (0.91\u0026ndash;1.22); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.468] or FT4 level [adjusted OR 1.28 (0.58\u0026ndash;2.80); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.533] had no association with prevalent fatty liver. High BMI [adjusted OR 1.05 (1.00\u0026ndash;1.11); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021] and high HbA1c [adjusted OR 1.98 (1.71\u0026ndash;2.30); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] were also independent factors associated with the prevalence of fatty liver (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eBinary regression models for prevalent fatty liver\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.71\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.71\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96 (0.71\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.782\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\u003e\u003cb\u003e1.05 (1.00-1.11)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.05 (1.00-1.11)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.05 (1.00-1.11)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.01 (1.73\u0026ndash;2.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.01 (1.73\u0026ndash;2.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.98 (1.71\u0026ndash;2.30)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (mU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.91\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4 (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28 (0.58\u0026ndash;2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT3 (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.01 (1.00-1.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003eHR, Hazard ratio; CI, Confidence interval; BMI, Body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; TG, Triglyceride; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; TSH, Thyroid-stimulating hormone; FT4, Free thyroxine; TT3, Total triiodothyronine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*Model: The models were adjusted for age, sex, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, and thyroid hormones (TSH for Model 1, FT4 for Model 2, and TT3 for Model 3).\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 between TT3 average and the development of fatty liver\u003c/h2\u003e \u003cp\u003eIn fatty liver risk cohort, 66.9% (891/1332) of the participants developed fatty liver with median time to onset as 38.0 months during the follow-up period of a median of 63.0 months (IQR 52.0\u0026ndash;71.0 months). Among the three thyroid hormone makers, TT3 average was positively associated with occurrence of fatty liver in the multivariable analysis [adjusted HR 1.01 (1.00\u0026ndash;1.02); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Model 3]. Participants with high TT3 average had a significantly higher risk to develop fatty liver than those of low TT3 average [adjusted HR 1.17 (1.03\u0026ndash;1.34); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016; Model 3]. The statistical significance was absent for TSH average [adjusted HR 0.93 (0.86\u0026ndash;1.01); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.099; Model 1] and for FT4 average [adjusted HR 1.17 (0.54\u0026ndash;2.53); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.677; Model 2]. Other factors associated with fatty liver occurrence were old age [adjusted HR 1.00 (1.00\u0026ndash;1.01); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048; Model 4], high HbA1c [adjusted HR 1.09 (1.00\u0026ndash;1.20); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042; Model 4] and female gender [adjusted HR 0.82 (0.70\u0026ndash;0.95); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011; Model 4] (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eMultivariate cox proportional hazards models for fatty liver risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\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=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eModel 4**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.00 (1.00-1.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.053\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.00 (1.00-1.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.00 (1.00-1.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.81 (0.70\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.82 (0.70\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.82 (0.71\u0026ndash;0.96)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.82 (0.70\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\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\u003e1.01 (0.98\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.10 (1.00-1.20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.10 (1.00-1.20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.09 (1.00-1.20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.09 (1.00-1.20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\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\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH average (mU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.86\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4 average (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17 (0.54\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT3 average (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.01 (1.00-1.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh TT3 average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.17 (1.03\u0026ndash;1.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003ea\u003c/sup\u003eHR, Hazard ratio; CI, Confidence interval; BMI, body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; TG, Triglyceride; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; TSH, Thyroid-stimulating hormone; FT4, Free thyroxine; TT3, Total triiodothyronine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e* Models 1, 2, and 3 were adjusted for age, sex, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, and thyroid hormone (TSH average for Model 1, FT4 average for Model 2, TT3 average for Model 3).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e** Model 4 was adjusted for age, sex, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, and high TT3 average.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between TT3 average and the resolution of fatty liver\u003c/h2\u003e \u003cp\u003eIn fatty liver resolution cohort, fatty liver resolved in 79.6% (265/333) of the participants with median time to onset as 23.0 months during the follow-up period of a median of 60.0 months (IQR 48.0\u0026ndash;70.0 months). Among the three thyroid hormone makers, TT3 average was inversely associated with resolution of fatty liver in the multivariable analysis [adjusted HR 0.97 (0.96\u0026ndash;0.99); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Model 3]. Participants with High TT3 average had a significantly lower chance to resolve fatty liver than those of low TT3 average [adjusted HR 0.64 (0.50\u0026ndash;0.82); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Model 4]. The statistical significance was absent for TSH average [adjusted HR 1.08 (0.91\u0026ndash;1.28); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.340; Model 1] and for FT4 average [adjusted HR 0.40 (0.10\u0026ndash;1.49); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.174; Model 2]. Younger age was also significantly associated with the resolution of fatty liver [adjusted HR 0.98 (0.97\u0026ndash;0.99); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039; Model 4] (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate cox proportional hazards models for fatty liver resolution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\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=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eModel 4**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.98 (0.97\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.98 (0.97\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.99 (0.98\u0026ndash;1.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.98 (0.97\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.72\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.72\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.82 (0.63\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.96 (0.72\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.793\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\u003e0.99 (0.94\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.94\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98 (0.94\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 (0.94\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.96-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.96-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.98 (0.96-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.81-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 (0.81\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09 (1.00-1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.90 (0.81-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.064\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\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH average (mU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.91\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4 average (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.10\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT3 average (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.97 (0.96\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh TT3 average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.64 (0.50\u0026ndash;0.82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003ea\u003c/sup\u003eHR, Hazard ratio; CI, Confidence interval; BMI, Body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; TG, Triglyceride; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; TSH, Thyroid-stimulating hormone; FT4, Free thyroxine; TT3, Total triiodothyronine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e* Models 1, 2, and 3 were adjusted for age, sex, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, and thyroid hormone (TSH average for Model 1, FT4 average for Model 2, TT3 average for Model 3).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e**Model 4 was adjusted for age, sex, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), AST level (IU/L), ALT level (IU/L), creatinine level (mg/dL), HbA1c, TG, HDL-C, LDL-C, High TT3 average.\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\u003eSensitivity analysis using the modified MAFLD\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the baseline cohort, modified MAFLD risk cohort, and modified MAFLD resolution cohort are shown in Supplementary Table\u0026nbsp;1. In sensitivity analysis using the modified MAFLD instead of simple fatty liver, similar associations between TT3 and modified MAFLD were observed. In baseline cohort, 249 (14.9%) participants have prevalent modified MAFLD. High TT3 level was also positively associated with modified MAFLD [adjusted OR 1.01 (1.00-1.01); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031, not shown in table] in binary logistic regression analysis with adjusting other factors. In modified MAFLD risk cohort, 705 out of 1416 participants (49.8%) developed modified MAFLD during the follow-up period of a median of 48.0 months (IQR 24.0\u0026ndash;64.0 months). In the multivariable analysis, the statistical significance was sustained for modified MAFLD [adjusted HR 1.01 (1.00-1.02); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Model 1] [adjusted HR 1.24 (1.07\u0026ndash;1.44); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; Model 2]. In modified MAFLD resolution cohort, modified MAFLD resolved in 204 out of 249 participants (81.9%). Multivariate cox hazard models showed that TT3 average is negatively associated with resolution of modified MAFLD [adjusted HR 0.97 (0.95\u0026ndash;0.99); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Model 1]. Compared with participants with low TT3 average, those in high TT3 average were significantly inversely associated with the resolution of modified MAFLD [adjusted HR 0.72 (0.54\u0026ndash;0.95); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024; Model 2] (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis: Relationship between TT3 average and modified MAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eModified MAFLD risk cohort\u003c/p\u003e \u003cp\u003efor incident MAFLD evaluation\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1416)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eModified MAFLD resolution cohort\u003c/p\u003e \u003cp\u003efor MAFLD resolution evaluation\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;249)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 1*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eModel 2**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.01 (1.00-1.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.01 (1.00-1.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.75 (0.63\u0026ndash;0.90)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.75 (0.63\u0026ndash;0.89)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76 (0.54\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.74 (0.52\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.079\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\u003e\u003cb\u003e1.14 (1.11\u0026ndash;1.17)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.14 (1.11\u0026ndash;1.17)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.92 (0.86\u0026ndash;0.98)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.93 (0.87\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.97-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT level (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.11 (1.00-1.24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.11 (1.00-1.24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.89\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.97 (0.87\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.658\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\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.98 (0.98\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.98 (0.98\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT3 average (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.01 (1.00-1.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.97 (0.95\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh TT3 average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.24 (1.07\u0026ndash;1.44)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.72 (0.54\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003ea\u003c/sup\u003eHR, Hazard ratio; CI, Confidence interval; MAFLD, Metabolic dysfunction-associated fatty liver; BMI, Body mass index; AST, Aspartate aminotransferase; ALT, Alanine transaminase; TG, Triglyceride; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; TSH, Thyroid-stimulating hormone; FT4, Free thyroxine; TT3, Total triiodothyronine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e*Model 1 was adjusted for age, sex, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), AST (IU/L), ALT (IU/L), HbA1c, TG, HDL-C, LDL-C, and TT3 average.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e**Model 2 was adjusted for age, sex, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), AST level (IU/L), ALT level (IU/L), HbA1c, TG, HDL-C, LDL-C, High TT3 average.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis according to baseline BMI or bodyweight change\u003c/h2\u003e \u003cp\u003eAfter dividing the participants in the fatty liver risk cohort into 3 subgroups according to baseline BMI (\u0026lt;\u0026thinsp;23, 23\u0026ndash;25, and \u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), Kaplan\u0026ndash;Meier curves for fatty liver risk according to the TT3 average status in normal (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.983) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), overweight (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.252) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and obese participants (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) were generated, respectively. In the multivariate analysis, the adjusted HRs of high TT3 average on incident fatty liver risk were 1.00 (0.81\u0026ndash;1.24; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.934), 1.14 (0.87\u0026ndash;1.48; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.319), and 1.46 (1.15\u0026ndash;1.84; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), respectively. The effect of high TT3 average level on incident fatty liver risk was evident in obese subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen the participants in the fatty liver risk cohort divided into 3 subgroups according to bodyweight change during follow up (bodyweight loss group, stable bodyweight group, bodyweight gain group), Kaplan\u0026ndash;Meier curves for incident fatty liver according to the TT3 average status showed that the participants with high TT3 average were more likely to develop fatty liver than those with low TT3 average in bodyweight loss group (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and stable bodyweight group (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), whereas not in bodyweight gain group (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.126) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Similar results were shown in multivariable analysis as follows according to bodyweight change: bodyweight loss group- adjusted HR 1.35 (1.00\u0026ndash;1.81, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), stable bodyweight group- adjusted HR 1.13 (1.09\u0026ndash;1.57, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and bodyweight gain group- adjusted HR 0.81 (0.61\u0026ndash;1.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.149) (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large and long-term followed longitudinal cohort study, we firstly demonstrated that TT3 was associated with development and resolution of fatty liver. We also showed that TT3 was also related to development and resolution of modified MAFLD. The association of high TT3 average with development of fatty liver was most prominent in obese participants (BMI of \u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), and the association was sustained in bodyweight stable or bodyweight loss group.\u003c/p\u003e \u003cp\u003eSeveral previous cross-sectional studies in euthyroid subjects showed a positive correlation between free triiodothyronine (FT3) and prevalent NAFLD\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In line with the study, we also found that higher TT3 level rather than TSH or FT4 was associated with increased prevalence of fatty liver. However, studying relationship between TT3 and development of fatty liver was an unmet need thus far - this is the first longitudinal study using survival analysis to demonstrate that TT3 is associated with development of fatty liver. The participants with high TT3 level during follow up were more likely to develop fatty liver comparing those with low TT3 level during follow up (HR 1.17). A previous study with longitudinal design showed that increased FT3 was indicated to be independently associated with increased hepatic steatosis \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, the study did not apply survival analysis and average follow-up duration was short (2.2 years). About 2 years of follow-up duration might be insufficient considering that median onset duration for the fatty liver occurrence was over 2 years in current study. Comparing the previous study, we traced the participants over 5 years (median 61.0 months). Notably, we also showed that TT3 level was also associated with the resolution of fatty liver when maintained in the lower normal level. It might assume an even stronger association between TT3 level and fatty liver. There are a few studies reporting that T3 level is not associated with the fatty liver \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and it could be explained by the fact that the difference of baseline demographics such as BMI or ethnicity.\u003c/p\u003e \u003cp\u003eA novel concept of MAFLD is now replacing the NAFLD which needs excluding excessive alcohol intake or other chronic liver disease\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For sensitivity analysis, we assessed modified MAFLD as an outcome replacing simple steatosis, and the result was similar to the main analysis. The results indicate that the previous association with the T3 and NAFLD can be postulated in the context of the MAFLD. Also, the fact that the sensitivity analysis including modified MAFLD was concordant with the main analysis supports the validity of our findings. Although lack of baseline data about HTN, insulin resistance, CRP limited the definite diagnosis of MAFLD, in our knowledge, this is the first study which evaluated the correlation between TT3 and development \u0026amp; resolution of fatty liver using the novel concept of MAFLD.\u003c/p\u003e \u003cp\u003eThe possible pathophysiology linking TT3 and fatty liver are as follows. At first, high TT3 could be a result of the compensatory mechanism. Obesity is proven cause of fatty liver, and obese patients tend to have an increased deiodinase activity which conversion of FT4 to FT3\u003csup\u003e6,19\u003c/sup\u003e as a compensatory mechanism to prevent further fat accumulation. In line with the concept, the impact of high TT3 average on development of fatty liver is prominent in obese participants in subgroup analysis of this study. To completely explain, nevertheless, it is insufficient because the impact of high TT3 average on development of fatty liver was remained despite steady or even declining body weight group when we performed a subgroup analysis according to bodyweight change during follow up. Second, it might be the manifestation of resistance of hepatic T3 uptake caused by repeated fat accumulation. High-fat diet and low levels of physical exercise accumulate excessive fat in hepatocytes and causes a production of proinflammatory factors such as IL-6 and TNF-α and reduction of antioxidants. These conditions decreased the activity of type 1 deiodinase, responsible for converting FT4 to T3, increased the activity of type 3 deiodinase, responsible for inactivating T3\u003csup\u003e20\u0026ndash;23\u003c/sup\u003e. In addition, serum T3 signals gene expression through THR-β isoform\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and obese patients showed decreased the THR in peripheral cells\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. These conditions indicate a T3 resistance state, which means decreased availability of T3 in hepatocyte despite of increased T3 production. This lower availability of T3 leads to a reduction in free fatty acid uptake by hepatocytes, lower mitochondrial β-oxidation and results in fatty liver disease, and further advanced stage. Recently, liver-directed selective THR-β agonist (resmetirom), a spotlighting possible medicine of MAFLD, showed the improved mitochondrial capacity and reduced liver fat that prevents progression to further stages of MAFLD in phase 2 trial\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Considering together the hopeful result of the trial and the association between TT3 average and development/resolution of fatty liver in current study, hepatic T3 uptake resistance needs to be considered as an important role of pathology and drug target for fatty liver.\u003c/p\u003e \u003cp\u003eThe current study has several strengths. First, we comprehensively demonstrated the association between TT3 and fatty liver through prevalence, incidence, or resolution. To our knowledge, this is the first study evaluating the relationship between TT3 and development and resolution of fatty liver using survival analysis in longitudinal data. Second, the sensitivity analysis adopting concept of MAFLD not only confirms the reliability of our study but also shed light on future research of relationship between MAFLD and thyroid hormone. Third, we proposed the possible mechanism (the concept of hepatic T3 resistance) that TT3 is positively associated with incidence of fatty liver through subgroup analysis and review of other articles. However, the present study also has limitations. First, we used USG to define fatty liver, which may not be objective. Despite this shortage, USG is the most widely accepted and recommended as first diagnostic modality for hepatic steatosis, and well-trained radiologist performed USG in current study. Also, there was only 2 pathological obese patients (BMI\u0026thinsp;\u0026ge;\u0026thinsp;35 kg/m\u003csup\u003e2\u003c/sup\u003e) who have possibility of prominent low sensitivity/specificity of USG to diagnosis fatty liver\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e in current study. The further study using more objective and quantifiable modality such as CT or MRI\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e is needed. Second, we could not use accurate definition of MAFLD because information of HTN, insulin resistance, and CRP was absent in row data. So, a future study with accurate definition of MAFLD is warranted to determine whether TT3 is associated with development and resolution of MAFLD. At last, this was a retrospective study involving the participants of a single ethnicity. Therefore, the results should be verified in additional large, and prospective studies.\u003c/p\u003e \u003cp\u003eIn conclusion, TT3 level in normal range was associated with development and resolution of either fatty liver or modified MAFLD in Korean euthyroid adults.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHye In Kim\u003c/strong\u003e: Conceptualization, Formal analysis, Writing \u0026ndash; original draft.\u0026nbsp;\u003cstrong\u003eJun Young Kim:\u003c/strong\u003e Conceptualization, Methodology, Writing \u0026ndash; original draft. \u003cstrong\u003eJung Hwan Cho:\u003c/strong\u003e Writing - review \u0026amp; editing. \u003cstrong\u003eJi Min Han:\u003c/strong\u003e Writing - review \u0026amp; editing. \u003cstrong\u003eSunghwan Suh:\u003c/strong\u003e Writing - review \u0026amp; editing. \u003cstrong\u003eJi Cheol Bae:\u003c/strong\u003e Conceptualization.\u003cstrong\u003e\u0026nbsp;Tae Hyuk Kim\u003c/strong\u003e: Investigation. \u003cstrong\u003eSun Wook Kim\u003c/strong\u003e: Investigation. \u003cstrong\u003eJong Ryeal Hahm\u003c/strong\u003e: Data curation, Supervision, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eJae Hoon Chung\u003c/strong\u003e: Data curation, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the fund of research promotion program, Gyeongsang National University, 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData described in the manuscript, code book, and analytic code will be made available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYounossi, Z. M. \u003cem\u003eet al.\u003c/em\u003e Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology (Baltimore, Md.) 64, 73\u0026ndash;84, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.28431\u003c/span\u003e\u003cspan address=\"10.1002/hep.28431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEslam, M. \u003cem\u003eet al.\u003c/em\u003e A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. Journal of hepatology 73, 202\u0026ndash;209, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2020.03.039\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2020.03.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCordeiro, A., Souza, L. L., Einicker-Lamas, M. \u0026amp; Pazos-Moura, C. C. Non-classic thyroid hormone signalling involved in hepatic lipid metabolism. The Journal of endocrinology 216, R47-57, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/joe-12-0542\u003c/span\u003e\u003cspan address=\"10.1530/joe-12-0542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeimberg, M., Olubadewo, J. O. \u0026amp; Wilcox, H. G. Plasma lipoproteins and regulation of hepatic metabolism of fatty acids in altered thyroid states. Endocrine reviews 6, 590\u0026ndash;607, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/edrv-6-4-590\u003c/span\u003e\u003cspan address=\"10.1210/edrv-6-4-590\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1985).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Tienhoven-Wind, L. \u0026amp; Dullaart, R. P. Low normal thyroid function as a determinant of increased large very low density lipoprotein particles. Clinical biochemistry 48, 489\u0026ndash;494, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clinbiochem.2015.01.015\u003c/span\u003e\u003cspan address=\"10.1016/j.clinbiochem.2015.01.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Pergola, G., Ciampolillo, A., Paolotti, S., Trerotoli, P. \u0026amp; Giorgino, R. Free triiodothyronine and thyroid stimulating hormone are directly associated with waist circumference, independently of insulin resistance, metabolic parameters and blood pressure in overweight and obese women. Clinical endocrinology 67, 265\u0026ndash;269, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2265.2007.02874.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2265.2007.02874.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReinehr, T. Obesity and thyroid function. Molecular and cellular endocrinology 316, 165\u0026ndash;171, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mce.2009.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.mce.2009.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoef, G. \u003cem\u003eet al.\u003c/em\u003e Body composition and metabolic parameters are associated with variation in thyroid hormone levels among euthyroid young men. European journal of endocrinology 167, 719\u0026ndash;726, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/eje-12-0447\u003c/span\u003e\u003cspan address=\"10.1530/eje-12-0447\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan den Berg, E. H. \u003cem\u003eet al.\u003c/em\u003e Higher free triiodothyronine is associated with non-alcoholic fatty liver disease in euthyroid subjects: the Lifelines Cohort Study. Metabolism: clinical and experimental 67, 62\u0026ndash;71, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.metabol.2016.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2016.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, G. \u003cem\u003eet al.\u003c/em\u003e Free triiodothyronine levels are positively associated with non-alcoholic fatty liver disease in euthyroid middle-aged subjects. Endocrine research 40, 188\u0026ndash;193, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/07435800.2014.987399\u003c/span\u003e\u003cspan address=\"10.3109/07435800.2014.987399\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarrell, G. C., Chitturi, S., Lau, G. K. \u0026amp; Sollano, J. D. Guidelines for the assessment and management of non-alcoholic fatty liver disease in the Asia-Pacific region: executive summary. Journal of gastroenterology and hepatology 22, 775\u0026ndash;777, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1440-1746.2007.05002.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1440-1746.2007.05002.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYajima, Y. \u003cem\u003eet al.\u003c/em\u003e Ultrasonographical diagnosis of fatty liver: significance of the liver-kidney contrast. The Tohoku journal of experimental medicine 139, 43\u0026ndash;50, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1620/tjem.139.43\u003c/span\u003e\u003cspan address=\"10.1620/tjem.139.43\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1983).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan, W. H. \u0026amp; Yeh, W. T. How to define obesity? Evidence-based multiple action points for public awareness, screening, and treatment: an extension of Asian-Pacific recommendations. Asia Pacific journal of clinical nutrition 17, 370\u0026ndash;374 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliamson, D. A., Bray, G. A. \u0026amp; Ryan, D. H. Is 5% weight loss a satisfactory criterion to define clinically significant weight loss? Obesity (Silver Spring, Md.) 23, 2319\u0026ndash;2320, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/oby.21358\u003c/span\u003e\u003cspan address=\"10.1002/oby.21358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKushner, R. F. \u0026amp; Ryan, D. H. Assessment and lifestyle management of patients with obesity: clinical recommendations from systematic reviews. Jama 312, 943\u0026ndash;952, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2014.10432\u003c/span\u003e\u003cspan address=\"10.1001/jama.2014.10432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, P. \u003cem\u003eet al.\u003c/em\u003e Free triiodothyronine is associated with the occurrence and remission of nonalcoholic fatty liver disease in euthyroid women. European journal of clinical investigation 49, e13070, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/eci.13070\u003c/span\u003e\u003cspan address=\"10.1111/eci.13070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIttermann, T. \u003cem\u003eet al.\u003c/em\u003e Inverse association between serum free thyroxine levels and hepatic steatosis: results from the Study of Health in Pomerania. Thyroid: official journal of the American Thyroid Association 22, 568\u0026ndash;574, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/thy.2011.0279\u003c/span\u003e\u003cspan address=\"10.1089/thy.2011.0279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao, Y., Gu, H., Wu, J. \u0026amp; Sui, J. Thyroid function is associated with non-alcoholic fatty liver disease in euthyroid subjects. Endocrine research 40, 74\u0026ndash;78, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/07435800.2014.952014\u003c/span\u003e\u003cspan address=\"10.3109/07435800.2014.952014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDall'Asta, C. \u003cem\u003eet al.\u003c/em\u003e Weight loss through gastric banding: effects on TSH and thyroid hormones in obese subjects with normal thyroid function. Obesity (Silver Spring, Md.) 18, 854\u0026ndash;857, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/oby.2009.320\u003c/span\u003e\u003cspan address=\"10.1038/oby.2009.320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNozaki, S. \u003cem\u003eet al.\u003c/em\u003e Stimulation of the activity and mRNA level of hepatic triacylglycerol lipase by triiodothyronine in HepG2 cells. Biochimica et biophysica acta 1127, 298\u0026ndash;302, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0005-2760(92)90235-n\u003c/span\u003e\u003cspan address=\"10.1016/0005-2760(92)90235-n\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1992).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. \u003cem\u003eet al.\u003c/em\u003e Peroxisomal proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1 alpha) enhances the thyroid hormone induction of carnitine palmitoyltransferase I (CPT-I alpha). The Journal of biological chemistry 279, 53963\u0026ndash;53971, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M406028200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M406028200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia, S. F. \u003cem\u003eet al.\u003c/em\u003e Myricetin alleviated hepatic steatosis by acting on microRNA-146b/thyroid hormone receptor b pathway in high-fat diet fed C57BL/6J mice. Food \u0026amp; function 10, 1465\u0026ndash;1477, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1039/c8fo01452c\u003c/span\u003e\u003cspan address=\"10.1039/c8fo01452c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlonso-Merino, E. \u003cem\u003eet al.\u003c/em\u003e Thyroid hormones inhibit TGF-β signaling and attenuate fibrotic responses. Proceedings of the National Academy of Sciences of the United States of America 113, E3451-3460, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1506113113\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1506113113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitter, M. J., Amano, I. \u0026amp; Hollenberg, A. N. Thyroid Hormone Signaling and the Liver. Hepatology (Baltimore, Md.) 72, 742\u0026ndash;752, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.31296\u003c/span\u003e\u003cspan address=\"10.1002/hep.31296\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendoza, A. \u0026amp; Hollenberg, A. N. New insights into thyroid hormone action. Pharmacology \u0026amp; therapeutics 173, 135\u0026ndash;145, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pharmthera.2017.02.012\u003c/span\u003e\u003cspan address=\"10.1016/j.pharmthera.2017.02.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshizuka, T. \u0026amp; Lazar, M. A. The nuclear receptor corepressor deacetylase activating domain is essential for repression by thyroid hormone receptor. Molecular endocrinology (Baltimore, Md.) 19, 1443\u0026ndash;1451, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/me.2005-0009\u003c/span\u003e\u003cspan address=\"10.1210/me.2005-0009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNannipieri, M. \u003cem\u003eet al.\u003c/em\u003e Expression of thyrotropin and thyroid hormone receptors in adipose tissue of patients with morbid obesity and/or type 2 diabetes: effects of weight loss. International journal of obesity (2005) 33, 1001\u0026ndash;1006, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ijo.2009.140\u003c/span\u003e\u003cspan address=\"10.1038/ijo.2009.140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegi, C. K., Babica, P., Bajard, L., Bienertova-Vasku, J. \u0026amp; Tarantino, G. Insights into the molecular targets and emerging pharmacotherapeutic interventions for nonalcoholic fatty liver disease. Metabolism: clinical and experimental 126, 154925, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.metabol.2021.154925\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2021.154925\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrison, S. A. \u003cem\u003eet al.\u003c/em\u003e Effects of Resmetirom on Noninvasive Endpoints in a 36-Week Phase 2 Active Treatment Extension Study in Patients With NASH. Hepatology communications 5, 573\u0026ndash;588, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep4.1657\u003c/span\u003e\u003cspan address=\"10.1002/hep4.1657\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrison, S. A. \u003cem\u003eet al.\u003c/em\u003e Resmetirom (MGL-3196) for the treatment of non-alcoholic steatohepatitis: a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial. Lancet (London, England) 394, 2012\u0026ndash;2024, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(19)32517-6\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(19)32517-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMottin, C. C. \u003cem\u003eet al.\u003c/em\u003e The role of ultrasound in the diagnosis of hepatic steatosis in morbidly obese patients. Obesity surgery 14, 635\u0026ndash;637, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1381/096089204323093408\u003c/span\u003e\u003cspan address=\"10.1381/096089204323093408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, S. H. \u003cem\u003eet al.\u003c/em\u003e Macrovesicular hepatic steatosis in living liver donors: use of CT for quantitative and qualitative assessment. Radiology 239, 105\u0026ndash;112, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2391050361\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2391050361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcPherson, S. \u003cem\u003eet al.\u003c/em\u003e Magnetic resonance imaging and spectroscopy accurately estimate the severity of steatosis provided the stage of fibrosis is considered. Journal of hepatology 51, 389\u0026ndash;397, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2009.04.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2009.04.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3790646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3790646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe positive relationship between triiodothyronine (T3) and fatty liver demonstrated only in cross-sectional study. In this longitudinal cohort study, we aimed to evaluated whether total T3 (TT3) is associated with the development/resolution of fatty liver. We included 1665 South Korean euthyroid adults with \u0026ge;\u0026thinsp;4 thyroid function tests. We explored the impact of TT3 average on development/resolution of either fatty liver (diagnosed by ultrasound) or modified metabolic dysfunction-associated fatty liver (MAFLD) using Cox proportional hazards regression models. During median 5 years follow-up, 891 (66.9%) participants among participants without fatty liver at baseline developed fatty liver, and 265 (79.6%) participants among participants with fatty liver at baseline resolved fatty liver. Compared with low TT3 average group, high TT3 average group was positively associated with development of fatty liver [adjusted HR 1.17 (1.03\u0026ndash;1.34); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016] and inversely associated with resolution of fatty liver [adjusted HR 0.64 (0.50\u0026ndash;0.82); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. The statistical significance was remained for development [adjusted HR 1.24 (1.07\u0026ndash;1.44); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004] and resolution [adjusted HR 0.72 (0.54\u0026ndash;0.95); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024] of modified MAFLD. Our finding provides longitudinal evidence that TT3 level was associated with development and resolution of either fatty liver or modified MAFLD.\u003c/p\u003e","manuscriptTitle":"Triiodothyronine is associated with incidence and resolution of fatty liver disease: a longitudinal study in euthyroid Korean adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 18:36:37","doi":"10.21203/rs.3.rs-3790646/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":"c842f54a-4732-4a80-b0bf-c075000b5a00","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27811579,"name":"Health sciences/Endocrinology"},{"id":27811580,"name":"Health sciences/Gastroenterology"},{"id":27811581,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-05-10T04:15:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 18:36:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3790646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3790646","identity":"rs-3790646","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}