Association Between Thyroid Hormone Sensitivity Indices and Metabolic- Associated Fatty Liver disease: Development and Validation of Risk Prediction Model in Retrospective Cohort Study

Association Between Thyroid Hormone Sensitivity Indices and Metabolic- Associated Fatty Liver disease: Development and Validation of Risk Prediction Model in Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Thyroid Hormone Sensitivity Indices and Metabolic- Associated Fatty Liver disease: Development and Validation of Risk Prediction Model in Retrospective Cohort Study Mujie Gao, Xinzhu Zhang, Hanjing Zhang, Rong Guo, Hailing Di, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7985491/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose This study aimed to assess the association between thyroid hormone sensitivity indices and metabolic-associated fatty liver disease (MAFLD) and to develop a predictive model for MAFLD based on these indices. Methods A total of 508 participants with normal thyroid function were retrospectively included, and randomly divided into a training group (n = 356) and a testing group (n = 152). The thyroid hormone sensitivity indices, including the Thyroid Feedback Quantile-based Index by FT 3 (TFQI FT3 ), FT 4 (TFQI FT4 ), and the free triiodothyronine to free thyroxine (FT 3 / FT 4 ) ratio, were calculated. Key variables were screened using LASSO regression, independent risk factors for MAFLD were found using multivariate logistic regression, and a visual nomogram prediction model was built. The receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to validate the model's performance. Results Among 508 euthyroid individuals, 136 (26.77%) met diagnostic criteria for MAFLD. Multiple logistic regression analysis in the training cohort identified TSH, FT 3 /FT 4 , TFQI FT3 , TFQI FT4 , sex, body mass index, alanine aminotransferase, triglycerides and fasting blood glucose as predictors of MAFLD ( P < 0.05). The central sensitivity index TFQI FT3 was positively correlated with MAFLD risk (OR = 4.653, 95% CI: 1.071–21.881), while TFQI FT4 was negatively correlated (OR = 0.059, 95% CI: 0.010–0.313). Based on the above results, a predictive nomogram for MAFLD risk was constructed. The nomogram model exhibited strong performance in both the training set (AUC = 0.830) and the testing set (AUC = 0.808), demonstrating excellent calibration curve fitting and significant clinical net benefit, as indicated by the DCA. Conclusions The thyroid hormone central sensitivity index (TFQI FT3, TFQI FT4 ) has independent predictive value for MAFLD risk. This study constructs an early screening and prediction model for MAFLD, providing new insights and directions for early diagnosis and treatment. Clinical trial number: Not applicable. Trial registration: Retrospectively registered. thyroid hormone sensitivity index metabolic-associated fatty liver disease nomogram prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background The prevalence of metabolic-associated fatty liver disease (MAFLD) has gradually increased with the improvement of living standards[ 1 ]. Notably, an international panel of experts proposed replacing nonalcoholic fatty liver disease (NAFLD) with MAFLD in 2020 to reflect its pathophysiological mechanisms more accurately[ 2 ]. The Third National Health and Nutrition Examination Survey in the United States indicated that the prevalence of MAFLD was approximately 19.3%[ 3 ]. After removing relevant influencing factors, another cross-sectional study in the U.S. found that the prevalence of MAFLD in adults reached 59%[ 4 ]. Additionally, a retrospective survey in China revealed a 28.33% prevalence of MAFLD in adults[ 5 ]. Thus, MAFLD has become a major global health problem as the prevalence of obesity continues to rise. Notably, MAFLD may progress to nonalcoholic steatohepatitis (NASH), liver fibrosis, and even hepatocellular carcinoma, severely impacting patients' quality of life and life expectancy. Data demonstrates that 58.9% of patients with MAFLD have NASH, while the prevalence of liver fibrosis is as high as 37.2% to 40%[ 6 ]. Importantly, MAFLD is a manifestation of metabolic syndrome in the liver. The accumulation of lipids in the liver is associated with metabolic and cardiovascular diseases such as hyperlipidemia, type 2 diabetes, hyperuricemia, and hypertension[ 7 ]. Moreover, the all-cause mortality rate in patients with MAFLD is significantly higher than that in those without the condition 2 . However, current strategies for preventing and treating MAFLD primarily focus on managing associated risk factors. Thus, it is urgent to conduct in-depth research on the pathogenesis and risk factors of MAFLD and explore new directions and targets for its prevention and treatment. Thyroid hormones act on multiple organs and targets throughout the body through various pathways. Notably, they play a role in maintaining glucose homeostasis by influencing pancreatic β-cell development and glucose metabolism[ 8 ]. Reduced thyroid hormone levels can contribute to insulin resistance[ 9 ], thereby increasing the risk of developing type 2 diabetes[ 10 ]. Additionally, thyroid hormones regulate the balance of energy metabolism in adipocytes, intervene in lipid synthesis and oxidation decomposition in hepatocytes, and thus maintain hepatic lipid homeostasis[ 11 ]. Hypothyroidism may accelerate hepatic steatosis and increase the risk of MAFLD[ 12 – 14 ]. However, it has also been reported that high FT 3 and low thyroid-stimulating hormone (TSH) levels were positively associated with the risk of developing NAFLD[ 15 ]. The discrepancies in the above findings may be due to the fact that thyroid function indices do not fully reflect the role of thyroid hormones in the development of MAFLD. Thyroid hormone sensitivity includes central sensitivity indices [thyroid-stimulating hormone thyroxine resistance index (TT 4 RI), thyroid-stimulating hormone index (TSHI), and thyroid feedback quantile-based index (TFQI)] and peripheral sensitivity indices, represented by the FT 3 / FT 4 ratio. Several studies have shown that reduced thyroid hormone sensitivity may be an independent risk factor for metabolic diseases[ 16 – 18 ]. However, the relationship between MAFLD and the thyroid hormone sensitivity index remains controversial and requires further investigation. To date, various predictive models for MAFLD progression have been developed based on patients from different regions and populations[ 19 , 20 ]. Nevertheless, few studies have introduced thyroid hormone sensitivity as an independent variable to assess the risk of developing MAFLD. Therefore, this study analyzed the association between thyroid hormone sensitivity indices and MAFLD. We aimed to thoroughly investigate the impact of thyroid hormone sensitivity on the risk of developing MAFLD. Additionally, a predictive model for MAFLD with thyroid hormone sensitivity was constructed to provide new insights and guidance for the early screening and intervention of MAFLD. 2. Materials and Methods 2.1 Study Participants A total of 508 subjects (243 males and 265 females) who underwent thyroid function tests at the Health Examination Center of the Third Hospital of Hebei Medical University from September 2022 to September 2024 were included in this study. To be eligible for this study, participants had to meet the following criteria: (1) age ≥ 18 years old; (2) normal thyroid function. Participants were excluded if they met any of the following criteria: (1) a history of thyroid dysfunction or thyroid surgery; (2) specific liver diseases other than MAFLD, such as alcoholic liver disease, viral hepatitis, or the use of medications like glucocorticoids and methotrexate, which may contribute to fatty liver and liver fibrosis[ 21 ]; (3) pregnancy or lactation women; (4) severe liver or kidney dysfunction [alanine aminotransferase (ALT) or aspartate aminotransferase (AST) > 3×ULN; Child-Pugh score ≥ B; serum creatinine (Scr) > 3×ULN or chronic kidney disease with uremia]; (5) malignant tumors. All subjects signed informed consent. The study complied with the ethical guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of the Third Hospital of Hebei Medical University (W2023-004-1). The diagnosis of MAFLD was based on the international expert consensus released in 2020, which defined MAFLD as the presence of hepatic fat accumulation (≥ 5% hepatic fat content confirmed by imaging or histology) in conjunction with overweight/obesity, type 2 diabetes, or at least two metabolic dysfunctions (e.g., hypertension, hyperlipidemia, or insulin resistance, etc.) [ 22 ]. 2.2 Data Collection and Measurements 2.2.1 General Information General participant information was collected, including name, gender, age, medical history and medication history. Standardized methods were used to measure height, weight, waist circumference, and blood pressure. The body mass index (BMI) was calculated: BMI = weight / height² (kg/m²). 2.2.2 Thyroid Function Testing and Index Calculation All participants were required to fast for at least 8 hours, and blood samples were collected from the antecubital vein the following morning. Thyroid function indicators: free triiodothyronine (FT 3 ), free thyroxine (FT 4 ), TSH, and thyroid autoimmune antibody: thyroid peroxidase antibody (TPOAb), thyroglobulin antibody (TGAb), were measured using the Maglumi 4000 Plus automated chemiluminescent immunoassay system (Shenzhen new industry company, China). TTFQI is calculated by the cumulative distribution function (CDF) formulas following: TFQI FT3/4 =cdfFT 3 / 4 -(1-cdf TSH), which ranges from − 1 to 1[ 23 ]. A negative TFQI indicates high sensitivity of the hypothalamus-pituitary-thyroid axis to thyroid hormones, while a positive value suggests low sensitivity, and a value of 0 indicates normal sensitivity. The FT 3 /FT 4 ratio was used to assess peripheral tissue sensitivity to thyroid hormones, with higher values suggesting greater peripheral sensitivity. 2.2.3 Other hematological indicators Biochemical indicators, including fasting blood glucose (FBG), albumin (ALB), ALT, AST, gamma-glutamyl transferase (GGT), homocysteine (HCY), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and serum uric acid (SUA), were measured using the Olympus AU2700 automated biochemical analyzer (Japan). 2.3 Statistical Analysis For statistical analyses, R 4.2.2 was used. The Shapiro-Wilk test was used to assess the normality of continuous variables. Normally distributed data were expressed as mean ± standard deviation ( x̄ ± s ) and the means of two groups were compared using the independent samples t -test. Non-normally distributed data were presented as median ( M, P25–P75 ) and compared using Mann–Whitney U tests. Categorical variables were expressed as counts and percentages and analyzed using chi-square tests. Variables with less than 10% missing values were imputed using median substitution. Lasso regression analysis was used to identify significantly associated variables in MAFLD preliminarily. Subsequently, multivariable logistic regression (enter method) was conducted to determine independent predictors and construct a nomogram for MAFLD risk prediction. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). P < 0.05 was considered statistically significant. 3. Results 3.1 Baseline Characteristics The participants consisted of 853 adults. Based on inclusion and exclusion criteria, 508 subjects with normal thyroid function were enrolled in this study (Fig. 1 ). Participants were randomly assigned to a training set (n = 356) and a validation set (n = 152) in a 7:3 ratio. Baseline characteristics showed no significant differences between the groups (Table 1 ). Table 1 Comparison of Baseline Characteristics Between Training and Test Sets Train(N = 356) Test(N = 152) Overall(N = 508) z/ \(\:{\chi\:}^{2}\) P age 59.00 (51.00, 67.00) 58.00 (46.00, 67.00) 58.00 (48.00, 67.00) -1.113 0.266 FT3 4.95 (4.53, 5.43) 4.87 (4.48, 5.38) 4.89 (4.50, 5.38) -0.766 0.443 FT4 10.56 (9.83, 12.02) 10.61 (9.45, 12.06) 10.61 (9.50, 11.45) -0.820 0.412 TSH 1.92 (1.24, 2.87) 2.01 (1.27, 2.97) 1.98 (1.25, 2.95) -0.440 0.660 Sex Female 179 (50.3%) 86 (56.6%) 265 (52.2%) -1.300 0.194 Male 177 (49.7%) 66 (43.4%) 243 (47.8%) TPOAb TPOAb Normal 314 (88.2%) 135 (88.8%) 449 (88.4%) -0.197 0.843 TPOAb Elevated 42 (11.8%) 17 (11.2%) 59 (11.6%) BMI Underweight 21 (5.9%) 4 (2.6%) 25 (4.9%) -1.057 0.291 Normal 120 (33.7%) 46 (30.3%) 166 (32.7%) Overweight 118 (33.1%) 61 (40.1%) 179 (35.2%) Obese 97 (27.2%) 41 (27.0%) 138 (27.2%) ALB Low 116 (32.6%) 50 (32.9%) 166 (32.7%) -0.191 0.848 Normal 237 (66.6%) 102 (67.1%) 339 (66.7%) High 3 (0.8%) 0 (0%) 3 (0.6%) ALT Low 6 (1.7%) 6 (3.9%) 12 (2.4%) -1.505 0.132 Normal 295 (82.9%) 128 (84.2%) 423 (83.3%) High 55 (15.4%) 18 (11.8%) 73 (14.4%) AST Low 29 (8.1%) 16 (10.5%) 45 (8.9%) -0.027 0.978 Normal 296 (83.1%) 119 (78.3%) 415 (81.7%) High 31 (8.7%) 17 (11.2%) 48 (9.4%) ALP Low 39 (11.0%) 12 (7.9%) 51 (10.0%) -0.232 0.817 Normal 286 (80.3%) 133 (87.5%) 419 (82.5%) High 31 (8.7%) 7 (4.6%) 38 (7.5%) γ-GGT Low 1 (0.3%) 0 (0%) 1 (0.2%) -0.236 0.813 Normal 283 (79.5%) 123 (80.9%) 406 (79.9%) High 72 (20.2%) 29 (19.1%) 101 (19.9%) Homocysteine Normal 280 (78.7%) 117 (77.0%) 397 (78.1%) -0.419 0.675 High 76 (21.3%) 35 (23.0%) 111 (21.9%) TC Normal 242 (68.0%) 104 (68.4%) 346 (68.1%) -0.098 0.922 High 114 (32.0%) 48 (31.6%) 162 (31.9%) TG Normal 242 (68.0%) 97 (63.8%) 339 (66.7%) -0.911 0.362 High 114 (32.0%) 55 (36.2%) 169 (33.3%) HDL-C Normal 224 (62.9%) 104 (68.4%) 328 (64.6%) -1.186 0.236 High 132 (37.1%) 48 (31.6%) 180 (35.4%) LDL-C Normal 271 (76.1%) 115 (75.7%) 386 (76.0%) -0.112 0.911 High 85 (23.9%) 37 (24.3%) 122 (24.0%) Apolipoprotein A Low 142 (39.9%) 59 (38.8%) 201 (39.6%) -0.202 0.840 Normal 154 (43.3%) 67 (44.1%) 221 (43.5%) High 60 (16.9%) 26 (17.1%) 86 (16.9%) Apolipoprotein B Low 117 (32.9%) 48 (31.6%) 165 (32.5%) -0.141 0.888 Normal 158 (44.4%) 70 (46.1%) 228 (44.9%) High 81 (22.8%) 34 (22.4%) 115 (22.6%) Lipoprotein(a) Normal 282 (79.2%) 116 (76.3%) 398 (78.3%) -0.725 0.468 High 74 (20.8%) 36 (23.7%) 110 (21.7%) FBG Low 6 (1.7%) 2 (1.3%) 8 (1.6%) -1.501 0.133 Normal 216 (60.7%) 82 (53.9%) 298 (58.7%) High 134 (37.6%) 68 (44.7%) 202 (39.8%) SUA Low 37 (10.4%) 12 (7.9%) 49 (9.6%) -0.556 0.578 Normal 266 (74.7%) 117 (77.0%) 383 (75.4%) High 53 (14.9%) 23 (15.1%) 76 (15.0%) 3.2 Risk Factor Analysis To minimize the influence of confounding variables and multicollinearity, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection. Using the “glmnet” package in R version 4.2.2, 10-fold cross-validation was performed to identify the optimal lambda value (lambda.min), and variables with non-zero coefficients were selected. The final screened variables included TSH, FT 3 /FT 4 , TFQI FT3 , TFQI FT4 , Sex, TPOAb, BMI, ALB, ALT, HDL, TC, TG, FBG, and SUA (Fig. 2 ). The above variables were then incorporated into a multivariable logistic regression model. The analysis identified that TSH, FT 3 /FT 4 , TFQI FT3 , TFQI FT4 , Sex, BMI, ALT, TG, and FBG were the independent risk factors for MAFLD in euthyroid individuals ( P < 0.05, Table 2 ). It was shown that TFQI FT3 was significantly positively correlated with the risk of MAFLD prevalence and TFQI FT4 was negatively associated. Table 2 Analysis of Risk Factors for MAFLD Using Multivariable Logistic Regression Model OR CI5 CI95 P (Intercept) 0.001 1.55*10^ (-5) 0.022 < 0.0001 TSH 1.371 1.097 1.757 0.007 FT 3 /FT 4 0.002 7.21E-06 0.224 0.024 TFQI FT3 4.653 1.071052 21.881 0.049 TFQI FT4 0.059 0.010022 0.313 0.001 sex 1.991 1.092993 3.676 0.026 TPOAb 0.660 0.255 1.601 0.371 BMI 2.773 1.956 4.028 < 0.0001 ALB 1.578 0.840 3.014 0.160 ALT 2.437 1.181 5.094 0.017 HDL 0.719 0.347 1.446 0.363 TC 0.653 0.348 1.200 0.175 TG 2.692 1.502 4.867 0.001 FBG 2.128 1.200 3.804 0.010 SUA 1.278 0.693 2.368 0.433 3.3 Construction of a new predictive model for predicting MAFLD Based on the results of multivariable logistic regression, a predictive nomogram for MAFLD risk was constructed using the following significant predictors: TSH, FT 3 /FT 4, TFQI FT3 , TFQI FT4 , Sex, BMI, ALT, TG and FBG ( P < 0.05). In the nomogram, each variable corresponded to a point scale; the total score was calculated by summing individual scores, which was then projected onto a probability axis to estimate the predicted risk of MAFLD. According to the total score, the risk was stratified as follows: low risk (< 149 points, probability ≤ 0.1), medium risk (149–165 points, probability 0.1–0.5), and high risk (≥ 166 points, probability ≥ 0.5). The model enabled visual risk assessment of MAFLD based on individual scores (Fig. 3 ). 3.4 Model evaluation and clinical utility The ROC curve analysis showed that the area under the curve (AUC) of the model was 0.830 in the training set and 0.808 in the validation set, suggesting good predictive performance (Fig. 4 ). The calibration curves also demonstrated a high consistency between predicted probabilities and observed outcomes (Fig. 5 ). Furthermore, decision curve analysis (Fig. 6 ) confirmed that the model achieved significant net clinical benefit (net benefit > 0) across a wide range of threshold probabilities, supporting its application in the early screening of MAFLD. 4. Discussion With the development of socioeconomic conditions and changes in lifestyle, the prevalence of MAFLD has shown a significant upward trend and has become one of the most common chronic liver diseases globally[ 1 ]. MAFLD patients frequently present with no obvious clinical symptoms or signs in the early stages, posing a challenge for timely identification and thereby increasing the risk of progression to cirrhosis or hepatocellular carcinoma. This study analyzed the relationship between thyroid hormone sensitivity and MAFLD. The results suggested that TFQI could serve as a novel predictor of MAFLD risk in individuals with normal thyroid function. Moreover, constructing a clinical prediction model provided a new direction for early risk stratification and personalized intervention of MAFLD. Thyroid hormone is a key regulator of metabolic homeostasis. It maintains lipid balance by regulating cholesterol synthesis, fatty acid metabolism, and lipoprotein transport. It also plays a key role in hepatic energy metabolism by regulating mitochondrial function and autophagy processes[ 24 , 25 ]. In the patients with hypothyroidism, reduced hepatic thyroid hormone signaling impaired lipid utilization, thereby accelerating the progression of MAFLD[ 26 ]. Due to the complex interplay among various thyroid hormones, relying on a single indicator to interpret the relationship between thyroid function and MAFLD may be insufficient. Some investigators have proposed using thyroid hormone sensitivity indices to evaluate the combined effects of thyroid hormones. Thyroid hormones regulate mitochondrial function and lipid metabolism gene expression through nuclear receptors (TRα/TRβ)[ 27 ]. Decreased sensitivity may weaken the liver's lipid oxidation capacity and promote fat deposition[ 27 ]. Thyroid hormone sensitivity could be classified into central and peripheral sensitivity. The former affects the regulation of the hypothalamic-pituitary-thyroid axis feedback loop. And the latter reflects the activity of deiodinases in peripheral tissues and reduces the effect of thyroid hormones in peripheral tissues. Laclaustra and the colleagues[ 23 ] first proposed TFQI, combining other central sensitivity indices reflecting thyroid hormone levels, and it is more stable than TT 4 RI and TSHI. Studies in Iranian found that TFQI was significantly associated with diabetes mellitus and hypertension in individuals with normal thyroid function[ 28 ]. Another epidemiological survey showed that the TFQI index was associated with risk factors for hyperuricemia, obesity, and cardiovascular diseases[ 29 ]. Some recent studies have explored the relationship between central thyroid hormone sensitivity and MAFLD. A cross-sectional study involving 129 participants with NAFLD revealed that elevated TFQI FT4 level was associated with a higher prevalence of liver fibrosis, however, TFQI FT3 was not calculated in that study[ 30 ]. Another cross-sectional analysis showed that high TFQI FT3 level significantly increased the risk of dyslipidemia and MAFLD among euthyroid patients[ 17 ]. A retrospective community analysis also confirmed that impaired central thyroid hormone sensitivity was associated with MAFLD and its progression to liver fibrosis[ 31 ]. Consistent with those findings, our study showed that TFQI FT3 was positively associated with the risk of MAFLD, supporting its role as an independent risk factor. Therefore, TFQI FT3 may be a promising predictive marker for MAFLD. The bioactivity of thyroid hormones is regulated by iodine tyrosine deiodinase types I, II, and III (D1, D2, D3), which form a group of membrane proteins characterized by disulfide oxidoreductase folds. These three deiodinases could activate or inactivate thyroid hormones, with D2 catalyzing the conversion of T 4 to the biologically active T 3 [ 32 ]. A cross-sectional study previously showed that increased fat accumulation triggered a compensatory mechanism, and the increase in deiodinase activity leaded to a high conversion of T 4 to T 3 [ 33 ]. Another study demonstrated that the mice with D2 gene knockout were more prone to insulin resistance and obesity[ 34 ]. If D2 activity remains intact, FT 4 may be efficiently converted into active T 3 , therefore effectively maintaining metabolic functions. Thus, we hypothesize that a compensatory mechanism may exist, in which sufficient production of FT 3 form FT 4 promotes fatty acid oxidation and cholesterol metabolism, ultimately reducing hepatic fat deposition and lowering the risk of MAFLD. Elevated TFQI FT4 reflects central resistance to FT 4 . Our findings revealed a significant negative association between TFQI FT4 and the risk of MAFLD. The underlying mechanisms remain under investigation. In future investigations, we plan to incorporate larger clinical datasets to strengthen the robustness of our findings further explore the relationship between TFQI FT4 and other components of metabolic syndrome. As the peripheral sensitivity index of thyroid hormone, a decreased ratio of FT 3 /FT 4 indicates a reduced sensitivity of peripheral organs to thyroid hormones. A retrospective study involving 18,766 adults with normal thyroid function showed that reduced sensitivity to thyroid hormones was associated with high RC levels[ 35 ]. Another study conducted in Turkey on obese adolescents revealed that the FT 3 /FT 4 ratio was higher in the patients with hepatic steatosis than those in control group[ 33 ]. Our research shows that FT 3 /FT 4 is negatively correlated with the risk of MAFLD, and the reduced peripheral sensitivity of thyroid hormones is associated with the risk of MAFLD. Given the genetic and racial differences within the studied population, the observed differences in the results may be attributed to population heterogeneity. Additionally, there may be limitations related to the sample size. Therefore, future studies should verify those findings by expanding the sample size or conducting stratified analyses. TSH has been demonstrated by numerous studies to be positively correlated with the risk of MAFLD[ 14 , 36 ]. Previous studies have shown that in individuals with normal thyroid function, relatively high levels of TSH are positively correlated with unfavorable lipid concentrations [ 37 ]. Our findings were consistent with the results above, that TSH was positively correlated with the risk of MAFLD. Previous research data showed that the risk of MAFLD in women is 19% lower than in men in the general population[ 38 ]. Another study on MAFLD in Asian children showed that the prevalence continued to rise in obese children and boys over 10 years old[ 39 ]. The difference in obesity, unbalanced diet, and lack of physical activity between men and may contribute to the occurrence and progression of MAFLD[ 40 ]. Moreover, elevated testosterone levels increase the risk of MAFLD[ 41 ], and the protective effect of estrogen on liver lipid metabolism may be a necessary reason for the lower prevalence in women[ 42 ]. The findings of this study, which indicated a higher risk of MAFLD in men, aligned with several previous studies, further confirming the gender differences in MAFLD populations. Obesity, dyslipidemia, and hyperglycemia constitute central components of metabolic syndrome. Obesity directly reflects an increase in body fat proportion, and the reduced clearance rate of TG further aggravates lipid metabolism disorder[ 43 ]. And progressive insulin resistance in MAFLD patients further subsequently elevates blood glucose levels. Consequently, abnormal fat accumulation may lead to lipid metabolism disorder, reducing insulin sensitivity and causing glycolipid metabolism disorder, increasing the risk of MAFLD[ 44 ]. This study drew a visualized line graph to display the risk model of MAFLD based on the above risk factors. It is highly operable and convenient for clinical application, and the risk of MAFLD may be evaluated by calculating specific scores. Therefore, this innovative non-invasive prediction tool has crucial clinical value for early screening of MAFLD and delay of disease progression. This study has several limitations. firstly, it didn’t establish causal relationships as a cross-sectional study. A prospective cohort study is needed to further verify the causal associations between various risk factors and the onset and progression of MAFLD. Additionally, this single-center retrospective study with a relatively small sample size may introduce selection bias. It is important to clarify that the diagnosis of hepatic steatosis was based on liver ultrasound rather than liver biopsy, which may result in inaccurate staging of MAFLD. Therefore, future studies should integrate additional clinical parameters and molecular biomarkers to optimize the model and improve its accuracy and reliability. 5. Conclusion In conclusion, we found that central thyroid hormone sensitivity indices (TFQI FT3 , TFQI FT4 ) were independently associated with MAFLD risk in euthyroid individuals, with TFQI FT3 showing a positive and TFQI FT4 a negative correlation. The nomogram model we constructed exhibited good predictive performance (AUC = 0.830) in training set, 0.808 in testing set), providing new insights of the impact of thyroid hormone sensitivity on the pathogenesis of MAFLD. Our study lays the foundation for integrating thyroid sensitivity markers into the early screening strategy of MAFLD. Further studies with larger, multi-center cohorts and mechanistic explorations are needed to validate these associations and optimize targeted intervention approaches. Declarations Ethics approval and consent to participate This study complied with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Third Hospital of Hebei Medical University (Approval No. W2023-004-1). Written informed consent, including agreement for data publication, was obtained from all participants or their legal agents. All participants were adults (age ≥ 18 years), and no minors or other vulnerable populations were included in this study. Consent for publication All participants or their legal agents provided consent for the publication of anonymized research data. Competing interests The authors declare that no conflicts of interest relevant to this article exist. Funding This work was supported by Natural Science Foundation of Hebei Province (Grant number: H2020206490); Medical Science Research Subject Plan of Hebei (Grant number: 20230095); Clinical Medicine Postdoctoral Research Support Program of Hebei Medical University (Grant number: PD2023002); Hebei Province Yanzhao Golden Talent Program (Grant number: B2024003014); Hebei Medical University Postdoctoral Startup Fund. Author Contribution Xuan Qiu: Conceptualization, Methodology, Funding acquisition, Supervision, Writing – review and editing; Xiaolin Zhang: Methodology, Validation, Writing – review and editing; Mujie Gao: Project administration, Funding acquisition, Visualization, Writing – original draft; Xinzhu Zhang: Investigation , Visualization, Writing – review and editing; Hanjing Zhang: Data curation; Rong Guo: Data curation; Hailing Di: Data curation; Kuanzhi Liu: Resources. Acknowledgement The authors gratefully acknowledge all participants whose contributions made this investigation possible. Data Availability Data will be made available on request. References Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64(1):73–84. Gofton C, Upendran Y, Zheng M-H, George J. MAFLD: How is it different from NAFLD? 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Sun H, Zhu W, Liu J, An Y, Wang Y, Wang G. Reduced Sensitivity to Thyroid Hormones Is Associated With High Remnant Cholesterol Levels in Chinese Euthyroid Adults. J Clin Endocrinol Metab. 2022;108(1):166–74. Chung GE, Kim D, Kim W, Yim JY, Park MJ, Kim YJ, Yoon JH, Lee HS. Non-alcoholic fatty liver disease across the spectrum of hypothyroidism. J Hepatol. 2012;57(1):150–6. Bulum T, Kolarić B, Duvnjak L. Insulin sensitivity modifies the relationship between thyroid function and lipid profile in euthyroid type 1 diabetic patients. Endocrine. 2012;42(1):139–45. Balakrishnan M, Patel P, Dunn-Valadez S, Dao C, Khan V, Ali H, El-Serag L, Hernaez R, Sisson A, Thrift AP, et al. Women Have a Lower Risk of Nonalcoholic Fatty Liver Disease but a Higher Risk of Progression vs Men: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol. 2021;19(1):61–e7115. Zou ZY, Zeng J, Ren TY, Huang LJ, Wang MY, Shi YW, Yang RX, Zhang QR, Fan JG. The burden and sexual dimorphism with nonalcoholic fatty liver disease in Asian children: A systematic review and meta-analysis. Liver Int. 2022;42(9):1969–80. Della Torre S. Beyond the X Factor: Relevance of Sex Hormones in NAFLD Pathophysiology. Cells 2021, 10(9). Ning L, Sun J. Associations between body circumference and testosterone levels and risk of metabolic dysfunction-associated fatty liver disease: a mendelian randomization study. BMC Public Health. 2023;23(1):602. Yuan S, Chen J, Dan L, Xie Y, Sun Y, Li X, Larsson SC. Homocysteine, folate, and nonalcoholic fatty liver disease: a systematic review with meta-analysis and Mendelian randomization investigation. Am J Clin Nutr. 2022;116(6):1595–609. Rosato V, Masarone M, Dallio M, Federico A, Aglitti A, Persico M. NAFLD and Extra-Hepatic Comorbidities: Current Evidence on a Multi-Organ Metabolic Syndrome. Int J Environ Res Public Health 2019, 16(18). Cho IY, Chang Y, Kang JH, Kim Y, Sung E, Shin H, Wild SH, Byrne CD, Ryu S. Long or Irregular Menstrual Cycles and Risk of Prevalent and Incident Nonalcoholic Fatty Liver Disease. J Clin Endocrinol Metab. 2022;107(6):e2309–17. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers invited by journal 04 Nov, 2025 Editor invited by journal 04 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 30 Oct, 2025 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. 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2","display":"","copyAsset":false,"role":"figure","size":314351,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve of the MAFLD Risk Prediction Model\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7985491/v1/72617475fb8f70a453375aea.jpeg"},{"id":96243977,"identity":"8b07c9a9-92f2-4531-a8d0-9d2c72a6c0bf","added_by":"auto","created_at":"2025-11-19 07:17:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51290,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram model of the MAFLD population\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7985491/v1/dd33c0fd62c7a774185aa385.png"},{"id":96244731,"identity":"ef119cad-56b6-440a-9d0f-4d7fe940eee4","added_by":"auto","created_at":"2025-11-19 07:19:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54584,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve of the Logistic Regression Model for the MAFLD Population\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7985491/v1/37e13835d379064fcf073700.png"},{"id":96243504,"identity":"4259d943-4760-4092-aa1f-674185ab6b9d","added_by":"auto","created_at":"2025-11-19 07:16:31","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":154713,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration Curve of the MAFLD Risk Prediction Model\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7985491/v1/3b336b1b8d7c6e5aa32f561a.jpeg"},{"id":96243146,"identity":"05832080-e515-4f94-a692-07cb0a28266d","added_by":"auto","created_at":"2025-11-19 07:15:43","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":126386,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Curve Analysis (DCA) of the MAFLD Risk Prediction Model\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7985491/v1/f29b242ba839dad20dd34049.jpeg"},{"id":96255331,"identity":"ba061944-1ef3-47a4-8a7f-501e099681fb","added_by":"auto","created_at":"2025-11-19 07:48:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1811739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7985491/v1/2da42e2c-66e8-4beb-b60a-f0f8e9f1b84d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Thyroid Hormone Sensitivity Indices and Metabolic- Associated Fatty Liver disease: Development and Validation of Risk Prediction Model in Retrospective Cohort Study","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThe prevalence of metabolic-associated fatty liver disease (MAFLD) has gradually increased with the improvement of living standards[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Notably, an international panel of experts proposed replacing nonalcoholic fatty liver disease (NAFLD) with MAFLD in 2020 to reflect its pathophysiological mechanisms more accurately[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The Third National Health and Nutrition Examination Survey in the United States indicated that the prevalence of MAFLD was approximately 19.3%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. After removing relevant influencing factors, another cross-sectional study in the U.S. found that the prevalence of MAFLD in adults reached 59%[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additionally, a retrospective survey in China revealed a 28.33% prevalence of MAFLD in adults[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, MAFLD has become a major global health problem as the prevalence of obesity continues to rise. Notably, MAFLD may progress to nonalcoholic steatohepatitis (NASH), liver fibrosis, and even hepatocellular carcinoma, severely impacting patients' quality of life and life expectancy. Data demonstrates that 58.9% of patients with MAFLD have NASH, while the prevalence of liver fibrosis is as high as 37.2% to 40%[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Importantly, MAFLD is a manifestation of metabolic syndrome in the liver. The accumulation of lipids in the liver is associated with metabolic and cardiovascular diseases such as hyperlipidemia, type 2 diabetes, hyperuricemia, and hypertension[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, the all-cause mortality rate in patients with MAFLD is significantly higher than that in those without the condition\u003csup\u003e2\u003c/sup\u003e. However, current strategies for preventing and treating MAFLD primarily focus on managing associated risk factors. Thus, it is urgent to conduct in-depth research on the pathogenesis and risk factors of MAFLD and explore new directions and targets for its prevention and treatment.\u003c/p\u003e\u003cp\u003eThyroid hormones act on multiple organs and targets throughout the body through various pathways. Notably, they play a role in maintaining glucose homeostasis by influencing pancreatic β-cell development and glucose metabolism[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Reduced thyroid hormone levels can contribute to insulin resistance[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], thereby increasing the risk of developing type 2 diabetes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, thyroid hormones regulate the balance of energy metabolism in adipocytes, intervene in lipid synthesis and oxidation decomposition in hepatocytes, and thus maintain hepatic lipid homeostasis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Hypothyroidism may accelerate hepatic steatosis and increase the risk of MAFLD[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, it has also been reported that high FT\u003csub\u003e3\u003c/sub\u003e and low thyroid-stimulating hormone (TSH) levels were positively associated with the risk of developing NAFLD[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The discrepancies in the above findings may be due to the fact that thyroid function indices do not fully reflect the role of thyroid hormones in the development of MAFLD.\u003c/p\u003e\u003cp\u003eThyroid hormone sensitivity includes central sensitivity indices [thyroid-stimulating hormone thyroxine resistance index (TT\u003csub\u003e4\u003c/sub\u003eRI), thyroid-stimulating hormone index (TSHI), and thyroid feedback quantile-based index (TFQI)] and peripheral sensitivity indices, represented by the FT\u003csub\u003e3\u003c/sub\u003e/ FT\u003csub\u003e4\u003c/sub\u003e ratio. Several studies have shown that reduced thyroid hormone sensitivity may be an independent risk factor for metabolic diseases[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the relationship between MAFLD and the thyroid hormone sensitivity index remains controversial and requires further investigation. To date, various predictive models for MAFLD progression have been developed based on patients from different regions and populations[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nevertheless, few studies have introduced thyroid hormone sensitivity as an independent variable to assess the risk of developing MAFLD. Therefore, this study analyzed the association between thyroid hormone sensitivity indices and MAFLD. We aimed to thoroughly investigate the impact of thyroid hormone sensitivity on the risk of developing MAFLD. Additionally, a predictive model for MAFLD with thyroid hormone sensitivity was constructed to provide new insights and guidance for the early screening and intervention of MAFLD.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Participants\u003c/h2\u003e\u003cp\u003eA total of 508 subjects (243 males and 265 females) who underwent thyroid function tests at the Health Examination Center of the Third Hospital of Hebei Medical University from September 2022 to September 2024 were included in this study. To be eligible for this study, participants had to meet the following criteria: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years old; (2) normal thyroid function. Participants were excluded if they met any of the following criteria: (1) a history of thyroid dysfunction or thyroid surgery; (2) specific liver diseases other than MAFLD, such as alcoholic liver disease, viral hepatitis, or the use of medications like glucocorticoids and methotrexate, which may contribute to fatty liver and liver fibrosis[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; (3) pregnancy or lactation women; (4) severe liver or kidney dysfunction [alanine aminotransferase (ALT) or aspartate aminotransferase (AST)\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026times;ULN; Child-Pugh score\u0026thinsp;\u0026ge;\u0026thinsp;B; serum creatinine (Scr)\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026times;ULN or chronic kidney disease with uremia]; (5) malignant tumors. All subjects signed informed consent. The study complied with the ethical guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of the Third Hospital of Hebei Medical University (W2023-004-1).\u003c/p\u003e\u003cp\u003eThe diagnosis of MAFLD was based on the international expert consensus released in 2020, which defined MAFLD as the presence of hepatic fat accumulation (\u0026ge;\u0026thinsp;5% hepatic fat content confirmed by imaging or histology) in conjunction with overweight/obesity, type 2 diabetes, or at least two metabolic dysfunctions (e.g., hypertension, hyperlipidemia, or insulin resistance, etc.) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection and Measurements\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 General Information\u003c/h2\u003e\u003cp\u003eGeneral participant information was collected, including name, gender, age, medical history and medication history. Standardized methods were used to measure height, weight, waist circumference, and blood pressure. The body mass index (BMI) was calculated: BMI\u0026thinsp;=\u0026thinsp;weight / height\u0026sup2; (kg/m\u0026sup2;).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Thyroid Function Testing and Index Calculation\u003c/h2\u003e\u003cp\u003eAll participants were required to fast for at least 8 hours, and blood samples were collected from the antecubital vein the following morning. Thyroid function indicators: free triiodothyronine (FT\u003csub\u003e3\u003c/sub\u003e), free thyroxine (FT\u003csub\u003e4\u003c/sub\u003e), TSH, and thyroid autoimmune antibody: thyroid peroxidase antibody (TPOAb), thyroglobulin antibody (TGAb), were measured using the Maglumi 4000 Plus automated chemiluminescent immunoassay system (Shenzhen new industry company, China).\u003c/p\u003e\u003cp\u003eTTFQI is calculated by the cumulative distribution function (CDF) formulas following: TFQI\u003csub\u003eFT3/4\u003c/sub\u003e=cdfFT\u003csub\u003e3\u003c/sub\u003e/\u003csub\u003e4\u003c/sub\u003e-(1-cdf TSH), which ranges from \u0026minus;\u0026thinsp;1 to 1[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A negative TFQI indicates high sensitivity of the hypothalamus-pituitary-thyroid axis to thyroid hormones, while a positive value suggests low sensitivity, and a value of 0 indicates normal sensitivity. The FT\u003csub\u003e3\u003c/sub\u003e/FT\u003csub\u003e4\u003c/sub\u003e ratio was used to assess peripheral tissue sensitivity to thyroid hormones, with higher values suggesting greater peripheral sensitivity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Other hematological indicators\u003c/h2\u003e\u003cp\u003eBiochemical indicators, including fasting blood glucose (FBG), albumin (ALB), ALT, AST, gamma-glutamyl transferase (GGT), homocysteine (HCY), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and serum uric acid (SUA), were measured using the Olympus AU2700 automated biochemical analyzer (Japan).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eFor statistical analyses, \u003cem\u003eR 4.2.2\u003c/em\u003e was used. The Shapiro-Wilk test was used to assess the normality of continuous variables. Normally distributed data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cem\u003ex̄\u003c/em\u003e \u0026plusmn; \u003cem\u003es\u003c/em\u003e) and the means of two groups were compared using the independent samples \u003cem\u003et\u003c/em\u003e-test. Non-normally distributed data were presented as median (\u003cem\u003eM, P25\u0026ndash;P75\u003c/em\u003e) and compared using Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e tests. Categorical variables were expressed as counts and percentages and analyzed using chi-square tests. Variables with less than 10% missing values were imputed using median substitution. Lasso regression analysis was used to identify significantly associated variables in MAFLD preliminarily. Subsequently, multivariable logistic regression (enter method) was conducted to determine independent predictors and construct a nomogram for MAFLD risk prediction. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\u003cp\u003eThe participants consisted of 853 adults. Based on inclusion and exclusion criteria, 508 subjects with normal thyroid function were enrolled in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants were randomly assigned to a training set (n\u0026thinsp;=\u0026thinsp;356) and a validation set (n\u0026thinsp;=\u0026thinsp;152) in a 7:3 ratio. Baseline characteristics showed no significant differences between the groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Baseline Characteristics Between Training and Test Sets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrain(N\u0026thinsp;=\u0026thinsp;356)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTest(N\u0026thinsp;=\u0026thinsp;152)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOverall(N\u0026thinsp;=\u0026thinsp;508)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.00\u003c/p\u003e\u003cp\u003e(51.00, 67.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.00\u003c/p\u003e\u003cp\u003e(46.00, 67.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.00\u003c/p\u003e\u003cp\u003e(48.00, 67.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.95\u003c/p\u003e\u003cp\u003e(4.53, 5.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.87\u003c/p\u003e\u003cp\u003e(4.48, 5.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.89\u003c/p\u003e\u003cp\u003e(4.50, 5.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.56\u003c/p\u003e\u003cp\u003e(9.83, 12.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.61\u003c/p\u003e\u003cp\u003e(9.45, 12.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.61\u003c/p\u003e\u003cp\u003e(9.50, 11.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003cp\u003e(1.24, 2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003cp\u003e(1.27, 2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003cp\u003e(1.25, 2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.660\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179 (50.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (56.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e265 (52.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177 (49.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (43.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e243 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPOAb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPOAb Normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e314 (88.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135 (88.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e449 (88.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.843\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPOAb Elevated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120 (33.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (30.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e166 (32.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118 (33.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (40.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179 (35.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (32.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e166 (32.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e237 (66.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (67.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e339 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e295 (82.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (84.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e423 (83.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e296 (83.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (78.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e415 (81.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (11.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286 (80.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133 (87.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e419 (82.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eγ-GGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e283 (79.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (80.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e406 (79.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (19.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomocysteine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e280 (78.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (77.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e397 (78.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (23.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e242 (68.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (68.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e346 (68.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (32.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e162 (31.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e242 (68.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 (63.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e339 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (32.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e169 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e224 (62.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (68.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e328 (64.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e180 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e271 (76.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (75.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e386 (76.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122 (24.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApolipoprotein A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142 (39.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (38.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e201 (39.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154 (43.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (44.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e221 (43.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApolipoprotein B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117 (32.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e165 (32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (44.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (46.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e228 (44.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipoprotein(a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e282 (79.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (76.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e398 (78.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (21.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e216 (60.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (53.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e298 (58.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 (44.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e202 (39.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e266 (74.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (77.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e383 (75.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (14.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76 (15.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Risk Factor Analysis\u003c/h2\u003e\u003cp\u003eTo minimize the influence of confounding variables and multicollinearity, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection. Using the \u0026ldquo;glmnet\u0026rdquo; package in \u003cem\u003eR\u003c/em\u003e version 4.2.2, 10-fold cross-validation was performed to identify the optimal lambda value (lambda.min), and variables with non-zero coefficients were selected. The final screened variables included TSH, FT\u003csub\u003e3\u003c/sub\u003e/FT\u003csub\u003e4\u003c/sub\u003e, TFQI\u003csub\u003eFT3\u003c/sub\u003e, TFQI\u003csub\u003eFT4\u003c/sub\u003e, Sex, TPOAb, BMI, ALB, ALT, HDL, TC, TG, FBG, and SUA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe above variables were then incorporated into a multivariable logistic regression model. The analysis identified that TSH, FT\u003csub\u003e3\u003c/sub\u003e/FT\u003csub\u003e4\u003c/sub\u003e, TFQI\u003csub\u003eFT3\u003c/sub\u003e, TFQI\u003csub\u003eFT4\u003c/sub\u003e, Sex, BMI, ALT, TG, and FBG were the independent risk factors for MAFLD in euthyroid individuals (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It was shown that TFQI\u003csub\u003eFT3\u003c/sub\u003e was significantly positively correlated with the risk of MAFLD prevalence and TFQI\u003csub\u003eFT4\u003c/sub\u003e was negatively associated.\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\u003eAnalysis of Risk Factors for MAFLD Using Multivariable Logistic Regression Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCI5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCI95\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55*10^ (-5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT\u003csub\u003e3\u003c/sub\u003e/FT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.21E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTFQI\u003csub\u003eFT3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.071052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTFQI\u003csub\u003eFT4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.092993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPOAb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Construction of a new predictive model for predicting MAFLD\u003c/h2\u003e\u003cp\u003eBased on the results of multivariable logistic regression, a predictive nomogram for MAFLD risk was constructed using the following significant predictors: TSH, FT\u003csub\u003e3\u003c/sub\u003e/FT\u003csub\u003e4,\u003c/sub\u003e TFQI\u003csub\u003eFT3\u003c/sub\u003e, TFQI\u003csub\u003eFT4\u003c/sub\u003e, Sex, BMI, ALT, TG and FBG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the nomogram, each variable corresponded to a point scale; the total score was calculated by summing individual scores, which was then projected onto a probability axis to estimate the predicted risk of MAFLD. According to the total score, the risk was stratified as follows: low risk (\u0026lt;\u0026thinsp;149 points, probability\u0026thinsp;\u0026le;\u0026thinsp;0.1), medium risk (149\u0026ndash;165 points, probability 0.1\u0026ndash;0.5), and high risk (\u0026ge;\u0026thinsp;166 points, probability\u0026thinsp;\u0026ge;\u0026thinsp;0.5). The model enabled visual risk assessment of MAFLD based on individual scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Model evaluation and clinical utility\u003c/h2\u003e\u003cp\u003eThe ROC curve analysis showed that the area under the curve (AUC) of the model was 0.830 in the training set and 0.808 in the validation set, suggesting good predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The calibration curves also demonstrated a high consistency between predicted probabilities and observed outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Furthermore, decision curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) confirmed that the model achieved significant net clinical benefit (net benefit\u0026thinsp;\u0026gt;\u0026thinsp;0) across a wide range of threshold probabilities, supporting its application in the early screening of MAFLD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith the development of socioeconomic conditions and changes in lifestyle, the prevalence of MAFLD has shown a significant upward trend and has become one of the most common chronic liver diseases globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. MAFLD patients frequently present with no obvious clinical symptoms or signs in the early stages, posing a challenge for timely identification and thereby increasing the risk of progression to cirrhosis or hepatocellular carcinoma. This study analyzed the relationship between thyroid hormone sensitivity and MAFLD. The results suggested that TFQI could serve as a novel predictor of MAFLD risk in individuals with normal thyroid function. Moreover, constructing a clinical prediction model provided a new direction for early risk stratification and personalized intervention of MAFLD.\u003c/p\u003e\u003cp\u003eThyroid hormone is a key regulator of metabolic homeostasis. It maintains lipid balance by regulating cholesterol synthesis, fatty acid metabolism, and lipoprotein transport. It also plays a key role in hepatic energy metabolism by regulating mitochondrial function and autophagy processes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In the patients with hypothyroidism, reduced hepatic thyroid hormone signaling impaired lipid utilization, thereby accelerating the progression of MAFLD[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Due to the complex interplay among various thyroid hormones, relying on a single indicator to interpret the relationship between thyroid function and MAFLD may be insufficient. Some investigators have proposed using thyroid hormone sensitivity indices to evaluate the combined effects of thyroid hormones. Thyroid hormones regulate mitochondrial function and lipid metabolism gene expression through nuclear receptors (TRα/TRβ)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Decreased sensitivity may weaken the liver's lipid oxidation capacity and promote fat deposition[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thyroid hormone sensitivity could be classified into central and peripheral sensitivity. The former affects the regulation of the hypothalamic-pituitary-thyroid axis feedback loop. And the latter reflects the activity of deiodinases in peripheral tissues and reduces the effect of thyroid hormones in peripheral tissues. Laclaustra and the colleagues[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] first proposed TFQI, combining other central sensitivity indices reflecting thyroid hormone levels, and it is more stable than TT\u003csub\u003e4\u003c/sub\u003eRI and TSHI. Studies in Iranian found that TFQI was significantly associated with diabetes mellitus and hypertension in individuals with normal thyroid function[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Another epidemiological survey showed that the TFQI index was associated with risk factors for hyperuricemia, obesity, and cardiovascular diseases[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Some recent studies have explored the relationship between central thyroid hormone sensitivity and MAFLD. A cross-sectional study involving 129 participants with NAFLD revealed that elevated TFQI\u003csub\u003eFT4\u003c/sub\u003e level was associated with a higher prevalence of liver fibrosis, however, TFQI\u003csub\u003eFT3\u003c/sub\u003e was not calculated in that study[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Another cross-sectional analysis showed that high TFQI\u003csub\u003eFT3\u003c/sub\u003e level significantly increased the risk of dyslipidemia and MAFLD among euthyroid patients[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A retrospective community analysis also confirmed that impaired central thyroid hormone sensitivity was associated with MAFLD and its progression to liver fibrosis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Consistent with those findings, our study showed that TFQI\u003csub\u003eFT3\u003c/sub\u003e was positively associated with the risk of MAFLD, supporting its role as an independent risk factor. Therefore, TFQI\u003csub\u003eFT3\u003c/sub\u003e may be a promising predictive marker for MAFLD.\u003c/p\u003e\u003cp\u003eThe bioactivity of thyroid hormones is regulated by iodine tyrosine deiodinase types I, II, and III (D1, D2, D3), which form a group of membrane proteins characterized by disulfide oxidoreductase folds. These three deiodinases could activate or inactivate thyroid hormones, with D2 catalyzing the conversion of T\u003csub\u003e4\u003c/sub\u003e to the biologically active T\u003csub\u003e3\u003c/sub\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A cross-sectional study previously showed that increased fat accumulation triggered a compensatory mechanism, and the increase in deiodinase activity leaded to a high conversion of T\u003csub\u003e4\u003c/sub\u003e to T\u003csub\u003e3\u003c/sub\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Another study demonstrated that the mice with D2 gene knockout were more prone to insulin resistance and obesity[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. If D2 activity remains intact, FT\u003csub\u003e4\u003c/sub\u003e may be efficiently converted into active T\u003csub\u003e3\u003c/sub\u003e, therefore effectively maintaining metabolic functions. Thus, we hypothesize that a compensatory mechanism may exist, in which sufficient production of FT\u003csub\u003e3\u003c/sub\u003e form FT\u003csub\u003e4\u003c/sub\u003e promotes fatty acid oxidation and cholesterol metabolism, ultimately reducing hepatic fat deposition and lowering the risk of MAFLD. Elevated TFQI\u003csub\u003eFT4\u003c/sub\u003e reflects central resistance to FT\u003csub\u003e4\u003c/sub\u003e. Our findings revealed a significant negative association between TFQI\u003csub\u003eFT4\u003c/sub\u003e and the risk of MAFLD. The underlying mechanisms remain under investigation. In future investigations, we plan to incorporate larger clinical datasets to strengthen the robustness of our findings further explore the relationship between TFQI\u003csub\u003eFT4\u003c/sub\u003e and other components of metabolic syndrome.\u003c/p\u003e\u003cp\u003eAs the peripheral sensitivity index of thyroid hormone, a decreased ratio of FT\u003csub\u003e3\u003c/sub\u003e/FT\u003csub\u003e4\u003c/sub\u003e indicates a reduced sensitivity of peripheral organs to thyroid hormones. A retrospective study involving 18,766 adults with normal thyroid function showed that reduced sensitivity to thyroid hormones was associated with high RC levels[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Another study conducted in Turkey on obese adolescents revealed that the FT\u003csub\u003e3\u003c/sub\u003e/FT\u003csub\u003e4\u003c/sub\u003e ratio was higher in the patients with hepatic steatosis than those in control group[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our research shows that FT\u003csub\u003e3\u003c/sub\u003e/FT\u003csub\u003e4\u003c/sub\u003e is negatively correlated with the risk of MAFLD, and the reduced peripheral sensitivity of thyroid hormones is associated with the risk of MAFLD. Given the genetic and racial differences within the studied population, the observed differences in the results may be attributed to population heterogeneity. Additionally, there may be limitations related to the sample size. Therefore, future studies should verify those findings by expanding the sample size or conducting stratified analyses.\u003c/p\u003e\u003cp\u003eTSH has been demonstrated by numerous studies to be positively correlated with the risk of MAFLD[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Previous studies have shown that in individuals with normal thyroid function, relatively high levels of TSH are positively correlated with unfavorable lipid concentrations [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our findings were consistent with the results above, that TSH was positively correlated with the risk of MAFLD.\u003c/p\u003e\u003cp\u003ePrevious research data showed that the risk of MAFLD in women is 19% lower than in men in the general population[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Another study on MAFLD in Asian children showed that the prevalence continued to rise in obese children and boys over 10 years old[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The difference in obesity, unbalanced diet, and lack of physical activity between men and may contribute to the occurrence and progression of MAFLD[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, elevated testosterone levels increase the risk of MAFLD[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and the protective effect of estrogen on liver lipid metabolism may be a necessary reason for the lower prevalence in women[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The findings of this study, which indicated a higher risk of MAFLD in men, aligned with several previous studies, further confirming the gender differences in MAFLD populations.\u003c/p\u003e\u003cp\u003eObesity, dyslipidemia, and hyperglycemia constitute central components of metabolic syndrome. Obesity directly reflects an increase in body fat proportion, and the reduced clearance rate of TG further aggravates lipid metabolism disorder[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. And progressive insulin resistance in MAFLD patients further subsequently elevates blood glucose levels. Consequently, abnormal fat accumulation may lead to lipid metabolism disorder, reducing insulin sensitivity and causing glycolipid metabolism disorder, increasing the risk of MAFLD[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This study drew a visualized line graph to display the risk model of MAFLD based on the above risk factors. It is highly operable and convenient for clinical application, and the risk of MAFLD may be evaluated by calculating specific scores. Therefore, this innovative non-invasive prediction tool has crucial clinical value for early screening of MAFLD and delay of disease progression.\u003c/p\u003e\u003cp\u003eThis study has several limitations. firstly, it didn\u0026rsquo;t establish causal relationships as a cross-sectional study. A prospective cohort study is needed to further verify the causal associations between various risk factors and the onset and progression of MAFLD. Additionally, this single-center retrospective study with a relatively small sample size may introduce selection bias. It is important to clarify that the diagnosis of hepatic steatosis was based on liver ultrasound rather than liver biopsy, which may result in inaccurate staging of MAFLD. Therefore, future studies should integrate additional clinical parameters and molecular biomarkers to optimize the model and improve its accuracy and reliability.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, we found that central thyroid hormone sensitivity indices (TFQI\u003csub\u003eFT3\u003c/sub\u003e, TFQI\u003csub\u003eFT4\u003c/sub\u003e) were independently associated with MAFLD risk in euthyroid individuals, with TFQI\u003csub\u003eFT3\u003c/sub\u003e showing a positive and TFQI\u003csub\u003eFT4\u003c/sub\u003e a negative correlation. The nomogram model we constructed exhibited good predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.830) in training set, 0.808 in testing set), providing new insights of the impact of thyroid hormone sensitivity on the pathogenesis of MAFLD. Our study lays the foundation for integrating thyroid sensitivity markers into the early screening strategy of MAFLD. Further studies with larger, multi-center cohorts and mechanistic explorations are needed to validate these associations and optimize targeted intervention approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e This study complied with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Third Hospital of Hebei Medical University (Approval No. W2023-004-1). Written informed consent, including agreement for data publication, was obtained from all participants or their legal agents. All participants were adults (age\u0026thinsp;\u0026ge;\u0026thinsp;18 years), and no minors or other vulnerable populations were included in this study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eAll participants or their legal agents provided consent for the publication of anonymized research data.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that no conflicts of interest relevant to this article exist.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by Natural Science Foundation of Hebei Province (Grant number: H2020206490); Medical Science Research Subject Plan of Hebei (Grant number: 20230095); Clinical Medicine Postdoctoral Research Support Program of Hebei Medical University (Grant number: PD2023002); Hebei Province Yanzhao Golden Talent Program (Grant number: B2024003014); Hebei Medical University Postdoctoral Startup Fund.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXuan Qiu: Conceptualization, Methodology, Funding acquisition, Supervision, Writing \u0026ndash; review and editing; Xiaolin Zhang: Methodology, Validation, Writing \u0026ndash; review and editing; Mujie Gao: Project administration, Funding acquisition, Visualization, Writing \u0026ndash; original draft; Xinzhu Zhang: Investigation , Visualization, Writing \u0026ndash; review and editing; Hanjing Zhang: Data curation; Rong Guo: Data curation; Hailing Di: Data curation; Kuanzhi Liu: Resources.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge all participants whose contributions made this investigation possible.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYounossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64(1):73\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGofton C, Upendran Y, Zheng M-H, George J. MAFLD: How is it different from NAFLD? Clin Mol Hepatol. 2023;29(Suppl):S17\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong R, Li Z, Zhang Y, Tan J, Chen Z. Comparison of NAFLD, MAFLD and MASLD characteristics and mortality outcomes in United States adults. Liver Int. 2024;44(4):1051\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng H, Pan L, Ran S, Wang M, Huang S, Zhao M, Cao Z, Yao Z, Xu L, Yang Q, et al. Prediction of MAFLD and NAFLD using different screening indexes: A cross-sectional study in U.S. adults. 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Mice with a targeted deletion of the type 2 deiodinase are insulin resistant and susceptible to diet induced obesity. PLoS ONE. 2011;6(6):e20832.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun H, Zhu W, Liu J, An Y, Wang Y, Wang G. Reduced Sensitivity to Thyroid Hormones Is Associated With High Remnant Cholesterol Levels in Chinese Euthyroid Adults. J Clin Endocrinol Metab. 2022;108(1):166\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung GE, Kim D, Kim W, Yim JY, Park MJ, Kim YJ, Yoon JH, Lee HS. Non-alcoholic fatty liver disease across the spectrum of hypothyroidism. J Hepatol. 2012;57(1):150\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBulum T, Kolarić B, Duvnjak L. Insulin sensitivity modifies the relationship between thyroid function and lipid profile in euthyroid type 1 diabetic patients. Endocrine. 2012;42(1):139\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalakrishnan M, Patel P, Dunn-Valadez S, Dao C, Khan V, Ali H, El-Serag L, Hernaez R, Sisson A, Thrift AP, et al. Women Have a Lower Risk of Nonalcoholic Fatty Liver Disease but a Higher Risk of Progression vs Men: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol. 2021;19(1):61\u0026ndash;e7115.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZou ZY, Zeng J, Ren TY, Huang LJ, Wang MY, Shi YW, Yang RX, Zhang QR, Fan JG. The burden and sexual dimorphism with nonalcoholic fatty liver disease in Asian children: A systematic review and meta-analysis. Liver Int. 2022;42(9):1969\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDella Torre S. Beyond the X Factor: Relevance of Sex Hormones in NAFLD Pathophysiology. Cells 2021, 10(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNing L, Sun J. Associations between body circumference and testosterone levels and risk of metabolic dysfunction-associated fatty liver disease: a mendelian randomization study. BMC Public Health. 2023;23(1):602.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan S, Chen J, Dan L, Xie Y, Sun Y, Li X, Larsson SC. Homocysteine, folate, and nonalcoholic fatty liver disease: a systematic review with meta-analysis and Mendelian randomization investigation. Am J Clin Nutr. 2022;116(6):1595\u0026ndash;609.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosato V, Masarone M, Dallio M, Federico A, Aglitti A, Persico M. NAFLD and Extra-Hepatic Comorbidities: Current Evidence on a Multi-Organ Metabolic Syndrome. Int J Environ Res Public Health 2019, 16(18).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho IY, Chang Y, Kang JH, Kim Y, Sung E, Shin H, Wild SH, Byrne CD, Ryu S. Long or Irregular Menstrual Cycles and Risk of Prevalent and Incident Nonalcoholic Fatty Liver Disease. J Clin Endocrinol Metab. 2022;107(6):e2309\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"thyroid hormone sensitivity index, metabolic-associated fatty liver disease, nomogram, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7985491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7985491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eThis study aimed to assess the association between thyroid hormone sensitivity indices and metabolic-associated fatty liver disease (MAFLD) and to develop a predictive model for MAFLD based on these indices.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 508 participants with normal thyroid function were retrospectively included, and randomly divided into a training group (n\u0026thinsp;=\u0026thinsp;356) and a testing group (n\u0026thinsp;=\u0026thinsp;152). The thyroid hormone sensitivity indices, including the Thyroid Feedback Quantile-based Index by FT\u003csub\u003e3\u003c/sub\u003e (TFQI\u003csub\u003eFT3\u003c/sub\u003e), FT\u003csub\u003e4\u003c/sub\u003e (TFQI\u003csub\u003eFT4\u003c/sub\u003e), and the free triiodothyronine to free thyroxine (FT\u003csub\u003e3\u003c/sub\u003e/ FT\u003csub\u003e4\u003c/sub\u003e) ratio, were calculated. Key variables were screened using LASSO regression, independent risk factors for MAFLD were found using multivariate logistic regression, and a visual nomogram prediction model was built. The receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to validate the model's performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 508 euthyroid individuals, 136 (26.77%) met diagnostic criteria for MAFLD. Multiple logistic regression analysis in the training cohort identified TSH, FT\u003csub\u003e3\u003c/sub\u003e/FT\u003csub\u003e4\u003c/sub\u003e, TFQI\u003csub\u003eFT3\u003c/sub\u003e, TFQI\u003csub\u003eFT4\u003c/sub\u003e, sex, body mass index, alanine aminotransferase, triglycerides and fasting blood glucose as predictors of MAFLD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The central sensitivity index TFQI\u003csub\u003eFT3\u003c/sub\u003e was positively correlated with MAFLD risk (OR\u0026thinsp;=\u0026thinsp;4.653, 95% CI: 1.071\u0026ndash;21.881), while TFQI\u003csub\u003eFT4\u003c/sub\u003e was negatively correlated (OR\u0026thinsp;=\u0026thinsp;0.059, 95% CI: 0.010\u0026ndash;0.313). Based on the above results, a predictive nomogram for MAFLD risk was constructed. The nomogram model exhibited strong performance in both the training set (AUC\u0026thinsp;=\u0026thinsp;0.830) and the testing set (AUC\u0026thinsp;=\u0026thinsp;0.808), demonstrating excellent calibration curve fitting and significant clinical net benefit, as indicated by the DCA.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe thyroid hormone central sensitivity index (TFQI\u003csub\u003eFT3,\u003c/sub\u003e TFQI\u003csub\u003eFT4\u003c/sub\u003e) has independent predictive value for MAFLD risk. This study constructs an early screening and prediction model for MAFLD, providing new insights and directions for early diagnosis and treatment.\u003c/p\u003e\u003ch2\u003eClinical trial number:\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e\u003cp\u003eRetrospectively registered.\u003c/p\u003e","manuscriptTitle":"Association Between Thyroid Hormone Sensitivity Indices and Metabolic- Associated Fatty Liver disease: Development and Validation of Risk Prediction Model in Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 11:12:22","doi":"10.21203/rs.3.rs-7985491/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-26T13:49:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139315179771236237747059038725682426926","date":"2026-03-24T19:05:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250057485064621217130475023614594100702","date":"2026-03-17T05:09:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158751421723118360418497015010760770157","date":"2026-02-05T13:38:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T00:42:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-04T09:48:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T22:59:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T22:59:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-10-30T05:53:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2c305ef2-8a51-4280-a608-c4a5cc094272","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-14T11:12:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 11:12:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7985491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7985491","identity":"rs-7985491","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}