Association between FT3/FT4 ratio and obesity in euthyroid patients with type 2 diabetes: a cross-sectional study with mediation and threshold analyses

Association between FT3/FT4 ratio and obesity in euthyroid patients with type 2 diabetes: a cross-sectional study with mediation and threshold analyses | 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 FT3/FT4 ratio and obesity in euthyroid patients with type 2 diabetes: a cross-sectional study with mediation and threshold analyses chunyan Zhu, xianglan Liu, junhua Yu, liangyan Hua, ziru Fang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7803754/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Thyroid hormone sensitivity, reflected by the free triiodothyronine-to-free thyroxine ratio (FT3/FT4), plays a critical role in metabolic regulation. This study aimed to investigate the association between FT3/FT4 and obesity in euthyroid patients with type 2 diabetes. Methods: A total of 1,408 euthyroid patients with type 2 diabetes were included. Multivariable linear regression, subgroup, mediation, and nonlinear analyses were performed to examine the associations between FT3/FT4 and body mass index (BMI). Results: FT3/FT4 was positively and independently associated with BMI in fully adjusted models (β = 7.03, 95% CI: 2.53–11.53, P = 0.0023). Subgroup analyses showed consistent associations across age, sex, and drinking categories, with no significant interactions. Mediation analyses indicated that waist circumference and visceral fat mediated 43.6% and 50.5% of the total effect, respectively, whereas subcutaneous fat was not significant. Nonlinear curve fitting revealed a threshold at FT3/FT4 = 0.41, below which FT3/FT4 was positively associated with BMI, while above it, the association plateaued. Conclusions: FT3/FT4 is independently associated with obesity in euthyroid patients with type 2 diabetes. Its partial mediation through central adiposity and threshold-dependent pattern suggest that FT3/FT4 may serve as a practical biomarker for early risk identification and personalized obesity management. Clinical trial number: not applicable. FT3/FT4 ratio Thyroid hormone sensitivity Obesity Type 2 diabetes Mediation analysis Figures Figure 1 Figure 2 Introduction Obesity and type 2 diabetes mellitus (T2DM) are two closely linked global health challenges. Their coexistence accelerates insulin resistance, worsens glycemic control, and markedly increases the risk of cardiovascular disease, kidney dysfunction, and premature mortality( 1 – 3 ). Epidemiological studies indicate that more than half of patients with T2DM are overweight or obese, underscoring the urgent need for accurate risk stratification( 4 ). Although anthropometric measures such as body mass index (BMI) and waist circumference are widely used, they do not fully capture the underlying biological processes( 5 ). Identifying reliable and easily accessible biomarkers is therefore essential for early risk identification and guiding individualized management in this population. Thyroid hormones are key regulators of energy expenditure, glucose and lipid metabolism, and body composition( 6 ). Even within the euthyroid range, subtle variations in thyroid function may influence metabolic outcomes( 7 ). Epidemiological and clinical studies have linked thyroid hormones to obesity, insulin resistance, and the risk of T2DM, suggesting a role in metabolic homeostasis( 8 ). However, most prior research has focused on individual parameters such as free triiodothyronine (FT3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH), with inconsistent results( 9 ). These inconsistencies may be explained by the fact that single hormone measurements do not fully capture tissue sensitivity to thyroid hormones. The ratio of FT3 to FT4 (FT3/FT4) has been proposed as a marker of thyroid hormone sensitivity, reflecting peripheral conversion and tissue responsiveness( 10 ). Recent studies suggest that higher FT3/FT4 is linked to insulin resistance, metabolic syndrome, and cardiovascular risk( 11 , 12 ). However, evidence on its relationship with obesity in euthyroid patients with T2DM is scarce. In particular, whether this association is mediated by fat distribution or follows a nonlinear pattern remains unclear. Therefore, the present study aimed to investigate the association between FT3/FT4 and BMI in euthyroid patients with T2DM. We further explored the potential mediating role of waist circumference, visceral fat, and subcutaneous fat, and assessed whether the relationship exhibited nonlinear and threshold effects. Materials and Methods Study Population This study was conducted at the Metabolic Management Center (MMC) of the Department of Endocrinology, Quzhou People’s Hospital, Wenzhou Medical University, Zhejiang Province, China. Consecutive adult patients with type 2 diabetes (T2DM) who attended the MMC between December 2022 and June 2025 were enrolled. The diagnosis of T2DM was based on the 2023 American Diabetes Association (ADA) criteria. Exclusion criteria were as follows: (i) acute diabetic complications (e.g., diabetic ketoacidosis or hyperosmolar hyperglycemic state); (ii) known thyroid dysfunction, current use of thyroid-related medications, or history of thyroid surgery/radioactive iodine therapy; (iii) recent use of medications known to interfere with thyroid function (e.g., glucocorticoids, amiodarone); (iv) severe hepatic or renal insufficiency; (v) active malignancy, cachexia, or other severe systemic diseases; (vi) pregnancy or lactation; and (vii) age < 18 years. The study protocol was approved by the Ethics Committee of Quzhou People’s Hospital, Wenzhou Medical University (Approval No. 2022–110). Written informed consent was obtained from all participants. Measurements Demographic data (age, sex), medical history, smoking status, and drinking status were collected at baseline. Anthropometric parameters: Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). Waist circumference (WC) was measured at the midpoint between the lowest rib and the iliac crest after normal expiration. Visceral fat (VF) and subcutaneous fat (SF) were assessed using a standardized body composition analyzer in the MMC. Blood pressure: Measured in the seated position after at least 5 minutes of rest using an automated sphygmomanometer; the average of two readings was used. Laboratory tests: HbA1c was measured by high-performance liquid chromatography (HPLC). Lipid profile (TC, TG, HDL-C, LDL-C), liver enzymes (AST, ALT), serum creatinine, uric acid, and high-sensitivity C-reactive protein (CRP) were measured by standard automated methods. Estimated glomerular filtration rate (eGFR) was calculated from serum creatinine using the CKD-EPI equation, adjusted for age and sex. Thyroid function tests, including free triiodothyronine (FT3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH), were simultaneously measured using chemiluminescence immunoassay under strict quality control. The reference ranges were: TSH 0.35–4.94µIU/mL, FT4 9.01–19.05pmol/L, and FT3 2.43–6.01pmol/L. Variable Definitions BMI: expressed in kg/m²; obesity was defined as BMI ≥ 28 kg/m² according to Chinese criteria( 13 ). FT3/FT4 ratio: calculated as FT3 (pmol/L) divided by FT4 (pmol/L). WC, VF, SF: indicators of central and abdominal adiposity measured under standardized MMC protocols. eGFR: estimated using the CKD-EPI equation based on serum creatinine, age, and sex. Smoking and drinking: classified as current or not current based on baseline report. TSH, FT3, FT4: treated as continuous variables and interpreted according to reference ranges. Statistical analysis Statistical analyses were performed to evaluate the association between FT3/FT4 and BMI. Continuous variables were summarized as mean (SD) or median (IQR) depending on distribution, and categorical variables as counts (percentages). Between-group comparisons were performed using the Kruskal-Wallis test for continuous variables and the χ² test (or Fisher’s exact test when appropriate) for categorical variables. Standardized mean differences were reported to assess baseline balance.The primary outcome was BMI. The main exposure was the FT3/FT4 ratio, analyzed as both a continuous variable and by quartiles (lowest quartile as reference). Linear regression models were fitted sequentially: Model 1 was unadjusted; Model 2 adjusted for age and sex; and Model 3 further adjusted for SBP, DBP, WBC, eGFR, LDL-C, HbA1c, uric acid, CRP, TSH, TC, TG, HDL-C, AST, ALT, smoking, and drinking. Skewed covariates (HbA1c, uric acid, CRP, TSH, TC, TG, HDL-C, AST, ALT) were Box–Cox transformed prior to analysis. Regression results were presented as β coefficients with 95% confidence intervals (CIs). Effect modification by smoking and drinking was evaluated using stratified analyses, and interaction was formally tested by including cross-product terms in the regression models. Nonlinear associations between FT3/FT4 and BMI were assessed using generalized additive models with penalized splines. To identify potential threshold effects, a two-piecewise linear regression model was applied, and the inflection point was estimated using likelihood ratio tests. Causal mediation analyses were conducted to explore whether central adiposity mediated the association between FT3/FT4 and BMI. Waist circumference (WC), visceral fat (VF), and subcutaneous fat (SF) were included separately as mediators. Each mediation model adjusted for the same covariates as the fully adjusted regression. Nonparametric bootstrapping with 1,000 resamples (percentile method) was used to estimate the average causal mediation effect (ACME), average direct effect (ADE), total effect, proportion mediated, and their 95% CIs. All analyses were based on available data. A two-sided P value < 0.05 was considered statistically significant. Statistical analyses were conducted using EmpowerStats ( www.empowerstats.com ) and R software (version 4.2.2). Ethics Statement This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Quzhou People’s Hospital, Wenzhou Medical University (Approval No. 2022–110; November 18, 2022). Written informed consent was obtained from all participants before enrollment. Results Clinical characteristics of the study population The baseline characteristics of the study population are summarized in Table 1. A total of 1,408 euthyroid patients with type 2 diabetes were included, including 1,127 patients with BMI < 28 kg/m² and 281 with BMI ≥ 28 kg/m². Patients with obesity were younger ( P < 0.001) and had higher diastolic and systolic blood pressure, HbA1c, total cholesterol, LDL-C, triglycerides, uric acid, AST, ALT, C-reactive protein, white blood cell counts, waist circumference, visceral fat, and subcutaneous fat levels compared with the non-obese group (all P < 0.05). HDL-C levels were lower in the obese group ( P < 0.001). FT3 and FT3/FT4 values were significantly higher in the obese group (both P < 0.001), while FT4 and TSH levels did not differ significantly between groups. Smoking and drinking status were also similar between groups. Table 1. Baseline characteristics of euthyroid patients with type 2 diabetes according to obesity status (BMI <28 vs. ≥28 kg/m²) Characteristic BMI<28 BMI≥28 P -value* N 1127 281 AGE (years) 55.24 ± 11.92 47.63 ± 15.25 <0.001 SEX (%) 0.823 Female 417 (37.00%) 106 (37.72%) Male 710 (63.00%) 175 (62.28%) Current drinking, n (%) 0.604 No 707 (67.46%) 173 (65.78%) Yes 341 (32.54%) 90 (34.22%) Current smoking, n (%) 0.938 No 712 (67.81%) 179 (68.06%) Yes 338 (32.19%) 84 (31.94%) DBP (mmHg) 74.55 ± 8.69 78.55 ± 9.70 <0.001 SBP (mmHg) 126.11 ± 13.72 129.81 ± 13.33 <0.001 HbA1C (%) 8.94 (7.37-11.02) 9.82 (8.40-11.00) 0.003 TC (mmol/L) 4.58 (3.87-5.41) 4.82 (4.07-5.55) 0.025 HDLC (mmol/L) 1.12 (0.94-1.30) 0.99 (0.85-1.18) <0.001 LDL-C (mmol/L) 2.75 ± 1.02 2.92 ± 0.98 0.008 TG (mg/dL） 1.77 (1.19-2.96) 2.37 (1.49-3.46) <0.001 EGFR (ml/min/1.73 m 2 ） 102.63 ± 14.72 107.72 ± 16.57 <0.001 UA (umol/L) 305.20 (256.00-370.60) 346.65 (303.02-416.00) <0.001 AST (U/L) 17.90 (14.70-23.80) 24.65 (17.67-42.40) <0.001 ALT (U/L) 20.60 (14.20-33.20) 37.00 (22.10-67.60) <0.001 CRP (mg/L） 1.39 (0.70-3.08) 2.92 (1.51-5.80) <0.001 WBC (×10 9 /L） 6.34 ± 1.84 6.97 ± 2.06 <0.001 WC (cm) 86.50 (82.00-91.50) 100.00 (96.00-106.25) <0.001 VF (cm 2 ) 85.00 (66.00-105.00) 131.00 (111.00-158.00) <0.001 SF (cm 2 ) 151.00 (122.00-186.00) 254.00 (212.00-291.00) <0.001 FT3 (pmol/L) 3.93 ± 0.53 4.09 ± 0.56 <0.001 FT4 (pmol/L) 12.67 ± 1.45 12.77 ± 1.40 0.283 TSH (μIU/mL) 1.37 (0.93-2.02) 1.47 (1.02-2.10) 0.110 FT3/FT4 0.31 ± 0.05 0.32 ± 0.05 0.005 Data are presented as mean ± SD for normally distributed continuous variables, median (interquartile range) for skewed variables, and n (%) for categorical variables. P values were calculated using independent t tests, Mann–Whitney U tests, or chi-squared tests as appropriate. Bold P values indicate statistical significance ( P < 0.05). Abbreviations: BMI, Body Mass Index (kg/m²); DBP, diastolic blood pressure (mmHg); SBP, systolic blood pressure (mmHg); A1C, glycated hemoglobin (%); TC, total cholesterol (mmol/L); HDLC, high-density lipoprotein cholesterol (mmol/L); LDLC, low-density lipoprotein cholesterol (mmol/L); TG, triglycerides (mg/dL); EGFR, estimated glomerular filtration rate (mL/min/1.73 m²); UA, uric acid (µmol/L); AST, aspartate aminotransferase (U/L); ALT, alanine aminotransferase (U/L); CRP, C-reactive protein (mg/L); WBC, white blood cell count (×10⁹/L); WC, waist circumference (cm); VF, visceral fat (cm²); SF, subcutaneous fat (cm²); FT3, free triiodothyronine (pmol/L); FT4, free thyroxine (pmol/L); TSH, thyroid-stimulating hormone (μIU/mL); FT3/FT4, ratio of FT3 to FT4. The baseline characteristics according to FT3/FT4 quartiles are shown in Table 2. Patients in higher quartiles were younger and more likely to be male (both P < 0.001). HbA1c, triglycerides, uric acid, AST, ALT, waist circumference, visceral fat, and subcutaneous fat levels increased across FT3/FT4 quartiles (all P < 0.05). In contrast, CRP levels decreased ( P < 0.001). FT3 levels increased and FT4 levels decreased from Q1 to Q4 (both P < 0.001). BMI showed a significant upward trend across FT3/FT4 quartiles ( P < 0.001). Table 2. Participant characteristics classified by FT3/FT4 quartiles. Characteristic Q1 Q2 Q3 Q4 P -value* N 331 373 347 357 AGE (years) 55.43 ± 13.13 55.07 ± 13.27 53.21 ± 12.87 51.23 ± 12.39 <0.001 SEX (%) <0.001 Female 143 (43.20%) 171 (45.84%) 99 (28.53%) 110 (30.81%) Male 188 (56.80%) 202 (54.16%) 248 (71.47%) 247 (69.19%) Current drinking, n (%) 0.005 No 224 (72.0%) 245 (71.6%) 204 (62.6%) 207 (62.3%) Yes 87 (28.0%) 97 (28.4%) 122 (37.4%) 125 (37.7%) Current smoking, n (%) 0.002 No 230 (74.0%) 246 (71.5%) 204 (62.6%) 211 (63.6%) Yes 81 (26.0%) 98 (28.5%) 122 (37.4%) 121 (36.4%) DBP (mmHg) 74.72 ± 9.31 74.48 ± 9.08 75.80 ± 8.55 76.39 ± 9.11 0.037 SBP (mmHg) 125.68 ± 14.30 127.56 ± 14.43 127.34 ± 13.2 126.71 ± 12.78 0.289 HbA1C (%) 10.47 (8.38-12.28) 9.62 (7.56-11.14) 8.90 (7.34-10.67) 8.32 (7.11-9.80) <0.001 TC (mmol/L) 4.49 (3.81-5.47) 4.62 (3.96-5.44) 4.69 (4.04-5.35) 4.67 (3.79-5.51) 0.633 HDLC (mmol/L) 1.12 (0.93-1.29) 1.10 (0.94-1.33) 1.08 (0.87-1.28) 1.08 (0.93-1.27) 0.245 LDL-C (mmol/L) 2.80 ± 1.05 2.79 ± 1.00 2.81 ± 1.00 2.73 ± 0.99 0.795 TG (mg/dL） 1.61 (1.08-2.66) 1.76 (1.20-2.95) 2.16 (1.38-3.36) 2.05 (1.30-3.60) <0.001 EGFR (ml/min/1.73 m 2 ) 102.12 ± 15.40 102.26 ± 16.26 102.78 ± 14.36 106.89 ± 14.40 <0.001 UA (umol/L) 308.60 (253.57-369.17) 305.20 (245.80-371.20) 319.05 (277.42-381.00) 326.60 (274.77-392.47) 0.002 AST(U/L) 17.40 (14.10-24.70) 18.30 (15.22-24.70) 18.70 (15.20-25.42) 21.05 (16.12-29.05) <0.001 ALT(U/L) 19.20 (13.30-30.80) 21.70 (15.57-36.60) 23.20 (15.20-37.80) 27.10 (18.25-45.85) <0.001 CRP (mg/L） 2.04 (0.99-4.58) 1.56 (0.69-3.70) 1.50 (0.78-3.25) 1.44 (0.71-3.23) <0.001 WBC (×10 9 /L) 6.49 ± 2.14 6.31 ± 1.73 6.44 ± 1.78 6.58 ± 1.91 0.519 WC (cm) 86.60 (81.50-94.00) 89.00 (82.00-95.00) 89.00 (84.00-96.00) 89.10 (84.60-95.50) <0.001 VF (cm 2 ) 86.00 (63.00-110.00) 93.00 (70.00-114.00) 96.00 (72.00-123.00) 99.00 (75.75-122.25) <0.001 SF (cm 2 ) 162.00 (123.00-204.00) 162.50 (126.00-208.25) 168.00 (135.00-220.00) 169.50 (133.75-216.75) 0.038 FT3 (pmol/L) 3.43 ± 0.40 3.84 ± 0.36 4.10 ± 0.38 4.43 ± 0.45 <0.001 FT4 (pmol/L) 13.83 ± 1.42 12.94 ± 1.18 12.41 ± 1.13 11.65 ± 1.13 <0.001 TSH (μIU/mL) 1.25 (0.83-1.86) 1.43 (0.99-2.07) 1.41 (1.00-1.99) 1.46 (0.99-2.12) 0.004 BMI (kg/m²) 24.20 (22.10-26.65) 24.50 (22.70-27.00) 25.30 (23.35-27.60) 25.50 (23.69-27.90) <0.001 Data are presented as mean ± SD for normally distributed continuous variables, median (interquartile range) for skewed variables, and n (%) for categorical variables. P values were calculated using independent t tests, Mann–Whitney U tests, or chi-squared tests as appropriate. Bold P values indicate statistical significance ( P < 0.05). Multivariable linear regression analyses for the association between thyroid parameters and BMI The associations between thyroid parameters and BMI were examined using multivariable linear regression models (Table 3). In crude models, FT3 and FT3/FT4 were positively associated with BMI, whereas FT4 showed no significant association. After full adjustment for potential confounders, FT3/FT4 remained significantly associated with BMI (β = 6.98, 95% CI: 2.49–11.48, P = 0.0024), and FT3 showed a weaker but statistically significant association (β = 0.72, 95% CI: 0.26–1.17, P = 0.0021). FT4 and TSH were not significantly associated with BMI in any model (all P > 0.05). A significant linear trend was observed across quartiles of FT3/FT4 ( P for trend = 0.0006). Table 3. Multivariable linear regression analyses for the association between thyroid parameters and BMI. Exposure Crude Model (Model 1) Partially Adjusted Model (Model 2) Fully Adjusted Model (Model 3) β (95% CI) p -value β (95% CI) p -value β (95% CI) p -value FT3 1.55 (1.19, 1.91) <0.0001 1.18 (0.80, 1.55) <0.0001 0.72 (0.26, 1.17) 0.0021 FT3 quartile Q1 1.0 1.0 1.0 Q2 0.95 (0.40, 1.51) 0.0008 0.83 (0.29, 1.38) 0.0029 0.78 (0.16, 1.39) 0.0133 Q3 1.06 (0.50, 1.62) 0.0002 0.83 (0.29, 1.38) 0.0029 0.80 (0.17, 1.42) 0.0125 Q4 2.11 (1.56, 2.67) <0.0001 1.58 (1.01, 2.14) <0.0001 1.08 (0.42, 1.73) 0.0014 P for trend <0.0001 <0.0001 0.0024 FT4 0.00 (-0.13, 0.14) 0.9450 -0.06 (-0.19, 0.08) 0.4173 -0.06 (-0.21, 0.09) 0.4082 FT4 quartile Q1 1.0 1.0 1.0 Q2 -0.12 (-0.69, 0.45) 0.6712 -0.18 (-0.73, 0.37) 0.5233 -0.19 (-0.78, 0.40) 0.5334 Q3 0.26 (-0.30, 0.82) 0.3676 0.12 (-0.43, 0.66) 0.6718 -0.19 (-0.79, 0.41) 0.5341 Q4 -0.13 (-0.69, 0.44) 0.6634 -0.34 (-0.89, 0.21) 0.2252 -0.38 (-1.00, 0.24) 0.2272 P for trend 0.9929 0.4172 0.2493 TSH 0.29 (0.06, 0.52) 0.0124 0.41 (0.18, 0.63) 0.0004 -0.27 (-1.06, 0.52) 0.5029 TSH quartile Q1 1.0 1.0 1.0 Q2 0.88 (0.32, 1.44) 0.0023 0.81 (0.27, 1.35) 0.0036 0.22 (-0.65, 1.09) 0.6201 Q3 0.97 (0.40, 1.53) 0.0008 0.94 (0.39, 1.48) 0.0008 0.11 (-1.11, 1.33) 0.8592 Q4 1.01 (0.45, 1.58) 0.0005 1.20 (0.65, 1.75) <0.0001 0.00 (-1.78, 1.78) 0.9992 P for trend 0.0006 <0.0001 0.9122 FT3/FT4 12.53 (8.71, 16.35) <0.0001 10.04 (6.27, 13.81) <0.0001 6.98 (2.49, 11.48) 0.0024 FT3/FT4 quartile Q1 1.0 1.0 1.0 Q2 0.62 (0.05, 1.18) 0.0317 0.58 (0.04, 1.13) 0.0358 0.41 (-0.23, 1.04) 0.2087 Q3 1.09 (0.52, 1.66) 0.0002 0.96 (0.40, 1.52) 0.0007 0.76 (0.11, 1.41) 0.0225 Q4 1.70 (1.14, 2.27) <0.0001 1.41 (0.86, 1.97) <0.0001 1.11 (0.45, 1.76) 0.0010 P for trend <0.0001 <0.0001 0.0006 β coefficients and 95% confidence intervals (CIs) were estimated using linear regression models with BMI as a continuous outcome. Thyroid parameters (FT3, FT4, TSH, and FT3/FT4) were analyzed both as continuous variables and as quartiles to assess linear trends. Model 1 was unadjusted; Model 2 was adjusted for age and sex; Model 3 was further adjusted for age, sex, diastolic blood pressure, systolic blood pressure, Current smoking, and Current drinking, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box–Cox transformed), uric acid (Box–Cox transformed), C-reactive protein (Box–Cox transformed), TSH (Box–Cox transformed), total cholesterol (Box–Cox transformed), triglycerides (Box–Cox transformed), HDL-C (Box–Cox transformed), AST (Box–Cox transformed), ALT (Box–Cox transformed). P values for linear trend across quartiles were obtained by modeling the median value of each quartile as a continuous variable. Bold P values indicate statistical significance (P < 0.05). Subgroup analyses of FT3/FT4 and BMI The subgroup analyses of the association between FT3/FT4 ratio and BMI are summarized in Table 4. The association remained statistically significant in both age subgroups (<60 years: β = 8.06, 95% CI: 2.64–13.48, P = 0.004; ≥60 years: β = 7.63, 95% CI: 0.20–15.06, P = 0.045) and in males (β = 6.65, 95% CI: 1.56–11.73, P = 0.011). The association was also significant among non-smokers (β = 9.76, 95% CI: 3.94–15.57, P = 0.001) and non-drinkers (β = 8.35, 95% CI: 2.54–14.17, P = 0.005). In females ( P = 0.145), smokers ( P = 0.509), and drinkers ( P = 0.070), the associations were not statistically significant. No significant interactions were observed across age, sex, smoking, or drinking status (all P for interaction > 0.05). Table 4. Subgroup analysis of the association between FT3/FT4 ratio and BMI. BMI FT3/FT4 N β (95% CI) P -value P for interaction AGE 0.578 =60 491 7.63 (0.20, 15.06) 0.045 SEX 0.976 Female 523 6.29 (-2.14, 14.73) 0.145 Male 885 6.65 (1.56, 11.73) 0.011 Current smoking 0.072 No 891 9.76 (3.94, 15.57) 0.001 YES 422 2.32 (-4.56, 9.21) 0.509 Current drinking 0.525 No 880 8.35 (2.54, 14.17) 0.005 YES 431 6.54 (-0.49, 13.57) 0.070 β and 95% confidence intervals (CIs) for the association between FT3/FT4 ratio and BMI were estimated using multivariable linear regression models. Analyses were stratified by age (<60 or ≥60 years), sex, current smoking status, and current drinking status. All models were adjusted for age, sex, diastolic blood pressure, systolic blood pressure, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box–Cox transformed), uric acid (Box–Cox transformed), C-reactive protein (Box–Cox transformed), TSH (Box–Cox transformed), total cholesterol (Box–Cox transformed), triglycerides (Box–Cox transformed), HDL-C (Box–Cox transformed), AST (Box–Cox transformed), ALT (Box–Cox transformed), Current drinking, and Current smoking. P values for interaction were calculated by including the cross-product term between FT3/FT4 ratio and each subgroup variable. Bold P values indicate statistical significance ( P < 0.05). Curve fitting and threshold effect analysis Generalized additive models demonstrated a nonlinear relationship between FT3/FT4 and BMI ( P for nonlinearity = 0.006) (Figure 1). Threshold effect analysis identified an inflection point at FT3/FT4 = 0.41. Below this threshold, FT3/FT4 was positively associated with BMI (β = 9.08, 95% CI: 4.13–14.03, P <0.001). Above the threshold, the association was not significant (β = –28.87, 95% CI: –64.85 to 7.10, P = 0.116). The difference between slopes was statistically significant ( P = 0.049) (Table 5). Figure 1 Legend The red solid line represents the estimated smooth curve for the association between FT3/FT4 ratio and BMI, based on a generalized additive model (GAM) with Gaussian distribution and identity link function. The blue dotted lines indicate the 95% confidence intervals. The model was adjusted for age, sex, diastolic blood pressure, systolic blood pressure, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box–Cox transformed), uric acid (Box–Cox transformed), C-reactive protein (Box–Cox transformed), TSH (Box–Cox transformed), total cholesterol (Box–Cox transformed), triglycerides (Box–Cox transformed), HDL-C (Box–Cox transformed), AST (Box–Cox transformed), ALT (Box–Cox transformed), current smoking, and current drinking. The smooth term for FT3/FT4 was statistically significant (edf = 1.58, F = 5.35, P = 0.0065), suggesting a non-linear positive association between FT3/FT4 and BMI. Tick marks on the x-axis represent individual observations. Table 5. Threshold Effect Analysis of FT3/FT4 on BMI Using Two-Piecewise Linear Regression Models BMI Adjusted β (95% CI) P -Value FT3/FT4 (Model I) 6.99 (2.61, 11.37) 0.0018 FT3/FT4 (Model II) Inflection point 0.41 FT3/FT4=0.41 -27.11 (-61.73, 7.51) 0.1252 Difference (Segment 2 – 1) -36.14 (-72.53, 0.25) 0.0520 Log likelihood ratio 0.049 Model I represents the linear relationship between FT3/FT4 and BMI after adjustment for age, sex, diastolic blood pressure, systolic blood pressure, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box–Cox transformed), uric acid (Box–Cox transformed), CRP (Box–Cox transformed), TSH (Box–Cox transformed), TC (Box–Cox transformed), TG (Box–Cox transformed), HDL-C (Box–Cox transformed), AST (Box–Cox transformed), ALT (Box–Cox transformed), smoking, and drinking status. Model II is a two-piecewise linear regression model with an estimated inflection point at FT3/FT4 = 0.41. The effect estimates (β) are shown for FT3/FT4 values below and above the threshold. The log-likelihood ratio test was used to compare Model II with the linear model to assess the presence of a threshold effect. Bold values indicate statistical significance (P < 0.05). Mediation analyses of adiposity indicators in the FT3/FT4–BMI relationship Causal mediation analyses were conducted to examine whether adiposity indices mediated the association between FT3/FT4 and BMI. As shown in Figure 2a, waist circumference partially mediated the relationship between FT3/FT4 and BMI, with a mediation effect of 0.23 (95% CI: 0.01–0.47, P =0.048), accounting for 43.6% of the total effect. Figure 2b shows a similar result for visceral fat, which mediated 50.5% of the total effect (estimate = 0.25, 95% CI: 0.02–0.49, P =0.028). For subcutaneous fat (Supplementary Figure 1), the mediation effect did not reach statistical significance ( P =0.066). Figure 2 Legend (a) Waist circumference (WC) partially mediated the association between FT3/FT4 ratio and BMI. The indirect effect was 0.23 (95% CI: 0.01–0.47, P = 0.048), accounting for 43.6% of the total effect. The total effect was 0.52 ( P < 0.0001), and the direct effect was 0.29 (95% CI: 0.10–0.48, P = 0.002). (b) Visceral fat (VF) showed a similar mediation effect, with an indirect effect of 0.25 (95% CI: 0.02–0.49, P = 0.028), accounting for 50.5% of the total effect. The total effect was 0.50 (P < 0.0001), while the direct effect was not statistically significant (0.25, 95% CI: –0.01–0.49, P = 0.07). Causal mediation analyses were performed using nonparametric bootstrap methods with 1,000 resamples, adjusting for age, sex, diastolic blood pressure, systolic blood pressure, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box–Cox transformed), uric acid (Box–Cox transformed), C-reactive protein (Box–Cox transformed), TSH (Box–Cox transformed), total cholesterol (Box–Cox transformed), triglycerides (Box–Cox transformed), HDL-C (Box–Cox transformed), AST (Box–Cox transformed), ALT (Box–Cox transformed), current smoking, and current drinking. Supplementary Figure 1 Legend Subcutaneous fat (SF) was examined as a potential mediator of the association between FT3/FT4 ratio and BMI. The indirect effect was 0.23 (95% CI: –0.02 to 0.49, P = 0.07), accounting for 46.5% of the total effect, but did not reach statistical significance. The total effect was 0.50 ( P < 0.0001), and the direct effect remained significant (0.27, 95% CI: 0.03–0.49, P = 0.038). Causal mediation analysis was performed using nonparametric bootstrap methods (1,000 resamples), adjusting for relevant confounders. Discussion In this study, FT3/FT4 was positively associated with BMI in euthyroid patients with type 2 diabetes, independent of major confounders. Exploratory analyses further revealed that central adiposity partially mediated this association, and a nonlinear threshold-dependent pattern was identified. Our results are in line with previous studies conducted in various populations. A community-based study reported that higher FT3/FT4 was significantly associated with both metabolically healthy and unhealthy obesity (ORs 1.678–2.883)(14). In a large health check-up cohort of 29,386 adults, FT3/FT4 was associated with fatty liver and multiple metabolic abnormalities(15). Another investigation demonstrated strong correlations between FT3/FT4 and visceral fat area, particularly among individuals with obesity(16). Moreover, FT3/FT4 has been linked to dyslipidemia, hyperuricemia, and altered body composition(17-19). Collectively, these findings support the notion that FT3/FT4 may serve as a useful biomarker for obesity and related metabolic traits. Nevertheless, some studies have reported inconsistent results. Several investigations found no significant associations between FT3/FT4 and metabolic syndrome in the general population(20), whereas others suggested sex-specific differences or variation across BMI strata(21, 22). These discrepancies may be explained by differences in study populations (e.g., general vs. diabetic, obesity severity, sex distribution), outcomes assessed (e.g., lipids, body composition, NAFLD), and methodological approaches, including the lack of mediation or nonlinear analyses. Importantly, most previous studies treated FT3/FT4 as a continuous variable without examining potential threshold effects or mediating pathways. In contrast, our study in euthyroid T2DM patients not only confirmed the independent association between FT3/FT4 and obesity but also revealed the mediating role of central adiposity and a potential threshold-dependent pattern, thereby extending the existing evidence base. Several biological mechanisms may underlie these associations. Obesity and inflammation disrupt the deiodinase profile, characterized by reduced DIO2 and increased DIO3 activity, which limit local T3 availability and create a resistance-like state(23, 24). As a compensatory response, circulating FT3 levels may rise, resulting in an increased FT3/FT4 ratio(25). Therefore, elevated FT3/FT4 may not reflect enhanced sensitivity but rather reduced tissue responsiveness accompanied by compensation, which aligns with our findings(26). Inflammatory cytokines such as TNF-α and IL-6 further suppress DIO2 and activate DIO3 through oxidative stress(19, 27), whereas T3 can inhibit NF-κB and JNK signaling, reduce IL-6–induced CRP production, and promote M2 macrophage polarization(28). These mechanisms may explain the observed decrease in CRP despite higher FT3/FT4(12). Energy metabolism and body composition provide another pathway(29, 30). T3 activates the AMPK–SIRT1–PGC1α and cAMP–PKA pathways, enhancing mitochondrial biogenesis, fatty acid mobilization, and thermogenesis(31, 32). Experimental studies showed that T2 induces browning of white adipose tissue, improves local inflammation and vascularization, and alleviates insulin resistance in high-fat diet models(32). Population studies demonstrated strong associations between FT3/FT4 and visceral fat area as well as the fat-to-muscle ratio(17, 33). Loss of skeletal muscle not only reduces DIO2-mediated T3 generation but also limits GLUT4-dependent glucose uptake, aggravating metabolic imbalance(34). These findings(35, 36) suggest that reduced thermogenesis and abnormal body composition may jointly mediate the FT3/FT4–obesity relationship, supported by our finding that waist circumference partly mediated this association. Finally, the relationship between FT3/FT4 and metabolic traits appears nonlinear. Population-based studies have reported threshold effects, where associations plateau beyond a certain FT3/FT4 level(37). This phenomenon may reflect a transition from compensation to decompensation(37). At lower FT3/FT4 levels, increased peripheral T4-to-T3 conversion enhances energy expenditure(38). However, with intensified inflammation, lipotoxicity, and insulin resistance, deiodinase dysregulation (↓DIO2, ↑DIO3) and reduced receptor sensitivity limit T3 action(39, 40). As a result, further increases in circulating FT3/FT4 may not translate into stronger tissue effects, leading to the threshold or plateau associations observed in our study. Our findings have important clinical implications. The FT3/FT4 ratio can be readily calculated from routine thyroid function tests, making it an easily accessible biomarker in clinical settings. Unlike BMI or waist circumference, which provide only anthropometric information, FT3/FT4 additionally reflects hormonal sensitivity and metabolic adaptation(35). This dual nature may help identify patients with T2DM who are at higher risk of obesity and related metabolic complications, even when conventional anthropometric measures are available. Moreover, the observed mediation effect of central adiposity and the threshold-dependent pattern highlight the importance of considering both hormonal and body composition factors when assessing obesity risk. Incorporating FT3/FT4 into risk stratification models may therefore improve early risk identification and inform personalized strategies for obesity management in this high-risk population. This study has several strengths. It included a large sample of euthyroid patients with T2DM and employed comprehensive statistical approaches, including multivariable regression, subgroup, mediation, and nonlinear analyses. These methods consistently supported the independent association between FT3/FT4 and BMI and provided novel insights into potential mediating pathways and threshold effects. Several limitations should also be considered. First, the cross-sectional design precludes causal inference, and longitudinal studies are needed to establish temporal relationships between FT3/FT4 and obesity. Second, although multiple confounders were adjusted for, residual confounding cannot be fully ruled out. Third, this was a single-center study in a Chinese population, which may limit the generalizability of the findings to other ethnic groups or clinical settings. Finally, we focused on BMI and imaging-derived fat indices to characterize adiposity, but did not assess other relevant outcomes such as long-term metabolic complications or cardiovascular events. Future longitudinal studies incorporating mechanistic exploration are warranted to validate and extend these findings. Conclusion In conclusion, the FT3/FT4 ratio was independently associated with BMI in euthyroid patients with T2DM, with central adiposity acting as a partial mediator and a nonlinear threshold effect identified. These findings suggest that FT3/FT4 may serve as a simple and informative biomarker for obesity risk stratification in this population. Abbreviations BMI, Body Mass Index (kg/m²); DBP, diastolic blood pressure (mmHg); SBP, systolic blood pressure (mmHg); A1C, glycated hemoglobin (%); TC, total cholesterol (mmol/L); HDLC, high-density lipoprotein cholesterol (mmol/L); LDLC, low-density lipoprotein cholesterol (mmol/L); TG, triglycerides (mg/dL); EGFR, estimated glomerular filtration rate (mL/min/1.73 m²); UA, uric acid (µmol/L); AST, aspartate aminotransferase (U/L); ALT, alanine aminotransferase (U/L); CRP, C-reactive protein (mg/L); WBC, white blood cell count (×10⁹/L); WC, waist circumference (cm); VF, visceral fat (cm²); SF, subcutaneous fat (cm²); FT3, free triiodothyronine (pmol/L); FT4, free thyroxine (pmol/L); TSH, thyroid-stimulating hormone (mIU/mL); FT3/FT4, ratio of FT3 to FT4. Declarations Acknowledgements Not applicable. Authors ’ contributions Authors and Affiliations The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, Zhengjiang China Chunyan Zhu: [email protected] , ORCID: 0009-0006-1148-1753. Xianglan Liu: [email protected] . Junhua Yu: [email protected] . Liangyan Hua: [email protected] . Ziru Fang: [email protected] . Yiming Zhang: [email protected] , ORCID: 0009-0006-2698-9883. Zichen Rao: [email protected] , ORCID: 0009-0005-6788-4829. CZ conceived the study, performed statistical analyses, and drafted the manuscript. ZR and YZ contributed to study design, data interpretation, methodological supervision, and critical revision of the manuscript. XL, JY, LH, and ZF were responsible for data collection, investigation, and reviewing the manuscript for important intellectual content. All authors read and approved the final version of the manuscript. Funding The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article. Availability of data and materials Data cannot be shared publicly because of patient privacy and confidentiality restrictions. Data are available from the Quzhou Affiliated Hospital of Wenzhou Medical University Institutional Data Access / Ethics Committee (contact via [email protected] ) for researchers who meet the criteria for access to confidential data. Ethics approval and consent to participate The studies involving humans were approved by The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital Medical Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. Consent for publication Not applicable Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Hill M, Yang Y, Zhang L, Sun Z, Jia G, Parrish A et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metab Clin Exp. 2021:154766. Scurt F, Ganz M, Herzog C, Bose K, Mertens P, Chatzikyrkou C. Association of metabolic syndrome and chronic kidney disease. Obes Rev. 2023;25. Adeva-Andany M, Fernández-Fernández C, Carneiro-Freire N, Castro-Quintela E, Pedre-Piñeiro A, Seco-Filgueira M. Insulin resistance underlies the elevated cardiovascular risk associated with kidney disease and glomerular hyperfiltration. Rev Cardiovasc Med. 2020;21(1):41–56. Prausmüller S, Resl M, Arfsten H, Spinka G, Wurm R, Neuhold S et al. Performance of the recommended ESC/EASD cardiovascular risk stratification model in comparison to SCORE and NT-proBNP as a single biomarker for risk prediction in type 2 diabetes mellitus. Cardiovasc Diabetol. 2021;20. Bandera E, Fay S, Giovannucci E, Leitzmann M, Marklew R, McTiernan A et al. The use and interpretation of anthropometric measures in cancer epidemiology: A perspective from the world cancer research fund international continuous update project. Int J Cancer. 2016;139. Yiruo SS, Yan Y, Zhang S, Liu S, Tang Z et al. Adipocyte thyroid hormone β receptor-mediated hormone action fine-tunes the intracellular glucose and lipid metabolism and systemic homeostasis. Diabetes. 2023. Pleić N, Gunjača I, Leko MB, Zemunik T. Thyroid function and metabolic syndrome: a two-sample bidirectional Mendelian randomization study. J Clin Endocrinol Metab. 2023. Wei Y, Li X, Cui R, Liu J, Wang G-Y. Associations between sensitivity to thyroid hormones and insulin resistance in euthyroid adults with obesity. Front Endocrinol. 2024;15. Chen Y, Li X-Z, Dai Y, Zhang J. The Association Between COVID-19 and Thyroxine Levels: A Meta-Analysis. Front Endocrinol. 2022;12. Lang X, Li Y, Zhang D, Zhang Y, Wu N, Zhang Y. FT3/FT4 ratio is correlated with all-cause mortality, cardiovascular mortality, and cardiovascular disease risk: NHANES 2007–2012. Front Endocrinol. 2022;13. Štěpánek L, Horáková D, Štěpánek L, Janout V, Janoutová J, Bouchalová K et al. Free triiodothyronine/free thyroxine (FT3/FT4) ratio is strongly associated with insulin resistance in euthyroid and hypothyroid adults: a cross-sectional study. Endokrynologia Polska. 2020. Neves J, Fontes-Carvalho R, Borges-Canha M, Leite A, Von Hafe M, Vale C et al. Association of thyroid function, within the euthyroid range, with cardiovascular risk: The EPIPorto study. Front Endocrinol. 2022;13. Liu M, Huang Y, Huang Z, Huang Q, Guo X, Wang Y et al. Prevalence of metabolic syndrome and its associated factors in Chinese patients with ankylosing spondylitis [Corrigendum]. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy. 2019;12:729-. Nie X, Ma X, Xu Y, Shen Y, Wang Y, Bao Y. Characteristics of Serum Thyroid Hormones in Different Metabolic Phenotypes of Obesity. Front Endocrinol (Lausanne). 2020;11:68. Yu L, Li Z, Yang R, Pan G, Cheng Q, He Y, et al. Impaired Sensitivity to Thyroid Hormones Is Associated With Elevated Blood Glucose in Coronary Heart Disease. Front Endocrinol (Lausanne). 2022;13:895843. Shi L, Wang J, Yang L, Zhao W, Zhang M, Yu A, et al. Impaired sensitivity to thyroid hormones is associated with ASCVD risk factors in Beijing in euthyroid population: a cross-sectional study. Front Endocrinol (Lausanne). 2025;16:1602202. Li Y, Zhang Q, Chen L, Wang Y, Ye Q, Liu W, et al. Impaired sensitivity to thyroid hormones is associated with increased body fat mass/muscle mass ratio (F/M) in the euthyroid population. Diabetol Metab Syndr. 2025;17(1):128. Cao B, Li K, Ke J, Zhao D. Impaired Sensitivity to Thyroid Hormones Is Associated With the Change of Abdominal Fat in Euthyroid Type 2 Diabetes Patients: A Retrospective Cohort Study. J Diabetes Res. 2024;2024:8462987. Zhou X, Zhang Y, Li Z. Impaired sensitivity to thyroid hormones is positively associated to metabolic syndrome severity in euthyroid Chinese adults as revealed by a cross-sectional study. Front Endocrinol (Lausanne). 2025;16:1552484. Duan X-Y, Fu J-L, Sun L-N, Mu Z-J, Xiu S-L. Association between sensitivity to thyroid hormones and non-high-density lipoprotein cholesterol levels in patients with type 2 diabetes mellitus. World J Diabetes. 2024;15(10):2081–92. Zhou L, Jiang L, An Y, Liu J, Wang G, Wang Y, et al. Association of Sensitivity to Thyroid Hormones and Non-Alcoholic Fatty Liver Disease and the Severity of Liver Fibrosis in Euthyroid Adults: A Retrospective Study. Diabetes Metab Syndr Obes. 2025;18:479–90. Chen F, Chen R, Zhou J, Xu W, Zhou J, Chen X, et al. Impaired Sensitivity to Thyroid Hormones is Associated with Central Obesity in Euthyroid Type 2 Diabetes Mellitus Patients with Overweight and Obesity. Diabetes Metab Syndr Obes. 2024;17:3379–96. Fontenelle L, Feitosa MM, Severo J, De Freitas TEC, Morais JBS, Torres–Leal F, et al. Thyroid Function in Human Obesity: Underlying Mechanisms. Horm Metab Res. 2016;48:787–94. Biondi B. Subclinical Hypothyroidism in Patients with Obesity and Metabolic Syndrome: A Narrative Review. Nutrients. 2023;16. Rohm T, Meier D, Olefsky J, Donath M. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55 1:31–55. Liu Z-M, Li G, Wu Y, Zhang D, Zhang S, Hao Y-T et al. Increased Central and Peripheral Thyroid Resistance Indices During the First Half of Gestation Were Associated With Lowered Risk of Gestational Diabetes—Analyses Based on Huizhou Birth Cohort in South China. Front Endocrinol. 2022;13. Małujło-Balcerska E, Pietras T. Deiodinase Types 1 and 3 and Proinflammatory Cytokine Values May Discriminate Depressive Disorder Patients from Healthy Controls. J Clin Med. 2023;12. Muazzez A, Shimi G, Balam FH, Ghorbani A, Zand H. Different Effects of Obesity and Fasting on the Expression of Type 3 Deiodinase and Thyroid Hormone Receptors in the Liver and Visceral Adipose Tissue of C57BL/6 Male Mice. Indian J Endocrinol Metab. 2024;28(3):320–6. Hopkins M, Blundell J. Energy balance, body composition, sedentariness and appetite regulation: pathways to obesity. Clin Sci. 2016;130 18:1615–28. Judge A, Dodd M. Metabolism. Essays in Biochemistry. 2020;64:607 – 47. Sabatino L, Vassalle C. Thyroid Hormones and Metabolism Regulation: Which Role on Brown Adipose Tissue and Browning Process? Biomolecules. 2025;15(3). Petito G, Cioffi F, Magnacca N, de Lange P, Senese R, Lanni A. Adipose Tissue Remodeling in Obesity: An Overview of the Actions of Thyroid Hormones and Their Derivatives. Pharmaceuticals (Basel). 2023;16(4). Yang S, Liao K, Zhou L, Zhang S, Wu J. Body roundness index, thyroid hormones, and threshold effects in US adults: a cross-sectional study from NHANES. Front Nutr. 2025;12:1539022. Yao N, Liu L, Jiang Y, Wang D, Lu Q, Zeng Y, et al. Peripheral Thyroid Hormone Sensitivity Mediates the Association Between Body Composition and Diabetes in Euthyroid Adults. J Cachexia Sarcopenia Muscle. 2025;16(5):e70061. Deng L, Zheng X, Shuai P, Yu X. Thyroid-Related Hormones Changes Predict Changes in Anthropometric Measures and Incidence of Obesity in Chinese Euthyroid Persons. Horm Metab Res. 2022;55:684–91. Nie X, Xiaojing, Xu Y, Shen Y, Wang Y, Bao Y. Characteristics of Serum Thyroid Hormones in Different Metabolic Phenotypes of Obesity. Front Endocrinol. 2020;11. Li Y, Pan T, Wang L, Wang Y, Gong Y, Wang G et al. Increased FT3/FT4 ratio in a certain range is associated with decreased glycemic variability in patients with type 2 diabetes. Sci Rep. 2024;14. Shu L, Hoo R, Wu X, Pan Y, Lee I, Cheong L et al. A-FABP mediates adaptive thermogenesis by promoting intracellular activation of thyroid hormones in brown adipocytes. Nat Commun. 2017;8. De Vries E, Surovtseva O, Vos W, Kunst R, Van Beeren M, Kwakkel J et al. Down regulation of type 3 deiodinase in the hypothalamus during inflammation. Thyroid: official J Am Thyroid Association. 2019. Russo S, Salas-Lucia F, Bianco A. Deiodinases and the Metabolic Code for Thyroid Hormone Action. Endocrinology. 2021. Additional Declarations No competing interests reported. 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06:47:38","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169588,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7803754/v1/1cc79727cc94eb2eded15c53.html"},{"id":94760214,"identity":"70be41ee-177a-4304-b85d-b78a35fad51e","added_by":"auto","created_at":"2025-10-30 11:54:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34028,"visible":true,"origin":"","legend":"\u003cp\u003eThe red solid line represents the estimated smooth curve for the association between FT3/FT4 ratio and BMI, based on a generalized additive model (GAM) with Gaussian distribution and identity link function. The blue dotted lines indicate the 95% confidence intervals. The model was adjusted for age, sex, diastolic blood pressure, systolic blood pressure, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box–Cox transformed), uric acid (Box–Cox transformed), C-reactive protein (Box–Cox transformed), TSH (Box–Cox transformed), total cholesterol (Box–Cox transformed), triglycerides (Box–Cox transformed), HDL-C (Box–Cox transformed), AST (Box–Cox transformed), ALT (Box–Cox transformed), current smoking, and current drinking. The smooth term for FT3/FT4 was statistically significant (edf = 1.58, F = 5.35, \u003cem\u003eP\u003c/em\u003e = 0.0065), suggesting a non-linear positive association between FT3/FT4 and BMI. Tick marks on the x-axis represent individual observations.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7803754/v1/6f0d9a9d0ad1a64ebe790d85.png"},{"id":94760217,"identity":"af12582c-6a23-4983-a198-e30488877a68","added_by":"auto","created_at":"2025-10-30 11:54:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60102,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Waist circumference (WC) partially mediated the association between FT3/FT4 ratio and BMI. The indirect effect was 0.23 (95% CI: 0.01–0.47, \u003cem\u003eP\u003c/em\u003e = 0.048), accounting for 43.6% of the total effect. The total effect was 0.52 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), and the direct effect was 0.29 (95% CI: 0.10–0.48, \u003cem\u003eP\u003c/em\u003e = 0.002).\u003c/p\u003e\n\u003cp\u003e(b) Visceral fat (VF) showed a similar mediation effect, with an indirect effect of 0.25 (95% CI: 0.02–0.49, \u003cem\u003eP\u003c/em\u003e= 0.028), accounting for 50.5% of the total effect. The total effect was 0.50 (P \u0026lt; 0.0001), while the direct effect was not statistically significant (0.25, 95% CI: –0.01–0.49, \u003cem\u003eP\u003c/em\u003e = 0.07).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7803754/v1/1cb9bcbbf6fd06b58d5905e3.png"},{"id":103197656,"identity":"b5596547-a99d-4fc9-8dc2-4cbf0d6eb59a","added_by":"auto","created_at":"2026-02-23 04:40:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1666294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7803754/v1/dbfde2c7-4236-47a8-a770-5b080d3f75a6.pdf"},{"id":94824420,"identity":"f12d6376-09ff-42f6-a772-53b528e41282","added_by":"auto","created_at":"2025-10-31 06:48:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":724000,"visible":true,"origin":"","legend":"","description":"","filename":"2022110MMC1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7803754/v1/4ec0fdd9e0d46667acd0627c.pdf"},{"id":94760219,"identity":"c7cff14d-71dc-4c88-92c8-1eb788351bd2","added_by":"auto","created_at":"2025-10-30 11:54:41","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27523,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7803754/v1/2b5d84e86d5a51c71539fce1.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between FT3/FT4 ratio and obesity in euthyroid patients with type 2 diabetes: a cross-sectional study with mediation and threshold analyses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity and type 2 diabetes mellitus (T2DM) are two closely linked global health challenges. Their coexistence accelerates insulin resistance, worsens glycemic control, and markedly increases the risk of cardiovascular disease, kidney dysfunction, and premature mortality(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Epidemiological studies indicate that more than half of patients with T2DM are overweight or obese, underscoring the urgent need for accurate risk stratification(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Although anthropometric measures such as body mass index (BMI) and waist circumference are widely used, they do not fully capture the underlying biological processes(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Identifying reliable and easily accessible biomarkers is therefore essential for early risk identification and guiding individualized management in this population.\u003c/p\u003e\u003cp\u003eThyroid hormones are key regulators of energy expenditure, glucose and lipid metabolism, and body composition(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Even within the euthyroid range, subtle variations in thyroid function may influence metabolic outcomes(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Epidemiological and clinical studies have linked thyroid hormones to obesity, insulin resistance, and the risk of T2DM, suggesting a role in metabolic homeostasis(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, most prior research has focused on individual parameters such as free triiodothyronine (FT3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH), with inconsistent results(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). These inconsistencies may be explained by the fact that single hormone measurements do not fully capture tissue sensitivity to thyroid hormones.\u003c/p\u003e\u003cp\u003eThe ratio of FT3 to FT4 (FT3/FT4) has been proposed as a marker of thyroid hormone sensitivity, reflecting peripheral conversion and tissue responsiveness(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Recent studies suggest that higher FT3/FT4 is linked to insulin resistance, metabolic syndrome, and cardiovascular risk(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, evidence on its relationship with obesity in euthyroid patients with T2DM is scarce. In particular, whether this association is mediated by fat distribution or follows a nonlinear pattern remains unclear.\u003c/p\u003e\u003cp\u003eTherefore, the present study aimed to investigate the association between FT3/FT4 and BMI in euthyroid patients with T2DM. We further explored the potential mediating role of waist circumference, visceral fat, and subcutaneous fat, and assessed whether the relationship exhibited nonlinear and threshold effects.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThis study was conducted at the Metabolic Management Center (MMC) of the Department of Endocrinology, Quzhou People\u0026rsquo;s Hospital, Wenzhou Medical University, Zhejiang Province, China. Consecutive adult patients with type 2 diabetes (T2DM) who attended the MMC between December 2022 and June 2025 were enrolled. The diagnosis of T2DM was based on the 2023 American Diabetes Association (ADA) criteria.\u003c/p\u003e\u003cp\u003eExclusion criteria were as follows: (i) acute diabetic complications (e.g., diabetic ketoacidosis or hyperosmolar hyperglycemic state); (ii) known thyroid dysfunction, current use of thyroid-related medications, or history of thyroid surgery/radioactive iodine therapy; (iii) recent use of medications known to interfere with thyroid function (e.g., glucocorticoids, amiodarone); (iv) severe hepatic or renal insufficiency; (v) active malignancy, cachexia, or other severe systemic diseases; (vi) pregnancy or lactation; and (vii) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years.\u003c/p\u003e\u003cp\u003e The study protocol was approved by the Ethics Committee of Quzhou People\u0026rsquo;s Hospital, Wenzhou Medical University (Approval No. 2022\u0026ndash;110). Written informed consent was obtained from all participants.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003eDemographic data (age, sex), medical history, smoking status, and drinking status were collected at baseline. Anthropometric parameters: Body mass index (BMI) was calculated as weight (kg) divided by height squared (m\u0026sup2;). Waist circumference (WC) was measured at the midpoint between the lowest rib and the iliac crest after normal expiration. Visceral fat (VF) and subcutaneous fat (SF) were assessed using a standardized body composition analyzer in the MMC. Blood pressure: Measured in the seated position after at least 5 minutes of rest using an automated sphygmomanometer; the average of two readings was used. Laboratory tests: HbA1c was measured by high-performance liquid chromatography (HPLC). Lipid profile (TC, TG, HDL-C, LDL-C), liver enzymes (AST, ALT), serum creatinine, uric acid, and high-sensitivity C-reactive protein (CRP) were measured by standard automated methods. Estimated glomerular filtration rate (eGFR) was calculated from serum creatinine using the CKD-EPI equation, adjusted for age and sex. Thyroid function tests, including free triiodothyronine (FT3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH), were simultaneously measured using chemiluminescence immunoassay under strict quality control. The reference ranges were: TSH 0.35\u0026ndash;4.94\u0026micro;IU/mL, FT4 9.01\u0026ndash;19.05pmol/L, and FT3 2.43\u0026ndash;6.01pmol/L.\u003c/p\u003e\n\u003ch3\u003eVariable Definitions\u003c/h3\u003e\n\u003cp\u003eBMI: expressed in kg/m\u0026sup2;; obesity was defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2; according to Chinese criteria(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFT3/FT4 ratio: calculated as FT3 (pmol/L) divided by FT4 (pmol/L).\u003c/p\u003e\u003cp\u003eWC, VF, SF: indicators of central and abdominal adiposity measured under standardized MMC protocols.\u003c/p\u003e\u003cp\u003eeGFR: estimated using the CKD-EPI equation based on serum creatinine, age, and sex.\u003c/p\u003e\u003cp\u003eSmoking and drinking: classified as current or not current based on baseline report.\u003c/p\u003e\u003cp\u003eTSH, FT3, FT4: treated as continuous variables and interpreted according to reference ranges.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed to evaluate the association between FT3/FT4 and BMI. Continuous variables were summarized as mean (SD) or median (IQR) depending on distribution, and categorical variables as counts (percentages). Between-group comparisons were performed using the Kruskal-Wallis test for continuous variables and the χ\u0026sup2; test (or Fisher\u0026rsquo;s exact test when appropriate) for categorical variables. Standardized mean differences were reported to assess baseline balance.The primary outcome was BMI. The main exposure was the FT3/FT4 ratio, analyzed as both a continuous variable and by quartiles (lowest quartile as reference). Linear regression models were fitted sequentially: Model 1 was unadjusted; Model 2 adjusted for age and sex; and Model 3 further adjusted for SBP, DBP, WBC, eGFR, LDL-C, HbA1c, uric acid, CRP, TSH, TC, TG, HDL-C, AST, ALT, smoking, and drinking. Skewed covariates (HbA1c, uric acid, CRP, TSH, TC, TG, HDL-C, AST, ALT) were Box\u0026ndash;Cox transformed prior to analysis. Regression results were presented as β coefficients with 95% confidence intervals (CIs). Effect modification by smoking and drinking was evaluated using stratified analyses, and interaction was formally tested by including cross-product terms in the regression models. Nonlinear associations between FT3/FT4 and BMI were assessed using generalized additive models with penalized splines.\u003c/p\u003e\u003cp\u003eTo identify potential threshold effects, a two-piecewise linear regression model was applied, and the inflection point was estimated using likelihood ratio tests. Causal mediation analyses were conducted to explore whether central adiposity mediated the association between FT3/FT4 and BMI. Waist circumference (WC), visceral fat (VF), and subcutaneous fat (SF) were included separately as mediators. Each mediation model adjusted for the same covariates as the fully adjusted regression. Nonparametric bootstrapping with 1,000 resamples (percentile method) was used to estimate the average causal mediation effect (ACME), average direct effect (ADE), total effect, proportion mediated, and their 95% CIs. All analyses were based on available data. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analyses were conducted using EmpowerStats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.empowerstats.com\" target=\"_blank\"\u003ewww.empowerstats.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and R software (version 4.2.2).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthics Statement\u003c/h3\u003e\n\u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Quzhou People\u0026rsquo;s Hospital, Wenzhou Medical University (Approval No. 2022\u0026ndash;110; November 18, 2022). Written informed consent was obtained from all participants before enrollment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eClinical characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of the study population are summarized in Table 1. A total of 1,408 euthyroid patients with type 2 diabetes were included, including 1,127 patients with BMI \u0026lt; 28 kg/m\u0026sup2; and 281 with BMI \u0026ge; 28 kg/m\u0026sup2;. Patients with obesity were younger (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and had higher diastolic and systolic blood pressure, HbA1c, total cholesterol, LDL-C, triglycerides, uric acid, AST, ALT, C-reactive protein, white blood cell counts, waist circumference, visceral fat, and subcutaneous fat levels compared with the non-obese group (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). HDL-C levels were lower in the obese group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). FT3 and FT3/FT4 values were significantly higher in the obese group (both \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), while FT4 and TSH levels did not differ significantly between groups. Smoking and drinking status were also similar between groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBaseline characteristics of euthyroid patients with type 2 diabetes according to obesity status (BMI \u0026lt;28 vs. \u0026ge;28 kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u0026lt;28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u0026ge;28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAGE (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.24 \u0026plusmn; 11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.63 \u0026plusmn; 15.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEX (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e417 (37.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106 (37.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e710 (63.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e175 (62.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e707 (67.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e173 (65.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e341 (32.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90 (34.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e712 (67.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e179 (68.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e338 (32.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84 (31.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.55 \u0026plusmn; 8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.55 \u0026plusmn; 9.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126.11 \u0026plusmn; 13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129.81 \u0026plusmn; 13.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHbA1C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;8.94 (7.37-11.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;9.82 (8.40-11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.58 (3.87-5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.82 (4.07-5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDLC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12 (0.94-1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.85-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.75 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.92 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTG (mg/dL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.77 (1.19-2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.37 (1.49-3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.63 \u0026plusmn; 14.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107.72 \u0026plusmn; 16.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e305.20 (256.00-370.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e346.65 (303.02-416.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.90 (14.70-23.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.65 (17.67-42.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.60 (14.20-33.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;37.00 (22.10-67.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP (mg/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.39 (0.70-3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.92 (1.51-5.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.34 \u0026plusmn; 1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.97 \u0026plusmn; 2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.50 (82.00-91.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.00 (96.00-106.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVF (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.00 (66.00-105.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e131.00 (111.00-158.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSF (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e151.00 (122.00-186.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e254.00 (212.00-291.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFT3 (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.93 \u0026plusmn; 0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.09 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFT4 (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.67 \u0026plusmn; 1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.77 \u0026plusmn; 1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTSH (\u0026mu;IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.37 (0.93-2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.47 (1.02-2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFT3/FT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.32 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; SD for normally distributed continuous variables, median (interquartile range) for skewed variables, and n (%) for categorical variables. P values were calculated using independent t tests, Mann\u0026ndash;Whitney U tests, or chi-squared tests as appropriate. Bold P values indicate statistical significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, Body Mass Index (kg/m\u0026sup2;); DBP, diastolic blood pressure (mmHg); SBP, systolic blood pressure (mmHg); A1C, glycated hemoglobin (%); TC, total cholesterol (mmol/L); HDLC, high-density lipoprotein cholesterol (mmol/L); LDLC, low-density lipoprotein cholesterol (mmol/L); TG, triglycerides (mg/dL); EGFR, estimated glomerular filtration rate (mL/min/1.73 m\u0026sup2;); UA, uric acid (\u0026micro;mol/L); AST, aspartate aminotransferase (U/L); ALT, alanine aminotransferase (U/L); CRP, C-reactive protein (mg/L); WBC, white blood cell count (\u0026times;10⁹/L); WC, waist circumference (cm); VF, visceral fat (cm\u0026sup2;); SF, subcutaneous fat (cm\u0026sup2;); FT3, free triiodothyronine (pmol/L); FT4, free thyroxine (pmol/L); TSH, thyroid-stimulating hormone (\u0026mu;IU/mL); FT3/FT4, ratio of FT3 to FT4.\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics according to FT3/FT4 quartiles are shown in Table 2. Patients in higher quartiles were younger and more likely to be male (both \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). HbA1c, triglycerides, uric acid, AST, ALT, waist circumference, visceral fat, and subcutaneous fat levels increased across FT3/FT4 quartiles (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). In contrast, CRP levels decreased (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). FT3 levels increased and FT4 levels decreased from Q1 to Q4 (both \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). BMI showed a significant upward trend across FT3/FT4 quartiles (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Participant characteristics classified by FT3/FT4 quartiles.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAGE (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.43 \u0026plusmn; 13.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.07 \u0026plusmn; 13.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.21 \u0026plusmn; 12.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.23 \u0026plusmn; 12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEX (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e143 (43.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e171 (45.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99 (28.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110 (30.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e188 (56.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e202 (54.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e248 (71.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e247 (69.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e224 (72.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e245 (71.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e204 (62.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e207 (62.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e230 (74.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e246 (71.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e204 (62.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e211 (63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98 (28.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.72 \u0026plusmn; 9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.48 \u0026plusmn; 9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.80 \u0026plusmn; 8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.39 \u0026plusmn; 9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;125.68 \u0026plusmn; 14.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127.56 \u0026plusmn; 14.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127.34 \u0026plusmn; 13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126.71 \u0026plusmn; 12.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHbA1C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.47 (8.38-12.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.62 (7.56-11.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.90 (7.34-10.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.32 (7.11-9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.49 (3.81-5.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;4.62 (3.96-5.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;4.69 (4.04-5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.67 (3.79-5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDLC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;1.12 (0.93-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10 (0.94-1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;1.08 (0.87-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;1.08 (0.93-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.80 \u0026plusmn; 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.79 \u0026plusmn; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.81 \u0026plusmn; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.73 \u0026plusmn; 0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTG \u0026nbsp;(mg/dL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;1.61 (1.08-2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.76 (1.20-2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.16 (1.38-3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.05 (1.30-3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.12 \u0026plusmn; 15.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.26 \u0026plusmn; 16.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.78 \u0026plusmn; 14.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106.89 \u0026plusmn; 14.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e308.60 (253.57-369.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e305.20 (245.80-371.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e319.05 (277.42-381.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;326.60 (274.77-392.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAST(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.40 (14.10-24.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.30 (15.22-24.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.70 (15.20-25.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.05 (16.12-29.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALT(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.20 (13.30-30.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.70 (15.57-36.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.20 (15.20-37.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.10 (18.25-45.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP (mg/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.04 (0.99-4.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.56 (0.69-3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50 (0.78-3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.44 (0.71-3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.49 \u0026plusmn; 2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.31 \u0026plusmn; 1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.44 \u0026plusmn; 1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.58 \u0026plusmn; 1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.60 (81.50-94.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89.00 (82.00-95.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89.00 (84.00-96.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89.10 (84.60-95.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVF (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.00 (63.00-110.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.00 (70.00-114.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96.00 (72.00-123.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99.00 (75.75-122.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSF (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e162.00 (123.00-204.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e162.50 (126.00-208.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e168.00 (135.00-220.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e169.50 (133.75-216.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFT3 (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.43 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.84 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.10 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.43 \u0026plusmn; 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFT4 (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.83 \u0026plusmn; 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.94 \u0026plusmn; 1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.41 \u0026plusmn; 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.65 \u0026plusmn; 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTSH (\u0026mu;IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.25 (0.83-1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;1.43 (0.99-2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.41 (1.00-1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.46 (0.99-2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.20 (22.10-26.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.50 (22.70-27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.30 (23.35-27.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.50 (23.69-27.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; SD for normally distributed continuous variables, median (interquartile range) for skewed variables, and n (%) for categorical variables. P values were calculated using independent t tests, Mann\u0026ndash;Whitney U tests, or chi-squared tests as appropriate. Bold P values indicate statistical significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable linear regression analyses for the association between thyroid parameters and BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe associations between thyroid parameters and BMI were examined using multivariable linear regression models (Table 3). In crude models, FT3 and FT3/FT4 were positively associated with BMI, whereas FT4 showed no significant association. After full adjustment for potential confounders, FT3/FT4 remained significantly associated with BMI (\u0026beta; = 6.98, 95% CI: 2.49\u0026ndash;11.48, \u003cem\u003eP\u003c/em\u003e = 0.0024), and FT3 showed a weaker but statistically significant association (\u0026beta; = 0.72, 95% CI: 0.26\u0026ndash;1.17, \u003cem\u003eP\u003c/em\u003e = 0.0021). FT4 and TSH were not significantly associated with BMI in any model (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). A significant linear trend was observed across quartiles of FT3/FT4 (\u003cem\u003eP\u003c/em\u003e for trend = 0.0006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Multivariable linear regression analyses for the association between thyroid parameters and BMI.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eCrude Model (Model 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003ePartially Adjusted Model (Model 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eFully Adjusted Model (Model 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.55 (1.19, 1.91)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.18 (0.80, 1.55)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72 (0.26, 1.17)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003equartile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.40, 1.51)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.83 (0.29, 1.38)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78 (0.16, 1.39)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0133\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.50, 1.62)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.83 (0.29, 1.38)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80 (0.17, 1.42)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0125\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.11 (1.56, 2.67)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.58 (1.01, 2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (0.42, 1.73)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00 (-0.13, 0.14)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06 (-0.19, 0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06 (-0.21, 0.09)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;quartile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.12 (-0.69, 0.45)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.18 (-0.73, 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.19 (-0.78, 0.40)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26 (-0.30, 0.82)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 (-0.43, 0.66)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.19 (-0.79, 0.41)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.13 (-0.69, 0.44)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.34 (-0.89, 0.21)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.38 (-1.00, 0.24)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.9929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.4172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.2493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29 (0.06, 0.52)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0124\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41 (0.18, 0.63)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.27 (-1.06, 0.52)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTSH\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;quartile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.88 (0.32, 1.44)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81 (0.27, 1.35)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22 (-0.65, 1.09)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.40, 1.53)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94 (0.39, 1.48)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11 (-1.11, 1.33)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 (0.45, 1.58)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.20 (0.65, 1.75)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00 (-1.78, 1.78)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.9122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT3/FT4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.53 (8.71, 16.35)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.04 (6.27, 13.81)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.98 (2.49, 11.48)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT3/FT4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;quartile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62 (0.05, 1.18)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58 (0.04, 1.13)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0358\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41 (-0.23, 1.04)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.09 (0.52, 1.66)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96 (0.40, 1.52)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.76 (0.11, 1.41)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0225\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.70 (1.14, 2.27)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.41 (0.86, 1.97)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11 (0.45, 1.76)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026beta; coefficients and 95% confidence intervals (CIs) were estimated using linear regression models with BMI as a continuous outcome. Thyroid parameters (FT3, FT4, TSH, and FT3/FT4) were analyzed both as continuous variables and as quartiles to assess linear trends. Model 1 was unadjusted; Model 2 was adjusted for age and sex; Model 3 was further adjusted for age, sex, diastolic blood pressure, systolic blood pressure, Current smoking, and Current drinking, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box\u0026ndash;Cox transformed), uric acid (Box\u0026ndash;Cox transformed), C-reactive protein (Box\u0026ndash;Cox transformed), TSH (Box\u0026ndash;Cox transformed), total cholesterol (Box\u0026ndash;Cox transformed), triglycerides (Box\u0026ndash;Cox transformed), HDL-C (Box\u0026ndash;Cox transformed), AST (Box\u0026ndash;Cox transformed), ALT (Box\u0026ndash;Cox transformed). \u003cem\u003eP\u0026nbsp;\u003c/em\u003evalues for linear trend across quartiles were obtained by modeling the median value of each quartile as a continuous variable. Bold P values indicate statistical significance (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analyses of FT3/FT4 and BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe subgroup analyses of the association between FT3/FT4 ratio and BMI are summarized in Table 4. The association remained statistically significant in both age subgroups (\u0026lt;60 years: \u0026beta; = 8.06, 95% CI: 2.64\u0026ndash;13.48, \u003cem\u003eP\u003c/em\u003e = 0.004; \u0026ge;60 years: \u0026beta; = 7.63, 95% CI: 0.20\u0026ndash;15.06, \u003cem\u003eP\u003c/em\u003e = 0.045) and in males (\u0026beta; = 6.65, 95% CI: 1.56\u0026ndash;11.73, \u003cem\u003eP\u003c/em\u003e = 0.011). The association was also significant among non-smokers (\u0026beta; = 9.76, 95% CI: 3.94\u0026ndash;15.57, \u003cem\u003eP\u003c/em\u003e = 0.001) and non-drinkers (\u0026beta; = 8.35, 95% CI: 2.54\u0026ndash;14.17, \u003cem\u003eP\u003c/em\u003e = 0.005). In females (\u003cem\u003eP\u003c/em\u003e = 0.145), smokers (\u003cem\u003eP\u003c/em\u003e = 0.509), and drinkers (\u003cem\u003eP\u003c/em\u003e = 0.070), the associations were not statistically significant. No significant interactions were observed across age, sex, smoking, or drinking status (all \u003cem\u003eP\u003c/em\u003e for interaction \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e\u003cstrong\u003eSubgroup analysis of the association between FT3/FT4 ratio and BMI.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFT3/FT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.06 (2.64, 13.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.63 (0.20, 15.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.29 (-2.14, 14.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.65 (1.56, 11.73)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.76 (3.94, 15.57)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32 (-4.56, 9.21)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent drinking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.35 (2.54, 14.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.54 (-0.49, 13.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026beta; and 95% confidence intervals (CIs) for the association between FT3/FT4 ratio and BMI were estimated using multivariable linear regression models. Analyses were stratified by age (\u0026lt;60 or \u0026ge;60 years), sex, current smoking status, and current drinking status. All models were adjusted for age, sex, diastolic blood pressure, systolic blood pressure, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box\u0026ndash;Cox transformed), uric acid (Box\u0026ndash;Cox transformed), C-reactive protein (Box\u0026ndash;Cox transformed), TSH (Box\u0026ndash;Cox transformed), total cholesterol (Box\u0026ndash;Cox transformed), triglycerides (Box\u0026ndash;Cox transformed), HDL-C (Box\u0026ndash;Cox transformed), AST (Box\u0026ndash;Cox transformed), ALT (Box\u0026ndash;Cox transformed), Current drinking, and Current smoking. P values for interaction were calculated by including the cross-product term between FT3/FT4 ratio and each subgroup variable. Bold \u003cem\u003eP\u003c/em\u003e values indicate statistical significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurve fitting and threshold effect analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneralized additive models demonstrated a nonlinear relationship between FT3/FT4 and BMI (\u003cem\u003eP\u003c/em\u003e for nonlinearity = 0.006) (Figure 1). Threshold effect analysis identified an inflection point at FT3/FT4 = 0.41. Below this threshold, FT3/FT4 was positively associated with BMI (\u0026beta; = 9.08, 95% CI: 4.13\u0026ndash;14.03, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001). Above the threshold, the association was not significant (\u0026beta; = \u0026ndash;28.87, 95% CI: \u0026ndash;64.85 to 7.10, \u003cem\u003eP\u003c/em\u003e = 0.116). The difference between slopes was statistically significant (\u003cem\u003eP\u003c/em\u003e = 0.049) (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1 Legend\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe red solid line represents the estimated smooth curve for the association between FT3/FT4 ratio and BMI, based on a generalized additive model (GAM) with Gaussian distribution and identity link function. The blue dotted lines indicate the 95% confidence intervals. The model was adjusted for age, sex, diastolic blood pressure, systolic blood pressure, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box\u0026ndash;Cox transformed), uric acid (Box\u0026ndash;Cox transformed), C-reactive protein (Box\u0026ndash;Cox transformed), TSH (Box\u0026ndash;Cox transformed), total cholesterol (Box\u0026ndash;Cox transformed), triglycerides (Box\u0026ndash;Cox transformed), HDL-C (Box\u0026ndash;Cox transformed), AST (Box\u0026ndash;Cox transformed), ALT (Box\u0026ndash;Cox transformed), current smoking, and current drinking. The smooth term for FT3/FT4 was statistically significant (edf = 1.58, F = 5.35, \u003cem\u003eP\u003c/em\u003e = 0.0065), suggesting a non-linear positive association between FT3/FT4 and BMI. Tick marks on the x-axis represent individual observations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Threshold Effect Analysis of FT3/FT4 on BMI Using Two-Piecewise Linear Regression Models\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted \u0026beta; (95% CI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFT3/FT4 (Model I)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.99 (2.61, 11.37)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFT3/FT4 (Model II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFT3/FT4\u0026lt;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.03 (4.20, 13.86)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFT3/FT4\u0026gt;=0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-27.11 (-61.73, 7.51)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDifference (Segment 2 \u0026ndash; 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-36.14 (-72.53, 0.25)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLog likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel I represents the linear relationship between FT3/FT4 and BMI after adjustment for age, sex, diastolic blood pressure, systolic blood pressure, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box\u0026ndash;Cox transformed), uric acid (Box\u0026ndash;Cox transformed), CRP (Box\u0026ndash;Cox transformed), TSH (Box\u0026ndash;Cox transformed), TC (Box\u0026ndash;Cox transformed), TG (Box\u0026ndash;Cox transformed), HDL-C (Box\u0026ndash;Cox transformed), AST (Box\u0026ndash;Cox transformed), ALT (Box\u0026ndash;Cox transformed), smoking, and drinking status.\u003c/p\u003e\n\u003cp\u003eModel II is a two-piecewise linear regression model with an estimated inflection point at FT3/FT4 = 0.41. The effect estimates (\u0026beta;) are shown for FT3/FT4 values below and above the threshold. The log-likelihood ratio test was used to compare Model II with the linear model to assess the presence of a threshold effect. Bold values indicate statistical significance (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation analyses of adiposity indicators in the FT3/FT4\u0026ndash;BMI relationship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCausal mediation analyses were conducted to examine whether adiposity indices mediated the association between FT3/FT4 and BMI. As shown in Figure 2a, waist circumference partially mediated the relationship between FT3/FT4 and BMI, with a mediation effect of 0.23 (95% CI: 0.01\u0026ndash;0.47, \u003cem\u003eP\u003c/em\u003e=0.048), accounting for 43.6% of the total effect. Figure 2b shows a similar result for visceral fat, which mediated 50.5% of the total effect (estimate = 0.25, 95% CI: 0.02\u0026ndash;0.49, \u003cem\u003eP\u003c/em\u003e=0.028). For subcutaneous fat (Supplementary Figure 1), the mediation effect did not reach statistical significance (\u003cem\u003eP\u003c/em\u003e=0.066).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2 Legend\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Waist circumference (WC) partially mediated the association between FT3/FT4 ratio and BMI. The indirect effect was 0.23 (95% CI: 0.01\u0026ndash;0.47, \u003cem\u003eP\u003c/em\u003e = 0.048), accounting for 43.6% of the total effect. The total effect was 0.52 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), and the direct effect was 0.29 (95% CI: 0.10\u0026ndash;0.48, \u003cem\u003eP\u003c/em\u003e = 0.002).\u003c/p\u003e\n\u003cp\u003e(b) Visceral fat (VF) showed a similar mediation effect, with an indirect effect of 0.25 (95% CI: 0.02\u0026ndash;0.49, \u003cem\u003eP\u003c/em\u003e = 0.028), accounting for 50.5% of the total effect. The total effect was 0.50 (P \u0026lt; 0.0001), while the direct effect was not statistically significant (0.25, 95% CI: \u0026ndash;0.01\u0026ndash;0.49, \u003cem\u003eP\u003c/em\u003e = 0.07).\u003c/p\u003e\n\u003cp\u003eCausal mediation analyses were performed using nonparametric bootstrap methods with 1,000 resamples, adjusting for age, sex, diastolic blood pressure, systolic blood pressure, white blood cell count, estimated glomerular filtration rate, LDL-C, HbA1c (Box\u0026ndash;Cox transformed), uric acid (Box\u0026ndash;Cox transformed), C-reactive protein (Box\u0026ndash;Cox transformed), TSH (Box\u0026ndash;Cox transformed), total cholesterol (Box\u0026ndash;Cox transformed), triglycerides (Box\u0026ndash;Cox transformed), HDL-C (Box\u0026ndash;Cox transformed), AST (Box\u0026ndash;Cox transformed), ALT (Box\u0026ndash;Cox transformed), current smoking, and current drinking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1 Legend\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubcutaneous fat (SF) was examined as a potential mediator of the association between FT3/FT4 ratio and BMI. The indirect effect was 0.23 (95% CI: \u0026ndash;0.02 to 0.49, \u003cem\u003eP\u003c/em\u003e = 0.07), accounting for 46.5% of the total effect, but did not reach statistical significance. The total effect was 0.50 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), and the direct effect remained significant (0.27, 95% CI: 0.03\u0026ndash;0.49, \u003cem\u003eP\u003c/em\u003e = 0.038). Causal mediation analysis was performed using nonparametric bootstrap methods (1,000 resamples), adjusting for relevant confounders.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, FT3/FT4 was positively associated with BMI in euthyroid patients with type 2 diabetes, independent of major confounders. Exploratory analyses further revealed that central adiposity partially mediated this association, and a nonlinear threshold-dependent pattern was identified.\u003c/p\u003e\n\u003cp\u003eOur results are in line with previous studies conducted in various populations. A community-based study reported that higher FT3/FT4 was significantly associated with both metabolically healthy and unhealthy obesity (ORs 1.678–2.883)(14). In a large health check-up cohort of 29,386 adults, FT3/FT4 was associated with fatty liver and multiple metabolic abnormalities(15). Another investigation demonstrated strong correlations between FT3/FT4 and visceral fat area, particularly among individuals with obesity(16). Moreover, FT3/FT4 has been linked to dyslipidemia, hyperuricemia, and altered body composition(17-19). Collectively, these findings support the notion that FT3/FT4 may serve as a useful biomarker for obesity and related metabolic traits.\u003c/p\u003e\n\u003cp\u003eNevertheless, some studies have reported inconsistent results. Several investigations found no significant associations between FT3/FT4 and metabolic syndrome in the general population(20), whereas others suggested sex-specific differences or variation across BMI strata(21, 22). These discrepancies may be explained by differences in study populations (e.g., general vs. diabetic, obesity severity, sex distribution), outcomes assessed (e.g., lipids, body composition, NAFLD), and methodological approaches, including the lack of mediation or nonlinear analyses. Importantly, most previous studies treated FT3/FT4 as a continuous variable without examining potential threshold effects or mediating pathways. In contrast, our study in euthyroid T2DM patients not only confirmed the independent association between FT3/FT4 and obesity but also revealed the mediating role of central adiposity and a potential threshold-dependent pattern, thereby extending the existing evidence base.\u003c/p\u003e\n\u003cp\u003eSeveral biological mechanisms may underlie these associations. Obesity and inflammation disrupt the deiodinase profile, characterized by reduced DIO2 and increased DIO3 activity, which limit local T3 availability and create a resistance-like state(23, 24). As a compensatory response, circulating FT3 levels may rise, resulting in an increased FT3/FT4 ratio(25). Therefore, elevated FT3/FT4 may not reflect enhanced sensitivity but rather reduced tissue responsiveness accompanied by compensation, which aligns with our findings(26). Inflammatory cytokines such as TNF-α and IL-6 further suppress DIO2 and activate DIO3 through oxidative stress(19, 27), whereas T3 can inhibit NF-κB and JNK signaling, reduce IL-6–induced CRP production, and promote M2 macrophage polarization(28). These mechanisms may explain the observed decrease in CRP despite higher FT3/FT4(12).\u003c/p\u003e\n\u003cp\u003eEnergy metabolism and body composition provide another pathway(29, 30). T3 activates the AMPK–SIRT1–PGC1α and cAMP–PKA pathways, enhancing mitochondrial biogenesis, fatty acid mobilization, and thermogenesis(31, 32). Experimental studies showed that T2 induces browning of white adipose tissue, improves local inflammation and vascularization, and alleviates insulin resistance in high-fat diet models(32). Population studies demonstrated strong associations between FT3/FT4 and visceral fat area as well as the fat-to-muscle ratio(17, 33). Loss of skeletal muscle not only reduces DIO2-mediated T3 generation but also limits GLUT4-dependent glucose uptake, aggravating metabolic imbalance(34). These findings(35, 36) suggest that reduced thermogenesis and abnormal body composition may jointly mediate the FT3/FT4–obesity relationship, supported by our finding that waist circumference partly mediated this association.\u003c/p\u003e\n\u003cp\u003eFinally, the relationship between FT3/FT4 and metabolic traits appears nonlinear. Population-based studies have reported threshold effects, where associations plateau beyond a certain FT3/FT4 level(37). This phenomenon may reflect a transition from compensation to decompensation(37). At lower FT3/FT4 levels, increased peripheral T4-to-T3 conversion enhances energy expenditure(38). However, with intensified inflammation, lipotoxicity, and insulin resistance, deiodinase dysregulation (↓DIO2, ↑DIO3) and reduced receptor sensitivity limit T3 action(39, 40). As a result, further increases in circulating FT3/FT4 may not translate into stronger tissue effects, leading to the threshold or plateau associations observed in our study.\u003c/p\u003e\n\u003cp\u003eOur findings have important clinical implications. The FT3/FT4 ratio can be readily calculated from routine thyroid function tests, making it an easily accessible biomarker in clinical settings. Unlike BMI or waist circumference, which provide only anthropometric information, FT3/FT4 additionally reflects hormonal sensitivity and metabolic adaptation(35). This dual nature may help identify patients with T2DM who are at higher risk of obesity and related metabolic complications, even when conventional anthropometric measures are available. Moreover, the observed mediation effect of central adiposity and the threshold-dependent pattern highlight the importance of considering both hormonal and body composition factors when assessing obesity risk. Incorporating FT3/FT4 into risk stratification models may therefore improve early risk identification and inform personalized strategies for obesity management in this high-risk population.\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. It included a large sample of euthyroid patients with T2DM and employed comprehensive statistical approaches, including multivariable regression, subgroup, mediation, and nonlinear analyses. These methods consistently supported the independent association between FT3/FT4 and BMI and provided novel insights into potential mediating pathways and threshold effects.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should also be considered. First, the cross-sectional design precludes causal inference, and longitudinal studies are needed to establish temporal relationships between FT3/FT4 and obesity. Second, although multiple confounders were adjusted for, residual confounding cannot be fully ruled out. Third, this was a single-center study in a Chinese population, which may limit the generalizability of the findings to other ethnic groups or clinical settings. Finally, we focused on BMI and imaging-derived fat indices to characterize adiposity, but did not assess other relevant outcomes such as long-term metabolic complications or cardiovascular events. Future longitudinal studies incorporating mechanistic exploration are warranted to validate and extend these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the FT3/FT4 ratio was independently associated with BMI in euthyroid patients with T2DM, with central adiposity acting as a partial mediator and a nonlinear threshold effect identified. These findings suggest that FT3/FT4 may serve as a simple and informative biomarker for obesity risk stratification in this population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u0026nbsp;BMI, Body Mass Index (kg/m\u0026sup2;); DBP, diastolic blood pressure (mmHg); SBP, systolic blood pressure (mmHg); A1C, glycated hemoglobin (%); TC, total cholesterol (mmol/L); HDLC, high-density lipoprotein cholesterol (mmol/L); LDLC, low-density lipoprotein cholesterol (mmol/L); TG, triglycerides (mg/dL); EGFR, estimated glomerular filtration rate (mL/min/1.73 m\u0026sup2;); UA, uric acid (\u0026micro;mol/L); AST, aspartate aminotransferase (U/L); ALT, alanine aminotransferase (U/L); CRP, C-reactive protein (mg/L); WBC, white blood cell count (\u0026times;10⁹/L); WC, waist circumference (cm); VF, visceral fat (cm\u0026sup2;); SF, subcutaneous fat (cm\u0026sup2;); FT3, free triiodothyronine (pmol/L); FT4, free thyroxine (pmol/L); TSH, thyroid-stimulating hormone (mIU/mL); FT3/FT4, ratio of FT3 to FT4.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, Zhengjiang China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChunyan Zhu: [email protected], ORCID: 0009-0006-1148-1753.\u003c/p\u003e\n\u003cp\u003eXianglan Liu: [email protected].\u003c/p\u003e\n\u003cp\u003eJunhua Yu: [email protected].\u003c/p\u003e\n\u003cp\u003eLiangyan Hua: [email protected].\u003c/p\u003e\n\u003cp\u003eZiru Fang: [email protected].\u003c/p\u003e\n\u003cp\u003eYiming Zhang: [email protected], ORCID: 0009-0006-2698-9883.\u003c/p\u003e\n\u003cp\u003eZichen Rao: [email protected], ORCID: 0009-0005-6788-4829.\u003c/p\u003e\n\u003cp\u003eCZ conceived the study, performed statistical analyses, and drafted the manuscript. ZR and YZ contributed to study design, data interpretation, methodological supervision, and critical revision of the manuscript. XL, JY, LH, and ZF were responsible for data collection, investigation, and reviewing the manuscript for important intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData cannot be shared publicly because of patient privacy and confidentiality restrictions. Data are available from the Quzhou Affiliated Hospital of Wenzhou Medical University Institutional Data Access / Ethics Committee (contact via [email protected]) for researchers who meet the criteria for access to confidential data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People\u0026apos;s Hospital Medical Ethics Committee. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe studies were conducted in accordance with the local legislation and institutional requirements.\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHill M, Yang Y, Zhang L, Sun Z, Jia G, Parrish A et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metab Clin Exp. 2021:154766.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScurt F, Ganz M, Herzog C, Bose K, Mertens P, Chatzikyrkou C. Association of metabolic syndrome and chronic kidney disease. Obes Rev. 2023;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdeva-Andany M, Fern\u0026aacute;ndez-Fern\u0026aacute;ndez C, Carneiro-Freire N, Castro-Quintela E, Pedre-Pi\u0026ntilde;eiro A, Seco-Filgueira M. Insulin resistance underlies the elevated cardiovascular risk associated with kidney disease and glomerular hyperfiltration. Rev Cardiovasc Med. 2020;21(1):41\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrausm\u0026uuml;ller S, Resl M, Arfsten H, Spinka G, Wurm R, Neuhold S et al. Performance of the recommended ESC/EASD cardiovascular risk stratification model in comparison to SCORE and NT-proBNP as a single biomarker for risk prediction in type 2 diabetes mellitus. Cardiovasc Diabetol. 2021;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBandera E, Fay S, Giovannucci E, Leitzmann M, Marklew R, McTiernan A et al. The use and interpretation of anthropometric measures in cancer epidemiology: A perspective from the world cancer research fund international continuous update project. Int J Cancer. 2016;139.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYiruo SS, Yan Y, Zhang S, Liu S, Tang Z et al. Adipocyte thyroid hormone β receptor-mediated hormone action fine-tunes the intracellular glucose and lipid metabolism and systemic homeostasis. Diabetes. 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePleić N, Gunjača I, Leko MB, Zemunik T. Thyroid function and metabolic syndrome: a two-sample bidirectional Mendelian randomization study. J Clin Endocrinol Metab. 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei Y, Li X, Cui R, Liu J, Wang G-Y. Associations between sensitivity to thyroid hormones and insulin resistance in euthyroid adults with obesity. Front Endocrinol. 2024;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Li X-Z, Dai Y, Zhang J. The Association Between COVID-19 and Thyroxine Levels: A Meta-Analysis. Front Endocrinol. 2022;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLang X, Li Y, Zhang D, Zhang Y, Wu N, Zhang Y. FT3/FT4 ratio is correlated with all-cause mortality, cardiovascular mortality, and cardiovascular disease risk: NHANES 2007\u0026ndash;2012. Front Endocrinol. 2022;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eŠtěp\u0026aacute;nek L, Hor\u0026aacute;kov\u0026aacute; D, Štěp\u0026aacute;nek L, Janout V, Janoutov\u0026aacute; J, Bouchalov\u0026aacute; K et al. Free triiodothyronine/free thyroxine (FT3/FT4) ratio is strongly associated with insulin resistance in euthyroid and hypothyroid adults: a cross-sectional study. Endokrynologia Polska. 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeves J, Fontes-Carvalho R, Borges-Canha M, Leite A, Von Hafe M, Vale C et al. Association of thyroid function, within the euthyroid range, with cardiovascular risk: The EPIPorto study. Front Endocrinol. 2022;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu M, Huang Y, Huang Z, Huang Q, Guo X, Wang Y et al. Prevalence of metabolic syndrome and its associated factors in Chinese patients with ankylosing spondylitis [Corrigendum]. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy. 2019;12:729-.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNie X, Ma X, Xu Y, Shen Y, Wang Y, Bao Y. Characteristics of Serum Thyroid Hormones in Different Metabolic Phenotypes of Obesity. Front Endocrinol (Lausanne). 2020;11:68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu L, Li Z, Yang R, Pan G, Cheng Q, He Y, et al. Impaired Sensitivity to Thyroid Hormones Is Associated With Elevated Blood Glucose in Coronary Heart Disease. Front Endocrinol (Lausanne). 2022;13:895843.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi L, Wang J, Yang L, Zhao W, Zhang M, Yu A, et al. Impaired sensitivity to thyroid hormones is associated with ASCVD risk factors in Beijing in euthyroid population: a cross-sectional study. Front Endocrinol (Lausanne). 2025;16:1602202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Zhang Q, Chen L, Wang Y, Ye Q, Liu W, et al. Impaired sensitivity to thyroid hormones is associated with increased body fat mass/muscle mass ratio (F/M) in the euthyroid population. Diabetol Metab Syndr. 2025;17(1):128.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao B, Li K, Ke J, Zhao D. Impaired Sensitivity to Thyroid Hormones Is Associated With the Change of Abdominal Fat in Euthyroid Type 2 Diabetes Patients: A Retrospective Cohort Study. J Diabetes Res. 2024;2024:8462987.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou X, Zhang Y, Li Z. Impaired sensitivity to thyroid hormones is positively associated to metabolic syndrome severity in euthyroid Chinese adults as revealed by a cross-sectional study. Front Endocrinol (Lausanne). 2025;16:1552484.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuan X-Y, Fu J-L, Sun L-N, Mu Z-J, Xiu S-L. Association between sensitivity to thyroid hormones and non-high-density lipoprotein cholesterol levels in patients with type 2 diabetes mellitus. World J Diabetes. 2024;15(10):2081\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou L, Jiang L, An Y, Liu J, Wang G, Wang Y, et al. Association of Sensitivity to Thyroid Hormones and Non-Alcoholic Fatty Liver Disease and the Severity of Liver Fibrosis in Euthyroid Adults: A Retrospective Study. Diabetes Metab Syndr Obes. 2025;18:479\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen F, Chen R, Zhou J, Xu W, Zhou J, Chen X, et al. Impaired Sensitivity to Thyroid Hormones is Associated with Central Obesity in Euthyroid Type 2 Diabetes Mellitus Patients with Overweight and Obesity. Diabetes Metab Syndr Obes. 2024;17:3379\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFontenelle L, Feitosa MM, Severo J, De Freitas TEC, Morais JBS, Torres\u0026ndash;Leal F, et al. Thyroid Function in Human Obesity: Underlying Mechanisms. Horm Metab Res. 2016;48:787\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBiondi B. Subclinical Hypothyroidism in Patients with Obesity and Metabolic Syndrome: A Narrative Review. Nutrients. 2023;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRohm T, Meier D, Olefsky J, Donath M. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55 1:31\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z-M, Li G, Wu Y, Zhang D, Zhang S, Hao Y-T et al. Increased Central and Peripheral Thyroid Resistance Indices During the First Half of Gestation Were Associated With Lowered Risk of Gestational Diabetes\u0026mdash;Analyses Based on Huizhou Birth Cohort in South China. Front Endocrinol. 2022;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMałujło-Balcerska E, Pietras T. Deiodinase Types 1 and 3 and Proinflammatory Cytokine Values May Discriminate Depressive Disorder Patients from Healthy Controls. J Clin Med. 2023;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuazzez A, Shimi G, Balam FH, Ghorbani A, Zand H. Different Effects of Obesity and Fasting on the Expression of Type 3 Deiodinase and Thyroid Hormone Receptors in the Liver and Visceral Adipose Tissue of C57BL/6 Male Mice. Indian J Endocrinol Metab. 2024;28(3):320\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHopkins M, Blundell J. Energy balance, body composition, sedentariness and appetite regulation: pathways to obesity. Clin Sci. 2016;130 18:1615\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJudge A, Dodd M. Metabolism. Essays in Biochemistry. 2020;64:607\u0026thinsp;\u0026ndash;\u0026thinsp;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSabatino L, Vassalle C. Thyroid Hormones and Metabolism Regulation: Which Role on Brown Adipose Tissue and Browning Process? Biomolecules. 2025;15(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetito G, Cioffi F, Magnacca N, de Lange P, Senese R, Lanni A. Adipose Tissue Remodeling in Obesity: An Overview of the Actions of Thyroid Hormones and Their Derivatives. Pharmaceuticals (Basel). 2023;16(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang S, Liao K, Zhou L, Zhang S, Wu J. Body roundness index, thyroid hormones, and threshold effects in US adults: a cross-sectional study from NHANES. Front Nutr. 2025;12:1539022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao N, Liu L, Jiang Y, Wang D, Lu Q, Zeng Y, et al. Peripheral Thyroid Hormone Sensitivity Mediates the Association Between Body Composition and Diabetes in Euthyroid Adults. J Cachexia Sarcopenia Muscle. 2025;16(5):e70061.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng L, Zheng X, Shuai P, Yu X. Thyroid-Related Hormones Changes Predict Changes in Anthropometric Measures and Incidence of Obesity in Chinese Euthyroid Persons. Horm Metab Res. 2022;55:684\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNie X, Xiaojing, Xu Y, Shen Y, Wang Y, Bao Y. Characteristics of Serum Thyroid Hormones in Different Metabolic Phenotypes of Obesity. Front Endocrinol. 2020;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Pan T, Wang L, Wang Y, Gong Y, Wang G et al. Increased FT3/FT4 ratio in a certain range is associated with decreased glycemic variability in patients with type 2 diabetes. Sci Rep. 2024;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShu L, Hoo R, Wu X, Pan Y, Lee I, Cheong L et al. A-FABP mediates adaptive thermogenesis by promoting intracellular activation of thyroid hormones in brown adipocytes. Nat Commun. 2017;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Vries E, Surovtseva O, Vos W, Kunst R, Van Beeren M, Kwakkel J et al. Down regulation of type 3 deiodinase in the hypothalamus during inflammation. Thyroid: official J Am Thyroid Association. 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRusso S, Salas-Lucia F, Bianco A. Deiodinases and the Metabolic Code for Thyroid Hormone Action. Endocrinology. 2021.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"FT3/FT4 ratio, Thyroid hormone sensitivity, Obesity, Type 2 diabetes, Mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-7803754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7803754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThyroid hormone sensitivity, reflected by the free triiodothyronine-to-free thyroxine ratio (FT3/FT4), plays a critical role in metabolic regulation. This study aimed to investigate the association between FT3/FT4 and obesity in euthyroid patients with type 2 diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,408 euthyroid patients with type 2 diabetes were included. Multivariable linear regression, subgroup, mediation, and nonlinear analyses were performed to examine the associations between FT3/FT4 and body mass index (BMI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFT3/FT4 was positively and independently associated with BMI in fully adjusted models (β = 7.03, 95% CI: 2.53–11.53, P = 0.0023). Subgroup analyses showed consistent associations across age, sex, and drinking categories, with no significant interactions. Mediation analyses indicated that waist circumference and visceral fat mediated 43.6% and 50.5% of the total effect, respectively, whereas subcutaneous fat was not significant. Nonlinear curve fitting revealed a threshold at FT3/FT4 = 0.41, below which FT3/FT4 was positively associated with BMI, while above it, the association plateaued.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFT3/FT4 is independently associated with obesity in euthyroid patients with type 2 diabetes. Its partial mediation through central adiposity and threshold-dependent pattern suggest that FT3/FT4 may serve as a practical biomarker for early risk identification and personalized obesity management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Association between FT3/FT4 ratio and obesity in euthyroid patients with type 2 diabetes: a cross-sectional study with mediation and threshold analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 11:54:36","doi":"10.21203/rs.3.rs-7803754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb6720a3-dad0-4c95-8511-d0fc2a31b849","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T04:39:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 11:54:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7803754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7803754","identity":"rs-7803754","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}