Association Between Thyroid-Stimulating Hormone and Diabetic Kidney Disease in Type 2 Diabetes: A Cross-Sectional Study with Mediation Analysis

Association Between Thyroid-Stimulating Hormone and Diabetic Kidney Disease in Type 2 Diabetes: A Cross-Sectional Study with Mediation Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Thyroid-Stimulating Hormone and Diabetic Kidney Disease in Type 2 Diabetes: A Cross-Sectional Study with Mediation Analysis Yuanyuan Zhang, Fanghong Zhao, Haochen Liu, Tianxiao Zhang, louyan Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8256993/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 Objective The relationship between thyroid-stimulating hormone (TSH) and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM) is unclear. This study aimed to clarify the relationship between TSH and DKD and to identify potential mediating factors. Methods A total of 1,390 patients with type 2 diabetes were divided into two groups: a non-diabetic kidney disease (Non-DKD) group and a diabetic kidney disease (DKD) group. We compared the differences in baseline data between the two groups, analyzed the association between TSH and renal function indicators, and categorized TSH levels into normal thyroid function and subclinical hypothyroidism (SCH) using a cutoff of 4.2 mIU/L. Logistic regression was used to compare the DKD prevalence between groups and clarify the TSH-DKD association. A restricted cubic spline model was used to determine if a non-linear relationship exists between TSH and DKD. Subgroup stratification analysis was conducted to examine potential confounding variables' modulation of the TSH-DKD association and their interaction. We also analyze potential mediating factors. Results A comparison of baseline data revealed that TSH levels were significantly higher in the DKD group than in the non-DKD group ( P < 0.05).TSH was significantly associated with renal function indicators (Cr, GFR-EPI, CysC, and UACR) ( P 4.2 mIU/L (SCH) compared to those with TSH ≤ 4.2 mIU/L(OR: 1.56,95% CI: 1.09–2.23, P = 0.016). In the restricted cubic spline model, Before adjusting for confounding factors, TSH levels exhibited a significant linear correlation with DKD risk. However, after adjusting for gender, lifestyle, and diabetes-related confounding factors, this association lost statistical significance.There was no interaction between subgroups. TSH indirectly influenced DKD via serum albumin. (β = 0.01, 95% CI: 0.00–0.02, P = 0.04). Conclusions Among individuals with type 2 diabetes, TSH shows a positive association with DKD, and this relationship might be partially mediated by albumin. Thyroid-stimulating hormone Diabetic kidney disease Albumin Figures Figure 1 Figure 2 Figure 3 Introduction Type 2 diabetes mellitus (T2DM) is one of the most serious public health challenges facing the world in the 21st century. According to the International Diabetes Federation (IDF), the global population of adults with diabetes reached 589 million in 2025, with one in every nine adults (aged 20–79) suffering from T2DM[1]. This number is projected to reach 853 million by 2050. As of 2024, China had an estimated 148 million adult patients with T2DM, making it the country with the largest patient population. Diabetic kidney disease (DKD), characterized by persistent proteinuria and progressive decline in kidney function, is the most severe microvascular complication of diabetes[2]. According to the 2024 Chinese Clinical Management Consensus on Diabetes Mellitus with Chronic Kidney Disease (CKD), around 32.5% of Chinese adults with T2DM also have CKD[3]. Over the past 30 years, the global incidence of CKD caused by T2DM has increased by 74% [4]. Between 50% and 70% of DKD patients progress to end-stage renal disease (ESRD) within 10 to 18 years, making DKD the leading cause of ESRD. DKD significantly increases the risk of renal failure and cardiovascular mortality, and its high medical costs impose a substantial financial burden on public health systems[5]. In recent years, the number of studies investigating the link between thyroid hormones and diabetes mellitus has increased. One seven-year longitudinal study found that variations in TSH and thyroid hormone levels, even within the normal reference range, are additional risk factors for developing T2DM [6]. A meta-analysis revealed that elevated TSH levels and reduced FT3 and FT4 levels are associated with an increased risk of developing type 2 diabetes. This risk increases progressively as TSH levels rise and thyroid hormone levels decline. In the general population, an increase of 1 mIU/L in TSH or a decrease of 1 pmol/L in FT3/FT4 corresponds to an 11%, 23% and 16.8% increase in the risk of type 2 diabetes, respectively [7]. Subclinical hypothyroidism (SCH) is characterised by elevated TSH levels and normal FT4 levels, and is a condition without symptoms[8]. Although SCH typically has no symptoms, it is associated with disorders of lipid metabolism, atherosclerosis, cardiac dysfunction and overt hypothyroidism[9]. SCH is also associated with hyperglycaemia and increased insulin resistance[10]. Previous studies have shown that SCH is associated with diabetic retinopathy[11], diabetic peripheral neuropathy[12], and cardiovascular disease[13] in patients with T2DM. Other studies have indicated a close association between chronic kidney disease (CKD) and SCH[14], and TSH has been associated with estimated glomerular filtration rate (eGFR)in the general population[15]. Research on type 1 diabetes has also revealed a significant link between thyroid dysfunction and a reduced glomerular filtration rate[16].Research has also found that eGFR was higher in the euthyroid group than in the SCH-T2DM group[17]. However, However, some studies have failed to demonstrate the association between SCH and DKD[11] .Similarly, recent studies have shown that TSH is not significantly associated with renal function markers in DKD patients[18]. Given the inconsistent findings in the literature regarding the TSH-DKD association, this study collected thyroid function test results and information on whether T2DM patients had DKD. Statistical analysis was then used to clarify the association between TSH and DKD in T2DM patients with normal thyroid function and SCH. The study also aimed to identify any underlying factors through which TSH might influence DKD. Subjects, materials, and methods Subjects From January 2021 to December 2023, a total of 1,486 patients with a confirmed diagnosis of type 2 diabetes mellitus (T2DM) were hospitalized at the Xi'an No.9 Hospital.All eligible patients were included, except for those meeting the following exclusion criteria: (a) type 1 diabetes mellitus, (b) a personal history of thyroid disease (regardless of treatment received), (c) acute diabetes complications, (d) severe liver dysfunction, (e) other chronic kidney diseases unrelated to DKD or undergoing dialysis, (f) pregnancy, (g) malignant tumors,(h)use of drugs affecting renal function (such as GLP-1 receptor agonists and SGLT2 inhibitors).A total of 1,390 patients with type 2 diabetes were finally included.Based on the diagnostic criteria for diabetic nephropathy, the patients were divided into two groups: the non-diabetic kidney disease (Non-DKD) group, which included 1,061 patients, and the diabetic kidney disease (DKD) group, which included 329 patients(as shown in Appendix Figure 1). The diagnostic criteria for type 2 diabetes mellitus (T2DM) are based on the standards set by the American Diabetes Association. Specifically, the criteria are:fasting plasma glucose (FPG) ≥7.0 mmol/L、Two-hour blood glucose ≥11.1 mmol/L (200 mg/dL) during an oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c) of at least 6.5%, or random blood glucose of at least 11.1 mmol/L (200 mg/dL) accompanied by typical hyperglycemic symptoms, such as polyuria, polydipsia, and unexplained weight loss. Meeting any one of these criteria is sufficient for a T2DM diagnosis. The definition of subclinical hypothyroidism (SCH) is elevated thyroid stimulating hormone (TSH) levels (>4.2 mIU/L) and normal free thyroxine (FT4) levels (9.0–25.0 pmol/L). Diabetic kidney disease（DKD）is diagnosed using the urine albumin-to-creatinine ratio (UACR), which classifies participants into categories based on renal disease: normal albuminuria (0–29.9 mg/g), microalbuminuria (30–299 mg/g), and nephropathy (≥300 mg/g creatinine). The UACR is calculated using the first morning urine sample. Alternatively, eGFR can be used if it is less than 60 mL/min/1.73 m². Materials We used a clinical case study questionnaire to gather demographic and anthropometric data, including gender, age, diabetes duration, alcohol consumption history, smoking history, hypertension history, and other disease history. A physician measured height, weight, and blood pressure (BP) using standard forms at the same time. Body mass index (BMI) was calculated by dividing weight (in kilograms) by height (in meters) squared. All patients had venous blood drawn while fasting, and the following laboratory parameters were measured: Fasting plasma glucose (FIB), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), Creatinine(Cr), estimated glomerular filtration rate (GFR-EPI), and cystatin-C (CysC) were measured using a Hitachi 008α automated analyzer (Hitachi Instruments (Suzhou) Co., Ltd., China). Glycated hemoglobin (HbA1c) is detected using on exchange high-performance liquid chromatography( Ha-8180 glycated hemoglobin analyzer,Produced by Arkray Inc. Japan). Serum free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and thyroglobulin antibody (TGAb) levels are measured by chemiluminescence (Roche Cobas e801, Germany). Simultaneously, morning fasting random urine samples are collected, and urine albumin concentration is determined using the rate scattering turbidimetric method combined with the creatinine oxidase method, calculating the urine albumin/creatinine ratio (UACR,mg/g).And determination of urinary microalbumin by immunoturbidimetry (Malb,mg/L). Statistical method All variables were tested for normal distribution. The mean and interquartile range of continuous data and the percentage of categorical data were used to describe the basic characteristics. The Spearman association test was used to analyze the association between TSH and renal function indicators, and logistic regression analysis established four models to investigate the relationship between TSH and DKD. The results are expressed as OR and 95%CI. We used a restricted cubic spline model to test for a nonlinear relationship between TSH and DKD. Stratified analyses were conducted based on sex, smoking history, drinking history, age, BMI, HbA1c, and FPG. These analyses examined whether potential confounding variables modulate the association between TSH and DKD, as well as tested for interactions between them. Finally, we applied mediation models based on Bayesian methods to investigate the direct impact of TSH on DKD and the indirect effect mediated by albumin. All statistical tests with P < 0.05 were considered significant.Data processing and statistical analysis were performed using the R software program. Results Demographic and characteristic information of the study subjects. The clinical characteristics of T2DM patients in both groups are shown in Table 1. A total of 1,390 patients were enrolled in the study: 1,061 (76.33%) in the non-DKD group and 329 (23.67%) in the DKD group. Significant differences were found between the two groups for the following variables:age,BMI,Wine,hypertension SBP, DBP,Diabetesduration,FPG,Albumin,Globulin,Cr,eGFR-EPI,UACR,CysC,Malb,TSH,FT3,FT4,TGAB,TG,TC ( P 0.05),as shown in Table 1. Significant association identified between TSH levels and renal function indicators. TSH showed positive correlations with Cr (r = 0.070, P = 0.009) and CysC (r = 0.106, P < 0.001), negatively associated with GFR-EPI (r = -0.148, P < 0.001), and positively associated with UACR (r = 0.076, P = 0.005). TSH was significantly associated with renal function indicators ( P < 0.05), as shown in Figure 1. Significant association between TSH levels and DKD after being adjusted for multiple co-variates Participants were divided into two groups based on TSH levels, with 4.2 mIU/L as the cutoff point: the euthyroid group and the SCH group. Using DKD as the dependent variable and TSH as the core variable, four different covariate-adjusted models were constructed to reflect this relationship. Model 1, which did not adjust for any variables, showed a positive association between TSH and DKD (OR:1.74, 95% CI:1.26–2.40, P < 0.001). Model 2, which adjusted for sex and age, also showed a positive association between TSH and DKD (OR: 1.59, 95% CI: 1.14–2.21, P = 0.007). Model 3 adjusted for sex, smoking, wine consumption, hypertension, age, and BMI. This model also showed a positive association between TSH and DKD (OR:1.59, 95% CI: 1.13–2.24, P = 0.008). Model 4 adjusted for sex, smoking, wine consumption, hypertension, age, BMI, SBP, DBP, diabetes duration, HbA1c, and FPG. TSH was positively associated with DKD (OR: 1.56, 95% CI: 1.09–2.23, P = 0.016), as shown in Table 2. TSH levels are associated with the overall risk of DKD using the restricted cubic scatterplot model The restricted cubic scatterplot model (see figure) shows that TSH levels are significantly associated with the overall risk of DKD ( P = 0.045). Before adjusting for confounding factors, TSH levels exhibited a significant linear correlation with DKD risk, implying that elevated TSH levels might elevate DKD risk. However, when TSH levels were in the lower range (approximately 0–2.14 mIU/L), they did not provide significant protection against DKD. TSH levels exceeding 2.14 mIU/L, especially when exceeding 4 mIU/L, significantly increased the risk of DKD onset, and the risk increased linearly with rising TSH levels. However, after full adjustment for covariates (sex, age, BMI, smoking, alcohol consumption, hypertension, diabetes duration, HbA1c, and FPG), the overall association was no longer statistically significant ( P for overall = 0.224), suggesting that the relationship is mediated or confounded by these factors. as shown in Figure 2. No significant heterogeneity was identified based on subgroup analysis DKD was used as the outcome variable, while TSH was used as the exposure variable. We divided TSH into normal thyroid function and SCH groups based on a cutoff of 4.2 mIU/L. The following variables were used as subgroup variables: gender, smoking history, drinking history, history of hypertension, age, BMI, duration of diabetes, HbA1c, FPG, TG, TC, LDL, and HDL. In the entire population (n = 1390), SCH was significantly associated with DKD (OR: 1.74, 95% CI: 1.26–2.40, P <0.001). Among males, SCH had a statistically significant effect on the risk of DKD ( P <0.05) in the following subgroups: non-smokers/smokers (showing a marginal trend), non-drinkers/drinkers, presence/absence of hypertension, age 7.0 mmol/L, TG ≥1.7mmol/L, TC <5.2mmol/L, LDL ≥1.8mmol/L, and HDL <1mmol/L. In women、aged ≥65 、 FPG ≥7.0 mmol/L、TG 0.05). The interaction P -values for all subgroups were greater than 0.05, indicating that factors such as gender, lifestyle habits, and disease indicators did not significantly interact and had limited modifying effects on the "TSH-DKD" association.However, the sample sizes for some subgroups, such as women and individuals aged 65 years and older, were small, and the results were inconsistent adjusting for Sex,Smoking,Wine,Hypertension, Age,BMI,Diabetes duration,Hbalc,FPG ,as shown in Figure 3. adjusting for Sex,Smoking,Wine,Hypertension, Age,BMI,Diabetes duration,Hbalc,FPG Significant mediation effects were identified for albumin We analyzed the relationship between TSH and DKD using albumin as a mediator variable, as shown in the table. Albumin was negatively associated with DKD ( P < 0.001), and TSH had a significant positive direct effect on DKD ( P < 0.001). TSH directly influences the occurrence and development of DKD; its direct effect accounts for 87.98% of the total effect.TSH also indirectly influences DKD through the mediator variable albumin ( P = 0.04), accounting for 12.02% of the total effect. Though the indirect effect is relatively small, it is still significant in the overall pathological mechanism.as shown in Table 3 and Appendix Figure 2. Discussion This study provides evidence that the link between thyrotropin（TSH） and Diabetic Kidney Disease (DKD) may operate through alterations in albumin metabolism. Our results demonstrated that higher TSH levels were significantly associated with the presence of DKD and poorer renal function, with subclinical hypothyroidism（SCH) conferring a 1.56-fold increase in DKD odds. However, the direct association between TSH and DKD, which displayed a linear pattern in initial analyses, did not persist after comprehensive adjustment for confounders. There was no interaction between subgroups.This underscores the complexity of the relationship and highlights albumin as a potential mechanistic pathway. Crucially, mediation analysis confirmed a significant indirect effect via albumin. We found that the prevalence of SCH in our T2DM cohort was 14.8% (206/1390).A systematic review and meta-analysis of 36 studies revealed a combined prevalence of SCH in patients with T2DM of 10.2%[12]. A cross-sectional study involving 205 patients with T2DM in Argentina found an SCH prevalence of 8%[19]. This discrepancy is due to the inconsistent thresholds that have been set.This study used a TSH cutoff of 4.2 mIU/L. This value is the 97.5th percentile, which was derived from major cohorts like NHANES that enrolled rigorously screened healthy individuals with no evidence of thyroid disease[20]. TSH levels were found to be significantly associated with indicators of renal function: there were positive associations with creatinine (Cr), cystatin C (CysC) and the urine albumin-to-creatinine ratio (UACR), and a negative association with the estimated glomerular filtration rate (eGFR)-EPI. Individuals with TSH levels greater than 4.2 mIU/L (SCH) were 1.56 times more likely to develop DKD than individuals with TSH levels of 4.2 mIU/L or less. Similar findings were observed in other clinical studies. Two cross-sectional studies involving 414 patients with T2DM without prior thyroid disease and 8,418 normoglycaemic individuals revealed that elevated UACR was positively associated with SCH, while elevated TSH levels were negatively associated with eGFR[17,21].Similarly, a prospective Indian cohort study found that, as DKD progressed from stage 3b to stage 5, the prevalence of SCH increased from 14.3% to 57.2%, suggesting a positive association between worsening renal function and SCH risk [22]. The restricted cubic spline model initially showed a linear, progressive increase in DKD risk with TSH levels above 2.14 mIU/L. However, this association lost statistical significance after adjusting for confounders like gender, lifestyle, and diabetes-related factors. The loss of significance in the fully adjusted RCS model suggests that the TSH–DKD association is confounded by factors such as blood pressure and diabetes duration. Key factors such as blood pressure, diabetes duration, and BMI likely share common pathogenic pathways with both thyroid dysfunction and renal damage, thereby attenuating the independent effect of TSH when statistically controlled for.Subgroup analyses ( by sex, age, and lipid levels) revealed varying trends but no significant interactions, potentially due to limited sample sizes in groups like women and the elderly.Previous studies have shown that hypothyroidism is 1.7 times more prevalent among women with type 2 diabetes than among men, with individuals over 60 years old facing an even greater risk[23].We observed that SCH significantly increased the risk of DKD in patients with dyslipidaemia, aligning with findings from previous studies[24], but not in those who were overweight/obese or had poor glycemic control. These findings suggest the TSH-DKD relationship is not independent or specific, as it is explained by confounders and not consistently modified by clinical subgroups. Future research should, therefore, explore mediating factors beyond common metabolic determinants. Albumin, synthesized by the liver, is the most abundant plasma protein. It is crucial for substance transport and exerts antioxidant and anti-inflammatory effects[25,26]. Thyroid dysfunction can impair albumin synthesis and function by exacerbating systemic inflammation and oxidative stress[27,28]. The resulting hypoalbuminaemia diminishes the kidneys' capacity to counteract inflammatory and oxidative damage, thereby accelerating DKD progression[29]. Furthermore, abnormal TSH levels may indirectly suppress hepatic albumin synthesis via their effects on thyroid hormones, leading to lower serum albumin concentrations[30,31]. This decline subsequently affects renal haemodynamics, exacerbating glomerular injury and albuminuria. Therefore, serum albumin is a pivotal mediator linking abnormal TSH levels to DKD pathophysiology through its antioxidant, anti-inflammatory, and haemodynamic regulatory actions. Understanding this mechanism may reveal novel targets for the early diagnosis and risk assessment of DKD. Future research should further explore the specific molecular pathways of the TSH-albumin-diabetes axis. For instance, cohort or interventional studies could clarify the causal role of serum albumin and evaluate whether interventions improving thyroid function or albumin levels can positively influence the prognosis of T2DM, thereby contributing to novel strategies for managing DKD. A recent Chinese study of 146 patients with biopsy-confirmed diabetic nephropathy revealed that those with elevated TSH and/or reduced FT3 levels had more severe proteinuria, renal insufficiency, and glomerular lesions. This suggests that regulating thyroid hormones may be renoprotective and improve outcomes[32]. Supporting this, a cohort study by Shin et al. demonstrated that thyroid hormone replacement therapy not only preserves renal function but also independently predicts renal prognosis in CKD patients with SCH [33]. Consequently, the impact of such therapy on renal prognosis in DKD patients merits further investigation. This study has several limitations. First, the cross-sectional design with a single thyroid function assessment precludes the establishment of causality and lacks prognostic insights; future longitudinal studies are needed. Second, the absence of data on inflammatory markers and renal pathology limits the exploration of underlying mechanisms. Third, the exclusion of patients on GLP-1 receptor agonists or SGLT2 inhibitors may affect the generalizability of our findings, given the renal protective effects of these drugs. Future research incorporating these patients and including prospective or interventional designs will be crucial to validate causality and assess the therapeutic potential of thyroid management in DKD. Conclusions In summary, our study demonstrates a cross-sectional association between higher TSH levels and DKD in T2DM patients, which is partially mediated by serum albumin. However, the absence of an independent, linear relationship after comprehensive adjustment suggests that TSH may be more of a risk marker within a complex metabolic milieu than a direct causal factor. Future longitudinal and interventional studies are warranted to clarify the causality and explore the therapeutic implications of modulating the TSH-albumin axis in DKD. Declarations Ethics approval and consent to participate：This study was approved by the Medical Biological Research Ethics Committee of the No.9 Hospital of Xi'an(Approval No.2025112). All patient data were deidentified for confidentiality, with the study adhering to the Declaration of Helsinki; informed consent was waived for the retrospective, de-identified data as approved by the ethics committee. Consent for publication：All presentations of case reports must have consent for publication. Availability of data and materials：The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests：The authors declare that they have no competing interests. Funding：This work was supported in part by the National Natural Science Foundation of China (No. 81700736), Xi’an Science and Technology Plan Project (24YXYJ0080), National Health Commission Capacity Building and Continuing Education Center Project (W2024SNJT45) and Natural Science Basic Research Plan in Shaanxi Province of China (Program No.2025JC-YBMS-924) Authors' contributions：ZFH conducted data collection，MLY and ZYY wrote the main manuscript text and ZTX LHC prepared figures . All authors reviewed the manuscript. Acknowledgements：not applicable. Clinical trial number: not applicable. References Ogle GD, Wang F, Haynes A, Gregory GA, King TW, Deng K et al. Global type 1 diabetes prevalence, incidence, and mortality estimates 2025: Results from the International diabetes Federation Atlas, 11th Edition, and the T1D Index Version 3.0. Diabetes Res Clin Pract. 2025 July;225:112277. Liang Y, Ding L, Tao M, Zhu Y. 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Tables Table 1 Socio-demography and clinical characteristics of the study subjects Variables Total (n = 1390) Non-DKD (n = 1061) DKD (n = 329) Statistic p Sex, n(%) χ²=0.55 0.457 Female 450 (32.37) 349 (32.89) 101 (30.70) male 940 (67.63) 712 (67.11) 228 (69.30) Age(y) 54.00 (47.00, 62.00) 53.00 (45.00, 61.00) 57.00(51.00, 64.00) Z=-6.46 <.001 BMI(kg/m2) 25.50 (23.50, 27.70) 25.30(23.40, 27.60) 25.90 (23.80, 28.00) Z=-2.29 0.022 Smoking, n(%) χ²=0.30 0.586 NO 755 (54.32) 572 (53.91) 183 (55.62) YES 635 (45.68) 489 (46.09) 146 (44.38) Wine, n(%) χ²=9.73 0.002 NO 1005 (72.30) 745 (70.22) 260 (79.03) YES 385 (27.70) 316 (29.78) 69 (20.97) hypertension, n(%) χ²=88.26 <.001 NO 793 (57.05) 679 (64.00) 114 (34.65) YES 597 (42.95) 382 (36.00) 215 (65.35) SBP(mmHg) 130.00 (120.00, 140.00) 124.00 (118.00, 138.00) 140.00 (128.00, 150.00) Z=-9.92 <.001 DBP(mmHg) 80.00(70.00, 86.00) 80.00(70.00, 85.00) 80.00(76.00, 90.00) Z=-4.21 <.001 Diabetes duration(y) 8.00 (3.00, 13.00) 6.00(2.00, 11.00) 11.00 (7.00, 16.00) Z=-9.47 <.001 HbA1c(%) 8.50(7.20, 10.10) 8.50 (7.20, 10.10) 8.30 (7.40, 10.10) Z=-0.13 0.899 FPG(mmol/L) 8.20(6.53, 10.50) 7.90(6.30, 10.26) 8.60(7.10, 11.00) Z=-3.98 <.001 Albumin(g/L) 42.50(40.10, 45.00) 42.90 (40.60, 45.20) 40.80 (37.90, 43.90) Z=-7.63 <.001 Globulin(g/L) 24.20(21.50, 27.20) 23.90 (21.30, 26.80) 25.30 (22.50, 28.90) Z=-5.14 <.001 Cr(μmol/L) GFR-EPI (ml/min/1.73m2) 86.85 (71.80, 98.56) 89.76 (77.03, 100.32) 72.37 (55.09, 88.11) Z=-12.21 <.001 UACR(mg/g) 1.16(0.64, 4.78) 0.90 (0.59, 1.84) 11.20(3.68, 44.12) Z=-18.62 <.001 CysC(mg/L) 0.86 (0.76, 0.99) 0.83 (0.75, 0.94) 0.99 (0.84, 1.25) Z=-11.24 <.001 Malb(mg/L) 7.44(5.00, 14.78) 6.39(5.00, 12.40) 13.70(5.82, 28.60) Z=-9.36 <.001 TSH(mIU/L) 2.14(1.39, 3.24) 2.11(1.37, 3.11) 2.24(1.46, 3.78) Z=-2.38 0.017 FT3(pmol/L) 4.66 (4.31, 5.04) 4.70 (4.36, 5.06) 4.56 (4.18, 4.93) Z=-4.18 <.001 FT4(pmol/L) 16.12 (14.74, 17.67) 16.20 (14.83, 17.72) 15.78(14.51, 17.48) Z=-2.17 0.030 TT3(nmol/L) 1.49 (1.31, 1.69) 1.49 (1.31, 1.70) 1.48 (1.31, 1.67) Z=-0.77 0.442 TT4(nmol/L) 107.32(95.43, 119.88) 107.00 (9550, 119.80) 107.90(94.90, 120.30) Z=-0.27 0.790 TPOAB(IU/ml) 57.00(0.00, 183.75) 66.00 (0.00, 197.00) 37.00(0.00, 146.00) Z=-1.93 0.054 TGAB(IU/ml) 235.00(63.00, 451.75) 272.00 (95.00, 489.00) 118.00 (23.00, 290.00) Z=-7.78 <.001 TG(mmol/L) 1.56(1.05, 2.35) 1.51 (1.03, 2.30) 1.72(1.15, 2.52) Z=-3.29 <.001 TC(mmol/L) 4.09(3.49, 4.73) 4.07 (3.48, 4.68) 4.19(3.52, 4.94) Z=-2.18 0.029 LDL(mmol/L) 2.33 (1.86, 2.86) 2.33 (1.84, 2.83) 2.40 (1.87, 2.91) Z=-1.15 0.249 HDL(mmol/L) 0.94 (0.80, 1.13) 0.94 (0.80, 1.13) 0.94(0.78, 1.13) Z=-0.87 0.383 Abbreviation:BMI,body mass index ; HTN, hypertension;SBP, systolic blood pressure;DBP,diastolic blood pressure;HbA1c,glycated hemoglobin；FPG,fasting plasma glucose;Cr Creatinine；eGFR-EPI,estimated glomerular filtration rate;UACR,urine-to-creatinine ratio;CysC,CystatinC;Malb,Microalbuminuria; TSH,thyroid-stimulating hormone;FT3,free triiodothyronine;FT4,free thyroxine;TT3,total triiodothyronine ；TT4,total thyroxine ；TPOAB,thyroid peroxidase autoantibodies ；TGAB,thyroglobulin autoantibodies;TG,triglycerides;TC,total cholesterol ;LDL,low-density lipoprotein ； HDL、high-density lipoprotein ； Table 2 Logistic Regression Model for the association between TSH and DKD Variables Model1 Model2 Model3 Model4 OR (95%CI) P OR (95%CI) P OR (95%CI) P OR (95%CI) P TSH＜4.2 1.00 1.00 1.00 1.00 TSH≥4.2 1.74 (1.26 ~2.40) <.001 1.59 (1.14 ~2.21) 0.007 1.59 (1.13 ~2.24) 0.008 1.56 (1.09 ~2.23) 0.016 Model1: Crude model Model2: Sex and age were adjusted Model3: Sex, smoking, wine, hypertension, age, and BMI were adjusted. Model4: Adjust: Sex, Smoking, Wine, hypertension, Age, BMI, SBP, DBP, Diabetes duration, HbA1c, FPG Table 3 Mediation effect of Albumin on the association between TSH and DKD Mediator Sample Exposure to Mediation Mediator to Outcome Direct effect Indirect effect Total effect Mediation Albumin 1390 -0.69 -1.42~0.03 P =0.062 -0.10 -0.12~-0.07 P ＜0.001 0.10 0.04~0.16 P ＜0.001 0.01 0.00~0.02 P =0.040 0.11 0.05~0.17 P ＜0.001 12.02 Additional Declarations No competing interests reported. Supplementary Files CRF.doc appendices.figure2.tif Appendix Figure 2.Mediation effect of Albumin on the association between TSH and DKD appendicesfigure1.tif Appendix Figure 1.Flowchart of research subjects and methods Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":613826,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots showing correlations between TSH and renal function indicators.\u003c/p\u003e\n\u003cp\u003eA. Comparison of TSH with Cr,\u003cstrong\u003eB\u003c/strong\u003e.Comparison of TSH with CysC,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e.Comparison of TSH with UACR,\u003cstrong\u003eD\u003c/strong\u003e.Comparison of TSH with GFR-EPI.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8256993/v1/9ca9b5234af64e7988b4bc30.png"},{"id":100689669,"identity":"bc5cc1f8-b615-47f2-bcbd-c41772c54e7a","added_by":"auto","created_at":"2026-01-20 13:45:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":368366,"visible":true,"origin":"","legend":"\u003cp\u003eRCS curve of TSH in DKD. \u003cstrong\u003eA.\u003c/strong\u003eother variables are not calibrated,\u003cstrong\u003eB\u003c/strong\u003e.sex, age, BMI, smoking, wine, hypertension, diabetes duration, HbA1c, and FPG were adjusted\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8256993/v1/b013c05354cdd8ad3413557c.png"},{"id":100689369,"identity":"a53bcad3-4745-4e56-8781-9fe309edce40","added_by":"auto","created_at":"2026-01-20 13:41:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1034287,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Subgroup Analysis between TSH and DKD\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8256993/v1/f8774a49431ef3ed2bcc055b.png"},{"id":107641429,"identity":"a076f16a-c67f-499d-ac51-7ad84e26fcab","added_by":"auto","created_at":"2026-04-23 13:26:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2239135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8256993/v1/f2d911b8-8735-425b-89f8-009e57239fc9.pdf"},{"id":100689752,"identity":"e79f22f6-5e26-48ef-b5b5-390cc3935ed4","added_by":"auto","created_at":"2026-01-20 13:46:03","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31744,"visible":true,"origin":"","legend":"","description":"","filename":"CRF.doc","url":"https://assets-eu.researchsquare.com/files/rs-8256993/v1/d7375741e663afbd3da28e09.doc"},{"id":100689318,"identity":"f316a2f5-fcf1-44cf-9ce5-428004f231e8","added_by":"auto","created_at":"2026-01-20 13:40:54","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAppendix Figure 2.\u003c/strong\u003eMediation effect of Albumin on the association between TSH and DKD\u003c/p\u003e","description":"","filename":"appendices.figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8256993/v1/4903a3cfdf0fc126b8d644d7.tif"},{"id":100689494,"identity":"e2b4562c-9db1-437b-b663-b92f790a076f","added_by":"auto","created_at":"2026-01-20 13:42:23","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":144203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAppendix Figure 1.\u003c/strong\u003eFlowchart of research subjects and methods\u003c/p\u003e","description":"","filename":"appendicesfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8256993/v1/7420751f368ae553fdf4e844.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Thyroid-Stimulating Hormone and Diabetic Kidney Disease in Type 2 Diabetes: A Cross-Sectional Study with Mediation Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is one of the most serious public health challenges facing the world in the 21st century. According to the International Diabetes Federation (IDF), the global population of adults with diabetes reached 589 million in 2025, with one in every nine adults (aged 20–79) suffering from T2DM[1]. This number is projected to reach 853 million by 2050. As of 2024, China had an estimated 148 million adult patients with T2DM, making it the country with the largest patient population. Diabetic kidney disease (DKD), characterized by persistent proteinuria and progressive decline in kidney function, is the most severe microvascular complication of diabetes[2]. According to the 2024 Chinese Clinical Management Consensus on Diabetes Mellitus with Chronic Kidney Disease (CKD), around 32.5% of Chinese adults with T2DM also have CKD[3]. Over the past 30 years, the global incidence of CKD caused by T2DM has increased by 74%\u0026nbsp;[4]. Between 50% and 70% of DKD patients progress to end-stage renal disease (ESRD) within 10 to 18 years, making DKD the leading cause of ESRD. DKD significantly increases the risk of renal failure and cardiovascular mortality, and its high medical costs impose a substantial financial burden on public health systems[5].\u003c/p\u003e\n\u003cp\u003eIn recent years, the number of studies investigating the link between thyroid hormones and diabetes mellitus has increased. One seven-year longitudinal study found that variations in TSH and thyroid hormone levels, even within the normal reference range, are additional risk factors for developing T2DM\u0026nbsp;[6]. A meta-analysis revealed that elevated TSH levels and reduced FT3 and FT4 levels are associated with an increased risk of developing type 2 diabetes. This risk increases progressively as TSH levels rise and thyroid hormone levels decline. In the general population, an increase of 1 mIU/L in TSH or a decrease of 1 pmol/L in FT3/FT4 corresponds to an 11%, 23% and 16.8% increase in the risk of type 2 diabetes, respectively\u0026nbsp;[7]. Subclinical hypothyroidism (SCH) is characterised by elevated TSH levels and normal FT4 levels, and is a condition without symptoms[8]. Although SCH typically has no symptoms, it is associated with disorders of lipid metabolism, atherosclerosis, cardiac dysfunction and overt hypothyroidism[9]. SCH is also associated with hyperglycaemia and increased insulin resistance[10]. Previous studies have shown that SCH is associated with diabetic retinopathy[11], diabetic peripheral neuropathy[12], and cardiovascular disease[13]\u0026nbsp;in patients with T2DM. Other studies have indicated a close association between chronic kidney disease (CKD) and SCH[14], and TSH has been associated with estimated glomerular filtration rate (eGFR)in the general population[15]. Research on type 1 diabetes has also revealed a significant link between thyroid dysfunction and a reduced glomerular filtration rate[16].Research has also found that eGFR was higher in the euthyroid group than in the SCH-T2DM group[17]. However, However, some studies have failed to demonstrate the association between SCH and DKD[11]\u0026nbsp;.Similarly, recent studies have shown that TSH is not significantly associated with renal function markers in DKD patients[18].\u003c/p\u003e\n\u003cp\u003eGiven the inconsistent findings in the literature regarding the TSH-DKD association, this study collected thyroid function test results and information on whether T2DM patients had DKD. Statistical analysis was then used to clarify the association between TSH and DKD in T2DM patients with normal thyroid function and SCH. The study also aimed to identify any underlying factors through which TSH might influence DKD.\u003c/p\u003e"},{"header":"Subjects, materials, and methods","content":"\u003cp\u003e\u003cstrong\u003eSubjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom January 2021 to December 2023, a total of 1,486 patients with a confirmed diagnosis of type 2 diabetes mellitus (T2DM) were hospitalized at the Xi'an No.9 Hospital.All eligible patients were included, except for those meeting the following exclusion criteria: (a) type 1 diabetes mellitus, (b) a personal history of thyroid disease (regardless of treatment received), (c) acute diabetes complications, (d) severe liver dysfunction, (e) other chronic kidney diseases unrelated to DKD or undergoing dialysis, (f) pregnancy, (g) malignant tumors,(h)use of drugs affecting renal function (such as GLP-1 receptor agonists and SGLT2 inhibitors).A total of 1,390 patients with type 2 diabetes were finally included.Based on the diagnostic criteria for diabetic nephropathy, the patients were divided into two groups: the non-diabetic kidney disease (Non-DKD) group, which included 1,061 patients, and the diabetic kidney disease (DKD) group, which included 329 patients(as shown in Appendix Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe diagnostic criteria for type 2 diabetes mellitus (T2DM) are based on the standards set by the American Diabetes Association. Specifically, the criteria are:fasting plasma glucose (FPG) ≥7.0 mmol/L、Two-hour blood glucose ≥11.1 mmol/L (200 mg/dL) during an oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c) of at least 6.5%, or random blood glucose of at least 11.1 mmol/L (200 mg/dL) accompanied by typical hyperglycemic symptoms, such as polyuria, polydipsia, and unexplained weight loss. Meeting any one of these criteria is sufficient for a T2DM diagnosis.\u003c/p\u003e\n\u003cp\u003eThe definition of subclinical hypothyroidism (SCH) is elevated thyroid stimulating hormone (TSH) levels (\u0026gt;4.2 mIU/L) and normal free thyroxine (FT4) levels (9.0–25.0 pmol/L).\u003c/p\u003e\n\u003cp\u003eDiabetic kidney disease（DKD）is diagnosed using the urine albumin-to-creatinine ratio (UACR), which classifies participants into categories based on renal disease: normal albuminuria (0–29.9 mg/g), microalbuminuria (30–299 mg/g), and nephropathy (≥300 mg/g creatinine). The UACR is calculated using the first morning urine sample. Alternatively, eGFR can be used if it is less than 60 mL/min/1.73 m².\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used\u0026nbsp;a clinical case study questionnaire\u0026nbsp;to gather demographic and anthropometric data, including gender, age, diabetes duration, alcohol consumption history, smoking history, hypertension history, and other disease history. A physician measured height, weight, and blood pressure (BP) using standard forms at the same time. Body mass index (BMI) was calculated by dividing weight (in kilograms) by height (in meters) squared. All patients had venous blood drawn while fasting, and the following laboratory parameters were measured: Fasting plasma glucose (FIB), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), Creatinine(Cr), estimated glomerular filtration rate (GFR-EPI), and cystatin-C (CysC) were measured using a Hitachi 008α automated analyzer (Hitachi Instruments (Suzhou) Co., Ltd., China). Glycated hemoglobin (HbA1c) is detected using on exchange high-performance liquid chromatography( Ha-8180 glycated hemoglobin analyzer,Produced by Arkray Inc. Japan). Serum free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and thyroglobulin antibody (TGAb) levels are measured by chemiluminescence (Roche Cobas e801, Germany). Simultaneously, morning fasting random urine samples are collected, and urine albumin concentration is determined using the rate scattering turbidimetric method combined with the creatinine oxidase method, calculating the urine albumin/creatinine ratio (UACR,mg/g).And determination of urinary microalbumin by immunoturbidimetry (Malb,mg/L).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll variables were tested for normal distribution. The mean and interquartile range of continuous data and the percentage of categorical data were used to describe the basic characteristics. The Spearman association test was used to analyze the association between TSH and renal function indicators, and logistic regression analysis established four models to investigate the relationship between TSH and DKD. The results are expressed as OR and 95%CI. We used a restricted cubic spline model to test for a nonlinear relationship between TSH and DKD. Stratified analyses were conducted based on sex, smoking history, drinking history, age, BMI, HbA1c, and FPG. These analyses examined whether potential confounding variables modulate the association between TSH and DKD, as well as tested for interactions between them. Finally, we applied mediation models based on Bayesian methods to investigate the direct impact of TSH on DKD and the indirect effect mediated by albumin. All statistical tests with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 were considered significant.Data processing and statistical analysis were performed using the R software program.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic and characteristic information of the study subjects.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical characteristics of T2DM patients in both groups are shown in Table 1. A total of 1,390 patients were enrolled in the study: 1,061 (76.33%) in the non-DKD group and 329 (23.67%) in the DKD group. Significant differences were found between the two groups for the following variables:age,BMI,Wine,hypertension SBP,\u003c/p\u003e\n\u003cp\u003eDBP,Diabetesduration,FPG,Albumin,Globulin,Cr,eGFR-EPI,UACR,CysC,Malb,TSH,FT3,FT4,TGAB,TG,TC (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). However, no significant differences were observed in terms of sex、\u0026nbsp;smoking、\u0026nbsp;HbA1c、TT3、TT4、TPOAB、LDL or HDL(\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05),as shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificant association identified between TSH levels and renal function indicators.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTSH showed positive correlations with Cr (r = 0.070, \u003cem\u003eP\u003c/em\u003e = 0.009) and CysC (r = 0.106, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001), negatively associated with GFR-EPI (r = -0.148, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001), and positively associated with UACR (r = 0.076, \u003cem\u003eP\u003c/em\u003e = 0.005). TSH was significantly associated with renal function indicators (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), as shown in\u0026nbsp;Figure\u0026nbsp;1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificant association between TSH levels and DKD after being adjusted for multiple co-variates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were divided into two groups based on TSH levels, with 4.2 mIU/L as the cutoff point: the euthyroid group and the SCH group. Using DKD as the dependent variable and TSH as the core variable, four different covariate-adjusted models were constructed to reflect this relationship. Model 1, which did not adjust for any variables, showed a positive association between TSH and DKD (OR:1.74, 95% CI:1.26–2.40,\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Model 2, which adjusted for sex and age, also showed a positive association between TSH and DKD (OR: 1.59, 95% CI: 1.14–2.21,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e= 0.007). Model 3 adjusted for sex, smoking, wine consumption, hypertension, age, and BMI. This model also showed a positive association between TSH and DKD (OR:1.59, 95% CI: 1.13–2.24,\u003cem\u003eP\u003c/em\u003e= 0.008). Model 4 adjusted for sex, smoking, wine consumption, hypertension, age, BMI, SBP, DBP, diabetes duration, HbA1c, and FPG. TSH was positively associated with DKD (OR: 1.56, 95% CI: 1.09–2.23,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 0.016),\u0026nbsp;as shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTSH levels are associated with the overall risk of DKD using the restricted cubic scatterplot model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe restricted cubic scatterplot model (see figure) shows that TSH levels are significantly associated with the overall risk of DKD (\u003cem\u003eP\u003c/em\u003e = 0.045). Before adjusting for confounding factors, TSH levels exhibited a significant linear correlation with DKD risk, implying that elevated TSH levels might elevate DKD risk. However, when TSH levels were in the lower range (approximately 0–2.14 mIU/L), they did not provide significant protection against DKD. TSH levels exceeding 2.14 mIU/L, especially when exceeding 4 mIU/L, significantly increased the risk of DKD onset, and the risk increased linearly with rising TSH levels. However, after full adjustment for covariates (sex, age, BMI, smoking, alcohol consumption, hypertension, diabetes duration, HbA1c, and FPG), the overall association was no longer statistically significant (\u003cem\u003eP\u003c/em\u003e for overall = 0.224), suggesting that the relationship is mediated or confounded by these factors. as shown in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo significant heterogeneity was identified based on subgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDKD was used as the outcome variable, while TSH was used as the exposure variable. We divided TSH into normal thyroid function and SCH groups based on a cutoff of 4.2 mIU/L. The following variables were used as subgroup variables: gender, smoking history, drinking history, history of hypertension, age, BMI, duration of diabetes, HbA1c, FPG, TG, TC, LDL, and HDL. In the entire population (n = 1390), SCH was significantly associated with DKD (OR: 1.74, 95% CI: 1.26–2.40,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Among males, SCH had a statistically significant effect on the risk of DKD (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in the following subgroups: non-smokers/smokers (showing a marginal trend), non-drinkers/drinkers, presence/absence of hypertension, age \u0026lt;65, different diabetes duration, different HbA1c levels, FPG \u0026gt;7.0 mmol/L, TG ≥1.7mmol/L, TC \u0026lt;5.2mmol/L, LDL ≥1.8mmol/L, and HDL \u0026lt;1mmol/L. In women、aged ≥65\u0026nbsp;、\u0026nbsp;FPG ≥7.0 mmol/L、TG \u0026lt;1.7mmol/L and TC ≥5.2, SCH had no significant effect on DKD risk (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). The interaction \u003cem\u003eP\u003c/em\u003e-values for all subgroups were greater than 0.05, indicating that factors such as gender, lifestyle habits, and disease indicators did not significantly interact and had limited modifying effects on the \"TSH-DKD\" association.However, the sample sizes for some subgroups, such as women and individuals aged 65 years and older, were small, and the results were inconsistent\u003c/p\u003e\n\u003ctable cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd height=\"128\"\u003e\u0026nbsp;\u003ctable cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eadjusting for Sex,Smoking,Wine,Hypertension,\u003c/p\u003e\n \u003cp\u003eAge,BMI,Diabetes duration,Hbalc,FPG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e,as shown in Figure 3.\u003c/p\u003e\n\u003ctable cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd height=\"128\"\u003e\u0026nbsp;\u003ctable cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eadjusting for Sex,Smoking,Wine,Hypertension,\u003c/p\u003e\n \u003cp\u003eAge,BMI,Diabetes duration,Hbalc,FPG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSignificant mediation effects were identified for albumin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the relationship between TSH and DKD using albumin as a mediator variable, as shown in the table. Albumin was negatively associated with DKD (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and TSH had a significant positive direct effect on DKD (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001). TSH directly influences the occurrence and development of DKD; its direct effect accounts for 87.98% of the total effect.TSH also indirectly influences DKD through the mediator variable albumin (\u003cem\u003eP\u003c/em\u003e= 0.04), accounting for 12.02% of the total effect. Though the indirect effect is relatively small, it is still significant in the overall pathological mechanism.as shown in Table 3 and Appendix Figure 2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides evidence that the link between thyrotropin（TSH）\u0026nbsp;and Diabetic Kidney Disease (DKD) may operate through alterations in albumin metabolism.\u0026nbsp;Our results demonstrated that higher TSH levels were significantly associated with the presence of DKD and poorer renal function, with subclinical hypothyroidism（SCH) conferring a 1.56-fold increase in DKD odds. However, the direct association between TSH and DKD, which displayed a linear pattern in initial analyses, did not persist after comprehensive adjustment for confounders. There was no interaction between subgroups.This underscores the complexity of the relationship and highlights albumin as a potential mechanistic pathway. Crucially, mediation analysis confirmed a significant indirect effect via albumin. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found that the prevalence of SCH in our T2DM cohort was 14.8% (206/1390).A systematic review and meta-analysis of 36 studies\u0026nbsp;revealed a combined prevalence of SCH in patients with T2DM of 10.2%[12]. A cross-sectional study involving 205 patients with T2DM in Argentina found an SCH prevalence of 8%[19]. This discrepancy is due to the inconsistent thresholds that have been set.This study used a TSH cutoff of 4.2 mIU/L. This value is the 97.5th percentile, which was derived from major cohorts like NHANES that enrolled rigorously screened healthy individuals with no evidence of thyroid disease[20].\u003c/p\u003e\n\u003cp\u003eTSH levels were found to be significantly associated with indicators of renal function: there were positive associations with creatinine (Cr), cystatin C (CysC) and the urine albumin-to-creatinine ratio (UACR), and a negative association with the estimated glomerular filtration rate (eGFR)-EPI. Individuals with TSH levels greater than 4.2 mIU/L (SCH) were 1.56 times more likely to develop DKD than individuals with TSH levels of 4.2 mIU/L or less. Similar findings were observed in other clinical studies. Two cross-sectional studies involving 414 patients with T2DM without prior thyroid disease and 8,418 normoglycaemic individuals revealed that elevated UACR was positively associated with SCH, while elevated TSH levels were negatively associated with eGFR[17,21].Similarly, a prospective Indian cohort study found that, as DKD progressed from stage 3b to stage 5, the prevalence of SCH increased from 14.3% to 57.2%, suggesting a positive association between worsening renal function and SCH risk [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe restricted cubic spline model initially showed a linear, progressive increase in DKD risk with TSH levels above 2.14 mIU/L. However, this association lost statistical significance after adjusting for confounders like gender, lifestyle, and diabetes-related factors. The loss of significance in the fully adjusted RCS model suggests that the TSH–DKD association is confounded by factors such as blood pressure and diabetes duration. Key factors such as blood pressure, diabetes duration, and BMI likely share common pathogenic pathways with both thyroid dysfunction and renal damage, thereby attenuating the independent effect of TSH when statistically controlled for.Subgroup analyses ( by sex, age, and lipid levels) revealed varying trends but no significant interactions, potentially due to limited sample sizes in groups like women and the elderly.Previous studies have shown that hypothyroidism is 1.7 times more prevalent among women with type 2 diabetes than among men, with individuals over 60 years old facing an even greater risk[23].We observed that SCH \u0026nbsp;significantly increased the risk of DKD in patients with dyslipidaemia, aligning with findings from previous studies[24], but not in those who were overweight/obese or had poor glycemic control. These findings suggest the TSH-DKD relationship is not independent or specific, as it is explained by confounders and not consistently modified by clinical subgroups. Future research should, therefore, explore mediating factors beyond common metabolic determinants.\u003c/p\u003e\n\u003cp\u003eAlbumin, synthesized by the liver, is the most abundant plasma protein. It is crucial for substance transport and exerts antioxidant and anti-inflammatory effects[25,26]. Thyroid dysfunction can impair albumin synthesis and function by exacerbating systemic inflammation and oxidative stress[27,28]. The resulting hypoalbuminaemia diminishes the kidneys' capacity to counteract inflammatory and oxidative damage, thereby accelerating DKD progression[29]. Furthermore, abnormal TSH levels may indirectly suppress hepatic albumin synthesis via their effects on thyroid hormones, leading to lower serum albumin concentrations[30,31]. This decline subsequently affects renal haemodynamics, exacerbating glomerular injury and albuminuria. Therefore, serum albumin is a pivotal mediator linking abnormal TSH levels to DKD pathophysiology through its antioxidant, anti-inflammatory, and haemodynamic regulatory actions. Understanding this mechanism may reveal novel targets for the early diagnosis and risk assessment of DKD. Future research should further explore the specific molecular pathways of the TSH-albumin-diabetes axis. For instance, cohort or interventional studies could clarify the causal role of serum albumin and evaluate whether interventions improving thyroid function or albumin levels can positively influence the prognosis of T2DM, thereby contributing to novel strategies for managing DKD.\u003c/p\u003e\n\u003cp\u003eA recent Chinese study of 146 patients with biopsy-confirmed diabetic nephropathy revealed that those with elevated TSH and/or reduced FT3 levels had more severe proteinuria, renal insufficiency, and glomerular lesions. This suggests that regulating thyroid hormones may be renoprotective and improve outcomes[32]. Supporting this, a cohort study by Shin et al. demonstrated that thyroid hormone replacement therapy not only preserves renal function but also independently predicts renal prognosis in CKD patients with SCH [33]. Consequently, the impact of such therapy on renal prognosis in DKD patients merits further investigation.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the cross-sectional design with a single thyroid function assessment precludes the establishment of causality and lacks prognostic insights; future longitudinal studies are needed. Second, the absence of data on inflammatory markers and renal pathology limits the exploration of underlying mechanisms. Third, the exclusion of patients on GLP-1 receptor agonists or SGLT2 inhibitors may affect the generalizability of our findings, given the renal protective effects of these drugs. Future research incorporating these patients and including prospective or interventional designs will be crucial to validate causality and assess the therapeutic potential of thyroid management in DKD.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study demonstrates a cross-sectional association between higher TSH levels and DKD in T2DM patients, which is partially mediated by serum albumin. However, the absence of an independent, linear relationship after comprehensive adjustment suggests that TSH may be more of a risk marker within a complex metabolic milieu than a direct causal factor. Future longitudinal and interventional studies are warranted to clarify the causality and explore the therapeutic implications of modulating the TSH-albumin axis in DKD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate：This study was approved by the Medical Biological Research Ethics Committee of the No.9 Hospital of Xi'an(Approval No.2025112). All patient data were deidentified for confidentiality, with the study adhering to the Declaration of Helsinki; informed consent was waived for the retrospective, de-identified data as approved by the ethics committee.\u003c/p\u003e\n\u003cp\u003eConsent for publication：All presentations of case reports must have consent for publication.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials：The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests：The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding：This work was supported in part by the National Natural Science Foundation of China (No. 81700736), Xi’an Science and Technology Plan Project (24YXYJ0080), National Health Commission Capacity Building and Continuing Education Center Project (W2024SNJT45) and Natural Science Basic Research Plan in Shaanxi Province of China (Program No.2025JC-YBMS-924)\u003c/p\u003e\n\u003cp\u003eAuthors' contributions：ZFH conducted data collection，MLY and ZYY wrote the main manuscript text and ZTX LHC prepared figures . All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements：not applicable.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOgle GD, Wang F, Haynes A, Gregory GA, King TW, Deng K et al. Global type 1 diabetes prevalence, incidence, and mortality estimates 2025: Results from the International diabetes Federation Atlas, 11th Edition, and the T1D Index Version 3.0. Diabetes Res Clin Pract. 2025 July;225:112277.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang Y, Ding L, Tao M, Zhu Y. The association of metabolic profile of folate with diabetic kidney disease: evidence from 2011\u0026ndash;2020 cycles of the NHANES. Ren Fail. 2024;46[2]:2420830.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Wang M, Yan Z. Recent advances in understanding the mechanisms by which sodium-glucose co-transporter type 2 inhibitors protect podocytes in diabetic nephropathy. Diabetol Metab Syndr. 2025;17[1]:84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia W, Yu R, Wang L, Zhu D, Guo L, Weng J, et al. Prevalence of chronic kidney disease among Chinese adults with diabetes: a nationwide population-based cross-sectional study. Lancet Reg Health West Pac. 2025;55:101463.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhang Y, Cao M, Yuan T, Ou S. Angiopoietin-like protein 4 dysregulation in kidney diseases: a promising biomarker and therapeutic target. Front Pharmacol. 2024;15:1475198.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJun JE, Jee JH, Bae JC, Jin SM, Hur KY, Lee MK, et al. Association Between Changes in Thyroid Hormones and Incident Type 2 Diabetes: A Seven-Year Longitudinal Study. Thyroid Off J Am Thyroid Assoc. 2017;271:29\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRong F, Dai H, Wu Y, Li J, Liu G, Chen H et al. Association between thyroid dysfunction and type 2 diabetes: a meta-analysis of prospective observational studies. BMC Med. 2021;19[1]:257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyneni R, Chawla HV, Grewal AS, Vivekanandan G, Ndakotsu A, Abubacker AP et al. Thyroxine Replacement for Subfertile Females With Subclinical Hypothyroidism and Autoimmune Thyroiditis: A Systematic Review. Cureus. 2021;13[8]:e16872.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKazempour-Ardebili S, Amouzegar A, Tohidi M, Amouzegar A, Azizi F. Prevalence of Subclinical Hypothyroidism in Chronic Kidney Disease in a Population-based Study: Tehran Thyroid Study. Int J Endocrinol Metab. 2021;19[2]:e103750.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaring AC, Rodondi N, Harrison S, Kanaya AM, Simonsick EM, Miljkovic I, et al. Thyroid function and prevalent and incident metabolic syndrome in older adults: the Health, Ageing and Body Composition Study. Clin Endocrinol (Oxf). 2012 June;766:911\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim BY, Kim CH, Jung CH, Mok JO, Suh KI, Kang SK. Association between subclinical hypothyroidism and severe diabetic retinopathy in Korean patients with type 2 diabetes. Endocr J. 2011;5812:1065\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan C, He X, Xia X, Li Y, Shi X, Shan Z et al. Subclinical Hypothyroidism and Type 2 Diabetes: A Systematic Review and Meta-Analysis. PLoS ONE. 2015;10[8]:e0135233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia F, Tian J, Deng F, Yang G, Long M, Cheng W, et al. Subclinical hypothyroidism and the associations with macrovascular complications and chronic kidney disease in patients with Type 2 diabetes. Diabet Med J Br Diabet Assoc. 2015;328:1097\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChonchol M, Lippi G, Salvagno G, Zoppini G, Muggeo M, Targher G. Prevalence of subclinical hypothyroidism in patients with chronic kidney disease. Clin J Am Soc Nephrol CJASN. 2008 Sept;35:1296\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsvold BO, Bj\u0026oslash;ro T, Vatten LJ. Association of thyroid function with estimated glomerular filtration rate in a population-based study: the HUNT study. Eur J Endocrinol. 2011;1641:101\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodacki M, Zajdenverg L, Dantas JR, de Oliveira JEP, Luiz RR, Cobas RA, et al. Should thyroid-stimulating hormone goals be reviewed in patients with type 1 diabetes mellitus? Results from the Brazilian Type 1 Diabetes Study Group. Diabet Med J Br Diabet Assoc. 2014;3112:1665\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurukawa S, Yamamoto S, Todo Y, Maruyama K, Miyake T, Ueda T, et al. Association between subclinical hypothyroidism and diabetic nephropathy in patients with type 2 diabetes mellitus. Endocr J. 2014;6110:1011\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi W, Song D, Chen D, Duan W, Zhang J. The association of thyroid parameters with markers of chronic kidney disease in euthyroid patients with type 2 diabetes. Endocr J 2023 July 28;70[7]:687\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJali MV, Kambar S, Jali SM, Pawar N, Nalawade P. Prevalence of thyroid dysfunction among type 2 diabetes mellitus patients. Diabetes Metab Syndr. 2017;11(Suppl 1):S105\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton TE, Davis S, Onstad L, Kopecky KJ. Thyrotropin levels in a population with no clinical, autoantibody, or ultrasonographic evidence of thyroid disease: implications for the diagnosis of subclinical hypothyroidism. J Clin Endocrinol Metab. 2008;934:1224\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun MT, Hsiao FC, Su SC, Pei D, Hung YJ. Thyrotropin as an independent factor of renal function and chronic kidney disease in normoglycemic euthyroid adults. Endocr Res. 2012;373:110\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBajaj S, Purwar N, Gupta A, Gupta P, Srivastava A. Prevalence of hypothyroidism in diabetic kidney disease and effect of thyroid hormone replacement on estimate glomerular filtration rate. Indian J Endocrinol Metab. 2016;206:795\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHollowell JG, Staehling NW, Flanders WD, Hannon WH, Gunter EW, Spencer CA, et al. Serum TSH, T(4), and thyroid antibodies in the United States population (1988 to 1994): National Health and Nutrition Examination Survey (NHANES III). J Clin Endocrinol Metab. 2002;872:489\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Tienhoven-Wind LJN, Dullaart RPF. Low-normal thyroid function and novel cardiometabolic biomarkers. Nutrients. 2015;72:1352\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCristancho C, Hemond CC. Serum Albumin Modifies the Effect of Peripheral Blood Monocytes on Severity of Diabetic Nephropathy in an Adult Population. Can J Diabetes. 2022;461:69\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Zhang Z, Teng Z, Zeng Q. Association of neutrophil-percentage-to-albumin ratio with all-cause and cardiovascular mortality in patients with diabetes and prediabetes from the NHANES 1999\u0026ndash;2018. Sci Rep. 2025;15[1]:15630.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeged\u0026uuml;s L, Bianco AC, Jonklaas J, Pearce SH, Weetman AP, Perros P. Primary hypothyroidism and quality of life. Nat Rev Endocrinol. 2022;184:230\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Liu H, Niu M, Wang Y, Xu R, Guo Y et al. Roles of long noncoding RNAs in human inflammatory diseases. Cell Death Discov. 2024;10[1]:235.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu X, Zhao L, Zhang Y, Li K, Yang J. The role and mechanism of the gut microbiota in the development and treatment of diabetic kidney disease. Front Physiol. 2023;14:1166685.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Ji X, Ni W, Luo Y, Ding B, Ma J, et al. Serum albumin and albuminuria predict the progression of chronic kidney disease in patients with newly diagnosed type 2 diabetes: a retrospective study. PeerJ. 2021;9:e11735.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Nawawy A, Elwafa RAHA, Khalil Abouahmed A, Rasheed RA, Omar OM. Evaluation of non-thyroidal illness syndrome in shock patients admitted to pediatric intensive care unit in a developing country. Eur J Pediatr. 2024;1832:769\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Q, Zhang J, Wang Y, Li H, Zhang R, Guo R, et al. Thyroid hormones and diabetic nephropathy: An essential relationship to recognize. Nephrol Carlton Vic. 2019;242:160\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin DH, Lee MJ, Kim SJ, Oh HJ, Kim HR, Han JH, et al. Preservation of renal function by thyroid hormone replacement therapy in chronic kidney disease patients with subclinical hypothyroidism. J Clin Endocrinol Metab. 2012;978:2732\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Socio-demography and clinical characteristics of the study subjects\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\" style=\"margin-right: calc(0%); width: 100%;\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003eTotal (n = 1390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eNon-DKD (n = 1061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eDKD (n = 329)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"31\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"31\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eSex, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e450 (32.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e349 (32.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e101 (30.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e940 (67.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e712 (67.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e228 (69.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eAge(y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e54.00 (47.00, 62.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e53.00 (45.00, 61.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e57.00(51.00, 64.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-6.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eBMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e25.50 (23.50, 27.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e25.30(23.40, 27.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e25.90 (23.80, 28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eSmoking, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e755 (54.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e572 (53.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e183 (55.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e635 (45.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e489 (46.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e146 (44.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eWine, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1005 (72.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e745 (70.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e260 (79.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e385 (27.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e316 (29.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e69 (20.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ehypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=88.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e793 (57.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e679 (64.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e114 (34.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e597 (42.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e382 (36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e215 (65.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eSBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e130.00 (120.00, 140.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e124.00 (118.00, 138.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e140.00 (128.00, 150.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eDBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e80.00(70.00, 86.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e80.00(70.00, 85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e80.00(76.00, 90.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003eduration(y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e8.00 (3.00, 13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6.00(2.00, 11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e11.00 (7.00, 16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-9.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHbA1c(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e8.50(7.20, 10.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e8.50 (7.20, 10.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e8.30 (7.40, 10.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eFPG(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e8.20(6.53, 10.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e7.90(6.30, 10.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e8.60(7.10, 11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eAlbumin(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e42.50(40.10, 45.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e42.90 (40.60, 45.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e40.80 (37.90, 43.90)\u003c/p\u003e\n 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0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCr(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eGFR-EPI\u003c/p\u003e\n \u003cp\u003e(ml/min/1.73m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e86.85 (71.80, 98.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e89.76 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15px;\"\u003e\n \u003cp\u003eMalb(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e7.44(5.00, 14.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6.39(5.00, 12.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e13.70(5.82, 28.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eTSH(mIU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e2.14(1.39, 3.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.11(1.37, 3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.24(1.46, 3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eFT3(pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e4.66 (4.31, 5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e4.70 (4.36, 5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e4.56 (4.18, 4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eFT4(pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e16.12 (14.74, 17.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e16.20 (14.83, 17.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e15.78(14.51, 17.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n 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7px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eTGAB(IU/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e235.00(63.00, 451.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e272.00 (95.00, 489.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e118.00 (23.00, 290.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n 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\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e4.19(3.52, 4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eLDL(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e2.33 (1.86, 2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.33 (1.84, 2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.40 (1.87, 2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHDL(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.94 (0.80, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.94 (0.80, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.94(0.78, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eZ=-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviation:BMI,body mass index ; HTN, hypertension;SBP, systolic blood pressure;DBP,diastolic blood pressure;HbA1c,glycated hemoglobin；FPG,fasting plasma glucose;Cr Creatinine；eGFR-EPI,estimated glomerular filtration rate;UACR,urine-to-creatinine ratio;CysC,CystatinC;Malb,Microalbuminuria;\u003c/p\u003e\n\u003cp\u003eTSH,thyroid-stimulating hormone;FT3,free triiodothyronine;FT4,free thyroxine;TT3,total triiodothyronine ；TT4,total thyroxine ；TPOAB,thyroid peroxidase autoantibodies ；TGAB,thyroglobulin autoantibodies;TG,triglycerides;TC,total cholesterol ;LDL,low-density lipoprotein ； HDL、high-density lipoprotein ；\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eLogistic Regression Model for the association between TSH and DKD\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTSH＜4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTSH\u0026ge;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.74\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.26 ~2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.59\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.14 ~2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.59\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.13 ~2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.56\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.09 ~2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel1: Crude model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\n \u003cp\u003eModel2: Sex and age were adjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\n \u003cp\u003eModel3: Sex, smoking, wine,\u0026nbsp;hypertension, age, and BMI were adjusted.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\n \u003cp\u003eModel4: Adjust: Sex, Smoking, Wine,\u0026nbsp;hypertension, Age, BMI, SBP, DBP, Diabetes duration, HbA1c, FPG\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\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Mediation effect of Albumin on the association between TSH and DKD\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMediator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eExposure to Mediation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eMediator to Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eeffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eMediation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003cp\u003e-1.42~0.03\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e=0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003cp\u003e-0.12~-0.07\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003cp\u003e0.04~0.16\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e0.00~0.02\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e=0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e0.05~0.17\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"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":"Thyroid-stimulating hormone, Diabetic kidney disease, Albumin","lastPublishedDoi":"10.21203/rs.3.rs-8256993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8256993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eObjective\u003c/b\u003e The relationship between thyroid-stimulating hormone (TSH) and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM) is unclear. This study aimed to clarify the relationship between TSH and DKD and to identify potential mediating factors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e A total of 1,390 patients with type 2 diabetes were divided into two groups: a non-diabetic kidney disease (Non-DKD) group and a diabetic kidney disease (DKD) group. We compared the differences in baseline data between the two groups, analyzed the association between TSH and renal function indicators, and categorized TSH levels into normal thyroid function and subclinical hypothyroidism (SCH) using a cutoff of 4.2 mIU/L. Logistic regression was used to compare the DKD prevalence between groups and clarify the TSH-DKD association. A restricted cubic spline model was used to determine if a non-linear relationship exists between TSH and DKD. Subgroup stratification analysis was conducted to examine potential confounding variables' modulation of the TSH-DKD association and their interaction. We also analyze potential mediating factors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e A comparison of baseline data revealed that TSH levels were significantly higher in the DKD group than in the non-DKD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).TSH was significantly associated with renal function indicators (Cr, GFR-EPI, CysC, and UACR) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). TSH showed a positive association with DKD. The probability of developing DKD was 1.56 times higher in subjects with TSH\u0026thinsp;\u0026gt;\u0026thinsp;4.2 mIU/L (SCH) compared to those with TSH\u0026thinsp;\u0026le;\u0026thinsp;4.2 mIU/L(OR: 1.56,95% CI: 1.09\u0026ndash;2.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). In the restricted cubic spline model, Before adjusting for confounding factors, TSH levels exhibited a significant linear correlation with DKD risk. However, after adjusting for gender, lifestyle, and diabetes-related confounding factors, this association lost statistical significance.There was no interaction between subgroups. TSH indirectly influenced DKD via serum albumin. (β\u0026thinsp;=\u0026thinsp;0.01, 95% CI: 0.00\u0026ndash;0.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e Among individuals with type 2 diabetes, TSH shows a positive association with DKD, and this relationship might be partially mediated by albumin.\u003c/p\u003e","manuscriptTitle":"Association Between Thyroid-Stimulating Hormone and Diabetic Kidney Disease in Type 2 Diabetes: A Cross-Sectional Study with Mediation Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 11:28:52","doi":"10.21203/rs.3.rs-8256993/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":"cffd093f-9a89-4d27-85ee-ff9e2fced9a3","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T13:26:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 11:28:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8256993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8256993","identity":"rs-8256993","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}