Correlates of thyroid nodules in patients with type 2 diabetes: a cross-sectional study

Correlates of thyroid nodules in patients with type 2 diabetes: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlates of thyroid nodules in patients with type 2 diabetes: a cross-sectional study Baolan Ji, Chao Tian, Wenhua Du, Yuanyuan Zhang, Bo Ban, Guanqi Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3241534/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Diabetes is an independent risk factor for thyroid nodules (TNs), however, the influencing factors of TNs have not been fully clarified under the condition of diabetes. We aimed to explore the correlates of TNs in type 2 diabetes (T2D) patients. Methods In this cross-sectional study, 1444 Chinese adults with T2D were included. Clinical and biochemical characteristics were collected. The overall prevalence of TNs was 45.6%. Spearman correlation analysis and logistic regression analysis were used successively to analyze the independent correlates of TNs. Results The results from univariate and further logistic regression analyses showed that female (OR: 1.656; 95% CI: 1.255–2.185), age (OR:1.017; 95% CI:1.005–1.030), UA (OR: 0.998; 95% CI: 0.997-1.000), free triiodothyronine (FT3) (OR: 1.400; 95% CI: 1.169–1.675) and peripheral atherosclerosis (PAS) (OR: 1.614; 95% CI: 1.155–2.255) independently correlated with TNs in all patients. Subsequently, stratified by sex and age, the results displayed that FT3 (OR: 1.585 ; 95% CI:1.211–2.073), PAS (OR: 1.759; 95% CI: 1.148–2.695) and duration of diabetes (OR: 1.037; 95% CI: 1.008–1.067) in female while age (OR: 1.020 ; 95% CI: 1.007–1.034) and PAS (OR: 1.802 ; 95% CI: 1.223–2.656) in male, and age (OR: 1.045 ; 95% CI: 1.025–1.065) and female (OR: 1.892; 95% CI: 1.361–2.629) in patients below 60 years old while female (OR: 1.643 ; 95% CI: 1.181–2.284), PAS (OR: 2.077; 95% CI: 1.269–3.401), FT3 (OR: 1.316 ; 95% CI: 1.044–1.661) and magnesium (OR: 7.399; 95% CI: 1.060-51.628 ) over 60 years old, significantly related to TNs. The prevalence of TNs was high in T2D patients. Conclusions The independent correlates of TNs were multifactorial, and there existed differences in different physiological states. Type 2 diabetes Thyroid nodules Correlates Background In recent years, the detection rates of thyroid nodules (TNs) have rapidly increased [ 1 ]. Although usually asymptomatic, some still exhibit malignancy, and then it is crucial to establish reasonable management strategies [ 2 , 3 ]. Epidemiological and observational studies consistently demonstrate that diabetes is an independent risk factor for TNs [ 4 – 7 ], however, the potential factors affecting TNs are as yet unclear but should be multifactorial under the condition of diabetes. Therefore, potential correlates of TNs were collected and analyzed among adult patients with type 2 diabetes (T2D) in this study. Methods Patients We retrospectively reviewed the medical records of patients with T2D from the Department of Endocrinology, Linyi People’s Hospital, between February 2020 and September 2021. The inclusion criteria were as follows: adults with T2D. The exclusion criteria included the subjects with type 1 diabetes and other types of diabetes, and missing data of thyroid ultrasound. Ultimately, a total of 1444 adult patients with T2D aged between 18 and 95 years old were eventually included in our study. Physical Examinations The height and weight of participants were measured. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in the nondominant arm of seated participants with an automated electronic device. Laboratory Measurements Following an overnight fast, blood samples were collected and analyzed in the morning for lipid profiles, including total cholesterol (TC), triglycerides (TG), high density lipoprotein-cholesterol (HDL-c), and low density lipoprotein-cholesterol (LDL-c); liver function, including alanine aminotransferase (ALT) and aspartate aminotransferase (AST); kidney function, including serum creatinine (Scr) and uric acid (UA); hemoglobin (Hb); glycosylated haemoglobin (HbA1c, high performance liquid chromatography); and electrolyte profiles including calcium (Ca), magnesium (Mg) and phosphate (P), were tested using a biochemical autoanalyzer (Cobas c 702, Roche, Germany). Measurements of thyroid function, including free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH) and anti-thyroperoxidase antibody (TPOAb), were tested by chemiluminescence immunoassay (SIEMENS, USA). Urinary creatinine (picric acid method) and urinary microalbumin (UMA, transmission turbidimetry) were tested by an autoanalyzer (Beckman Coulter AU5821), and urinary albumin to creatinine ratio (UACR) was calculated. Definition of complications and comorbidities According to the corresponding criteria, diabetic neuropathy (DN) [ 8 ], retinopathy (DR) [ 9 ] and peripheral neuropathy (DPN) (assessed by electromyography) were diagnosed, respectively. Peripheral atherosclerosis (PAS) including intima-media thickness increasing, plaque formation, stenosis and occlusion of carotid or lower extremity artery, was assessed by vascular ultrasonography. TNs were evaluated by thyroid ultrasound. Self-reported current cigarette smoking, alcohol drinking, age of menarche and age of menopause were collected. Smoker was defined as smoking at least one cigarette per day in the last month, and drinker was defined as alcohol consumption ≥ two times per week. Parameter calculations 1. Body mass index (BMI) = weight (kg) / height 2 (m 2 ); 2. TG / HDL-c ratio = TG (mmol/L) / (HDL-c) (mmol/L) Statistical Analysis Statistical analysis was performed using SPSS 26.0 (SPSS, Inc., Chicago, USA). Data were presented as mean ± SD for normally distributed variables, and median (interquartile ranges) for abnormal distributions. Independent-Samples T test and Mann-Whitney U test were used for comparisons of normally and abnormally distributed continuous variables between two groups, respectively. Categorical variables were presented as percentage (%), and were compared by Chi-square test. Spearman correlation analysis and logistic regression analysis were used successively to analyze the independent correlates of TNs. Statistical differences were defined by P -value (two-tailed) less than 0.05. Results Baseline clinical and biochemical characteristics The clinical and biochemical characteristics of the participants are shown in Table 1. A total of 1444 patients with T2D with a mean age of 58.8 ± 13.0 years were enrolled in this study. The overall prevalence of TNs was 45.6%. Then, the subjects were divided into two groups including TNs (with TNs) group and NTNs (without TNs) group, and the level of each variable was compared between the two groups (Table 1). The results showed that the age, duration of diabetes, SBP, HDL-c, FT3, and the percentage of female, DR and PAS were significantly increased, and the TG / HDL-c ratio, UA, Scr, UMA and the percentage of smoking, DN were significantly decreased in the TNs group (all P -value < 0.05). However, there was no obvious differences in the drinking, BMI, DBP, age of menarche, age of menopause, TC, LDL-c, TG, HbA1c, ALT, AST, UACR, Hb, FT4, TSH, TPOAb, Ca, Mg, P, and the percentage of DPN between the two groups (all P -value > 0.05). Correlation between TNs and all variables by univariate analysis As shown in Table 2, a spearman correlation analysis was performed to analyze the relationship between TNs and each variable. The results showed that TNs was related positively to sex (female), age, duration of diabetes, SBP, HDL-c, FT3, Mg, DR and PAS (all P -value < 0.05), while negatively to smoking, TG/HDL-c ratio, UA, Scr, UMA, Hb and DN (all P -value < 0.05). However, there was no significant association between TNs and drinking, BMI, DBP, age of menarche, age of menopause, TC, LDL-c, TG, HbA1c, ALT, AST, UACR, FT4, TSH, TPOAb, Ca, P and DPN in all patients (all P -value > 0.05). Subsequently, stratified by sex and age, the results displayed that the duration of diabetes, FT3, Ca, DR and PAS were positively, and the UACR and UMA were negatively related to TNs in female (all P -value < 0.05), while the age, SBP, Mg, DR and PAS were positively, and the TG/HDL-c, AST, and UA were negatively related to TNs in male (all P -value < 0.05). Additionally, the sex (female), age, DR and PAS were positively, and the smoking, UA, Scr and Hb were negatively related to TNs in patients below 60 years old (all P -value < 0.05), while the sex (female), HDL-c, FT3, Ca, Mg and PAS were positively, and the UMA and DN were negatively related to TNs in patients over 60 years old (all P -value < 0.05). Independent c orrelates of TNs by logistic regression analysis Firstly, in the overall population, TNs was served as the dependent variable, and the sex, age, duration of diabetes, smoking, SBP, HDL-c, TG/HDL-c ratio, UA, Scr, UMA, Hb, FT3, Mg, DN, DR and PAS were as the independent variables according to the results of univariate analysis (Table 1 and Table 2). A logistic regression analysis was performed to analyze the independent correlates of TNs (Table 3), and the results found that after adjusting for the other variables, the sex (female), age, FT3 and PAS was significantly positively related to TNs (all P -value < 0.05), while the UA was independently and negatively related to TNs ( P -value = 0.046). Subsequently, stratified by sex and age, the results displayed that the FT3, PAS and duration of diabetes adjusting for the UACR, UMA, Ca and DR in female, while the age and PAS adjusting for the SBP, TG/HDL-c, AST, UA, Mg and DR in male, was significantly related to TNs (all P -value < 0.05). Besides, the age and female adjusting for the smoking, UA, Scr, Hb, DR and PAS in patients below 60 years old while the female, PAS, FT3 and Mg adjusting for the HDL-c, UMA, Ca and DN in patients over 60 years old, was closely related to TNs (all P -value < 0.05). Discussion This study demonstrated that there was a high prevalence of TNs in patients with T2D, and the correlates of TNs were involved in multiple factors. Strong evidences demonstrate that female and age are highly related to TNs [ 4 , 10 – 13 ]. Our results also supported the conclusion that the percentage of female was significantly increased in TNs group. The mechanism of the gender difference on TNs was unclear, and the underlying reason might be related to the effect of oestrogen in the propagation of thyroid stem/progenitor cells which were probably involved in be the origin of non-functioning TNs in females [ 14 ]. Additionally, it should be noted that our study suggested that the age might have a more significant impact on TNs in male patients. Additionally, studies displayed that although the prevalence of TNs increased with advancing age, such nodules had a lower risk of malignancy, whereas identified cancers were more likely to be of high-risk histology [ 15 , 16 ]. Therefore, it was essential to further explore the effect of age on TNs and carefully weigh the risks and benefits of TNs diagnosis and treatment in older adults. There might exist association between the function of pituitary-thyroid axis and TNs, but it was not fully clarified. Some studies showed a positive association between TSH level and TNs and cancer in adults [ 17 ], but others displayed that TSH measurement may not be served as a single effective tool to detect or exclude TNs [ 18 ]. In our study, there was no obvious difference in TSH level between TNs and NTNs groups. Additionally, inconsistent with previous studies [ 19 , 20 ], we found that FT3 rather than FT4 were positively correlated with TNs in patients with T2D in the whole population, and subgroup analyses demonstrate that FT3 was obviously related to TNs in female and in patients over 60 years old. Thus, further researches should be carried out to explore the roles of the function of pituitary-thyroid axis on the occurrence and progression of TNs. In addition, there was no consistent conclusion about the association between UA and TNs. In the studies of Huang et al. [ 21 ] and Li et al. [ 10 ], UA was an independent risk factor for the formation of TNs, while in the meta-analysis by Hu et al. [ 22 ], the data showed no correlation between the incidence of TNs and the presence or absence of hyperuricemia in the overall population but a bidirectional regulatory effect of UA on TNs in different genders. However, in the present study, we found that UA was negatively related to TNs, and may be a protective factor for TNs in the overall population, but there existed no obvious correlation between UA and TNs in different subgroup analyses. Therefore, further large sample studies were needed to analyze the relationships and mechanism between UA and TNs by stratified analysis. Moreover, serum Mg level was closely associated with TNs in patients over 60 years old in our study. In the study of Zeng et al. [ 23 ], the results showed that higher level of Mg significantly increased the risks of TNs among healthy subjects. However, other studies demonstrated significant and negative associations between Mg and thyroid cancer [ 24 , 25 ]. Thus, the relationship of Mg and TNs and potential mechanisms were needed to be further explored. Finally, our results showed that the percentage of PAS was significantly higher in TNs group than that in NTNs group, and a significant relationship existed between PAS and TNs. Unfortunately, there were few evidences to support our data. Inhibitors of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins), potent cholesterol-lowering drugs, presented multiple vascular protective actions [ 26 ]. Accumulating evidences indicated an antiproliferative effect of statins on TNs [ 27 – 30 ]. Overall, the specific relationship and mechanism between PAS and TNs, and the preventive and therapeutic effects of statins on TNs needed to be further elaborated. A few limitations to our study should be considered. Firstly, because of a cross-sectional study, we could not draw a direct cause-effect relationship between the correlates and TNs, so longitudinal studies should be conducted to further support our data. Secondly, we only conducted a preliminary analysis of correlates of TNs, further studies will be performed to explore the effects of the above correlates on the size, morphology and pathology of TNs. Additionally, the potential mechanisms needed to be further explored. Conclusions The prevalence of TNs was high in adults with T2D. The independent correlates of TNs were multifactorial, and there existed differences in different physiological states. Abbreviations BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; LDL-c, low-density lipoprotein cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; UA, uric acid; Scr, serum creatinine; UACR, urinary albumin to creatinine ratio; UMA, urinary microalbumin; Hb, hemoglobin; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; TPOAb, anti-thyroperoxidase antibody; Ca, calcium; Mg, magnesium; P, phosphate; DN, diabetic nephropathy; DR, diabetic retinopathy; DPN, diabetic peripheral neuropathy; PAS, peripheral atherosclerosis; TNs, thyroid nodules; NTNs, without thyroid nodules Declarations Ethics approval and consent to participate The study was approved by the Human Ethics Committee of the Linyi People’s Hospital. All procedures were performed in accordance with ethical standards laid out in the Declaration of Helsinki. Informed consent was obtained from the patients. Consent for publication Not applicable. Availability of data and materials Data availability from the authors on request. Competing interests All authors declare that they have no conflict of interest. Funding This research was supported by grants from the Postdoctoral Program of Affiliated Hospital of Jining Medical University (JYFY322152). Authors' contributions G.Q.G. and B.B. conceived and designed the study. B.L.J. performed the statistical analysis and drafted the manuscript. C.T. participated in data collection and drafted the manuscript. W.H.D. and Y.Y.Z. participated in data collection. All authors revised and approved the final manuscript. Acknowledgements Not applicable. References Qu MY, Tang W, Cui XY, et al. Increased Prevalence of Thyroid Nodules Across Nearly 10 Years in Shanghai, China. 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Tables Table 1 Clinical and biochemical characteristics Variables All TNs group NTNs group P -value Number 1444 658 786 Sex (female, n, %) 657 (45.5%) 346 (52.6%) 311 (39.6%) <0.001 Age (years) 58.8 ± 13.0 60.96 ± 11.84 56.91 ± 13.62 <0.001 Duration of diabetes (years) 10.0 (4.0 ~ 14.0) 10.0 (4.0 ~ 15.0) 8.0 (3.0 ~ 14.0) 0.008 Smoking (%) 315 (21.8%) 120 (18.2%) 195 (24.8%) 0.002 Drinking (%) 266 (18.4%) 115 (17.5%) 151 (19.2%) 0.218 BMI (kg/m 2 ) 25.69 ± 3.73 25.65 ± 3.37 25.72 ± 4.02 0.717 SBP (mmHg) 129.2 ± 19.2 130.8 ± 19.8 127.8 ± 18.5 0.003 DBP (mmHg) 80.0 ± 12.2 80.0 ± 12.2 80.0 ± 12.2 0.994 Age of menarche (years) 15.3 ± 1.9 15.3 ± 1.8 15.2 ± 1.9 0.839 Age of menopause (years) 50.0 ± 3.6 49.8 ± 3.8 50.2 ± 3.3 0.198 TC (mmol/L) 4.7 ± 1.5 4.65 ± 1.26 4.78 ± 1.63 0.111 LDL-c (mmol/L) 2.99 ± 1.08 3.00 ± 1.10 2.98 ± 1.06 0.683 TG (mmol/L) 1.39 (0.98 ~ 2.05) 1.35 (0.96 ~ 2.03) 1.42 (0.99 ~ 2.10) 0.161 HDL-c (mmol/L) 1.14 ± 0.33 1.16 ± 0.34 1.12 ± 0.33 0.023 TG/HDL-c ratio 1.28 (0.81 ~ 2.07) 1.22 (0.79 ~ 2.03) 1.33 (0.82 ~ 2.17) 0.031 HbA1c (%) 140.85 ± 20.0 9.34 ± 2.26 9.41 ± 2.32 0.578 ALT (U/L) 18.20 (13.03 ~ 26.90) 18.00 (13.10 ~ 25.30) 18.30 (13.00 ~ 29.20) 0.157 AST (U/L) 17.60 (14.00 ~ 23.00) 17.10 (14.20 ~ 21.90) 18.10 (13.90 ~ 24.00) 0.087 UA (μmolL) 298.08 ± 97.43 285.94 ± 88.65 308.41 ± 103.25 <0.001 Scr (μmol/L) 67.64 ± 22.27 66.19 ± 21.49 68.88 ± 22.84 0.025 UACR (mg/g) 12.70 (5.70 ~ 53.40) 11.80 (5.60 ~ 43.75) 13.20 (5.90 ~ 59.40) 0.294 UMA (mg/L) 12.20 (4.80 ~ 44.35) 10.50 (4.40 ~ 37.90) 13.70 (5.35 ~ 52.20) 0.019 Hb (g/L) 140.85 ± 19.99 139.89 ± 18.89 141.64 ± 20.83 0.578 FT3 (pmol/L) 4.52 ± 0.81 4.58 ± 0.76 4.47 ± 0.85 0.008 FT4 (pmol/L) 16.47 ± 2.90 16.44 ± 3.01 16.50 ± 2.81 0.710 TSH (uIU/mL) 1.84 (1.15 ~ 2.70) 1.84 (1.19 ~ 2.78) 1.83 (1.13 ~ 2.62) 0.274 TPOAb (IU/mL) 38.50 (33.20 ~ 46.20) 37.50 (33.00 ~ 44.90) 39.20 (33.60 ~ 47.60) 0.057 Ca (mmol/L) 2.32 ± 0.12 2.32 ± 0.11 2.31 ± 0.12 0.079 Mg (mmol/L) 0.87 ± 0.09 0.88 ± 0.07 0.87 ± 0.10 0.090 P (mmol/L) 1.19 ± 0.21 1.19 ± 0.20 1.19 ± 0.21 0.589 Complications DN (%) 388 (26.9%) 159 (24.2%) 229 (29.1%) 0.019 DR (%) 566 (39.2%) 289 (43.9%) 277 (35.2%) <0.001 DPN (%) 632 (43.8%) 296 (45.0%) 336 (42.7%) 0.212 PAS (%) 1035 (71.7%) 529 (80.4%) 506 (64.4%) <0.001 BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; LDL-c, low-density lipoprotein cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; UA, uric acid; Scr, serum creatinine; UACR, urinary albumin to creatinine ratio; UMA, urinary microalbumin; Hb, hemoglobin; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; TPOAb, anti-thyroperoxidase antibody; Ca, calcium; Mg, magnesium; P, phosphate; DN, diabetic nephropathy; DR, diabetic retinopathy; DPN, diabetic peripheral neuropathy; PAS, peripheral atherosclerosis; TNs, thyroid nodules; NTNs, without thyroid nodules. Data were presented as mean ± SD for normally distributed variables, and median (interquartile ranges) for abnormal distributions. Independent-Samples T test and Mann-Whitney U test were used for comparisons of normally and abnormally distributed continuous variables between TNs and NTNs groups, respectively. Categorical variables were presented as percentage (%), and were compared by chi-square test. Statistical differences were defined by P- value (two-tailed) less than 0.05. Table 2 The correlation between TNs and different variables by univariate analysis Variables In all In female In male Age < 60 Age ≥ 60 Correlation coefficient P -value Correlation coefficient P -value Correlation coefficient P -value Correlation coefficient P -value Correlation coefficient P -value Sex 0.130 <0.001 0.146 <0.001 0.091 0.013 Age 0.146 <0.001 0.063 0.105 0.182 <0.001 0.166 <0.001 0.021 0.566 Duration of diabetes 0.076 0.008 0.096 0.023 0.058 0.138 0.066 0.128 0.022 0.555 Smoking -0.079 0.003 0.022 0.576 -0.036 0.317 -0.105 0.006 -0.039 0.282 Drinking -0.022 0.398 0.037 0.343 0.049 0.168 -0.044 0.249 0.014 0.695 BMI -0.017 0.554 0.043 0.305 -0.052 0.165 -0.035 0.385 0.025 0.527 SBP 0.070 0.008 0.059 0.129 0.073 0.040 0.050 0.194 0.061 0.095 DBP 0.008 0.756 0.011 0.774 0.044 0.216 0.035 0.364 0.024 0.515 Age of menarche 0.022 0.583 0.025 0.540 -0.062 0.319 0.030 0.576 Age of menopause -0.088 0.061 -0.086 0.067 -0.100 0.308 -0.091 0.089 TC -0.022 0.409 -0.043 0.284 -0.035 0.330 -0.019 0.615 0.000 0.994 LDL-c 0.002 0.926 -0.007 0.858 -0.006 0.868 0.002 0.959 0.022 0.557 TG -0.038 0.161 -0.014 0.721 -0.061 0.094 -0.021 0.591 -0.018 0.623 HDL-c 0.064 0.017 0.002 0.955 0.051 0.159 0.026 0.510 0.075 0.043 TG/HDL-c ratio -0.058 0.031 -0.018 0.650 -0.073 0.045 -0.017 0.659 -0.061 0.100 HbA1c -0.014 0.599 0.042 0.299 -0.055 0.132 -0.014 0.721 -0.002 0.955 ALT -0.038 0.157 0.041 0.305 -0.062 0.086 -0.054 0.163 0.005 0.898 AST -0.046 0.087 0.020 0.615 -0.083 0.022 -0.038 0.324 -0.052 0.162 UA -0.100 <0.001 -0.060 0.133 -0.074 0.040 -0.123 0.001 -0.059 0.111 Scr -0.073 0.007 -0.023 0.565 0.013 0.714 -0.114 0.003 -0.066 0.075 UACR -0.029 0.294 -0.084 0.038 0.008 0.819 -0.017 0.659 -0.071 0.060 UMA -0.064 0.019 -0.098 0.016 -0.009 0.798 -0.037 0.342 -0.083 0.027 Hb -0.063 0.019 0.065 0.103 -0.044 0.226 -0.105 0.006 0.051 0.164 FT3 0.057 0.037 0.164 <0.001 0.055 0.145 0.023 0.571 0.140 <0.001 FT4 -0.043 0.118 -0.024 0.556 -0.026 0.489 -0.023 0.558 -0.036 0.349 TSH 0.030 0.274 -0.023 0.562 0.030 0.428 0.030 0.444 0.030 0.434 TPOAb -0.065 0.056 -0.080 0.110 -0.081 0.083 -0.074 0.121 -0.059 0.232 Ca 0.038 0.156 0.092 0.020 -0.006 0.860 0.004 0.907 0.100 0.007 Mg 0.072 0.007 0.056 0.159 0.107 0.003 0.005 0.891 0.107 0.004 P 0.012 0.641 -0.014 0.716 -0.007 0.854 -0.015 0.689 0.066 0.074 Complications DN -0.056 0.034 -0.057 0.143 -0.025 0.486 -0.052 0.176 -0.072 0.049 DR 0.089 0.001 0.082 0.036 0.095 0.007 0.085 0.026 0.062 0.090 DPN 0.022 0.394 0.026 0.513 0.016 0.659 0.065 0.089 -0.053 0.147 PAS 0.177 <0.001 0.176 <0.001 0.179 <0.001 0.131 0.001 0.175 <0.001 BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; LDL-c, low-density lipoprotein cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; UA, uric acid; Scr, serum creatinine; Hb, hemoglobin; UACR, urinary albumin to creatinine ratio; UMA, urinary microalbumin; Hb, hemoglobin; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; TPOAb, anti-thyroperoxidase antibody; Ca, calcium; Mg, magnesium; P, phosphate; DN, diabetic nephropathy; DR, diabetic retinopathy; DPN, diabetic peripheral neuropathy; PAS, peripheral atherosclerosis; TNs, thyroid nodules. Correlation coefficients between TNs and different variables were determined by Spearman's correlation analysis. Table 3 The relative risks for TNs by logistic regression analysis Variables B SE Wald P -value OR 95.0 % CI for OR In all Female 0.504 0.141 12.710 <0.001 1.656 1.255-2.185 Age 0.017 0.006 7.576 0.006 1.017 1.005-1.030 UA -0.002 0.001 3.985 0.046 0.998 0.997-1.000 FT3 0.336 0.092 13.452 <0.001 1.400 1.169-1.675 PAS 0.479 0.171 7.877 0.005 1.614 1.155-2.255 In female FT3 0.460 0.137 11.280 0.001 1.585 1.211-2.073 PAS 0.565 0.218 6.727 0.009 1.759 1.148-2.695 Duration of diabetes 0.036 0.015 6.114 0.013 1.037 1.008- 1.067 In male Age 0.020 0.007 8.666 0.003 1.020 1.007-1.034 PAS 0.589 0.198 8.856 0.003 1.802 1.223-2.656 Age < 60 Age 0.044 0.010 19.997 <0.001 1.045 1.025-1.065 Female 0.638 0.168 14.417 <0.001 1.892 1.361-2.629 Age ≥ 60 Female 0.496 0.168 8.697 0.003 1.643 1.181-2.284 PAS 0.731 0.251 8.454 0.004 2.077 1.269-3.401 FT3 0.275 0.119 5.378 0.020 1.316 1.044-1.661 Mg 2.001 0.991 4.077 0.043 7.399 1.060-51.628 UA, uric acid; FT3, free triiodothyronine; PAS, peripheral atherosclerosis; Mg, magnesium; TNs, thyroid nodules; CI, confidence interval; OR, odd ratio; SE, standard error. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3241534","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":224758975,"identity":"7dfd5d34-7ae5-425e-b661-fde387b4c0ac","order_by":0,"name":"Baolan Ji","email":"","orcid":"","institution":"Linyi People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Baolan","middleName":"","lastName":"Ji","suffix":""},{"id":224758976,"identity":"2aa7c9ca-2e81-465e-9077-8510e0fdd882","order_by":1,"name":"Chao Tian","email":"","orcid":"","institution":"Linyi People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Tian","suffix":""},{"id":224758977,"identity":"e93b4da1-be07-4143-b86c-1552b8ccc1a5","order_by":2,"name":"Wenhua Du","email":"","orcid":"","institution":"Linyi People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenhua","middleName":"","lastName":"Du","suffix":""},{"id":224758978,"identity":"0db2be93-7942-4c9d-864e-d87da0f437c1","order_by":3,"name":"Yuanyuan Zhang","email":"","orcid":"","institution":"Linyi People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":224758979,"identity":"ef8d5c1d-6366-40a2-9b07-aabacf6d37bf","order_by":4,"name":"Bo Ban","email":"","orcid":"","institution":"Affiliated Hospital of Jining Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Ban","suffix":""},{"id":224758980,"identity":"52decabc-4754-407f-bdde-ff431041baaf","order_by":5,"name":"Guanqi Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYNACAxDBfABIHCBJC1sCVAsz0VbxGBCnRT4i+dmDNwUMdv3tPd8kPtTcYTBn78fvOsMbaeaGcwwYkmecObtNcsaxZwyWPYfx22I4I8FMGuikZIYbudukedgOMxjcSCakJf0bWIv8jZxn0n/+AbXcf0zALxI5YFvsDG7ksEkztoFsIeB9A543ZZJAvyQYnjlmbNnbd5jH4EyyAX5b2tO3Sbz5w2Avd7z54Y0f3w7LGRw/+AC/LQeABA/D/8QGBgYWCTCbEJBvgCizB1LMHwgqHwWjYBSMghEJAIoySE2twCn0AAAAAElFTkSuQmCC","orcid":"","institution":"Linyi People’s Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guanqi","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2023-08-07 09:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3241534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3241534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44733446,"identity":"9609b2c1-cd55-4d11-8496-445f57011aa8","added_by":"auto","created_at":"2023-10-16 22:05:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":316090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3241534/v1/fefcecc0-d06a-4dbd-a93e-464fe97cc51f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlates of thyroid nodules in patients with type 2 diabetes: a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eIn recent years, the detection rates of thyroid nodules (TNs) have rapidly increased [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although usually asymptomatic, some still exhibit malignancy, and then it is crucial to establish reasonable management strategies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Epidemiological and observational studies consistently demonstrate that diabetes is an independent risk factor for TNs [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], however, the potential factors affecting TNs are as yet unclear but should be multifactorial under the condition of diabetes. Therefore, potential correlates of TNs were collected and analyzed among adult patients with type 2 diabetes (T2D) in this study.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e We retrospectively reviewed the medical records of patients with T2D from the Department of Endocrinology, Linyi People\u0026rsquo;s Hospital, between February 2020 and September 2021. The inclusion criteria were as follows: adults with T2D. The exclusion criteria included the subjects with type 1 diabetes and other types of diabetes, and missing data of thyroid ultrasound. Ultimately, a total of 1444 adult patients with T2D aged between 18 and 95 years old were eventually included in our study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhysical Examinations\u003c/h2\u003e \u003cp\u003eThe height and weight of participants were measured. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in the nondominant arm of seated participants with an automated electronic device.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory Measurements\u003c/h2\u003e \u003cp\u003eFollowing an overnight fast, blood samples were collected and analyzed in the morning for lipid profiles, including total cholesterol (TC), triglycerides (TG), high density lipoprotein-cholesterol (HDL-c), and low density lipoprotein-cholesterol (LDL-c); liver function, including alanine aminotransferase (ALT) and aspartate aminotransferase (AST); kidney function, including serum creatinine (Scr) and uric acid (UA); hemoglobin (Hb); glycosylated haemoglobin (HbA1c, high performance liquid chromatography); and electrolyte profiles including calcium (Ca), magnesium (Mg) and phosphate (P), were tested using a biochemical autoanalyzer (Cobas c 702, Roche, Germany). Measurements of thyroid function, including free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH) and anti-thyroperoxidase antibody (TPOAb), were tested by chemiluminescence immunoassay (SIEMENS, USA). Urinary creatinine (picric acid method) and urinary microalbumin (UMA, transmission turbidimetry) were tested by an autoanalyzer (Beckman Coulter AU5821), and urinary albumin to creatinine ratio (UACR) was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of complications and comorbidities\u003c/h2\u003e \u003cp\u003eAccording to the corresponding criteria, diabetic neuropathy (DN) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], retinopathy (DR) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and peripheral neuropathy (DPN) (assessed by electromyography) were diagnosed, respectively. Peripheral atherosclerosis (PAS) including intima-media thickness increasing, plaque formation, stenosis and occlusion of carotid or lower extremity artery, was assessed by vascular ultrasonography. TNs were evaluated by thyroid ultrasound. Self-reported current cigarette smoking, alcohol drinking, age of menarche and age of menopause were collected. Smoker was defined as smoking at least one cigarette per day in the last month, and drinker was defined as alcohol consumption\u0026thinsp;\u0026ge;\u0026thinsp;two times per week.\u003c/p\u003e \u003cp\u003e \u003cb\u003eParameter calculations\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e1. Body mass index (BMI)\u0026thinsp;=\u0026thinsp;weight (kg) / height\u003csup\u003e2\u003c/sup\u003e (m\u003csup\u003e2\u003c/sup\u003e);\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e2. TG / HDL-c ratio\u0026thinsp;=\u0026thinsp;TG (mmol/L) / (HDL-c) (mmol/L)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS 26.0 (SPSS, Inc., Chicago, USA). Data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for normally distributed variables, and median (interquartile ranges) for abnormal distributions. Independent-Samples T test and Mann-Whitney U test were used for comparisons of normally and abnormally distributed continuous variables between two groups, respectively. Categorical variables were presented as percentage (%), and were compared by Chi-square test. Spearman correlation analysis and logistic regression analysis were used successively to analyze the independent correlates of TNs. Statistical differences were defined by \u003cem\u003eP\u003c/em\u003e-value (two-tailed) less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline clinical and biochemical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical and biochemical characteristics of the participants are shown in Table 1. A total of 1444 patients with T2D with a mean age of 58.8 \u0026plusmn; 13.0 years were enrolled in this study. The overall\u0026nbsp;prevalence\u0026nbsp;of TNs was 45.6%. Then,\u0026nbsp;the subjects were divided into two groups including TNs (with TNs) group and NTNs (without TNs) group, and the level of each variable was compared between the two groups (Table 1).\u0026nbsp;The results showed that the age, duration of diabetes, SBP, HDL-c, FT3, and the percentage of female, DR and PAS were significantly increased, and the TG / HDL-c ratio, UA, Scr, UMA and the percentage of smoking, DN were significantly decreased in the TNs group (all \u003cem\u003eP\u003c/em\u003e-value\u0026nbsp;\u0026lt; 0.05). However, there was no obvious differences in the drinking, BMI, DBP, age of menarche, age of menopause, TC, LDL-c, TG, HbA1c, ALT, AST, UACR, Hb, FT4, TSH, TPOAb, Ca, Mg, P, and the percentage of DPN between the two groups (all \u003cem\u003eP\u003c/em\u003e-value\u0026nbsp;\u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between TNs and all variables by univariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, a spearman correlation analysis was performed to analyze the relationship between TNs and each variable. The results showed that TNs was related positively to sex (female), age, duration of diabetes, SBP, HDL-c, FT3, Mg, DR and PAS (all \u003cem\u003eP\u003c/em\u003e-value\u0026nbsp;\u0026lt; 0.05), while negatively to smoking, TG/HDL-c ratio, UA, Scr, UMA, Hb and DN (all \u003cem\u003eP\u003c/em\u003e-value\u0026nbsp;\u0026lt; 0.05). However, there was no significant association between TNs and drinking, BMI, DBP, age of menarche, age of menopause, TC, LDL-c, TG, HbA1c, ALT, AST, UACR, FT4, TSH, TPOAb,\u0026nbsp;Ca, P and DPN\u0026nbsp;in all patients (all\u003cem\u003e\u0026nbsp;P\u003c/em\u003e-value\u0026nbsp;\u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Subsequently, stratified by sex and age, the results displayed that the duration of diabetes, FT3, Ca, DR and PAS were positively, and the UACR and UMA were negatively related to TNs in female (all \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05), while the age, SBP, Mg, DR and PAS were positively, and the TG/HDL-c, AST, and UA were negatively related to TNs in male (all \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05). Additionally, the sex (female), age, DR and PAS were positively, and the smoking, UA, Scr and Hb were negatively related to TNs in patients below 60 years old (all \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05), while the sex (female), HDL-c, FT3, Ca, Mg and PAS were positively, and the UMA and DN were negatively related to TNs in patients over 60 years old (all \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent c\u003c/strong\u003e\u003cstrong\u003eorrelates of TNs\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;by logistic regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, in the overall population, TNs was served as the dependent variable, and the\u0026nbsp;sex, age,\u0026nbsp;duration of diabetes, smoking, SBP, HDL-c, TG/HDL-c ratio, UA, Scr,\u0026nbsp;UMA, Hb, FT3, Mg, DN, DR and PAS were as the independent variables according to the results of\u0026nbsp;univariate analysis (Table 1 and Table 2). A logistic regression analysis was performed to analyze the independent\u003cem\u003e\u0026nbsp;\u003c/em\u003ecorrelates of TNs (Table 3), and\u0026nbsp;the results found that after adjusting for the other variables, the sex (female), age, FT3 and PAS\u0026nbsp;was significantly positively related to TNs (all \u003cem\u003eP\u003c/em\u003e-value\u0026nbsp;\u0026lt; 0.05), while the UA was independently and negatively related to TNs (\u003cem\u003eP\u003c/em\u003e-value\u0026nbsp;= 0.046).\u0026nbsp;Subsequently, stratified by sex and age, the results displayed that the FT3, PAS and\u0026nbsp;duration of diabetes\u0026nbsp;adjusting for\u0026nbsp;the UACR, UMA, Ca and DR in female,\u0026nbsp;while the age and PAS\u0026nbsp;adjusting for\u0026nbsp;the SBP, TG/HDL-c, AST, UA, Mg and DR in male, was significantly related to TNs\u0026nbsp;(all \u003cem\u003eP\u003c/em\u003e-value\u0026nbsp;\u0026lt; 0.05). Besides, the age and female\u0026nbsp;adjusting for\u0026nbsp;the smoking, UA, Scr, Hb, DR and PAS in patients below 60 years old while the female, PAS, FT3 and Mg\u0026nbsp;adjusting for\u0026nbsp;the HDL-c, UMA, Ca and DN in patients over 60 years old, was closely related to TNs\u0026nbsp;(all \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated that there was a high prevalence of TNs in patients with T2D, and the correlates of TNs were involved in multiple factors.\u003c/p\u003e \u003cp\u003eStrong evidences demonstrate that female and age are highly related to TNs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our results also supported the conclusion that the percentage of female was significantly increased in TNs group. The mechanism of the gender difference on TNs was unclear, and the underlying reason might be related to the effect of oestrogen in the propagation of thyroid stem/progenitor cells which were probably involved in be the origin of non-functioning TNs in females [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, it should be noted that our study suggested that the age might have a more significant impact on TNs in male patients. Additionally, studies displayed that although the prevalence of TNs increased with advancing age, such nodules had a lower risk of malignancy, whereas identified cancers were more likely to be of high-risk histology [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, it was essential to further explore the effect of age on TNs and carefully weigh the risks and benefits of TNs diagnosis and treatment in older adults.\u003c/p\u003e \u003cp\u003eThere might exist association between the function of pituitary-thyroid axis and TNs, but it was not fully clarified. Some studies showed a positive association between TSH level and TNs and cancer in adults [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], but others displayed that TSH measurement may not be served as a single effective tool to detect or exclude TNs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our study, there was no obvious difference in TSH level between TNs and NTNs groups. Additionally, inconsistent with previous studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], we found that FT3 rather than FT4 were positively correlated with TNs in patients with T2D in the whole population, and subgroup analyses demonstrate that FT3 was obviously related to TNs in female and in patients over 60 years old. Thus, further researches should be carried out to explore the roles of the function of pituitary-thyroid axis on the occurrence and progression of TNs.\u003c/p\u003e \u003cp\u003eIn addition, there was no consistent conclusion about the association between UA and TNs. In the studies of Huang et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and Li et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], UA was an independent risk factor for the formation of TNs, while in the meta-analysis by Hu et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the data showed no correlation between the incidence of TNs and the presence or absence of hyperuricemia in the overall population but a bidirectional regulatory effect of UA on TNs in different genders. However, in the present study, we found that UA was negatively related to TNs, and may be a protective factor for TNs in the overall population, but there existed no obvious correlation between UA and TNs in different subgroup analyses. Therefore, further large sample studies were needed to analyze the relationships and mechanism between UA and TNs by stratified analysis.\u003c/p\u003e \u003cp\u003eMoreover, serum Mg level was closely associated with TNs in patients over 60 years old in our study. In the study of Zeng et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the results showed that higher level of Mg significantly increased the risks of TNs among healthy subjects. However, other studies demonstrated significant and negative associations between Mg and thyroid cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, the relationship of Mg and TNs and potential mechanisms were needed to be further explored.\u003c/p\u003e \u003cp\u003eFinally, our results showed that the percentage of PAS was significantly higher in TNs group than that in NTNs group, and a significant relationship existed between PAS and TNs. Unfortunately, there were few evidences to support our data. Inhibitors of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins), potent cholesterol-lowering drugs, presented multiple vascular protective actions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Accumulating evidences indicated an antiproliferative effect of statins on TNs [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Overall, the specific relationship and mechanism between PAS and TNs, and the preventive and therapeutic effects of statins on TNs needed to be further elaborated.\u003c/p\u003e \u003cp\u003eA few limitations to our study should be considered. Firstly, because of a cross-sectional study, we could not draw a direct cause-effect relationship between the correlates and TNs, so longitudinal studies should be conducted to further support our data. Secondly, we only conducted a preliminary analysis of correlates of TNs, further studies will be performed to explore the effects of the above correlates on the size, morphology and pathology of TNs. Additionally, the potential mechanisms needed to be further explored.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe prevalence of TNs was high in adults with T2D. The independent correlates of TNs were multifactorial, and there existed differences in different physiological states.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; LDL-c, low-density lipoprotein cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; ALT, alanine aminotransferase;\u0026nbsp;AST, aspartate aminotransferase; UA, uric acid; Scr, serum creatinine; UACR, urinary albumin to creatinine ratio; UMA, urinary microalbumin; Hb, hemoglobin; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; TPOAb, anti-thyroperoxidase antibody; Ca, calcium; Mg, magnesium; P, phosphate; DN, diabetic nephropathy; DR, diabetic retinopathy; DPN, diabetic peripheral neuropathy; PAS, peripheral atherosclerosis; TNs, thyroid nodules; NTNs, without thyroid nodules\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Human Ethics Committee of the Linyi People\u0026rsquo;s Hospital. All procedures were performed in accordance with ethical standards laid out in the Declaration of Helsinki. Informed consent was obtained from the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData availability from the authors on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by grants from the Postdoctoral Program of Affiliated Hospital of Jining Medical University (JYFY322152).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.Q.G. and B.B. conceived and designed the study. B.L.J. performed the statistical analysis and drafted the manuscript. C.T. participated in data collection and drafted the manuscript. W.H.D. and Y.Y.Z. participated in data collection. All authors revised and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eQu MY, Tang W, Cui XY, et al. Increased Prevalence of Thyroid Nodules Across Nearly 10 Years in Shanghai, China. Curr Med Sci. 2023;43(1):191\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander EK, Cibas ES. Diagnosis of thyroid nodules. Lancet Diabetes Endocrinol. 2022;10(7):533\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander EK, Doherty GM, Barletta JA. Management of thyroid nodules. 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Influence of long-term statin use in type 2 diabetic patients on thyroid nodularity in iodine-sufficient area. Exp Clin Endocrinol Diabetes. 2011;119(8):497\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Clinical and biochemical characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"745\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003eTNs group\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003eNTNs group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eNumber\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e1444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eSex (female, n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e657 (45.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e346 (52.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e311 (39.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e58.8 \u0026plusmn; 13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e60.96 \u0026plusmn; 11.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e56.91 \u0026plusmn; 13.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of diabetes (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e10.0 (4.0 ~ 14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e10.0 (4.0 ~ 15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e8.0 (3.0 ~ 14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e315 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e120 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e195 (24.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eDrinking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e266 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e115 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e151 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e25.69 \u0026plusmn; 3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e25.65 \u0026plusmn; 3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e25.72 \u0026plusmn; 4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e129.2 \u0026plusmn; 19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e130.8 \u0026plusmn; 19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e127.8 \u0026plusmn; 18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e80.0 \u0026plusmn; 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e80.0 \u0026plusmn; 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e80.0 \u0026plusmn; 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eAge of menarche (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e15.3 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e15.3 \u0026plusmn; 1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e15.2 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eAge of menopause (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e50.0 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e49.8 \u0026plusmn; 3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e50.2 \u0026plusmn; 3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e4.7 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e4.65 \u0026plusmn; 1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e4.78 \u0026plusmn; 1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e2.99 \u0026plusmn; 1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e3.00 \u0026plusmn; 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e2.98 \u0026plusmn; 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e1.39 (0.98 ~ 2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.35 (0.96 ~ 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.42 (0.99 ~ 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e1.14 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.16 \u0026plusmn; 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.12 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eTG/HDL-c ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e1.28 (0.81 ~ 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.22 (0.79 ~ 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.33 (0.82 ~ 2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e140.85 \u0026plusmn; 20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e9.34 \u0026plusmn; 2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e9.41 \u0026plusmn; 2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e18.20 (13.03 ~ 26.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e18.00 (13.10 ~ 25.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e18.30 (13.00 ~ 29.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e17.60 (14.00 ~ 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e17.10 (14.20 ~ 21.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e18.10 (13.90 ~ 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eUA (\u0026mu;molL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e298.08 \u0026plusmn; 97.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e285.94 \u0026plusmn; 88.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e308.41 \u0026plusmn; 103.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eScr (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e67.64 \u0026plusmn; 22.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e66.19 \u0026plusmn; 21.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e68.88 \u0026plusmn; 22.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eUACR (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e12.70 (5.70 ~ 53.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e11.80 (5.60 ~ 43.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e13.20 (5.90 ~ 59.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eUMA (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e12.20 (4.80 ~ 44.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e10.50 (4.40 ~ 37.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e13.70 (5.35 ~ 52.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eHb (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e140.85 \u0026plusmn; 19.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e139.89 \u0026plusmn; 18.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e141.64 \u0026plusmn; 20.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eFT3 (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e4.52 \u0026plusmn; 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e4.58 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e4.47 \u0026plusmn; 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eFT4 (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e16.47 \u0026plusmn; 2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e16.44 \u0026plusmn; 3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e16.50 \u0026plusmn; 2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eTSH (uIU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e1.84 (1.15 ~ 2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.84 (1.19 ~ 2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.83 (1.13 ~ 2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eTPOAb (IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e38.50 (33.20 ~ 46.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e37.50 (33.00 ~ 44.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e39.20 (33.60 ~ 47.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\"\u003e\n \u003cp\u003eCa (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e2.32 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e2.32 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e2.31 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\"\u003e\n \u003cp\u003eMg (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e0.88 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\"\u003e\n \u003cp\u003eP (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e1.19 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.19 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e1.19 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003eComplications\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; DN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e388 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e159 (24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e229 (29.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; DR (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e566 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e289 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e277 (35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; DPN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e632 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e296 (45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e336 (42.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.80965147453083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; PAS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.179624664879356%\" valign=\"top\"\u003e\n \u003cp\u003e1035 (71.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e529 (80.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.58176943699732%\" valign=\"top\"\u003e\n \u003cp\u003e506 (64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; LDL-c, low-density lipoprotein cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; UA, uric acid; Scr, serum creatinine; UACR, urinary albumin to creatinine ratio; UMA, urinary microalbumin; Hb, hemoglobin; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; TPOAb, anti-thyroperoxidase antibody; Ca, calcium; Mg, magnesium; P, phosphate; DN, diabetic nephropathy; DR, diabetic retinopathy; DPN, diabetic peripheral neuropathy; PAS, peripheral atherosclerosis; TNs, thyroid nodules; NTNs, without thyroid nodules. Data were presented as mean \u0026plusmn; SD for normally distributed variables, and median (interquartile ranges) for abnormal distributions. Independent-Samples T test and Mann-Whitney U test were used for comparisons of normally and abnormally distributed continuous variables between TNs and NTNs groups, respectively. Categorical variables were presented as percentage (%), and were compared by chi-square test. Statistical differences were defined by \u003cem\u003eP-\u003c/em\u003evalue (two-tailed) less than 0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Table 2 The correlation between TNs and different variables by univariate analysis\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1018\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.833005893909627%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.81532416502947%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIn all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5717092337917484%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.12770137524558%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIn female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5717092337917484%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.092337917485267%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIn male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5717092337917484%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.717092337917485%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAge \u0026lt; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5717092337917484%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.12770137524558%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAge \u0026ge; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.829493087557603%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.71889400921659%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8433179723502304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.138248847926267%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6036866359447%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8433179723502304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.175115207373272%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.870967741935484%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8433179723502304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.944700460829493%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6036866359447%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8433179723502304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.138248847926267%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6036866359447%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eAge of menarche\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eAge of menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eTG/HDL-c ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.114\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eUACR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eUMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eFT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n 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\u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eFT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eTPOAb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\"\u003e\n \u003cp\u003eCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\"\u003e\n \u003cp\u003eMg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003eComplications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; DR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; DPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.818449460255152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; PAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.224730127576056%\" valign=\"top\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.575073601570167%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519136408243376%\" valign=\"top\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.556427870461237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.322865554465162%\" valign=\"top\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5701668302257115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635917566241414%\" valign=\"top\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4769381746810595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\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\u003eBMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; LDL-c, low-density lipoprotein cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; UA, uric acid; Scr, serum creatinine; Hb, hemoglobin; UACR, urinary albumin to creatinine ratio; UMA, urinary microalbumin; Hb, hemoglobin; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; TPOAb, anti-thyroperoxidase antibody; Ca, calcium; Mg, magnesium; P, phosphate; DN, diabetic nephropathy; DR, diabetic retinopathy; DPN, diabetic peripheral neuropathy; PAS, peripheral atherosclerosis; TNs, thyroid nodules. Correlation coefficients between TNs and different variables were determined by Spearman\u0026apos;s correlation analysis.\u003c/p\u003e\n\u003cp\u003eTable 3 The relative risks for TNs by logistic regression analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e95.0 % CI for OR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn all\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e12.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.255-2.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e7.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.005-1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;UA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e3.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e0.997-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;FT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e13.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.169-1.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;PAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e7.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.155-2.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;FT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e11.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.211-2.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;PAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e6.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.148-2.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Duration of diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e6.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.008- 1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e8.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.007-1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; PAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e8.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.223-2.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge \u0026lt; 60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e19.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.025-1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e14.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.361-2.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge \u0026ge; 60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e8.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.181-2.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; PAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e8.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e2.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.269-3.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; FT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e5.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e1.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.044-1.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e2.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e4.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e7.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.060-51.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUA, uric acid; FT3, free triiodothyronine; PAS, peripheral atherosclerosis; Mg, magnesium; TNs, thyroid nodules; CI, confidence interval; OR, odd ratio; SE, standard error.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes, Thyroid nodules, Correlates","lastPublishedDoi":"10.21203/rs.3.rs-3241534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3241534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiabetes is an independent risk factor for thyroid nodules (TNs), however, the influencing factors of TNs have not been fully clarified under the condition of diabetes. We aimed to explore the correlates of TNs in type 2 diabetes (T2D) patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, 1444 Chinese adults with T2D were included. Clinical and biochemical characteristics were collected. The overall prevalence of TNs was 45.6%. Spearman correlation analysis and logistic regression analysis were used successively to analyze the independent correlates of TNs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results from univariate and further logistic regression analyses showed that female (OR: 1.656; 95% CI: 1.255\u0026ndash;2.185), age (OR:1.017; 95% CI:1.005\u0026ndash;1.030), UA (OR: 0.998; 95% CI: 0.997-1.000), free triiodothyronine (FT3) (OR: 1.400; 95% CI: 1.169\u0026ndash;1.675) and peripheral atherosclerosis (PAS) (OR: 1.614; 95% CI: 1.155\u0026ndash;2.255) independently correlated with TNs in all patients. Subsequently, stratified by sex and age, the results displayed that FT3 (OR: 1.585 ; 95% CI:1.211\u0026ndash;2.073), PAS (OR: 1.759; 95% CI: 1.148\u0026ndash;2.695) and duration of diabetes (OR: 1.037; 95% CI: 1.008\u0026ndash;1.067) in female while age (OR: 1.020 ; 95% CI: 1.007\u0026ndash;1.034) and PAS (OR: 1.802 ; 95% CI: 1.223\u0026ndash;2.656) in male, and age (OR: 1.045 ; 95% CI: 1.025\u0026ndash;1.065) and female (OR: 1.892; 95% CI: 1.361\u0026ndash;2.629) in patients below 60 years old while female (OR: 1.643 ; 95% CI: 1.181\u0026ndash;2.284), PAS (OR: 2.077; 95% CI: 1.269\u0026ndash;3.401), FT3 (OR: 1.316 ; 95% CI: 1.044\u0026ndash;1.661) and magnesium (OR: 7.399; 95% CI: 1.060-51.628 ) over 60 years old, significantly related to TNs. The prevalence of TNs was high in T2D patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe independent correlates of TNs were multifactorial, and there existed differences in different physiological states.\u003c/p\u003e","manuscriptTitle":"Correlates of thyroid nodules in patients with type 2 diabetes: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-11 05:35:17","doi":"10.21203/rs.3.rs-3241534/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":"e17f9959-781e-4e2f-bad0-2c36ee130e58","owner":[],"postedDate":"August 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:33:22+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-11 05:35:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3241534","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3241534","identity":"rs-3241534","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}