Peripheral artery disease is not a risk factor of major adverse cardiovascular events in thyroxine using diabetic patients: a retrospective study

Peripheral artery disease is not a risk factor of major adverse cardiovascular events in thyroxine using diabetic patients: a retrospective 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 Peripheral artery disease is not a risk factor of major adverse cardiovascular events in thyroxine using diabetic patients: a retrospective study Chih-Wei Hsu, Chia-Hung Lin, Pi-Hua Liu, Yi-Hsuan Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3932875/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 Aims: This retrospective study investigated the occurrence of major adverse cardiovascular events ( MACE) in thyroxine using diabetic patients and compared the risk factors between the MACE and non-MACE groups. Methods: We used longitudinal claims data from 2008 to 2017 from the Chang Gung Research Database. Diabetic patients who used thyroxine were included. The primary outcome was the occurrence of MACE. The secondary outcomes were the differences between the two groups (MACE vs. no MACE). Results: After 1:1 group matching by propensity score between MACE and non-MACE group by sex, age, the interval of using thyroxine, there were 416 patients in each group. Patients with worse renal function (eGFR < 45 ml/min/1.73 m2), hypertension, history of diabetic microvascular complications, end stage renal disease (ESRD), coronary heart disease (CHD), heart failure, cerebrovascular accident (CVA) and diabetic foot infection had a higher risk of experiencing MACE. Free T4 had weak positive correlation with HDL, and TSH had weak positive correlation with LDL and negative correlation with HDL (correlation coefficient, p value: 0.131, 0.022; 0.124, 0.016; -0.157, 0.003, respectively). There’s no optimal cutoff points according to the Receiver operating characteristic (ROC) curve analysis of the best discrimination point between TSH/free T4/LDL and MACE attack. Conclusions: In thyroxine using diabetic patients, patients with worse renal function, hypertension, history of diabetic microvascular complications, ESRD, CHD, heart failure, CVA and diabetic foot infection had a higher risk of experiencing MACE, but peripheral artery disease (PAD) was not a significant risk of MACE. major adverse cardiovascular events risk factors type 2 diabetes thyroxine supplement peripheral artery disease Figures Figure 1 Figure 2 Figure 3 1. Introduction Cardiovascular disease (CVD) is a major cause of morbidity and mortality worldwide, and an abnormal lipid profile, characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG) and low levels of high-density lipoprotein cholesterol (HDL-C), mainly contributes to atherosclerosis and is a well-established risk factor for CVD. Furthermore, there were several other well-known risk factors for CVD, including hypertension, diabetes mellitus, obesity and cigarette smoking[ 1 ]. In addition to this, peripheral artery disease (PAD), one of atherosclerotic diseases, also played an important role in cardiovascular and cerebrovascular ischemic events[ 2 ]. The thyroid gland regulates lipid metabolism, blood pressure, vasculature, and angiogenesis[ 3 ], and alterations in thyroid function can have a significant impact on lipid profiles[ 4 ] by regulating the expression of lipolytic and lipogenic genes[ 5 ]. Numerous research studies have indicated that variations in thyroid function, such as hypothyroidism and hyperthyroidism, can have a significant impact on lipid metabolism. In individuals with hypothyroidism, LDL cholesterol, triglycerides, and total cholesterol levels are elevated, while HDL cholesterol levels are decreased[ 6 ]. This condition can potentially increase the risk of atherosclerosis. Furthermore, hypothyroidism has been found to disrupt blood pressure regulation, potentially leading to the development of systolic and diastolic high blood pressure due to increased vascular resistance [ 7 ] and arterial stiffness [ 8 ]. Additionally, hypothyroidism affects the vasculature by causing endothelial dysfunction [ 9 ], which is an early stage of atherosclerosis. This has been linked to a decrease in NO availability, further indicating a relationship between hypothyroidism and atherosclerosis [ 10 ]. In contrast, hyperthyroidism is associated with reduced levels of LDL cholesterol and total cholesterol, with no significant effect on HDL cholesterol levels [ 11 ]. Although thyroid hormone replacement therapy is commonly used to treat hypothyroidism, there is a paucity of research on the effects of thyroid hormone replacement therapy in patients with diabetes mellitus (DM). While some studies have suggested that thyroid hormone replacement therapy may improve lipid profiles in patients with DM [ 12 ], even after achieving a normal TSH following thyroxine replacement, LDL and total cholesterol levels were still higher than in individuals with normal thyroid function[ 13 ]. Moreover, the impact of thyroid hormone replacement therapy on major adverse cardiovascular events (MACE) in patients with DM remains unclear. In this study, we aim to investigate the effects of thyroid hormone replacement therapy on lipid profiles and MACE risk in patients with DM. By elucidating the impact of thyroid hormone replacement therapy on lipid metabolism and CVD risk in this population, we hope to provide valuable insights into the management of dyslipidemia and CVD in patients with DM. 2. Materials and Methods 2.1. Data source We collected an existing claims dataset to establish a retrospective cohort study from 2008 to 2017 from the Chang Gung Research Database (CGRD), which is a de-identified database of medical records from CGMH, Linkou branch. The CGMH, Linkou branch, founded in 1978, is one of the largest medical institutions in Taiwan. Currently, it has a total number of approximately 3700 beds, and each year, it serves 4 million outpatient visits, 200,000 emergency visits, and 100,000 inpatients. This study was approved by the CGMH Institutional Review Board (IRB). We used the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), combined with the Nineth Revision, Clinical Modification (ICD-9-CM), an international medical diagnosis code, based on the timing of the transition, to ascertain the diagnosis of diabetes mellitus, hypothyroidism and atherosclerotic cardiovascular disease and other underlying disease of these participants (Supplementary table 1 ). 2.2. Study population From 2008 to 2017, we identified 6519 type 2 diabetes patients who used thyroxine, and in these patients, there were 2267 patients had major adverse cardiac events(MACE). We further excluded 1566 patients who had MACE before beginning taking thyroxine, and used the rest 701 patients to be matched with those who had no MACE attacked even under thyroxine using by age, sex and the time of beginning thyroxin. 416 participants in each group were selected after using propensity score matching (Fig. 1 ). These participants received thyroxine due to the following reasons: primary or secondary hypothyroidism, post-procedure hypothyroidism, hyperthyroidism with suppression and supplement therapy and others. The following biochemical data were collected in 1 year before the event of MACE occurred : serum creatinine, HbA1c, eGFR, urine albumin/creatinine ratio (UACR), aspartate aminotransferase (AST), alanine aminotransferase (ALT), uric acid, thyroid stimulating hormone (TSH), free-T4, T3, thyroid peroxidase antibodies (Anti-TPO), anti-thyroid stimulating hormone receptor antibodies(Anti-TSH), triglyceride(TG), low density lipoprotein (LDL), high density lipoprotein (HDL) and thyroglobulin(Tg). Cardiovascular and diabetic drug using were also analyzed, including angiotensin converting enzyme inhibitors/angiotensin receptor blockers(ACEi/ARB), diuretics, metformin, sulfonylurea, thiazolidinedione (TZD), acarbose, Dipeptidyl peptidase-4 inhibitor(DPP-4 i), glucagon like peptide-1 receptor agonist (GLP-1 RA) and sodium-glucose cotransporter-2 inhibitors (SGLT-2 i). These drugs were prescribed at least three months before MACE attacked or at least three months before the last follow-up with at least combined with 3 months thyroxine using in those without MACE. We also analyzed the prevalence of cormobidities which were diagnosed before MACE. The diagnosis were used the ICD-9-CM, ICD-10-CM, and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10PCS), including hypertension (HTN), coronary heart disease(CHD), heart failure, cerebrovascular accident(CVA), diabetic microvascular complications, end stage renal disease (ESRD), peripheral artery disease (PAD), diabetic foot infection and lower-extremity amputation (LEA). Diabetic microvascular complications included diabetic nephropathy, neuropathy, and retinopathy. 2.3. Main outcome measures The primary outcome of this study was the occurrence of MACE. The secondary outcome were the differences between the two groups (MACE vs. no MACE) of thyroid function (the level of free T4, T3, TSH, Anti-TSH antibody and thyroglobulin), LDL, HDL, TG, and HbA1c level. 2.4. Statistical analysis The baseline characteristics of the four groups (the reasons why used thyroxine) used one-way analysis of variance (ANOVA) for continuous variables, and a chi-square test for categorical variables. The relationship between thyroid function, lipid profile and HbA1c were analyzed by Pearson correlation coefficient. After matching with age, sex by propensitiy score, the risk of MACE was evaluted based on the underlying disease, baseline renal function and HbA1c level. The odds ratio (OR) and 95% confidence intervals (CIs) for MACE attacked were derived from logistic regresstion models using subjects had no MACE attacked as the reference group (OR = 1). After matching with age, sex by propensitiy score, the risk of MACE was evaluted based on the underlying disease, baseline renal function and HbA1c level were analyzed using logistic regression, with the results presented as ORs with 95% CI.the odds ratio (OR) was determined by logistic regression. The diagnostic performance was evaluated by the area under curve (AUC). The optimal cutoff point was derived from the receiver operator characteristic (ROC) curve with the shortest distance to sensitivity = 1 and 1 – specificity = 0. The sensitivity was the probability that the prediction would be positive for subjects with MACE attacked, and the specificity was the probability that the prediction would be negative for subjects without MACE attacked. Statistics were analyzed using SAS software (version 9·4; SAS Institute Inc., Cary, NC, USA). Two-sided p -values < 0.05 were considered significantly. 3. Results From 2008 to 2017, a total of 6519 type 2 diabetes patients who used thyroxine registered in the CGRD. Among these patients, during the studying period, there were 2267 patients who had MACE and 4252 patients who didn’t. We excluded 1566 patients who had MACE before thyroxine use and there were 701 patients left. Then, we used 1:1 group matching by propensity score between MACE and non-MACE group by sex, age, the interval of using thyroxine. After group matching, we analyzed MACE and non-MACE groups with 416 patients in each group. 3.1 Demographic characteristics in patients grouped according to thyroxine using reason. Demographic characteristics of thyroxine using participants, such as sex, age, and biochemistry data were presented in Table 1. The patients using thyroxine due to primary hypothyroidism or secondary hypothyroidism were older (65.48 vs. 63.33, 61.88 and 63.61 years old, respectively). The proportion of female participants was 95.83 % in the group of hyperthyroidism with suppression and supplement therapy, which was higher than that in other groups. Besides, patients using thyroxine due to primary hypothyroidism or secondary hypothyroidism had higher creatinine and lower eGFR level (1.72 vs. 1.24, 0.96, 1.35 mg/dl and 63.07 vs. 72.87, 77.07, 71.25 ml/ min/1.73 m2 respectively). Moreover, the group of primary hypothyroidism or secondary hypothyroidism had a higher proportion of diabetic microvascular complications, ACEi/ARB usage and diuretics usage. 3.2 Correlation between thyroid function, lipid profile and HbA1c The relationship between thyroid function, lipid profile and HbA1c were analyzed by Pearson correlation coefficient (Table 2). Free T4 had weak positive correlation with HDL (Correlation coefficient, p value: 0.131, 0.022, respectively), and TSH had weak positive correlation with LDL and negative correlation with HDL (correlation coefficient, p value: 0.124, 0.016; -0.157, 0.003, respectively). 3.3 Subgroup analyses of MACE risk factors Subgroup analyses of MACE risk factors were summarized in Figure 2. Patients with worse renal function (eGFR < 45 ml/min/1.73 m2 ), hypertension, history of diabetic microvascular complications, ESRD, CHD, heart failure, CVA and dibetic foot infection had a higher risk of experiencing MACE. On the other hand, PAD and LEA were not risk factors for experiencing MACE under patients with thyroxine using. Furthermore, we also used the TSH level 5uIU/mL, Free T4 level 0.7ng/dL and T3 level 70ng/dL as cut point because these values were the closest to average, but there was no significant finding of different occurrence rate of MACE. Moreover, we used univariate and multivariate logistic regression to analyze the risk factors on MACE (Table 3). 3.4 Receiver operating characteristic (ROC) curve analysis of the best discrimination point between TSH/free T4/LDL and MACE To explore a best discrimination point of thyroid function for MACE attack, we tried to analyze the best point by ROC curve, representing the largest sum of sensitivity and specificity. However, there’s no optimal cutoff points according to the ROC curve analysis (Figure 3). 4. Discussion There were three main findings in our study. First, in Pearson correlation coefficient analysis, free T4 had weak positive correlation with HDL, and TSH had weak positive correlation with LDL and negative correlation with HDL. Second, worse renal function, hypertension, history of diabetic microvascular complications, ESRD, CHD, heart failure, CVA and diabetic foot infection had a higher risk of experiencing MACE. However, PAD and LEA were not risk factors for experiencing MACE under type 2 diabetes patients with thyroxine using. Third, for the discrimination point of thyroid function for MACE attack in ROC curve analysis, there’s no optimal cutoff points. We'll discuss about our results compared with previous studies in the following sections. According to previous studies, thyroid function significantly affects lipid metabolism. Within reference TSH level, there was significant positive correlation between TSH level and total cholesterol, LDL-C, non-HDL-C and TG, and negative correlation with HDL-C. And in different subgroups, the level of the correlation changed. However, the study didn’t analyze the subgroup of diabetic patients[ 14 ]. Wang JJ et al. assess of causal association between thyroid function and lipid metabolism via a genetic analysis, and the results demonstrated that increased TSH levels were significantly associated with higher total cholesterol (TC) and LDL levels, and the FT3:FT4 ratio was significantly associated with TC and LDL levels[ 15 ]. Jung et al. analyzed the association between thyroid function and lipid profiles, apolipoproteins, and HDL function. TC, TG, LDL-C, apoB levels, and the apoA-I/II ratio were significantly increased in the overt hypothyroid state and recovered to baseline values with levothyroxine replacement[ 16 ]. Thyroid hormone influences lipid metabolism in many ways. T3 can mediate gene activation to control lipid metabolism, increase bile acid flow and furtherly enhance serum cholesterol uptake by liver, regular thermogenesis and reduce body weight by stimulating brown adipose tissue activity[ 4 , 17 ]. In type 2 diabetes patients, higher TSH and lower T3 and T4 level were noted compared with non-diabetic control group by a case control study. Meanwhile, higher serum TC, LDL-C and TG level were also seen in type 2 diabetes. And it was obvious that significant positive correlation between TSH and TC, LDL-C and TG, and negative correlation between T3/T4 and TC, LDL-C and TG were also found[ 18 ]. Another retrospective study also disclosed TSH level was higher in patients with diabetes than those without diabetes[ 19 ]. Moreover, dysthyroid states also affect the heart not only the rhythm but also structure which furtherly increase mortality and the risk of MACE[ 20 ]. Although the results of correlation between thyroid function and lipid profile, MACE by previous studies were consistent with current study, there was no previous studies focusing on thyroxine supplement in type 2 diabetes patients. However, based on real-world data, it seems that treating patients with subclinical hypothyroidism using thyroxine does not provide significant benefits for all-cause mortality and MACE[ 29 ]. Additionally, in patients with hyperthyroidism, the risk of MACE and heart failure increases[ 30 ]. Therefore, we further analyzed whether there is a controlled threshold for thyroid hormone concentration that can achieve the lowest incidence of MACE. In our study, there’s no optimal cutoff points according to the ROC curve analysis of the best discrimination point between TSH/free T4 and MACE attack. There was a population-based prospective cohort study executed in Finland by Langen et al. which presented that compared with TSH within the reference range, high TSH level was related to a greater risk of total mortality and sudden cardiac death whereas low TSH was not associated with MACE. And TSH level did not have a linear relation with any of the cardiac outcomes and showed a U-shaped association with total mortality[ 31 ]. Although the diabetic population was around 4.7% the study, but there was no further subgroup analysis. This study had some limitations. First, this was a nonrandomized, retrospective, observational study; therefore, selection bias was possible despite comprehensive propensity score matching and our setting the index date as MACE attack. Some patients might not be included if the clinical physicians missed to put the codes on diagnostic system. Second, biochemical results were not intact in some patients and there were missed data. Third, the patient number was relatedly low. In the future, we can design prospective study to collect longer follow up results for these groups of diabetes patients. 5. Conclusions In thyroxine using diabetic patients, worse renal function, hypertension, history of diabetic microvascular complications, ESRD, CHD, heart failure, CVA and diabetic foot infection increased the risk of experiencing MACE, however, PAD was not a significant risk of MACE. Declarations Ethics approval and consent to participate This study was approved by the CGMH Institutional Review Board (IRB). The consent was waived by the IRB. Consent for publication Availability of data and materials Not applicable. Competing interests The authors declare that they have no conflicts of interest. Funding This research was supported by grants from Chang Gung Memorial Hospital (CMRPG3M2141、CFRPG3L0031). Authors’ contributions Chih-Wei Hsu wrote the manuscript and participated in data collection, Chia-Hung Lin analyzed the data, Pi-Hua Liu analyzed and presented the data, and Yi-Hsuan Lin reviewed and edited the manuscript. All authors were involved in data interpretation and in the critical revision and approval of the manuscript. Yi-Hsuan Lin is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript. Acknowledgements The authors wish to acknowledge Yi-Chia Chen for assistance with statistical analysis. References Leritz EC, McGlinchey RE, Kellison I, Rudolph JL, Milberg WP. Cardiovascular Disease Risk Factors and Cognition in the Elderly. Curr Cardiovasc Risk Rep. 2011;5(5):407–12. 10.1007/s12170-011-0189-x . Perez Mejias EL, Faxas SM, Taveras NT, et al. Peripheral Artery Disease as a Risk Factor for Myocardial Infarction. Cureus. 2021;13(6):e15655. 10.7759/cureus.15655 . Ichiki T. Thyroid Hormone and Vascular Remodeling. J Atheroscler Thromb. 2016;23(3):266–75. 10.5551/jat.32755 . Rizos CV, Elisaf MS, Liberopoulos EN. Effects of thyroid dysfunction on lipid profile. Open Cardiovasc Med J. 2011;5:76–84. 10.2174/1874192401105010076 . Mullur R, Liu YY, Brent GA. Thyroid hormone regulation of metabolism. Physiol Rev. 2014;94(2):355–82. 10.1152/physrev.00030.2013 . Duntas LH, Brenta G. The effect of thyroid disorders on lipid levels and metabolism. Med Clin North Am. 2012;96(2):269–81. 10.1016/j.mcna.2012.01.012 . Graettinger JS, Muenster JJ, Checchia CS, Grissom RL, Campbell JA. A correlation of clinical and hemodynamic studies in patients with hypothyroidism. J Clin Invest. 1958;37(4):502–10. 10.1172/JCI103631 . Obuobie K, Smith J, Evans LM, John R, Davies JS, Lazarus JH. Increased central arterial stiffness in hypothyroidism. J Clin Endocrinol Metab. 2002;87(10):4662–6. 10.1210/jc.2002–020493 . Lekakis J, Papamichael C, Alevizaki M, et al. Flow-mediated, endothelium-dependent vasodilation is impaired in subjects with hypothyroidism, borderline hypothyroidism, and high-normal serum thyrotropin (TSH) values. Thyroid. 1997;7(3):411–4. 10.1089/thy.1997.7.411 . Taddei S, Caraccio N, Virdis A, et al. Impaired endothelium-dependent vasodilatation in subclinical hypothyroidism: beneficial effect of levothyroxine therapy. J Clin Endocrinol Metab. 2003;88(8):3731–7. 10.1210/jc.2003–030039 . Biondi B, Cooper DS. The clinical significance of subclinical thyroid dysfunction. Endocr Rev. 2008;29(1):76–131. 10.1210/er.2006-0043 . Monzani F, Caraccio N, Kozakowa M, et al. Effect of levothyroxine replacement on lipid profile and intima-media thickness in subclinical hypothyroidism: a double-blind, placebo- controlled study. J Clin Endocrinol Metab. 2004;89(5):2099–106. 10.1210/jc.2003–031669 . McAninch EA, Rajan KB, Miller CH, Bianco AC. Systemic Thyroid Hormone Status During Levothyroxine Therapy In Hypothyroidism: A Systematic Review and Meta-Analysis. J Clin Endocrinol Metab. 2018;103(12):4533–42. 10.1210/jc.2018 – 01361 . Asvold BO, Vatten LJ, Nilsen TI, Bjoro T. The association between TSH within the reference range and serum lipid concentrations in a population-based study. The HUNT Study. Eur J Endocrinol. 2007;156(2):181–6. 10.1530/eje.1.02333 . Wang JJ, Zhuang ZH, Shao CL, et al. Assessment of causal association between thyroid function and lipid metabolism: a Mendelian randomization study. Chin Med J (Engl). 2021;134(9):1064–9. 10.1097/CM9.0000000000001505 . Jung KY, Ahn HY, Han SK, Park YJ, Cho BY, Moon MK. Association between thyroid function and lipid profiles, apolipoproteins, and high-density lipoprotein function. J Clin Lipidol. 2017;11(6):1347–53. 10.1016/j.jacl.2017.08.015 . Duntas LH, Brenta G. A Renewed Focus on the Association Between Thyroid Hormones and Lipid Metabolism. Front Endocrinol (Lausanne). 2018;9:511. 10.3389/fendo.2018.00511 . Juliette AV, P P. Association between thyroid hormones & lipid profile in type 2 diabetes mellitus patients- A case control study in tertiary care hospital. Int J Clin Biochem Res. 2021;8(1):25–8. 10.18231/j.ijcbr.2021.006 . Saha H, Sarkar HKB, Khan S, Sana N, Sugawara A, Choudhury S. A Comparative Study of Thyroid Hormone and Lipid Status of Patient with and without Diabetes in Adults. Open J Endocr Metabolic Dis. 2013;3:113–9. 10.4236/ojemd.2013.32017 . Faber J, Selmer C. Cardiovascular disease and thyroid function. Front Horm Res. 2014;43:45–56. 10.1159/000360558 . Criqui MH, Langer RD, Fronek A, et al. Mortality over a period of 10 years in patients with peripheral arterial disease. N Engl J Med. 1992;326(6):381–6. 10.1056/NEJM199202063260605 . Criqui MH, Ninomiya JK, Wingard DL, Ji M, Fronek A. Progression of peripheral arterial disease predicts cardiovascular disease morbidity and mortality. J Am Coll Cardiol. 2008;52(21):1736–42. 10.1016/j.jacc.2008.07.060 . Saw J, Bhatt DL, Moliterno DJ, et al. The influence of peripheral arterial disease on outcomes: a pooled analysis of mortality in eight large randomized percutaneous coronary intervention trials. J Am Coll Cardiol. 2006;48(8):1567–72. 10.1016/j.jacc.2006.03.067 . Criqui MH. Peripheral arterial disease–epidemiological aspects. Vasc Med. 2001;6(3 Suppl):3–7. 10.1177/1358836X0100600i102 . Mya MM, Aronow WS. Increased prevalence of peripheral arterial disease in older men and women with subclinical hypothyroidism. J Gerontol Biol Sci Med Sci. 2003;58(1):68–9. 10.1093/gerona/58.1.m68 . Mazzeffi MA, Lin HM, Flynn BC, O'Connell TL, DeLaet DE. Hypothyroidism and the risk of lower extremity arterial disease. Vasc Health Risk Manag. 2010;6:957–62. 10.2147/VHRM.S13535 . Wang P, Du R, Lin L, et al. Association between Free Triiodothyronine Levels and Peripheral Arterial Disease in Euthyroid Participants. Biomed Environ Sci. 2017;30(2):128–33. 10.3967/bes2017.016 . Ohba K, Iwaki T. Role of thyroid hormone in an experimental model of atherosclerosis: the potential mediating role of immune response and autophagy. Endocr J. 2022;69(9):1043–52. 10.1507/endocrj.EJ22-0177[1] . Andersen MN, Olsen AS, Madsen JC, et al. Long-Term Outcome in Levothyroxine Treated Patients With Subclinical Hypothyroidism and Concomitant Heart Disease. J Clin Endocrinol Metab. 2016;101(11):4170–7. 10.1210/jc.2016–2226 . Selmer C, Olesen JB, Hansen ML, et al. Subclinical and overt thyroid dysfunction and risk of all-cause mortality and cardiovascular events: a large population study. J Clin Endocrinol Metab. 2014;99(7):2372–82. 10.1210/jc.2013–4184 . Langen VL, Niiranen TJ, Puukka P, et al. Thyroid-stimulating hormone and risk of sudden cardiac death, total mortality and cardiovascular morbidity. Clin Endocrinol (Oxf). 2018;88(1):105–13. 10.1111/cen.13472 . Tables Table 1 Demographic characteristics of thyroxine using participants. Primary hypothyroidism or Secondary hypothyroidism (n=316) Post-procedure hypothyroidism (n=151) Hyperthyroidism with suppression and supplement therapy (n=24) other (n=341) p-value Age (year) 65.48 ± 10.66 63.33 ± 10.01 a 61.88 ± 9.92 63.61 ± 9.99 a 0.040* Sex (n, %) 0.002* Female 232 (73.42) 126 (83.44) a 23 (95.83) a 241 (70.67) bc Male 84 (26.58) 25 (16.56) a 1 (4.17) a 100 (29.33) bc Hypertension(n, %) 198 (62.66) 104 (68.87) 16 (66.67) 215 (63.05) 0.570 Diabetic microvascular complications (n, %) 135 (42.72) 63 (41.72) 8 (33.33) 107 (31.38) ab 0.015* ESRD (n, %) 9 (2.85) 3 (1.99) 0 (0) 6 (1.76) 0.679 PAD (n,%) 1 (0.32) 1 (0.66) 0 (0) 1 (0.29) 0.912 CHD 74 (23.42) 33 (21.85) 3 (12.5) 70 (20.53) 0.567 Heart failure 41 (12.97) 19 (12.58) 2 (8.33) 36 (10.56) 0.731 CVA 59 (18.67) 19 (12.58) 5 (20.83) 60 (17.6) 0.391 Diabetic foot infection (n, %) 5 (1.58) 5 (3.31) 1 (4.17) 5 (1.47) 0.431 LEA (n,%) 1 (0.32) 0 (0) 0 (0) 2 (0.59) 0.769 HbAlc (%, mmol/mol) 7.17 ± 1.66 (213) 7.26 ± 1.2 (100) 6.94 ± 1.29 (20) 7.15 ± 1.61 (216) 0.846 Creatinine (mg/dL) 1.72 ± 2.12 (269) 1.24 ± 1.67 (120) a 0.96 ± 0.73 (21) 1.35 ± 1.6 (248) a 0.025* eGFR (ml/min/1.73 m^2) 63.07 ± 31.96 (269) 72.87 ± 27.47 (120) a 77.07 ± 30.03 (21) a 71.25 ± 30.18 (248) a 0.002* UACR (mg/g) 365.44 ± 972.63 (59) 388.15 ± 1353.65 (38) 529.02 ± 1254.2 (6) 330.23 ± 1119.87 (64) 0.977 AST (U/L) 39.31 ± 42.58 (172) 29.85 ± 17.72 (60) 43.71 ± 33.56 (14) 31.84 ± 17.56 (152) 0.061 ALT (U/L) 31.84 ± 41.80 (249) 27.23 ± 23.52 (103) 35.58 ± 33.85 (19) 28.98 ± 21.36 (221) 0.502 Uric acid (mg/dL) 6.20 ± 1.98 (119) 6.22 ± 1.97 (45) 7.13 ± 2.57 (9) 6.13 ± 1.59 (110) 0.485 TSH (uIU/mL) 12.53 ± 29.96 (204) 8.27 ± 21.3 (91) 6.07 ± 15.44 (18) 6.35 ± 22.92 (173) 0.116 Free T4 (ng/dL) 1.07 ± 0.39 (178) 1.26 ± 0.40 (74) ad 1.38 ± 0.8 (18) ad 1.13 ± 0.39 (144) <0.001* T3 (ng/dL) 72.73 ± 23.34 (50) 86.30 ± 50.78 (20) 148.16 ± 84.7 (6) abd 77.95 ± 36.45 (42) <0.001* Anti-TPO Ab (IU/mL) 85.539 ± 177.179 (110) 25.719 ± 64.188 (37) 158.44 ± 242.176 (13) 122.107 ± 277.816 (93) b <0.001* Anti-TSH Ab (IU/mL) 0.78 ± 0.49 (4) 0 0 4 ± 4.5 (6) 0.200 TG (mg/dL) 153.33 ± 95.78 (208) 162.86 ± 99.81 (100) 151.33 ± 104.92 (18) 153.23 ± 127.85 (217) 0.890 LDL-C (mg/dL) 103.68 ± 38.42 (204) 103.96 ± 35.22 (96) 88.27 ± 38.33 (18) 102.16 ± 33.16 (209) 0.358 HDL-C (mg/dL) 47.64 ± 13.98 (195) 47.34 ± 12.77 (94) 45.38 ± 15.75 (18) 47.87 ± 14.23 (198) 0.905 ACEi/ARB usage (n, %) 108 (34.18) 58 (38.41) 8 (33.33) 91 (26.69) ab 0.046* Diuretics usage (n, %) 86 (27.22) 27 (17.88) a 9 (37.5) b 54 (15.84) ac <0.001* Metformin (n, %) 73 (23.1) 44 (29.14) 5 (20.83) 80 (23.46) 0.483 Sulphonylurea/Glinide (n, %) 90 (28.48) 51 (33.77) 7 (29.17) 84 (24.63) 0.215 TZD (n, %) 14 (4.43) 4 (2.65) 1 (4.17) 16 (4.69) 0.766 Acarbose (n, %) 28 (8.86) 9 (5.96) 3 (12.5) 28 (8.21) 0.619 DPP-4i (n, %) 73 (23.1) 29 (19.21) 5 (20.83) 65 (19.06) 0.599 GLP-1 RA (n, %) 5 (1.58) 0 (0) 1 (4.17) 3 (0.88) 0.199 SGLT2i (n, %) 5 (1.58) 2 (1.32) 0 (0) 6 (1.76) 0.914 *denote p value <0.05 a denote significant difference with Primary hypothyroidism or Secondary hypothyroidism group b denote significant difference with Post-procedure hypothyroidism group c denote significant difference with Hyperthyroidism with suppression and supplement therapy group d denote significant difference with other group Abbreviations: HTN, hypertension; ESRD, end stage renal disease; PAD, peripheral artery disease; CHD, coronary heart disease; CVA, cerebrovascular accident; LEA: lower extremities amputation; HbA1c: glycated hemoglobulin; eGFR: estimated Glomerular filtration rate; URAC: urine albumin to creatinine ratio; AST: aspartate aminotransferase; ALT: Alanine aminotransferase; TSH: thyroid stimulating hormone; Anti-TPO Ab: antithyroid peroxidase antibody; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; ACEi, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blockers; TZD, thiazolidinedione; DPP-4i, dipeptidyl peptidase-4 inhibitor; GLP-1 RA, glucagon-like peptide-1 receptor agonist; SGLT-2i, sodium-glucose co-transporter 2 inhibitor Table 2 Correlation between thyroid function, lipid profile and HbA1c Free T4 T3 TSH Anti-TPO Ab Anti-TSH Ab Correlation coefficient p-value Correlation coefficient p-value Correlation coefficient p-value Correlation coefficient p-value Correlation coefficient p-value LDL -0.006 0.916 0.019 0.868 0.124 0.016* -0.032 0.641 -0.265 0.491 HDL 0.131 0.022* 0.043 0.701 -0.157 0.003* 0.159 0.023* -0.144 0.711 HbA1c -0.040 0.467 -0.050 0.644 -0.053 0.294 -0.026 0.706 0.495 0.175 *denote p value <0.05 TSH: thyroid stimulating hormone; Anti-TPO Ab: antithyroid peroxidase antibody; LDL-C: low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; HbA1c: glycated hemoglobulin Table 3 Predictors of MACE in patients with thyroxine using type 2 diabetes by logistic regression Univariate Multivariate model 1 Multivariate model 2 Multivariate model 3 Multivariate model 4 Variables Odds ratio (95%CI) P value Odds ratio (95%CI) P value Odds ratio (95%CI) P value Odds ratio (95%CI) P value Odds ratio (95%CI) P value Age < 65 yr 1.03 (0.78 , 1.35) 0.8893 1.59 (0.94 - 2.72) 0.087 1.57 (0.92 - 2.68) 0.0974 ≥ 65 yr Sex Female 1 (0.73 , 1.37) 1 1.08 (0.59 - 1.95) 0.8106 1.12 (0.62 - 2.02) 0.6973 Male eGFR < 30 ml/min/1.73 m2 1.69 (1.07 , 2.67) 0.0256* 1.44 (0.65 - 3.18) 0.3669 1.55 (0.68 - 3.51) 0.2961 ≥ 30 ml/min/1.73 m2 eGFR < 45 ml/min/1.73 m2 1.61 (1.1 , 2.36) 0.0175* 1.1 (0.54 - 2.21) 0.7957 1.19 (0.58 - 2.43) 0.6363 ≥ 45 ml/min/1.73 m2 TSH < 5 0.95 (0.64 - 1.4) 0.8437 0.55 (0.22 - 1.35) 0.1932 0.64 (0.36 - 1.14) 0.1297 0.65 (0.36 - 1.15) 0.1394 0.64 (0.36 - 1.13) 0.1224 ≥ 5 Free T4 < 0.7 0.95 (0.51 - 1.78) 0.8747 0.66 (0.37 - 1.16) 0.1487 0.55 (0.22 - 1.34) 0.1884 0.58 (0.23 - 1.44) 0.2397 0.58 (0.23 - 1.44) 0.2404 ≥ 0.7 Hypertension Yes 2.22 (1.66 , 2.96) <0.001* 2.2 (1.21 - 3.98) 0.0097* 2.27 (1.26 - 4.11) 0.0066* 2.38 (1.29 - 4.37) 0.0053* 2.44 (1.33 - 4.47) 0.0038* No Diabetic microvascular complication Yes 2.23 (1.67 , 2.97) <0.001* 0.91 (0.51 - 1.61) 0.7458 0.96 (0.54 - 1.72) 0.895 0.92 (0.51 - 1.63) 0.765 0.96 (0.53 - 1.73) 0.8932 No ESRD Yes 17.68 (2.34 , 133.49) <0.001* 5.42 (0.50 - 58.22) 0.1631 6.61 (0.63 - 69.4) 0.1155 4.54 (0.43 - 47.98) 0.2084 5.53 (0.54 - 56.96) 0.1504 No CHD Yes 6.93 (4.57 , 10.52) <0.001* 12.58 (6.00 - 26.40) <0.001* 12.49 (5.95 - 26.21) <0.001* 12.31 (5.86 - 25.87) <0.001* 12.26 (5.83 - 25.78) <0.001* No CVA Yes 69.83 (22.03 , 221.37) <0.001* 44.05 (13.06 - 148.56) <0.001* 43.69 (12.94 - 147.53) <0.001* 45.86 (13.52 - 155.59) <0.001* 45.76 (13.46 - 155.6) <0.001* No Diabetic foot infection Yes 4.44 (1.26 , 15.7) 0.0201* 2.63 (0.6 - 11.44) 0.1984 2.43 (0.56 - 10.61) 0.2387 2.91 (0.65 - 13.00) 0.1617 2.6 (0.58 - 11.64) 0.2111 No *denote p value <0.05 Abbreviations: eGFR: estimated Glomerular filtration rate; TSH: thyroid stimulating hormone; ESRD, end stage renal disease; CHD, coronary heart disease; CVA, cerebrovascular accident Additional Declarations No competing interests reported. Supplementary Files supplementarytable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3932875","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271865030,"identity":"c6bd695e-0d42-4601-91ec-f4faa86e42e9","order_by":0,"name":"Chih-Wei Hsu","email":"","orcid":"","institution":"Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chih-Wei","middleName":"","lastName":"Hsu","suffix":""},{"id":271865031,"identity":"ce43026e-fcb2-4742-bd8a-a60087549b40","order_by":1,"name":"Chia-Hung Lin","email":"","orcid":"","institution":"Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chia-Hung","middleName":"","lastName":"Lin","suffix":""},{"id":271865032,"identity":"1be340c0-8083-4f40-82c0-b08af7561831","order_by":2,"name":"Pi-Hua Liu","email":"","orcid":"","institution":"Chang Gung University","correspondingAuthor":false,"prefix":"","firstName":"Pi-Hua","middleName":"","lastName":"Liu","suffix":""},{"id":271865033,"identity":"5f1355dc-7cdd-46d5-b28d-6603073bc745","order_by":3,"name":"Yi-Hsuan Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDACCebGhx8M2ORA7AMPiNPC2GwsUcFnDNaSQKSWNgmeM3KJDSAOUVoMbjc2G0i2maXPDzv8EGiLnZxuAyEtdw42PihsS8vdeDvNAKgl2djsACEtNxJBthzL3Tg7AaTlQOI2IrS0SfC2/U83nJ3+gQQtPGfYEuSlc4i0RRLoMGAgsxlukM4pOJBgQIRf+G4kHwRFpbz87PTNHz5U2MkR1KIAU2AAZhgQUA4C8g3ojFEwCkbBKBgF6AAA+bdLfjlU8qcAAAAASUVORK5CYII=","orcid":"","institution":"Chang Gung Memorial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yi-Hsuan","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-02-06 05:24:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3932875/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3932875/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51019935,"identity":"9604d488-0cbb-47e6-9d2d-564196394e2b","added_by":"auto","created_at":"2024-02-12 19:39:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":519468,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of subject recruitment from the CGRD\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3932875/v1/06d96e3b46f6579b62d5bae5.jpg"},{"id":51019936,"identity":"df94d8af-5e50-4727-a8ea-25924d9092fe","added_by":"auto","created_at":"2024-02-12 19:39:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":499678,"visible":true,"origin":"","legend":"\u003cp\u003eCox proportional hazard models for MACE in thyroxine using type 2 diabetes mellitus patients. The results presented as patient number and percentage. Abbreviations: MACE: major adverse cardiovascular events; CI: Confidence interval; HbA1c: glycated hemoglobulin; eGFR: estimated Glomerular filtration rate; URAC: urine albumin to creatinine ratio; TSH: thyroid stimulating hormone; ESRD, end stage renal disease; CHD, coronary heart disease; CVA, cerebrovascular accident; PAD, peripheral artery disease; LEA: lower extremities amputation.\u003c/p\u003e","description":"","filename":"Figure22.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3932875/v1/8f3b8695b2865e40eb8e756a.jpg"},{"id":51019937,"identity":"c02f210e-ec28-499d-bd5a-8553443e3b94","added_by":"auto","created_at":"2024-02-12 19:39:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155505,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve of MACE by (A) TSH level, (B) free T4 level, and (C) LDL level. Abbreviation: ROC: receiver operating characteristic curve; MACE: major adverse cardiovascular events; TSH: thyroid stimulating hormone; LDL-C: low-density lipoprotein cholesterol\u003c/p\u003e","description":"","filename":"Figure32.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3932875/v1/faee7cd15422f998acf3a744.jpg"},{"id":56988266,"identity":"500885bc-4a27-4755-bf96-bd4242173ab9","added_by":"auto","created_at":"2024-05-23 05:38:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1887348,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3932875/v1/eb99ea85-79a3-484f-a929-cd093a24ced7.pdf"},{"id":51019934,"identity":"fe7199e5-e588-452b-aa79-da1ec47ee3f5","added_by":"auto","created_at":"2024-02-12 19:38:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":76265,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3932875/v1/3f61d9ba3e188c16ab18f970.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Peripheral artery disease is not a risk factor of major adverse cardiovascular events in thyroxine using diabetic patients: a retrospective study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) is a major cause of morbidity and mortality worldwide, and an abnormal lipid profile, characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG) and low levels of high-density lipoprotein cholesterol (HDL-C), mainly contributes to atherosclerosis and is a well-established risk factor for CVD. Furthermore, there were several other well-known risk factors for CVD, including hypertension, diabetes mellitus, obesity and cigarette smoking[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition to this, peripheral artery disease (PAD), one of atherosclerotic diseases, also played an important role in cardiovascular and cerebrovascular ischemic events[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe thyroid gland regulates lipid metabolism, blood pressure, vasculature, and angiogenesis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and alterations in thyroid function can have a significant impact on lipid profiles[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] by regulating the expression of lipolytic and lipogenic genes[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Numerous research studies have indicated that variations in thyroid function, such as hypothyroidism and hyperthyroidism, can have a significant impact on lipid metabolism. In individuals with hypothyroidism, LDL cholesterol, triglycerides, and total cholesterol levels are elevated, while HDL cholesterol levels are decreased[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This condition can potentially increase the risk of atherosclerosis. Furthermore, hypothyroidism has been found to disrupt blood pressure regulation, potentially leading to the development of systolic and diastolic high blood pressure due to increased vascular resistance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and arterial stiffness [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, hypothyroidism affects the vasculature by causing endothelial dysfunction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which is an early stage of atherosclerosis. This has been linked to a decrease in NO availability, further indicating a relationship between hypothyroidism and atherosclerosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, hyperthyroidism is associated with reduced levels of LDL cholesterol and total cholesterol, with no significant effect on HDL cholesterol levels [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough thyroid hormone replacement therapy is commonly used to treat hypothyroidism, there is a paucity of research on the effects of thyroid hormone replacement therapy in patients with diabetes mellitus (DM). While some studies have suggested that thyroid hormone replacement therapy may improve lipid profiles in patients with DM [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], even after achieving a normal TSH following thyroxine replacement, LDL and total cholesterol levels were still higher than in individuals with normal thyroid function[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, the impact of thyroid hormone replacement therapy on major adverse cardiovascular events (MACE) in patients with DM remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we aim to investigate the effects of thyroid hormone replacement therapy on lipid profiles and MACE risk in patients with DM. By elucidating the impact of thyroid hormone replacement therapy on lipid metabolism and CVD risk in this population, we hope to provide valuable insights into the management of dyslipidemia and CVD in patients with DM.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data source\u003c/h2\u003e \u003cp\u003eWe collected an existing claims dataset to establish a retrospective cohort study from 2008 to 2017 from the Chang Gung Research Database (CGRD), which is a de-identified database of medical records from CGMH, Linkou branch. The CGMH, Linkou branch, founded in 1978, is one of the largest medical institutions in Taiwan. Currently, it has a total number of approximately 3700 beds, and each year, it serves 4\u0026nbsp;million outpatient visits, 200,000 emergency visits, and 100,000 inpatients. This study was approved by the CGMH Institutional Review Board (IRB). We used the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), combined with the Nineth Revision, Clinical Modification (ICD-9-CM), an international medical diagnosis code, based on the timing of the transition, to ascertain the diagnosis of diabetes mellitus, hypothyroidism and atherosclerotic cardiovascular disease and other underlying disease of these participants (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study population\u003c/h2\u003e \u003cp\u003eFrom 2008 to 2017, we identified 6519 type 2 diabetes patients who used thyroxine, and in these patients, there were 2267 patients had major adverse cardiac events(MACE). We further excluded 1566 patients who had MACE before beginning taking thyroxine, and used the rest 701 patients to be matched with those who had no MACE attacked even under thyroxine using by age, sex and the time of beginning thyroxin. 416 participants in each group were selected after using propensity score matching (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese participants received thyroxine due to the following reasons: primary or secondary hypothyroidism, post-procedure hypothyroidism, hyperthyroidism with suppression and supplement therapy and others. The following biochemical data were collected in 1 year before the event of MACE occurred : serum creatinine, HbA1c, eGFR, urine albumin/creatinine ratio (UACR), aspartate aminotransferase (AST), alanine aminotransferase (ALT), uric acid, thyroid stimulating hormone (TSH), free-T4, T3, thyroid peroxidase antibodies (Anti-TPO), anti-thyroid stimulating hormone receptor antibodies(Anti-TSH), triglyceride(TG), low density lipoprotein (LDL), high density lipoprotein (HDL) and thyroglobulin(Tg). Cardiovascular and diabetic drug using were also analyzed, including angiotensin converting enzyme inhibitors/angiotensin receptor blockers(ACEi/ARB), diuretics, metformin, sulfonylurea, thiazolidinedione (TZD), acarbose, Dipeptidyl peptidase-4 inhibitor(DPP-4 i), glucagon like peptide-1 receptor agonist (GLP-1 RA) and sodium-glucose cotransporter-2 inhibitors (SGLT-2 i). These drugs were prescribed at least three months before MACE attacked or at least three months before the last follow-up with at least combined with 3 months thyroxine using in those without MACE.\u003c/p\u003e \u003cp\u003eWe also analyzed the prevalence of cormobidities which were diagnosed before MACE. The diagnosis were used the ICD-9-CM, ICD-10-CM, and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10PCS), including hypertension (HTN), coronary heart disease(CHD), heart failure, cerebrovascular accident(CVA), diabetic microvascular complications, end stage renal disease (ESRD), peripheral artery disease (PAD), diabetic foot infection and lower-extremity amputation (LEA). Diabetic microvascular complications included diabetic nephropathy, neuropathy, and retinopathy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Main outcome measures\u003c/h2\u003e \u003cp\u003eThe primary outcome of this study was the occurrence of MACE. The secondary outcome were the differences between the two groups (MACE vs. no MACE) of thyroid function (the level of free T4, T3, TSH, Anti-TSH antibody and thyroglobulin), LDL, HDL, TG, and HbA1c level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the four groups (the reasons why used thyroxine) used one-way analysis of variance (ANOVA) for continuous variables, and a chi-square test for categorical variables. The relationship between thyroid function, lipid profile and HbA1c were analyzed by Pearson correlation coefficient. After matching with age, sex by propensitiy score, the risk of MACE was evaluted based on the underlying disease, baseline renal function and HbA1c level. The odds ratio (OR) and 95% confidence intervals (CIs) for MACE attacked were derived from logistic regresstion models using subjects had no MACE attacked as the reference group (OR\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eAfter matching with age, sex by propensitiy score, the risk of MACE was evaluted based on the underlying disease, baseline renal function and HbA1c level were analyzed using logistic regression, with the results presented as ORs with 95% CI.the odds ratio (OR) was determined by logistic regression.\u003c/p\u003e \u003cp\u003eThe diagnostic performance was evaluated by the area under curve (AUC). The optimal cutoff point was derived from the receiver operator characteristic (ROC) curve with the shortest distance to sensitivity\u0026thinsp;=\u0026thinsp;1 and 1 \u0026ndash; specificity\u0026thinsp;=\u0026thinsp;0. The sensitivity was the probability that the prediction would be positive for subjects with MACE attacked, and the specificity was the probability that the prediction would be negative for subjects without MACE attacked.\u003c/p\u003e \u003cp\u003eStatistics were analyzed using SAS software (version 9\u0026middot;4; SAS Institute Inc., Cary, NC, USA). Two-sided \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eFrom 2008 to 2017, a total of 6519 type 2 diabetes patients who used thyroxine registered in the CGRD. Among these patients, during the studying period, there were 2267 patients who had MACE and 4252 patients who didn\u0026rsquo;t. We excluded 1566 patients who had MACE before thyroxine use and there were 701 patients left. Then, we used 1:1 group matching by propensity score between MACE and non-MACE group by sex, age, the interval of using thyroxine. After group matching, we analyzed MACE and non-MACE groups with 416 patients in each group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Demographic characteristics in patients grouped according to thyroxine using reason. \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic characteristics of thyroxine using participants, such as sex, age, and biochemistry data were presented in Table 1. The patients using thyroxine due to primary hypothyroidism or secondary hypothyroidism were older (65.48 vs. 63.33, 61.88 and 63.61 years old, respectively). \u0026nbsp;The proportion of female participants was 95.83 % in the group of hyperthyroidism with suppression and supplement therapy, which was higher than that in other groups. Besides, patients using thyroxine due to primary hypothyroidism or secondary hypothyroidism had higher creatinine and lower eGFR level (1.72 vs. 1.24, 0.96, 1.35 mg/dl and 63.07 vs. 72.87, 77.07, 71.25 ml/ min/1.73 m2 respectively). Moreover, the group of primary hypothyroidism or secondary hypothyroidism had a higher proportion of diabetic microvascular complications, ACEi/ARB usage and diuretics usage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Correlation between thyroid function, lipid profile and HbA1c\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between thyroid function, lipid profile and HbA1c were analyzed by Pearson correlation coefficient (Table 2). Free T4 had weak positive correlation with HDL (Correlation coefficient, p value: 0.131, 0.022, respectively), and TSH had weak positive correlation with LDL and negative correlation with HDL (correlation coefficient, p value: 0.124, 0.016; -0.157, 0.003, respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Subgroup analyses of MACE risk factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Subgroup analyses of MACE risk factors were summarized in Figure 2. \u0026nbsp;Patients with worse renal function (eGFR \u0026lt; 45 ml/min/1.73 m2 ), hypertension, history of diabetic microvascular complications, ESRD, CHD, heart failure, CVA and dibetic foot infection had a higher risk of experiencing MACE. On the other hand, PAD and LEA were not risk factors for experiencing MACE under patients with thyroxine using. Furthermore, we also used the TSH level 5uIU/mL, Free T4 level 0.7ng/dL and T3 level 70ng/dL as cut point because these values were the closest to average, but there was no significant finding of different occurrence rate of MACE.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, we used univariate and multivariate logistic regression to analyze the risk factors on MACE (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Receiver operating characteristic (ROC) curve analysis of the best discrimination point between TSH/free T4/LDL and MACE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore a best discrimination point of thyroid function for MACE attack, we tried to analyze the best point by ROC curve, representing the largest sum of sensitivity and specificity. However, there\u0026rsquo;s no optimal cutoff points according to the ROC curve analysis (Figure 3).\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThere were three main findings in our study. First, in Pearson correlation coefficient analysis, free T4 had weak positive correlation with HDL, and TSH had weak positive correlation with LDL and negative correlation with HDL. Second, worse renal function, hypertension, history of diabetic microvascular complications, ESRD, CHD, heart failure, CVA and diabetic foot infection had a higher risk of experiencing MACE. However, PAD and LEA were not risk factors for experiencing MACE under type 2 diabetes patients with thyroxine using. Third, for the discrimination point of thyroid function for MACE attack in ROC curve analysis, there\u0026rsquo;s no optimal cutoff points. We'll discuss about our results compared with previous studies in the following sections.\u003c/p\u003e \u003cp\u003eAccording to previous studies, thyroid function significantly affects lipid metabolism. Within reference TSH level, there was significant positive correlation between TSH level and total cholesterol, LDL-C, non-HDL-C and TG, and negative correlation with HDL-C. And in different subgroups, the level of the correlation changed. However, the study didn\u0026rsquo;t analyze the subgroup of diabetic patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Wang JJ et al. assess of causal association between thyroid function and lipid metabolism via a genetic analysis, and the results demonstrated that increased TSH levels were significantly associated with higher total cholesterol (TC) and LDL levels, and the FT3:FT4 ratio was significantly associated with TC and LDL levels[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Jung et al. analyzed the association between thyroid function and lipid profiles, apolipoproteins, and HDL function. TC, TG, LDL-C, apoB levels, and the apoA-I/II ratio were significantly increased in the overt hypothyroid state and recovered to baseline values with levothyroxine replacement[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thyroid hormone influences lipid metabolism in many ways. T3 can mediate gene activation to control lipid metabolism, increase bile acid flow and furtherly enhance serum cholesterol uptake by liver, regular thermogenesis and reduce body weight by stimulating brown adipose tissue activity[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In type 2 diabetes patients, higher TSH and lower T3 and T4 level were noted compared with non-diabetic control group by a case control study. Meanwhile, higher serum TC, LDL-C and TG level were also seen in type 2 diabetes. And it was obvious that significant positive correlation between TSH and TC, LDL-C and TG, and negative correlation between T3/T4 and TC, LDL-C and TG were also found[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Another retrospective study also disclosed TSH level was higher in patients with diabetes than those without diabetes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, dysthyroid states also affect the heart not only the rhythm but also structure which furtherly increase mortality and the risk of MACE[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although the results of correlation between thyroid function and lipid profile, MACE by previous studies were consistent with current study, there was no previous studies focusing on thyroxine supplement in type 2 diabetes patients.\u003c/p\u003e \u003cp\u003eHowever, based on real-world data, it seems that treating patients with subclinical hypothyroidism using thyroxine does not provide significant benefits for all-cause mortality and MACE[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, in patients with hyperthyroidism, the risk of MACE and heart failure increases[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, we further analyzed whether there is a controlled threshold for thyroid hormone concentration that can achieve the lowest incidence of MACE.\u003c/p\u003e \u003cp\u003eIn our study, there\u0026rsquo;s no optimal cutoff points according to the ROC curve analysis of the best discrimination point between TSH/free T4 and MACE attack. There was a population-based prospective cohort study executed in Finland by Langen et al. which presented that compared with TSH within the reference range, high TSH level was related to a greater risk of total mortality and sudden cardiac death whereas low TSH was not associated with MACE. And TSH level did not have a linear relation with any of the cardiac outcomes and showed a U-shaped association with total mortality[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Although the diabetic population was around 4.7% the study, but there was no further subgroup analysis.\u003c/p\u003e \u003cp\u003eThis study had some limitations. First, this was a nonrandomized, retrospective, observational study; therefore, selection bias was possible despite comprehensive propensity score matching and our setting the index date as MACE attack. Some patients might not be included if the clinical physicians missed to put the codes on diagnostic system. Second, biochemical results were not intact in some patients and there were missed data. Third, the patient number was relatedly low. In the future, we can design prospective study to collect longer follow up results for these groups of diabetes patients.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn thyroxine using diabetic patients, worse renal function, hypertension, history of diabetic microvascular complications, ESRD, CHD, heart failure, CVA and diabetic foot infection increased the risk of experiencing MACE, however, PAD was not a significant risk of MACE.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the CGMH Institutional Review Board (IRB). The consent was waived by the IRB.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by grants from Chang Gung Memorial Hospital (CMRPG3M2141、CFRPG3L0031).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChih-Wei Hsu wrote the manuscript and participated in data collection, Chia-Hung Lin analyzed the data, Pi-Hua Liu analyzed and presented the data, and Yi-Hsuan Lin reviewed and edited the manuscript. All authors were involved in data interpretation and in the critical revision and approval of the manuscript. Yi-Hsuan Lin is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge Yi-Chia Chen for assistance with statistical analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLeritz EC, McGlinchey RE, Kellison I, Rudolph JL, Milberg WP. Cardiovascular Disease Risk Factors and Cognition in the Elderly. Curr Cardiovasc Risk Rep. 2011;5(5):407\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12170-011-0189-x\u003c/span\u003e\u003cspan address=\"10.1007/s12170-011-0189-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez Mejias EL, Faxas SM, Taveras NT, et al. Peripheral Artery Disease as a Risk Factor for Myocardial Infarction. Cureus. 2021;13(6):e15655. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.15655\u003c/span\u003e\u003cspan address=\"10.7759/cureus.15655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIchiki T. Thyroid Hormone and Vascular Remodeling. J Atheroscler Thromb. 2016;23(3):266\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5551/jat.32755\u003c/span\u003e\u003cspan address=\"10.5551/jat.32755\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRizos CV, Elisaf MS, Liberopoulos EN. Effects of thyroid dysfunction on lipid profile. Open Cardiovasc Med J. 2011;5:76\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/1874192401105010076\u003c/span\u003e\u003cspan address=\"10.2174/1874192401105010076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMullur R, Liu YY, Brent GA. Thyroid hormone regulation of metabolism. Physiol Rev. 2014;94(2):355\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/physrev.00030.2013\u003c/span\u003e\u003cspan address=\"10.1152/physrev.00030.2013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuntas LH, Brenta G. The effect of thyroid disorders on lipid levels and metabolism. Med Clin North Am. 2012;96(2):269\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mcna.2012.01.012\u003c/span\u003e\u003cspan address=\"10.1016/j.mcna.2012.01.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraettinger JS, Muenster JJ, Checchia CS, Grissom RL, Campbell JA. A correlation of clinical and hemodynamic studies in patients with hypothyroidism. J Clin Invest. 1958;37(4):502\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/JCI103631\u003c/span\u003e\u003cspan address=\"10.1172/JCI103631\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObuobie K, Smith J, Evans LM, John R, Davies JS, Lazarus JH. Increased central arterial stiffness in hypothyroidism. J Clin Endocrinol Metab. 2002;87(10):4662\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2002\u0026ndash;020493\u003c/span\u003e\u003cspan address=\"10.1210/jc.2002\u0026ndash;020493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLekakis J, Papamichael C, Alevizaki M, et al. Flow-mediated, endothelium-dependent vasodilation is impaired in subjects with hypothyroidism, borderline hypothyroidism, and high-normal serum thyrotropin (TSH) values. Thyroid. 1997;7(3):411\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/thy.1997.7.411\u003c/span\u003e\u003cspan address=\"10.1089/thy.1997.7.411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaddei S, Caraccio N, Virdis A, et al. Impaired endothelium-dependent vasodilatation in subclinical hypothyroidism: beneficial effect of levothyroxine therapy. J Clin Endocrinol Metab. 2003;88(8):3731\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2003\u0026ndash;030039\u003c/span\u003e\u003cspan address=\"10.1210/jc.2003\u0026ndash;030039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiondi B, Cooper DS. The clinical significance of subclinical thyroid dysfunction. Endocr Rev. 2008;29(1):76\u0026ndash;131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/er.2006-0043\u003c/span\u003e\u003cspan address=\"10.1210/er.2006-0043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonzani F, Caraccio N, Kozakowa M, et al. Effect of levothyroxine replacement on lipid profile and intima-media thickness in subclinical hypothyroidism: a double-blind, placebo- controlled study. J Clin Endocrinol Metab. 2004;89(5):2099\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2003\u0026ndash;031669\u003c/span\u003e\u003cspan address=\"10.1210/jc.2003\u0026ndash;031669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcAninch EA, Rajan KB, Miller CH, Bianco AC. Systemic Thyroid Hormone Status During Levothyroxine Therapy In Hypothyroidism: A Systematic Review and Meta-Analysis. J Clin Endocrinol Metab. 2018;103(12):4533\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2018\u0026thinsp;\u0026ndash;\u0026thinsp;01361\u003c/span\u003e\u003cspan address=\"10.1210/jc.2018\u0026thinsp;\u0026ndash;\u0026thinsp;01361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsvold BO, Vatten LJ, Nilsen TI, Bjoro T. The association between TSH within the reference range and serum lipid concentrations in a population-based study. The HUNT Study. Eur J Endocrinol. 2007;156(2):181\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/eje.1.02333\u003c/span\u003e\u003cspan address=\"10.1530/eje.1.02333\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang JJ, Zhuang ZH, Shao CL, et al. Assessment of causal association between thyroid function and lipid metabolism: a Mendelian randomization study. Chin Med J (Engl). 2021;134(9):1064\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CM9.0000000000001505\u003c/span\u003e\u003cspan address=\"10.1097/CM9.0000000000001505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung KY, Ahn HY, Han SK, Park YJ, Cho BY, Moon MK. Association between thyroid function and lipid profiles, apolipoproteins, and high-density lipoprotein function. J Clin Lipidol. 2017;11(6):1347\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacl.2017.08.015\u003c/span\u003e\u003cspan address=\"10.1016/j.jacl.2017.08.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuntas LH, Brenta G. A Renewed Focus on the Association Between Thyroid Hormones and Lipid Metabolism. Front Endocrinol (Lausanne). 2018;9:511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2018.00511\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2018.00511\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuliette AV, P P. Association between thyroid hormones \u0026amp; lipid profile in type 2 diabetes mellitus patients- A case control study in tertiary care hospital. Int J Clin Biochem Res. 2021;8(1):25\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18231/j.ijcbr.2021.006\u003c/span\u003e\u003cspan address=\"10.18231/j.ijcbr.2021.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha H, Sarkar HKB, Khan S, Sana N, Sugawara A, Choudhury S. A Comparative Study of Thyroid Hormone and Lipid Status of Patient with and without Diabetes in Adults. Open J Endocr Metabolic Dis. 2013;3:113\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4236/ojemd.2013.32017\u003c/span\u003e\u003cspan address=\"10.4236/ojemd.2013.32017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaber J, Selmer C. Cardiovascular disease and thyroid function. Front Horm Res. 2014;43:45\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000360558\u003c/span\u003e\u003cspan address=\"10.1159/000360558\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriqui MH, Langer RD, Fronek A, et al. Mortality over a period of 10 years in patients with peripheral arterial disease. N Engl J Med. 1992;326(6):381\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJM199202063260605\u003c/span\u003e\u003cspan address=\"10.1056/NEJM199202063260605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriqui MH, Ninomiya JK, Wingard DL, Ji M, Fronek A. Progression of peripheral arterial disease predicts cardiovascular disease morbidity and mortality. J Am Coll Cardiol. 2008;52(21):1736\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2008.07.060\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2008.07.060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaw J, Bhatt DL, Moliterno DJ, et al. The influence of peripheral arterial disease on outcomes: a pooled analysis of mortality in eight large randomized percutaneous coronary intervention trials. J Am Coll Cardiol. 2006;48(8):1567\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2006.03.067\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2006.03.067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriqui MH. Peripheral arterial disease\u0026ndash;epidemiological aspects. Vasc Med. 2001;6(3 Suppl):3\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1358836X0100600i102\u003c/span\u003e\u003cspan address=\"10.1177/1358836X0100600i102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMya MM, Aronow WS. Increased prevalence of peripheral arterial disease in older men and women with subclinical hypothyroidism. J Gerontol Biol Sci Med Sci. 2003;58(1):68\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/gerona/58.1.m68\u003c/span\u003e\u003cspan address=\"10.1093/gerona/58.1.m68\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMazzeffi MA, Lin HM, Flynn BC, O'Connell TL, DeLaet DE. Hypothyroidism and the risk of lower extremity arterial disease. Vasc Health Risk Manag. 2010;6:957\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/VHRM.S13535\u003c/span\u003e\u003cspan address=\"10.2147/VHRM.S13535\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang P, Du R, Lin L, et al. Association between Free Triiodothyronine Levels and Peripheral Arterial Disease in Euthyroid Participants. Biomed Environ Sci. 2017;30(2):128\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3967/bes2017.016\u003c/span\u003e\u003cspan address=\"10.3967/bes2017.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhba K, Iwaki T. Role of thyroid hormone in an experimental model of atherosclerosis: the potential mediating role of immune response and autophagy. Endocr J. 2022;69(9):1043\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1507/endocrj.EJ22-0177[1]\u003c/span\u003e\u003cspan address=\"10.1507/endocrj.EJ22-0177[1]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen MN, Olsen AS, Madsen JC, et al. Long-Term Outcome in Levothyroxine Treated Patients With Subclinical Hypothyroidism and Concomitant Heart Disease. J Clin Endocrinol Metab. 2016;101(11):4170\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2016\u0026ndash;2226\u003c/span\u003e\u003cspan address=\"10.1210/jc.2016\u0026ndash;2226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelmer C, Olesen JB, Hansen ML, et al. Subclinical and overt thyroid dysfunction and risk of all-cause mortality and cardiovascular events: a large population study. J Clin Endocrinol Metab. 2014;99(7):2372\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2013\u0026ndash;4184\u003c/span\u003e\u003cspan address=\"10.1210/jc.2013\u0026ndash;4184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangen VL, Niiranen TJ, Puukka P, et al. Thyroid-stimulating hormone and risk of sudden cardiac death, total mortality and cardiovascular morbidity. Clin Endocrinol (Oxf). 2018;88(1):105\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/cen.13472\u003c/span\u003e\u003cspan address=\"10.1111/cen.13472\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Demographic characteristics of thyroxine using participants.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"730\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary hypothyroidism or Secondary hypothyroidism\u003c/p\u003e\n \u003cp\u003e(n=316)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003ePost-procedure hypothyroidism\u0026nbsp;\u003cbr\u003e\u0026nbsp; (n=151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003eHyperthyroidism with suppression\u0026nbsp;\u003cbr\u003e\u0026nbsp;and supplement therapy\u003c/p\u003e\n \u003cp\u003e(n=24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003cp\u003e(n=341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e65.48 \u0026plusmn; 10.66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e63.33 \u0026plusmn; 10.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e61.88 \u0026plusmn; 9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e63.61 \u0026plusmn; 9.99\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eSex (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e232 (73.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e126 (83.44)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e23 (95.83)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e241 (70.67)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e84 (26.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e25 (16.56)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e1 (4.17)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e100 (29.33)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension(n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e198 (62.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e104 (68.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e16 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e215 (63.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetic microvascular complications (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e135 (42.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e63 (41.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e8 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e107 (31.38)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eESRD (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e9 (2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e3 (1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e6 (1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003ePAD (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e74 (23.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e33 (21.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e3 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e70 (20.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e41 (12.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e19 (12.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e2 (8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e36 (10.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eCVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e59 (18.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e19 (12.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e5 (20.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e60 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetic foot infection (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e5 (3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e1 (4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eLEA (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e2 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eHbAlc (%, mmol/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e7.17 \u0026plusmn; 1.66 (213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e7.26 \u0026plusmn; 1.2 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e6.94 \u0026plusmn; 1.29 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e7.15 \u0026plusmn; 1.61 (216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e1.72 \u0026plusmn; 2.12 (269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e1.24 \u0026plusmn; 1.67 (120)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e0.96 \u0026plusmn; 0.73 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e1.35 \u0026plusmn; 1.6 (248)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.025*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eeGFR (ml/min/1.73 m^2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e63.07 \u0026plusmn; 31.96 (269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e72.87 \u0026plusmn; 27.47 (120)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e77.07 \u0026plusmn; 30.03 (21)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e71.25 \u0026plusmn; 30.18 (248)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eUACR (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e365.44 \u0026plusmn; 972.63 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e388.15 \u0026plusmn; 1353.65 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e529.02 \u0026plusmn; 1254.2 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e330.23 \u0026plusmn; 1119.87 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e39.31 \u0026plusmn; 42.58 (172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e29.85 \u0026plusmn; 17.72 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e43.71 \u0026plusmn; 33.56 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e31.84 \u0026plusmn; 17.56 (152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e31.84 \u0026plusmn; 41.80 (249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e27.23 \u0026plusmn; 23.52 (103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e35.58 \u0026plusmn; 33.85 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e28.98 \u0026plusmn; 21.36 (221)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eUric acid (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e6.20 \u0026plusmn; 1.98 (119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e6.22 \u0026plusmn; 1.97 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e7.13 \u0026plusmn; 2.57 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e6.13 \u0026plusmn; 1.59 (110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eTSH (uIU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e12.53 \u0026plusmn; 29.96 (204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e8.27 \u0026plusmn; 21.3 (91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e6.07 \u0026plusmn; 15.44 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e6.35 \u0026plusmn; 22.92 (173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eFree T4 (ng/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e1.07 \u0026plusmn; 0.39 (178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e1.26 \u0026plusmn; 0.40 (74)\u003csup\u003ead\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e1.38 \u0026plusmn; 0.8 (18)\u003csup\u003ead\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e1.13 \u0026plusmn; 0.39 (144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" 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=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eT3 (ng/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e72.73 \u0026plusmn; 23.34 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e86.30 \u0026plusmn; 50.78 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e148.16 \u0026plusmn; 84.7 (6)\u003csup\u003eabd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e77.95 \u0026plusmn; 36.45 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" 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=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eAnti-TPO Ab (IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e85.539 \u0026plusmn; 177.179 (110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e25.719 \u0026plusmn; 64.188 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e158.44 \u0026plusmn; 242.176 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e122.107 \u0026plusmn; 277.816 (93)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" 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=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eAnti-TSH Ab (IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e0.78 \u0026plusmn; 0.49 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e4 \u0026plusmn; 4.5 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eTG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e153.33 \u0026plusmn; 95.78 (208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e162.86 \u0026plusmn; 99.81 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e151.33 \u0026plusmn; 104.92 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e153.23 \u0026plusmn; 127.85 (217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e103.68 \u0026plusmn; 38.42 (204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e103.96 \u0026plusmn; 35.22 (96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e88.27 \u0026plusmn; 38.33 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e102.16 \u0026plusmn; 33.16 (209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e47.64 \u0026plusmn; 13.98 (195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e47.34 \u0026plusmn; 12.77 (94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e45.38 \u0026plusmn; 15.75 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e47.87 \u0026plusmn; 14.23 (198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eACEi/ARB usage (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e108 (34.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e58 (38.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e8 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e91 (26.69)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.046*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eDiuretics usage (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e86 (27.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e27 (17.88)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e9 (37.5)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e54 (15.84)\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" 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=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eMetformin (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e73 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e44 (29.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e5 (20.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e80 (23.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eSulphonylurea/Glinide (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e90 (28.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e51 (33.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e7 (29.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e84 (24.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eTZD (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e14 (4.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e4 (2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e1 (4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e16 (4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eAcarbose (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e28 (8.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e9 (5.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e3 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e28 (8.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eDPP-4i (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e73 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e29 (19.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e5 (20.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e65 (19.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eGLP-1 RA (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e1 (4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e3 (0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.319615912208505%\" valign=\"top\"\u003e\n \u003cp\u003eSGLT2i (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.106995884773664%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.872427983539094%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558299039780522%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e6 (1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.327846364883403%\" valign=\"top\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*denote p value \u0026lt;0.05\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003edenote significant difference with Primary hypothyroidism or Secondary hypothyroidism group\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003edenote significant difference with Post-procedure hypothyroidism group\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003edenote significant difference with Hyperthyroidism with suppression and supplement therapy group\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003edenote significant difference with other group\u003c/p\u003e\n\u003cp\u003eAbbreviations: HTN, hypertension; ESRD, end stage renal disease; PAD, peripheral artery disease; CHD, coronary heart disease; CVA, cerebrovascular accident; LEA: lower extremities amputation; HbA1c: glycated hemoglobulin; eGFR: estimated Glomerular filtration rate; URAC: urine albumin to creatinine ratio; AST: aspartate aminotransferase; ALT: Alanine aminotransferase; TSH: thyroid stimulating hormone; Anti-TPO Ab: antithyroid peroxidase antibody; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; ACEi, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blockers; TZD, thiazolidinedione; DPP-4i, dipeptidyl peptidase-4 inhibitor; GLP-1 RA, glucagon-like peptide-1 receptor agonist; SGLT-2i, sodium-glucose co-transporter 2 inhibitor\u003c/p\u003e\n\u003cp\u003eTable 2 Correlation between thyroid function, lipid profile and HbA1c\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.392996108949417%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.93644617380026%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFree T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898832684824903%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.93644617380026%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.93644617380026%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAnti-TPO Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898832684824903%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAnti-TSH Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.392996108949417%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.614785992217898%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.614785992217898%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.392996108949417%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.614785992217898%\" valign=\"top\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e-0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.614785992217898%\" valign=\"top\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.392996108949417%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003e0.022*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.614785992217898%\" valign=\"top\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e-0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003e0.023*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e-0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.614785992217898%\" valign=\"top\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.392996108949417%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e-0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.614785992217898%\" valign=\"top\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.652399481193256%\" valign=\"top\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.284046692607005%\" valign=\"top\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.614785992217898%\" valign=\"top\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*denote p value \u0026lt;0.05\u003c/p\u003e\n\u003cp\u003eTSH: thyroid stimulating hormone; Anti-TPO Ab: antithyroid peroxidase antibody; LDL-C: low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; HbA1c: glycated hemoglobulin\u003c/p\u003e\n\u003cp\u003eTable 3 Predictors of MACE in patients with thyroxine using type 2 diabetes by logistic regression\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate model 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate model 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate model 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate model 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Odds ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Odds ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Odds ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Odds ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Odds ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026lt; 65 yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e1.03 (0.78 , 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.8893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e1.59 (0.94 - 2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e1.57 (0.92 - 2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.0974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026ge;\u0026nbsp;65 yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e1 (0.73 , 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e1.08 (0.59 - 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.8106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e1.12 (0.62 - 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.6973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026lt; 30 ml/min/1.73 m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e1.69 (1.07 , 2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.0256*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e1.44 (0.65 - 3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.3669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e1.55 (0.68 - 3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.2961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026ge;\u0026nbsp;30 ml/min/1.73 m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026lt; 45 ml/min/1.73 m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e1.61 (1.1 , 2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.0175*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e1.1 (0.54 - 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.7957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e1.19 (0.58 - 2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.6363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026ge;\u0026nbsp;45 ml/min/1.73 m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eTSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026lt; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.95 (0.64 - 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.8437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.55 (0.22 - 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.64 (0.36 - 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e0.65 (0.36 - 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.64 (0.36 - 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026ge; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eFree T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026lt; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.95 (0.51 - 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.8747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.66 (0.37 - 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.55 (0.22 - 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e0.58 (0.23 - 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.2397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.58 (0.23 - 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.2404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u0026ge; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e2.22 (1.66 , 2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e2.2 (1.21 - 3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.0097*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e2.27 (1.26 - 4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.0066*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e2.38 (1.29 - 4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.0053*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e2.44 (1.33 - 4.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.0038*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eDiabetic microvascular complication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e2.23 (1.67 , 2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.91 (0.51 - 1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.7458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.96 (0.54 - 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e0.92 (0.51 - 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.96 (0.53 - 1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.8932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eESRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e17.68 (2.34 , 133.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e5.42 (0.50 - 58.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e6.61 (0.63 - 69.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e4.54 (0.43 - 47.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.2084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e5.53 (0.54 - 56.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e6.93 (4.57 , 10.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e12.58 (6.00 - 26.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e12.49 (5.95 - 26.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e12.31 (5.86 - 25.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e12.26 (5.83 - 25.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eCVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e69.83 (22.03 , 221.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e44.05 (13.06 - 148.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e43.69 (12.94 - 147.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e45.86 (13.52 - 155.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e45.76 (13.46 - 155.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eDiabetic foot infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e4.44 (1.26 , 15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.0201*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e2.63 (0.6 - 11.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e2.43 (0.56 - 10.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.2387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e2.91 (0.65 - 13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.1617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e2.6 (0.58 - 11.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.2111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.35483870967742%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*denote p value \u0026lt;0.05\u003c/p\u003e\n\u003cp\u003eAbbreviations: eGFR: estimated Glomerular filtration rate; TSH: thyroid stimulating hormone; ESRD, end stage renal disease; CHD, coronary heart disease; CVA, cerebrovascular accident\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":"major adverse cardiovascular events, risk factors, type 2 diabetes, thyroxine supplement, peripheral artery disease","lastPublishedDoi":"10.21203/rs.3.rs-3932875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3932875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims:\u003c/h2\u003e \u003cp\u003eThis retrospective study investigated the occurrence of major adverse cardiovascular events \u003cem\u003e(\u003c/em\u003eMACE) in thyroxine using diabetic patients and compared the risk factors between the MACE and non-MACE groups.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe used longitudinal claims data from 2008 to 2017 from the Chang Gung Research Database. Diabetic patients who used thyroxine were included. The primary outcome was the occurrence of MACE. The secondary outcomes were the differences between the two groups (MACE vs. no MACE).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAfter 1:1 group matching by propensity score between MACE and non-MACE group by sex, age, the interval of using thyroxine, there were 416 patients in each group. Patients with worse renal function (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;45 ml/min/1.73 m2), hypertension, history of diabetic microvascular complications, end stage renal disease (ESRD), coronary heart disease (CHD), heart failure, cerebrovascular accident (CVA) and diabetic foot infection had a higher risk of experiencing MACE. Free T4 had weak positive correlation with HDL, and TSH had weak positive correlation with LDL and negative correlation with HDL (correlation coefficient, p value: 0.131, 0.022; 0.124, 0.016; -0.157, 0.003, respectively). There\u0026rsquo;s no optimal cutoff points according to the Receiver operating characteristic (ROC) curve analysis of the best discrimination point between TSH/free T4/LDL and MACE attack.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eIn thyroxine using diabetic patients, patients with worse renal function, hypertension, history of diabetic microvascular complications, ESRD, CHD, heart failure, CVA and diabetic foot infection had a higher risk of experiencing MACE, but peripheral artery disease (PAD) was not a significant risk of MACE.\u003c/p\u003e","manuscriptTitle":"Peripheral artery disease is not a risk factor of major adverse cardiovascular events in thyroxine using diabetic patients: a retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-12 19:38:49","doi":"10.21203/rs.3.rs-3932875/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":"ea54dd55-efdc-4f30-9018-7338f2ec1c66","owner":[],"postedDate":"February 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-23T05:30:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-12 19:38:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3932875","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3932875","identity":"rs-3932875","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}