An Exploration of the Association between Goiter and Type 2 Diabetes Leveraging the CHNS Database

An Exploration of the Association between Goiter and Type 2 Diabetes Leveraging the CHNS Database | 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 An Exploration of the Association between Goiter and Type 2 Diabetes Leveraging the CHNS Database Jiajia Song, Xiaofang Han, Kemei Liu, Fei Zhai, Dechao Yin, Xiaohuan Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7194919/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 The comorbidity of multiple diseases is receiving growing attention, and the coexistence of goiter and type 2 diabetes mellitus (T2DM) is an important one. Thus, this study aims to utilize the widely representative China Health and Nutrition Survey (CHNS) database for relevant investigations. Methods Covariates had potential association with T2DM were obtained from the database. Univariate and multivariate logistic regression models were used to evaluate the association of goiter with T2DM. Additionally, the Receiver Operating Characteristic (ROC) and smooth curves were drawn for investigating the linear correlation between goiter and T2DM, as well as the potential prediction value of goiter on T2DM risk. Results Among 10,148 eligible participants, 2,109 had T2DM and 155 had goiter. After adjusting for covariates, a positive association between goiter and increased risk of T2DM was observed (odds ratio [OR] = 2.62, 95% confidence interval [CI]: 1.29–4.81). The smooth curve showed that there is a nonlinear association between goiter and T2DM. Additionally, there was a potential prediction value of goiter on the risk of T2DM, with the area under the curve (AUC) of ROC curve of 0.809. Conclusion This study identified an association between goiter and T2DM, and however, the causal relationship between them and specific mechanisms needs further clarification. T2DM CHNS Goiter Receiver Operating Characteristic Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Goiter refers to the pathological enlargement of the thyroid gland caused by various etiologies, which can be diagnosed through visual inspection, palpation, or ultrasound examination[ 1 ]. It is classified into diffuse and nodular types based on morphology[ 2 ], as well as toxic (associated with hyperthyroidism and suppressed TSH levels) or non-toxic (related to normal TSH levels) based on function[ 3 ]. Causes of goiter include factors such as iodine deficiency and autoimmune diseases. The prevalence of simple goiter is as high as 5% in iodine-sufficient areas and can exceed 10% in iodine-deficient regions[ 4 ].When accompanied by thyroid dysfunction, whether hyperthyroidism or hypothyroidism, goiter significantly harms the cardiovascular, digestive, and reproductive systems. Also, even euthyroid nodular goiter patients show markedly elevated cardiovascular disease (CVD) risk[ 5 ]. Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by persistent hyperglycemia due to insufficient insulin secretion and/or insulin resistance (IR), accompanied by abnormal lipid and protein metabolism. Recently, according to China’s 2018–2019 epidemiological data, DM prevalence was 11.9% using the 1999 WHO diagnostic criteria and 12.4% by the 2010 ADA standards[ 6 ]. In the absence of appropriate management, persistent hyperglycemia in this population may lead to the development of a spectrum of severe complications. Long-term hyperglycemia causes multi-system damage including atherosclerotic cardiovascular disease (ASCVD), stroke, retinopathy, nephropathy, neuropathy, and foot complications, significantly reducing quality of life and increasing mortality[ 7 ]. Current methods for treating hyperglycemia include diet control, appropriate increase in exercise volume, and drug therapy, and the common medicines include sulfonylureas and other insulin secretagogues, biguanides, α-glycosidase inhibitors, thiazolidinediones, and insulin[ 8 ]. Although these treatments demonstrate glycemic control efficacy, they fail to address the fundamental pathophysiology of the disease. Besides, traditional therapeutic regimens are frequently accompanied by adverse effects, such as hypoglycemia, weight gain, or gastrointestinal disturbances[ 9 ]. Prolonged administration may also induce therapeutic resistance, potentially compromising clinical outcomes. Consequently, an urgent need exists to develop innovative pathogenesis mechanism related to modalities and therapeutic strategies. Epidemiological investigations have revealed an elevated prevalence of thyroid disorders among individuals with DM, with this statistical association being particularly pronounced in iodine-deficient regions. While this correlation suggests a potential pathophysiological interplay between these two endocrinopathies, the precise mechanistic pathways underlying their co-occurrence require further elucidation [ 10 , 11 ].The Pomerania Health Study found that metformin suppresses both endemic and sporadic goiters, indicating an underlying metformin-thyroid interaction, and however, the effect may arise from direct drug actions rather than modulating T2DM pathology. Therefore, further studies are needed to clarify the T2DM-goiter relationship and its mechanisms[ 12 ]. In recent years, studies using the China Health and Nutrition Survey (CHNS) data have identified two independent risk factors correlating with T2DM, including waist circumference-age interaction and adult ultra-processed food intake[ 13 , 14 ]. An other CHNS-based study showed that regional gut microbiota variations were also significantly linked to glycemic traits and T2DM risk[ 15 ]. Nevertheless, research on clarifying the relationship between goiter and T2DM risk remains limited up to now. Herein, this study utilized data from the CHNS database with the aim of exploring the association of goiter and T2DM. Focusing on Chinese population characteristics, this study aims to offer new insights into the thyroid-diabetes interplay and provide some information for early-stage detection and targeted prevention in T2DM, thereby reduce patient morbidity while enhance quality of life. Materials and methods Study design and participants In this cross-sectional study, data was extracted from the CHNS database. The CHNS is a long-term open cohort study jointly conducted by the University of North Carolina and the Chinese Center for Disease Control and Prevention and has completed ten survey rounds since 1989 across 15 provinces, encompassing demographic, nutritional, and health data[ 13 ]. This project adopted a multi-stage random cluster sampling approach to gather information regarding key phenotypes, dietary habits, and health outcomes from over 30,000 participants hailing from 15 provinces or megacities across China (with 6 located in the northern region and 9 in the southern region). The data was collected in multiple rounds spanning from 1989 to 2018, specifically 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, 2015, and 2018. More detailed information on this survey is shown elsewhere: https://www.cpc.unc.edu/projects/china . Initially, 16,799 adult participants that neither disability nor pregnant/lactating in the database were included. The exclusion criteria were (1) missing data on total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP), diastolic blood pressure (DBP), physical activity or body fat (2) diagnosed with myocardial infarction, stroke, cancer, fracture, asthma or taking blood pressure medication, (3) having an extreme daily energy intake (> 6000 kcal or 4000 kcal or < 600 kcal for female), (4) with a body mass index (BMI) level of 60 kg/m2, and (5) having a physical activity level of > 1260 MET·h/w. Finally, 10,148 people were eligible for further analysis. The CHNS follows the Helsinki Declaration guidelines for research. It has approvals from relevant Institutional Review Boards and all participants have signed consent forms, safeguarding scientific integrity, methodology and participants' rights. Diagnosis of goiter and T2DM In the CHNS, goiter presence and morphology (diffuse/nodular) were assessed through physical examinations. T2DM was defined through the following methods. Self-reported DM was identified based on the questionnaire, and if the subject responded "yes" to the question: "Confirmed diabetes?", he/she was recognized as a patient with DM. Moreover, DM could also be defined according to the following criteria: fasting blood glucose was equal to or higher than 7.0 mmol/L, or glycosylated hemoglobin (HbA1c) was equal to or higher than 48 mmol/L (which is equivalent to 6.5%). However, it should be noted that fasting blood samples were only collected by the CHNS in 2009. Consequently, fasting blood glucose and HbA1c data were only available in mid-2009. In the analysis, participants were divided into T2DM group and non-T2DM group. Covariates selection Data on variables as potential confounding factors were also extracted from the database, including demographic information (age, ethnicity, gender, educational level, place of residence, health insurance and employment status), physical examination data (height, weight and BMI) and health condition (hypertension and smoking). In this study, we examined both the pooled and 2009 data of variables. Statistical analysis Continuous variables were presented using mean and standard deviation [Mean (SD)], and t test was utilized for comparation between T2DM group and non-T2DM group. Categorical variables were presented as percentage with proportion [n (%)], and chi-square (χ²) test was used for the comparation. Univariate and multivariate logistic regression models were employed to investigate the associations of covariates with T2DM, and of goiter with T2DM, respectively. This model calculated the adjusted odds ratio (OR) and its corresponding 95% confidence interval (CI). Model 1 was an unadjusted model, Model 2 adjusted for demographic characteristic (age, gender and ethnicity), and Model 3 adjusted for educational level, health insurance, employment status, place of residence, height, weight, BMI, smoking status and hypertension in addition to the Model 2. A line graph was drawn to show the association between goiter and T2DM. Also, the receiver operator characteristic (ROC) curve with area under the curve (AUC) was utilized to reflect the potential prediction value of goiter on T2DM risk. All statistical analyses were conducted via R software, a widely recognized and powerful tool for data analysis in epidemiology and statistics. To ensure the reliability of results, the threshold for statistical significance was set at P < 0.05. Results Characteristics of participants Figure 1 showed the flowchart of participants screening. 16,799 adults without disability or women being pregnant/lactating were initially included. After excluding individuals meeting the exclusion criteria (n = 6651), 10,148 participants were eligible for further analysis. Comparation of characteristics in participants between T2DM group and non-T2DM group was shown in Table 1 . Among the total population, 2,109 had T2DM and 155 had goiter. The average age of patients with T2DM was significantly higher than those without T2DM (59.87 years vs. 47.33 years). The proportion of people with the ethnicity of Han was significantly higher than that in non-T2DM group (94.6% vs. 90.4%). More than half of individuals in non-T2DM group were female, while the proportion of male patients was higher in T2DM group ( P = 0.018). The average weight and BMI levels were both significantly higher in T2DM group than those in non-T2DM group (65.05 kg vs. 60.04 kg). Patients with hypertension occupied 41.6% in T2DM group, while the number in non-T2DM group was 10.2% ( P < 0.001). Table 1 Characteristics of participants in T2DM group and non-T2DM group Variables Non-T2DM (n = 61739) T2DM (n = 2109) P Age, years, mean (SD) 47.33 (15.68) 59.87 (11.89) < 0.001 Ethnicity, n (%) < 0.001 Han 55822 (90.4) 1996 (94.6) Other 5917 (9.6) 113 (5.4) Gender, n (%) 0.018 Female 32430 (52.5) 1052 (49.9) Male 29309 (47.5) 1057 (50.1) Educational level, n (%) 0.012 Middle school or vocational 32951 (53.4) 1059 (50.2) Primary school or below 24209 (39.2) 873 (41.4) University or higher 4579 (7.4) 177 (8.4) Place of residence, n (%) < 0.001 Rural 39281 (63.6) 984 (46.7) Urban 22458 (36.4) 1125 (53.3) Health insurance, n (%) < 0.001 Yes 37677 (61.0) 1760 (83.5) No 24062 (39.0) 349 (16.5) Employment status, n (%) < 0.001 Yes 37871 (61.3) 678 (32.1) No 23868 (38.7) 1431 (67.9) Height, cm, mean (SD) 160.95 (8.17) 161.17 (8.16) 0.222 Weight, kg, mean (SD) 60.04 (10.19) 65.05 (9.75) < 0.001 BMI, kg/m 2 , mean (SD) 23.12 (3.23) 25.02 (3.29) < 0.001 Hypertension, n (%) < 0.001 Yes 6284 (10.2) 878 (41.6) No 55455 (89.8) 1231 (58.4) Smoking, n (%) < 0.001 Current 16892 (27.4) 489 (23.2) Never 43106 (69.8) 1483 (70.3) Past 1741 (2.8) 137 (6.5) Goiter, n (%) 0.015 Yes 144 (0.2) 11 (0.5) No 61595 (99.8) 2098 (99.5) Statistics included t test and chi-square test. T2DM: type 2 diabetes mellitus, SD: standard deviation, BMI: body mass index. Association between goiter and T2DM The associations of covariates with T2DM were first explored (Fig. 2 ). It could be clearly seen that goiter, age, weight, urban residence, health insurance and non-work was positively associated with T2DM risk, whereas non-hypertension and educational level of primary school or below were negatively associated with T2DM risk (all P < 0.05). Then, the association between goiter and T2DM was investigated. According to Table 2 , goiter was associated with increased odds of T2DM after adjusting for all selected covariates (OR = 2.62, 95%CI: 1.29–4.81). Table 2 Association between goiter and T2DM Variable Model 1 Model 2 Model 3 OR (95% CI) P OR (95% CI) P OR (95% CI) P Goiter 2.24 (1.14–3.95) 0.01 2.44 (1.23–4.38) 0.005 2.62 (1.29–4.81) 0.004 T2DM: type 2 diabetes mellitus, OR: odds ratio, CI: confidence interval. Model 1: unadjusted model. Model 2: adjusted for age, gender and ethnicity. Model 3: adjusted for age, gender, ethnicity, educational level, health insurance, employment status, place of residence, height, weight, BMI, smoking status and hypertension. ROC and nonlinear correlation analysis Moreover, the ROC curve suggested that goiter has a potential prediction value on T2DM risk, with an AUC of 0.809 (Fig. 3 ). A smooth curve was constructed further to verify the nonlinear association between goiter and T2DM (Fig. 4 ). The probability of developing T2DM steadily increased as the status shifted from not having a goiter to having a goiter. Discussion The medical community has increasingly recognized the importance of disease comorbidities. Existing studies indicate that thyroid dysfunction may contribute to DM pathogenesis by impairing both insulin secretion and sensitivity[ 16 ]. Epidemiological data consistently show elevated T2DM risk among patients with thyroid disorders, pointing to potential underlying physiological connections[ 14 ]. In this study, we explored the relationship between goiter and T2DM in Chinese population based on the CHNS database with a large sample size. The results suggested a significantly positive association between goiter and T2DM risk after covariate adjustment. Participants were stratified into T2DM group and non-T2DM group, as well as various covariates were examined, thus, it was better to understand the patterns of T2DM prevalence in China. Over the past 50 years, the aging population has increased significantly, contributing to the rise in T2DM prevalence, with nearly half of patients aged 65 years or older. This trend highlights the impact of demographic shifts on T2DM rates, emphasizing the need for targeted interventions for older adults[ 9 , 17 ]. The observed effects may stem from age-related declines in metabolic efficiency, where cellular responsiveness to insulin diminishes, contributing to IR. Concurrently, pancreatic function deteriorates over time, reducing insulin secretion efficiency. Additionally, heightened systemic inflammation and oxidative stress disrupt insulin signaling pathways and compromise the body’s ability to produce insulin effectively. These interconnected physiological changes collectively contribute to the metabolic dysregulation associated with aging[ 18 ]. In terms of gender, while no significant association of gender with T2DM was observed in this study, gender itself has been reported to influence the risk of T2DM complications through various mechanisms. For example, women have a relatively higher risk of CVDs and kidney diseases, whereas men are more prone to diabetic retinopathy and painless diabetic neuropathy[ 19 ]. Obesity plays a significant role in DM. Being overweight or obese causes the body to develop IR[ 20 ], making it difficult for glucose to enter blood cells, thereby increasing the risk of T2DM. Fat cells are also more resistant to insulin, and abdominal fat significantly impacts the development of T2DM[ 21 ]. Experimental evidence has confirmed that weight loss achieved through dietary interventions can lead to long-term remission in patients with T2DM. The greater the weight loss, the higher the DM remission rate, and weight management helps delay or prevent the onset of clinical complications of DM[ 22 ]. Among lifestyle characteristics, place of residence, health insurance, smoking, and work status were also significantly associated with the risk of T2DM. A previous study has found that people living in areas with poor sports facilities have higher rates of obesity and T2DM[ 23 ]. At the same time, for every interquartile (IQR) increase in community green space, the risk of DM decreases by 21% (HR = 0.79, 95%CI: 0.63–0.99)[ 24 ]. Besides, underweight is more common in iodine-sufficient urban areas, while obesity is more prevalent in iodine-deficient suburban regions[ 25 ]. After adjusting for significant risk factors such as DM, hypothyroidism/hyperthyroidism, phosphorus intake, dairy and seafood consumption, adult women with low dietary iodine intake were associated with a higher risk of T2DM[ 26 ]. Higher placental iodine concentrations are associated with a lower incidence of gestational diabetes mellitus (GDM). It has been confirmed that lower placental iodine load is linked to changes in plasma insulin levels, homeostasis model assessment of insulin resistance (HOMA-IR) index, and β-cell activity[ 27 ]. It is evident that various factors related to residential areas, such as sports facilities, green space coverage, and iodine levels, influence people’s health to varying degrees, particularly the risk of developing DM. The incidence of acquired immune deficiency syndrome (AIDS) among individuals without health insurance (24%) is significantly higher than that in the insured group (4%) ( P = 0.004), and however, there is no significant difference in the prevalence of diseases such as hypertension, DM, and tuberculosis[ 28 ]. Nevertheless, stable medical insurance coverage can help achieve glycemic control and blood pressure management[ 29 ]. Moreover, whether one has health insurance directly influences the treatment plan for Graves’ disease[ 30 ]. The present study found that the risk of T2DM is significantly increased among uninsured individuals. Research indicates that half of the uninsured population in rural and urban areas is under 40 years old, with the rural uninsured population being larger than the urban one[ 31 ]. Therefore, health insurance status and place of residence may significantly impact disease treatment and management. Smoking could increase the risk of prediabetes and DM in the general population[ 32 ] and also raises the likelihood of developing thyroid goiter and nodules requiring hospitalization[ 33 ]. Clinical data indicated that smoking can slightly reduce BMI and alter body composition. The longer the duration of smoking, the higher the waist-to-hip ratio, along with increased visceral and subcutaneous fat[ 34 ]. A decrease in BMI is a risk factor for thyroid tumors[ 35 ]. At the same time, smoking can reduce thyroid-stimulating hormone[ 36 ], stimulate the growth of thyroid tumors[ 37 , 38 ], and have potential anti-estrogenic effects. Estrogen further promotes thyroid tumorigenesis through estrogen receptors or vascular growth factor signaling pathways[ 39 ]. A significant association was observed between goiter and T2DM after adjusting multiple covariates, and goiter has a potential prediction value on T2DM risk, with an AUC of ROC of 0.809. The biological mechanisms under relationship between goiter and T2DM are complex and diverse. Thyroid hormone deficiency (such as hypothyroidism) can reduce the expression and function of glucose transporter 4 (GLUT4) in skeletal muscle and adipose tissue, leading to decreased insulin-mediated glucose uptake and exacerbating IR. Hypothyroidism patients may experience approximately 30% reduction in insulin sensitivity, elevated fasting blood glucose and glycated hemoglobin (HbA1c) levels[ 40 ]. Therefore, thyroid enlargement may be associated with IR and hyperinsulinemia[ 41 ]. A study has suggested that patients with T2DM who also have thyroid enlargement may face a 2–3 times higher risk of cardiovascular events[ 42 , 43 ]. On the other hand, thyroid hormone levels may lead to goiter formation, indicating that thyroid dysfunction may contribute to the pathogenesis of T2DM by affecting insulin sensitivity and β-cell function[ 44 ]. Additionally, both T2DM and thyroid diseases are associated with autoimmune mechanisms. Thyroid enlargement may be a manifestation of autoimmune thyroid disease, with anti-thyroid peroxidase antibodies (TPO-Ab) and anti-thyroglobulin antibodies (TGAb) serving as key biochemical markers of autoimmune responses[ 45 ]. TPOAb positivity, TGAb, IA-2 antibodies, and lower serum fasting C-peptide levels contribute to the progression of β-cell failure. Autoimmune reactions may simultaneously attack pancreatic and thyroid tissues, damaging pancreatic β-cells and impairing insulin secretion, thereby increasing the risk of developing T2DM[ 46 ]. Findings in the current study, that is, the positive association between goiter and increased T2DM risk, may provide valuable insights for early clinical screening and intervention and implementing preventive measures in T2DM. Thus, routine follow-up for T2DM patients should incorporate thyroid function evaluation, particularly for those presenting with signs of thyroid enlargement, which may achieve more comprehensive health management and better disease control. Furthermore, integrating multiple covariates reinforced the role of goiter as an independent risk factor for T2DM. In fact, lifestyle and genetic factors may interact complexly with goiter and DM, whether incorporating additional biomarkers may further improve predictive accuracy of goiter on T2DM risk, multicenter validation studies would be necessary. Limitations could not be ignored in the results explanation. Due to the cross-sectional study design, sample size constraints and potential selection bias are inescapable, as well as causal association between goiter and T2DM could not be concluded. While the CHNS provides representative data, generalizability may be limited by participant selection criteria, and mechanistic experiments are needed to enhance external validity. Future multi-center studies with larger samples should validate these associations and explore underlying biology. Conclusion Our CHNS analysis confirms a significant goiter-T2DM association, highlighting their population health significance. Clinicians should consider thyroid status in diabetes risk assessment. Further research should elucidate mechanisms and translate findings into improved patient management and prevention strategies. Declarations Ethics approval and consent to participate The study design was in accordance with the Declaration of Helsinki. The CHNS database has been approved by the relevant ethical review boards, and the participants have given informed consent. Ethical approval was not required because of the public characteristics of the data of this database. Clinical trial number Not applicable. Consent for publication None. Availability of data and material The datasets analyzed in this study are publicly available summary statistics. Data used can be obtained upon a reasonable request to the corresponding author. Competing interests The authors have stated that they have no competing interest. Funding This research was supported by the Key Project of Natural Science of Bengbu Medical University(2024byzd430) . Authors’ contributions JS conceived the study. 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The Relationship Between Thyroid Function and the Prevalence of Type 2 Diabetes Mellitus in Euthyroid Subjects. J Clin Endocrinol Metab. 2017. 102(2): 434-442. Wiersinga WM, Poppe KG, Effraimidis G. Hyperthyroidism: aetiology, pathogenesis, diagnosis, management, complications, and prognosis. Lancet Diabetes Endocrinol. 2023. 11(4): 282-298. Murao S, Kondo S, Ohashi J, et al. Anti-thyroid peroxidase antibody, IA-2 antibody, and fasting C-peptide levels predict beta cell failure in patients with latent autoimmune diabetes in adults (LADA)--a 5-year follow-up of the Ehime study. Diabetes Res Clin Pract. 2008. 80(1): 114-21. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7194919","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502796805,"identity":"99286f95-f74e-4fe9-bc06-d8ccde29d695","order_by":0,"name":"Jiajia Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie2QsQrCMBCGDwqZqt0kIjSv0NLFoQ+TIjiJi4tjpaKL4triS3R0vFBwik/gIvgCARcHQVM3F5NRMN9w0/9x9x+Aw/GTIEd1T0MSFIhqbqdkotqMky49ZqKUdmu8xidNFsIkaTpLizxb4EWUvscJSIWdHFjQw+9KhG2XIZkSb1tj/wBxtecGRdfXW/wZgVONsQQenQ0Ky5HrLjRbweSCepoVwLcStQqgsFHaLvrJPCH0GIlcUnMXVsqRUvdnyHbF9faYpywYmA6j8iNBDfGWYI0WKYfD4fhrXtx9U/MHmYOxAAAAAElFTkSuQmCC","orcid":"","institution":"Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jiajia","middleName":"","lastName":"Song","suffix":""},{"id":502796806,"identity":"5c9c764a-22ed-4783-a365-7b359eea6da9","order_by":1,"name":"Xiaofang Han","email":"","orcid":"","institution":"Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofang","middleName":"","lastName":"Han","suffix":""},{"id":502796807,"identity":"02d454a5-78f2-476e-9abc-0c0f48a6a1cd","order_by":2,"name":"Kemei Liu","email":"","orcid":"","institution":"Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kemei","middleName":"","lastName":"Liu","suffix":""},{"id":502796808,"identity":"038653b6-e46c-44ef-9b79-1cfa86b09f22","order_by":3,"name":"Fei Zhai","email":"","orcid":"","institution":"Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Zhai","suffix":""},{"id":502796809,"identity":"ff2bd5ee-0863-402e-962f-d64d38014eb7","order_by":4,"name":"Dechao Yin","email":"","orcid":"","institution":"Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dechao","middleName":"","lastName":"Yin","suffix":""},{"id":502796810,"identity":"2df6c580-a05b-4ca3-b712-01d4ba864dc5","order_by":5,"name":"Xiaohuan Zhu","email":"","orcid":"","institution":"Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohuan","middleName":"","lastName":"Zhu","suffix":""},{"id":502796811,"identity":"4174659c-1829-4896-97f1-213a18d8baa8","order_by":6,"name":"Ting Hu","email":"","orcid":"","institution":"Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-07-23 09:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7194919/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7194919/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89641965,"identity":"bb5f1e5e-5585-4e2d-8638-bb4664609ea6","added_by":"auto","created_at":"2025-08-22 08:12:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1064573,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chat of participants screening.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7194919/v1/5350cdd787834e7c6ba88392.png"},{"id":89641961,"identity":"b2cec271-e6e2-4035-bbcd-7a3c93130fc1","added_by":"auto","created_at":"2025-08-22 08:12:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":638113,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot on associations of variables with T2DM.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7194919/v1/ab07f0ab1a88fb9189b8fa5c.png"},{"id":89640219,"identity":"77dd7f74-a34e-412b-830e-25afd90665b4","added_by":"auto","created_at":"2025-08-22 08:04:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134268,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the prediction value of goiter on T2DM risk.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7194919/v1/c7a4d8ac576fc79f1ca381e3.png"},{"id":89640224,"identity":"144ee9b8-db7e-409c-8364-97d9b82d6717","added_by":"auto","created_at":"2025-08-22 08:04:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":141408,"visible":true,"origin":"","legend":"\u003cp\u003eSmooth curve of correlation between goiter and T2DM.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7194919/v1/d538caa946420984fb8cd36e.png"},{"id":105898043,"identity":"c05e6cc1-dac2-48df-a5ce-81ca6fbe7f79","added_by":"auto","created_at":"2026-04-01 08:59:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2992268,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7194919/v1/6a9897c5-bb07-408a-9065-c1db183ca7ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Exploration of the Association between Goiter and Type 2 Diabetes Leveraging the CHNS Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGoiter refers to the pathological enlargement of the thyroid gland caused by various etiologies, which can be diagnosed through visual inspection, palpation, or ultrasound examination[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is classified into diffuse and nodular types based on morphology[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], as well as toxic (associated with hyperthyroidism and suppressed TSH levels) or non-toxic (related to normal TSH levels) based on function[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Causes of goiter include factors such as iodine deficiency and autoimmune diseases. The prevalence of simple goiter is as high as 5% in iodine-sufficient areas and can exceed 10% in iodine-deficient regions[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].When accompanied by thyroid dysfunction, whether hyperthyroidism or hypothyroidism, goiter significantly harms the cardiovascular, digestive, and reproductive systems. Also, even euthyroid nodular goiter patients show markedly elevated cardiovascular disease (CVD) risk[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eType 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by persistent hyperglycemia due to insufficient insulin secretion and/or insulin resistance (IR), accompanied by abnormal lipid and protein metabolism. Recently, according to China\u0026rsquo;s 2018\u0026ndash;2019 epidemiological data, DM prevalence was 11.9% using the 1999 WHO diagnostic criteria and 12.4% by the 2010 ADA standards[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the absence of appropriate management, persistent hyperglycemia in this population may lead to the development of a spectrum of severe complications. Long-term hyperglycemia causes multi-system damage including atherosclerotic cardiovascular disease (ASCVD), stroke, retinopathy, nephropathy, neuropathy, and foot complications, significantly reducing quality of life and increasing mortality[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Current methods for treating hyperglycemia include diet control, appropriate increase in exercise volume, and drug therapy, and the common medicines include sulfonylureas and other insulin secretagogues, biguanides, α-glycosidase inhibitors, thiazolidinediones, and insulin[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although these treatments demonstrate glycemic control efficacy, they fail to address the fundamental pathophysiology of the disease. Besides, traditional therapeutic regimens are frequently accompanied by adverse effects, such as hypoglycemia, weight gain, or gastrointestinal disturbances[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Prolonged administration may also induce therapeutic resistance, potentially compromising clinical outcomes. Consequently, an urgent need exists to develop innovative pathogenesis mechanism related to modalities and therapeutic strategies.\u003c/p\u003e\u003cp\u003eEpidemiological investigations have revealed an elevated prevalence of thyroid disorders among individuals with DM, with this statistical association being particularly pronounced in iodine-deficient regions. While this correlation suggests a potential pathophysiological interplay between these two endocrinopathies, the precise mechanistic pathways underlying their co-occurrence require further elucidation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].The Pomerania Health Study found that metformin suppresses both endemic and sporadic goiters, indicating an underlying metformin-thyroid interaction, and however, the effect may arise from direct drug actions rather than modulating T2DM pathology. Therefore, further studies are needed to clarify the T2DM-goiter relationship and its mechanisms[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In recent years, studies using the China Health and Nutrition Survey (CHNS) data have identified two independent risk factors correlating with T2DM, including waist circumference-age interaction and adult ultra-processed food intake[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. An other CHNS-based study showed that regional gut microbiota variations were also significantly linked to glycemic traits and T2DM risk[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Nevertheless, research on clarifying the relationship between goiter and T2DM risk remains limited up to now.\u003c/p\u003e\u003cp\u003eHerein, this study utilized data from the CHNS database with the aim of exploring the association of goiter and T2DM. Focusing on Chinese population characteristics, this study aims to offer new insights into the thyroid-diabetes interplay and provide some information for early-stage detection and targeted prevention in T2DM, thereby reduce patient morbidity while enhance quality of life.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy design and participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this cross-sectional study, data was extracted from the CHNS database. The CHNS is a long-term open cohort study jointly conducted by the University of North Carolina and the Chinese Center for Disease Control and Prevention and has completed ten survey rounds since 1989 across 15 provinces, encompassing demographic, nutritional, and health data[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This project adopted a multi-stage random cluster sampling approach to gather information regarding key phenotypes, dietary habits, and health outcomes from over 30,000 participants hailing from 15 provinces or megacities across China (with 6 located in the northern region and 9 in the southern region). The data was collected in multiple rounds spanning from 1989 to 2018, specifically 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, 2015, and 2018. More detailed information on this survey is shown elsewhere: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cpc.unc.edu/projects/china\u003c/span\u003e\u003cspan address=\"https://www.cpc.unc.edu/projects/china\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eInitially, 16,799 adult participants that neither disability nor pregnant/lactating in the database were included. The exclusion criteria were (1) missing data on total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP), diastolic blood pressure (DBP), physical activity or body fat (2) diagnosed with myocardial infarction, stroke, cancer, fracture, asthma or taking blood pressure medication, (3) having an extreme daily energy intake (\u0026gt;\u0026thinsp;6000 kcal or \u0026lt;\u0026thinsp;800 kcal for male, \u0026gt;\u0026thinsp;4000 kcal or \u0026lt;\u0026thinsp;600 kcal for female), (4) with a body mass index (BMI) level of \u0026lt;\u0026thinsp;10 kg/m2 or \u0026gt;\u0026thinsp;60 kg/m2, and (5) having a physical activity level of \u0026gt;\u0026thinsp;1260 MET\u0026middot;h/w. Finally, 10,148 people were eligible for further analysis. The CHNS follows the Helsinki Declaration guidelines for research. It has approvals from relevant Institutional Review Boards and all participants have signed consent forms, safeguarding scientific integrity, methodology and participants' rights.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnosis of goiter and T2DM\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the CHNS, goiter presence and morphology (diffuse/nodular) were assessed through physical examinations. T2DM was defined through the following methods. Self-reported DM was identified based on the questionnaire, and if the subject responded \"yes\" to the question: \"Confirmed diabetes?\", he/she was recognized as a patient with DM. Moreover, DM could also be defined according to the following criteria: fasting blood glucose was equal to or higher than 7.0 mmol/L, or glycosylated hemoglobin (HbA1c) was equal to or higher than 48 mmol/L (which is equivalent to 6.5%). However, it should be noted that fasting blood samples were only collected by the CHNS in 2009. Consequently, fasting blood glucose and HbA1c data were only available in mid-2009. In the analysis, participants were divided into T2DM group and non-T2DM group.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData on variables as potential confounding factors were also extracted from the database, including demographic information (age, ethnicity, gender, educational level, place of residence, health insurance and employment status), physical examination data (height, weight and BMI) and health condition (hypertension and smoking). In this study, we examined both the pooled and 2009 data of variables.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were presented using mean and standard deviation [Mean (SD)], and t test was utilized for comparation between T2DM group and non-T2DM group. Categorical variables were presented as percentage with proportion [n (%)], and chi-square (χ\u0026sup2;) test was used for the comparation.\u003c/p\u003e\u003cp\u003eUnivariate and multivariate logistic regression models were employed to investigate the associations of covariates with T2DM, and of goiter with T2DM, respectively. This model calculated the adjusted odds ratio (OR) and its corresponding 95% confidence interval (CI). Model 1 was an unadjusted model, Model 2 adjusted for demographic characteristic (age, gender and ethnicity), and Model 3 adjusted for educational level, health insurance, employment status, place of residence, height, weight, BMI, smoking status and hypertension in addition to the Model 2. A line graph was drawn to show the association between goiter and T2DM. Also, the receiver operator characteristic (ROC) curve with area under the curve (AUC) was utilized to reflect the potential prediction value of goiter on T2DM risk.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted via R software, a widely recognized and powerful tool for data analysis in epidemiology and statistics. To ensure the reliability of results, the threshold for statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eCharacteristics of participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the flowchart of participants screening. 16,799 adults without disability or women being pregnant/lactating were initially included. After excluding individuals meeting the exclusion criteria (n\u0026thinsp;=\u0026thinsp;6651), 10,148 participants were eligible for further analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eComparation of characteristics in participants between T2DM group and non-T2DM group was shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the total population, 2,109 had T2DM and 155 had goiter. The average age of patients with T2DM was significantly higher than those without T2DM (59.87 years vs. 47.33 years). The proportion of people with the ethnicity of Han was significantly higher than that in non-T2DM group (94.6% vs. 90.4%). More than half of individuals in non-T2DM group were female, while the proportion of male patients was higher in T2DM group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). The average weight and BMI levels were both significantly higher in T2DM group than those in non-T2DM group (65.05 kg vs. 60.04 kg). Patients with hypertension occupied 41.6% in T2DM group, while the number in non-T2DM group was 10.2% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of participants in T2DM group and non-T2DM group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-T2DM\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61739)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eT2DM\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2109)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.33 (15.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e59.87 (11.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55822 (90.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1996 (94.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5917 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32430 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1052 (49.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29309 (47.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1057 (50.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school or vocational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32951 (53.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1059 (50.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24209 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e873 (41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4579 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of residence, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39281 (63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e984 (46.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22458 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1125 (53.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth insurance, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37677 (61.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1760 (83.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24062 (39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e349 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37871 (61.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e678 (32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23868 (38.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1431 (67.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, cm, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160.95 (8.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e161.17 (8.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, kg, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.04 (10.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e65.05 (9.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.12 (3.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e25.02 (3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6284 (10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e878 (41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55455 (89.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1231 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16892 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e489 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43106 (69.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1483 (70.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1741 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoiter, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61595 (99.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2098 (99.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eStatistics included t test and chi-square test.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eT2DM: type 2 diabetes mellitus, SD: standard deviation, BMI: body mass index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation between goiter and T2DM\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe associations of covariates with T2DM were first explored (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It could be clearly seen that goiter, age, weight, urban residence, health insurance and non-work was positively associated with T2DM risk, whereas non-hypertension and educational level of primary school or below were negatively associated with T2DM risk (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Then, the association between goiter and T2DM was investigated. According to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, goiter was associated with increased odds of T2DM after adjusting for all selected covariates (OR\u0026thinsp;=\u0026thinsp;2.62, 95%CI: 1.29\u0026ndash;4.81).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between goiter and T2DM\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoiter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.24 (1.14\u0026ndash;3.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.44 (1.23\u0026ndash;4.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.62 (1.29\u0026ndash;4.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eT2DM: type 2 diabetes mellitus, OR: odds ratio, CI: confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1: unadjusted model.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 2: adjusted for age, gender and ethnicity.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 3: adjusted for age, gender, ethnicity, educational level, health insurance, employment status, place of residence, height, weight, BMI, smoking status and hypertension.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eROC and nonlinear correlation analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMoreover, the ROC curve suggested that goiter has a potential prediction value on T2DM risk, with an AUC of 0.809 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A smooth curve was constructed further to verify the nonlinear association between goiter and T2DM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The probability of developing T2DM steadily increased as the status shifted from not having a goiter to having a goiter.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe medical community has increasingly recognized the importance of disease comorbidities. Existing studies indicate that thyroid dysfunction may contribute to DM pathogenesis by impairing both insulin secretion and sensitivity[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Epidemiological data consistently show elevated T2DM risk among patients with thyroid disorders, pointing to potential underlying physiological connections[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this study, we explored the relationship between goiter and T2DM in Chinese population based on the CHNS database with a large sample size. The results suggested a significantly positive association between goiter and T2DM risk after covariate adjustment.\u003c/p\u003e\u003cp\u003eParticipants were stratified into T2DM group and non-T2DM group, as well as various covariates were examined, thus, it was better to understand the patterns of T2DM prevalence in China. Over the past 50 years, the aging population has increased significantly, contributing to the rise in T2DM prevalence, with nearly half of patients aged 65 years or older. This trend highlights the impact of demographic shifts on T2DM rates, emphasizing the need for targeted interventions for older adults[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The observed effects may stem from age-related declines in metabolic efficiency, where cellular responsiveness to insulin diminishes, contributing to IR. Concurrently, pancreatic function deteriorates over time, reducing insulin secretion efficiency. Additionally, heightened systemic inflammation and oxidative stress disrupt insulin signaling pathways and compromise the body\u0026rsquo;s ability to produce insulin effectively. These interconnected physiological changes collectively contribute to the metabolic dysregulation associated with aging[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In terms of gender, while no significant association of gender with T2DM was observed in this study, gender itself has been reported to influence the risk of T2DM complications through various mechanisms. For example, women have a relatively higher risk of CVDs and kidney diseases, whereas men are more prone to diabetic retinopathy and painless diabetic neuropathy[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Obesity plays a significant role in DM. Being overweight or obese causes the body to develop IR[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], making it difficult for glucose to enter blood cells, thereby increasing the risk of T2DM. Fat cells are also more resistant to insulin, and abdominal fat significantly impacts the development of T2DM[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Experimental evidence has confirmed that weight loss achieved through dietary interventions can lead to long-term remission in patients with T2DM. The greater the weight loss, the higher the DM remission rate, and weight management helps delay or prevent the onset of clinical complications of DM[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong lifestyle characteristics, place of residence, health insurance, smoking, and work status were also significantly associated with the risk of T2DM. A previous study has found that people living in areas with poor sports facilities have higher rates of obesity and T2DM[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. At the same time, for every interquartile (IQR) increase in community green space, the risk of DM decreases by 21% (HR\u0026thinsp;=\u0026thinsp;0.79, 95%CI: 0.63\u0026ndash;0.99)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Besides, underweight is more common in iodine-sufficient urban areas, while obesity is more prevalent in iodine-deficient suburban regions[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. After adjusting for significant risk factors such as DM, hypothyroidism/hyperthyroidism, phosphorus intake, dairy and seafood consumption, adult women with low dietary iodine intake were associated with a higher risk of T2DM[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Higher placental iodine concentrations are associated with a lower incidence of gestational diabetes mellitus (GDM). It has been confirmed that lower placental iodine load is linked to changes in plasma insulin levels, homeostasis model assessment of insulin resistance (HOMA-IR) index, and β-cell activity[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is evident that various factors related to residential areas, such as sports facilities, green space coverage, and iodine levels, influence people\u0026rsquo;s health to varying degrees, particularly the risk of developing DM. The incidence of acquired immune deficiency syndrome (AIDS) among individuals without health insurance (24%) is significantly higher than that in the insured group (4%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and however, there is no significant difference in the prevalence of diseases such as hypertension, DM, and tuberculosis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Nevertheless, stable medical insurance coverage can help achieve glycemic control and blood pressure management[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, whether one has health insurance directly influences the treatment plan for Graves\u0026rsquo; disease[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The present study found that the risk of T2DM is significantly increased among uninsured individuals. Research indicates that half of the uninsured population in rural and urban areas is under 40 years old, with the rural uninsured population being larger than the urban one[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, health insurance status and place of residence may significantly impact disease treatment and management. Smoking could increase the risk of prediabetes and DM in the general population[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and also raises the likelihood of developing thyroid goiter and nodules requiring hospitalization[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Clinical data indicated that smoking can slightly reduce BMI and alter body composition. The longer the duration of smoking, the higher the waist-to-hip ratio, along with increased visceral and subcutaneous fat[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A decrease in BMI is a risk factor for thyroid tumors[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. At the same time, smoking can reduce thyroid-stimulating hormone[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], stimulate the growth of thyroid tumors[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and have potential anti-estrogenic effects. Estrogen further promotes thyroid tumorigenesis through estrogen receptors or vascular growth factor signaling pathways[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA significant association was observed between goiter and T2DM after adjusting multiple covariates, and goiter has a potential prediction value on T2DM risk, with an AUC of ROC of 0.809. The biological mechanisms under relationship between goiter and T2DM are complex and diverse. Thyroid hormone deficiency (such as hypothyroidism) can reduce the expression and function of glucose transporter 4 (GLUT4) in skeletal muscle and adipose tissue, leading to decreased insulin-mediated glucose uptake and exacerbating IR. Hypothyroidism patients may experience approximately 30% reduction in insulin sensitivity, elevated fasting blood glucose and glycated hemoglobin (HbA1c) levels[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, thyroid enlargement may be associated with IR and hyperinsulinemia[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A study has suggested that patients with T2DM who also have thyroid enlargement may face a 2\u0026ndash;3 times higher risk of cardiovascular events[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. On the other hand, thyroid hormone levels may lead to goiter formation, indicating that thyroid dysfunction may contribute to the pathogenesis of T2DM by affecting insulin sensitivity and β-cell function[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Additionally, both T2DM and thyroid diseases are associated with autoimmune mechanisms. Thyroid enlargement may be a manifestation of autoimmune thyroid disease, with anti-thyroid peroxidase antibodies (TPO-Ab) and anti-thyroglobulin antibodies (TGAb) serving as key biochemical markers of autoimmune responses[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. TPOAb positivity, TGAb, IA-2 antibodies, and lower serum fasting C-peptide levels contribute to the progression of β-cell failure. Autoimmune reactions may simultaneously attack pancreatic and thyroid tissues, damaging pancreatic β-cells and impairing insulin secretion, thereby increasing the risk of developing T2DM[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFindings in the current study, that is, the positive association between goiter and increased T2DM risk, may provide valuable insights for early clinical screening and intervention and implementing preventive measures in T2DM. Thus, routine follow-up for T2DM patients should incorporate thyroid function evaluation, particularly for those presenting with signs of thyroid enlargement, which may achieve more comprehensive health management and better disease control. Furthermore, integrating multiple covariates reinforced the role of goiter as an independent risk factor for T2DM. In fact, lifestyle and genetic factors may interact complexly with goiter and DM, whether incorporating additional biomarkers may further improve predictive accuracy of goiter on T2DM risk, multicenter validation studies would be necessary. Limitations could not be ignored in the results explanation. Due to the cross-sectional study design, sample size constraints and potential selection bias are inescapable, as well as causal association between goiter and T2DM could not be concluded. While the CHNS provides representative data, generalizability may be limited by participant selection criteria, and mechanistic experiments are needed to enhance external validity. Future multi-center studies with larger samples should validate these associations and explore underlying biology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur CHNS analysis confirms a significant goiter-T2DM association, highlighting their population health significance. Clinicians should consider thyroid status in diabetes risk assessment. Further research should elucidate mechanisms and translate findings into improved patient management and prevention strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design was in accordance with the Declaration of Helsinki. The CHNS database has been approved by the relevant ethical review boards, and the participants have given informed consent. Ethical approval was not required because of the public characteristics of the data of this database.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are publicly available summary statistics. Data used can be obtained upon a reasonable request to the corresponding author.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have stated that they have no competing interest.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Key Project of Natural Science of Bengbu Medical University(2024byzd430) .\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJS conceived the study. JS, XH, KL, FZ and DY collected the data and wrote the manuscript. XZ and TH performed statistical analysis. JS participated in the revising of a manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\n\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDean DS, Gharib H. Epidemiology of thyroid nodules. Best Pract Res Clin Endocrinol Metab. 2008. 22(6): 901-11.\u003c/li\u003e\n\u003cli\u003eBaloch ZW, Asa SL, Barletta JA, et al. Overview of the 2022 WHO Classification of Thyroid Neoplasms. Endocr Pathol. 2022. 33(1): 27-63.\u003c/li\u003e\n\u003cli\u003eKnobel M. Etiopathology, clinical features, and treatment of diffuse and multinodular nontoxic goiters. J Endocrinol Invest. 2016. 39(4): 357-73.\u003c/li\u003e\n\u003cli\u003eBrix TH, Heged\u0026uuml;s L. Genetic and environmental factors in the aetiology of simple goitre. Ann Med. 2000. 32(3): 153-6.\u003c/li\u003e\n\u003cli\u003eAydoğan Y, Altay M, \u0026Uuml;nsal O, et al. 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Diabetes Res Clin Pract. 2008. 80(1): 114-21.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"T2DM, CHNS, Goiter, Receiver Operating Characteristic","lastPublishedDoi":"10.21203/rs.3.rs-7194919/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7194919/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e\u003cp\u003eThe comorbidity of multiple diseases is receiving growing attention, and the coexistence of goiter and type 2 diabetes mellitus (T2DM) is an important one. Thus, this study aims to utilize the widely representative China Health and Nutrition Survey (CHNS) database for relevant investigations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eCovariates had potential association with T2DM were obtained from the database. Univariate and multivariate logistic regression models were used to evaluate the association of goiter with T2DM. Additionally, the Receiver Operating Characteristic (ROC) and smooth curves were drawn for investigating the linear correlation between goiter and T2DM, as well as the potential prediction value of goiter on T2DM risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 10,148 eligible participants, 2,109 had T2DM and 155 had goiter. After adjusting for covariates, a positive association between goiter and increased risk of T2DM was observed (odds ratio [OR]\u0026thinsp;=\u0026thinsp;2.62, 95% confidence interval [CI]: 1.29\u0026ndash;4.81). The smooth curve showed that there is a nonlinear association between goiter and T2DM. Additionally, there was a potential prediction value of goiter on the risk of T2DM, with the area under the curve (AUC) of ROC curve of 0.809.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study identified an association between goiter and T2DM, and however, the causal relationship between them and specific mechanisms needs further clarification.\u003c/p\u003e","manuscriptTitle":"An Exploration of the Association between Goiter and Type 2 Diabetes Leveraging the CHNS Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 08:04:01","doi":"10.21203/rs.3.rs-7194919/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":"a8bffaa2-e432-49a6-b8e4-f8243bade439","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T08:57:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 08:04:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7194919","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7194919","identity":"rs-7194919","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}