Age-Related Enhancement of HPT Axis Sensitivity to Thyroid Hormones Protects Metabolic and Cardiovascular Health

Age-Related Enhancement of HPT Axis Sensitivity to Thyroid Hormones Protects Metabolic and Cardiovascular Health | 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 Age-Related Enhancement of HPT Axis Sensitivity to Thyroid Hormones Protects Metabolic and Cardiovascular Health Lei Zhao, Runqing Mu, Xin Zhang, Shuo Wang, Min Zhao, Hong Shang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6780346/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The regulation and secretion of hormones in the endocrine system change with aging, including a decline in serum free triiodothyronine (fT3) and an increase in thyroid-stimulating hormone (TSH) within their reference ranges. While these changes may influence age-related disease risks, the role of hypothalamic-pituitary-thyroid (HPT) axis sensitivity in metabolic and cardiovascular health remains unclear. Methods This study enrolled 13,646 participants (6,221 males, 7,425 females). Metabolic (BMI, SUA, FPG, lipoproteins) and cardiovascular (ECG, CK-MB, NT-proBNP) indices were measured. HPT sensitivity was quantified using Thyroid Feedback Quantile-Based Index (TFQI). Multivariable logistic regression adjusted for age (per 10-year increment), sex, and outcome-specific confounders (BMI, SUA, FPG, non-HDL for ST-segment analysis). Nonlinear relationships between age and TFQI were analyzed using restricted cubic splines (RCS). Result Metabolic indicators were generally elevated in participants with decreased HPT axis sensitivity to fT3 (Q4) compared to the reference group (Q1). Restricted cubic spline analysis revealed a significant age-dependent decline in TFQI-fT3 values, with an inflection point at 48 years (p 48 years) (OR 0.79, 95% CI 0.65–0.97, p = 0.023), whereas no significant association was observed in younger adults (≤ 48 years) (all p > 0.05). For cardiovascular outcomes, higher fT3 sensitivity was significantly associated with reduced risk of abnormal ST segments in both age groups (younger: OR 0.57, 95% CI 0.32–0.97, p = 0.046; older: OR 0.68, 95% CI 0.48–0.95, p = 0.027). Conversely, decreased fT4 sensitivity (TFQI-fT4 Q4) specifically increased ST-segment risk in older adults (OR 1.43, 95% CI 1.04–1.96, p = 0.025), with no significant effect in younger adults (OR 1.11, 95% CI 0.65–1.82, p = 0.693). Conclusion Enhanced HPT axis sensitivity to fT3 increased with age (inflection at 48 years) and was associated with reduced risks of metabolic syndrome (in > 48-year-olds) and ST-segment abnormalities (all adults). These findings suggest age-specific TH regulation influences metabolic and cardiovascular health, warranting further research into underlying mechanisms. HPT axis sensitivity Thyroid Feedback Quantile-Based Index (TFQI) Metabolic syndrome (MetS) Cardiovascular risk Aging Figures Figure 1 Figure 2 Introduction Thyroid hormones play a pivotal role in regulating metabolism and the cardiovascular system. Abnormal levels of triiodothyronine (T3) and thyroxine (T4) are linked to type 2 diabetes (T2DM), dyslipidemia, nonalcoholic fatty liver disease (NAFLD), and atrial fibrillation[1-4]. Emerging evidence suggests that even within reference ranges, variations in thyroid hormone levels associate with metabolic and cardiovascular impairments[5, 6]. The Penn Heart Study demonstrated that individuals with high-normal thyroid-stimulating hormone (TSH) (4.51-19.99 mIU/L) and normal FT4 had an increased risk of atrial fibrillation (AF) compared to those with euthyroidism (HR 1.82, 95% CI 1.27-2.61)[7], highlighting the clinical relevance of subtle thyroid alterations. Thyroid function varies with age. Aging population often exhibit decreased free serum T3 levels and elevated serum TSH levels, reflecting age-related changes in thyroid regulation[8]. Among western populations aged ≥70 years, the prevalence of TSH levels of 4.5 mIU/L or higher is approximately 15%[9], and this prevalence reaches 20%[10] in the Chinese population. Meta-analyses indicate that significantly elevated TSH levels (10 mIU/L or greater) in the elderly are associated with cardiovascular morbidity and all-cause mortality[11, 12]. However, moderately elevated TSH levels (4.5-7.0 mIU/L) have not shown a statistically significant impact on aging and disease development[13, 14]. This paradox suggests that conventional thyroid markers may not fully capture age-related physiological adaptations. The hypothalamic-pituitary-thyroid (HPT) axis maintains thyroid hormonal balance through feedback loops. Analyzing combined indices rather than single measurements can provide new insights into thyroid function and metabolism. Central and peripheral thyroid sensitivity can be reflected by various indicators. However, existing studies on HPT sensitivity present three critical gaps: (1) inconsistent findings across euthyroid and hypothyroid populations[15-17]; (2) limited data on age-dependent sensitivity changes; and (3) unclear cardiovascular implications. To address these unclear questions, we conducted a cross-sectional analysis of 13,730 adults (aged 18-92 years), systematically evaluating how HPT sensitivity to thyroid hormones varies with age and associates with metabolic/cardiovascular risk stratification. Methods Study Population This study employed a whole cluster, stratified random sampling method to collect 13,730 participants from six geographically diverse regions in China between January- July 2014. The age and sex composition and the urban/rural ratio of each community were determined by using the 2010 national census data of China[18]. Inclusion criteria comprised: (i) subjects who were not pregnant; (ii) no administration of iodine-containing substances (including drugs and contrast media) within 3 months prior to enrollment and no current thyroid-directed pharmacotherapy; and (iii) no diagnosed developmental/psychiatric disorders. Fasting blood and spot urine samples were collected from each participant. Serum was obtained by centrifugation of the blood samples and stored at 4℃ prior to testing in the central laboratories of their respective city (Shanghai, Beijing, Guangzhou, Chengdu, Xi’an, and Shenyang). Participants underwent thyroid and liver ultrasonography for NAFLD, as well as an electrocardiogram (ECG) performed by trained and evaluated observers. Laboratory Measurements fT4, fT3, TSH, TPOAb, and TgAb levels were measured using an electrochemi-luminescence immunoassay (Cobas e601/E170, Roche Diagnostic, USA) with manufacturer-defined reference ranges: fT3(3.1-6.8pmol/L), fT4(12.0-22.0pmol/L), TSH (0.27–4.2 mIU/L), TPOAb (<34.0 IU/mL), and TgAb (115.0 IU/mL). Lipid profiles, including serum triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and serum uric acid (SUA) were measured by using Roche Modular automatic biochemical analyzer (Roche Diagnostics). Diagnostic criteria for metabolism and cardiovascular disorders are detailed in supplementary table 1[19-26]. Cardiovascular risk was quantified using Framingham Risk Score (FRS) incorporating age, sex, lipids, blood pressure, and smoking status (defined as >20 cigarettes/day)[27]. Smoking status was assessed using a detailed questionnaire, defining regular smokers as those smoking more than 20 cigarettes per day. HPT Sensitivity Indices Three indices were calculated. Firstly, the thyrotropin T4 resistance index (TT4RI) was derived by multiplying FT4 (pmol/L) with TSH (mIU/L). Secondly, the TSH index (TSHI) was determined using the following formula, TSH (mIU/L) + 0.1345 · FT4 (pmol/L). Lastly, the thyroid feedback quantile-based index (TFQI) is a new index calculated using the cumulative distribution function based on fT3 or fT4 and TSH[28]. The indices, TFQI, TSHI, and TT4RI, serve as indicators of central thyroid hormone sensitivity, providing insights into the HPT axis's response to peripheral thyroid hormones[29]. Statistical Analysis All statistical analyses were performed using R software, version 4.2.2, and MSTATA software. Continuous variables were reported as mean±SD or median (IQR) based on distribution normality. Multivariable logistic regression analysis was adjusted for age (per 10-year increment), sex, BMI, and outcome-specific covariates (e.g., SUA/FPG for metabolic outcomes). Nonlinear relationships between age and TFQI were examined using a linear regression model with Restricted cubic splines (RCS). By age (≤48 vs >48 years) based on RCS-derived inflection points, the data were divided into two distinct segments based on this inflection point. Ethical Approval The study protocols were approved by the Medical Ethics Committee of China Medical University. All participants provided informed consent before participating in the study. Results Clinical characteristics of subjects The study enrolled 13,730 participants. After excluding 84 individuals due to incomplete information on thyroid function data, the final sample size was 13,646 participants. The participants had a mean age of 47 ± 15 years (range: 18-92 years), with 6,221 men and 7,425 women. The median (IQR) levels of FT3, FT4, and TSH were 4.84 (IQR: 4.45-5.28) pmol/L, 16.35 (IQR:15.0-17.8) pmol/L and 2.18 (IQR: 1.48-3.13) mIU/L, respectively. Mean (SD) values for BMI, UA, FPG, and non-HDL-C were 24.0 (3.7), 316 (89), 5.41 (1.25), and 3.41 (1.01). For CK, CK-MB, and NT-proBNP, the mean (SD) values were 125 (741), 1.64 (1.39), and 60 (149). Detailed diagnostic criteria for metabolism and cardiovascular disorders are provided in Supplementary Table 1. Participants were divided into four groups based on their HPT sensitivity IQR. Table 1 presents the clinical features of subjects in different HPT sensitivity groups. Age, sex, metabolic, and cardiovascular indicators were included. TFQI values range from -1 and 1, with positive values indicating poor thyroid hormone sensitivity, 0 indicating normal thyroid hormone sensitivity, and negative values indicating good sensitivity. Higher TSHI and TT4RI values correspond to lower central sensitivity to thyroid hormone. Individuals with lower HPT sensitivity (Q4) were younger and had a lower proportion of females compared to those with higher sensitivity (Q1). Metabolic indicators generally increased with decreased HPT sensitivity to fT3 (Q4) compared to normal or reduced sensitivity (Q1), a trend not observed for fT4 sensitivity. Age-Related Changes in HPT Sensitivity Age appears to correlate with thyroid hormone sensitivity (Table 1). To further explore this relationship, we analyzed thyroid hormone sensitivity across different age groups (Table 2). Results indicate that TFQI fT3 significantly decreases with age, while TFQI fT4 shows a similar but less pronounced trend. Trends for TT4RI and TSHI associated with age were unclear. We employed RCS analysis to examine the nonlinear association between age and TFQI fT3 , adjusting for sex and race (Figure 1). RCS analysis demonstrated a gradual decrease in TFQI fT3 levels with increasing age. HPT Sensitivity and metabolic and cardiovascular disorders To analyze the impact of TFQI on metabolic and cardiovascular disorders, we used logistic regression for multivariable analysis in adults. Table 3 shows that in the older adults (aged over 48 years), increased HPT sensitivity to fT3 (TFQI-fT3 Q1) is a protective factor against MetS (OR 0.79, p=0.023). However, this trend was not statistically significant in the adult population or for HPT sensitivity to fT4. Table 4 reveals that increased HPT sensitivity to fT3 (TFQI-fT3 Q1) is significantly associated with a decreased risk of abnormal ST segment in both younger adults (OR 0.57, p=0.046) and older adults (aged over 48 years) (OR 0.68, p=0.027). In contrast, while increased HPT sensitivity to fT4 (TFQI-fT4 Q1) was not significantly associated, decreased HPT sensitivity to fT4 (TFQI-fT4 Q4) was a risk factor for abnormal ST segment in adults (OR 1.43, p=0.025). The RCS analysis, adjusting for the effects of Age and Sex suggested a non-linear association of TFQI fT4 and TFQI fT3 with MetS and abnormal ST (Figure 2). Discussion Our population-based study reveals a clinically significant age-dependent enhancement in HPT axis sensitivity to fT3, with distinct metabolic and cardiovascular implications. Three key findings were promoted: (1) TFQI-fT3 demonstrates a nonlinear decline with age (inflection at 48 years); (2) Increased fT3 sensitivity associates with reduced MetS and ST-abnormality risks in adults >48 years; (3) This protective pattern exhibits hormone specificity, as fT4 sensitivity showed opposing cardiovascular effects. These observations extend current understanding of thyroid aging by integrating HPT axis dynamics with clinical outcomes. Circulating thyroid hormones are synthesized and released by the thyroid gland, regulated by the HPT axis. The HPT axis regulates thyroid hormone homeostasis through a classic feedback loop including hypothalamic TRH stimulates pituitary TSH secretion, which promotes thyroidal release of T4 and T3[30]. T3's activity is about ten times higher than T4's, and T4 can be converted to T3 by deiodinases, DIO1 and DIO2, in extra-thyroidal tissues. DIO1, primarily active in the liver and kidneys, generates 15-20% of the total circulating T3[31], while DIO2, found in brown adipose tissue, the pituitary gland, the brain, and the heart, is responsible for the majority of T3 production[32]. Thyroid hormones regulate metabolic processes and maintain homeostasis within their normal range. They significantly impact basal metabolic rate, glucose absorption, lipolysis, and serum cholesterol levels[33-35]. These hormones also affect the cardiovascular system by modulating endothelial cell function, heart rate, and myocardial oxygen consumption[36]. Clinical thyroid dysfunctions, such as hyperthyroidism and hypothyroidism, are strongly associated with obesity, dyslipidemia, atrial fibrillation, and other CVDs[37, 38]. Subclinical thyroid conditions, often without overt symptoms, also carry significant risks for CVD and metabolic disorders. Studies show that subclinical hypothyroidism is associated with vascular abnormalities like increased systemic vascular resistance and altered endothelial-mediated vasorelaxation and vascular compliance[39, 40]. Subsequent evidence links impaired sensitivity to hyperuricemia, abdominal fat, hypertension, and diabetes [29, 41-43]. Our results corroborate Laclaustra et al. (2019) report linking impaired thyroid sensitivity (higher TFQI) to metabolic dysfunction[28], but uniquely identify an age-dependent protective effect of fT3 sensitivity. The lower MetS risk (OR 0.79) in high-sensitivity older adults (>48 years) may reflect optimized energy homeostasis during aging. Which is the hypothesis supported by animal studies showing DIO2 upregulation preserves cardiac function in aged mice[44]. The age-related TSH rise with stable/falling T3 levels has been interpreted variably - as subclinical hypothyroidism or adaptive response [45, 46]. Recent studies found that the set point of the HPT axis and peripheral sensitivity to thyroid hormones vary across different age groups[28]. In our study, TFQI fT3 significantly decreases with increasing age, indicating that normal low levels of FT3 are more likely to cause elevated TSH during aging, indirectly explaining higher TSH levels in the old adults. Our RCS-derived inflection point (48 years) suggests midlife may mark a critical transition in thyroid regulation. Mild evaluated TSH levels are considered a sign of healthy aging in some studies. For example, patients aged 85–89 with elevated TSH had lower mortality (HR 0.77, 95% CI 0.63–0.94)[47], and patients aged 70–79 with TSH values between 4.5 and 7 mIU/L showed improved mobility[48]. Rakov et al. revealed that aging induces organ-specific hypersensitivity to T4 despite systemic T3 decline, suggesting central and peripheral mechanisms may compensate for reduced thyroid output[49]. Our TFQI-fT3 findings extend this pattern by showing enhanced central sensitivity to fT3 specifically protects metabolic and cardiovascular health in older adults. In the older adults (aged over 48 years), increased HPT sensitivity to fT3 (TFQI-fT3 Q1) is protective against MetS (OR 0.79, p=0.023) and decreases the risk of abnormal ST segments. Subclinical hypothyroidism and its risk of CVDs are controversial, especially in the elderly. Some studies show that treating persistent subclinical hypothyroidism (TSH 5·0-10·0 mIU/L) reduces cardiovascular events in adults, but not in those over 70[50]. Our results suggest that higher HPT axis sensitivity to fT3 protects against metabolic and cardiovascular injuries, providing some evidence for considering therapeutic intervention for subclinical hypothyroidism in the elderly. The main strengths of our study lie in (1) Nationally representative sampling (n=13,646) with standardized sample collection; (2) Novel application of TFQI to characterize aging HPT sensitivity changes and its association with metabolic/cardiovascular outcomes. However, our study has limitations. Multicenter investigations across different regions face potential impacts from geographic, environmental, and dietary factors, which may influence disease outcomes. Additionally, as a cross-sectional survey, this study cannot determine the precise underlying mechanism linking aging, thyroid function, metabolic disorders, and CVD risk. Future prospective studies are needed. Declarations Ethics approval and consent to participate The study protocols were approved by the Medical Ethics Committee of China Medical University. All participants provided informed consent before participating in the study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Information This work was supported by the National Key Technologies R&D Program provided by Ministry of Science and Technology of the People's Republic of China (Project Grant # 2022YFC3602300, Sub-project Grant # 2022YFC3602302), China postdoctoral Science Foundation (Grant # 2022YFC3602300, Grant # 2024T171044). Authors’ Contributions Author 1 (Hong Shang): Supervision (lead); project administration (lead); writing- review and editing (equal).Author 2 (Min Zhao): Supervision (lead); project administration (lead); writing-review and editing (equal).Author 3 (Lei Zhao): Formal analysis (lead); writing-original draft (lead); writing - review and editing (equal).Author 4 (Runqing Mu): Data curation (lead); Formal analysis (supporting); writing - review and editing (equal).Author 5 (Shou Wang): Data curation (supporting); formal analysis (supporting); review and editing (equal).Author 6 (Xin Zhang): Data curation (supporting); formal analysis (supporting); review and editing (equal). Acknowledgements The authors sincerely appreciate the continuous support, assistance, and cooperation from the sub-centers of China Consortium of Reference Intervals project (CHINRIP), including: 1. Department of Laboratory Science, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong, China. 2. Department of Clinical Laboratory Medicine, Zhongshan Hospital of Fudan University, Shanghai, China. 3. Department of Laboratory Medicine, Peking University Third Hospital, Beijing, China. 4. National Center for Clinical Laboratories, Beijing Hospital, Beijing, China. 5. Department of Laboratory medicine, West China Hospital, Sichuan University, Sichuan, China. 6. Department of Clinical Laboratory Medicine, Xijing Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, China. The authors also extend their gratitude to all other participants for their contributions to this study. References Biondi B, Kahaly GJ, Robertson RP. Thyroid Dysfunction and Diabetes Mellitus: Two Closely Associated Disorders. Endocr Rev. 2019;40(3):789–824. Hatziagelaki E, Paschou SA, Schön M, Psaltopoulou T, Roden M. NAFLD and thyroid function: pathophysiological and therapeutic considerations. Trends Endocrinol Metab. 2022;33(11):755–68. Mavromati M, Jornayvaz FR. Hypothyroidism-Associated Dyslipidemia: Potential Molecular Mechanisms Leading to NAFLD. Int J Mol Sci 2021, 22(23). Baumgartner C, da Costa BR, Collet TH, Feller M, Floriani C, Bauer DC, Cappola AR, Heckbert SR, Ceresini G, Gussekloo J, et al. Thyroid Function Within the Normal Range, Subclinical Hypothyroidism, and the Risk of Atrial Fibrillation. Circulation. 2017;136(22):2100–16. Manolis AA, Manolis TA, Melita H, Manolis AS. Subclinical thyroid dysfunction and cardiovascular consequences: An alarming wake-up call? Trends Cardiovasc Med. 2020;30(2):57–69. Floriani C, Gencer B, Collet TH, Rodondi N. Subclinical thyroid dysfunction and cardiovascular diseases: 2016 update. Eur Heart J. 2018;39(7):503–7. Kannan L, Shaw PA, Morley MP, Brandimarto J, Fang JC, Sweitzer NK, Cappola TP, Cappola AR. Thyroid Dysfunction in Heart Failure and Cardiovascular Outcomes. Circulation Heart Fail. 2018;11(12):e005266. Gauthier BR, Sola-García A, Cáliz-Molina M, Lorenzo PI, Cobo-Vuilleumier N, Capilla-González V, Martin-Montalvo A. Thyroid hormones in diabetes, cancer, and aging. Aging Cell. 2020;19(11):e13260. Vadiveloo T, Donnan PT, Murphy MJ, Leese GP. Age- and gender-specific TSH reference intervals in people with no obvious thyroid disease in Tayside, Scotland: the Thyroid Epidemiology, Audit, and Research Study (TEARS). J Clin Endocrinol Metab. 2013;98(3):1147–53. Zhao L, Teng D, Shi X, Li Y, Ba J, Chen B, Du J, He L, Lai X, Li Y, et al. The Effect of Increased Iodine Intake on Serum Thyrotropin: A Cross-Sectional, Chinese Nationwide Study. Thyroid: official J Am Thyroid Association. 2020;30(12):1810–9. Gencer B, Collet TH, Virgini V, Bauer DC, Gussekloo J, Cappola AR, Nanchen D, den Elzen WP, Balmer P, Luben RN, et al. Subclinical thyroid dysfunction and the risk of heart failure events: an individual participant data analysis from 6 prospective cohorts. Circulation. 2012;126(9):1040–9. Ning Y, Cheng YJ, Liu LJ, Sara JD, Cao ZY, Zheng WP, Zhang TS, Han HJ, Yang ZY, Zhang Y, et al. What is the association of hypothyroidism with risks of cardiovascular events and mortality? A meta-analysis of 55 cohort studies involving 1,898,314 participants. BMC Med. 2017;15(1):21. Ojala AK, Schalin-Jäntti C, Pitkälä KH, Tilvis RS, Strandberg TE. Serum thyroid-stimulating hormone and cognition in older people. Age Ageing. 2016;45(1):155–7. Takamura N, Hayashida N, Maeda T. Risk of coronary heart disease and mortality for adults with subclinical hypothyroidism. JAMA. 2010;304(22):2481–2. author reply 2482. Nie X, Ma X, Xu Y, Shen Y, Wang Y, Bao Y. Increased Serum Adipocyte Fatty Acid-Binding Protein Levels Are Associated with Decreased Sensitivity to Thyroid Hormones in the Euthyroid Population. Thyroid: official J Am Thyroid Association. 2020;30(12):1718–23. Sun Y, Teng D, Zhao L, Shi X, Li Y, Shan Z, Teng W. Impaired Sensitivity to Thyroid Hormones Is Associated with Hyperuricemia, Obesity, and Cardiovascular Disease Risk in Subjects with Subclinical Hypothyroidism. Thyroid: official J Am Thyroid Association. 2022;32(4):376–84. Lai S, Li J, Wang Z, Wang W, Guan H. Sensitivity to Thyroid Hormone Indices Are Closely Associated With NAFLD. Front Endocrinol. 2021;12:766419. China. Totpcotpsro 2010 National Bureau of statistics of the People’s Republic of China. China, NHPSotPsRo. WS/T 428-2013 Criteria of weight for adults http://wwwnhcgovcn/ewebeditor/uploadfile/2013/08/20130808135715967 pdf 2013. Chinese Society of Endocrinrinology CMA, Li C. Guideline for the diagnosis and management of hyperuricemia and gout in China(2019). Chin J Endocrinol Metabolism. 2020;36(1):13. Berberich AJ, Hegele RA. A Modern Approach to Dyslipidemia. Endocr Rev. 2022;43(4):611–53. Genuth S, Alberti KG, Bennett P, Buse J, Defronzo R, Kahn R, Kitzmiller J, Knowler WC, Lebovitz H, Lernmark A, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care. 2003;26(11):3160–7. Narkiewicz K. Diagnosis and management of hypertension in obesity. Obes reviews: official J Int Association Study Obes. 2006;7(2):155–62. FitzGerald JD, Dalbeth N, Mikuls T, Brignardello-Petersen R, Guyatt G, Abeles AM, Gelber AC, Harrold LR, Khanna D, King C, et al. 2020 American College of Rheumatology Guideline for the Management of Gout. Arthritis Care Res. 2020;72(6):744–60. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SC. Jr. et al: Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735–52. Sharma S, Drezner JA, Baggish A, Papadakis M, Wilson MG, Prutkin JM, La Gerche A, Ackerman MJ, Borjesson M, Salerno JC, et al. International Recommendations for Electrocardiographic Interpretation in Athletes. J Am Coll Cardiol. 2017;69(8):1057–75. Sud M, Sivaswamy A, Chu A, Austin PC, Anderson TJ, Naimark DMJ, Farkouh ME, Lee DS, Roifman I, Thanassoulis G, et al. Population-Based Recalibration of the Framingham Risk Score and Pooled Cohort Equations. J Am Coll Cardiol. 2022;80(14):1330–42. Laclaustra M, Moreno-Franco B, Lou-Bonafonte JM, Mateo-Gallego R, Casasnovas JA, Guallar-Castillon P, Cenarro A, Civeira F. Impaired Sensitivity to Thyroid Hormones Is Associated With Diabetes and Metabolic Syndrome. Diabetes Care. 2019;42(2):303–10. Sun H, Zhu W, Liu J, An Y, Wang Y, Wang G. Reduced Sensitivity to Thyroid Hormones Is Associated With High Remnant Cholesterol Levels in Chinese Euthyroid Adults. J Clin Endocrinol Metab. 2022;108(1):166–74. Jabbar A, Pingitore A, Pearce SH, Zaman A, Iervasi G, Razvi S. Thyroid hormones and cardiovascular disease. Nat reviews Cardiol. 2017;14(1):39–55. Berry MJ, Banu L, Larsen PR. Type I iodothyronine deiodinase is a selenocysteine-containing enzyme. Nature. 1991;349(6308):438–40. Moreno JC, Klootwijk W, van Toor H, Pinto G, D'Alessandro M, Lèger A, Goudie D, Polak M, Grüters A, Visser TJ. Mutations in the iodotyrosine deiodinase gene and hypothyroidism. N Engl J Med. 2008;358(17):1811–8. Oppenheimer JH, Schwartz HL, Lane JT, Thompson MP. Functional relationship of thyroid hormone-induced lipogenesis, lipolysis, and thermogenesis in the rat. J Clin Investig. 1991;87(1):125–32. Cheng SY, Leonard JL, Davis PJ. Molecular aspects of thyroid hormone actions. Endocr Rev. 2010;31(2):139–70. Mottis A, Mouchiroud L, Auwerx J. Emerging roles of the corepressors NCoR1 and SMRT in homeostasis. Genes Dev. 2013;27(8):819–35. Razvi S, Jabbar A, Pingitore A, Danzi S, Biondi B, Klein I, Peeters R, Zaman A, Iervasi G. Thyroid Hormones and Cardiovascular Function and Diseases. J Am Coll Cardiol. 2018;71(16):1781–96. Liu J, Wu G, Li S, Cheng L, Ye X. The genetic association between hyperthyroidism and heart failure: a Mendelian randomization study. Front Endocrinol. 2024;15:1344282. Kuczaj A, Warwas S, Danel A, Przybyłowski P, Hrapkowicz T. Thyroid Dysfunction in Advanced Heart Failure Patients and Its Correlation with Amiodarone Therapy: A Two-Year Study. Biomedicines 2024, 12(3). Chen X, Zhang N, Cai Y, Shi J. Evaluation of left ventricular diastolic function using tissue Doppler echocardiography and conventional doppler echocardiography in patients with subclinical hypothyroidism aged < 60 years: a meta-analysis. J Cardiol. 2013;61(1):8–15. Owen PJ, Sabit R, Lazarus JH. Thyroid disease and vascular function. Thyroid: official J Am Thyroid Association. 2007;17(6):519–24. Mehran L, Delbari N, Amouzegar A, Hasheminia M, Tohidi M, Azizi F. Reduced Sensitivity to Thyroid Hormone Is Associated with Diabetes and Hypertension. J Clin Endocrinol Metab. 2022;107(1):167–76. Lu Y, Wang J, An Y, Liu J, Wang Y, Wang G, Leng S. Impaired sensitivity to thyroid hormones is associated with hyperuricemia in a Chinese euthyroid population. Front Endocrinol. 2023;14:1132543. Cao B, Li K, Ke J, Zhao D. Impaired Sensitivity to Thyroid Hormones Is Associated With the Change of Abdominal Fat in Euthyroid Type 2 Diabetes Patients: A Retrospective Cohort Study. Journal of diabetes research 2024, 2024:8462987. Brent GA. Mechanisms of thyroid hormone action. J Clin Investig. 2012;122(9):3035–43. Hollowell JG, Staehling NW, Flanders WD, Hannon WH, Gunter EW, Spencer CA, Braverman LE, Serum TSH. T(4), and thyroid antibodies in the United States population (1988 to 1994): National Health and Nutrition Examination Survey (NHANES III). J Clin Endocrinol Metab. 2002;87(2):489–99. Bremner AP, Feddema P, Leedman PJ, Brown SJ, Beilby JP, Lim EM, Wilson SG, O'Leary PC, Walsh JP. Age-related changes in thyroid function: a longitudinal study of a community-based cohort. J Clin Endocrinol Metab. 2012;97(5):1554–62. Gussekloo J, van Exel E, de Craen AJ, Meinders AE, Frölich M, Westendorp RG. Thyroid status, disability and cognitive function, and survival in old age. JAMA. 2004;292(21):2591–9. Du Puy RS, Poortvliet RKE, Mooijaart SP, den Elzen WPJ, Jagger C, Pearce SHS, Arai Y, Hirose N, Teh R, Menzies O, et al. Outcomes of Thyroid Dysfunction in People Aged Eighty Years and Older: An Individual Patient Data Meta-Analysis of Four Prospective Studies (Towards Understanding Longitudinal International Older People Studies Consortium). Thyroid: official J Am Thyroid Association. 2021;31(4):552–62. Rakov H, De Angelis M, Renko K, Hönes GS, Zwanziger D, Moeller LC, Schramm KW, Führer D. Aging Is Associated with Low Thyroid State and Organ-Specific Sensitivity to Thyroxine. Thyroid: official J Am Thyroid Association. 2019;29(12):1723–33. Razvi S, Weaver JU, Butler TJ, Pearce SH. Levothyroxine treatment of subclinical hypothyroidism, fatal and nonfatal cardiovascular events, and mortality. Arch Intern Med. 2012;172(10):811–7. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(Suppl 1):S62–69. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr., Jones DW, Materson BJ, Oparil S, Wright JT Jr, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–72. Cotter TG, Rinella M. Nonalcoholic Fatty Liver Disease 2020: The State of the Disease. Gastroenterology. 2020;158(7):1851–64. Tables Table 1: Clinical Features of Subjects in Groups with Different Sensitivity to Thyroid Hormones. TFQI FT3 1 TFQI FT4 2 Q1 (N=3697) Q2 (N=3058) Q3 (N=3316) Q4 (N=3575) P-value Q1 (N=3181) Q2 (N=3487) Q3 (N=3630) Q4 (N=3348) P-value Age(mean±SD) 49.81 ± 14.95 48.46 ± 14.69 47.05 ± 14.75 44.25 ± 14.80 <.001 48.90 ± 14.65 48.31 ± 14.46 47.33 ± 14.81 45.03 ± 15.59 <.001 Sex (%) male 1126 (30.5%) 1232 (40.3%) 1699 (51.2%) 2164 (60.5%) <.001 1292 (40.6%) 1471 (42.2%) 1687 (46.5%) 1771 (52.9%) <.001 female 2571 (69.5%) 1826 (59.7%) 1617 (48.8%) 1411 (39.5%) 1889 (59.4%) 2016 (57.8%) 1943 (53.5%) 1577 (47.1%) metabolism index(mean±SD) BMI 23.38 ± 3.64 23.92 ± 3.66 24.11 ± 3.60 24.61 ± 3.83 <.001 24.10 ± 3.72 24.18 ± 3.72 23.92 ± 3.70 23.80 ± 3.70 <.001 WHR 0.85 ± 0.08 0.86 ± 0.08 0.87 ± 0.07 0.89 ± 0.76 <.001 0.86 ± 0.07 0.86 ± 0.09 0.86 ± 0.16 0.87 ± 0.77 0.649 FPG 5.41 ± 1.38 5.39 ± 1.16 5.45 ± 1.30 5.41 ± 1.15 0.275 5.34 ± 1.11 5.43 ± 1.24 5.41 ± 1.22 5.46 ± 1.41 0.001 SUA 296 ± 85 310 ± 87 320 ± 90 335 ± 90 <.001 308.35 ± 91.83 311.25 ± 88.26 315.32 ± 88.58 327.27 ± 88.27 <.001 TG 1.30 ± 1.14 1.40 ± 1.24 1.46 ± 1.20 1.54 ± 1.15 <.001 1.42 ± 1.31 1.45 ± 1.28 1.42 ± 1.10 1.41 ± 1.04 0.431 TC 4.97 ± 1.03 4.90 ± 1.02 4.84 ± 0.98 4.75 ± 0.96 <.001 4.90 ± 1.04 4.90 ± 1.03 4.86 ± 0.98 4.80 ± 0.95 <.001 HDL 1.55 ± 0.40 1.46 ± 0.38 1.43 ± 0.39 1.37 ± 0.37 <.001 1.46 ± 0.38 1.45 ± 0.38 1.46 ± 0.40 1.45 ± 0.39 0.336 LDL 3.04 ± 0.91 3.03 ± 0.90 2.99 ± 0.88 2.94 ± 0.85 <.001 3.03 ± 0.92 3.04 ± 0.91 2.99 ± 0.87 2.94 ± 0.85 <.001 non-HDL 3.35 ± 0.98 3.39 ± 1.01 3.45 ± 1.03 3.45 ± 1.00 <.001 3.36 ± 1.02 3.41 ± 1.01 3.41 ± 1.01 3.45 ± 0.98 0.013 SBP 124± 19 125 ± 21 126 ± 17 126 ± 17 <.001 125 ± 18 126 ± 20 126 ± 18 126 ± 18 0.017 DBP 76 ± 10 77 ± 10 78 ± 10 78 ± 10 <.001 77 ± 11 78 ± 11 78 ± 11 78 ± 11 0.002 Cardiovascular Index(mean±SD) HR 75 ± 10 76 ± 11 76 ± 11 76 ± 11 <.001 75 ± 10 76 ± 11 76 ± 11 76 ± 11 <.001 CK 133.62 ± 1362.59 111.14 ± 118.18 121.77 ± 185.37 131.32 ± 364.34 0.595 132.46 ± 1048.62 112.97 ± 116.22 132.73 ± 992.94 122.47 ± 331.73 0.643 CK-MB 1.59 ± 1.06 1.63 ± 1.35 1.69 ± 1.25 1.66 ± 1.79 0.015 1.77 ± 1.23 1.66 ± 1.37 1.65 ± 1.82 1.49 ± 0.95 <.001 NT-proBNP 73.90 ± 215.92 61.13 ± 115.80 53.63 ± 95.43 49.72 ± 125.73 <.001 60.60 ± 115.96 56.51 ± 105.45 59.10 ± 143.60 63.12 ± 207.96 0.313 hs-cTNI 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.01 0.787 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.01 0.00 ± 0.01 0.893 1: TFQI FT3 : Thyroid feedback quantile-based index by fT3 is calculated as cumulative distribution function (cdf) fT3-(1-cdf TSH). 2: TFQI FT4 : Thyroid feedback quantile-based index by fT4 is calculated as cumulative distribution function (cdf) fT4-(1-cdf TSH). The population was divided into four parts based on the quartile ranges of TFQI. Abbreviations: BMI, body mass index; WHR, waist hip rate; FPG, fasting plasma glucose; SUA, serum uric acid; TG, triglyceride; TG, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; CK, creatine kinase; NT-proBNP, N-Terminal pro-brain natriuretic peptide; hs-cTNI, highly sensitive cardiac troponin I. Table 2: Sensitivity to Thyroid Hormones indexes in different age groups. Age group (years) p-value 18-29 30-39 40-49 50-59 60-69 70-79 ≥80 N = 2,122 N = 2,403 N = 2,742 N = 3,056 N = 2,373 N = 896 N = 54 TFQI fT3 1 <0.001 Median (IQR) 0.10 (-0.15, 0.35) 0.05 (-0.18, 0.28) 0.00 (-0.24, 0.24) 0.00 (-0.24, 0.22) -0.04 (-0.27, 0.19) -0.09 (-0.35, 0.14) -0.18 (-0.36, 0.06) TFQI fT4 2 <0.001 Median (IQR) 0.09 (-0.14, 0.34) 0.03 (-0.18, 0.25) -0.02 (-0.24, 0.19) 0.00 (-0.23, 0.22) -0.01 (-0.21, 0.19) -0.02 (-0.26, 0.20) -0.01 (-0.17, 0.24) TT4RI 3 <0.001 Median (IQR) 37 (26, 51) 36 (25, 50) 34 (24, 49) 35 (24, 51) 37 (24, 54) 35 (23, 56) 39 (27, 71) TSHI 4 <0.001 Median (IQR) 3.08 (2.69, 3.45) 3.01 (2.63, 3.37) 2.94 (2.55, 3.31) 2.98 (2.55, 3.36) 2.99 (2.59, 3.39) 2.97 (2.54, 3.42) 3.03 (2.65, 3.65) 1: TFQI FT3 : Thyroid feedback quantile-based index by fT3 is calculated as cumulative distribution function (cdf) fT3-(1-cdf TSH). 2: TFQI FT4 : Thyroid feedback quantile-based index by fT4 is calculated as cumulative distribution function (cdf) fT4-(1-cdf TSH). The population was divided into four parts based on the quartile ranges of TFQI. 3: TT4RI: Thyrotropin T4 resistance index was derived by multiplying FT4 (pmol/L) with TSH (mIU/L). 4: TSHI: The TSH index was determined as ln TSH (mIU/L) + 0.1345 · FT4 (pmol/L). Table 3: Multivariable analysis of influencing factors for Metabolic Syndrome (MetS) using logistic regression in adult (≤48 years old) and elderly (>48 years old) population TFQI-fT3 Model for MetS 1 TFQI-fT4 Model for MetS 1 Population TFQI groups 2 N Event N OR 3 95% CI 3 p-value N Event N OR 3 95% CI 3 p-value Younger Adults 4 (≤48 years old) Reference 3,228 122 — — 3,586 146 — — Increased 1,674 42 0.99 0.68, 1.42 0.956 1,531 57 1.03 0.75, 1.42 0.835 Decreased 2,108 113 1.17 0.89, 1.53 0.254 1,893 74 0.87 0.64, 1.16 0.335 Older Adults 5 (>48 years old) Reference 3,146 329 — — 3,531 355 — — Increased 2,023 165 0.79 0.65, 0.97 0.023 1,650 147 0.85 0.69, 1.04 0.109 Decreased 1,467 173 1.11 0.91, 1.35 0.297 1,455 165 1.13 0.92, 1.37 0.236 1: Multivariable logistic regression analysis models adjusted for potential factor including age (10 years), sex, TFQI fT3 (or TFQI fT4 ) groups with the outcome of Metabolic Syndrome (MetS). 2: TFQI groups are defined by first dividing the data into four quartiles, denoted as Q1, Q2, Q3, and Q4, based on the interquartile range. The groups are then classified as follows: Q1 is labeled as the "Increase" group, representing the lowest quartile. Q2 and Q3 are combined and labeled as the "Reference" groups. Q4 is labeled as the "Decrease" group, representing the highest quartile. 3: OR = Odds Ratio, CI = Confidence Interval. 4: Number in data frame = 7010, Number in model = 7010, Missing = 0, AIC = 2131.9, C-statistic = 0.744, H&L = Chi-sq (8) 2.49 (p=0.962). 5: Number in data frame = 6636, Number in model = 6636, Missing = 0, AIC = 4198.9, C-statistic = 0.64, H&L = Chi-sq (8) 18.06 (p=0.021). Table 4: Multivariable analysis of influencing factors for Abnormal ST segment using logistic regression in adult (≤48 years old) and elderly (>48 years old) population TFQI-fT3 Model for abnormal ST segment 1 TFQI-fT4 Model for anormal ST segment 1 Population TFQI groups 2 N Event N OR 3 95% CI 3 p-value N Event N OR 3 95% CI 3 p-value Younger Adults 4 (≤48 years old) Reference 3,225 49 — — 3,581 47 — — Increased 1,672 18 0.57 0.32, 0.97 0.046 1,530 16 0.72 0.39, 1.25 0.256 Decreased 2,103 19 0.75 0.43, 1.27 0.301 1,889 23 1.11 0.65, 1.82 0.693 Older Adults 5 (>48 years old) Reference 3,136 106 — — 3,521 112 — — Increased 2,019 52 0.68 0.48, 0.95 0.027 1,649 37 0.7 0.47, 1.01 0.063 Decreased 1,463 55 1.25 0.89, 1.73 0.198 1,448 64 1.43 1.04, 1.96 0.025 1: Multivariable logistic regression analysis models adjusted for potential factor including age (10 years), sex, BMI, SUA, FPG, non-HDL, TFQIfT3(or TFQIfT4) groups with the outcome of abnormal ST segment. 2: TFQI groups are defined by first dividing the data into four quartiles, denoted as Q1, Q2, Q3, and Q4, based on the interquartile range. The groups are then classified as follows: Q1 is labeled as the "Increase" group, representing the lowest quartile. Q2 and Q3 are combined and labeled as the "Reference" groups. Q4 is labeled as the "Decrease" group, representing the highest quartile. 3: OR=Odds Ratio, CI=Confidence Interval. 4: Number in data frame=6636, Number in model=6636, Missing=0, AIC=4202.2, C-statistic=0.638, H&L=Chi-sq (8) 18.52 (p=0.018). 5: Number in data frame=6636, Number in model=6618, Missing=18, AIC=1844.1, C-statistic=0.659, H&L=Chi-sq (8) 8.35 (p=0.400). 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. 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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-6780346","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466347966,"identity":"be9e7242-4121-4b25-9581-91ffa53b5b12","order_by":0,"name":"Lei Zhao","email":"","orcid":"","institution":"The First Hospital of China Medical University: The First Affiliated Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhao","suffix":""},{"id":466347967,"identity":"45f736ef-4514-4884-b4c8-ff17d1f3cb79","order_by":1,"name":"Runqing Mu","email":"","orcid":"","institution":"The First Hospital of China Medical University: The First Affiliated Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Runqing","middleName":"","lastName":"Mu","suffix":""},{"id":466347968,"identity":"010345d9-58c0-40c6-984b-e9ef2fd9f624","order_by":2,"name":"Xin Zhang","email":"","orcid":"","institution":"The First Hospital of China Medical University: The First Affiliated Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""},{"id":466347969,"identity":"0f636b78-345a-4200-b8f4-a3b02bcc7a57","order_by":3,"name":"Shuo Wang","email":"","orcid":"","institution":"The First Hospital of China Medical University: The First Affiliated Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Wang","suffix":""},{"id":466347970,"identity":"c1df5717-de68-46f9-9831-43eacd382f1c","order_by":4,"name":"Min Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACZjBikIPw2EjQYkyCFoguhsQGorXotvMe/lxQY5PeP+2MAcOHssMM/LMb8GsxO8yXYDzjWFrujNs5Bowzzh1mkLhzgJAWHoNkHrbDuRukcwyYedsOMxhIJBDWcpjn3+F0A5CWv0RqMWwGGp4A1sJIpBZjZt6+NMMZt9MKDvacS+eRuEFIy/kzxp95vtnI889O3vjgR5m1HP8MAlpQwAEg5iFB/SgYBaNgFIwCXAAA/C48N1/yZ7EAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7235-3268","institution":"The First Affiliated Hospital of China Medical University: The First Hospital of China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhao","suffix":""},{"id":466347971,"identity":"d5d83acb-deb3-4cd1-ab9e-90ba454138a9","order_by":5,"name":"Hong Shang","email":"","orcid":"","institution":"The First Hospital of China Medical University: The First Affiliated Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Shang","suffix":""}],"badges":[],"createdAt":"2025-05-30 02:50:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6780346/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6780346/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84216430,"identity":"ec4fc891-2ae2-465b-9341-2be5971b3a73","added_by":"auto","created_at":"2025-06-09 10:44:29","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112756,"visible":true,"origin":"","legend":"\u003cp\u003eTFQI\u003csub\u003eFT3\u003c/sub\u003e: Thyroid feedback quantile-based index by fT3 is calculated as cumulative distribution function (cdf) fT3-(1-cdf TSH). The linear regression was adjusted for Sex. Y-axis represents the Predicted Values to present TFQI\u003csub\u003efT3\u003c/sub\u003e for any value of Age compared to individuals with 48 of Age.\u003c/p\u003e\n\u003cp\u003eAssociation between Age and TFQI\u003csub\u003efT3\u003c/sub\u003e with the RCS function.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6780346/v1/d47ec956bf5da55179963e90.jpeg"},{"id":84216844,"identity":"f5a7781e-8ce4-4095-8ede-749c08df5ff7","added_by":"auto","created_at":"2025-06-09 10:52:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":273483,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between TFQI and cardio (abnormal ST) and metabolic (Mets) function with the RCS function.\u003c/p\u003e\n\u003cp\u003eA) Association between TFQI\u003csub\u003efT3\u003c/sub\u003e and abnormal.ST with the RCS function. B) Association between TFQI\u003csub\u003efT4\u003c/sub\u003e and abnormal.ST with the RCS function. C) Association between TFQI\u003csub\u003efT3\u003c/sub\u003e and MetS with the RCS function. D) Association between TFQI\u003csub\u003efT3\u003c/sub\u003e and MetS with the RCS function. Y-axis represents the OR to present abnormal.ST or MetS for any value of TFQI\u003csub\u003efT3\u003c/sub\u003e and TFQI\u003csub\u003efT4 \u003c/sub\u003e.The logistic regression was adjusted for Sex and Age. TFQI: Thyroid feedback quantile-based index by fT3 or fT4 is calculated as cumulative distribution function (cdf) fT3 or fT4-(1-cdf TSH). MetS: Metabolic Syndrome.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6780346/v1/8c5087bcae79763b16860f4a.jpeg"},{"id":86769473,"identity":"37d0d6b9-4437-4b0c-8148-c215cfc156be","added_by":"auto","created_at":"2025-07-15 11:33:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1437229,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6780346/v1/9ed27e23-223c-49dc-91f3-b51e51aa8b09.pdf"},{"id":84216428,"identity":"deea2de2-aeaa-4b94-a577-95e97f2f4de0","added_by":"auto","created_at":"2025-06-09 10:44:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25292,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6780346/v1/c40f449c6c3137a609934f37.docx"}],"financialInterests":"","formattedTitle":"Age-Related Enhancement of HPT Axis Sensitivity to Thyroid Hormones Protects Metabolic and Cardiovascular Health","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid hormones play a pivotal role in regulating metabolism and the cardiovascular system. Abnormal levels of triiodothyronine (T3) and thyroxine (T4) are linked to type 2 diabetes (T2DM), dyslipidemia, nonalcoholic fatty liver disease (NAFLD), and atrial fibrillation[1-4]. Emerging evidence suggests that even within reference ranges, variations in thyroid hormone levels associate with metabolic and cardiovascular impairments[5, 6]. The Penn Heart Study demonstrated that individuals with high-normal thyroid-stimulating hormone (TSH) (4.51-19.99 mIU/L) and normal FT4 had an increased risk of atrial fibrillation (AF) compared to those with euthyroidism (HR 1.82, 95% CI 1.27-2.61)[7], highlighting the clinical relevance of subtle thyroid alterations.\u003c/p\u003e\n\u003cp\u003eThyroid function varies with age. Aging population often exhibit decreased free serum T3 levels and elevated serum TSH levels, reflecting age-related changes in thyroid regulation[8]. Among western populations aged \u0026ge;70 years, the prevalence of TSH levels of 4.5 mIU/L or higher is approximately 15%[9], and this prevalence reaches 20%[10] in the Chinese population. Meta-analyses indicate that significantly elevated TSH levels (10 mIU/L or greater) in the elderly are associated with cardiovascular morbidity and all-cause mortality[11, 12]. However, moderately elevated TSH levels (4.5-7.0 mIU/L) have not shown a statistically significant impact on aging and disease development[13, 14].\u0026nbsp;This paradox suggests that conventional thyroid markers may not fully capture age-related physiological adaptations.\u003c/p\u003e\n\u003cp\u003eThe hypothalamic-pituitary-thyroid (HPT) axis maintains thyroid hormonal balance through feedback loops. Analyzing combined indices rather than single measurements can provide new insights into thyroid function and metabolism. Central and peripheral thyroid sensitivity can be reflected by various indicators. However, existing studies on HPT sensitivity present three critical gaps: (1) inconsistent findings across euthyroid and hypothyroid populations[15-17]; (2) limited data on age-dependent sensitivity changes; and (3) unclear cardiovascular implications. To address these unclear questions, we conducted a cross-sectional analysis of 13,730 adults (aged 18-92 years), systematically evaluating how HPT sensitivity to thyroid hormones varies with age and associates with metabolic/cardiovascular risk stratification.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Population\u003c/p\u003e\n\u003cp\u003eThis study employed a whole cluster, stratified random sampling method to collect 13,730 participants from six geographically diverse regions in China between January- July 2014. The age and sex composition and the urban/rural ratio of each community were determined by using the 2010 national census data of China[18]. Inclusion criteria comprised: (i) subjects who were not pregnant; (ii) no administration of iodine-containing substances (including drugs and contrast media) within 3 months prior to enrollment and no current thyroid-directed pharmacotherapy; and (iii) no diagnosed developmental/psychiatric disorders. Fasting blood and spot urine samples were collected from each participant. Serum was obtained by centrifugation of the blood samples and stored at 4℃ prior to testing in the central laboratories of their respective city (Shanghai, Beijing, Guangzhou, Chengdu, Xi\u0026rsquo;an, and Shenyang). Participants underwent thyroid and liver ultrasonography for NAFLD, as well as an electrocardiogram (ECG) performed by trained and evaluated observers.\u003c/p\u003e\n\u003cp\u003eLaboratory Measurements\u003c/p\u003e\n\u003cp\u003efT4, fT3, TSH, TPOAb, and TgAb levels were measured using an electrochemi-luminescence immunoassay (Cobas e601/E170, Roche Diagnostic, USA) with manufacturer-defined reference ranges: fT3(3.1-6.8pmol/L), fT4(12.0-22.0pmol/L), TSH (0.27\u0026ndash;4.2 mIU/L), TPOAb (\u0026lt;34.0 IU/mL), and TgAb (115.0 IU/mL). Lipid profiles, including serum triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and serum uric acid (SUA) were measured by using Roche Modular automatic biochemical analyzer (Roche Diagnostics). Diagnostic criteria for metabolism and cardiovascular disorders are detailed in supplementary table 1[19-26].\u0026nbsp;Cardiovascular risk was quantified using Framingham Risk Score (FRS) incorporating age, sex, lipids, blood pressure, and smoking status (defined as \u0026gt;20 cigarettes/day)[27]. Smoking status was assessed using a detailed questionnaire, defining regular smokers as those smoking more than 20 cigarettes per day.\u003c/p\u003e\n\u003cp\u003eHPT Sensitivity Indices\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree indices were calculated. Firstly, the thyrotropin T4 resistance index (TT4RI) was derived by multiplying FT4 (pmol/L) with TSH (mIU/L). Secondly, the TSH index (TSHI) was determined using the following formula, TSH (mIU/L) + 0.1345 \u0026middot; FT4 (pmol/L). Lastly, the thyroid feedback quantile-based index (TFQI) is a new index calculated using the cumulative distribution function based on fT3 or fT4 and TSH[28].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe indices, TFQI, TSHI, and TT4RI, serve as indicators of central thyroid hormone sensitivity, providing insights into the HPT axis\u0026apos;s response to peripheral thyroid hormones[29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R software, version 4.2.2, and MSTATA software. Continuous variables were reported as mean\u0026plusmn;SD or median (IQR) based on distribution normality. Multivariable logistic regression analysis was adjusted for age (per 10-year increment), sex, BMI, and outcome-specific covariates (e.g., SUA/FPG for metabolic outcomes). Nonlinear relationships between age and TFQI were examined using a linear regression model with Restricted cubic splines (RCS). By age (\u0026le;48 vs \u0026gt;48 years) based on RCS-derived inflection points, the data were divided into two distinct segments based on this inflection point.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical Approval\u003c/p\u003e\n\u003cp\u003eThe study protocols were approved by the Medical Ethics Committee of China Medical University. All participants provided informed consent before participating in the study.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eClinical characteristics of subjects\u003c/p\u003e\n\u003cp\u003eThe study enrolled 13,730 participants. After excluding 84 individuals due to incomplete information on thyroid function data, the final sample size was 13,646 participants. The participants had a mean age of 47 \u0026plusmn; 15 years (range: 18-92 years), with 6,221 men and 7,425 women. The median (IQR) levels of FT3, FT4, and TSH were 4.84 (IQR: 4.45-5.28) pmol/L, 16.35 (IQR:15.0-17.8) pmol/L and 2.18 (IQR: 1.48-3.13) mIU/L, respectively. Mean (SD) values for BMI, UA, FPG, and non-HDL-C were 24.0 (3.7), 316 (89), 5.41 (1.25), and 3.41 (1.01). For CK, CK-MB, and NT-proBNP, the mean (SD) values were 125 (741), 1.64 (1.39), and 60 (149). Detailed diagnostic criteria for metabolism and cardiovascular disorders are provided in Supplementary Table 1. Participants were divided into four groups based on their HPT sensitivity IQR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 presents the clinical features of subjects in different HPT sensitivity groups. Age, sex, metabolic, and cardiovascular indicators were included. TFQI values range from -1 and 1, with positive values indicating poor thyroid hormone sensitivity, 0 indicating normal thyroid hormone sensitivity, and negative values indicating good sensitivity. Higher TSHI and TT4RI values correspond to lower central sensitivity to thyroid hormone. Individuals with lower HPT sensitivity (Q4) were younger and had a lower proportion of females compared to those with higher sensitivity (Q1). Metabolic indicators generally increased with decreased HPT sensitivity to fT3 (Q4) compared to normal or reduced sensitivity (Q1), a trend not observed for fT4 sensitivity. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAge-Related Changes in HPT Sensitivity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAge appears to correlate with thyroid hormone sensitivity (Table 1). To further explore this relationship, we analyzed thyroid hormone sensitivity across different age groups (Table 2). Results indicate that TFQI\u003csub\u003efT3\u003c/sub\u003e significantly decreases with age, while TFQI\u003csub\u003efT4\u003c/sub\u003e shows a similar but less pronounced trend. Trends for TT4RI and TSHI associated with age were unclear. We employed RCS analysis to examine the nonlinear association between age and TFQI\u003csub\u003efT3\u003c/sub\u003e, adjusting for sex and race (Figure 1). RCS analysis demonstrated a gradual decrease in TFQI\u003csub\u003efT3\u003c/sub\u003e levels with increasing age.\u003c/p\u003e\n\u003cp\u003eHPT Sensitivity and metabolic and cardiovascular disorders\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo analyze the impact of TFQI on metabolic and cardiovascular disorders, we used logistic regression for multivariable analysis in adults. Table 3 shows that in the older adults (aged over 48 years), increased HPT sensitivity to fT3 (TFQI-fT3 Q1) is a protective factor against MetS (OR 0.79, p=0.023). However, this trend was not statistically significant in the adult population or for HPT sensitivity to fT4. Table 4 reveals that increased HPT sensitivity to fT3 (TFQI-fT3 Q1) is significantly associated with a decreased risk of abnormal ST segment in both younger adults (OR 0.57, p=0.046) and older adults (aged over 48 years) (OR 0.68, p=0.027). In contrast, while increased HPT sensitivity to fT4 (TFQI-fT4 Q1) was not significantly associated, decreased HPT sensitivity to fT4 (TFQI-fT4 Q4) was a risk factor for abnormal ST segment in adults (OR 1.43, p=0.025). The RCS analysis, adjusting for the effects of Age and Sex suggested a non-linear association of TFQI\u003csub\u003efT4\u003c/sub\u003e and TFQI\u003csub\u003efT3\u003c/sub\u003e with MetS and abnormal ST (Figure 2).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur population-based study reveals a clinically significant age-dependent enhancement in HPT axis sensitivity to fT3, with distinct metabolic and cardiovascular implications. Three key findings were promoted: (1) TFQI-fT3 demonstrates a nonlinear decline with age (inflection at 48 years); (2) Increased fT3 sensitivity associates with reduced MetS and ST-abnormality risks in adults \u0026gt;48 years; (3) This protective pattern exhibits hormone specificity, as fT4 sensitivity showed opposing cardiovascular effects. These observations extend current understanding of thyroid aging by integrating HPT axis dynamics with clinical outcomes.\u003c/p\u003e\n\u003cp\u003eCirculating thyroid hormones are synthesized and released by the thyroid gland, regulated by the HPT axis. The HPT axis regulates thyroid hormone homeostasis through a classic feedback loop including hypothalamic TRH stimulates pituitary TSH secretion, which promotes thyroidal release of T4 and T3[30]. T3\u0026apos;s activity is about ten times higher than T4\u0026apos;s, and T4 can be converted to T3 by deiodinases, DIO1 and DIO2, in extra-thyroidal tissues. DIO1, primarily active in the liver and kidneys, generates 15-20% of the total circulating T3[31], while DIO2, found in brown adipose tissue, the pituitary gland, the brain, and the heart, is responsible for the majority of T3 production[32].\u003c/p\u003e\n\u003cp\u003eThyroid hormones regulate metabolic processes and maintain homeostasis within their normal range. They significantly impact basal metabolic rate, glucose absorption, lipolysis, and serum cholesterol levels[33-35]. These hormones also affect the cardiovascular system by modulating endothelial cell function, heart rate, and myocardial oxygen consumption[36]. Clinical thyroid dysfunctions, such as hyperthyroidism and hypothyroidism, are strongly associated with obesity, dyslipidemia, atrial fibrillation, and other CVDs[37, 38]. Subclinical thyroid conditions, often without overt symptoms, also carry significant risks for CVD and metabolic disorders. Studies show that subclinical hypothyroidism is associated with vascular abnormalities like increased systemic vascular resistance and altered endothelial-mediated vasorelaxation and vascular compliance[39, 40]. Subsequent evidence links impaired sensitivity to hyperuricemia, abdominal fat, hypertension, and diabetes [29, 41-43]. Our results corroborate Laclaustra et al. (2019) report linking impaired thyroid sensitivity (higher TFQI) to metabolic dysfunction[28], but uniquely identify an age-dependent protective effect of fT3 sensitivity. The lower MetS risk (OR 0.79) in high-sensitivity older adults (\u0026gt;48 years) may reflect optimized energy homeostasis during aging. Which is the hypothesis supported by animal studies showing DIO2 upregulation preserves cardiac function in aged mice[44].\u003c/p\u003e\n\u003cp\u003eThe age-related TSH rise with stable/falling T3 levels has been interpreted variably - as subclinical hypothyroidism or adaptive response [45, 46]. Recent studies found that the set point of the HPT axis and peripheral sensitivity to thyroid hormones vary across different age groups[28]. In our study, TFQI\u003csub\u003efT3\u003c/sub\u003e significantly decreases with increasing age, indicating that normal low levels of FT3 are more likely to cause elevated TSH during aging, indirectly explaining higher TSH levels in the old adults. Our RCS-derived inflection point (48 years) suggests midlife may mark a critical transition in thyroid regulation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMild evaluated TSH levels are considered a sign of healthy aging in some studies. For example, patients aged 85\u0026ndash;89 with elevated TSH had lower mortality (HR 0.77, 95% CI 0.63\u0026ndash;0.94)[47], and patients aged 70\u0026ndash;79 with TSH values between 4.5 and 7 mIU/L showed improved mobility[48]. Rakov et al. revealed that aging induces organ-specific hypersensitivity to T4 despite systemic T3 decline, suggesting central and peripheral mechanisms may compensate for reduced thyroid output[49]. Our TFQI-fT3 findings extend this pattern by showing enhanced central sensitivity to fT3 specifically protects metabolic and cardiovascular health in older adults. In the older adults (aged over 48 years), increased HPT sensitivity to fT3 (TFQI-fT3 Q1) is protective against MetS (OR 0.79, p=0.023) and decreases the risk of abnormal ST segments. Subclinical hypothyroidism and its risk of CVDs are controversial, especially in the elderly. Some studies show that treating persistent subclinical hypothyroidism (TSH 5\u0026middot;0-10\u0026middot;0 mIU/L) reduces cardiovascular events in adults, but not in those over 70[50]. Our results suggest that higher HPT axis sensitivity to fT3 protects against metabolic and cardiovascular injuries, providing some evidence for considering therapeutic intervention for subclinical hypothyroidism in the elderly. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe main strengths of our study lie in (1) Nationally representative sampling (n=13,646) with standardized sample collection; (2) Novel application of TFQI to characterize aging HPT sensitivity changes and its association with metabolic/cardiovascular outcomes. However, our study has limitations. Multicenter investigations across different regions face potential impacts from geographic, environmental, and dietary factors, which may influence disease outcomes. Additionally, as a cross-sectional survey, this study cannot determine the precise underlying mechanism linking aging, thyroid function, metabolic disorders, and CVD risk. Future prospective studies are needed.\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 protocols were approved by the Medical Ethics Committee of China Medical University. All participants provided informed consent before participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Technologies R\u0026amp;D Program provided by Ministry of Science and Technology of the People\u0026apos;s Republic of China (Project Grant # 2022YFC3602300, Sub-project Grant # 2022YFC3602302), China postdoctoral Science Foundation (Grant # 2022YFC3602300, Grant # 2024T171044).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor 1 (Hong Shang): Supervision (lead); project administration (lead); writing- review and editing (equal).Author 2 (Min Zhao): Supervision (lead); project administration (lead); writing-review and editing (equal).Author 3 (Lei Zhao): Formal analysis (lead); writing-original draft (lead); writing - review and editing (equal).Author 4 (Runqing Mu): Data curation (lead); Formal analysis (supporting); writing - review and editing (equal).Author 5 (Shou Wang): Data curation (supporting); formal analysis (supporting); review and editing (equal).Author 6 (Xin Zhang): Data curation (supporting); formal analysis (supporting); review and editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely appreciate the continuous support, assistance, and cooperation from the sub-centers of China Consortium of Reference Intervals project (CHINRIP), including:\u003c/p\u003e\n\u003cp\u003e1. Department of Laboratory Science, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong, China.\u003c/p\u003e\n\u003cp\u003e2. Department of Clinical Laboratory Medicine, Zhongshan Hospital of Fudan University, Shanghai, China.\u003c/p\u003e\n\u003cp\u003e3. Department of Laboratory Medicine, Peking University Third Hospital, Beijing, China.\u003c/p\u003e\n\u003cp\u003e4. National Center for Clinical Laboratories, Beijing Hospital, Beijing, China.\u003c/p\u003e\n\u003cp\u003e5. Department of Laboratory medicine, West China Hospital, Sichuan University, Sichuan, China.\u003c/p\u003e\n\u003cp\u003e6. Department of Clinical Laboratory Medicine, Xijing Hospital, the Fourth Military Medical University, Xi\u0026apos;an, Shaanxi, China.\u003c/p\u003e\n\u003cp\u003eThe authors also extend their gratitude to all other participants for their contributions to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBiondi B, Kahaly GJ, Robertson RP. Thyroid Dysfunction and Diabetes Mellitus: Two Closely Associated Disorders. Endocr Rev. 2019;40(3):789\u0026ndash;824.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHatziagelaki E, Paschou SA, Sch\u0026ouml;n M, Psaltopoulou T, Roden M. NAFLD and thyroid function: pathophysiological and therapeutic considerations. Trends Endocrinol Metab. 2022;33(11):755\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMavromati M, Jornayvaz FR. Hypothyroidism-Associated Dyslipidemia: Potential Molecular Mechanisms Leading to NAFLD. Int J Mol Sci 2021, 22(23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaumgartner C, da Costa BR, Collet TH, Feller M, Floriani C, Bauer DC, Cappola AR, Heckbert SR, Ceresini G, Gussekloo J, et al. Thyroid Function Within the Normal Range, Subclinical Hypothyroidism, and the Risk of Atrial Fibrillation. Circulation. 2017;136(22):2100\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManolis AA, Manolis TA, Melita H, Manolis AS. Subclinical thyroid dysfunction and cardiovascular consequences: An alarming wake-up call? Trends Cardiovasc Med. 2020;30(2):57\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFloriani C, Gencer B, Collet TH, Rodondi N. Subclinical thyroid dysfunction and cardiovascular diseases: 2016 update. Eur Heart J. 2018;39(7):503\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKannan L, Shaw PA, Morley MP, Brandimarto J, Fang JC, Sweitzer NK, Cappola TP, Cappola AR. Thyroid Dysfunction in Heart Failure and Cardiovascular Outcomes. Circulation Heart Fail. 2018;11(12):e005266.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGauthier BR, Sola-Garc\u0026iacute;a A, C\u0026aacute;liz-Molina M, Lorenzo PI, Cobo-Vuilleumier N, Capilla-Gonz\u0026aacute;lez V, Martin-Montalvo A. Thyroid hormones in diabetes, cancer, and aging. Aging Cell. 2020;19(11):e13260.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVadiveloo T, Donnan PT, Murphy MJ, Leese GP. Age- and gender-specific TSH reference intervals in people with no obvious thyroid disease in Tayside, Scotland: the Thyroid Epidemiology, Audit, and Research Study (TEARS). J Clin Endocrinol Metab. 2013;98(3):1147\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao L, Teng D, Shi X, Li Y, Ba J, Chen B, Du J, He L, Lai X, Li Y, et al. The Effect of Increased Iodine Intake on Serum Thyrotropin: A Cross-Sectional, Chinese Nationwide Study. Thyroid: official J Am Thyroid Association. 2020;30(12):1810\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGencer B, Collet TH, Virgini V, Bauer DC, Gussekloo J, Cappola AR, Nanchen D, den Elzen WP, Balmer P, Luben RN, et al. Subclinical thyroid dysfunction and the risk of heart failure events: an individual participant data analysis from 6 prospective cohorts. Circulation. 2012;126(9):1040\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNing Y, Cheng YJ, Liu LJ, Sara JD, Cao ZY, Zheng WP, Zhang TS, Han HJ, Yang ZY, Zhang Y, et al. What is the association of hypothyroidism with risks of cardiovascular events and mortality? A meta-analysis of 55 cohort studies involving 1,898,314 participants. BMC Med. 2017;15(1):21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOjala AK, Schalin-J\u0026auml;ntti C, Pitk\u0026auml;l\u0026auml; KH, Tilvis RS, Strandberg TE. Serum thyroid-stimulating hormone and cognition in older people. Age Ageing. 2016;45(1):155\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakamura N, Hayashida N, Maeda T. Risk of coronary heart disease and mortality for adults with subclinical hypothyroidism. JAMA. 2010;304(22):2481\u0026ndash;2. author reply 2482.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNie X, Ma X, Xu Y, Shen Y, Wang Y, Bao Y. Increased Serum Adipocyte Fatty Acid-Binding Protein Levels Are Associated with Decreased Sensitivity to Thyroid Hormones in the Euthyroid Population. Thyroid: official J Am Thyroid Association. 2020;30(12):1718\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Teng D, Zhao L, Shi X, Li Y, Shan Z, Teng W. Impaired Sensitivity to Thyroid Hormones Is Associated with Hyperuricemia, Obesity, and Cardiovascular Disease Risk in Subjects with Subclinical Hypothyroidism. Thyroid: official J Am Thyroid Association. 2022;32(4):376\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai S, Li J, Wang Z, Wang W, Guan H. Sensitivity to Thyroid Hormone Indices Are Closely Associated With NAFLD. Front Endocrinol. 2021;12:766419.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina. Totpcotpsro 2010 National Bureau of statistics of the People\u0026rsquo;s Republic of China.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina, NHPSotPsRo. WS/T 428-2013 Criteria of weight for adults \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wwwnhcgovcn/ewebeditor/uploadfile/2013/08/20130808135715967\u003c/span\u003e\u003cspan address=\"http://wwwnhcgovcn/ewebeditor/uploadfile/2013/08/20130808135715967\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003epdf\u003c/em\u003e 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChinese Society of Endocrinrinology CMA, Li C. Guideline for the diagnosis and management of hyperuricemia and gout in China(2019). Chin J Endocrinol Metabolism. 2020;36(1):13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerberich AJ, Hegele RA. A Modern Approach to Dyslipidemia. Endocr Rev. 2022;43(4):611\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenuth S, Alberti KG, Bennett P, Buse J, Defronzo R, Kahn R, Kitzmiller J, Knowler WC, Lebovitz H, Lernmark A, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care. 2003;26(11):3160\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarkiewicz K. Diagnosis and management of hypertension in obesity. Obes reviews: official J Int Association Study Obes. 2006;7(2):155\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzGerald JD, Dalbeth N, Mikuls T, Brignardello-Petersen R, Guyatt G, Abeles AM, Gelber AC, Harrold LR, Khanna D, King C, et al. 2020 American College of Rheumatology Guideline for the Management of Gout. Arthritis Care Res. 2020;72(6):744\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SC. Jr. et al: Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma S, Drezner JA, Baggish A, Papadakis M, Wilson MG, Prutkin JM, La Gerche A, Ackerman MJ, Borjesson M, Salerno JC, et al. International Recommendations for Electrocardiographic Interpretation in Athletes. J Am Coll Cardiol. 2017;69(8):1057\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSud M, Sivaswamy A, Chu A, Austin PC, Anderson TJ, Naimark DMJ, Farkouh ME, Lee DS, Roifman I, Thanassoulis G, et al. Population-Based Recalibration of the Framingham Risk Score and Pooled Cohort Equations. J Am Coll Cardiol. 2022;80(14):1330\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaclaustra M, Moreno-Franco B, Lou-Bonafonte JM, Mateo-Gallego R, Casasnovas JA, Guallar-Castillon P, Cenarro A, Civeira F. Impaired Sensitivity to Thyroid Hormones Is Associated With Diabetes and Metabolic Syndrome. Diabetes Care. 2019;42(2):303\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Zhu W, Liu J, An Y, Wang Y, Wang G. Reduced Sensitivity to Thyroid Hormones Is Associated With High Remnant Cholesterol Levels in Chinese Euthyroid Adults. J Clin Endocrinol Metab. 2022;108(1):166\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJabbar A, Pingitore A, Pearce SH, Zaman A, Iervasi G, Razvi S. Thyroid hormones and cardiovascular disease. Nat reviews Cardiol. 2017;14(1):39\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerry MJ, Banu L, Larsen PR. Type I iodothyronine deiodinase is a selenocysteine-containing enzyme. Nature. 1991;349(6308):438\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno JC, Klootwijk W, van Toor H, Pinto G, D'Alessandro M, L\u0026egrave;ger A, Goudie D, Polak M, Gr\u0026uuml;ters A, Visser TJ. Mutations in the iodotyrosine deiodinase gene and hypothyroidism. N Engl J Med. 2008;358(17):1811\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOppenheimer JH, Schwartz HL, Lane JT, Thompson MP. Functional relationship of thyroid hormone-induced lipogenesis, lipolysis, and thermogenesis in the rat. J Clin Investig. 1991;87(1):125\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng SY, Leonard JL, Davis PJ. Molecular aspects of thyroid hormone actions. Endocr Rev. 2010;31(2):139\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMottis A, Mouchiroud L, Auwerx J. Emerging roles of the corepressors NCoR1 and SMRT in homeostasis. Genes Dev. 2013;27(8):819\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazvi S, Jabbar A, Pingitore A, Danzi S, Biondi B, Klein I, Peeters R, Zaman A, Iervasi G. Thyroid Hormones and Cardiovascular Function and Diseases. J Am Coll Cardiol. 2018;71(16):1781\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Wu G, Li S, Cheng L, Ye X. The genetic association between hyperthyroidism and heart failure: a Mendelian randomization study. Front Endocrinol. 2024;15:1344282.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuczaj A, Warwas S, Danel A, Przybyłowski P, Hrapkowicz T. Thyroid Dysfunction in Advanced Heart Failure Patients and Its Correlation with Amiodarone Therapy: A Two-Year Study. Biomedicines 2024, 12(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Zhang N, Cai Y, Shi J. Evaluation of left ventricular diastolic function using tissue Doppler echocardiography and conventional doppler echocardiography in patients with subclinical hypothyroidism aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years: a meta-analysis. J Cardiol. 2013;61(1):8\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwen PJ, Sabit R, Lazarus JH. Thyroid disease and vascular function. Thyroid: official J Am Thyroid Association. 2007;17(6):519\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehran L, Delbari N, Amouzegar A, Hasheminia M, Tohidi M, Azizi F. Reduced Sensitivity to Thyroid Hormone Is Associated with Diabetes and Hypertension. J Clin Endocrinol Metab. 2022;107(1):167\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Y, Wang J, An Y, Liu J, Wang Y, Wang G, Leng S. Impaired sensitivity to thyroid hormones is associated with hyperuricemia in a Chinese euthyroid population. Front Endocrinol. 2023;14:1132543.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao B, Li K, Ke J, Zhao D. Impaired Sensitivity to Thyroid Hormones Is Associated With the Change of Abdominal Fat in Euthyroid Type 2 Diabetes Patients: A Retrospective Cohort Study. \u003cem\u003eJournal of diabetes research\u003c/em\u003e 2024, 2024:8462987.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrent GA. Mechanisms of thyroid hormone action. J Clin Investig. 2012;122(9):3035\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHollowell JG, Staehling NW, Flanders WD, Hannon WH, Gunter EW, Spencer CA, Braverman LE, Serum TSH. T(4), and thyroid antibodies in the United States population (1988 to 1994): National Health and Nutrition Examination Survey (NHANES III). J Clin Endocrinol Metab. 2002;87(2):489\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBremner AP, Feddema P, Leedman PJ, Brown SJ, Beilby JP, Lim EM, Wilson SG, O'Leary PC, Walsh JP. Age-related changes in thyroid function: a longitudinal study of a community-based cohort. J Clin Endocrinol Metab. 2012;97(5):1554\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGussekloo J, van Exel E, de Craen AJ, Meinders AE, Fr\u0026ouml;lich M, Westendorp RG. Thyroid status, disability and cognitive function, and survival in old age. JAMA. 2004;292(21):2591\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Puy RS, Poortvliet RKE, Mooijaart SP, den Elzen WPJ, Jagger C, Pearce SHS, Arai Y, Hirose N, Teh R, Menzies O, et al. Outcomes of Thyroid Dysfunction in People Aged Eighty Years and Older: An Individual Patient Data Meta-Analysis of Four Prospective Studies (Towards Understanding Longitudinal International Older People Studies Consortium). Thyroid: official J Am Thyroid Association. 2021;31(4):552\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRakov H, De Angelis M, Renko K, H\u0026ouml;nes GS, Zwanziger D, Moeller LC, Schramm KW, F\u0026uuml;hrer D. Aging Is Associated with Low Thyroid State and Organ-Specific Sensitivity to Thyroxine. Thyroid: official J Am Thyroid Association. 2019;29(12):1723\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazvi S, Weaver JU, Butler TJ, Pearce SH. Levothyroxine treatment of subclinical hypothyroidism, fatal and nonfatal cardiovascular events, and mortality. Arch Intern Med. 2012;172(10):811\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(Suppl 1):S62\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr., Jones DW, Materson BJ, Oparil S, Wright JT Jr, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCotter TG, Rinella M. Nonalcoholic Fatty Liver Disease 2020: The State of the Disease. Gastroenterology. 2020;158(7):1851\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Clinical Features of Subjects in Groups with Different Sensitivity to Thyroid Hormones.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI\u003csub\u003eFT3\u003c/sub\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI\u003csub\u003eFT4\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=3697)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=3058)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=3316)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4 (N=3575)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=3181)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=3487)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=3630)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=3348)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(mean\u0026plusmn;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e49.81 \u0026plusmn; 14.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e48.46 \u0026plusmn; 14.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e47.05 \u0026plusmn; 14.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e44.25 \u0026plusmn; 14.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e48.90 \u0026plusmn; 14.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e48.31 \u0026plusmn; 14.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e47.33 \u0026plusmn; 14.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e45.03 \u0026plusmn; 15.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1126 (30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1232 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1699 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2164 (60.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1292 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1471 (42.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1687 (46.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1771 (52.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003efemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2571 (69.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1826 (59.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1617 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1411 (39.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1889 (59.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2016 (57.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1943 (53.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1577 (47.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emetabolism index(mean\u0026plusmn;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e23.38 \u0026plusmn; 3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e23.92 \u0026plusmn; 3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e24.11 \u0026plusmn; 3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e24.61 \u0026plusmn; 3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e24.10 \u0026plusmn; 3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e24.18 \u0026plusmn; 3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e23.92 \u0026plusmn; 3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e23.80 \u0026plusmn; 3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.85 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.86 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.89 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.86 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.86 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.86 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFPG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e5.41 \u0026plusmn; 1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5.39 \u0026plusmn; 1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5.45 \u0026plusmn; 1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5.41 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e5.34 \u0026plusmn; 1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5.43 \u0026plusmn; 1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5.41 \u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5.46 \u0026plusmn; 1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e296 \u0026plusmn; 85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e310 \u0026plusmn; 87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e320 \u0026plusmn; 90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e335 \u0026plusmn; 90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e308.35 \u0026plusmn; 91.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e311.25 \u0026plusmn; 88.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e315.32 \u0026plusmn; 88.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e327.27 \u0026plusmn; 88.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.30 \u0026plusmn; 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.40 \u0026plusmn; 1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.46 \u0026plusmn; 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.54 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.42 \u0026plusmn; 1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.45 \u0026plusmn; 1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.42 \u0026plusmn; 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.41 \u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4.97 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4.90 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4.84 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4.75 \u0026plusmn; 0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4.90 \u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4.90 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4.86 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4.80 \u0026plusmn; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.55 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.46 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.43 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.37 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.46 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.45 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.46 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.45 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.04 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.03 \u0026plusmn; 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.99 \u0026plusmn; 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.94 \u0026plusmn; 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.03 \u0026plusmn; 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.04 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.99 \u0026plusmn; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.94 \u0026plusmn; 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003enon-HDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.35 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.39 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.45 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.45 \u0026plusmn; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.36 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.41 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.41 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.45 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e124\u0026plusmn; 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e125 \u0026plusmn; 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e126 \u0026plusmn; 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e126 \u0026plusmn; 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e125 \u0026plusmn; 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e126 \u0026plusmn; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e126 \u0026plusmn; 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e126 \u0026plusmn; 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e76 \u0026plusmn; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e77 \u0026plusmn; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e78 \u0026plusmn; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e78 \u0026plusmn; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e77 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e78 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e78 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e78 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular Index(mean\u0026plusmn;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e75 \u0026plusmn; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e76 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e76 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e76 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e75 \u0026plusmn; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e76 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e76 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e76 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e133.62 \u0026plusmn; 1362.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e111.14 \u0026plusmn; 118.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e121.77 \u0026plusmn; 185.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e131.32 \u0026plusmn; 364.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e132.46 \u0026plusmn; 1048.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e112.97 \u0026plusmn; 116.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e132.73 \u0026plusmn; 992.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e122.47 \u0026plusmn; 331.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCK-MB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.59 \u0026plusmn; 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.63 \u0026plusmn; 1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.69 \u0026plusmn; 1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.66 \u0026plusmn; 1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.77 \u0026plusmn; 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.66 \u0026plusmn; 1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.65 \u0026plusmn; 1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.49 \u0026plusmn; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT-proBNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e73.90 \u0026plusmn; 215.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e61.13 \u0026plusmn; 115.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e53.63 \u0026plusmn; 95.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e49.72 \u0026plusmn; 125.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e60.60 \u0026plusmn; 115.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e56.51 \u0026plusmn; 105.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e59.10 \u0026plusmn; 143.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e63.12 \u0026plusmn; 207.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehs-cTNI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1: TFQI\u003csub\u003eFT3\u003c/sub\u003e: Thyroid feedback quantile-based index by fT3 is calculated as cumulative distribution function (cdf) fT3-(1-cdf TSH).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2: TFQI\u003csub\u003eFT4\u003c/sub\u003e: Thyroid feedback quantile-based index by fT4 is calculated as cumulative distribution function (cdf) fT4-(1-cdf TSH). The population was divided into four parts based on the quartile ranges of TFQI.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; WHR, waist hip rate; FPG, fasting plasma glucose; SUA, serum uric acid; TG, triglyceride; TG, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; CK, creatine kinase; NT-proBNP, N-Terminal pro-brain natriuretic peptide; hs-cTNI, highly sensitive cardiac troponin I.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Sensitivity to Thyroid Hormones indexes in different age groups.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18-29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-39\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40-49\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50-59\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e60-69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e70-79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 2,122\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 2,403\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 2,742\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 3,056\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 2,373\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 896\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI\u003csub\u003efT3\u003c/sub\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.15, 0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.18, 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.24, 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.24, 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.04\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.27, 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.09\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.35, 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.18\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.36, 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI\u003csub\u003efT4\u0026nbsp;\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003cp\u003e(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.14, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-0.18, 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.24, 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.23, 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-0.21, 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-0.26, 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.01\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.17, 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTT4RI\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003cp\u003e(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e37\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(26, 51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e36\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(25, 50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e34\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(24, 49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e35\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(24, 51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e37\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(24, 54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e35\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(23, 56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(27, 71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSHI\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003cp\u003e(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.08\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.69, 3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.63, 3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.55, 3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.55, 3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.59, 3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.54, 3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.03\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.65, 3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\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\u003e1: TFQI\u003csub\u003eFT3\u003c/sub\u003e: Thyroid feedback quantile-based index by fT3 is calculated as cumulative distribution function (cdf) fT3-(1-cdf TSH).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2: TFQI\u003csub\u003eFT4\u003c/sub\u003e: Thyroid feedback quantile-based index by fT4 is calculated as cumulative distribution function (cdf) fT4-(1-cdf TSH). The population was divided into four parts based on the quartile ranges of TFQI.\u003c/p\u003e\n\u003cp\u003e3: TT4RI: Thyrotropin T4 resistance index was derived by multiplying FT4 (pmol/L) with TSH (mIU/L).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4: TSHI: The TSH index was determined as ln TSH (mIU/L) + 0.1345 \u0026middot; FT4 (pmol/L).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Multivariable analysis of influencing factors for Metabolic Syndrome (MetS) using logistic regression in adult (\u0026le;48 years old) and elderly (\u0026gt;48 years old) population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI-fT3 Model for MetS\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI-fT4 Model for MetS\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI groups\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003eYounger Adults\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(\u0026le;48 years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e3,228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3,586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eIncreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1,674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.68, 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1,531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.75, 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eDecreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2,108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.89, 1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1,893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.64, 1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003eOlder Adults\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(\u0026gt;48 years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e3,146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3,531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncreased\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e165\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.65, 0.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1,650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.69, 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eDecreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1,467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.91, 1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1,455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.92, 1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1: Multivariable logistic regression analysis models adjusted for potential factor including age (10 years), sex, TFQI\u003csub\u003efT3\u003c/sub\u003e(or TFQI\u003csub\u003efT4\u003c/sub\u003e) groups with the outcome of Metabolic Syndrome (MetS).\u003c/p\u003e\n\u003cp\u003e2: TFQI groups are defined by first dividing the data into four quartiles, denoted as Q1, Q2, Q3, and Q4, based on the interquartile range. The groups are then classified as follows: Q1 is labeled as the \u0026quot;Increase\u0026quot; group, representing the lowest quartile. Q2 and Q3 are combined and labeled as the \u0026quot;Reference\u0026quot; groups. Q4 is labeled as the \u0026quot;Decrease\u0026quot; group, representing the highest quartile.\u003c/p\u003e\n\u003cp\u003e3: OR = Odds Ratio, CI = Confidence Interval.\u003c/p\u003e\n\u003cp\u003e4: Number in data frame = 7010, Number in model = 7010, Missing = 0, AIC = 2131.9, C-statistic = 0.744, H\u0026amp;L = Chi-sq (8) 2.49 (p=0.962).\u003c/p\u003e\n\u003cp\u003e5: Number in data frame = 6636, Number in model = 6636, Missing = 0, AIC = 4198.9, C-statistic = 0.64, H\u0026amp;L = Chi-sq (8) 18.06 (p=0.021).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 4: Multivariable analysis of influencing factors for Abnormal ST segment using logistic regression in adult (\u0026le;48 years old) and elderly (\u0026gt;48 years old) population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI-fT3 Model for abnormal ST segment\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI-fT4 Model for anormal ST segment\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFQI groups\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003eYounger Adults\u003csup\u003e\u0026nbsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(\u0026le;48 years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e3,225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3,581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eIncreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,672\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.32, 0.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1,530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.39, 1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eDecreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2,103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.43, 1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1,889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.65, 1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003eOlder Adults\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(\u0026gt;48 years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e3,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncreased\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.68\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.48, 0.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1,649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.47, 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eDecreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1,463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.89, 1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,448\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.04, 1.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1: Multivariable logistic regression analysis models adjusted for potential factor including age (10 years), sex, BMI, SUA, FPG, non-HDL, TFQIfT3(or TFQIfT4) groups with the outcome of abnormal ST segment.\u003c/p\u003e\n\u003cp\u003e2: TFQI groups are defined by first dividing the data into four quartiles, denoted as Q1, Q2, Q3, and Q4, based on the interquartile range. The groups are then classified as follows: Q1 is labeled as the \u0026quot;Increase\u0026quot; group, representing the lowest quartile. Q2 and Q3 are combined and labeled as the \u0026quot;Reference\u0026quot; groups. Q4 is labeled as the \u0026quot;Decrease\u0026quot; group, representing the highest quartile.\u003c/p\u003e\n\u003cp\u003e3: OR=Odds Ratio, CI=Confidence Interval.\u003c/p\u003e\n\u003cp\u003e4: Number in data frame=6636, Number in model=6636, Missing=0, AIC=4202.2, C-statistic=0.638, H\u0026amp;L=Chi-sq (8) 18.52 (p=0.018).\u003c/p\u003e\n\u003cp\u003e5: Number in data frame=6636, Number in model=6618, Missing=18, AIC=1844.1, C-statistic=0.659, H\u0026amp;L=Chi-sq (8) 8.35 (p=0.400). \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\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":"HPT axis sensitivity, Thyroid Feedback Quantile-Based Index (TFQI), Metabolic syndrome (MetS), Cardiovascular risk, Aging","lastPublishedDoi":"10.21203/rs.3.rs-6780346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6780346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe regulation and secretion of hormones in the endocrine system change with aging, including a decline in serum free triiodothyronine (fT3) and an increase in thyroid-stimulating hormone (TSH) within their reference ranges. While these changes may influence age-related disease risks, the role of hypothalamic-pituitary-thyroid (HPT) axis sensitivity in metabolic and cardiovascular health remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study enrolled 13,646 participants (6,221 males, 7,425 females). Metabolic (BMI, SUA, FPG, lipoproteins) and cardiovascular (ECG, CK-MB, NT-proBNP) indices were measured. HPT sensitivity was quantified using Thyroid Feedback Quantile-Based Index (TFQI). Multivariable logistic regression adjusted for age (per 10-year increment), sex, and outcome-specific confounders (BMI, SUA, FPG, non-HDL for ST-segment analysis). Nonlinear relationships between age and TFQI were analyzed using restricted cubic splines (RCS).\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eMetabolic indicators were generally elevated in participants with decreased HPT axis sensitivity to fT3 (Q4) compared to the reference group (Q1). Restricted cubic spline analysis revealed a significant age-dependent decline in TFQI-fT3 values, with an inflection point at 48 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); TFQI-fT4 showed a similar but attenuated trend. For metabolic outcomes, increased fT3 sensitivity (TFQI-fT3 Q1) was associated with lower MetS risk in older adults (\u0026gt;\u0026thinsp;48 years) (OR 0.79, 95% CI 0.65\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;0.023), whereas no significant association was observed in younger adults (\u0026le;\u0026thinsp;48 years) (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For cardiovascular outcomes, higher fT3 sensitivity was significantly associated with reduced risk of abnormal ST segments in both age groups (younger: OR 0.57, 95% CI 0.32\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;0.046; older: OR 0.68, 95% CI 0.48\u0026ndash;0.95, p\u0026thinsp;=\u0026thinsp;0.027). Conversely, decreased fT4 sensitivity (TFQI-fT4 Q4) specifically increased ST-segment risk in older adults (OR 1.43, 95% CI 1.04\u0026ndash;1.96, p\u0026thinsp;=\u0026thinsp;0.025), with no significant effect in younger adults (OR 1.11, 95% CI 0.65\u0026ndash;1.82, p\u0026thinsp;=\u0026thinsp;0.693).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEnhanced HPT axis sensitivity to fT3 increased with age (inflection at 48 years) and was associated with reduced risks of metabolic syndrome (in \u0026gt;\u0026thinsp;48-year-olds) and ST-segment abnormalities (all adults). These findings suggest age-specific TH regulation influences metabolic and cardiovascular health, warranting further research into underlying mechanisms.\u003c/p\u003e","manuscriptTitle":"Age-Related Enhancement of HPT Axis Sensitivity to Thyroid Hormones Protects Metabolic and Cardiovascular Health","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 10:44:24","doi":"10.21203/rs.3.rs-6780346/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":"0a1f8ad4-f1f0-423c-9446-8504d989a605","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-15T11:25:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 10:44:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6780346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6780346","identity":"rs-6780346","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}