Relationship between sensitivity to thyroid hormone and cardiovascular-kidney-metabolic syndrome in U.S. adults: Evidence from the 2007–2012 national health and nutrition examination survey

Relationship between sensitivity to thyroid hormone and cardiovascular-kidney-metabolic syndrome in U.S. adults: Evidence from the 2007–2012 national health and nutrition examination survey | 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 Article Relationship between sensitivity to thyroid hormone and cardiovascular-kidney-metabolic syndrome in U.S. adults: Evidence from the 2007–2012 national health and nutrition examination survey Zelu Xia, Rongjia Xu, Chen Jiang, Zuo Chen, Yizhan Zhang, Shentao Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6273789/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study investigated the relevance of thyroid hormone sensitivity (THS) to cardiovascular-kidney-metabolic (CKM) syndrome risk in euthyroid U.S. adults, given unclear prior evidence. Methods The cross-sectional study analyzed 8,009 euthyroid adults from NHANES (2007–2012) to assess associations between THS and CKM syndrome. Central THS indices (TSHI, TT4RI, TFQI) and peripheral FT3/FT4 ratio were evaluated. Participants were stratified into five CKM stages (stage 0: no CKM; stages 1–4: increasing severity). Weighted logistic regression examined associations between thyroid sensitivity and CKM stages, while restricted cubic spline (RCS) together with piecewise linear regression models explored nonlinearity and threshold effects. Results In total, 2,381/8,009 participants were classified as CKM syndrome stage 0. TFQI and FT3/FT4 showed a relevance to CKM syndrome stages 1–3 ( P 0.05). For each additional unit increase in TFQI and FT3/FT4, the risk of developing CKM syndrome also increased. Compared with low FT3/FT4 levels, high FT3/FT4 level indicated higher odds of developing CKM syndrome stages 1–3. Significant interaction effects (P-interaction < 0.05) emerged across sex/age/ethnicity subgroups stratified by CKM stage 2 ~ 3. Males, older adults ≥ 60y, and Black individuals with elevated FT3/FT4 demonstrated higher progression odds versus respective referent groups. Conclusion THS indices (TFQI, FT3/FT4) are potential biomarkers for CKM risk stratification in euthyroid populations. And there are differences in gender, age and race, which requires further vertical research to verify the causal relationship. Health sciences/Endocrinology Health sciences/Endocrinology/Endocrine system and metabolic diseases cardiorenal-kidney-metabolic syndrome sensitivity to thyroid hormone TFQI FT3/FT4 NHANES Figures Figure 1 Figure 2 Introduction CKM syndrome is a multifaceted clinical condition, featuring the interaction of metabolic risk factors, CKD, and cardiovascular problems. Consequently, this syndrome indicates a high occurrence of multi-organ failure and adverse effects on the cardiovascular system( 1 ). CKM syndrome exhibits complex inter-system interactions and high mortality rate, making it a major public health concern ( 2 , 3 , 4 ). Simple and effective biochemical markers for its early detection and management of CKM syndrome are therefore required. The term ‘THS’ refers to the degree of response to thyroid hormones, and is a crucial factor in controlling metabolism, cardiovascular health, and energy regulation. This index includes both central and peripheral sensitivity ( 5 ). According to existing studies, abnormal THS greatly affects disease development and prognosis. For example, hypothyroidism or hyperthyroidism may lead to abnormal heart function, thereby facilitating the development of cardiovascular disease (CVD) ( 6 ). Additionally, changes in THS may indirectly influence insulin sensitivity and lipid metabolism by altering metabolic rate and energy expenditure, which are important components of CKM syndrome ( 7 ). However, researches have not well explored the precise link between THS and CKM. Against such backdrop, our study aimed at examining such linkage in a population without thyroid disorder. Recent research involving the Chinese population underscores the contribution of abnormal THS to CKM syndrome management, but there is still a gap in studies concerning other ethnicities( 8 ). Utilizing NHANES data, the cross-sectional study engaged in assessing central THS by thyroid feedback quantile-based index (TFQI), thyrotrophic thyroxine resistance index (TT4RI), and thyroid-stimulating hormone index (TSHI), and assessing peripheral THS by free triiodothyronine/free thyroxine (FT3/FT4). We subsequently examined the correlation of different THS markers with various CKM stages in euthyroid subjects, for elucidating the possible THS-CKM linkage, and finally providing evidence to improve the prevention and management of this condition. Subjects and methods Study Population The study employed data from 3 NHANES cycles spanning from 2007 to 2012 (downloaded in https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val ). The NHANES is a comprehensive, four-stage, nationwide probability sampling survey aimed at choosing a sample representing the non-institutionalized U.S. civilian population ( 9 ). The study obtained the approval from the National Institutes of Health Ethics Advisory Committee, and acquired the all participants’ written informed consent. A total of 30,442 participants completed the NHANES and fasting tests at the mobile test centers. After excluding 11,823 participants aged < 18 years, 10,111 with insufficient thyroid function indices or abnormal thyroid function, and 499 with insufficient data to confirm CKM syndrome or exclude pregnancy, the study included a final sample of 8,009 participants (Fig. 1). THS index assessment Central THS evaluation was conducted using three different indices, with the corresponding formulas given below: TSHI = lnTSH (mIU/L) + 0.1345 FT4 (pmol/L) TT4RI = FT4 (pmol/L) × TSH (mIU/L) TFQI = (cdfFT4) - (1 - cdfTSH). TFQI was measured on a scale from − 1 to 1, and negative and positive numbers respectively denoted heightened and reduced sensitivity of the hypothalamic-pituitary-thyroid axis to thyroid hormone change ( 10 ). The FT3/FT4 ratio marked the peripheral THS. These indices were divided into quartiles: low, mid-lower, mid-upper, and high. Assessment of CKM syndrome CKM syndrome features concurrent subclinical or clinical CVD, CKD, and metabolic disorders ( 1 ). Subclinical CVD referred to the case of a 10-year CVD risk ≥ 20%, or an elevated chronic kidney disease risk, predicted using a simplified CKM syndrome risk algorithm( 11 ) incorporating various variables listed in Table S1 . Clinical CVD was defined as any history of stroke, heart attack, chronic heart failure, or coronary heart disease. We collected data using standardized questionnaires and physical examinations, and analyzed blood and spot urine samples at a central laboratory. The 2021 updated CKD-EPI Eq. (12) was adopted for estimating the glomerular filtration rate. The Kidney Disease: Improving Global Outcomes Classification system stratifies CKD risk based on different thresholds of estimated glomerular filtration rate (< 30, 30–44, 45–59, and ≥ 60 mL/min/1.73 m 2 ) and urinary albumin/creatinine (ALB/Cr) ratio (< 30, 30–299, and ≥ 300 mg/g). CKD referred to a urinary ALB/Cr ratio ≥ 300 mg/g or an eGFR < 60 mL/min/1.73m 2 ( 13 ). Metabolic disorders included overweight or obesity, abdominal obesity, pre-diabetes, diabetes mellitus, hypertension, abnormal lipid levels and metabolic syndrome. Diabetes features a fasting blood sugar(FBS) level > 7 mmol/L (126 mg/dL), a higher/glycosylated hemoglobin ≥ 6.5%, a 2-hour blood glucose level in an OGTT of 11 mmol/L (200mg/dL) or more, self-reported diagnosis, or use of hypoglycemic drugs. Hypertension was diagnosed based on a systolic blood pressure(SBP) > 140 mmHg, a diastolic blood pressure(DBP) > 90 mmHg, or the administer of antihypertensive drugs. CVD was determined by a history of stroke, heart attack and failure, coronary artery disease, or angina under self report. Participants were categorized into five CKM syndrome stages, according to the severity of their clinical condition: stage 0 (normal), stage 1 (characterized by obesity or pre-diabetes only), stage 2 (characterized by at least one metabolic disorder other than obesity or CKD), stage 3 (subclinical CVD with a concurrent metabolic disorder or CKD), stage 4 (clinical CVD with concurrent metabolic disorder or CKD) ( 14 ). Detailed staging criteria are presented in Table S2. Assessment of other covariates Covariates used here encompassed age, sex (male or female), race, current smoking status (everyday, some days, not at all, missing), current alcohol consumption status (yes, no, missing), and household poverty income ratio (PIR: <3 indicates low, ≥ 3 indicates high). The NHANES database provides public access to the comprehensive measurement specific to all variables here. Statistical Analysis In accordance with the guidelines from the National Center for Health Statistics, the strata and main sampling units for complex, multi-stage, and probability sampling designs were all considered. MEC sample weights were applied for all analyses, including personnel involved in testing at the mobile test center. Continuous variables followed the format of the mean of the weighted 95% confidence interval (CI), while categorical variables followed the format of frequency of weighted percentages. ANOVA was employed for comparing continuous variables, and an adjusted χ 2 test served for comparing categorical variables among various TFQI and FT3/FT4 levels. Given the low number of missing data for covariates, multiple imputation designed for survey data was applied to address missing covariates rather than excluding those participants. For elucidating the relevance of THS indices to CKM syndrome stage, we used a polynomial logistic regression model with stage 0 as the reference group. The odds ratios (ORs) and 95% CI were measured for assessing the association between each THS index (as a continuous variable or stratified by quartiles) and CKM syndrome stage. A basic model incorporating only the THS indices, a partially adjusted model including demographic covariates (age, sex, race), and a fully adjusted model including demographic and lifestyle factors (current smoker, current alcohol drinker, PIR, and education) were constructed. With the aim of ensuring that the results were robust, we conducted several further analyses. Given the demographic variations in THS and CKM syndrome, analyses of subgroups and interactions were conducted according to age, sex, and race/ethnicity. Additionally, taking stage 0 as a control, we performed RCS analysis to investigate any possible nonlinear connections between various THS indices and CKM syndrome stages. Statistical analyses relied on SAS 9.4 (SAS Institute) and R 4.4.1 (R Foundation), with two-tailed P-values < 0.05 defining statistical significance. Results Baseline characteristics Our study enrolled 8,009 participants aged ≥ 18 years as a representative sample of 101,708,798 non-institutionalized U.S. adults. The mean age of participants was 45.587 (95% CI, 45.166–46.008) years, with 48.795% (n = 3,908) being females and 44.712% (n = 3,581) being non-Hispanic Whites. Participants with CKM syndrome stage 1–4 were older and had higher TFQI, TT4RI, and TSHI index values, with a lower FT3/FT4 ratio ( P < 0.001) compared to those at stage 0. Significant differences in sex, race, education, marital status, smoking status, and PIR were found between the groups ( P 0.05) (Table 1 ). Table 1 Basic characteristics of participants with CKM syndrome mellitus (n = 8009) in the NHANES 2007–2012, weighted. Variables* Total CKM in different installments (N = 8009) 0(N = 2381) 1(N = 1236) 2(N = 1760) 3(N = 2414) 4(N = 218) P-value Age,weighted mean(95%CI) 45.587 (45.166 ,46.008) 38.709 (37.982 39.451) 34.696 (33.953 35.455) 41.806 (41.123 42.500) 56.855 (56.216 57.501) 69.121 (67.558 70.720) < 0.001 Sex,n(weighted %) < 0.001 Male 4101 (51.205%) 1194 (50.147%) 560 (45.307%) 955 (54.261%) 1266 (52.444%) 126 (57.798%) Female 3908 (48.795%) 1187 (49.853%) 676 (54.693%) 805 (45.739%) 1148 (47.556%) 92 (42.202%) Race,n(weighted %) < 0.001 Mexican American 1326 (16.556%) 305 (12.810%) 254 (20.550%) 419 (23.807%) 334 (13.836%) 14 (6.422%) Other Hispanic 899 (11.225%) 223 (9.366%) 149 (12.055%) 261 (14.830%) 253 (10.481%) 13 (5.963%) Non-Hispanic White 3581 (44.712%) 1138 (47.795%) 455 (36.812%) 700 (39.773%) 1166 (48.302%) 122 (55.963%) Non-Hispanic Black 1634 (20.402%) 420 (17.640%) 318 (25.728%) 300 (17.045%) 530 (21.955%) 66 (30.275%) Other Race - Including Multi-Racial Missing 569 (7.105%) 295 (12.390%) 60 (4.854%) 80 (4.545%) 131 (5.427%) 3 (1.376%) Education level,n(weighted %) < 0.001 Less than 9th grade 915 (11.425%) 203 (8.526%) 92 (7.443%) 248 (14.091%) 330 (13.670%) 42 (19.266%) 9-11th grade (Includes 12th grade with no diploma) 1428 (17.830%) 432 (18.144%) 232 (18.770%) 341 (19.375%) 371 (15.369%) 52 (23.853%) High school graduate/GED or equivalent 2002 (24.997%) 619 (25.997%) 324 (26.214%) 444 (25.227%) 563 (23.322%) 52 (23.853%) Some college or AA degree 2082 (25.996%) 558 (23.436%) 343 (27.751%) 473 (26.875%) 663 (27.465%) 45 (20.642%) College graduate or above 1574 (19.653%) 563 (23.646%) 245 (19.822%) 254 (14.432%) 485 (20.091%) 27 (12.385%) Marital status,n(weighted %) § < 0.001 Married 4017 (52.730%) 1035 (48.162%) 547 (48.109%) 906 (53.294%) 1426 (59.072%) 103 (47.248%) Widowed 594 (7.797%) 134 (6.235%) 24 (2.111%) 82 (4.824%) 296 (12.262%) 58 (26.606%) Divorced 839 (11.013%) 219 (10.191%) 110 (9.675%) 193 (11.353%) 291 (12.055%) 26 (11.927%) Separated 262 (3.439%) 81 (3.769%) 25 (2.199%) 78 (4.588%) 72 (2.983%) 6 (2.752%) Never married 1324 (17.380%) 489 (22.755%) 309 (27.177%) 270 (15.882%) 234 (9.693%) 22 (10.092%) Living with partner 580 (7.614%) 190 (8.841%) 122 (10.730%) 171 (10.059%) 94 (3.894%) 3 (1.376%) Smoking,n(weighted %) < 0.001 Every day 1867 (23.311%) 681 (28.601%) 284 (22.977%) 443 (25.170%) 425 (17.606%) 34 (15.596%) Some days 275 (3.434%) 88 (3.696%) 43 (3.479%) 80 (4.545%) 61 (2.527%) 3 (1.376%) Not at all 1717 (21.438%) 386 (16.212%) 154 (12.460%) 317 (18.011%) 773 (32.022%) 87 (39.908%) Drink,n(weighted %) § 0.065 Yes 5225 (67.840%) 1525 (69.508%) 778 (67.243%) 1186 (68.994%) 1597 (66.156%) 139 (63.761%) No 2477 (32.160%) 669 (30.492%) 379 (32.757%) 533 (31.006%) 817 (33.844%) 79 (36.239%) PIR,n(weighted %) 0.011 3 2618 (35.809%) 800 (36.934%) 385 (34.131%) 514 (31.572%) 866 (39.561%) 53 (26.500%) TFQI,weighted mean(95%CI) -0.001 (-0.013 ,0.012) 0.209 (0.196 0.223) 0.199 (0.180 0.219) 0.178 (0.164 0.193) 0.211 (0.198 0.225) 0.243 (0.203 0.291) 0.002 FT3/FT4,weighted mean(95%CI) 4.115 (4.095 ,4.135) 3.947 (3.915 3.979) 4.117 (4.075 4.160) 4.236 (4.199 4.273) 3.906 (3.875 3.938) 3.352 (3.249 3.458) < 0.001 TT4RI,weighted mean(95%CI) 17.701 (17.383 ,18.020) 14.670 (14.354 14.992) 14.536 (14.112 14.973) 15.080 (14.709 15.461) 16.380 (16.048 16.720) 17.831 (16.606 19.147) < 0.001 TSHI,weighted mean(95%CI) 1.564 (1.554 ,1.574) 1.522 (1.509 1.535) 1.508 (1.491 1.525) 1.499 (1.485 1.514) 1.563 (1.551 1.576) 1.683 (1.640 1.728) < 0.001 TSH(mIU/L),weighted mean(95%CI) 1.709 (1.682 ,1.736) 1.477 (1.447 1.507) 1.478 (1.435 1.522) 1.481 (1.446 1.516) 1.569 (1.537 1.603) 1.613 (1.504 1.731) < 0.001 FT3(pg/ml),weighted mean(95%CI) 3.189 (3.177 ,3.200) 3.214 (3.200 3.229) 3.181 (3.160 3.202) 3.148 (3.129 3.167) 3.109 (3.094 3.124) 2.975 (2.920 3.030) < 0.001 (FT4ng/dl),weighted mean(95%CI) 0.798 (0.796 ,0.801) 0.782 (0.777 0.787) 0.781 (0.774 0.788) 0.787 (0.781 0.793) 0.797 (0.791 0.803) 0.812 (0.791 0.834) < 0.001 Central obesity,n(weighted %) < 0.001 Yes 4319 (53.927%) 99 (4.158%) 777 (62.864%) 1295 (73.580%) 1960 (81.193%) 188 (86.239%) NO 3690 (46.073%) 2282 (95.842%) 459 (37.136%) 465 (26.420%) 454 (18.807%) 30 (13.761%) Hyperlipidemia,n(weighted %) < 0.001 Yes 4584 (57.236%) 908 (38.135%) 0 (0.000%) 1320 (75.000%) 2181 (90.348%) 175 (80.275%) NO 3425 (42.764%) 1473 (61.865%) 1236 (100.000%) 440 (25.000%) 233 (9.652%) 43 (19.725%) Hypertension,n(weighted %) < 0.001 Yes 2649 (33.075%) 441 (18.522%) 0 (0.000%) 590 (33.523%) 1438 (59.569%) 180 (82.569%) NO 5360 (66.925%) 1940 (81.478%) 1236 (100.000%) 1170 (66.477%) 976 (40.431%) 38 (17.431%) T2DM,n(weighted %) < 0.001 Yes 1511 (18.866%) 199 (8.358%) 0 (0.000%) 338 (19.205%) 844 (34.963%) 130 (59.633%) NO 6498 (81.134%) 2182 (91.642%) 1236 (100.000%) 1422 (80.795%) 1570 (65.037%) 88 (40.367%) CVD,n(weighted %) < 0.001 Yes 616 (7.691%) 130 (5.460%) 7 (0.566%) 59 (3.352%) 202 (8.368%) 218 (100.000%) NO 7393 (92.309%) 2251 (94.540%) 1229 (99.434%) 1701 (96.648%) 2212 (91.632%) 0 (0.000%) Low HDL-C,n(weighted %) < 0.001 Yes 2613 (32.626%) 409 (17.178%) 297 (24.029%) 878 (49.886%) 925 (38.318%) 104 (47.706%) NO 5396 (67.374%) 1972 (82.822%) 939 (75.971%) 882 (50.114%) 1489 (61.682%) 114 (52.294%) CKD,n(weighted %) < 0.001 Yes 1410 (17.605%) 349 (14.658%) 0 (0.000%) 247 (14.034%) 596 (24.689%) 218 (100.000%) NO 6599 (82.395%) 2032 (85.342%) 1236 (100.000%) 1513 (85.966%) 1818 (75.311%) 0 (0.000%) CKD, chronic kidney disease; CKM, cardiovascular-kidney-metabolic; CVD, cardiovascular disease; FT3/FT4 ,free triiodothyronine/free thyroxine ;NHANES,National Health and Nutrition Examination Survey;PIR, poverty/income ratio;T2DM,Type 2 Diabetes Mellitus;TFQI, thyroid feedback quantile-based index ;TSH, thyroid stimulating hormone; TSHRI,thyroid stimulating hormone resistance index ;TT4RI ,thyrotrophic thyroxine resistance index ; *Weighted to be nationally representative. §Variable categories may not sum to 100% because of missing data. Participants were stratified by TFQl quartiles. Baseline comparisons revealed that participants with high TFQI were older, more frequently non-Hispanic White, had at least a college degree, were smokers, married or widowed, and more easily developed CKM syndrome stages 3–4 versus those with low TFQI. Additionally, the 10-year CVD hazard ratio (HR) and the prevalence of central obesity, hypertension, diabetes mellitus, clinical CVD, and CKD significantly increased as the TFQI quartile increased ( P < 0.05), with no significant differences between sex, hyperlipidemia, low- and high-density lipoprotein cholesterol, alcohol consumption status, TT4RI, TSHI, or FT3/FT4 subgroups (Table S3). Baseline comparison of the different FT3/FT4 subgroups revealed that participants possessing a low FT3/FT4 ratio presented a larger likelihood of being older, female, non-Hispanic White, non-smokers, and with a higher PIR (> 3) than those with a high FT3/FT4 ratio. Additionally, the distribution of education, marital status, current alcohol drinking status, TFQI, TT4RI, TSHI, central obesity, and hypertension were significantly different among the subgroups ( P < 0.05). Conversely, CKM syndrome stages, 10-year CVD HR, and prevalence of hyperlipidemia, diabetes mellitus, clinical CVD, and CKD showed no inter-group differences (Table S4). Correlation between CKM syndrome and THS indices In the completely adjusted model (Table 2.1 ), TFQI and FT3/FT4 exhibited an obvious relevance to the risks of CKM syndrome stages 1–3 compared with stage 0 ( P < 0.05). The CKM syndrome risk increased with each unit increase in TFQI and FT3/FT4 (CKM syndrome stage 1: TFQI, 2.145 [95% CI, 1.065–4.317], FT3/FT4, 1.386 [95% CI, 1.147–1.674]; stage 2: TFQI, 2.155 [95% CI, 2.272–3.652], FT3/FT4, 2.055 [95% CI, 1.727–2.446]; stage 3: TFQI, 2.318 [95% CI, 1.419–3.786], FT3/FT4, 1.961 [95% CI, 1.678–2.292]). However, no association was found between CKM syndrome stage 4 and TT4RI or TSHI. Further correlation analysis of CKM syndrome and TSH indices stratified by quartile revealed that, high FT3/FT4 ratio more easily triggered the onset of CKM syndrome stage 1 (OR, 1.467 [95% CI, 1.067–2.016]), 2 (OR, 3.489 [95% CI, 2.442–4.87]), and 3 (OR, 2.969 [95% CI, 2.104–4.19]), but not CKM syndrome stage 4. Compared with low TFQI, TT4RI, and TSHI, no significant correlations were found between the high-level indicators and the risk of CKM syndrome stages 1–4 (Table 2.2 ). However, high FT3 levels indicated a lower risk of developing CKM syndrome stages 2–4 than low FT3 levels (stage 2: 0.587 [95% CI, 0.454–0.759]; stage 3: 0.473 [95% CI, 0.349–0.64]; stage: 0.158 [95% CI, 0.073–0.346]). Further, elevated FT4 concentrations demonstrated significant correlation with increased progression to advanced-stage (stages 3–4) CKM syndrome compared to lower levels (stage 3: 1.523 [95% CI, 1.06–2.188]; stage 4: 3.471 [95% CI, 1.485–8.112]). The unadjusted and partially adjusted results are presented in Tables S5 and S6. The relevance of the FT3/FT4 ratio to CKM syndrome stages 3 and 4 was unstable in the unadjusted model, but became significant or positive in models with age, sex, race, and ethnicity adjusted. Subgroup analyses based on these variables revealed a similar association between different THS indices and CKM syndrome stages as in the primary analysis (Tables S7-S9). Notably, FT3/FT4 ratio exhibited an obvious relevance to CKM syndrome stages 2 and 3 across different sex, age, race, and ethnicity subgroups. The risk of CKM syndrome stage 3 was higher in Black individuals, those aged ≥ 60 years, and males with higher FT3/FT4 ratios compared to in White individuals, those aged < 60 years, and females with lower FT3/FT4 ratios (Black: 3.494 [95% CI, 1.83–6.67]; White: 2.72 [95% CI, 1.841–4.017]; male: 3.308 [95% CI, 1.863–5.874]; female: 3.105 [95% CI, 2.004–4.812]; aged ≥ 60: 2.59 [95% CI, 1.467–4.893]; aged < 60: 2.069 [95% CI, 1.395–3.068]; all interaction P < 0.05), respectively. However, White individuals, those aged ≥ 60 years, and males with higher FT3/FT4 ratios were more likely to develop CKM syndrome stage 2 than Black individuals, those aged < 60 years, and females with lower FT3/FT4 ratios (White: 3.04 [95% CI, 2.117–4.367]; Black: 2.99 [95% CI, 1.614–5.539]; male: 3.949 [95% CI, 2.436–6.403]; female: 2.199 [95% CI, 1.312–3.685]; aged ≥ 60: 4.395 [95%CI, 1.665–11.605], age < 60: 2.185 [95%CI, 1.481–3.223]; all interactions P < 0.05), respectively. Table 2.1 Logistic regression analysis results of thyroid hormone sensitivity indices and CKM stage 1 to 4 in fully adjusted modelshted. Stage* 1 2 3 4 OR(95%CI) † P-value OR(95%CI) P-value OR(95%CI) P-value OR(95%CI) P-value TFQI (%) 2.145 (1.065 ,4.317) 0.034 2.155 (1.272 ,3.652) 0.006 2.318 (1.419 ,3.786) 0.002 3.245 (0.755 ,13.95) 0.108 FT3/FT4 (%) 1.386 (1.147 ,1.674) 0.002 2.055 (1.727 ,2.446) 0 1.961 (1.678 ,2.292) 0 0.829 (0.501 ,1.373) 0.449 TT4RI 0.995 (0.968 ,1.023) 0.704 0.987 (0.965 ,1.011) 0.271 0.963 (0.944 ,0.983) 0.001 0.983 (0.944 ,1.023) 0.38 TSHRI 0.879 (0.339 ,2.277) 0.78 1.92 (0.747 ,4.932) 0.165 2.26 (1.114 ,4.587) 0.026 0.502 (0.073 ,3.452) 0.466 TSH 0.955 (0.862 ,1.057) 0.356 0.961 (0.858 ,1.076) 0.472 1.128 (1.003 ,1.27) 0.045 1.352 (1.069 ,1.71) 0.014 FT3 (pg/ml) 0.812 (0.638 ,1.035) 0.089 0.644 (0.46 ,0.901) 0.013 0.479 (0.354 ,0.648) 0 0.238 (0.124 ,0.459) 0 FT4 (ng/dl) 1.226 (0.626 ,2.402) 0.536 1.711 (0.842 ,3.477) 0.13 3.463 (1.735 ,6.912) 0.001 20.641 (4.148 ,102.723) 0.001 CKM, cardiovascular-kidney-metabolic; FT3/FT4, free triiodothyronine/free thyroxine ;TFQI, thyroid feedback quantile-based index ;TSH, thyroid stimulating hormone; TSHRI, thyroid stimulating hormone resistance index ;TT4RI, thyrotrophic thyroxine resistance index ; * Weighted to be nationally representative. † Models were fully adjusted for age, sex, race and ethnicity, education level, marital status, current smoker (yes, no), current drinker (yes, no, or missing). Table 2.2 Logistic regression analysis results of thyroid hormone sensitivity indices stratified by quartile and CKM stage 1 to 4 in fully adjusted modelshted. Stage 1 2 3 4 N(weighted %)* OR(95%CI) P-value N(weighted %) OR(95%CI) P-value N(weighted %) OR(95%CI) P-value N(weighted %) OR(95%CI) P-value TFQI (%) 1 355 (28.7%) Reference 482 (27.4%) Reference 518 (21.5%) Reference 27 (12.4%) Reference 2 304 (24.6%) 0.878 (0.597 ,1.291) 0.465 449 (25.5%) 1.219 (0.795 ,1.867) 0.322 589 (24.4%) 1.133 (0.681 ,1.887) 0.592 46 (21.1%) 0.621 (0.191 ,2.017) 0.384 3 288 (23.3%) 1.129 (0.652 ,1.954) 0.628 462 (26.2%) 1.941 (1.042 ,3.616) 0.039 624 (25.8%) 1.436 (0.618 ,3.337) 0.357 60 (27.5%) 1.026 (0.197 ,5.355) 0.972 4 289 (23.4%) 1.056 (0.587 ,1.898) 0.839 367 (20.9%) 1.221 (0.609 ,2.448) 0.532 683 (28.3%) 1.797 (0.737 ,4.38) 0.171 85 (39.0%) 0.605 (0.076 ,4.825) 0.598 FT3/FT4(%) 1 224 (18.1%) Reference 274 (15.6%) Reference 697 (28.9%) Reference 126 (57.8%) Reference 2 288 (23.3%) 1.136 (0.846 ,1.525) 0.358 330 (18.8%) 1.436 (1.118 ,1.845) 0.009 544 (22.5%) 1.317 (0.973 ,1.783) 0.07 40 (18.3%) 0.509 (0.249 ,1.041) 0.062 3 393 (31.8%) 1.181 (0.834 ,1.674) 0.312 552 (31.4%) 2.388 (1.71 ,3.335) 0 622 (25.8%) 1.847 (1.358 ,2.513) 0.001 33 (15.1%) 0.672 (0.309 ,1.46) 0.28 4 331 (26.8%) 1.467 (1.067 ,2.016) 0.023 604 (34.3%) 3.489 (2.442 ,4.987) 0 551 (22.8%) 2.969 (2.104 ,4.19) 0 19 (8.7%) 0.636 (0.226 ,1.784) 0.351 TSH 1 320 (25.9%) Reference 458 (26.0%) Reference 575 (23.8%) Reference 48 (22.0%) Reference 2 316 (25.6%) 0.95 (0.743 ,1.215) 0.651 434 (24.7%) 0.911 (0.724 ,1.145) 0.379 552 (22.9%) 0.885 (0.687 ,1.141) 0.305 50 (22.9%) 1.104 (0.506 ,2.411) 0.78 3 307 (24.8%) 0.964 (0.738 ,1.259) 0.763 441 (25.1%) 0.918 (0.686 ,1.229) 0.525 622 (25.8%) 0.911 (0.7 ,1.186) 0.446 52 (23.9%) 0.9 (0.393 ,2.061) 0.781 4 293 (23.7%) 0.87 (0.69 ,1.098) 0.209 427 (24.3%) 0.896 (0.674 ,1.192) 0.407 665 (27.5%) 1.098 (0.774 ,1.556) 0.561 68 (31.2%) 1.828 (0.952 ,3.509) 0.066 FT3(pg/ml) 1 220 (17.8%) Reference 373 (21.2%) Reference 564 (23.4%) Reference 79 (36.2%) Reference 2 372 (30.1%) 0.801 (0.638 ,1.005) 0.054 547 (31.1%) 0.874 (0.656 ,1.164) 0.315 757 (31.4%) 0.801 (0.616 ,1.043) 0.089 63 (28.9%) 0.539 (0.3 ,0.968) 0.041 3 243 (19.7%) 0.641 (0.503 ,0.816) 0.002 369 (21.0%) 0.676 (0.535 ,0.856) 0.004 492 (20.4%) 0.609 (0.466 ,0.794) 0.002 33 (15.1%) 0.454 (0.243 ,0.85) 0.019 4 401 (32.4%) 0.809 (0.639 ,1.024) 0.072 471 (26.8%) 0.587 (0.454 ,0.759) 0.001 601 (24.9%) 0.473 (0.349 ,0.64) 0 43 (19.7%) 0.158 (0.073 ,0.346) 0 FT4(ng/dl) 1 167 (13.5%) Reference 226 (12.8%) Reference 310 (12.8%) Reference 30 (13.8%) Reference 2 381 (30.8%) 0.918 (0.708 ,1.19) 0.474 530 (30.1%) 1.003 (0.79 ,1.274) 0.975 642 (26.6%) 1.152 (0.946 ,1.402) 0.138 43 (19.7%) 1.638 (0.82 ,3.271) 0.141 3 390 (31.6%) 1.08 (0.824 ,1.415) 0.536 559 (31.8%) 0.906 (0.681 ,1.205) 0.455 734 (30.4%) 1.093 (0.842 ,1.418) 0.461 61 (28.0%) 1.719 (0.852 ,3.466) 0.115 4 298 (24.1%) 0.807 (0.609 ,1.067) 0.117 445 (25.3%) 1.189 (0.859 ,1.645) 0.259 728 (30.2%) 1.523 (1.06 ,2.188) 0.027 84 (38.5%) 3.471 (1.485 ,8.112) 0.009 RCS analysis Figure 2 and Figure S1 illustrate the non-linear relationship between CKM syndrome stages 1–4 and different THS indices. RCS regression analysis revealed a non-linear relevance of TFQI to CKM syndrome stage 2 ( P 0.05), a non-linear relevance of TT4RI to CKM syndrome stages 2–3 ( P < 0.05), a linear relevance of TT4RI to CKM syndrome stage 1 ( P = 0.065), a non-linear relevance of TSHI to CKM syndrome stages 2–3 ( P < 0.05), and a linear relevance of TSHI to CKM syndrome stage 4 ( P = 0.065). In CKM syndrome stage 2, the TFQI, TT4RI, and TSHI index values were approximately − 0.25, 33, and 1.55, while in CKM syndrome stage 3, TT4RI and TSHI were approximately 28 and 1.6, respectively. These data indicated changes in the direction or strength of the association between these THS indices and CKM syndrome. Two piecewise linear regression models using different THS indices as the regression variables revealed that the risk of CKM syndrome stage 2 decreased as TFQI and TSHI increased above the inflection point (TFQI: 0.716 [95% CI, 0.521, 0.985], TSHI: 0.451 [0.233, 0.875]), whereas the risk of CKM syndrome stages 2–3 increased as TT4RI increased below the inflection point (CKM syndrome stage 2: 1.013 [95% CI, 1.001–1.025]; CKM syndrome stage 3: 1.02 [95% CI, 1.008–1.032]) (Table 3 ). Table 3 The different THS indices and CKM stages in the piecewise linear regression models OR(95%CI) P-value CKM stage 2 Inflection point (TFQI) TFQI≥-0.25 0.716(0.521,0.985) 0.045 TFQI<-0.25 0.725(0.295,1.782) 0.486 Inflection point (TT4RI) TT4RI ≥ 33 0.994(0.954,1.036) 0.791 TT4RI ≤ 33 1.013(1.001,1.025) 0.034 Inflection point (TSHI) TSHI ≥ 1.6 0.451(0.233,0.875) 0.023 TSHI ≤ 1.6 0.778(0.426,1.42) 0.417 CKM stage 3 Inflection point (TT4RI) TT4RI ≥ 28 0.991(0.963,1.019) 0.523 TT4RI ≤ 28 1.02(1.008,1.032) 0.002 Inflection point (TSHI) TSHI ≥ 1.6 0.804(0.456,1.418) 0.456 TSHI ≤ 1.6 1.134(0.661,1.944) 0.65 Discussion This investigation analyzed data from a demographically diverse cohort of American adults, revealing significant correlations between both central and peripheral THS measures and CKM syndrome. TFQI had a stronger connection with the onset of CKM syndrome compared to other central THS indices. Individuals with high FT3/FT4 ratios were more likely to develop CKM syndrome stages 1 to 3 than those with low FT3/FT4 ratios. These findings highlight abnormal thyroid hormone sensitivity as a potential driver of CKM syndrome development in patients without thyroid dysfunction. Compared with other measures of THS, the FT3/FT4 ratio could serve as potential epidemiological tools for quantifying the contribution of abnormal THS in CKM syndrome development. As a pioneering investigation, this research explores the association between THS indices and CKM syndrome stage among adult populations in the U.S.. Although numerous studies have linked decreased THS to the onset of obesity, CVD, diabetes, renal disease, and metabolic syndrome in general populations, the association between THS concentrations and CKM syndrome severity remains poorly characterized. Thyroid hormones can increase energy expenditure, heat production ( 15 ), glucose and fatty acid oxidation in the muscles ( 16 ) and liver ( 17 ), and lipolysis of adipose tissues ( 18 ). Thus, thyroid hormones play important roles in numerous processes, including weight loss, lipid reduction, and insulin sensitivity, by influencing the basic metabolism and energy stability of the entire body. These hormones are chiefly controlled by the hypothalamus-pituitary-thyroid axis, cellular membrane transport, and iodothyronine deiodinases (DiO) in the peripheral tissues ( 19 , 20 ). As such, THS impairment can be systematically divided into central and peripheral types. Impaired central THS disrupts the feedback circuits in the central nervous system, causing a relative increase in TSH levels ( 19 ) TFQI has recently attracted significant attention as a composite measure of THS; TFQI is a more accurate measure of thyroid hormone homeostasis in the population with normal thyroid function compared with those with single thyroid function indices. A number of earlier studies have shown a significant connection between impaired central THS and the risks of proteinuria, metabolic syndrome, CVD, hyperuricemia, diabetes, and hypertension ( 21 , 22 , 23 , 24 ). FT3/FT4 is a positive measure of peripheral THS sensitivity ( 25 ). Increased or impaired peripheral THS is associated with a variety of diseases ( 26 , 27 ), possibly caused by the adaptive regulation of thyroid hormones under pathological conditions ( 28 ). Increased, but not impaired, peripheral THS has been linked to the development of insulin resistance, fatty liver, obesity and osteoporosis ( 29 , 30 , 31 , 32 ). An overweight status and excessive adipose tissue are important aspects of CKM syndrome pathogenesis. Obesity has been shown to be associated with energy metabolism imbalance ( 33 ). Further, the energy metabolism imbalance caused by thyroid hormone resistance may play a pivotal role in the progression of CKM syndrome. Obesity caused by impaired THS may be attributed to TSH-mediated fat synthesis ( 34 , 35 ), underscoring the importance of central thyroid hormone resistance. Additionally, insufficient relative thyroid hormone production reduces resting energy and heat production, leading to fat accumulation and obesity ( 36 ). Various studies have previously investigated the relationship between impaired central THS and obesity. Using data from the NHANES, Laclaustra et al. ( 10 ) found that impaired central THS was associated with an increased body mass index (BMI) among euthyroid participants. Conversely, Sun et al. ( 22 ) observed an inverse relationship in mainland China: subclinical hypothyroidism patients exhibiting central THS dysfunction showed statistically significant protection against obesity. This inconsistency may be attributed to the differences in the study population. Our results predominantly align with those reported by Laclaustra et al.; however, we found a non-linear relationship between TFQI and CKM syndrome stage 2 incidence, which may contribute to the discrepancy in findings. As such, studies with larger samples are warranted to address these discrepancies. Interestingly, Wang et al. ( 37 ) found that gene-driven changes in serum TSH levels did not influence BMI or obesity, and that an increase in BMI increased FT3 levels without influencing FT4 levels, suggesting defects in downstream signaling. Furthermore, Biondi et al.( 31 ) revealed that euthyroid individuals with obesity had higher FT3, TSH and FT3/FT4 indices than those without obesity, suggesting that elevated thyrotropin may be a result, rather than a cause, of increased BMI. Given that obesity represents the predominant underlying metabolic disorder of CKM syndrome stages 1–2, elevated FT3/FT4 ratio is associated with a higher risk of developing CKM syndrome. Diabetes, CKD, and CVD are all major clinical features of CKM syndrome. These clinical characteristics are predominantly found in individuals with CKM syndrome stage 2–4, in which inflammatory reactions and hemodynamic changes caused by thyroid resistance may be important stage-specific features. Furthermore, prior evidence have shown that impaired central THS links to the development of these diseases. For example, Roos et al. ( 38 ) reported a positive association between HOMA insulin resistance index (HOMA-IR) and TSH while FT4 exhibited an inverse relationship with HOMA-IR in euthyroid adults. Further, Wang et al. ( 38 ) demonstrated an association between TFQI and albuminuria. Several studies have further confirmed that impaired central THS increases the risk of CVD ( 22 , 39 , 40 ). The association between thyroid function abnormalities and these clinical features could be attributed to several factors. First, elevated TSH binds to the thyrotropin receptor in adipose tissues, stimulating the production of excessive C-reactive proteins and mediating inflammation. This process, in turn, causes hyperinsulinemia and insulin resistance ( 41 ). Additionally, increased thyroid hormones regulate adipokines in adipose tissue, driving insulin resistance and higher blood glucose levels ( 42 ). Second, resistance to thyroid hormones may change the immune microenvironment in kidney disease, boosting inflammatory responses and inflicting kidney damage ( 43 ). Finally, thyroid hormones affect peripheral hemodynamics, resulting in increased cardiac filling and altered cardiac contraction patterns ( 44 ). Conversely, decreased thyroid hormones can directly impair ventricular diastolic function and stroke volume, increased arterial resistance, and further influence cardiac function ( 45 ). Our findings demonstrated that THS exhibited a strong association with the progression of CKM syndrome from stages 1 through 3, as revealed by the current investigation. These results suggest that THS is particularly useful to screen out high-risk groups for early-stage CKM syndrome. Further, these results indicate that THS may have a multi-faceted potential impact on CKM syndrome stages 1 to 3. While we did not identify any significant correlation between THS indices and CKM syndrome stage 4, this does not mean that thyroid hormone has lost its effect on patients of this stage. Instead, we speculate that this discrepancy may be related to the interaction of multiple diseases in CKM syndrome stage 4, although further research is needed to confirm this. Indeed, CVD predominantly occurs in CKM syndrome stage 4; as such, this stage is often accompanied by a variety of critical conditions, with a high risk of death. Moreover, in light of the post hoc power analysis (Power > 0.9), we have determined that our study was adequately powered to detect significant associations at CKM Stage 4(Table S10). Consequently, The non-significant findings in Stage 4 are not due to a lack of power but may be attributed to smaller effect sizes or other confounding factors that require further investigation. In the future researches, THS may need to be combined with more biochemical indicators to evaluate the disease. However, after the consideration of factors including sex, age, race, smoking, and alcohol use, we found that the risk of developing CKM syndrome stage 4 was associated with higher FT4 levels, indicating a need for closer monitoring of individuals with borderline FT4 levels. The association between THS indices and CKM syndrome varied across age subgroups. Individuals aged ≥ 60 years with higher FT3/FT4 ratios showed an elevated risk of CKM syndrome stages 1–3 than those aged < 60. In our study, we found that younger individuals tended to have higher peripheral THS values, confirming the results of prior studies ( 46 , 47 ). This finding indicates the need for close monitoring of elevated FT3/FT4 in older adults, and screening for potential CKM syndrome risk groups, which could help to optimize CKM syndrome management and reduce the overall CKM syndrome burden. Differences in interaction were also identified in different sex and race subgroups. In particular, among the subset of individuals with higher FT3/FT4 ratios, Black individuals and males had a higher risk of CKM syndrome stage 3 compared to White individuals and females, respectively. These results are in agreement with reported sex difference in the link between thyroid dysfunction and obesity ( 48 ). Consistent with our findings, a prior Chinese cross-sectional study revealed stronger associations between thyroid hormone sensitivity and CKM syndrome in elderly males. However, their analysis results focused predominantly on central thyroid sensitivity (TSHI) rather than peripheral sensitivity indices (FT3/FT4 ratio) ( 8 ). This discrepancy may stem from: 1) Ethnic-specific variations in thyroid axis regulation; 2) Our implementation of stricter diagnostic criteria. Therefore, more studies are required to clarify the fundamental mechanisms underlying these differences in sex and race. This study has several limitations. Firstly, its cross-sectional design prevented the establishment of any causal relationship between THS and CKM syndrome, highlighting the need for further cohort studies and interventional trials. Second, due to limitations in the data source thyroid-associated antibodies were not examined, which may have confounded the relationship between THS and CKM syndrome, as autoimmunity may play a role in thyroid sensitivity. Third, rather than relying on clinical diagnosis, we defined subclinical CVD as a 10-year CVD risk ≥ 20% or a high risk of CKD, which may have impacted our outcome. Thus, more clinical data are needed in future research to address this issue. Fourth, our study used a representative U.S. population, with individuals of different races. Our results consequently revealed racial differences between Black and White individuals. As such, further validation is required to confirm the generalizability of our results. Fifth, although we adjusted for mixed factors as much as possible, other potential mixed factors (such as diet, physical activity, stress level, etc.) may also have affected the results. Finally, although our research confirms the relationship between THS and CKM syndrome, an investigation of the relevant mechanisms is lacking, and further experimental verification is therefore necessary. Conclusions Overall, our study revealed a potential link between central and peripheral THS and CKM syndrome among euthyroid participants. Impaired central THS and increased peripheral THS may play a role in the development or progression of CKM syndrome, even in the absence of obvious thyroid dysfunction. Further, we found TFQI and FT3/FT4 as key contributors to heterogeneity in CKM syndrome, which may have important implications for CKM syndrome risk assessment and management in euthyroid individuals. However, further trials are required to determine the future clinical use of this index in these patients. Declarations Ethical Statement This manuscript used de-identified patient data and thus did not require institutional review board or ethical board approval. Data availability statement In this study, the analyzed data are publicly available and can be retrieved from the designated repository: https://www.cdc.gov/nchs/nhanes/ Disclosure The authors dedlare that there is no contlict of interest that could be perceived as prejudicing the impartiality of this work. Funding The authors affirm that the research was carried out without any commercial or financial affiliations. Author contributions ZX and RX conceived and designed the study. RX and YZ handled data collection and analysis. 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A Cross-Sectional Study Examining the Parametric Thyroid Feedback Quantile Index and Its Relationship with Metabolic and Cardiovascular Diseases. Thyroid: Official J. Am. Thyroid Association . 32 , 1488–1499. https://doi.org/10.1089/thy.2022.0025) (2022). Liu, F. et al. Association between sensitivity to thyroid hormones and risk of arrhythmia in patients with coronary heart disease: a RCSCD-TCM study in China. Endocr. 2023 79 349–357. ( https://doi.org/10.1007/s12020-022-03223-4) Su, X., Peng, H., Chen, X., Wu, X. & Wang, B. Hyperlipidemia and hypothyroidism. Clin. Chim. Acta . 527 , 61–70. https://doi.org/10.1016/j.cca.2022.01.006) (2022). Teixeira, P., de FDS, Dos Santos, P. B. & Pazos-Moura, C. C. The role of thyroid hormone in metabolism and metabolic syndrome. Therapeutic Adv. Endocrinol. Metabolism 2020 11 2042018820917869. ( https://doi.org/10.1177/2042018820917869 ). Han, Z. et al. The role of thyroid hormone in the renal immune microenvironment. Int. Immunopharmacol. 119 , 110172. https://doi.org/10.1016/j.intimp.2023.110172) (2023). Corona, G., Solaroli, E., Tortorici, G. & Sforza, A. [Heart and thyroid interactions]. Giornale Italiano Di Cardiologia () 2019 20 342–350. (2006). https://doi.org/10.1714/3165.31467 ). Cappola, A. R. et al. Thyroid and Cardiovascular Disease: Research Agenda for Enhancing Knowledge, Prevention, and Treatment. Circulation 139 , 2892–2909. https://doi.org/10.1161/CIRCULATIONAHA.118.036859) (2019). Wang, Z. et al. Serum FT3/FT4, but not TSH is associated with handgrip strength in euthyroid U.S. population: evidence from NHANES. Front. Endocrinol. 15 , 1323026. https://doi.org/10.3389/fendo.2024.1323026) (2024). Chen, X. et al. Relationship of gender and age on thyroid hormone parameters in a large Chinese population. Archives Endocrinol. Metabolism . 64 , 52–58. https://doi.org/10.20945/2359-3997000000179) (2020). Wang, B. et al. an. Sex Differences in the Associations of Obesity With Hypothyroidism and Thyroid Autoimmunity Among Chinese Adults. Frontiers in Physiology 9 1397. (2018). https://doi.org/10.3389/fphys.2018.01397 ). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFiguresandTables1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6273789","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435234307,"identity":"1c116fed-39ab-42a8-a634-0db4dde9f685","order_by":0,"name":"Zelu Xia","email":"","orcid":"","institution":"Beichen District Hospital of Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zelu","middleName":"","lastName":"Xia","suffix":""},{"id":435234308,"identity":"4f8494b9-ecc3-4df1-85e1-7ea5e7e39019","order_by":1,"name":"Rongjia Xu","email":"","orcid":"","institution":"First Teaching Hospital of Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rongjia","middleName":"","lastName":"Xu","suffix":""},{"id":435234309,"identity":"526fd8a7-8a43-4027-b528-9006d204c63b","order_by":2,"name":"Chen Jiang","email":"","orcid":"","institution":"First Teaching Hospital of Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Jiang","suffix":""},{"id":435234310,"identity":"5cee36f2-cc76-4195-836a-803f84370877","order_by":3,"name":"Zuo Chen","email":"","orcid":"","institution":"Beichen District Hospital of Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zuo","middleName":"","lastName":"Chen","suffix":""},{"id":435234311,"identity":"22fef14d-50bd-419f-abc6-96e7a977faa1","order_by":4,"name":"Yizhan Zhang","email":"","orcid":"","institution":"First Teaching Hospital of Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yizhan","middleName":"","lastName":"Zhang","suffix":""},{"id":435234312,"identity":"9743ddee-1ae8-40ec-8379-5e4268df6fd5","order_by":5,"name":"Shentao Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYHACNgjF3tj4QMJAQo6Nvf0AMVoMGBh4Dh82sCiwMObjOZNApBaJtDSJig8VifMkHAzwqje4kf7swccdf+T5GXIMJG4YSKS3STAkMPyo2IZHS4654cwzBoYzG4DEDAOJ3DbpxgOMPWdu49RidiOHTZq3zYBxw8Eeg2QJkBaZAwnMjG34tKQ/k/7bZmC/4TCPweE/QIexSSQYENCSYCbN2GaQuOEYW2ID0JYEglrsz7wxk+xtM06e2cN8mAGoxbANGMgH8flFsj39mcTPNjnbfvmH7T8k/tTJy7e3H3zwowK3FuzgAInqR8EoGAWjYBSgAQD2BVh04rQniQAAAABJRU5ErkJggg==","orcid":"","institution":"First Teaching Hospital of Tianjin University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Shentao","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-03-21 03:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6273789/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6273789/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79677374,"identity":"b59c70b7-f0db-4baa-a5df-3beddbd71ab1","added_by":"auto","created_at":"2025-04-01 12:22:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147722,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6273789/v1/7542b6e184065b0ea618b85d.png"},{"id":79677376,"identity":"ebdca3ae-d932-428b-a2cb-3c62ba8e8d00","added_by":"auto","created_at":"2025-04-01 12:22:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180561,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6273789/v1/3b9f928f09705d27f36e31da.png"},{"id":79680576,"identity":"3031df5a-b745-4b54-be0f-2834f383eaf8","added_by":"auto","created_at":"2025-04-01 12:46:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1601723,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6273789/v1/b782dfa9-398d-49ac-bcec-06e484cffd16.pdf"},{"id":79677384,"identity":"79d615e0-3551-433a-8f12-39a140456b8f","added_by":"auto","created_at":"2025-04-01 12:22:24","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":405786,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresandTables1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6273789/v1/c303a85abed70808c5ac1a25.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between sensitivity to thyroid hormone and cardiovascular-kidney-metabolic syndrome in U.S. adults: Evidence from the 2007–2012 national health and nutrition examination survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCKM syndrome is a multifaceted clinical condition, featuring the interaction of metabolic risk factors, CKD, and cardiovascular problems. Consequently, this syndrome indicates a high occurrence of multi-organ failure and adverse effects on the cardiovascular system(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). CKM syndrome exhibits complex inter-system interactions and high mortality rate, making it a major public health concern (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Simple and effective biochemical markers for its early detection and management of CKM syndrome are therefore required.\u003c/p\u003e \u003cp\u003eThe term \u0026lsquo;THS\u0026rsquo; refers to the degree of response to thyroid hormones, and is a crucial factor in controlling metabolism, cardiovascular health, and energy regulation. This index includes both central and peripheral sensitivity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). According to existing studies, abnormal THS greatly affects disease development and prognosis. For example, hypothyroidism or hyperthyroidism may lead to abnormal heart function, thereby facilitating the development of cardiovascular disease (CVD) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, changes in THS may indirectly influence insulin sensitivity and lipid metabolism by altering metabolic rate and energy expenditure, which are important components of CKM syndrome (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, researches have not well explored the precise link between THS and CKM.\u003c/p\u003e \u003cp\u003eAgainst such backdrop, our study aimed at examining such linkage in a population without thyroid disorder. Recent research involving the Chinese population underscores the contribution of abnormal THS to CKM syndrome management, but there is still a gap in studies concerning other ethnicities(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Utilizing NHANES data, the cross-sectional study engaged in assessing central THS by thyroid feedback quantile-based index (TFQI), thyrotrophic thyroxine resistance index (TT4RI), and thyroid-stimulating hormone index (TSHI), and assessing peripheral THS by free triiodothyronine/free thyroxine (FT3/FT4). We subsequently examined the correlation of different THS markers with various CKM stages in euthyroid subjects, for elucidating the possible THS-CKM linkage, and finally providing evidence to improve the prevention and management of this condition.\u003c/p\u003e"},{"header":"Subjects and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe study employed data from 3 NHANES cycles spanning from 2007 to 2012 (downloaded in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The NHANES is a comprehensive, four-stage, nationwide probability sampling survey aimed at choosing a sample representing the non-institutionalized U.S. civilian population (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The study obtained the approval from the National Institutes of Health Ethics Advisory Committee, and acquired the all participants\u0026rsquo; written informed consent. A total of 30,442 participants completed the NHANES and fasting tests at the mobile test centers. After excluding 11,823 participants aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years, 10,111 with insufficient thyroid function indices or abnormal thyroid function, and 499 with insufficient data to confirm CKM syndrome or exclude pregnancy, the study included a final sample of 8,009 participants (Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTHS index assessment\u003c/h3\u003e\n\u003cp\u003eCentral THS evaluation was conducted using three different indices, with the corresponding formulas given below:\u003c/p\u003e \u003cp\u003eTSHI\u0026thinsp;=\u0026thinsp;lnTSH (mIU/L)\u0026thinsp;+\u0026thinsp;0.1345 FT4 (pmol/L)\u003c/p\u003e \u003cp\u003eTT4RI\u0026thinsp;=\u0026thinsp;FT4 (pmol/L) \u0026times; TSH (mIU/L)\u003c/p\u003e \u003cp\u003eTFQI = (cdfFT4) - (1 - cdfTSH).\u003c/p\u003e \u003cp\u003eTFQI was measured on a scale from \u0026minus;\u0026thinsp;1 to 1, and negative and positive numbers respectively denoted heightened and reduced sensitivity of the hypothalamic-pituitary-thyroid axis to thyroid hormone change (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The FT3/FT4 ratio marked the peripheral THS. These indices were divided into quartiles: low, mid-lower, mid-upper, and high.\u003c/p\u003e\n\u003ch3\u003eAssessment of CKM syndrome\u003c/h3\u003e\n\u003cp\u003eCKM syndrome features concurrent subclinical or clinical CVD, CKD, and metabolic disorders (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Subclinical CVD referred to the case of a 10-year CVD risk\u0026thinsp;\u0026ge;\u0026thinsp;20%, or an elevated chronic kidney disease risk, predicted using a simplified CKM syndrome risk algorithm(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) incorporating various variables listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Clinical CVD was defined as any history of stroke, heart attack, chronic heart failure, or coronary heart disease.\u003c/p\u003e \u003cp\u003eWe collected data using standardized questionnaires and physical examinations, and analyzed blood and spot urine samples at a central laboratory. The 2021 updated CKD-EPI Eq.\u0026nbsp;(12) was adopted for estimating the glomerular filtration rate. \u003cem\u003eThe Kidney Disease: Improving Global Outcomes Classification system\u003c/em\u003e stratifies CKD risk based on different thresholds of estimated glomerular filtration rate (\u0026lt;\u0026thinsp;30, 30\u0026ndash;44, 45\u0026ndash;59, and \u0026ge;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) and urinary albumin/creatinine (ALB/Cr) ratio (\u0026lt;\u0026thinsp;30, 30\u0026ndash;299, and \u0026ge;\u0026thinsp;300 mg/g). CKD referred to a urinary ALB/Cr ratio\u0026thinsp;\u0026ge;\u0026thinsp;300 mg/g or an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetabolic disorders included overweight or obesity, abdominal obesity, pre-diabetes, diabetes mellitus, hypertension, abnormal lipid levels and metabolic syndrome. Diabetes features a fasting blood sugar(FBS) level\u0026thinsp;\u0026gt;\u0026thinsp;7 mmol/L (126 mg/dL), a higher/glycosylated hemoglobin\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, a 2-hour blood glucose level in an OGTT of 11 mmol/L (200mg/dL) or more, self-reported diagnosis, or use of hypoglycemic drugs. Hypertension was diagnosed based on a systolic blood pressure(SBP)\u0026thinsp;\u0026gt;\u0026thinsp;140 mmHg, a diastolic blood pressure(DBP)\u0026thinsp;\u0026gt;\u0026thinsp;90 mmHg, or the administer of antihypertensive drugs. CVD was determined by a history of stroke, heart attack and failure, coronary artery disease, or angina under self report.\u003c/p\u003e \u003cp\u003eParticipants were categorized into five CKM syndrome stages, according to the severity of their clinical condition: stage 0 (normal), stage 1 (characterized by obesity or pre-diabetes only), stage 2 (characterized by at least one metabolic disorder other than obesity or CKD), stage 3 (subclinical CVD with a concurrent metabolic disorder or CKD), stage 4 (clinical CVD with concurrent metabolic disorder or CKD) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Detailed staging criteria are presented in Table S2.\u003c/p\u003e\n\u003ch3\u003eAssessment of other covariates\u003c/h3\u003e\n\u003cp\u003eCovariates used here encompassed age, sex (male or female), race, current smoking status (everyday, some days, not at all, missing), current alcohol consumption status (yes, no, missing), and household poverty income ratio (PIR: \u0026lt;3 indicates low, \u0026ge;\u0026thinsp;3 indicates high). The NHANES database provides public access to the comprehensive measurement specific to all variables here.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e In accordance with the guidelines from the National Center for Health Statistics, the strata and main sampling units for complex, multi-stage, and probability sampling designs were all considered. MEC sample weights were applied for all analyses, including personnel involved in testing at the mobile test center. Continuous variables followed the format of the mean of the weighted 95% confidence interval (CI), while categorical variables followed the format of frequency of weighted percentages. ANOVA was employed for comparing continuous variables, and an adjusted χ\u003csup\u003e2\u003c/sup\u003e test served for comparing categorical variables among various TFQI and FT3/FT4 levels. Given the low number of missing data for covariates, multiple imputation designed for survey data was applied to address missing covariates rather than excluding those participants.\u003c/p\u003e \u003cp\u003eFor elucidating the relevance of THS indices to CKM syndrome stage, we used a polynomial logistic regression model with stage 0 as the reference group. The odds ratios (ORs) and 95% CI were measured for assessing the association between each THS index (as a continuous variable or stratified by quartiles) and CKM syndrome stage. A basic model incorporating only the THS indices, a partially adjusted model including demographic covariates (age, sex, race), and a fully adjusted model including demographic and lifestyle factors (current smoker, current alcohol drinker, PIR, and education) were constructed.\u003c/p\u003e \u003cp\u003eWith the aim of ensuring that the results were robust, we conducted several further analyses. Given the demographic variations in THS and CKM syndrome, analyses of subgroups and interactions were conducted according to age, sex, and race/ethnicity. Additionally, taking stage 0 as a control, we performed RCS analysis to investigate any possible nonlinear connections between various THS indices and CKM syndrome stages.\u003c/p\u003e \u003cp\u003eStatistical analyses relied on SAS 9.4 (SAS Institute) and R 4.4.1 (R Foundation), with two-tailed P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 defining statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eOur study enrolled 8,009 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years as a representative sample of 101,708,798 non-institutionalized U.S. adults. The mean age of participants was 45.587 (95% CI, 45.166\u0026ndash;46.008) years, with 48.795% (n\u0026thinsp;=\u0026thinsp;3,908) being females and 44.712% (n\u0026thinsp;=\u0026thinsp;3,581) being non-Hispanic Whites. Participants with CKM syndrome stage 1\u0026ndash;4 were older and had higher TFQI, TT4RI, and TSHI index values, with a lower FT3/FT4 ratio (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those at stage 0. Significant differences in sex, race, education, marital status, smoking status, and PIR were found between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); however, there was no notable variation in alcohol consumption (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of participants with CKM syndrome mellitus (n\u0026thinsp;=\u0026thinsp;8009) in the NHANES 2007\u0026ndash;2012, weighted.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eCKM in different installments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;8009)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(N\u0026thinsp;=\u0026thinsp;2381)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(N\u0026thinsp;=\u0026thinsp;1236)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(N\u0026thinsp;=\u0026thinsp;1760)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(N\u0026thinsp;=\u0026thinsp;2414)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4(N\u0026thinsp;=\u0026thinsp;218)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,weighted mean(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.587 (45.166 ,46.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.709 (37.982 39.451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.696 (33.953 35.455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.806 (41.123 42.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.855 (56.216 57.501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69.121 (67.558 70.720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4101 (51.205%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1194 (50.147%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e560 (45.307%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e955 (54.261%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1266 (52.444%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e126 (57.798%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3908 (48.795%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1187 (49.853%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e676 (54.693%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e805 (45.739%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1148 (47.556%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92 (42.202%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1326 (16.556%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305 (12.810%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e254 (20.550%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e419 (23.807%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e334 (13.836%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14 (6.422%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e899 (11.225%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223 (9.366%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149 (12.055%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e261 (14.830%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e253 (10.481%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13 (5.963%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3581 (44.712%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1138 (47.795%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e455 (36.812%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e700 (39.773%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1166 (48.302%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e122 (55.963%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1634 (20.402%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e420 (17.640%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e318 (25.728%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e300 (17.045%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e530 (21.955%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e66 (30.275%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race - Including Multi-Racial Missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e569 (7.105%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e295 (12.390%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60 (4.854%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80 (4.545%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e131 (5.427%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3 (1.376%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e915 (11.425%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203 (8.526%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92 (7.443%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e248 (14.091%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e330 (13.670%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42 (19.266%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade (Includes 12th grade with no diploma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1428 (17.830%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e432 (18.144%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e232 (18.770%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e341 (19.375%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e371 (15.369%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52 (23.853%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2002 (24.997%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e619 (25.997%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e324 (26.214%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e444 (25.227%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e563 (23.322%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52 (23.853%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2082 (25.996%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e558 (23.436%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e343 (27.751%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e473 (26.875%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e663 (27.465%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45 (20.642%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1574 (19.653%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e563 (23.646%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e245 (19.822%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e254 (14.432%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e485 (20.091%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27 (12.385%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status,n(weighted %)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4017 (52.730%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1035 (48.162%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e547 (48.109%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e906 (53.294%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1426 (59.072%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e103 (47.248%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e594 (7.797%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134 (6.235%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (2.111%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82 (4.824%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e296 (12.262%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58 (26.606%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e839 (11.013%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219 (10.191%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110 (9.675%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e193 (11.353%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e291 (12.055%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26 (11.927%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e262 (3.439%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81 (3.769%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (2.199%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78 (4.588%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72 (2.983%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6 (2.752%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1324 (17.380%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e489 (22.755%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e309 (27.177%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e270 (15.882%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e234 (9.693%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22 (10.092%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e580 (7.614%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190 (8.841%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122 (10.730%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e171 (10.059%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94 (3.894%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3 (1.376%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvery day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1867 (23.311%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e681 (28.601%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e284 (22.977%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e443 (25.170%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e425 (17.606%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34 (15.596%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275 (3.434%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (3.696%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (3.479%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80 (4.545%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61 (2.527%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3 (1.376%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1717 (21.438%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e386 (16.212%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154 (12.460%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e317 (18.011%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e773 (32.022%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87 (39.908%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink,n(weighted %)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5225 (67.840%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1525 (69.508%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e778 (67.243%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1186 (68.994%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1597 (66.156%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e139 (63.761%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2477 (32.160%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e669 (30.492%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e379 (32.757%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e533 (31.006%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e817 (33.844%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79 (36.239%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4693 (64.191%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1366 (63.066%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e743 (65.869%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1114 (68.428%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1323 (60.439%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e147 (73.500%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2618 (35.809%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e800 (36.934%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e385 (34.131%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e514 (31.572%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e866 (39.561%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53 (26.500%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFQI,weighted mean(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001 (-0.013 ,0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.209 (0.196 0.223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199 (0.180 0.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.178 (0.164 0.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.211 (0.198 0.225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.243 (0.203 0.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3/FT4,weighted mean(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.115 (4.095 ,4.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.947 (3.915 3.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.117 (4.075 4.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.236 (4.199 4.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.906 (3.875 3.938)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.352 (3.249 3.458)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT4RI,weighted mean(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.701 (17.383 ,18.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.670 (14.354 14.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.536 (14.112 14.973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.080 (14.709 15.461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.380 (16.048 16.720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.831 (16.606 19.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSHI,weighted mean(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.564 (1.554 ,1.574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.522 (1.509 1.535)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.508 (1.491 1.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.499 (1.485 1.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.563 (1.551 1.576)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.683 (1.640 1.728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH(mIU/L),weighted mean(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.709 (1.682 ,1.736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.477 (1.447 1.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.478 (1.435 1.522)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.481 (1.446 1.516)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.569 (1.537 1.603)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.613 (1.504 1.731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3(pg/ml),weighted mean(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.189 (3.177 ,3.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.214 (3.200 3.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.181 (3.160 3.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.148 (3.129 3.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.109 (3.094 3.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.975 (2.920 3.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(FT4ng/dl),weighted mean(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.798 (0.796 ,0.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.782 (0.777 0.787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.781 (0.774 0.788)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.787 (0.781 0.793)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.797 (0.791 0.803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.812 (0.791 0.834)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral obesity,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4319 (53.927%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (4.158%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e777 (62.864%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1295 (73.580%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1960 (81.193%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e188 (86.239%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3690 (46.073%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2282 (95.842%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459 (37.136%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e465 (26.420%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e454 (18.807%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30 (13.761%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4584 (57.236%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e908 (38.135%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1320 (75.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2181 (90.348%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e175 (80.275%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3425 (42.764%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1473 (61.865%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1236 (100.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e440 (25.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e233 (9.652%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43 (19.725%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2649 (33.075%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e441 (18.522%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e590 (33.523%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1438 (59.569%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e180 (82.569%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5360 (66.925%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1940 (81.478%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1236 (100.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1170 (66.477%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e976 (40.431%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38 (17.431%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1511 (18.866%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199 (8.358%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e338 (19.205%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e844 (34.963%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e130 (59.633%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6498 (81.134%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2182 (91.642%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1236 (100.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1422 (80.795%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1570 (65.037%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88 (40.367%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e616 (7.691%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130 (5.460%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (0.566%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59 (3.352%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e202 (8.368%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e218 (100.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7393 (92.309%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2251 (94.540%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1229 (99.434%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1701 (96.648%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2212 (91.632%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0 (0.000%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow HDL-C,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2613 (32.626%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e409 (17.178%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e297 (24.029%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e878 (49.886%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e925 (38.318%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e104 (47.706%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5396 (67.374%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1972 (82.822%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e939 (75.971%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e882 (50.114%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1489 (61.682%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e114 (52.294%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD,n(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1410 (17.605%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e349 (14.658%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e247 (14.034%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e596 (24.689%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e218 (100.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6599 (82.395%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2032 (85.342%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1236 (100.000%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1513 (85.966%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1818 (75.311%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0 (0.000%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCKD, chronic kidney disease; CKM, cardiovascular-kidney-metabolic; CVD, cardiovascular disease; FT3/FT4 ,free triiodothyronine/free thyroxine ;NHANES,National Health and Nutrition Examination Survey;PIR, poverty/income ratio;T2DM,Type 2 Diabetes Mellitus;TFQI, thyroid feedback quantile-based index ;TSH, thyroid stimulating hormone; TSHRI,thyroid stimulating hormone resistance index ;TT4RI ,thyrotrophic thyroxine resistance index ;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Weighted to be nationally representative.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026sect;Variable categories may not sum to 100% because of missing data.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eParticipants were stratified by TFQl quartiles. Baseline comparisons revealed that participants with high TFQI were older, more frequently non-Hispanic White, had at least a college degree, were smokers, married or widowed, and more easily developed CKM syndrome stages 3\u0026ndash;4 versus those with low TFQI. Additionally, the 10-year CVD hazard ratio (HR) and the prevalence of central obesity, hypertension, diabetes mellitus, clinical CVD, and CKD significantly increased as the TFQI quartile increased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no significant differences between sex, hyperlipidemia, low- and high-density lipoprotein cholesterol, alcohol consumption status, TT4RI, TSHI, or FT3/FT4 subgroups (Table S3).\u003c/p\u003e \u003cp\u003eBaseline comparison of the different FT3/FT4 subgroups revealed that participants possessing a low FT3/FT4 ratio presented a larger likelihood of being older, female, non-Hispanic White, non-smokers, and with a higher PIR (\u0026gt;\u0026thinsp;3) than those with a high FT3/FT4 ratio. Additionally, the distribution of education, marital status, current alcohol drinking status, TFQI, TT4RI, TSHI, central obesity, and hypertension were significantly different among the subgroups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, CKM syndrome stages, 10-year CVD HR, and prevalence of hyperlipidemia, diabetes mellitus, clinical CVD, and CKD showed no inter-group differences (Table S4).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation between CKM syndrome and THS indices\u003c/h3\u003e\n\u003cp\u003eIn the completely adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e), TFQI and FT3/FT4 exhibited an obvious relevance to the risks of CKM syndrome stages 1\u0026ndash;3 compared with stage 0 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The CKM syndrome risk increased with each unit increase in TFQI and FT3/FT4 (CKM syndrome stage 1: TFQI, 2.145 [95% CI, 1.065\u0026ndash;4.317], FT3/FT4, 1.386 [95% CI, 1.147\u0026ndash;1.674]; stage 2: TFQI, 2.155 [95% CI, 2.272\u0026ndash;3.652], FT3/FT4, 2.055 [95% CI, 1.727\u0026ndash;2.446]; stage 3: TFQI, 2.318 [95% CI, 1.419\u0026ndash;3.786], FT3/FT4, 1.961 [95% CI, 1.678\u0026ndash;2.292]). However, no association was found between CKM syndrome stage 4 and TT4RI or TSHI. Further correlation analysis of CKM syndrome and TSH indices stratified by quartile revealed that, high FT3/FT4 ratio more easily triggered the onset of CKM syndrome stage 1 (OR, 1.467 [95% CI, 1.067\u0026ndash;2.016]), 2 (OR, 3.489 [95% CI, 2.442\u0026ndash;4.87]), and 3 (OR, 2.969 [95% CI, 2.104\u0026ndash;4.19]), but not CKM syndrome stage 4. Compared with low TFQI, TT4RI, and TSHI, no significant correlations were found between the high-level indicators and the risk of CKM syndrome stages 1\u0026ndash;4 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e). However, high FT3 levels indicated a lower risk of developing CKM syndrome stages 2\u0026ndash;4 than low FT3 levels (stage 2: 0.587 [95% CI, 0.454\u0026ndash;0.759]; stage 3: 0.473 [95% CI, 0.349\u0026ndash;0.64]; stage: 0.158 [95% CI, 0.073\u0026ndash;0.346]). Further, elevated FT4 concentrations demonstrated significant correlation with increased progression to advanced-stage (stages 3\u0026ndash;4) CKM syndrome compared to lower levels (stage 3: 1.523 [95% CI, 1.06\u0026ndash;2.188]; stage 4: 3.471 [95% CI, 1.485\u0026ndash;8.112]). The unadjusted and partially adjusted results are presented in Tables S5 and S6. The relevance of the FT3/FT4 ratio to CKM syndrome stages 3 and 4 was unstable in the unadjusted model, but became significant or positive in models with age, sex, race, and ethnicity adjusted. Subgroup analyses based on these variables revealed a similar association between different THS indices and CKM syndrome stages as in the primary analysis (Tables S7-S9). Notably, FT3/FT4 ratio exhibited an obvious relevance to CKM syndrome stages 2 and 3 across different sex, age, race, and ethnicity subgroups. The risk of CKM syndrome stage 3 was higher in Black individuals, those aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, and males with higher FT3/FT4 ratios compared to in White individuals, those aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, and females with lower FT3/FT4 ratios (Black: 3.494 [95% CI, 1.83\u0026ndash;6.67]; White: 2.72 [95% CI, 1.841\u0026ndash;4.017]; male: 3.308 [95% CI, 1.863\u0026ndash;5.874]; female: 3.105 [95% CI, 2.004\u0026ndash;4.812]; aged\u0026thinsp;\u0026ge;\u0026thinsp;60: 2.59 [95% CI, 1.467\u0026ndash;4.893]; aged\u0026thinsp;\u0026lt;\u0026thinsp;60: 2.069 [95% CI, 1.395\u0026ndash;3.068]; all interaction \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively. However, White individuals, those aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, and males with higher FT3/FT4 ratios were more likely to develop CKM syndrome stage 2 than Black individuals, those aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, and females with lower FT3/FT4 ratios (White: 3.04 [95% CI, 2.117\u0026ndash;4.367]; Black: 2.99 [95% CI, 1.614\u0026ndash;5.539]; male: 3.949 [95% CI, 2.436\u0026ndash;6.403]; female: 2.199 [95% CI, 1.312\u0026ndash;3.685]; aged\u0026thinsp;\u0026ge;\u0026thinsp;60: 4.395 [95%CI, 1.665\u0026ndash;11.605], age\u0026thinsp;\u0026lt;\u0026thinsp;60: 2.185 [95%CI, 1.481\u0026ndash;3.223]; all interactions \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis results of thyroid hormone sensitivity indices and CKM stage 1 to 4 in fully adjusted modelshted.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStage*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR(95%CI)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFQI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.145 (1.065 ,4.317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.155 (1.272 ,3.652)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.318 (1.419 ,3.786)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.245 (0.755 ,13.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3/FT4 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.386 \u003c/p\u003e \u003cp\u003e(1.147 ,1.674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.055 \u003c/p\u003e \u003cp\u003e(1.727 ,2.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.961 \u003c/p\u003e \u003cp\u003e(1.678 ,2.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.829 (0.501 ,1.373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT4RI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.995 (0.968 ,1.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.987 (0.965 ,1.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.963 (0.944 ,0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.983 (0.944 ,1.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSHRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.879 (0.339 ,2.277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92 (0.747 ,4.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.26 (1.114 ,4.587)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.502 (0.073 ,3.452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.955 (0.862 ,1.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.961 (0.858 ,1.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.128 (1.003 ,1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.352 (1.069 ,1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.812 (0.638 ,1.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.644 (0.46 ,0.901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.479 (0.354 ,0.648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.238 (0.124 ,0.459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4 (ng/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.226 (0.626 ,2.402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.711 (0.842 ,3.477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.463 (1.735 ,6.912)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.641 (4.148 ,102.723)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eCKM, cardiovascular-kidney-metabolic; FT3/FT4, free triiodothyronine/free thyroxine ;TFQI, thyroid feedback quantile-based index ;TSH, thyroid stimulating hormone; TSHRI, thyroid stimulating hormone resistance index ;TT4RI, thyrotrophic thyroxine resistance index ;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* Weighted to be nationally representative.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u0026dagger; Models were fully adjusted for age, sex, race and ethnicity, education level, marital status, current smoker (yes, no), current drinker (yes, no, or missing).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis results of thyroid hormone sensitivity indices stratified by quartile and CKM stage 1 to 4 in fully adjusted modelshted.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"25\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c25\" namest=\"c25\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eN(weighted %)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eN(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eN(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003eN(weighted %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFQI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e355 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e482 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e518 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e27 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e304 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.878 (0.597 ,1.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e449 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.219 (0.795 ,1.867)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e589 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.133 (0.681 ,1.887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e46 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.621 (0.191 ,2.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e288 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.129 (0.652 ,1.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e462 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.941 (1.042 ,3.616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e624 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.436 (0.618 ,3.337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e60 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e1.026 (0.197 ,5.355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e289 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.056 (0.587 ,1.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e367 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.221 (0.609 ,2.448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e683 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.797 (0.737 ,4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e85 (39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.605 (0.076 ,4.825)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3/FT4(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e224 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e274 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e697 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e126 (57.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e288 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.136 (0.846 ,1.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e330 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.436 (1.118 ,1.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e544 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.317 (0.973 ,1.783)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e40 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.509 (0.249 ,1.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e393 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.181 (0.834 ,1.674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e552 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.388 (1.71 ,3.335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e622 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.847 (1.358 ,2.513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e33 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.672 (0.309 ,1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e331 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.467 (1.067 ,2.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e604 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.489 (2.442 ,4.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e551 (22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e2.969 (2.104 ,4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e19 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.636 (0.226 ,1.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e320 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e458 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e575 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e48 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e316 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95 (0.743 ,1.215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e434 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.911 (0.724 ,1.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e552 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.885 (0.687 ,1.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e50 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e1.104 (0.506 ,2.411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e307 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.964 (0.738 ,1.259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e441 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.918 (0.686 ,1.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e622 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.911 (0.7 ,1.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e52 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.9 (0.393 ,2.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e293 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87 (0.69 ,1.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e427 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.896 (0.674 ,1.192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e665 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.098 (0.774 ,1.556)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e68 (31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e1.828 (0.952 ,3.509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e220 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e373 (21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e564 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e79 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e372 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.801 (0.638 ,1.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e547 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.874 (0.656 ,1.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e757 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.801 (0.616 ,1.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e63 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.539 (0.3 ,0.968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e243 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.641 (0.503 ,0.816)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e369 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.676 (0.535 ,0.856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e492 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.609 (0.466 ,0.794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e33 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.454 (0.243 ,0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e401 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.809 (0.639 ,1.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e471 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.587 (0.454 ,0.759)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e601 (24.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.473 (0.349 ,0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e43 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.158 (0.073 ,0.346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4(ng/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e167 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e226 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e310 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e30 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e381 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.918 (0.708 ,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e530 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.003 (0.79 ,1.274)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e642 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.152 (0.946 ,1.402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e43 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e1.638 (0.82 ,3.271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e390 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08 (0.824 ,1.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e559 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.906 (0.681 ,1.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e734 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.093 (0.842 ,1.418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e61 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e1.719 (0.852 ,3.466)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e298 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.807 (0.609 ,1.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e445 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.189 (0.859 ,1.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e728 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.523 (1.06 ,2.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e84 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e3.471 (1.485 ,8.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRCS analysis\u003c/h2\u003e \u003cp\u003eFigure 2 and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e illustrate the non-linear relationship between CKM syndrome stages 1\u0026ndash;4 and different THS indices. RCS regression analysis revealed a non-linear relevance of TFQI to CKM syndrome stage 2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a linear relevance of TFQI to CKM syndrome stage 4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.087), a linear relevance of FT3/FT4 ratio to CKM syndrome stages 2\u0026ndash;4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), a non-linear relevance of TT4RI to CKM syndrome stages 2\u0026ndash;3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a linear relevance of TT4RI to CKM syndrome stage 1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065), a non-linear relevance of TSHI to CKM syndrome stages 2\u0026ndash;3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and a linear relevance of TSHI to CKM syndrome stage 4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065). In CKM syndrome stage 2, the TFQI, TT4RI, and TSHI index values were approximately \u0026minus;\u0026thinsp;0.25, 33, and 1.55, while in CKM syndrome stage 3, TT4RI and TSHI were approximately 28 and 1.6, respectively. These data indicated changes in the direction or strength of the association between these THS indices and CKM syndrome. Two piecewise linear regression models using different THS indices as the regression variables revealed that the risk of CKM syndrome stage 2 decreased as TFQI and TSHI increased above the inflection point (TFQI: 0.716 [95% CI, 0.521, 0.985], TSHI: 0.451 [0.233, 0.875]), whereas the risk of CKM syndrome stages 2\u0026ndash;3 increased as TT4RI increased below the inflection point (CKM syndrome stage 2: 1.013 [95% CI, 1.001\u0026ndash;1.025]; CKM syndrome stage 3: 1.02 [95% CI, 1.008\u0026ndash;1.032]) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe different THS indices and CKM stages in the piecewise linear regression models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCKM stage 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point (TFQI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFQI\u0026ge;-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.716(0.521,0.985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFQI\u0026lt;-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.725(0.295,1.782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point (TT4RI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT4RI\u0026thinsp;\u0026ge;\u0026thinsp;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.994(0.954,1.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT4RI\u0026thinsp;\u0026le;\u0026thinsp;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.013(1.001,1.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point (TSHI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSHI\u0026thinsp;\u0026ge;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.451(0.233,0.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSHI\u0026thinsp;\u0026le;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.778(0.426,1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCKM stage 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point (TT4RI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT4RI\u0026thinsp;\u0026ge;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.991(0.963,1.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT4RI\u0026thinsp;\u0026le;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02(1.008,1.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point (TSHI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSHI\u0026thinsp;\u0026ge;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.804(0.456,1.418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSHI\u0026thinsp;\u0026le;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.134(0.661,1.944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis investigation analyzed data from a demographically diverse cohort of American adults, revealing significant correlations between both central and peripheral THS measures and CKM syndrome. TFQI had a stronger connection with the onset of CKM syndrome compared to other central THS indices. Individuals with high FT3/FT4 ratios were more likely to develop CKM syndrome stages 1 to 3 than those with low FT3/FT4 ratios. These findings highlight abnormal thyroid hormone sensitivity as a potential driver of CKM syndrome development in patients without thyroid dysfunction. Compared with other measures of THS, the FT3/FT4 ratio could serve as potential epidemiological tools for quantifying the contribution of abnormal THS in CKM syndrome development.\u003c/p\u003e \u003cp\u003eAs a pioneering investigation, this research explores the association between THS indices and CKM syndrome stage among adult populations in the U.S.. Although numerous studies have linked decreased THS to the onset of obesity, CVD, diabetes, renal disease, and metabolic syndrome in general populations, the association between THS concentrations and CKM syndrome severity remains poorly characterized. Thyroid hormones can increase energy expenditure, heat production (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), glucose and fatty acid oxidation in the muscles (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and liver (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and lipolysis of adipose tissues (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Thus, thyroid hormones play important roles in numerous processes, including weight loss, lipid reduction, and insulin sensitivity, by influencing the basic metabolism and energy stability of the entire body. These hormones are chiefly controlled by the hypothalamus-pituitary-thyroid axis, cellular membrane transport, and iodothyronine deiodinases (DiO) in the peripheral tissues (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). As such, THS impairment can be systematically divided into central and peripheral types.\u003c/p\u003e \u003cp\u003eImpaired central THS disrupts the feedback circuits in the central nervous system, causing a relative increase in TSH levels (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) TFQI has recently attracted significant attention as a composite measure of THS; TFQI is a more accurate measure of thyroid hormone homeostasis in the population with normal thyroid function compared with those with single thyroid function indices. A number of earlier studies have shown a significant connection between impaired central THS and the risks of proteinuria, metabolic syndrome, CVD, hyperuricemia, diabetes, and hypertension (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). FT3/FT4 is a positive measure of peripheral THS sensitivity (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Increased or impaired peripheral THS is associated with a variety of diseases (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), possibly caused by the adaptive regulation of thyroid hormones under pathological conditions (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Increased, but not impaired, peripheral THS has been linked to the development of insulin resistance, fatty liver, obesity and osteoporosis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn overweight status and excessive adipose tissue are important aspects of CKM syndrome pathogenesis. Obesity has been shown to be associated with energy metabolism imbalance (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Further, the energy metabolism imbalance caused by thyroid hormone resistance may play a pivotal role in the progression of CKM syndrome. Obesity caused by impaired THS may be attributed to TSH-mediated fat synthesis (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), underscoring the importance of central thyroid hormone resistance. Additionally, insufficient relative thyroid hormone production reduces resting energy and heat production, leading to fat accumulation and obesity (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Various studies have previously investigated the relationship between impaired central THS and obesity. Using data from the NHANES, Laclaustra et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) found that impaired central THS was associated with an increased body mass index (BMI) among euthyroid participants. Conversely, Sun et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) observed an inverse relationship in mainland China: subclinical hypothyroidism patients exhibiting central THS dysfunction showed statistically significant protection against obesity. This inconsistency may be attributed to the differences in the study population. Our results predominantly align with those reported by Laclaustra et al.; however, we found a non-linear relationship between TFQI and CKM syndrome stage 2 incidence, which may contribute to the discrepancy in findings. As such, studies with larger samples are warranted to address these discrepancies. Interestingly, Wang et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) found that gene-driven changes in serum TSH levels did not influence BMI or obesity, and that an increase in BMI increased FT3 levels without influencing FT4 levels, suggesting defects in downstream signaling. Furthermore, Biondi et al.(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) revealed that euthyroid individuals with obesity had higher FT3, TSH and FT3/FT4 indices than those without obesity, suggesting that elevated thyrotropin may be a result, rather than a cause, of increased BMI. Given that obesity represents the predominant underlying metabolic disorder of CKM syndrome stages 1\u0026ndash;2, elevated FT3/FT4 ratio is associated with a higher risk of developing CKM syndrome.\u003c/p\u003e \u003cp\u003eDiabetes, CKD, and CVD are all major clinical features of CKM syndrome. These clinical characteristics are predominantly found in individuals with CKM syndrome stage 2\u0026ndash;4, in which inflammatory reactions and hemodynamic changes caused by thyroid resistance may be important stage-specific features. Furthermore, prior evidence have shown that impaired central THS links to the development of these diseases. For example, Roos et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) reported a positive association between HOMA insulin resistance index (HOMA-IR) and TSH while FT4 exhibited an inverse relationship with HOMA-IR in euthyroid adults. Further, Wang et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) demonstrated an association between TFQI and albuminuria. Several studies have further confirmed that impaired central THS increases the risk of CVD (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The association between thyroid function abnormalities and these clinical features could be attributed to several factors. First, elevated TSH binds to the thyrotropin receptor in adipose tissues, stimulating the production of excessive C-reactive proteins and mediating inflammation. This process, in turn, causes hyperinsulinemia and insulin resistance (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Additionally, increased thyroid hormones regulate adipokines in adipose tissue, driving insulin resistance and higher blood glucose levels (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Second, resistance to thyroid hormones may change the immune microenvironment in kidney disease, boosting inflammatory responses and inflicting kidney damage (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Finally, thyroid hormones affect peripheral hemodynamics, resulting in increased cardiac filling and altered cardiac contraction patterns (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Conversely, decreased thyroid hormones can directly impair ventricular diastolic function and stroke volume, increased arterial resistance, and further influence cardiac function (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings demonstrated that THS exhibited a strong association with the progression of CKM syndrome from stages 1 through 3, as revealed by the current investigation. These results suggest that THS is particularly useful to screen out high-risk groups for early-stage CKM syndrome. Further, these results indicate that THS may have a multi-faceted potential impact on CKM syndrome stages 1 to 3. While we did not identify any significant correlation between THS indices and CKM syndrome stage 4, this does not mean that thyroid hormone has lost its effect on patients of this stage. Instead, we speculate that this discrepancy may be related to the interaction of multiple diseases in CKM syndrome stage 4, although further research is needed to confirm this. Indeed, CVD predominantly occurs in CKM syndrome stage 4; as such, this stage is often accompanied by a variety of critical conditions, with a high risk of death. Moreover, in light of the post hoc power analysis (Power\u0026thinsp;\u0026gt;\u0026thinsp;0.9), we have determined that our study was adequately powered to detect significant associations at CKM Stage 4(Table S10). Consequently, The non-significant findings in Stage 4 are not due to a lack of power but may be attributed to smaller effect sizes or other confounding factors that require further investigation. In the future researches, THS may need to be combined with more biochemical indicators to evaluate the disease. However, after the consideration of factors including sex, age, race, smoking, and alcohol use, we found that the risk of developing CKM syndrome stage 4 was associated with higher FT4 levels, indicating a need for closer monitoring of individuals with borderline FT4 levels.\u003c/p\u003e \u003cp\u003eThe association between THS indices and CKM syndrome varied across age subgroups. Individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years with higher FT3/FT4 ratios showed an elevated risk of CKM syndrome stages 1\u0026ndash;3 than those aged\u0026thinsp;\u0026lt;\u0026thinsp;60. In our study, we found that younger individuals tended to have higher peripheral THS values, confirming the results of prior studies (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). This finding indicates the need for close monitoring of elevated FT3/FT4 in older adults, and screening for potential CKM syndrome risk groups, which could help to optimize CKM syndrome management and reduce the overall CKM syndrome burden. Differences in interaction were also identified in different sex and race subgroups. In particular, among the subset of individuals with higher FT3/FT4 ratios, Black individuals and males had a higher risk of CKM syndrome stage 3 compared to White individuals and females, respectively. These results are in agreement with reported sex difference in the link between thyroid dysfunction and obesity (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Consistent with our findings, a prior Chinese cross-sectional study revealed stronger associations between thyroid hormone sensitivity and CKM syndrome in elderly males. However, their analysis results focused predominantly on central thyroid sensitivity (TSHI) rather than peripheral sensitivity indices (FT3/FT4 ratio) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This discrepancy may stem from: 1) Ethnic-specific variations in thyroid axis regulation; 2) Our implementation of stricter diagnostic criteria. Therefore, more studies are required to clarify the fundamental mechanisms underlying these differences in sex and race.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, its cross-sectional design prevented the establishment of any causal relationship between THS and CKM syndrome, highlighting the need for further cohort studies and interventional trials. Second, due to limitations in the data source thyroid-associated antibodies were not examined, which may have confounded the relationship between THS and CKM syndrome, as autoimmunity may play a role in thyroid sensitivity. Third, rather than relying on clinical diagnosis, we defined subclinical CVD as a 10-year CVD risk\u0026thinsp;\u0026ge;\u0026thinsp;20% or a high risk of CKD, which may have impacted our outcome. Thus, more clinical data are needed in future research to address this issue. Fourth, our study used a representative U.S. population, with individuals of different races. Our results consequently revealed racial differences between Black and White individuals. As such, further validation is required to confirm the generalizability of our results. Fifth, although we adjusted for mixed factors as much as possible, other potential mixed factors (such as diet, physical activity, stress level, etc.) may also have affected the results. Finally, although our research confirms the relationship between THS and CKM syndrome, an investigation of the relevant mechanisms is lacking, and further experimental verification is therefore necessary.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, our study revealed a potential link between central and peripheral THS and CKM syndrome among euthyroid participants. Impaired central THS and increased peripheral THS may play a role in the development or progression of CKM syndrome, even in the absence of obvious thyroid dysfunction. Further, we found TFQI and FT3/FT4 as key contributors to heterogeneity in CKM syndrome, which may have important implications for CKM syndrome risk assessment and management in euthyroid individuals. However, further trials are required to determine the future clinical use of this index in these patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthical Statement\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThis manuscript used de-identified patient data and thus did not require institutional review board or ethical board approval.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eData availability statement \u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eIn this study, the analyzed data are publicly available and can be retrieved from the designated repository: https://www.cdc.gov/nchs/nhanes/\u003c/p\u003e\n\u003ch3\u003eDisclosure\u003c/h3\u003e\n\u003cp\u003eThe authors dedlare that there is no contlict of interest that could be perceived as prejudicing the impartiality of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that the research was carried out without any commercial or financial affiliations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZX and RX conceived and designed the study. RX and YZ handled data collection and analysis. ZX and RX created the tables and figures. SW and JC provided critical revisions to the manuscript\u0026rsquo;s structure and core logic. ZC provided suggestions for the writing and conception of the article. All authors participated in drafting, revising, and approving the final manuscript. All the authors contributed to the article and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNdumele, C. E. et al. 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Sex Differences in the Associations of Obesity With Hypothyroidism and Thyroid Autoimmunity Among Chinese Adults. \u003cem\u003eFrontiers in Physiology\u003c/em\u003e 9 1397. (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphys.2018.01397\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2018.01397\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"cardiorenal-kidney-metabolic syndrome, sensitivity to thyroid hormone, TFQI, FT3/FT4, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6273789/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6273789/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study investigated the relevance of thyroid hormone sensitivity (THS) to cardiovascular-kidney-metabolic (CKM) syndrome risk in euthyroid U.S. adults, given unclear prior evidence.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe cross-sectional study analyzed 8,009 euthyroid adults from NHANES (2007\u0026ndash;2012) to assess associations between THS and CKM syndrome. Central THS indices (TSHI, TT4RI, TFQI) and peripheral FT3/FT4 ratio were evaluated. Participants were stratified into five CKM stages (stage 0: no CKM; stages 1\u0026ndash;4: increasing severity). Weighted logistic regression examined associations between thyroid sensitivity and CKM stages, while restricted cubic spline (RCS) together with piecewise linear regression models explored nonlinearity and threshold effects.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn total, 2,381/8,009 participants were classified as CKM syndrome stage 0. TFQI and FT3/FT4 showed a relevance to CKM syndrome stages 1\u0026ndash;3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no correlation was observed at stage 4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For each additional unit increase in TFQI and FT3/FT4, the risk of developing CKM syndrome also increased. Compared with low FT3/FT4 levels, high FT3/FT4 level indicated higher odds of developing CKM syndrome stages 1\u0026ndash;3. Significant interaction effects (P-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05) emerged across sex/age/ethnicity subgroups stratified by CKM stage 2\u0026thinsp;~\u0026thinsp;3. Males, older adults\u0026thinsp;\u0026ge;\u0026thinsp;60y, and Black individuals with elevated FT3/FT4 demonstrated higher progression odds versus respective referent groups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTHS indices (TFQI, FT3/FT4) are potential biomarkers for CKM risk stratification in euthyroid populations. And there are differences in gender, age and race, which requires further vertical research to verify the causal relationship.\u003c/p\u003e","manuscriptTitle":"Relationship between sensitivity to thyroid hormone and cardiovascular-kidney-metabolic syndrome in U.S. adults: Evidence from the 2007–2012 national health and nutrition examination survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 12:22:19","doi":"10.21203/rs.3.rs-6273789/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":"09e118ce-fcf0-4483-a1fb-6b992c8519d9","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46344408,"name":"Health sciences/Endocrinology"},{"id":46344409,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases"}],"tags":[],"updatedAt":"2025-04-01T12:22:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 12:22:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6273789","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6273789","identity":"rs-6273789","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}