Iodine nutrition, bamboo shoot consumption, thiocyanate exposure, thyroid function, weight and height, and associations among them in the tribal children of Tripura, North-east India

Iodine nutrition, bamboo shoot consumption, thiocyanate exposure, thyroid function, weight and height, and associations among them in the tribal children of Tripura, North-east India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Iodine nutrition, bamboo shoot consumption, thiocyanate exposure, thyroid function, weight and height, and associations among them in the tribal children of Tripura, North-east India Indrajit Ray, Amar K. Chandra, Saru Kumar Debbarma, Sekhar Kumar Mookerjee, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7229013/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tripura’s tribal people consume iodized salt and bamboo shoot which contains antithyroid constituents like thiocyanate. But information regarding ‘the status and impacts of iodine nutrition, bamboo shoot consumption (BSC) and thiocyanate exposure on their thyroid function and health’ was found scanty. Therefore, this cross-sectional study aimed at assessing iodine nutrition, BSC, thiocyanate exposure, thyroid function, weight and height, and associations among them in Tripura’s tribal children. Methods This cross-sectional study included 127 children aged 6–12 years (boys-59 and girls-68) from Deorachhara, Unakoti and Naithangchhara villages. In this study, thyroid volume (TV), free thyroxine (FT 4 ), free triiodothyronine (FT 3 ), thyroid stimulating hormone (TSH), thyroglobulin antibody (Tg-Ab), thyroid peroxidase antibody (TPO-Ab), urinary iodine (UI), urinary thiocyanate (USCN), salt iodine (SI), water iodine (WI), weight, height and BSC were assessed. Results Medians of BSC (grams/person/day) and UI (µg/L) were 83.3 and 103 in Deorachhara, 119 and 100 in Naithangchhara, and 166.7 and 143 in Unakoti, respectively. Unakoti having higher BSC (p = 0.037) showed higher levels of TSH (p = 0.034) and FT 3 (p = 0.001), and lower levels of FT 4 (p = 0.002), weight (p = 0.000) and height (p = 0.009) compared to Deorachhara. There were positive correlations between TV and height in Unakoti having highest BSC (p < 0.02), between Tg-Ab and TPO-Ab proportionate BSC in Unakoti (p < 0.001) and Naithangchhara (p < 0.05), and between USCN and TPO-Ab in boys (p < 0.05). Deorachhara having lowest BSC showed a negative correlation between TV and Tg-Ab (p < 0.05) and a positive correlation between TV and weight (p < 0.001). Besides, Naithangchhara having lower UI (p = 0.014) showed lower levels of FT 4 (p = 0.001), FT 3 (p = 0.000) and TSH (p = 0.000), and higher Tg-Ab level (p = 0.003) compared to Unakoti. Conclusion Tripura’s studied tribal children had lower normal free thyroid hormone levels and poor health, and were at risk of developing autoimmune thyroid diseases, probably because of BSC and lower adequate iodine. Thyroid Volume Thyroxine Triiodothyronine Thyrotropin Thyroid Antibodies Introduction Iodine, an essential trace element, is required by mammals for the production of thyroid hormones (TH) – thyroxine (T 4 ) and triiodothyronine (T 3 ) [ 1 , 2 ]. Thyroid gland produces 100% of T 4 and 20% of T 3 in man, and remaining 80% of T 3 is produced by extrathyroidal deiodination of T 4 in liver, kidney, brain, pituitary and brown fat [ 1 ]. Less than 1% of T 4 and T 3 remains as free F 4 (FT 4 ) and free F 3 (FT 3 ) [ 1 ]. Free TH, being accessible to peripheral tissues, regulate various biological activities [ 3 ]. To produce sufficient TH, daily iodine consumption (IC) should be at least enough to ensure a median urinary iodine (UI) level of 100 µg/L indicating adequate iodine consumption (AIC) [ 4 ]. Foodstuffs deficient in iodine and/or rich in goitrogens may cause iodine deficiency (ID) [ 1 , 2 , 5 ] which decreases TH [ 1 ]. In low TH (hypothyroidism), hypothalamic thyrotropin releasing hormone (TRH) stimulates pituitary gland to produce thyroid stimulating hormone (TSH)/thyrotropin which, in turn, stimulates thyroid gland to release TH (predominantly FT 4 ). Besides, hyperthyroidism i.e., high TH (predominantly FT 4 ) decrease TSH inhibiting pituitary gland and hypothalamus [ 6 ]. Prolonged TSH action produces goiter (enlargement of thyroid gland) by stimulating hypertrophy and hyperplasia [ 1 ]. Beside hypothyroidism and goiter, ID may also (a) increase the incidences of stillbirth, spontaneous abortion, congenital anomalies, low birth weight, young one’s mortality, deaf-mutism, squint, spastic diplegia, cretinism [ 5 ], short stature [ 4 ], delayed bone maturation, retarded sexual maturation [ 1 ], (b) damage brain in young ones [ 5 ], and (c) lower intelligence quotient up to 13.5 points [ 4 ]. All these disorders are known as iodine deficiency disorders (IDD). Besides, both ID [ 7 ] and excess IC [ 4 , 7 ] may cause autoimmune thyroid diseases (AITD) by increasing thyroglobulin antibody (Tg-Ab) and thyroid peroxidase antibody (TPO-Ab). Excess IC may convert the higher Tg-Ab or TPO-Ab state to overt hypothyroidism [ 8 ]. To control IDD, major strategies are sustainably eliminating ID by supplementing exogenous iodine through universal salt iodization (USI) program and monitoring IC periodically [ 4 ]. In the context of thyroid function, Tripura’s tribal people hold special relevance, because they traditionally consume bamboo shoot (BS) which contains antithyroid constituents like thiocyanate [ 9 ]. Besides, the State of Tripura is iodine deficient [ 10 ] and found goiter endemic [ 11 , 12 ], where effectiveness of the prophylactic USI program introduced in 1988 [ 11 ] was not satisfactory [ 12 ]. But information regarding ‘the status and impacts of iodine nutrition, bamboo shoot consumption (BSC) and thiocyanate exposure on Tripura’s tribal population’s thyroid function and health’ was found scanty. Therefore, this study aimed at assessing thyroid pathophysiology, thyroid volume (TV), FT 4 , FT 3 , TSH, Tg-Ab, TPO-Ab, UI, urinary thiocyanate (USCN), salt iodine (SI), water iodine (WI), weight, height and BSC, and associations among them in Tripura’s tribal children. Methods Study Population Three hilly villages Deorachhara, Unakoti and Naithangchhara were randomly selected from the Tripura Tribal Autonomous District Council, North-east India. Deorachhara and Unakoti are in Unakoti district, and Naithangchhara is in Dhalai district. These villages were sparsely populated and under USI program, besides all were similar in climate, economy, culture, education, dietary habits and available health facilities. As school-aged children have ability to reflect current status of iodine nutrition [ 5 ] and urine samples collected at random from 40 subjects of an area represent current status of iodine nutrition in that area [ 13 ], the willing tribal children aged 6–12 years receiving no medical treatment were selected randomly ensuring at least 40 subjects from each village. Thus, a total of 127 children (boys-59 and girls-68) were selected from these three villages. The study was conducted during 2015–2018. Selected children’s demographic characteristics have been shown in Table 1 . Table 1 Demographic characteristics, and levels of TV, FT 4 , FT 3 , TSH, Tg-Ab, TPO-Ab, UI, USCN, WI, SI and BSC by village and total, and mean differences’ significance levels among villages in Tripura’s tribal children Variable Deorachhara (DC) ( n = 45) (M/F = 0.80) Unakoti (UK) ( n = 41) (M/F = 0.78) Naithangchhara (NC) ( n = 41) (M/F = 1.05) Total ( n = 127) (M/F = 0.87) Mean differences’ significance level* Mean (SD)/ Median Mean (SD)/ Median Mean (SD)/ Median Mean (SD)/ Median DC vs. UK UK vs. NC DC vs. NC Age (y) 9.02 (2.02)/9.56 8.37 (1.45)/8.24 8.68 (1.49)/8.75 8.70 (1.69)/8.77 0.084 (NS) 0.341 (NS) 0.371 (NS) Weight (kg) 24.2 (6.6)/24.0 20.0 (3.6)/19.7 21.5 (3.7)/20.2 22.0 (5.2)/20.9 0.000 (S) 0.077 (NS) 0.016 (S) Height (cm) 124.5 (12.3)/ 125.4 118.7 (7.7)/119.3 119.1 (9.2)/118.9 120.9 (10.3)/120.4 0.009 (S) 0.868 (NS) 0.020 (S) BMI 15.3 (1.7)/14.9 14.08 (1.1)/14.1 15.0 (0.9)/15.1 14.8 (1.4)/14.6 0.000 (S) 0.000 (S) 0.370 (NS) BSA (m 2 ) 0.92 (0.17)/0.93 0.82 (0.10)/0.82 0.84 (0.11)/0.82 0.86 (0.14)/0.84 0.001 (S) 0.311 (NS) 0.028 (S) TV (ml) 0.85 (0.34)/0.86 0.83 (0.23)/0.85 0.79 (0.20)/0.76 0.82 (0.27)/0.82 0.650 (NS) 0.392 (NS) 0.250 (NS) FT 4 (pmol/L) 22.8 (6.1)/22.1 19.2 (4.8)/19.7 16.3 (3.5)/17.0 19.5 (5.6)/20.1 0.002 (S) 0.001 (S) 0.000 (S) FT 3 (pmol/L) 4.91 (1.44)/5.01 6.02 (1.57)/5.87 4.37 (0.84)/4.22 5.09 (1.48)/5.04 0.001 (S) 0.000 (S) 0.033 (S) TSH (mIU/L) 2.66 (1.23)/2.13 3.25 (1.34)/3.02 1.40 (0.90)/1.31 2.44 (1.39)/2.23 0.034 (S) 0.000 (S) 0.000 (S) Tg-Ab (IU/ml) 12.2 (2.5)/11.5 13.1 (5.2)/12.0 20.6 (15.4)/14.7 15.2 (10.0)/12.5 0.335 (NS) 0.003 (S) 0.001 (S) TPO-Ab (IU/ml) 4.39 (1.59)/4.05 4.61 (2.04)/4.15 7.86 (21.40)/1.40 5.58 (12.25)/3.89 0.583 (NS) 0.333 (NS) 0.301 (NS) UI (µg/L) 117.8 (69.3)/103 130.7 (49.9)/143 103.4 (51.0)/100 117.3 (58.4)/108 0.321 (NS) 0.014 (S) 0.269 (NS) USCN (mg/dl) 1.08 (0.67)/0.9 0.95 (0.36)/0.9 0.92 (0.88)/0.5 0.99 (0.67)/0.8 0.228 (NS) 0.844 (NS) 0.324 (NS) WI (µg/L) 2.18 (2.91)/1.1 6.32 (5.54)/4.5 5.83 (3.79)/5.0 4.69 (4.56)/3 0.000 (S) 0.642 (NS) 0.000 (S) SI (ppm) 22.2 (11.0)/26.6 20.5 (12.3)/20 21.0 (7.4)/21.0 21.2 (10.4)/20.7 0.511 (NS) 0.840 (NS) 0.549 (NS) BS (g/person /day) 125.6 (78.4)/83.3 (FN = 15) 194.4 (118.0)/166.7 (FN = 23) 131.7 (86.0)/119.0 (FN = 23) 153.8 (101.4)/119.0 (FN = 61) 0.037 @ (S) 0.046 @ (S) 0.826 # (NS) Note BMI: Body mass index, BSA: Body surface area, TV: Thyroid volume, FT 4 : Free thyroxine, FT 3 : Free triiodothyronine, TSH: Thyroid stimulating hormone, Tg-Ab: Thyroglobulin antibody, TPO-Ab: Thyroid peroxidase antibody, UI: Urinary iodine, USCN: Urinary thiocyanate, WI: Water iodine, SI: Salt iodine, BSC: Bamboo shoot consumption, n: Children number, M/F: Male/Female ratio, FN: Families’ number, S: Significant, NS: Not significant *: Two-tail t-test ( @ : Assuming Unequal Variances, # : Assuming Equal Variances) and z-test at α = 0.05 were performed to find out mean differences’ significance level Anthropometric Measurements Weight and height have been widely and successfully used with reference to one another or age to assess health and nutritional risk, especially in children [ 14 ]. So, weight and height were measured using a digital weighing scale and an anthropometric rod, and recorded to nearest 100 grams and centimeters, respectively. Body mass index (BMI) was calculated using the formula: (weight in kilograms)/(height in meters) 2 [ 15 ]. Body surface area (BSA) ‘in meters 2 ’ was calculated using Dubois and Dubois formula: (weight in kilograms) 0.425 x (height in centimeters) 0.725 x 71.84 x 10 − 4 [ 4 ]. Weight, height and BMI of each child were compared with those of CDC (Centers for Disease Control and Prevention of the United States) reference z-scores for the child’s sex and age [ 16 ]. A child with <-2SD (SD, standard deviation) weight-for-age, <-2SD height-for-age, 1SD BMI-for-age or > 2SD BMI-for-age was considered as underweight, stunted, thin, overweight or obese, respectively [ 15 ]. Thyroid Ultrasonography Echogenicity of thyroid gland was studied for detecting nodules and measuring TV following WHO (World Health Organization) ultrasonographic guidelines [ 4 ], using a portable compact color doppler ultrasound scanner system with a 7.5 MHz linear array transducer (GE LOGIQ E BT 12 Console with 12 L RS Linear Probe, Wipro GE Healthcare Pvt. Ltd) by a radiologist. Each thyroid lobe’s volume (milliliters) was calculated by the formula: [0.479 x length (centimeters) x width (centimeters) x depth (centimeters)]. Thyroid volume was calculated by adding the volumes of right and left lobes. Thyroid volume of each child was compared with the 97th percentile reference TV of iodine-sufficient children for the child’s sex and age [ 4 ] to diagnose as goitrous or normal. Serum levels of FT 4 , FT 3 , TSH, Tg-Ab and TPO-Ab Serum was separated within 2 hours of fasting venous blood collection from each child and stored at -20 o C until analysis. Serum levels of FT 4, FT 3 , TSH, Tg-Ab and TPO-Ab were measured by ELISA microwell methods using the kits of FT 4 (Product Code: 1225 − 300, Accu-Bind ELISA Microwells, Monobind Inc., Lake Forest, CA 92630, USA), FT 3 (Calbiotech Inc., 10461 Austin Dr. Spring Valley, CA, 91978), TSH (Product Code: 325 − 300, Accu-Bind ELISA Microwells, Monobind Inc., Lake Forest, CA 92630, USA), anti-Tg-Ab (EUROIMMUN, Medizinische, Labordiagnostika, AG) and anti-TPO-Ab (EUROIMMUN, Medizinische, Labordiagnostika, AG), respectively. Diagnostic tests to evaluate thyroid function : (a) Diagnostic criteria and values (as per assay manuals) :- AITD: Tg-Ab (Negative: <100 IU/ml, Positive: ≥100 IU/ml); TPO-Ab (Negative: <50 IU/ml, Positive: ≥50 IU/ml). (b) Diagnostic criteria [ 17 ] and values (as per assay manuals) : Euthyroid: normal TSH (0.39–6.16 µIU/ml [0.39–6.16 mIU/L]) and normal FT 4 (0.8-2.0 ng/dl [10.3–25.7 pmol/L]); Euthyroid hypothyroxinemia: normal TSH and low FT 4 (< 0.8 ng/dl [ 2.0 ng/dl [> 25.7 pmol/L]) and low FT 3 (< 1.4 pg/ml [ 6.16 µIU/ml [> 6.16 mIU/L]) and low FT 4 [Hashimoto’s thyroiditis: Primary hypothyroidism with positive Tg-Ab and positive TPO-Ab]; Secondary/Central hypothyroidism: low TSH (< 0.39 µIU/ml [< 0.39 mIU/L]) and low FT 4 ; Subclinical hypothyroidism: high TSH and normal FT 4 ; Primary hyperthyroidism: low TSH and high FT 4 [Grave’s disease: Primary hyperthyroidism with positive Tg-Ab, positive TPO-Ab and positive TRAb (thyrotropin receptor antibody)]; Secondary/TSH-mediated/central hyperthyroidism: high TSH and high FT 4 ; Subclinical hyperthyroidism: low TSH, normal FT 4 and high FT 3 (> 4.2 pg/ml [> 6.45 pmol/L]). Urinary Iodine Level Urine samples were collected from the selected children and stored at 4 o C until analysis. Urine samples were collected ensuring at least 40 samples from each village [ 13 ]. Urine samples’ iodine contents were assayed following Karmarkar et al. method [ 18 ]. Urinary Thiocyanate Level The dietary consumption of thiocyanate in a population is determined from the USCN [ 19 ]. So, urine samples’ thiocyanate contents were estimated following Aldridge modified method of Michajlovskij and Langer [ 20 ]. Salt Iodine Level Iodized salt samples were collected from the families of selected children, ensuring at least 35 salt samples from each village, as 35 salt samples collected randomly from an area reflect USI program status in that area [ 21 ]. Salt samples’ iodine contents were estimated following iodometric titration method [ 4 ]. Water Iodine Level Samples of water used for drinking and house-hold works were collected from the families of selected children and stored at 4 o C until analysis. Water samples’ iodine contents were assayed following Karmarkar et al. method [ 18 ]. Bamboo Shoot Consumption To determine per capita daily BSC in the selected villages, 61 randomly selected tribal families consisting of 270 members were interviewed regarding their consumed BS’s types and forms, duration of BS availability in a year, consumption frequency per week, consumption amount per day, number of family members, and the information thus obtained was recorded. Statistical Analysis Means (SDs) and medians of age, TV, FT 4 , FT 3 , TSH, Tg-Ab, TPO-Ab, UI, USCN, WI, SI, weight, height and BSC were calculated using Microsoft Excel 2007. Two tail z-test (for n [children number] ≥ 30) and t-test (for n < 30) of Microsoft Excel Analysis Tool Pak were used to calculate mean differences’ significance levels (p-values). First order partial correlation coefficients (r) were calculated using the formulae: \(\:{r}_{12.3}=({r}_{12}\:-{r}_{13}\:\text{x}\:{r}_{23})/\sqrt{(1-{r}_{13}^{2})(1-\:{r}_{23}^{2})}\) …. \(\:{r}_{23.1}=({r}_{23}\:-{r}_{12}\:\text{x}\:{r}_{13})/\sqrt{(1-{r}_{12}^{2})(1-\:{r}_{13}^{2})}\) [where 1 = UI, 2 = SI and 3 = WI], and 9th order partial ‘r’ using the formulae: \(\:{\:r}_{12.3456789\text{X}\text{Y}}=({r}_{12.3456789\text{X}}-{r}_{1\text{Y}.3456789\text{X}}\:\text{x}\:\:{r}_{2\text{Y}.3456789\text{X}})/\sqrt{(1-{r}_{1\text{Y}.3456789\text{X}}^{2})(1-\:{r}_{2\text{Y}.3456789\text{X}}^{2})}\) , …., \(\:{r}_{\text{X}\text{Y}.123456789\:}=({r}_{\text{X}\text{Y}.12345678\:}-{r}_{9\text{X}.12345678}\:\text{x}\:{r}_{9\text{Y}.12345678})/\sqrt{(1-{r}_{9\text{X}.12345678}^{2})(1-\:{r}_{9\text{Y}.12345678}^{2})}\) [where 1 = age, 2 = TV, 3 = FT 4 , 4 = FT 3 , 5 = TSH, 6 = Tg-Ab, 7 = TPO-Ab, 8 = USCN, 9 = UI, X = weight, and Y = height], and their p-values were obtained when t-values were calculated using the formula t= \(\:\text{r}/\sqrt{(1-{\text{r}}^{2})/(\text{n}-\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{v}\text{a}\text{r}\text{i}\text{a}\text{l}\text{e}\text{s})}\) , and consulted with the critical t value table of two-tail test [ 22 ]. Results Means (SDs) and medians of the studied variables, and mean differences’ significance levels (p-values) are shown in Table 1 . Dietary survey on BS showed an overall median BSC of 119 grams/person/day for about 6 months from May to October. The studied families mainly consumed muli ( Melocanna baccifera ), mritinga ( Bambusa tulda ), rupai ( Dendrocalamus longispathus ), dalu ( Schizostachyum dullooa ), barak ( Bambusa balcooa ) and jai or bari ( Bambusa vulgaris var. vulgaris ) types of BS in cooked forms as curry, fry, prickle and boiled BS. Unakoti having significantly higher BSC showed significantly higher levels of TSH and FT 3 , and significantly lower levels of FT 4 , weight, height, BMI and BSA compared to Deorachhara. Besides, Naithangchhara having significantly lower UI showed significantly lower levels of FT 4 , FT 3 and TSH, and significantly higher levels of Tg-Ab and BMI compared to Unakoti. Table 2 shows the prevalence of underweight, stunting and thinness. Unakoti showed highest prevalence of underweight, and thinness, whereas Naithangchhara showed highest stunting prevalence. Table 2 Prevalence of underweight, stunting and thinness by village, sex and total in Tripura’s tribal children Type of malnutrition Prevalence Deorachhara Unakoti Naithangchhara Boys Girls Total Underweight (<-2SD weight-for-age) 11 (24.4%) 21 (51.2%) 18 (43.9%) 25 (42.4%) 25 (36.8%) 50 (39.4%) Stunted (<-2SD height-for-age) 12 (26.7%) 18 (43.9%) 21 (51.2%) 27 (45.8%) 24 (35.3%) 51 (40.2%) Thin (<-2SD BMI-for-age) 4 (8.9%) 10 (24.4%) 2 (4.9%) 4 (6.8%) 12 (17.6%) 16 (12.6%) Malnutrition data presented in n (%), where n is children number Table 3 represents the distribution pattern of FT 4 , FT 3 , TSH, Tg-Ab, TPO-Ab, UI, WI and SI. Normal levels of FT 4 , FT 3 and TSH were observed in 92.9%, 81.1% and 93.7% children, respectively. Iodine level was ≥ 15 parts per million (ppm) in 80.3% salt samples. Table 3 Distribution pattern of FT 4 , FT 3 , TSH, Tg-Ab, TPO-Ab, UI, WI and SI in Tripura’s tribal children Data presented in n (%), where n is children number Note FT 4 : Free thyroxine, FT 3 : Free triiodothyronine, TSH: Thyroid stimulating hormone, Tg-Ab: Thyroglobulin antibody, TPO-Ab: Thyroid peroxidase antibody, UI: Urinary iodine, WI: Water iodine, SI: Salt iodine, ID: Iodine deficiency *Values as per assay manuals Table 4 shows 9th order partial ‘r’ between different variable pairs along with p-values. There were significant positive correlations between TV and height in Unakoti, between Tg-Ab and TPO-Ab in Unakoti, Naithangchhara and total children, between TV and weight in Deorachhara and total children, between TPO-Ab and FT 3 in Deorachhara, between FT 4 and TSH in girls, between FT 4 and weight in total children, between age and Tg-Ab in total children, between UI and height in Naithangchhara and total children, between USCN and FT 4 in Naithangchhara and total children, between UI and TPO-AB and between USCN and TPO-Ab in boys. Besides, there were significant negative corrections between TPO-Ab and TSH in Unakoti, between TV and Tg-Ab in Deorachhara, between Tg-Ab and FT 3 in boys, between TSH and weight in total children, between Tg-Ab and weight in boys, between age and UI in Naithangchhara and boys, and between UI and weight in total children. Table 4 Ninth order partial correlation coefficients (r) and significance levels (p-values) for some variable pairs involving age, TV, FT 4 , FT 3 , TSH, Tg-Ab, TPO-Ab, UI, USCN, weight and height by village, sex and total Variable pair Deorachhara (n = 45) Unakoti (n = 41) Naithangchhara (n = 41) Boys (n = 59) Girls (n = 68) Total (n = 127) TV & weight r = 0.5351, p 0.05 (NS) r = 0.0823, p > 0.05 (NS) r = 0.2048, p > 0.05 (NS) r = 0.3213, p < 0.02 (S) r = 0.2625, p 0.05 (NS) r = 0.4432, p 0.05 (NS) r = 0.0235, p > 0.05 (NS) r = 0.1428, p > 0.05 (NS) r = 0.0967, p > 0.05 (NS) FT 4 & TSH r = − 0.1582, p > 0.05 (NS) r = 0.2472, p > 0.05 (NS) r = − 0.0623, p > 0.05 (NS) r = 0.1102, p > 0.05 (NS) r = 0.2676, p 0.05 (NS) FT 4 & weight r = 0.1623, p > 0.05 (NS) r = − 0.2080, p > 0.05 (NS) r = 0.0853, p > 0.05 (NS) r = 0.2408, p > 0.05 (NS) r = 0.0801, p > 0.05 (NS) r = 0.2005, p < 0.05 (S) Tg-Ab & TV r = − 0.3743, p 0.05 (NS) r = 0.0725, p > 0.05 (NS) r = 0.0323, p > 0.05 (NS) r = 0.1646, p > 0.05 (NS) r = 0.0710, p > 0.05 (NS) TPO-Ab & TSH r = 0.2909, p > 0.05 (NS) r = − 0.3578, p 0.05 (NS) r = − 0.0313, p > 0.05 (NS) r = − 0.0941, p > 0.05 (NS) r = − 0.0719, p > 0.05 (NS) Tg-Ab & TPO-Ab r = 0.1073, p > 0.05 (NS) r = 0.6258, p < 0.001) (S) r = 0.3773, p < 0.05) (S) r = 0.7366, p < 0.001 (S) r = 0.3220, p < 0.02 (S) r = 0.3822, p 0.05 (NS) r = 0.0506, p > 0.05 (NS) r = 0.2498, p > 0.05 (NS) r = 0.1031, p > 0.05 (NS) r = 0.2749, p < 0.05 (S) r = 0.2025, p 0.05 (NS) r = 0.00184, p > 0.05 (NS) r = − 0.1067, p > 0.05 (NS) r = − 0.2770, p 0.05 (NS) r = − 0.0974, p > 0.05 (NS) UI & TPO-Ab r = 0.1311, p > 0.05 (NS) r = 0.0606, p > 0.05 (NS) r = 0.1083, p > 0.05 (NS) r = 0.3658, p 0.05 (NS) r = 0.0731, p > 0.05 (NS) UI & weight r = − 0.3232, p > 0.05 (NS) r = 0.1021, p > 0.05 (NS) r = − 0.2893, p > 0.05 (NS) r = − 0.2899, p 0.05 (NS) r = − 0.1878, p 0.05 (NS) r = 0.2534, p > 0.05 (NS) r = 0.3733, p < 0.05) (S) r = 0.3958, p 0.05 (NS) r = 0.2425, p 0.05 (NS) r = − 0.2124, p > 0.05 (NS) r = − 0.3865, p < 0.05) (S) r = − 0.3208, p 0.05 (NS) r = − 0.1580, p > 0.05 (NS) USCN & TPO-Ab r = − 0.0780, p > 0.05 (NS) r = 0.0955, p > 0.05 (NS) r = − 0.1556, p > 0.05 (NS) r = 0.3196, p 0.05 (NS) r = − 0.0280, p > 0.05 (NS) USCN & FT 4 r = 0.0091, p > 0.05 (NS) r = 0.0396, p > 0.05 (NS) r = 0.5613, p 0.05 (NS) r = 0.2068, p > 0.05 (NS) r = 0.1969, p < 0.05 (S) TPO-Ab & FT 3 r = 0.4543, p 0.05 (NS) r = − 0.2117, p > 0.05 (NS) r = 0.2278, p > 0.05 (NS) r = − 0.0092, p > 0.05 (NS) r = 0.0060, p > 0.05 (NS) TSH & weight r = − 0.0775, p > 0.05 (NS) r = 0.0647, p > 0.05 (NS) r = 0.2352, p > 0.05 (NS) r = − 0.2553, p > 0.05 (NS) r = − 0.2616, p < 0.05 (S) r = − 0.2423, p 0.05 (NS) r = − 0.0158, p > 0.05 (NS) r = 0.0376, p > 0.05 (NS) r = 0.2952, 0 0.05 (NS) r = − 0.0089, p > 0.05 (NS) Tg-Ab & weight r = 0.2731, p > 0.05 (NS) r = − 0.1613, p > 0.05 (NS) r = − 0.2382, p > 0.05 (NS) r = − 0.3274, p 0.05 (NS) r = − 0.1257, p > 0.05 (NS) TPO-Ab & weight r = 0.0628, p > 0.05 (NS) r = 0.1632, p > 0.05 (NS) r = 0.1679, p > 0.05 (NS) r = 0.3196, 0 0.05 (NS) r = 0.0597, p > 0.05 (NS) Note TV: Thyroid volume, FT 4 : Free thyroxine, FT 3 : Free triiodothyronine, TSH: Thyroid stimulating hormone, Tg-Ab: Thyroglobulin antibody, TPO-Ab: Thyroid peroxidase antibody, UI: Urinary iodine, USCN: Urinary thiocyanate, n: Children number; S: Significant, NS: Not significant Ninth order partial ‘r’ involving BMI and BSA replacing weight and height, respectively were also calculated (not shown in table). There were significant positive correlations between TV and BSA in Unakoti, Deorachhara and total children, between FT 4 and BSA in total children, and between UI and BSA in Unakoti and boys. There were significant negative correlations between UI and BMI in Deorachhara and total children, and between Tg-Ab and BMI in boys. There were no significant differences between the means of boys and girls for the study variables (not shown in table). First order partial correlation among UI, WI and SI showed a significant positive correlation between UI and WI (not shown in table). Discussion This study revealed the status of iodine nutrition, BSC, thiocyanate exposure, thyroid function, physical health, and associations among them in Tripura’s studied children. For optimal TH production, thyroid gland requires varying amounts of iodine based on age (years): 90 µg/day for children under 6, 120 µg/day for children aged 6–12, and 150 µg/day for adolescents and adults. Consistently meeting these requirements typically results in a median UI level of 100 µg/L, indicating AIC across all age groups [ 4 ]. Median UI levels of 100–199 µg/L representing a range of AIC indicate adequate iodine nutrition [ 4 ]. Besides, median UI levels (µg/L) of < 20, 20–49, 50–99, 200–299 and ≥ 300 indicate severe ID, moderate ID, mild ID, more than AIC and excess IC, respectively [ 4 ]. As per these criteria, the studied children had AIC. Bioavailability of iodine in an area is generally reflected in its WI. An area is considered as severe, moderate, relative iodine deficient or iodine sufficient depending on whether WI level (µg/L) is 10–20 or > 20, respectively [ 23 ]. As per these criteria, the studied villages were severe to moderate iodine deficient, overall being severe iodine deficient which confirmed our previous report [ 10 ]. However, this study showed a positive correlation between UI and WI (p < 0.05) as was observed by Lv et al [ 24 ]. All these suggested that though these villages were iodine deficient, water available in these villages was a significant source of the consumed iodine. Generally, achieving ≥ 15 ppm iodine in ≥ 90% salt samples indicate USI program’s satisfactory effectiveness [ 4 ]. As per this criterion, effectiveness of the ongoing USI program under this study was not satisfactory. However, USI program under this study was better in effectiveness than that of 1996-98 [ 12 ]. Besides, SI had no correlation with UI. All these suggested that the ongoing USI program was not a significant source of consumed iodine. Usually, Total Goiter Rates (TGR) of 0.0-4.9%, 5.0-19.9%, 20.0-29.9% and ≥ 30% indicate ‘no’, ‘mild’, ‘moderate’ and ‘severe’ IDD, respectively [ 4 ]. Besides, thyroid nodule prevalence in children normally varies within 1.0-1.5% [ 25 ]. This study showing all children with normal TV and 0.8% children with thyroid nodules suggested that IDD were not major problems in these villages. This study’s TV was lower than that of excess IC area [ 26 ]. This study’s village-wise and overall means/medians of FT 4 , FT 3 and TSH were in normal ranges. This study’s overall means of FT 4 and FT 3 were higher and lower, respectively than that of excess IC area [ 26 ], and higher in both cases than that of AIC area [ 27 ]. This study’s overall median TSH was lower than that of excess IC [ 26 ] and AIC areas [ 27 ]. Overall, 86.1% studied children were euthyroid. This study also showed high FT 3 in 17.3% children, being highest (34.1% children) in Unakoti having highest mean and median levels of BSC, TSH and FT 3 . All these suggested that BS might have increased FT 3 , probably by elevating TSH. This study’s thyroid dysfunction prevalence (1.6% subclinical hypothyroidism and 2.4% euthyroid hypothyroxinemia) was lower than that of AIC area [ 27 ]. This study’s overall median Tg-Ab was lower than that of AIC area [ 27 ], and the prevalence of positive Tg-Ab (0%) and positive TPO-Ab (0.8%) were lower than that of excess IC area [ 26 ]. Generally, AITD prevalence in children varies within 0.5-3% in areas having no ID [ 28 ]. Thus, it appeared that positive thyroid auto-antibodies were not major problems in these villages. This study’s median BSC (grams/person/day) during BS-growing seasons varied from 83.3 in Deorachhara, 119 in Naithangchhara to 166.7 in Unakoti, overall being 119 which were higher than that of Australia and New Zealand (where BSC varied within 31–70 grams/person/day) [ 29 ]. This study showing higher BSC (p = 0.037) associated with higher levels of TSH (p = 0.034) and FT 3 (p = 0.001), and lower levels of FT 4 (p = 0.002), height (p = 0.009), weight (p = 0.000), BMI (p = 0.00) and BSA (p = 0.001) in Unakoti than in Deorachhara -suggested that higher BSC might have lowered FT 4 , subsequently impacting height, weight, BMI, and BSA, with a concurrent compensatory increase in TSH and FT 3 . This scenario aligned with the known effects of primary hypothyroidism. Besides, this study showing the trends of positive correlation between Tg-Ab and TPO-Ab proportionate to BSC in three villages, being significant in Unakoti (p < 0.001), Naithangchhara (p < 0.05) and total children (p < 0.001) suggested a positive association between Tg-Ab and TPO-Ab, and this was possibly because of increased BSC. This study also showed the trends of positive to negative correlations (i) between TV and height, being significantly positive in Unakoti having highest BSC (p < 0.02), and (ii) between TPO-Ab and TSH, being significantly negative in Unakoti having highest BSC (p < 0.05). These correlations suggested (i) a positive association between TV and height, a finding that has been previously reported [ 30 ], and (ii) a negative association between TPO-Ab and TSH, a finding that contradicted previously reported positive correlation between these two [ 31 ], and these were possibly because of higher BSC. Furthermore, this study showed the trends of positive to negative correlations (i) between TV and Tg-Ab, being significantly negative in Deorachhara having lowest BSC (p < 0.05), and (ii) between FT 4 and weight, being positive in the village having lower BSC and significantly positive in total children (p < 0.05). These correlations suggested (i) a negative association between TV and Tg-Ab, a finding that has been previously reported [ 32 ], and (ii) a positive association between FT 4 and weight in total children, and these were possibly because of lower BSC. But this study was unable to ascertain cause-effect relationship among the variables of these correlations. This study also showed the trends of positive to negative correlations between TV and weight, being significantly positive in Deorachhara having lowest BSC (p < 0.001) and total children (p < 0.01). These correlations suggesting a positive association between TV and weight, a finding that has been previously reported [ 30 ], indicated that higher weight enhanced TV, a finding that has also been previously reported [ 33 ], and these were probably because of lower BSC. Besides, this study showed the trends of positive to negative correlations between TSH and FT 4 , being positive in the village having higher BSC and significantly positive in girls (p < 0.05). This positive correlation suggested that elevated TSH enhanced FT 4 in girls, potentially increasing the risk of TSH-mediated hyperthyroidism, and this effect was possibly because of higher BSC. Literature has already reported BS’s association with reduced T 4 and increased TV in rats [ 34 ]. Besides, there have been some reports on BS’s antithyroid action. Bamboo shoot has been reported to contain thiocyanate [ 12 ] which decreases thyroidal iodine uptake [ 29 ], and 2,4-dihydroxy benzoic acid [ 35 ] which inhibits thyroid peroxidase (TPO) activity [ 36 ]. Bamboo shoot has also been reported to reduce TH synthesis decreasing the activities of sodium-potassium adenosine triphosphatase (Na+-K+-ATPase) and deiodinase [ 37 ]. However, it has already been reported that iodine can partially mitigate BS’s antithyroid action [ 34 , 38 ]. This study’s median UI (µg/L) were 103 in Deorachhara, 143 in Unakoti, and 100 in Naithangchhara. This study showing lower UI (p = 0.014) associated with lower levels of FT 4 (p = 0.001), FT 3 (p = 0.000) and TSH (p = 0.000), and higher levels of Tg-Ab (p = 0.003) and BMI (p = 0.000) in Naithangchhara than in Unakoti - suggested that lower AIC might have (i) decreased FT 4 and FT 3 , which remained associated with decreased TSH, and (ii) increased BMI and Tg-Ab. This scenario reflected a potential risk of secondary/central hypothyroidism. These findings appeared reflecting the findings of a previous study [ 27 ] that reported TgAb’s strong associations with lower levels of UI, FT 4 and FT 3 in an area having median UI of 150 µg/L. Besides, this study showed the trends of positive correlation between TV and BSA proportionate to UI in three villages, being significant in Unakoti (p < 0.01), Deorachhara (p < 0.01) and total children (p < 0.001). These correlations suggested a positive association between TV and BSA, a finding that has been previously reported [ 30 ], and this was possibly because of higher AIC. This study showing the trends of negative to positive correlations between FT 4 and BSA, being positive in the village having lower UI and significantly positive in total children (p < 0.01) suggested a positive association between FT 4 and BSA, and this was possibly because of lower AIC. However, this study was unable to decide cause-effect relationship among the variables of these correlations. Furthermore, this study showed the trends of negative to positive correlations (i) between age and Tg-Ab, being positive in the village having lower UI and significantly positive in total children (p < 0.05), and (ii) between Tg-Ab and FT 3 , being negative in the village having lower UI and significantly negative in boys (p < 0.05). These correlations suggested (i) an increase in Tg-Ab with the advancement of age in total children, a finding that has been previously reported in females [ 39 ], and (ii) a negative association between Tg-Ab and FT 3 in boys, a finding that has been previously reported in school children [ 27 ], and these were probably because of lower AIC. This study showing a positive correlation between USCN and TPO-Ab (p < 0.05) in boys suggested that thiocyanate increased TPO-Ab in boys, a finding that supported the reported hypothesis that excess thiocyanate may promote thyroid autoantibodies [ 40 ]. This study showing a positive correlation between UI and TPO-Ab (p < 0.01) being associated with the positive correlation between USCN and TPO-Ab in boys suggested that both increased iodine excretion and increased thiocyanate elevated TPO-Ab, potentially indicating ID in boys. Because increased UI, while sometimes linked to increased IC, can also reflect increased iodine excretion, especially when influenced by thiocyanate [ 41 ]. This study showing a negative correlation between age and UI (p < 0.05) being associated with the positive correlation between UI and TPO-Ab in boys suggested that problem of increased TPO-Ab probably got reduced with the advancement of age in boys. This study showing a positive correlation between TPO-Ab and FT 3 in Deorachhara having highest mean USCN (p < 0.01) suggested a positive association between TPO-Ab and FT 3 , a finding that contradicted the previously reported negative correlation [ 31 ], implying a probable link between elevated levels of TPO-Ab and FT 3 with ID which was potentially mediated by thiocyanate in Deorachhara. Because an increase in T 3 by ID has already been reported [ 4 ] and an increase in TPO-Ab by ID has already been suggested in this study. Furthermore, this study showing positive correlations between USCN and FT 4 in Naithangchhara having lowest UI (p < 0.001) and total children (p < 0.05) suggested that increased thiocyanate raised FT 4 in children, and lower AIC might have favored this. However, thiocyanate has already been reported to increase FT 4 [ 42 ]. The mechanism through which thiocyanate increases FT 4 is still unclear, as thiocyanate decreases TH synthesis inhibiting iodide transport and incorporation of iodide to thyroglobulin [ 42 ]. However, thiocyanate has been reported to decrease protein-bound T 4 levels and thereby to increase FT 4 in rats [ 42 ]. Unakoti having highest BSC showed highest prevalence of underweight (51.2%) and thinness (24.4%), whereas Deorachhara having lowest BSC showed lowest prevalence of underweight (24.4%) and stunting (26.7%). Besides, Naithangchhara having lowest UI showed highest stunting prevalence (51.2%) and lowest thinness prevalence (4.9%). All these suggested that BS might have increased underweight and thinness, whereas lower AIC might have increased stunting and thereby lowered thinness. By sex, boys had highest prevalence of underweight (42.4) and stunting (45.8%), whereas girls had highest thinness prevalence (17.6%). All these suggested that boys were more susceptible to underweight and stunting, and girls to thinness. This study’s overall means of age, weight, height, BMI and BSA were lower than that of the children drinking water containing higher iodine and having higher obesity prevalence [ 43 ]. This study showing positive correlations between UI and height in total children (p < 0.01), and between UI and BSA in boys (p < 0.02), and negative correlations between UI and weight (p < 0.05), and between UI and BMI in total children (p < 0.02) suggested that higher IC increased height sometimes increasing BSA, decreased weight and thereby lowered BMI, whereas lower IC increased weight and thereby enhanced BMI. The increase in height by increased IC was possibly because of boosting of bone development and growth by increased TH, specially FT 3 , resulted from increased IC. Because FT 3 has been reported to stimulate bone development and growth [ 44 ]. Besides, the decreases in weight and thereby decreases in BMI by increased IC might be linked to body fat reduction resulted from enhanced calorigenesis by increased TH, particularly FT 3 , resulted from increased IC. Because FT 3 has also been reported to elevate calorigenesis, increasing basal metabolic rate, thermogenesis and lipolysis [ 44 ]. Furthermore, this study showing positive correlations between FT 4 and weight (p < 0.05), and between FT 4 and BSA (p < 0.01) in total children suggested that increased FT 4 associated with increased weight was reflecting a trend of weight gain (associated with increased BSA). Because a study observed that individuals with higher baseline FT 4 showed preference for a high fat and a high protein diet and, therefore, hypothesized that individuals with higher baseline FT 4 would gain more weight [ 45 ]. Besides, TSH exhibiting negative correlation with weight in total children (p < 0.01) in this study suggested that TSH reduced weight which was likely to be associated with body fat burning. Because literature reported that (i) the presence of TSH receptors in liver and adipocytes (in addition to thyroid) suggests TSH’s role in influencing thermogenesis, adipogenesis, lipolysis/lipogenesis, and (ii) increased TSH in obese one is a compensatory mechanism to counter weight gain [ 45 ]. This study showing a positive correlation between Tg-Ab and height (p < 0.05) in boys suggested that an increase in height might have produced relative ID by increasing iodine requirement against lower AIC, and this ID was probably linked to raised Tg-Ab in boys. This study showing negative correlations between Tg-Ab and weight (p < 0.05), and between Tg-Ab and BMI (p < 0.05) suggested that Tg-Ab remained associated with lower weight, and consequently with lower BMI. However, this study couldn't settle cause-effect relationship between higher Tg-Ab and lower weight. However, this study’s Tg-Ab and TPO-Ab showing no positive associations with TSH suggested that they were not risk factors for differentiated thyroid cancer (DTC). Because the combination of Tg-Ab, TPO-Ab and high TSH was identified as a risk factor for DTC [ 46 ]. This study had several limitations. The study (1) being cross-sectional, was not always able to determine cause-effect relationship among the variable pairs, and (2) did not consider (a) intakes of (i) dietary nitrates, fluorides, calcium, magnesium and isothiocyanate which reduce absorption and incorporation of dietary iodine, and (ii) vegetables of cruciferae family, cassava, maize, peanuts, walnuts, lima beans, sweet potatoes, millets, soya bean containing glucosinolates and goitrins which block iodine incorporation in TH and suppress T 4 secretion, and (b) statuses of iron, zinc, copper, selenium, vitamin A and vanadium - deficiency of which may cause hypothyroidism [ 1 ]. The strengths of this study were (1) to conduct the study in three different areas, and (2) to investigate fourteen different variables in each area. This research design allowed for a more robust and nuanced understanding of the phenomena being studied, by capturing diverse perspectives and considering different interplays of multiple variables across three different areas. In summary, Tripura’s studied tribal children had lower normal FT 4 level and poor health, and were at risk of developing TSH-mediated hyperthyroidism and AITD (increased TPO-Ab and Tg-Ab levels), and these were possibly because of thiocyanate and other antithyroid substances derived from consumed BS. Besides, they had lower normal levels of FT 4 and FT 3 , and were at risk of weight gain and increased Tg-Ab level, and these were possibly because of lower AIC. However, further studies are needed to confirm these findings and to elucidate the underlying mechanisms. Abbreviations AIC Adequate iodine consumption AITD Autoimmune thyroid diseases BMI Body mass index BS Bamboo shoot BSA Body surface area BSC Bamboo shoot consumption CDC Centers for Disease Control and Prevention of the United States DTC Differentiated thyroid cancer FT 3 Free triiodothyronine FT 4 Free thyroxine IC Iodine consumption ID Iodine deficiency IDD Iodine deficiency disorders Na+-K+-ATPase Sodium-potassium adenosine triphosphatase SD Standard deviation SI Salt iodine Tg-Ab Thyroglobulin antibody TGR Total Goiter Rate TH Thyroid hormones TPO Thyroid peroxidase TPO-Ab Thyroid peroxidase antibody TRH Thyrotropin releasing hormone TSH Thyroid stimulating hormone TV Thyroid volume, UI Urinary iodine USCN Urinary thiocyanate USI Universal salt iodization WHO World Health Organization WI Water iodine Declarations Acknowledgments Funding This study was funded by the Department of Biotechnology (DBT), Ministry of Science Technology, Government of India, New Delhi under North Eastern Region (NER) Twinning Project MAP-240/2013 (Sanction Number BT/.471/NE/TBP/2013, dated 26 th December, 2014). Author Ajoy Datta received fellowship from DBT, Author contributions Indrajit Ray: Resources acquisition, methodology, investigation, data acquisition, statistical analysis, data interpretation, and manuscript preparation. Amar K. Chandra: Original conceptualization, visualization, supervision, data evaluation and interpretation. Saru Kumar Debbarma: Data evaluation and interpretation. Sekhar Kumar Mookerjee: Investigation, data evaluation and interpretation. Ajoy Datta: Investigation and data acquisition. Laishram Hemchandra Singh: Project team member. All authors: Writing, editing and approving the final manuscript. Conflict of Interest No conflict of interest regarding this article was reported. Ethical approval This study was approved by the Institutional Human Ethical Committee, Department of Physiology, University of Calcutta, Kolkata, India (IHEC/PHY/CU/H-P 34/13, dated 3 rd October, 2013). 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In: Dunn JT, Medeiros-Neto GA (eds) Endemic Goiter and Cretinism: Continuing Threats to World Health, Pan American Health Organization (PAHO), Washington, DC, USA, Scientific Publication No. 292, pp. 262–272 Michajlovskij N, Langer P (1958) Studies on relations between thiocyanate formation and goitrogenic properties of foods. I. Preformed thiocyanate content of some foods [in German]. Hoppe Seylers Z Physiol Chem 312:26–30. https://pubmed.ncbi.nlm.nih.gov/13598397/ World Health Organization (WHO) (1994) Indicators for assessing iodine deficiency disorders and their control through salt iodization. Micronutrient Series, WHO/NUT/94.6, Geneva. https://iris.who.int/handle/10665/70715 . Accessed 28 May 2025 Das D, Das A (2015) Statistics in Biology and Psychology, Sixth edn. Academic, Kolkata Zeltser ME, Aidarkhanov BA, Berezhnaya IM, Speransky GG, Bazarbekova RB, Nurbekova AA, Levina SA, Mandrovnaya NV, Aripova AA (1992) Iodine deficiency and its clinical manifestations in Kazakhstan. International Council for Control of Iodine Deficiency Disorders (ICCIDD). IDD Newsl 8(1):5–6 Lv S, Wang Y, Xu D, Rutherford S, Chong Z, Du Y, Jia L, Zhao J (2013) Drinking water contributes to excessive iodine intake among children in Hebei, China. Eur J Clin Nutr 67:961–965. https://doi.org/10.1038/ejcn.2013.127 Richman DM, Benson CB, Doubilet PM, Peters HE, Huang SA, Asch E, Wassner AJ, Smith JR, Cherella CE, Frates MC (2018) Thyroid Nodules in Pediatric Patients: Sonographic Characteristics and Likelihood of Cancer. Radiol 288(2):591–599. https://doi.org/10.1148/radiol.2018171170 Chen W, Ho Y, Wang W, Tan L, Bian J, Pearce EN, Zimmermann MB, Shen J, Zhang W (2018) Adverse effects on thyroid of Chinese children exposed to long-term iodine excess: optimal and safe Tolerance Upper Intake Levels of iodine for 7- to 14-y-old children. Am J Clin Nutr 107:780–788. https://doi.org/10.1093/ajcn/ nqy011 Tamang B, Khatiwada S, Gelal B, Shrestha S, Mehta KD, Baral N, Shah GS, Lamsal M (2019) Association of antithyroglobulin antibody with iodine nutrition and thyroid dysfunction in Nepalese children. Thyroid Res 12:1–8. https://doi.org/10.1186_s13044-019-0067-z Zimmermann MB, Aeberli I, Andersson M, Assey V, Yorg JAJ, Jooste P, Jukic T, Kartono D, Kusic Z, Pretell E, San Luis TOL Jr, Untoro J, Timmer A (2013) Thyroglobulin Is a Sensitive Measure of Both Deficient and Excess Iodine Intakes in Children and Indicates No Adverse Effects on Thyroid Function in the UIC Range of 100–299 µg/L: A UNICEF/ICCIDD Study Group Report. J Clin Endocrinol Metab 98(3):1271–1280. https://doi.org/10.1210/jc.2012-3952 Food Standards Australia New Zealand (FSANZ) (2004) Cyanogenic glycosides in cassava and bamboo shoots: A human health risk assessment. Technical Report Series No. 28, 1–24. https://www.foodstandards.gov.au/publications/documents/28_Cyanogenic_glycosides.pdf . Accessed 19 April 2021 Aydıner O, Aydıner EK, Akpınar I, Turan S, Bereket A (2015) Normative Data of Thyroid Volume-Ultrasonographic Evaluation of 422 Subjects Aged 0–55 Years. Clin Res Pediatr Endocrinol 7(2):98–101. https://doi.org/10.4274/jcrpe.1818 Gautam B, Poudel S, Pokhrel S (2024) Significance of thyroid peroxidase antibodies (anti-TPO antibodies) in patients undergoing thyroid function tests. J Gandaki Med College-Nepal 17(2):111–116. https://doi.org/10.3126/jgmc-n.v17i2.71641 Lin Z, Lu C, Teng D, Sun Y, Liu T, Li Y, Shan Z, Teng W (2023) Influencing Factors and New Reference Intervals of Adult Thyroid Volume in IodineSufficient Areas of China. Biol Trace Elem Res 201:5652–5661. https://doi.org/10.1007/s12011-023-03635-x Liu L, Liu L, Li M, Du Y, Liu P, Fan L, Meng F (2022) Study on association between height, weight, iodine supplementation and thyroid volume. Br J Nutr 127:1358–1366. https://doi.org/10.1017/S0007114521002622 Chandra AK, Ghosh D, Mukhopadhyay S, Tripathy S (2004) Effect of bamboo shoot, Bambusa arundinacea (Retz.) Wild. on thyroid status under conditions of varying iodine intake in rats. Indian J Exp Biol 42:781–786. https://pubmed.ncbi.nlm.nih.gov/15573527/ Dey SK, Brahma BK, Goyal AK (2018) GC-MS profiling of phytocomponents from hydromethanolic fraction of Bambusa tulda leaf: a potential candidate for anti-diabetic activity. Int J Life Sci Res 6(3):382–388. https://www.researchgate.net/publication/327670191_GC-MS_profiling_of_phytocomponents_from_hydromethanolic_fraction_of_Bambusa_tulda_leaf_a_potential_candidate_for_anti-diabetic_activity . Accessed 19 April 2021 Khadem S, Marles RJ (2010) Monocyclic Phenolic Acids; Hydroxy- and Polyhydroxybenzoic Acids: Occurrence and Recent Bioactivity Studies. Molecules 15(11):7985–8005. https://doi.org/10.3390/molecules15117985 Sarkar D, Chandra AK, Chakraborty A, Ghosh S, Chattopadhyay S, Singh LH, Ray I (2020) Effects of bamboo shoots ( Bambusa balcooa ) on thyroid hormone synthesizing regulatory elements at cellular and molecular levels in thyrocytes. J Ethnopharmacol 250:112463. https://doi.org/10.1016/j.jep.2019.112463 Sarkar D, Chandra AK, Chattopadhyay S, Biswas M, Das S, Singh LH, Ray I (2020) Possible mechanism of bamboo shoots ( Bambusa balcooa ) induced thyroid disruption – An in vitro study. Hum Exp Toxicol 10:960327120958037. https://doi.org/10.1177/0960327120958037 Ma C, Li D, Yin Y, Wu J, Guo X, Zhang R, Hu Y, Zou Y, Li W, Wang D, Cheng X, Qiu L (2019) Establishing thresholds and effects of gender, age, and season for thyroglobulin and thyroid peroxidase antibodies by mining real-world big data. Clin Biochem 74:36–41. https://doi.org/10.1016/j.clinbiochem.2019.08.011 Singh LH, Chandra AK, Yumnam SD, Sarkar D, Manglem RK, Dhabali T, Mookerjee S, Ray I (2021) Thiocyanate in excess develops goiter followed by autoimmune thyroid diseases even after effective salt iodization in a rural community of north east India. Ecotox Environ Safe 208:111711. https://doi.org/10.1016/j.ecoenv.2020.111711 Miller JK, Moss BR, Swanson EW, Lyke WA (1975) Effect of Thyroid Status and Thiocyanate on Absorption and Excretion of Iodine by Cattle. J Dairy Sci 58(4):526–531. https://www.journalofdairyscience.org/article/S0022-0302(75)84602-9/pdf . Accessed 6 June 2025 Leko MB, Gunjaca I, Pleic N, Zemunik T (2021) Environmental Factors Affecting Thyroid-Stimulating Hormone and Thyroid Hormone Levels. Int J Mol Sci 22:6521. https://doi.org/10.3390/ijms22126521 Chen W, Li X, Wu Y, Bian J, Shen J, Jiang W, Tan L, Wang X, Wang W, Pearce EN, Zimmermann MB, Carriquiry AL, Zhang W (2017) Associations between iodine intake, thyroid volume, and goiter rate in school-aged Chinese children from areas with high iodine drinking water concentrations. Am J Clin Nutr 105:228–233. https://doi.org/10.3945/ajcn.116.139725 Leung AM, Brent GA (2016) The Influence of Thyroid Hormone on Growth Hormone Secretion and Action (Chap. 4). In: Cohen LE (ed) Growth Hormone Deficiency: Physiology and Clinical Management. Springer, pp 28–46. https://doi.org/10.1007/978-3-319-28038-7 Stanikova D, Krajcovicova L, Lobotkova D, Vitariusova E, Ticha L, Pribilincova Z, Ukropcova B, Ukropec J, Stanik J (2023) Thyroid hormone levels and BMI-SDS changes in adolescents with obesity. Front Endocrinol 14:1304970. https://doi.org/10.3389/fendo.2023.1304970 Frohlich E, Wahl R (2017) Thyroid Autoimmunity: Role of Anti-thyroid Antibodies in Thyroid and Extra-Thyroidal Diseases. Front Immunol 8:521. https://doi.org/10.3389/fimmu.2017.00521 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7229013","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496163820,"identity":"7b7db96c-18cb-4258-87e6-5f76458acdeb","order_by":0,"name":"Indrajit Ray","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYJCCAwxsDAyMzUDGByCPjZ14LcyMD2eAtDATZQ8biGBmNuYB0wQUm7efMTxcUHZPnrmd/5i0za9t8nzMDIwfPubg1iJzJsfg8IxzxYaNzcxs0rl9tw3bmBmYJWduw61FggGohbctgRGipec2I1ALGzMvPi38b8Ba7MFaLHtu2xPWIgGxJRGohdmY4cftRCK0PCs4zHMuIRmoxfBhb8Pt5DZmxmb8fuFP3vyZpyzBdmP/wQcHfvy5bTu/vfngh494tDAwcBiAKcMGIMHYBmIyNuBTDwTsD8CUPJj8Q0DxKBgFo2AUjEgAAFb6TYiICjh0AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3498-5501","institution":"Ramkrishna Mahavidyalaya (Affiliated to Tripura University), Government of Tripura, Kailashahar, Unakoti District, Tripura, INDIA","correspondingAuthor":true,"prefix":"","firstName":"Indrajit","middleName":"","lastName":"Ray","suffix":""},{"id":496163821,"identity":"4115b2a2-7857-4908-84dd-833ee873c942","order_by":1,"name":"Amar K. Chandra","email":"","orcid":"","institution":"University of Calcutta - Rajabazar Science College Campus","correspondingAuthor":false,"prefix":"","firstName":"Amar","middleName":"K.","lastName":"Chandra","suffix":""},{"id":496163822,"identity":"0f09b28e-1b8a-4421-ae99-aae6a23d882c","order_by":2,"name":"Saru Kumar Debbarma","email":"","orcid":"","institution":"Agartala Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Saru","middleName":"Kumar","lastName":"Debbarma","suffix":""},{"id":496163823,"identity":"70f3e4ab-0377-4b6f-aa03-cb22ee2e1a98","order_by":3,"name":"Sekhar Kumar Mookerjee","email":"","orcid":"","institution":"Calcutta Medical College and Hospital: Medical College and Hospital Kolkata","correspondingAuthor":false,"prefix":"","firstName":"Sekhar","middleName":"Kumar","lastName":"Mookerjee","suffix":""},{"id":496163824,"identity":"38ebbe28-a7f3-435f-bbc4-e249df2f7ef3","order_by":4,"name":"Ajoy Datta","email":"","orcid":"","institution":"Ramkrishna Mahavidyalaya (Affiliated to Tripura University), Government of Tripura, Kailashahar, Unakoti District, Tripura, INDIA","correspondingAuthor":false,"prefix":"","firstName":"Ajoy","middleName":"","lastName":"Datta","suffix":""},{"id":496163825,"identity":"e2a862c3-8bf6-43d3-ae60-e06a2a4b1a4c","order_by":5,"name":"Laishram Hemchandra Singh","email":"","orcid":"","institution":"DM College of Science","correspondingAuthor":false,"prefix":"","firstName":"Laishram","middleName":"Hemchandra","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2025-07-28 02:31:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7229013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7229013/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88395751,"identity":"c3c48b8d-d722-4d1b-bddf-d64fcd268f6b","added_by":"auto","created_at":"2025-08-06 06:01:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1288289,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7229013/v1/a8f7a42a-4de7-437c-a3df-713b907a6d34.pdf"}],"financialInterests":"","formattedTitle":"Iodine nutrition, bamboo shoot consumption, thiocyanate exposure, thyroid function, weight and height, and associations among them in the tribal children of Tripura, North-east India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIodine, an essential trace element, is required by mammals for the production of thyroid hormones (TH) – thyroxine (T\u003csub\u003e4\u003c/sub\u003e) and triiodothyronine (T\u003csub\u003e3\u003c/sub\u003e) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thyroid gland produces 100% of T\u003csub\u003e4\u003c/sub\u003e and 20% of T\u003csub\u003e3\u003c/sub\u003e in man, and remaining 80% of T\u003csub\u003e3\u003c/sub\u003e is produced by extrathyroidal deiodination of T\u003csub\u003e4\u003c/sub\u003e in liver, kidney, brain, pituitary and brown fat [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Less than 1% of T\u003csub\u003e4\u003c/sub\u003e and T\u003csub\u003e3\u003c/sub\u003e remains as free F\u003csub\u003e4\u003c/sub\u003e (FT\u003csub\u003e4\u003c/sub\u003e) and free F\u003csub\u003e3\u003c/sub\u003e (FT\u003csub\u003e3\u003c/sub\u003e) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Free TH, being accessible to peripheral tissues, regulate various biological activities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo produce sufficient TH, daily iodine consumption (IC) should be at least enough to ensure a median urinary iodine (UI) level of 100 µg/L indicating adequate iodine consumption (AIC) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Foodstuffs deficient in iodine and/or rich in goitrogens may cause iodine deficiency (ID) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] which decreases TH [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In low TH (hypothyroidism), hypothalamic thyrotropin releasing hormone (TRH) stimulates pituitary gland to produce thyroid stimulating hormone (TSH)/thyrotropin which, in turn, stimulates thyroid gland to release TH (predominantly FT\u003csub\u003e4\u003c/sub\u003e). Besides, hyperthyroidism i.e., high TH (predominantly FT\u003csub\u003e4\u003c/sub\u003e) decrease TSH inhibiting pituitary gland and hypothalamus [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prolonged TSH action produces goiter (enlargement of thyroid gland) by stimulating hypertrophy and hyperplasia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Beside hypothyroidism and goiter, ID may also (a) increase the incidences of stillbirth, spontaneous abortion, congenital anomalies, low birth weight, young one’s mortality, deaf-mutism, squint, spastic diplegia, cretinism [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], short stature [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], delayed bone maturation, retarded sexual maturation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], (b) damage brain in young ones [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and (c) lower intelligence quotient up to 13.5 points [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. All these disorders are known as iodine deficiency disorders (IDD). Besides, both ID [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and excess IC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] may cause autoimmune thyroid diseases (AITD) by increasing thyroglobulin antibody (Tg-Ab) and thyroid peroxidase antibody (TPO-Ab). Excess IC may convert the higher Tg-Ab or TPO-Ab state to overt hypothyroidism [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To control IDD, major strategies are sustainably eliminating ID by supplementing exogenous iodine through universal salt iodization (USI) program and monitoring IC periodically [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the context of thyroid function, Tripura’s tribal people hold special relevance, because they traditionally consume bamboo shoot (BS) which contains antithyroid constituents like thiocyanate [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Besides, the State of Tripura is iodine deficient [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and found goiter endemic [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], where effectiveness of the prophylactic USI program introduced in 1988 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] was not satisfactory [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. But information regarding ‘the status and impacts of iodine nutrition, bamboo shoot consumption (BSC) and thiocyanate exposure on Tripura’s tribal population’s thyroid function and health’ was found scanty. Therefore, this study aimed at assessing thyroid pathophysiology, thyroid volume (TV), FT\u003csub\u003e4\u003c/sub\u003e, FT\u003csub\u003e3\u003c/sub\u003e, TSH, Tg-Ab, TPO-Ab, UI, urinary thiocyanate (USCN), salt iodine (SI), water iodine (WI), weight, height and BSC, and associations among them in Tripura’s tribal children.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThree hilly villages Deorachhara, Unakoti and Naithangchhara were randomly selected from the Tripura Tribal Autonomous District Council, North-east India. Deorachhara and Unakoti are in Unakoti district, and Naithangchhara is in Dhalai district. These villages were sparsely populated and under USI program, besides all were similar in climate, economy, culture, education, dietary habits and available health facilities. As school-aged children have ability to reflect current status of iodine nutrition [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and urine samples collected at random from 40 subjects of an area represent current status of iodine nutrition in that area [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the willing tribal children aged 6–12 years receiving no medical treatment were selected randomly ensuring at least 40 subjects from each village. Thus, a total of 127 children (boys-59 and girls-68) were selected from these three villages. The study was conducted during 2015–2018. Selected children’s demographic characteristics have been shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eDemographic characteristics, and levels of TV, FT\u003csub\u003e4\u003c/sub\u003e, FT\u003csub\u003e3\u003c/sub\u003e, TSH, Tg-Ab, TPO-Ab, UI, USCN, WI, SI and BSC by village and total, and mean differences’ significance levels among villages in Tripura’s tribal children\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeorachhara (DC)\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 45)\u003c/p\u003e\u003cp\u003e(M/F = 0.80)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnakoti (UK)\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 41)\u003c/p\u003e\u003cp\u003e(M/F = 0.78)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNaithangchhara (NC) (\u003cem\u003en\u003c/em\u003e = 41)\u003c/p\u003e\u003cp\u003e(M/F = 1.05)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 127)\u003c/p\u003e\u003cp\u003e(M/F = 0.87)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMean differences’ significance level*\u003c/p\u003e\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\u003cp\u003eMean (SD)/\u003c/p\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean (SD)/\u003c/p\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean (SD)/\u003c/p\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean (SD)/\u003c/p\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDC vs. UK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUK vs. NC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDC vs. NC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.02 (2.02)/9.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.37 (1.45)/8.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.68 (1.49)/8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.70 (1.69)/8.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.084 (NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.341 (NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWeight\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.2 (6.6)/24.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.0 (3.6)/19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.5 (3.7)/20.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.0 (5.2)/20.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.077 (NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHeight\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124.5 (12.3)/ 125.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118.7 (7.7)/119.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119.1 (9.2)/118.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120.9 (10.3)/120.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.3 (1.7)/14.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.08 (1.1)/14.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.0 (0.9)/15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.8 (1.4)/14.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBSA\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92 (0.17)/0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82 (0.10)/0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84 (0.11)/0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86 (0.14)/0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTV\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.85 (0.34)/0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83 (0.23)/0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79 (0.20)/0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82 (0.27)/0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFT\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.8 (6.1)/22.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.2 (4.8)/19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.3 (3.5)/17.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.5 (5.6)/20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFT\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.91 (1.44)/5.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.02 (1.57)/5.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.37 (0.84)/4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.09 (1.48)/5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTSH\u003c/em\u003e (mIU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.66 (1.23)/2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.25 (1.34)/3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40 (0.90)/1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.44 (1.39)/2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTg-Ab\u003c/em\u003e (IU/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.2 (2.5)/11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.1 (5.2)/12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.6 (15.4)/14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.2 (10.0)/12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTPO-Ab\u003c/em\u003e (IU/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.39 (1.59)/4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.61 (2.04)/4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.86 (21.40)/1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.58 (12.25)/3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUI\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(µg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117.8 (69.3)/103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130.7 (49.9)/143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103.4 (51.0)/100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e117.3 (58.4)/108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUSCN\u003c/em\u003e (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08 (0.67)/0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95 (0.36)/0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92 (0.88)/0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99 (0.67)/0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWI\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(µg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.18 (2.91)/1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.32 (5.54)/4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.83 (3.79)/5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.69 (4.56)/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSI\u003c/p\u003e\u003cp\u003e(ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.2 (11.0)/26.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.5 (12.3)/20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.0 (7.4)/21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.2 (10.4)/20.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBS\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(g/person\u003c/p\u003e\u003cp\u003e/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125.6 (78.4)/83.3\u003c/p\u003e\u003cp\u003e(FN = 15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194.4 (118.0)/166.7\u003c/p\u003e\u003cp\u003e(FN = 23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131.7 (86.0)/119.0\u003c/p\u003e\u003cp\u003e(FN = 23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e153.8 (101.4)/119.0\u003c/p\u003e\u003cp\u003e(FN = 61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.037\u003csup\u003e\u003cb\u003e@\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.046\u003csup\u003e\u003cb\u003e@\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.826\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e BMI: Body mass index, BSA: Body surface area, TV: Thyroid volume, FT\u003csub\u003e4\u003c/sub\u003e: Free thyroxine, FT\u003csub\u003e3\u003c/sub\u003e: Free triiodothyronine, TSH: Thyroid stimulating hormone, Tg-Ab: Thyroglobulin antibody, TPO-Ab: Thyroid peroxidase antibody, UI: Urinary iodine, USCN: Urinary thiocyanate, WI: Water iodine, SI: Salt iodine, BSC: Bamboo shoot consumption, n: Children number, M/F: Male/Female ratio, FN: Families’ number, S: Significant, NS: Not significant\u003c/p\u003e\u003cp\u003e*: Two-tail t-test (\u003csup\u003e@\u003c/sup\u003e: Assuming Unequal Variances, \u003csup\u003e#\u003c/sup\u003e: Assuming Equal Variances) and z-test at α = 0.05 were performed to find out mean differences’ significance level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnthropometric Measurements\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWeight and height have been widely and successfully used with reference to one another or age to assess health and nutritional risk, especially in children [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. So, weight and height were measured using a digital weighing scale and an anthropometric rod, and recorded to nearest 100 grams and centimeters, respectively. Body mass index (BMI) was calculated using the formula: (weight in kilograms)/(height in meters)\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Body surface area (BSA) ‘in meters\u003csup\u003e2\u003c/sup\u003e’ was calculated using Dubois and Dubois formula: (weight in kilograms)\u003csup\u003e0.425\u003c/sup\u003e x (height in centimeters)\u003csup\u003e0.725\u003c/sup\u003e x 71.84 x 10\u003csup\u003e− 4\u003c/sup\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Weight, height and BMI of each child were compared with those of CDC (Centers for Disease Control and Prevention of the United States) reference z-scores for the child’s sex and age [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A child with \u0026lt;-2SD (SD, standard deviation) weight-for-age, \u0026lt;-2SD height-for-age, \u0026lt;-2SD BMI-for-age, \u0026gt;1SD BMI-for-age or \u0026gt; 2SD BMI-for-age was considered as underweight, stunted, thin, overweight or obese, respectively [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eThyroid Ultrasonography\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEchogenicity of thyroid gland was studied for detecting nodules and measuring TV following WHO (World Health Organization) ultrasonographic guidelines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], using a portable compact color doppler ultrasound scanner system with a 7.5 MHz linear array transducer (GE LOGIQ E BT 12 Console with 12 L RS Linear Probe, Wipro GE Healthcare Pvt. Ltd) by a radiologist. Each thyroid lobe’s volume (milliliters) was calculated by the formula: [0.479 x length (centimeters) x width (centimeters) x depth (centimeters)]. Thyroid volume was calculated by adding the volumes of right and left lobes. Thyroid volume of each child was compared with the 97th percentile reference TV of iodine-sufficient children for the child’s sex and age [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] to diagnose as goitrous or normal.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSerum levels of FT\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e, \u003cb\u003eFT\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e, \u003cb\u003eTSH, Tg-Ab and TPO-Ab\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSerum was separated within 2 hours of fasting venous blood collection from each child and stored at -20\u003csup\u003eo\u003c/sup\u003eC until analysis. Serum levels of FT\u003csub\u003e4,\u003c/sub\u003e FT\u003csub\u003e3\u003c/sub\u003e, TSH, Tg-Ab and TPO-Ab were measured by ELISA microwell methods using the kits of FT\u003csub\u003e4\u003c/sub\u003e (Product Code: 1225 − 300, Accu-Bind ELISA Microwells, Monobind Inc., Lake Forest, CA 92630, USA), FT\u003csub\u003e3\u003c/sub\u003e (Calbiotech Inc., 10461 Austin Dr. Spring Valley, CA, 91978), TSH (Product Code: 325 − 300, Accu-Bind ELISA Microwells, Monobind Inc., Lake Forest, CA 92630, USA), anti-Tg-Ab (EUROIMMUN, Medizinische, Labordiagnostika, AG) and anti-TPO-Ab (EUROIMMUN, Medizinische, Labordiagnostika, AG), respectively.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDiagnostic tests to evaluate thyroid function\u003c/em\u003e: (a) \u003cem\u003eDiagnostic criteria and values (as per assay manuals)\u003c/em\u003e:- AITD: Tg-Ab (Negative: \u0026lt;100 IU/ml, Positive: ≥100 IU/ml); TPO-Ab (Negative: \u0026lt;50 IU/ml, Positive: ≥50 IU/ml). (b) \u003cem\u003eDiagnostic criteria\u003c/em\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] \u003cem\u003eand values (as per assay manuals)\u003c/em\u003e: Euthyroid: normal TSH (0.39–6.16 µIU/ml [0.39–6.16 mIU/L]) and normal FT\u003csub\u003e4\u003c/sub\u003e (0.8-2.0 ng/dl [10.3–25.7 pmol/L]); Euthyroid hypothyroxinemia: normal TSH and low FT\u003csub\u003e4\u003c/sub\u003e (\u0026lt; 0.8 ng/dl [\u0026lt; 10.3 pmol/L]); Euthyroid hyperthyroxinemia: normal TSH, high FT\u003csub\u003e4\u003c/sub\u003e (\u0026gt; 2.0 ng/dl [\u0026gt; 25.7 pmol/L]) and low FT\u003csub\u003e3\u003c/sub\u003e (\u0026lt; 1.4 pg/ml [\u0026lt; 2.15 pmol/L]); Primary/Overt hypothyroidism: high TSH (\u0026gt; 6.16 µIU/ml [\u0026gt; 6.16 mIU/L]) and low FT\u003csub\u003e4\u003c/sub\u003e [Hashimoto’s thyroiditis: Primary hypothyroidism with positive Tg-Ab and positive TPO-Ab]; Secondary/Central hypothyroidism: low TSH (\u0026lt; 0.39 µIU/ml [\u0026lt; 0.39 mIU/L]) and low FT\u003csub\u003e4\u003c/sub\u003e; Subclinical hypothyroidism: high TSH and normal FT\u003csub\u003e4\u003c/sub\u003e; Primary hyperthyroidism: low TSH and high FT\u003csub\u003e4\u003c/sub\u003e [Grave’s disease: Primary hyperthyroidism with positive Tg-Ab, positive TPO-Ab and positive TRAb (thyrotropin receptor antibody)]; Secondary/TSH-mediated/central hyperthyroidism: high TSH and high FT\u003csub\u003e4\u003c/sub\u003e; Subclinical hyperthyroidism: low TSH, normal FT\u003csub\u003e4\u003c/sub\u003e and high FT\u003csub\u003e3\u003c/sub\u003e (\u0026gt; 4.2 pg/ml [\u0026gt; 6.45 pmol/L]).\u003c/p\u003e\u003cp\u003e\u003cb\u003eUrinary Iodine Level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUrine samples were collected from the selected children and stored at 4\u003csup\u003eo\u003c/sup\u003eC until analysis. Urine samples were collected ensuring at least 40 samples from each village [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Urine samples’ iodine contents were assayed following Karmarkar \u003cem\u003eet al.\u003c/em\u003e method [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eUrinary Thiocyanate Level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe dietary consumption of thiocyanate in a population is determined from the USCN [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. So, urine samples’ thiocyanate contents were estimated following Aldridge modified method of Michajlovskij and Langer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eSalt Iodine Level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIodized salt samples were collected from the families of selected children, ensuring at least 35 salt samples from each village, as 35 salt samples collected randomly from an area reflect USI program status in that area [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Salt samples’ iodine contents were estimated following iodometric titration method [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eWater Iodine Level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSamples of water used for drinking and house-hold works were collected from the families of selected children and stored at 4\u003csup\u003eo\u003c/sup\u003eC until analysis. Water samples’ iodine contents were assayed following Karmarkar \u003cem\u003eet al.\u003c/em\u003e method [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eBamboo Shoot Consumption\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo determine per capita daily BSC in the selected villages, 61 randomly selected tribal families consisting of 270 members were interviewed regarding their consumed BS’s types and forms, duration of BS availability in a year, consumption frequency per week, consumption amount per day, number of family members, and the information thus obtained was recorded.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eMeans (SDs) and medians of age, TV, FT\u003csub\u003e4\u003c/sub\u003e, FT\u003csub\u003e3\u003c/sub\u003e, TSH, Tg-Ab, TPO-Ab, UI, USCN, WI, SI, weight, height and BSC were calculated using Microsoft Excel 2007. Two tail z-test (for n [children number] ≥ 30) and t-test (for n \u0026lt; 30) of Microsoft Excel Analysis Tool Pak were used to calculate mean differences’ significance levels (p-values). First order partial correlation coefficients (r) were calculated using the formulae: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{12.3}=({r}_{12}\\:-{r}_{13}\\:\\text{x}\\:{r}_{23})/\\sqrt{(1-{r}_{13}^{2})(1-\\:{r}_{23}^{2})}\\)\u003c/span\u003e\u003c/span\u003e …. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{23.1}=({r}_{23}\\:-{r}_{12}\\:\\text{x}\\:{r}_{13})/\\sqrt{(1-{r}_{12}^{2})(1-\\:{r}_{13}^{2})}\\)\u003c/span\u003e\u003c/span\u003e [where 1 = UI, 2 = SI and 3 = WI], and 9th order partial ‘r’ using the formulae:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:r}_{12.3456789\\text{X}\\text{Y}}=({r}_{12.3456789\\text{X}}-{r}_{1\\text{Y}.3456789\\text{X}}\\:\\text{x}\\:\\:{r}_{2\\text{Y}.3456789\\text{X}})/\\sqrt{(1-{r}_{1\\text{Y}.3456789\\text{X}}^{2})(1-\\:{r}_{2\\text{Y}.3456789\\text{X}}^{2})}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e, ….,\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{\\text{X}\\text{Y}.123456789\\:}=({r}_{\\text{X}\\text{Y}.12345678\\:}-{r}_{9\\text{X}.12345678}\\:\\text{x}\\:{r}_{9\\text{Y}.12345678})/\\sqrt{(1-{r}_{9\\text{X}.12345678}^{2})(1-\\:{r}_{9\\text{Y}.12345678}^{2})}\\)\u003c/span\u003e\u003c/span\u003e [where 1 = age, 2 = TV, 3 = FT\u003csub\u003e4\u003c/sub\u003e, 4 = FT\u003csub\u003e3\u003c/sub\u003e, 5 = TSH, 6 = Tg-Ab, 7 = TPO-Ab, 8 = USCN, 9 = UI, X = weight, and Y = height], and their p-values were obtained when t-values were calculated using the formula t= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{r}/\\sqrt{(1-{\\text{r}}^{2})/(\\text{n}-\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{v}\\text{a}\\text{r}\\text{i}\\text{a}\\text{l}\\text{e}\\text{s})}\\)\u003c/span\u003e\u003c/span\u003e, and consulted with the critical t value table of two-tail test [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMeans (SDs) and medians of the studied variables, and mean differences\u0026rsquo; significance levels (p-values) are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Dietary survey on BS showed an overall median BSC of 119 grams/person/day for about 6 months from May to October. The studied families mainly consumed muli (\u003cem\u003eMelocanna baccifera\u003c/em\u003e), mritinga (\u003cem\u003eBambusa tulda\u003c/em\u003e), rupai (\u003cem\u003eDendrocalamus longispathus\u003c/em\u003e), dalu (\u003cem\u003eSchizostachyum dullooa\u003c/em\u003e), barak (\u003cem\u003eBambusa balcooa\u003c/em\u003e) and jai or bari (\u003cem\u003eBambusa vulgaris\u003c/em\u003e var. \u003cem\u003evulgaris\u003c/em\u003e) types of BS in cooked forms as curry, fry, prickle and boiled BS. Unakoti having significantly higher BSC showed significantly higher levels of TSH and FT\u003csub\u003e3\u003c/sub\u003e, and significantly lower levels of FT\u003csub\u003e4\u003c/sub\u003e, weight, height, BMI and BSA compared to Deorachhara. Besides, Naithangchhara having significantly lower UI showed significantly lower levels of FT\u003csub\u003e4\u003c/sub\u003e, FT\u003csub\u003e3\u003c/sub\u003e and TSH, and significantly higher levels of Tg-Ab and BMI compared to Unakoti.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the prevalence of underweight, stunting and thinness. Unakoti showed highest prevalence of underweight, and thinness, whereas Naithangchhara showed highest stunting prevalence.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrevalence of underweight, stunting and thinness by village, sex and total in Tripura\u0026rsquo;s tribal children\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eType of malnutrition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeorachhara\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnakoti\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNaithangchhara\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoys\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGirls\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight (\u0026lt;-2SD weight-for-age)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (42.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (39.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStunted (\u0026lt;-2SD height-for-age)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThin (\u0026lt;-2SD BMI-for-age)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eMalnutrition data presented in n (%), where n is children number\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e represents the distribution pattern of FT\u003csub\u003e4\u003c/sub\u003e, FT\u003csub\u003e3\u003c/sub\u003e, TSH, Tg-Ab, TPO-Ab, UI, WI and SI. Normal levels of FT\u003csub\u003e4\u003c/sub\u003e, FT\u003csub\u003e3\u003c/sub\u003e and TSH were observed in 92.9%, 81.1% and 93.7% children, respectively. Iodine level was \u0026ge;\u0026thinsp;15 parts per million (ppm) in 80.3% salt samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Distribution pattern of FT\u003csub\u003e4\u003c/sub\u003e, FT\u003csub\u003e3\u003c/sub\u003e, TSH, Tg-Ab, TPO-Ab, UI, WI and SI in Tripura\u0026rsquo;s tribal children\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"740\" height=\"483\"\u003e\u003c/p\u003e\n\u003ctable cellspacing=\"0\" cellpadding=\"0\" align=\"left\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" align=\"left\"\u003e\n \u003cp\u003eData presented in n (%), where n is children number\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e FT\u003csub\u003e4\u003c/sub\u003e:\u003csub\u003e\u0026nbsp;\u003c/sub\u003eFree thyroxine, FT\u003csub\u003e3\u003c/sub\u003e:\u003csub\u003e\u0026nbsp;\u003c/sub\u003eFree triiodothyronine, TSH: Thyroid stimulating hormone, Tg-Ab: Thyroglobulin antibody, TPO-Ab: Thyroid peroxidase antibody, UI: Urinary iodine, WI: Water iodine, SI: Salt iodine, ID: Iodine deficiency\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e*Values as per assay manuals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows 9th order partial \u0026lsquo;r\u0026rsquo; between different variable pairs along with p-values. There were significant positive correlations between TV and height in Unakoti, between Tg-Ab and TPO-Ab in Unakoti, Naithangchhara and total children, between TV and weight in Deorachhara and total children, between TPO-Ab and FT\u003csub\u003e3\u003c/sub\u003e in Deorachhara, between FT\u003csub\u003e4\u003c/sub\u003e and TSH in girls, between FT\u003csub\u003e4\u003c/sub\u003e and weight in total children, between age and Tg-Ab in total children, between UI and height in Naithangchhara and total children, between USCN and FT\u003csub\u003e4\u003c/sub\u003e in Naithangchhara and total children, between UI and TPO-AB and between USCN and TPO-Ab in boys. Besides, there were significant negative corrections between TPO-Ab and TSH in Unakoti, between TV and Tg-Ab in Deorachhara, between Tg-Ab and FT\u003csub\u003e3\u003c/sub\u003e in boys, between TSH and weight in total children, between Tg-Ab and weight in boys, between age and UI in Naithangchhara and boys, and between UI and weight in total children.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNinth order partial correlation coefficients (r) and significance levels (p-values) for some variable pairs involving age, TV, FT\u003csub\u003e4\u003c/sub\u003e, FT\u003csub\u003e3\u003c/sub\u003e, TSH, Tg-Ab, TPO-Ab, UI, USCN, weight and height by village, sex and total\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable pair\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeorachhara\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnakoti\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNaithangchhara\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoys\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGirls\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;127)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTV \u0026amp;\u003c/p\u003e\n \u003cp\u003eweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.5351,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0014, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0823,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2048, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3213, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2625, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTV \u0026amp;\u003c/p\u003e\n \u003cp\u003eheight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0844, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.4432, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0610,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0235, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1428,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0967, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFT\u003csub\u003e4\u003c/sub\u003e \u0026amp;\u003c/p\u003e\n \u003cp\u003eTSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1582, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2472, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0623, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1102, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2676, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1589, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFT\u003csub\u003e4\u003c/sub\u003e \u0026amp;\u003c/p\u003e\n \u003cp\u003eweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1623, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2080, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0853,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2408, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0801, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2005, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTg-Ab \u0026amp;\u003c/p\u003e\n \u003cp\u003eTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.3743, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0788, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0725,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0323, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1646,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0710, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPO-Ab \u0026amp; TSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2909, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.3578, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2597, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0313, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0941, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0719, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTg-Ab \u0026amp; TPO-Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1073, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.6258, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3773, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.7366, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3220, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3822, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge \u0026amp;\u003c/p\u003e\n \u003cp\u003eTg-Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2373, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0506, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2498,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1031, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2749, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2025, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTg-Ab \u0026amp;\u003c/p\u003e\n \u003cp\u003eFT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0574, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.00184, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1067, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2770, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0676, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0974, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUI \u0026amp;\u003c/p\u003e\n \u003cp\u003eTPO-Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1311, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0606, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1083,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3658, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0234, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0731, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUI \u0026amp;\u003c/p\u003e\n \u003cp\u003eweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.3232, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1021, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2893, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2899,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1904, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1878,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUI \u0026amp;\u003c/p\u003e\n \u003cp\u003eheight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2758, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2534, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3733, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3958, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1520, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2425, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge \u0026amp;\u003c/p\u003e\n \u003cp\u003eUI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0765, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2124,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.3865, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.3208, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0170, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1580, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSCN \u0026amp; TPO-Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0780, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0955,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1556, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3196, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1170, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0280, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSCN \u0026amp;\u003c/p\u003e\n \u003cp\u003eFT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0091, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0396, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.5613,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1520, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2068, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1969, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPO-Ab \u0026amp; FT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.4543, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1762, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2117, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2278, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0092, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0060, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSH \u0026amp; weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0775, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0647,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2352,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2553, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2616, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2423, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTg-Ab \u0026amp;\u003c/p\u003e\n \u003cp\u003eheight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1852,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0158,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0376,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2952,\u003c/p\u003e\n \u003cp\u003e0\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1465,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0089,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTg-Ab \u0026amp; weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2731, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1613, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2382, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.3274, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0859, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.1257, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPO-Ab \u0026amp;\u003c/p\u003e\n \u003cp\u003eweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0628,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1632,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.1679,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3196,\u003c/p\u003e\n \u003cp\u003e0\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0945,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.0597,\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e TV: Thyroid volume, FT\u003csub\u003e4\u003c/sub\u003e: Free thyroxine, FT\u003csub\u003e3\u003c/sub\u003e: Free triiodothyronine, TSH: Thyroid stimulating hormone, Tg-Ab: Thyroglobulin antibody, TPO-Ab: Thyroid peroxidase antibody, UI: Urinary iodine, USCN: Urinary thiocyanate, n: Children number; S: Significant, NS: Not significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNinth order partial \u0026lsquo;r\u0026rsquo; involving BMI and BSA replacing weight and height, respectively were also calculated (not shown in table). There were significant positive correlations between TV and BSA in Unakoti, Deorachhara and total children, between FT\u003csub\u003e4\u003c/sub\u003e and BSA in total children, and between UI and BSA in Unakoti and boys. There were significant negative correlations between UI and BMI in Deorachhara and total children, and between Tg-Ab and BMI in boys. There were no significant differences between the means of boys and girls for the study variables (not shown in table).\u003c/p\u003e\n\u003cp\u003eFirst order partial correlation among UI, WI and SI showed a significant positive correlation between UI and WI (not shown in table).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study revealed the status of iodine nutrition, BSC, thiocyanate exposure, thyroid function, physical health, and associations among them in Tripura\u0026rsquo;s studied children.\u003c/p\u003e\u003cp\u003eFor optimal TH production, thyroid gland requires varying amounts of iodine based on age (years): 90 \u0026micro;g/day for children under 6, 120 \u0026micro;g/day for children aged 6\u0026ndash;12, and 150 \u0026micro;g/day for adolescents and adults. Consistently meeting these requirements typically results in a median UI level of 100 \u0026micro;g/L, indicating AIC across all age groups [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Median UI levels of 100\u0026ndash;199 \u0026micro;g/L representing a range of AIC indicate adequate iodine nutrition [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Besides, median UI levels (\u0026micro;g/L) of \u0026lt;\u0026thinsp;20, 20\u0026ndash;49, 50\u0026ndash;99, 200\u0026ndash;299 and \u0026ge;\u0026thinsp;300 indicate severe ID, moderate ID, mild ID, more than AIC and excess IC, respectively [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As per these criteria, the studied children had AIC.\u003c/p\u003e\u003cp\u003eBioavailability of iodine in an area is generally reflected in its WI. An area is considered as severe, moderate, relative iodine deficient or iodine sufficient depending on whether WI level (\u0026micro;g/L) is \u0026lt;\u0026thinsp;4, 4\u0026ndash;10, \u0026gt;\u0026thinsp;10\u0026ndash;20 or \u0026gt;\u0026thinsp;20, respectively [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As per these criteria, the studied villages were severe to moderate iodine deficient, overall being severe iodine deficient which confirmed our previous report [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, this study showed a positive correlation between UI and WI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as was observed by Lv \u003cem\u003eet al\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All these suggested that though these villages were iodine deficient, water available in these villages was a significant source of the consumed iodine.\u003c/p\u003e\u003cp\u003eGenerally, achieving\u0026thinsp;\u0026ge;\u0026thinsp;15 ppm iodine in \u0026ge;\u0026thinsp;90% salt samples indicate USI program\u0026rsquo;s satisfactory effectiveness [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As per this criterion, effectiveness of the ongoing USI program under this study was not satisfactory. However, USI program under this study was better in effectiveness than that of 1996-98 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Besides, SI had no correlation with UI. All these suggested that the ongoing USI program was not a significant source of consumed iodine.\u003c/p\u003e\u003cp\u003eUsually, Total Goiter Rates (TGR) of 0.0-4.9%, 5.0-19.9%, 20.0-29.9% and \u0026ge;\u0026thinsp;30% indicate \u0026lsquo;no\u0026rsquo;, \u0026lsquo;mild\u0026rsquo;, \u0026lsquo;moderate\u0026rsquo; and \u0026lsquo;severe\u0026rsquo; IDD, respectively [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Besides, thyroid nodule prevalence in children normally varies within 1.0-1.5% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This study showing all children with normal TV and 0.8% children with thyroid nodules suggested that IDD were not major problems in these villages. This study\u0026rsquo;s TV was lower than that of excess IC area [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study\u0026rsquo;s village-wise and overall means/medians of FT\u003csub\u003e4\u003c/sub\u003e, FT\u003csub\u003e3\u003c/sub\u003e and TSH were in normal ranges. This study\u0026rsquo;s overall means of FT\u003csub\u003e4\u003c/sub\u003e and FT\u003csub\u003e3\u003c/sub\u003e were higher and lower, respectively than that of excess IC area [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and higher in both cases than that of AIC area [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This study\u0026rsquo;s overall median TSH was lower than that of excess IC [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and AIC areas [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Overall, 86.1% studied children were euthyroid. This study also showed high FT\u003csub\u003e3\u003c/sub\u003e in 17.3% children, being highest (34.1% children) in Unakoti having highest mean and median levels of BSC, TSH and FT\u003csub\u003e3\u003c/sub\u003e. All these suggested that BS might have increased FT\u003csub\u003e3\u003c/sub\u003e, probably by elevating TSH. This study\u0026rsquo;s thyroid dysfunction prevalence (1.6% subclinical hypothyroidism and 2.4% euthyroid hypothyroxinemia) was lower than that of AIC area [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study\u0026rsquo;s overall median Tg-Ab was lower than that of AIC area [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and the prevalence of positive Tg-Ab (0%) and positive TPO-Ab (0.8%) were lower than that of excess IC area [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Generally, AITD prevalence in children varies within 0.5-3% in areas having no ID [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Thus, it appeared that positive thyroid auto-antibodies were not major problems in these villages.\u003c/p\u003e\u003cp\u003eThis study\u0026rsquo;s median BSC (grams/person/day) during BS-growing seasons varied from 83.3 in Deorachhara, 119 in Naithangchhara to 166.7 in Unakoti, overall being 119 which were higher than that of Australia and New Zealand (where BSC varied within 31\u0026ndash;70 grams/person/day) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study showing higher BSC (p\u0026thinsp;=\u0026thinsp;0.037) associated with higher levels of TSH (p\u0026thinsp;=\u0026thinsp;0.034) and FT\u003csub\u003e3\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.001), and lower levels of FT\u003csub\u003e4\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.002), height (p\u0026thinsp;=\u0026thinsp;0.009), weight (p\u0026thinsp;=\u0026thinsp;0.000), BMI (p\u0026thinsp;=\u0026thinsp;0.00) and BSA (p\u0026thinsp;=\u0026thinsp;0.001) in Unakoti than in Deorachhara -suggested that higher BSC might have lowered FT\u003csub\u003e4\u003c/sub\u003e, subsequently impacting height, weight, BMI, and BSA, with a concurrent compensatory increase in TSH and FT\u003csub\u003e3\u003c/sub\u003e. This scenario aligned with the known effects of primary hypothyroidism. Besides, this study showing the trends of positive correlation between Tg-Ab and TPO-Ab proportionate to BSC in three villages, being significant in Unakoti (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Naithangchhara (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) suggested a positive association between Tg-Ab and TPO-Ab, and this was possibly because of increased BSC. This study also showed the trends of positive to negative correlations (i) between TV and height, being significantly positive in Unakoti having highest BSC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.02), and (ii) between TPO-Ab and TSH, being significantly negative in Unakoti having highest BSC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These correlations suggested (i) a positive association between TV and height, a finding that has been previously reported [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and (ii) a negative association between TPO-Ab and TSH, a finding that contradicted previously reported positive correlation between these two [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and these were possibly because of higher BSC. Furthermore, this study showed the trends of positive to negative correlations (i) between TV and Tg-Ab, being significantly negative in Deorachhara having lowest BSC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and (ii) between FT\u003csub\u003e4\u003c/sub\u003e and weight, being positive in the village having lower BSC and significantly positive in total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These correlations suggested (i) a negative association between TV and Tg-Ab, a finding that has been previously reported [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and (ii) a positive association between FT\u003csub\u003e4\u003c/sub\u003e and weight in total children, and these were possibly because of lower BSC. But this study was unable to ascertain cause-effect relationship among the variables of these correlations. This study also showed the trends of positive to negative correlations between TV and weight, being significantly positive in Deorachhara having lowest BSC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These correlations suggesting a positive association between TV and weight, a finding that has been previously reported [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], indicated that higher weight enhanced TV, a finding that has also been previously reported [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and these were probably because of lower BSC. Besides, this study showed the trends of positive to negative correlations between TSH and FT\u003csub\u003e4\u003c/sub\u003e, being positive in the village having higher BSC and significantly positive in girls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This positive correlation suggested that elevated TSH enhanced FT\u003csub\u003e4\u003c/sub\u003e in girls, potentially increasing the risk of TSH-mediated hyperthyroidism, and this effect was possibly because of higher BSC. Literature has already reported BS\u0026rsquo;s association with reduced T\u003csub\u003e4\u003c/sub\u003e and increased TV in rats [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Besides, there have been some reports on BS\u0026rsquo;s antithyroid action. Bamboo shoot has been reported to contain thiocyanate [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] which decreases thyroidal iodine uptake [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and 2,4-dihydroxy benzoic acid [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] which inhibits thyroid peroxidase (TPO) activity [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Bamboo shoot has also been reported to reduce TH synthesis decreasing the activities of sodium-potassium adenosine triphosphatase (Na+-K+-ATPase) and deiodinase [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, it has already been reported that iodine can partially mitigate BS\u0026rsquo;s antithyroid action [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study\u0026rsquo;s median UI (\u0026micro;g/L) were 103 in Deorachhara, 143 in Unakoti, and 100 in Naithangchhara. This study showing lower UI (p\u0026thinsp;=\u0026thinsp;0.014) associated with lower levels of FT\u003csub\u003e4\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.001), FT\u003csub\u003e3\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.000) and TSH (p\u0026thinsp;=\u0026thinsp;0.000), and higher levels of Tg-Ab (p\u0026thinsp;=\u0026thinsp;0.003) and BMI (p\u0026thinsp;=\u0026thinsp;0.000) in Naithangchhara than in Unakoti - suggested that lower AIC might have (i) decreased FT\u003csub\u003e4\u003c/sub\u003e and FT\u003csub\u003e3\u003c/sub\u003e, which remained associated with decreased TSH, and (ii) increased BMI and Tg-Ab. This scenario reflected a potential risk of secondary/central hypothyroidism. These findings appeared reflecting the findings of a previous study [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] that reported TgAb\u0026rsquo;s strong associations with lower levels of UI, FT\u003csub\u003e4\u003c/sub\u003e and FT\u003csub\u003e3\u003c/sub\u003e in an area having median UI of 150 \u0026micro;g/L. Besides, this study showed the trends of positive correlation between TV and BSA proportionate to UI in three villages, being significant in Unakoti (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Deorachhara (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These correlations suggested a positive association between TV and BSA, a finding that has been previously reported [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and this was possibly because of higher AIC. This study showing the trends of negative to positive correlations between FT\u003csub\u003e4\u003c/sub\u003e and BSA, being positive in the village having lower UI and significantly positive in total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) suggested a positive association between FT\u003csub\u003e4\u003c/sub\u003e and BSA, and this was possibly because of lower AIC. However, this study was unable to decide cause-effect relationship among the variables of these correlations. Furthermore, this study showed the trends of negative to positive correlations (i) between age and Tg-Ab, being positive in the village having lower UI and significantly positive in total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and (ii) between Tg-Ab and FT\u003csub\u003e3\u003c/sub\u003e, being negative in the village having lower UI and significantly negative in boys (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These correlations suggested (i) an increase in Tg-Ab with the advancement of age in total children, a finding that has been previously reported in females [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and (ii) a negative association between Tg-Ab and FT\u003csub\u003e3\u003c/sub\u003e in boys, a finding that has been previously reported in school children [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and these were probably because of lower AIC.\u003c/p\u003e\u003cp\u003eThis study showing a positive correlation between USCN and TPO-Ab (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in boys suggested that thiocyanate increased TPO-Ab in boys, a finding that supported the reported hypothesis that excess thiocyanate may promote thyroid autoantibodies [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This study showing a positive correlation between UI and TPO-Ab (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) being associated with the positive correlation between USCN and TPO-Ab in boys suggested that both increased iodine excretion and increased thiocyanate elevated TPO-Ab, potentially indicating ID in boys. Because increased UI, while sometimes linked to increased IC, can also reflect increased iodine excretion, especially when influenced by thiocyanate [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This study showing a negative correlation between age and UI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) being associated with the positive correlation between UI and TPO-Ab in boys suggested that problem of increased TPO-Ab probably got reduced with the advancement of age in boys. This study showing a positive correlation between TPO-Ab and FT\u003csub\u003e3\u003c/sub\u003e in Deorachhara having highest mean USCN (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) suggested a positive association between TPO-Ab and FT\u003csub\u003e3\u003c/sub\u003e, a finding that contradicted the previously reported negative correlation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], implying a probable link between elevated levels of TPO-Ab and FT\u003csub\u003e3\u003c/sub\u003e with ID which was potentially mediated by thiocyanate in Deorachhara. Because an increase in T\u003csub\u003e3\u003c/sub\u003e by ID has already been reported [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and an increase in TPO-Ab by ID has already been suggested in this study.\u003c/p\u003e\u003cp\u003eFurthermore, this study showing positive correlations between USCN and FT\u003csub\u003e4\u003c/sub\u003e in Naithangchhara having lowest UI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggested that increased thiocyanate raised FT\u003csub\u003e4\u003c/sub\u003e in children, and lower AIC might have favored this. However, thiocyanate has already been reported to increase FT\u003csub\u003e4\u003c/sub\u003e [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The mechanism through which thiocyanate increases FT\u003csub\u003e4\u003c/sub\u003e is still unclear, as thiocyanate decreases TH synthesis inhibiting iodide transport and incorporation of iodide to thyroglobulin [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, thiocyanate has been reported to decrease protein-bound T\u003csub\u003e4\u003c/sub\u003e levels and thereby to increase FT\u003csub\u003e4\u003c/sub\u003e in rats [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnakoti having highest BSC showed highest prevalence of underweight (51.2%) and thinness (24.4%), whereas Deorachhara having lowest BSC showed lowest prevalence of underweight (24.4%) and stunting (26.7%). Besides, Naithangchhara having lowest UI showed highest stunting prevalence (51.2%) and lowest thinness prevalence (4.9%). All these suggested that BS might have increased underweight and thinness, whereas lower AIC might have increased stunting and thereby lowered thinness. By sex, boys had highest prevalence of underweight (42.4) and stunting (45.8%), whereas girls had highest thinness prevalence (17.6%). All these suggested that boys were more susceptible to underweight and stunting, and girls to thinness.\u003c/p\u003e\u003cp\u003eThis study\u0026rsquo;s overall means of age, weight, height, BMI and BSA were lower than that of the children drinking water containing higher iodine and having higher obesity prevalence [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This study showing positive correlations between UI and height in total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and between UI and BSA in boys (p\u0026thinsp;\u0026lt;\u0026thinsp;0.02), and negative correlations between UI and weight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and between UI and BMI in total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.02) suggested that higher IC increased height sometimes increasing BSA, decreased weight and thereby lowered BMI, whereas lower IC increased weight and thereby enhanced BMI. The increase in height by increased IC was possibly because of boosting of bone development and growth by increased TH, specially FT\u003csub\u003e3\u003c/sub\u003e, resulted from increased IC. Because FT\u003csub\u003e3\u003c/sub\u003e has been reported to stimulate bone development and growth [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Besides, the decreases in weight and thereby decreases in BMI by increased IC might be linked to body fat reduction resulted from enhanced calorigenesis by increased TH, particularly FT\u003csub\u003e3\u003c/sub\u003e, resulted from increased IC. Because FT\u003csub\u003e3\u003c/sub\u003e has also been reported to elevate calorigenesis, increasing basal metabolic rate, thermogenesis and lipolysis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, this study showing positive correlations between FT\u003csub\u003e4\u003c/sub\u003e and weight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and between FT\u003csub\u003e4\u003c/sub\u003e and BSA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in total children suggested that increased FT\u003csub\u003e4\u003c/sub\u003e associated with increased weight was reflecting a trend of weight gain (associated with increased BSA). Because a study observed that individuals with higher baseline FT\u003csub\u003e4\u003c/sub\u003e showed preference for a high fat and a high protein diet and, therefore, hypothesized that individuals with higher baseline FT\u003csub\u003e4\u003c/sub\u003e would gain more weight [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Besides, TSH exhibiting negative correlation with weight in total children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in this study suggested that TSH reduced weight which was likely to be associated with body fat burning. Because literature reported that (i) the presence of TSH receptors in liver and adipocytes (in addition to thyroid) suggests TSH\u0026rsquo;s role in influencing thermogenesis, adipogenesis, lipolysis/lipogenesis, and (ii) increased TSH in obese one is a compensatory mechanism to counter weight gain [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This study showing a positive correlation between Tg-Ab and height (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in boys suggested that an increase in height might have produced relative ID by increasing iodine requirement against lower AIC, and this ID was probably linked to raised Tg-Ab in boys. This study showing negative correlations between Tg-Ab and weight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and between Tg-Ab and BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggested that Tg-Ab remained associated with lower weight, and consequently with lower BMI. However, this study couldn't settle cause-effect relationship between higher Tg-Ab and lower weight.\u003c/p\u003e\u003cp\u003eHowever, this study\u0026rsquo;s Tg-Ab and TPO-Ab showing no positive associations with TSH suggested that they were not risk factors for differentiated thyroid cancer (DTC). Because the combination of Tg-Ab, TPO-Ab and high TSH was identified as a risk factor for DTC [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study had several limitations. The study (1) being cross-sectional, was not always able to determine cause-effect relationship among the variable pairs, and (2) did not consider (a) intakes of (i) dietary nitrates, fluorides, calcium, magnesium and isothiocyanate which reduce absorption and incorporation of dietary iodine, and (ii) vegetables of cruciferae family, cassava, maize, peanuts, walnuts, lima beans, sweet potatoes, millets, soya bean containing glucosinolates and goitrins which block iodine incorporation in TH and suppress T\u003csub\u003e4\u003c/sub\u003e secretion, and (b) statuses of iron, zinc, copper, selenium, vitamin A and vanadium - deficiency of which may cause hypothyroidism [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The strengths of this study were (1) to conduct the study in three different areas, and (2) to investigate fourteen different variables in each area. This research design allowed for a more robust and nuanced understanding of the phenomena being studied, by capturing diverse perspectives and considering different interplays of multiple variables across three different areas.\u003c/p\u003e\u003cp\u003eIn summary, Tripura\u0026rsquo;s studied tribal children had lower normal FT\u003csub\u003e4\u003c/sub\u003e level and poor health, and were at risk of developing TSH-mediated hyperthyroidism and AITD (increased TPO-Ab and Tg-Ab levels), and these were possibly because of thiocyanate and other antithyroid substances derived from consumed BS. Besides, they had lower normal levels of FT\u003csub\u003e4\u003c/sub\u003e and FT\u003csub\u003e3\u003c/sub\u003e, and were at risk of weight gain and increased Tg-Ab level, and these were possibly because of lower AIC. However, further studies are needed to confirm these findings and to elucidate the underlying mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Adequate iodine consumption\u003c/p\u003e\n\u003cp\u003eAITD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Autoimmune thyroid diseases\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body mass index\u003c/p\u003e\n\u003cp\u003eBS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Bamboo shoot\u003c/p\u003e\n\u003cp\u003eBSA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body surface area\u003c/p\u003e\n\u003cp\u003eBSC\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eBamboo shoot consumption\u003c/p\u003e\n\u003cp\u003eCDC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Centers for Disease Control and Prevention of the United States\u003c/p\u003e\n\u003cp\u003eDTC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Differentiated thyroid cancer\u003c/p\u003e\n\u003cp\u003eFT\u003csub\u003e3\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Free triiodothyronine\u003c/p\u003e\n\u003cp\u003eFT\u003csub\u003e4\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Free thyroxine\u003c/p\u003e\n\u003cp\u003eIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Iodine consumption\u003c/p\u003e\n\u003cp\u003eID \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Iodine deficiency\u003c/p\u003e\n\u003cp\u003eIDD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Iodine deficiency disorders\u003c/p\u003e\n\u003cp\u003eNa+-K+-ATPase \u0026nbsp; Sodium-potassium adenosine triphosphatase\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Standard deviation\u003c/p\u003e\n\u003cp\u003eSI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Salt iodine\u003c/p\u003e\n\u003cp\u003eTg-Ab \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Thyroglobulin antibody\u003c/p\u003e\n\u003cp\u003eTGR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total Goiter Rate\u003c/p\u003e\n\u003cp\u003eTH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Thyroid hormones\u003c/p\u003e\n\u003cp\u003eTPO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Thyroid peroxidase\u003c/p\u003e\n\u003cp\u003eTPO-Ab \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Thyroid peroxidase antibody\u003c/p\u003e\n\u003cp\u003eTRH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Thyrotropin releasing hormone\u003c/p\u003e\n\u003cp\u003eTSH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Thyroid stimulating hormone\u003c/p\u003e\n\u003cp\u003eTV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Thyroid volume, \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Urinary iodine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUSCN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Urinary thiocyanate\u003c/p\u003e\n\u003cp\u003eUSI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Universal salt iodization\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; World Health Organization\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; WI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Water iodine\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This study was funded by the Department of Biotechnology (DBT), Ministry of Science Technology, Government of India, New Delhi under North Eastern Region (NER) Twinning Project MAP-240/2013 (Sanction Number BT/.471/NE/TBP/2013, dated 26\u003csup\u003eth\u003c/sup\u003e December, 2014). Author Ajoy Datta received fellowship from DBT,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndrajit Ray: Resources acquisition, methodology, investigation, data acquisition, statistical analysis, data interpretation, and manuscript preparation. \u0026nbsp;Amar K. Chandra: \u0026nbsp;Original conceptualization, visualization, supervision, data evaluation and interpretation. Saru Kumar Debbarma: Data evaluation and interpretation. Sekhar Kumar Mookerjee: Investigation, data evaluation and interpretation. Ajoy Datta: Investigation and data acquisition. Laishram Hemchandra Singh: Project team member. All authors: Writing, editing and approving the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest regarding this article was reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Human Ethical Committee, Department of Physiology, University of Calcutta, Kolkata, India (IHEC/PHY/CU/H-P 34/13, dated 3\u003csup\u003erd\u003c/sup\u003e October, 2013). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and the Helsinki Declaration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consents of the studied children were obtained in writing from their guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e Datasets are available from the corresponding author on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOpinion of the Scientific Committee on Food on the Tolerable Upper Iodine Level of Iodine. Scientific Committee on Food (SCF) of European Commission (EC) (2002) SCF/CS/NUT/UPPLEV/26 Final. 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Front Immunol 8:521. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2017.00521\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2017.00521\" 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":"Thyroid Volume, Thyroxine, Triiodothyronine, Thyrotropin, Thyroid Antibodies ","lastPublishedDoi":"10.21203/rs.3.rs-7229013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7229013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTripura\u0026rsquo;s tribal people consume iodized salt and bamboo shoot which contains antithyroid constituents like thiocyanate. But information regarding \u0026lsquo;the status and impacts of iodine nutrition, bamboo shoot consumption (BSC) and thiocyanate exposure on their thyroid function and health\u0026rsquo; was found scanty. Therefore, this cross-sectional study aimed at assessing iodine nutrition, BSC, thiocyanate exposure, thyroid function, weight and height, and associations among them in Tripura\u0026rsquo;s tribal children.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis cross-sectional study included 127 children aged 6\u0026ndash;12 years (boys-59 and girls-68) from Deorachhara, Unakoti and Naithangchhara villages. In this study, thyroid volume (TV), free thyroxine (FT\u003csub\u003e4\u003c/sub\u003e), free triiodothyronine (FT\u003csub\u003e3\u003c/sub\u003e), thyroid stimulating hormone (TSH), thyroglobulin antibody (Tg-Ab), thyroid peroxidase antibody (TPO-Ab), urinary iodine (UI), urinary thiocyanate (USCN), salt iodine (SI), water iodine (WI), weight, height and BSC were assessed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMedians of BSC (grams/person/day) and UI (\u0026micro;g/L) were 83.3 and 103 in Deorachhara, 119 and 100 in Naithangchhara, and 166.7 and 143 in Unakoti, respectively. Unakoti having higher BSC (p\u0026thinsp;=\u0026thinsp;0.037) showed higher levels of TSH (p\u0026thinsp;=\u0026thinsp;0.034) and FT\u003csub\u003e3\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.001), and lower levels of FT\u003csub\u003e4\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.002), weight (p\u0026thinsp;=\u0026thinsp;0.000) and height (p\u0026thinsp;=\u0026thinsp;0.009) compared to Deorachhara. There were positive correlations between TV and height in Unakoti having highest BSC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.02), between Tg-Ab and TPO-Ab proportionate BSC in Unakoti (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Naithangchhara (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and between USCN and TPO-Ab in boys (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Deorachhara having lowest BSC showed a negative correlation between TV and Tg-Ab (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a positive correlation between TV and weight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Besides, Naithangchhara having lower UI (p\u0026thinsp;=\u0026thinsp;0.014) showed lower levels of FT\u003csub\u003e4\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.001), FT\u003csub\u003e3\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.000) and TSH (p\u0026thinsp;=\u0026thinsp;0.000), and higher Tg-Ab level (p\u0026thinsp;=\u0026thinsp;0.003) compared to Unakoti.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTripura\u0026rsquo;s studied tribal children had lower normal free thyroid hormone levels and poor health, and were at risk of developing autoimmune thyroid diseases, probably because of BSC and lower adequate iodine.\u003c/p\u003e","manuscriptTitle":"Iodine nutrition, bamboo shoot consumption, thiocyanate exposure, thyroid function, weight and height, and associations among them in the tribal children of Tripura, North-east India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 05:45:35","doi":"10.21203/rs.3.rs-7229013/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":"6baf4fb5-8887-4e9e-9bab-ef585aec1956","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T05:45:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 05:45:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7229013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7229013","identity":"rs-7229013","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}