Association between TSH levels within the reference range and adiposity markers at the baseline of the ELSA-Brasil study.

BACKGROUND: The association of thyrotropin (TSH) with overall (body mass index, BMI), visceral (waist circumference and steatosis), and upper subcutaneous (neck circumference, NC) adiposity markers is still controversial, and the aim of this study is to assess these associations in the baseline data of a large cohort from ELSA-Brasil. METHODS AND
FINDINGS: This cross-sectional study included 11,224 participants with normal thyroid function (normal TSH levels). BMI, waist circumference, NC and steatosis, defined by hepatic attenuation (mild or moderate/severe) were the explicative variables. TSH levels were log transformed (logTSH), and multivariate linear regression models were generated to estimate the associations between logTSH and BMI (continuous and categorized), waist circumference, NC, and steatosis after adjusting for sociodemographic characteristics, health behaviors, and comorbidities. The mean age was 51.5±8.9 years, 5,793 (51.6%) participants were women, 21.8% (n = 2,444) were obese, and 15.1% of the sample was TPOAb positive. The TSH levels were significantly higher in the obese group than in the reference group (<25.0 kg/m2). In the multivariable linear regression models, significant associations of logTSH with BMI and obesity were found. LogTSH was associated with waist circumference only among women. NC and steatosis were not related to TSH levels.
CONCLUSIONS: TSH levels were associated with overall adiposity and obesity. Further studies may elucidate reference levels of TSH according to BMI status.

Introduction

Thyrotropin (TSH) is a well-recognized pituitary hormone that binds to its receptor on the thyroid gland, promoting thyroid function. However, TSH receptors (TSHR) are also expressed in many other tissues, including adipose tissue. TSHR activation has been linked to white adipose tissue lipolysis in animal and human cell models[1] and has been associated with triglyceride accumulation in animal models.[2] TSHR stimulation is also involved in the regulation of thermogenesis, and thyroid hormones might contribute to the control of energy expenditure and metabolic rate.[3] Thus, the effects of TSHR activation on adipose tissue may potentially impact body composition.[4]

Thyroid dysfunctions, namely, hyper- or hypothyroidism, are often associated with weight changes. Conversely, treatment of subclinical hypothyroidism does not seem to be beneficial for weight loss purposes.[5] A systematic review that included 29 studies showed an association between TSH and adiposity markers, such as body mass index (BMI) and waist circumference among individuals with normal thyroid function in 18 studies,[6] even independently of free thyroxine levels.[7] However, among these studies, some adjusted for smoking status[7-17] and others do not. [18-21] Also, the upper reference limit of TSH was not the same among the studies.[7-21] Interestingly, a data analysis of 14 cohorts with 55,412 individuals with thyroid function within the normal range demonstrated that BMI was not different among the lower (0.45–1.49 mIU/L) up to the higher TSH quartile (3.50–4.49 mIU/L). This analysis included studies from Europe, United States, Australia, Asia and South America.[22] Data of 16,902 participants with serum TSH within the reference range from five population-based studies from Germany, Denmark and Netherlands showed the association of TSH with BMI and waist circumference in cross-sectional analysis. However, longitudinal analyses of the four prospective studies included showed that higher TSH levels at baseline studies were related to a decrease of BMI and waist circumference.[23]

Body mass index has been considered the best marker of overall obesity and is mainly used in large epidemiological studies.[24-26] Waist circumference and steatosis are proxies of visceral adiposity.[27] These adipose deposits are metabolically different from subcutaneous deposits. In the Framingham Heart Cohort, neck circumference (NC), a measure of subcutaneous fat, has been independently associated with adverse cardiometabolic risk factors, perhaps acting as a source of circulating free fatty acids.[28] The influence of TSH on visceral adipose tissue and upper body subcutaneous fat remains to be elucidated.

In light of these points, our aim is to assess the association of TSH with overall (BMI), visceral (waist circumference and steatosis), and upper subcutaneous (neck circumference) adiposity markers in the baseline data of participants with normal thyroid function from the Brazilian Longitudinal Study of Adult Health (Estudo Longitudinal da Saúde do Adulto–ELSA-Brasil), a large Brazilian cohort in progress.

Methods

Study design and population

This cross-sectional analysis is a subproject of the Brazilian Longitudinal Study of Adult Health (ELSA–Brasil), which has been described previously.[29,30] Briefly, the baseline cohort comprises 15,105 active or retired civil servants of universities or research institutions from six cities in Brazil who were enrolled between August 2008 and December 2010; the participants were aged from 35–74 years and were mostly female (54%) middle-aged (78% aged < 60 years) adults. All participants were volunteers and signed an informed consent form. All the six Institutional Review Boards (Universidade Federal de Minas Gerais, Universidade de São Paulo, Universidade Federal do Espírito Santo, Universidade Federal do Rio Grande do Sul, Universidade Federal da Bahia e Fundação Oswaldo Cruz) approved this study and also the National Commission for Ethics in Research (CONEP / MS 976/2006) This analysis was approved by the Publications Committee of ELSA-Brasil (PubliELSA 17_0567). The quality and control of the collection and storage of data were ensured via training sessions, certifications, and renewal of certifications of those performing the interviews, clinical examinations and laboratory tests involved in the study protocol.[30]

We excluded participants who were using levothyroxine (n = 1,072) or antithyroid agents (thiamazole or propylthiouracil n = 20), those with missing values for TSH (n = 11), and those with TSH levels <0.40 mcUI/mL (n = 224) or >4.0 mcUI/mL (n = 877). We also excluded participants who were using any drugs that can interfere with TSH laboratory assays (amiodarone, alfa interferon, beta interferon, bromocriptine, carbamazepine, carbidopa, phenytoin, furosemide, haloperidol, heparin, levodopa, lithium carbonate, metoclopramide, valproic acid, oxcarbazepine, propranolol, phenobarbital, primidone, and rifampicin, n = 508),[31] amfepramone (n = 10), mazindol (n = 1), or orlistat (n = 13); participants who previously underwent bariatric surgery (n = 96); and patients with self-reported cirrhosis/hepatitis (n = 1,049).

After the above mentioned participants were excluded, 11,224 participants remained in the analysis. For the steatosis analysis, the quality of the ultrasound images of 3,231 participants was unacceptable, and these exams were excluded. The final sample for steatosis was 8,993.

Laboratory measurements

Blood samples were collected after a 12-hour fasting.[30] TSH was assayed for all participants by an immunoenzymatic bead-based technique using Siemens reagent L2KTS2 with an analytical sensitivity of 0.004 mU/L (Siemens IMMULITE 2000 Immunoassay System® ‒ Siemens Healthcare Diagnostics, Deerfield, IL, USA). The presence of thyroid peroxidase antibody (TPOAb) was tested by electrochemiluminescence (Immulite, Los Angeles, CA, USA) and was defined as positive if the result was ≥ 35 UI/mL. All samples were stored frozen at -80°C until the date of transportation to the Central Laboratory. All laboratory analyses were performed at a single research center (University of São Paulo).[32,33] The cutoff values of TSH were similar to those used in the National Health and Nutritional Examination Survey (NHANES III).[34]

Other laboratory tests were performed, and the methodology and equipment utilized were as follows: fasting plasma glucose and total cholesterol (enzymatic colorimetric method), HDL cholesterol (homogeneous colorimetric method without precipitation), triglycerides (glycerol phosphate peroxidase). Glycated hemoglobin A1c was assessed using high-pressure chromatography (Bio-Rad Laboratories, Hercules, CA, USA)[32]. Intra-assay coefficients of variation (CV) of the TSH, HDL cholesterol, triglycerides and A1c were, respectively, 3.32%, 2.0%,1.6%, 0.86%. Inter-assay CV for lower and higher control of TSH were 4.65 and 5.89, respectively.[33]

Anthropometric data

Anthropometric parameters were measured using standardized and calibrated instruments according to the study protocol. Weight (kg) and height (cm) were measured while the participant was barefoot, wearing light clothing and standing straight with the head level using Toledo scales (to the nearest 100 g) and a stadiometer (accuracy of 0.1 cm), respectively. Waist circumference was measured by inelastic tapes (cm) and used as a proxy of visceral adiposity.[29] The average of two measures was used for analyses. Since BMI [weight(kg)/height(m)2] is considered the most practical measure of overall obesity for populations, it was used as a proxy of “overall obesity” in this study[24-26,35]. BMI was categorized according to the World Health Organization criteria: 18.5–24.9 kg/m2—normal weight (reference category in analyses with BMI as a categorical variable), 25–29.9 kg/m2 –overweight and ≥ 30kg/m2- obesity.[35] Neck circumference was measured with an inelastic tape (mm) right above the cricoid cartilage and perpendicular to the long axis of the neck, with the participant in a sitting position.[29] It was used as a proxy of subcutaneous adipose tissue in our study.

Hepatic ultrasound protocol and steatosis

Liver ultrasound examinations were performed on the participants by board-certified radiologists or by radiology technicians after adequate training using the same models of equipment: a high-resolution B-mode scanner (SSA-790A, Aplio XG, Toshiba Medical System, Tokyo, Japan) and a convex array transducer (model PVT-375BT), with a central frequency of 3.5 MHz and a fundamental frequency of 1.9–5.0 MHz. After the acquisition process, the B-mode hepatic ultrasound images were read by board-certified radiologists at the ELSA-Brasil site, which was established as the ELSA-Brasil ultrasound reading center. Hepatic attenuation was classified as normal or abnormal as follows: mild, moderate or severe.[36] Steatosis was defined by hepatic attenuation (mild or moderate/severe) and was used as a proxy of abdominal fat.

Other participant characteristics

Baseline data collection (standardized interviews, anthropometric and blood pressure measurements, blood tests) was performed at the workplace and at the six research centers. Race/skin color (white, black, or mixed: brown, Asian, native) and educational level [≥12 years (university degree); 9–11 years (high school); ≤8 years (elementary school)] were self-reported. Participants were classified as nonsmokers or as current smokers if they had smoked at least 100 cigarettes (five packs of cigarettes) throughout their lifetime and currently smoked. Alcohol use was categorized as low (weekly consumption ≤ 175 g), moderate (176–350 g) or high (>350 g).

Leisure and transportation-related physical activity was verified through the long form of the International Physical Activity Questionnaire (IPAQ), and participants were categorized into three groups: low, moderate or high activity, according to the sum of the metabolic equivalents per week (combination of activity type, frequency and duration). Diabetes mellitus was defined by insulin and antidiabetic drug use or by a self-reported medical diagnosis of diabetes. Cardiovascular disease was defined by the combination of self-reported heart failure, cardiac revascularization, stroke or previous myocardial infarction.[10] The glomerular filtration rate was estimated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.[37]

Statistical analysis

The normality of the distribution of the data was assessed by histograms, kurtosis and Shapiro-Wilk tests. TSH (response variable) had a skewed distribution and was log-transformed (logTSH). Descriptive and univariate analyses were used to compare logTSH with BMI (continuous and categorized), waist circumference, NC, and steatosis (explicative variables); sociodemographic characteristics; health behaviors; and comorbidities. Multivariate linear regression models were generated to estimate the associations between logTSH and (1) BMI (continuous or categorized as 18.5–25.0 kg/m2 (reference), ≥ 25–29.9 kg/m2, and ≥ 30 kg/m2), (2) waist circumference (continuous by sex), (3) NC (continuous by sex), and (4) hepatic steatosis (no, mild, moderate/severe). All models were adjusted for the following covariates: Model 1- age (years) and sex (only for BMI and steatosis); Model 2- Model 1 plus race/skin color (white, black or mixed), education, TPOAb (negative/positive), diabetes (yes/no), cardiovascular disease (yes/no), and glomerular filtration rate; and Model 3 –Model 2 plus lifestyle behaviors (smoking, alcohol use, physical activity). We tested whether BMI and diabetes, BMI and smoking and BMI and TPOAb were effect modifiers on the associations of LogTSH by multiplicative interaction terms between BMI/diabetes, BMI/smoking and BMI/ TPOAb. Sensitivity analyses according to TPOAb status (negative/positive) and smoking status (current smoker/nonsmoker) were performed when the p value of the interaction term was < .05. All p values given are two-sided with the level of significance set to p < .05. We used the STATA™ package 14.0 (Stata Corporation, College Station, TX, USA) for all analyses.

Results

Among the 11,224 participants included in the present analysis, the mean age was 51.5±8.9 years, and almost fifty percent were female and had a university degree. Twenty-two percent (n = 2,444) of the sample had obesity, and 40.5% (n = 4,548) were overweight. The median TSH level was 1.48 mcUI/mL [interquartile range (IQR 1.04–2.18], and 15.1% of the sample was TPOAb positive. Among BMI categories, TSH levels were significantly higher in the obesity group than in the reference group (<25.0 kg/m2), with median values of 1.53 mcUI/mL [Interquartile range (IQR) 1.07–2.23] and 1.47 mcUI/mL [IQR1.04–2.12], respectively. TSH levels were not different between the overweight and normal weight groups (p = 0.18) or the overweight and obesity groups (p = 0.32). The characteristics of the study population are depicted in Table 1.

Table 1

Demographic, lifestyle characteristics, thyrotropin, anthropometric data, and comorbidities of study population: Brazilian Longitudinal Study of Adult Health (2008–2010) n = 11,224.

Characteristic *	n (%)
Sex
Male	5,440(48.5)
Female	5,784(51.5)
Age(years)	51.5±8.9*
Skin color/race**
White	5,540 (50.0)
Black	1,891 (17.0)
Mixed	3,658 (33.0)
Educational level
Elementary school	1,462(13.0)
High school	4,020(35.8)
University degree	5,742 (51.2)
TSH (mcUI/mL)	1.48[1.04–2.18]∞
TPOAb positive	1,690(15.1)
Smoking status
No	9,719(86.6)
Yes	1,505(13.4)
Alcohol Consumption**
<175g/week	10,152(91.0)
175-349g/week	775(6.4)
≥ 350g/week	285(2.6)
Leisure physical activity**
Highly active	793(7.1)
Moderate active	1,759(15.9)
Low active	8,524(77.0)
BMI (kg/m2)	26.9 ± 4.6*
Waist circumference (cm)
Male	95.0 ± 11.5*
Female	87.1 ± 12.4*
Neck circumference (cm)
Male	39.5 ± 2.8*
Female	33.9 ± 2.5*
Hepatic attenuation§
Normal	5,453 (60.6)
Mild	3,267 (36.4)
Moderate/severe	273(3.0)
Comorbidities and exams
Diabetes mellitus (Yes)	1,005 (8.9)
CVD (Yes)	521(4.6)
GFR(mL/min/1.73 m2)	97.0[94.5–118.1] ∞
Fasting plasma glucose(mg/dL)	100.2[94,2–108.8] ∞
Glycated hemoglobin A1c(%)	5.3[4.9–5.8] ∞
Total cholesterol(mg/dL)	212[186–239] ∞
HDLc(mg/dL)	54[46–64] ∞
Tryglicerides(mg/dL)	114[81–165] ∞

∞Median [Interquartile Range]

* Mean± standard deviation; Skin color- self-reported

Mixed- brown, Asian, or native; TSH- Thyrotropin; TPOAb- Thyroid peroxidase antibody; BMI- body mass index; CVD- cardiovascular disease; GFR- glomerular filtration rate; HDLc- HDL cholesterol.

**Differences in total frequency (n) of characteristics are due to missing values.

§ Final sample for steatosis n = 8,993.

In univariate analysis, logTSH was associated with BMI and waist circumference. LogTSH was not associated with neck circumference or steatosis. The association between TSH levels and BMI categorized as reference, ≥ 25–29.9 kg/m2, and ≥ 30 kg/m2 was only significant for the obesity category.

In multivariable linear regression models, a significant association was found between logTSH and BMI. The final model also included sex, skin color/race, glomerular filtration rate, TPOAb positivity and smoking. As thyrotropin levels were log-transformed, for each 1 kg/m2 increase in BMI, TSH increased by 0.4%. LogTSH was associated with waist circumference only among women (β exponential (eβ) = 1.001[1.0006–1.003] p<0.05), and the final model included skin color/race, education, TPOAb positivity, alcohol use and smoking status. LogTSH was not associated with neck circumference or steatosis (Table 2).

Table 2

Linear regression to estimate the association between thyrotropin1 and body mass index, waist circumference, neck circumference, and hepatic steatosis.

Brazilian Longitudinal Study of Adult Health (2008–2010) n = 11,224.

	Crude	Final Model
	eβ [CI95%]	eβ [CI95%]
BMI(kg/m2)	1.004[1.002–1.006]*	1.004[1.003–1.007]*
Waist(cm)
Male	1.001[1.0002–1.002]*	1.000[0.999–1.002] ∞
Female	1.001[0.99–1.002] ∞	1.001[1.0006–1.003]*
Neck(cm)
Male	1.004[0.99–1.008] ∞	1.002[0.998–1.007] ∞
Female	0.999[0.99–1.005] ∞	1.004[0.999–1.010] ∞
Steatosis§
No	Reference	Reference
Mild	0.993[0.97–1.014] ∞	0.991[0.970–1.012] ∞
Moderate/Severe	1.019[0.96–1.083] ∞	1.005[0.946–1.067] ∞

Thyrotropin1- Log TSH; eβ – β exponential; [CI95%]–confidence interval 95%

BMI- body mass index

*p value < .05

∞ p value> .05

§ For steatosis analysis n = 8,993.

The final model of the association between logTSH and categorized BMI was significant only for the obesity category and included sex, race/skin color, glomerular filtration rate, TPOAb positivity, and smoking (Table 3).

Table 3

Linear regression to estimate the association between thyrothropin1 and body mass index status.

Brazilian Longitudinal Study of Adult Health (baseline 2008–2010) n = 11,224.

	Crude	Final Model
	eβ [CI95%]	eβ [CI95%]
BMI status
Normal weight	Reference	Reference
Overweight	1.01[0.99–1.03] ∞	1.02[0.99–1.04] ∞
Obesity	1.04[1.01–1.07] *	1.05[1.02–1.08]*

Thyrothropin1-logTSH; eβ – β exponential; [CI95%]–confidence interval 95%; BMI-

body mass index; Normal weight- BMI <25.0 kg/m2; Overweight- BMI ≥25–29.9 kg/m2

Obesity- BMI ≥30.0 kg/m2

* p value < .05

∞ p value> .05

The association between LogTSH and adiposity markers were not influenced by diabetes (p value for the interaction terms = 0.47 for BMI, 0.99 for categorized BMI, 0.99 for waist circumference, 0.99 for NC and 0.97 for steatosis). The association of LogTSH with BMI, categorized BMI, waist and neck circumference were not influenced by smoking (p value for the interaction terms were not significant). However, the interaction term significance was borderline for the association LogTSH and waist among women (p = 0.08), and for moderate/severe steatosis (p = 0.05). The association of LogTSH with BMI, categorized BMI, and waist circumference were not influenced by TPOAb (p value for the interaction terms were not significant). However, the association between LogTSH and neck circumference among men was influenced by TPOAb (p value for the interaction = 0.04). The association of LogTSH and moderate/severe steatosis was also influenced by TPOAb (p value for the interaction = 0.01).

After stratification by smoking status, LogTSH remained associated with waist circumference only among women, and there was no association with steatosis (Table 4).

Table 4

Association between Thyrotropin and waist, and with steatosis after stratification by smoking.

Brazilian Longitudinal Study of Adult Health (2008–2010).

	Smokers Final Model eβ [CI95%]	Non smokers Final Model eβ [CI95%]
Waist2
Men	1.000[0.997–1.003] §	1.000[0.999–1.002] §
Women	1.003[1.0001–1.006]*	1.001[1.0002–1.002]*
Steatosis3
No	Reference	Reference
Mild	0.996[0.938–1.056] §	0.992[0.970–1.016] §
Moderate/Severe	0.791[0.624–1.002] §	1.027[0.965–1.093] §

1Thyrotropin- Log TSH

2n = n = 11,224

3n = 8,993; eβ – β exponential; [CI95%]–confidence interval 95%

Waist- waist circumference

* p value < .05

§p value>.05

After analyses stratified by TPOAb, in the TPOAb-positive group, LogTSH was associated neck circumference among men and with moderate/severe steatosis (Table 5).

Table 5

Association between thyrothropin1, waist and neck circumference, and steatosis after stratification by thyroid peroxidase antibody.

Brazilian Longitudinal Study of Adult Health (2008–2010).

	TPOAb positive Final Model1 eβ [CI95%]	TPOAb negative Final Model1 eβ [CI95%]
Neck
Men	1.015[1.003–1.029]*	0.999[0.995–1.005] §
Women	1.006[0.994–1.018] §	1.004[0.997–1.009] §
Steatosis2
No
Mild	Reference	Reference
Moderate/Severe	1.025[0.963–1.089] §	0.985[0.963–1.008] §
	1.198[1.016–1.413]*	0.981[0.919–1.046] §

Thyrothropin1- logTSH; eβ – β exponential; [CI95%]–confidence interval 95%

Neck- neck circumference; Waist- waist circumference; TPOAb—Thyroid peroxidase antibody.

*p value < .05

§p value>.05. For neck analysis-n = 11,224 For steatosis analysis, n = 8,993.

Discussion

Among the baseline participants of the ELSA-Brasil who had serum TSH concentrations within the reference range, higher TSH levels were associated with a higher BMI after all adjustments for confounders. In the analysis with BMI categories, the association of higher TSH levels was present only among participants with a BMI≥ 30 kg/m2, in comparison with normal weight and overweight categories. Interestingly, TSH levels were also associated with waist circumference only among women. TSH levels were associated neither with steatosis, which is a proxy of visceral adiposity, nor with neck circumference, an upper body subcutaneous adipose tissue deposit that is a predictor of cardiovascular diseases.[28] Similar to our study, several previous studies have found a direct association between TSH and BMI, irrespective of sex.[7-11] In samples composed of only women[38,39] or men,[18] the TSH-BMI association was also demonstrated. However, some authors reported no association[12-14,19,40] or associations only among men[41] or among women.[15] These divergences could partially result from diverse methodological aspects among both cross-sectional and longitudinal studies with regard to exclusion criteria, model adjustments, stratification, BMI levels of the groups, and even TSH cut-offs. Although clinical or subclinical hypo- or hyperthyroidism, previous thyroid disorder, or use of medication related to a thyroid disorder were included in the exclusion criteria in these studies, only some of the studies mentioned the exclusion of drugs that interfere with thyroid function.[7,39]

Adipocytes may play a role in TSH physiologic regulation. Whether this role is mediated by insulin resistance or is related to the adipose tissue accumulation per se is still a controversy among authors. Nannipieri et al. found a reduction in TSHR expression in the subcutaneous and visceral adipose tissue of people with severe obesity compared to the expression in the adipose tissue in lean individuals. After major weight loss induced by bariatric surgery, TSHR expression increases significantly in subcutaneous adipose tissue, and there was a reduction in circulating TSH and free triiodothyronine levels.[42] Fontenelle et al. noted that adiposity accumulation results in an adaptive processes to increase energy expenditure, such as changes in the activity of deiodinases, which could be associated with alterations in TSH levels.[4] With the increase of adipose tissue, the resulting hyperinsulinemia and insulin resistance decreases activity of type 2 deiodinase in thyrotrophic cells, reducing intracellular availability of triiodothyronine. This resembles a ‘‘state of hypothyroidism” and may increase TSH levels, which might contribute to explain the higher levels of TSH in individuals with obesity. However, the relationship between TSH and BMI may be independent of insulin resistance, according to some studies.[43,44] Since diabetes—a condition associated with insulin resistance- was not a modifier of the association between BMI and TSH, our findings suggest that the association TSH-BMI is independent of insulin resistance.

We found a direct association between TSH and waist circumference, only among women, in the overall population. This result is different from the findings of HUNT 2 and 3, in which an increase in TSH was associated with an increased waist circumference among euthyroid women and men over a 10.5-year follow-up.[16] The result also differs from the 2007–2008 analysis of the National Health and Nutrition Examination Survey (NHANES), in which every 1-quartile increase in waist circumference was associated with an increase in TSH for women and men.[7] TSH levels were not associated with waist circumference among Japanese men or women.[41] A cross-sectional analysis that used data from four population-based cohort studies and one population-based cross-sectional study from Germany, the Netherlands and Denmark found that serum TSH within the reference range was positively associated with waist circumference.[23] Conversely, our findings are similar to those reported by DePergola et al., who found a positive weak correlation between TSH and waist circumference (r = 0.17) only among obese or overweight women.[19] Also, at a cross-sectional analysis of Study of Health in Pomerania the association of waist circumference and BMI and TSH were found only in women.[45] Two other reviews included cross-sectional studies that found an association between TSH and waist circumference irrespective of sex, among people with overweight or obesity or in a large sample of women.[4,6] It is unclear whether these findings might be related to sex differences in subcutaneous abdominal and visceral tissue proportions because women had proportionally higher fat mass than men. Camhi et al. found that, for a given level of waist circumference or BMI, women had higher levels of subcutaneous fat than men,[46] and as demonstrated by Nannipieri et al., TSHR expression is reduced in adipose tissue.[42] Perhaps more accurate measures of visceral and subcutaneous adipose tissue, as Dual-energy X-ray absorptiometry or computed tomography, could contribute to explaining the different results between studies.

Similar to a meta-analysis with 26,147 participants in which no association of nonalcoholic fat liver disease with TSH levels was found,[47] we did not find a significant association between TSH and hepatic steatosis. Moreover, a cross-sectional analysis of a population-based Study of Health in Pomerania Trend (SHIP Trend) found no association between visceral adipose tissue, measured using magnetic resonance imaging, and TSH levels.[48]

According to some authors, current smoking may modify the association between TSH and BMI.[11,12,41,49] However, in this ELSA-Brasil study, associations of TSH with waist and steatosis remained the same after stratification by smoking status. It is still controversial whether the presence of TPOAb can modify the TSH-BMI association. In present study, the presence of TPOAb modified the association of TSH with NC among men and the association of TSH with steatosis only within the moderate to severe group. In a Brazilian study of women with normal TSH values, BMI was significantly related to TSH only among the TPOAb-positive group.[49] However, in a sample of nonobese Chinese individuals with normal thyroid function, TPOAb was not correlated with BMI,[50] and after a 10-year follow-up of 1,100 euthyroid persons, no association was observed between change in TSH and TPOAb status and weight change.[14]

Despite the significant associations between TSH and BMI and between TSH and waist circumference at the baseline of the ELSA-Brasil study, the clinical relevance of these associations is not well understood. We wonder whether other individual factors have a stronger influence on this association. Moreover, the clinical meaning of this association needs to be more thoroughly investigated in longitudinal studies, particularly regarding whether TSH reference levels should change in accordance with BMI. In the HUNT2 Study, the increase in TSH was related to a discrete increase in weight and BMI after 10.5 years in both sexes.[16] In an Iranian population, changes in TSH levels over 10 years were associated neither with the incidence of metabolic syndrome nor with any of its components, such as obesity or waist circumference.[14] In the DanThyr Study, TSH levels were not a determinant of weight change after 11 years of follow-up, despite the presence of an association between weight change and change in TSH levels.[13] Contrasting with a positive cross-sectional association demonstrated between TSH and BMI and waist circumference for women and men, Tiller at al. found that high TSH serum levels were associated with decreased anthropometric markers at longitudinal data.[45]

The large sample of adults from a middle-income country, the methodological rigor used in data collection, the centralized analysis of the laboratory tests, the quality assurance control, the exclusion of drugs that interfere with thyroid function, the large set of covariates and the stratified analysis for smoking and TPOAb status are strengths of this ELSA-Brasil study. Few studies have investigated the association of TSH and other adipose depots, such as neck circumference, waist circumference and steatosis. The limitations of this study are the absence of free thyroxine data, the lack of body composition data that prevents us from better assessing the impact of TSH on lean or fat mass, and the use of hepatic ultrasound, which might have limited the accuracy of the detection of mild steatosis.[27]

In conclusion, TSH levels were associated with overall adiposity and obesity in this cross-sectional analysis of the ELSA-Brazil cohort study. On the other hand, the visceral adiposity markers, steatosis and waist circumference, had no association or only an association among females, respectively. Upper adiposity deposits, represented by neck circumference, were not related to TSH levels. Smoking status and TPOAb presence were relevant but did not modify the main associations found. This interesting topic remains controversial and deserves further study, especially the reference levels of TSH according to BMI status.

14 Nov 2019

PONE-D-19-24212

Association between TSH levels and adiposity markers at the baseline of the Brazilian Longitudinal Study of Adult Health (ELSA–Brasil)

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Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: In this manuscript the authors investigated the association of TSH with markers of obesity in 11224 individuals.

Comments:

Title:

- Please remove the word longitudinal, since only cross-sectional data was considered.

- Mention that only TSH levels in the reference range were considered

Introduction:

Lines 59-62: Here some more studies should be included, e.g. the Asvold studies and PMID:27393002 ; please specify which studies adjusted for smoking.

Change "maximum reference value" to "upper reference limit".

Lines 65-66: I don't agree that the BMI is the best marker of overall obesity. Do you have a reference for this?

Material & Methods:

- When were blood samples taken? It is well known that TSH follows a circadian rhythm peaking in the morning which may have affect the results.

- Were all examiners certified for the examinations? Do you have data on intra- and inter observer variation?

- I suggest to not adjust for Diabtes and cardiovascular disease, since this may introduce a collider-stratification bias.

Results

Line 217: Change multivariate to multivariable

Table 2 needs reformatting - it is very hard to read. Please just state whether the p-value is below or above 0.05. Statistical tests are yes/no decisions.

- It seems that you used stepwise procedures on the potentially confounding variables (or what do you mean by final model?). This is no good procedure, because we are not in the world of prediction. The aim of this manuscript is to investigate whether TSH is associated with markers of body composition - thus, to draw causal inference. For this it is better to define the model a priori to data analysis based on a Directed Acyclic Graph (DAG) and afterwards all models are run with the same confounder set.

- Stratified analyses: Did you test whether the interaction terms of smoking or TPOab with logTSH were significantly associated with markers of body composition? If not significant, stratified analyses are not useful.

Reviewer #2: This is a large population based cross sectional study assessed the association between TSH levels and adiposity markers at the baseline of the Brazilian Longitudinal Study of Adult Health (ELSA–Brasil).

The authors found significant associations of SH with BMI and obesity and the association of TSH with waist circumference only among women. NC and steatosis were not related to TSH levels.

The study has strength of good design and methodology considering various confounders and large sample size and assessed 2 variables e.g. NC and steatosis which less have been studied previously. Although, the subject is not a new challenging issue especially within cross sectional design which can not show any cause and effect relationship and thyroid hormones especially T3/T4 ratio have not been assessed.

Following points should be addressed

Introduction is well written.

In methods CVs of laboratory measurements should be mentioned.

In Results Section Table 1 the sex-split results of the association of BMI with TSH is not reported, while for NC, WC and steatosis the results reported sex splitted and also adjusted foe sex in model 1!!!???? Please explain

If there is interaction between sex and TSH with BMI, all analysis should be sex splitted instead of adjusting for sex.

It is not clear all over the text (results and discussion), whether the association of TSH and BMI was assessed within 3 categories of BMI and this relationship was significant only in obese individuals or TSH was associated with obesity compared with other categories of normal and overweight ???

**********

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Reviewer #1: No

Reviewer #2: Yes: Ladan Mehran

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7 Jan 2020

Reviewer 1: Thank you for the valuable suggestions.

Here are the answers:

Title:

- Please remove the word longitudinal, since only cross-sectional data was considered.

- Mention that only TSH levels in the reference range were considered

- We changed the title for:

“Association between TSH levels in the reference range and adiposity markers at the baseline of the ELSA–Brasil study”

- The word longitudinal is related to the name of the cohort study “Brazilian Longitudinal Study of Adult Health” but, in fact, this analysis is cross-sectional, from the baseline data of the cohort.

Introduction:

Lines 59-62: Here some more studies should be included, e.g. the Asvold studies and PMID:27393002 ; please specify which studies adjusted for smoking.

We updated the References list including other studies as Asvold BO et al. (JAMA Intern Med. 2015 Jun; 175(6): 1037–1047) and Tiller D et al. (Thyroid. 2016;26:1205-14). We also specified which studies adjusted or not for smoking

Line 59-60- However, among these studies, some adjusted for smoking status7-17 and others do not. 18-21

Change "maximum reference value" to "upper reference limit".

We changed the word maximum by upper:

Line 60- “Also, the upper reference limit of TSH was not the same among the studies.7-21”

Lines 65-66: I don't agree that the BMI is the best marker of overall obesity. Do you have a reference for this?

- We mentioned that BMI is the best marker of overall obesity because this is the indirect measurement most commonly used and that recommended by the World Health Organization for the evaluation of overweight/obesity in adults. The World Health Organization stated that “ A crude population measure of obesity is the body mass index (BMI)” and “BMI provides the most useful population-level measure of overweight and obesity as it is the same for both sexes and for all ages of adults”.

24.World Health Organization. Obesity and overweight. Key facts. Available at: https://www.who.int/en/news-room/fact-sheets/detail/obesity-and- overweighthttps:// Accessed:11/25/2019.

- Also an Endocrine Society Scientific Statement of 2018 stated that “BMI provides the most useful population-level measurement of overweight and obesity, and numerous large population studies across multiple continents have demonstrated its utility as an estimate of risk.

25. Bray G, Heisel WE, Afshin A, Jensen MD, Dietz WH, Long M,et al. The Science of Obesity Management: An Endocrine Society Scientific Statement. Endocr Rev. 2018;39:79-132.

- We used the term “overall obesity” as assessed by BMI, in order to differentiate from abdominal obesity, assessed by waist circumference.

26. Hu F. Measurements of adiposity and body composition. In: Hu FB, ed. Obesity Epidemiology. New York, United States: Oxford University Press; 2008:53-83.

- We rephrased the Introduction and the Methods section in order to clarify the use of BMI as a marker of adiposity:

Line 71- Body mass index has been considered the best marker of overall obesity and is mainly used in large epidemiological studies.24-26

Line 144: Since BMI is considered the most practical measure of overall obesity for populations24-26,35 , we used BMI as a proxy of overall obesity in this study.

Material & Methods:

- When were blood samples taken? It is well known that TSH follows a circadian rhythm peaking in the morning which may have affect the results.

We rephrased the Laboratory Measurements at Methods Section

Line 118 - Blood samples were collected after an overnight fast by “Blood samples were collected after a 12-hour fasting.30”

All the blood samples were collected with the same protocol, so do not impose bias to these analyses. We referenced studies where TSH was assessed in non-fasting blood samples (Asvold, Bjergved), but also others that used fasting samples (Kithara, Fox, Sakurai).

- Were all examiners certified for the examinations? Do you have data on intra- and inter observer variation?

All the anthropometric parameters were carried out using standard equipment and techniques by trained, certified professionals under rigorous quality control. The intra-class correlation coeficient for repeat measurements of waist circumference, an anthropometric measure sources of variability, was 0.98 (95% CI 0.85–1.0).

29.-Aquino S, Aquino EML, Barreto SM, Carvalho MS, Chor D, Duncan BB et al. ELSA-Brasil (Brazilian Longitudinal Study of Adult Health): objectives and design. Am J Epidemiol. 2012;175:315-24.

30.Schmidt MI, Griep RH, Passos VM, Luft VC, Goulart AC, Menezes GM de S et al. Strategies and development of quality assurance and control in the ELSA-Brasil. Rev Saúde Pública 2013; 47(Suppl 2): 105–112.

- I suggest to not adjust for Diabetes and cardiovascular disease, since this may introduce a collider-stratification bias.

We decided to adjust for diabetes and cardiovascular because these conditions may impose lifestyle modifications that could contribute to anthropometric alterations. As suggested, we performed a new analysis without adjustments for cardiovascular disease or diabetes and final models had no significantly different results: for BMI, eβ=1.005[1.003-1.007] p<0.01; for categorical BMI, overweight category: eβ=1.018[0.997-1.039] p=0.09, obesity category: eβ=1.051[1.025-1.077] p<0.01; for waist circumference, among men: eβ=1.000[0.999-1.002] p=0.20, among women: eβ=1.002[1.0006-1.003] p=0.002; for neck circumference, among men: eβ=1.003[0.998-1.007] p=0.26, among women: eβ=1.005[0.999-1.009] p=0.08; for steatosis, mild: eβ=0.99[0.97-1.014] p=0.49, moderate/severe: eβ=1.007[0.95-1.069] p=0.83.

Results

Line 217: Change multivariate to multivariable

- We reworded the text as suggested “In multivariable linear regression models” (Line 234)

Table 2 needs reformatting - it is very hard to read. Please just state whether the p-value is below or above 0.05. Statistical tests are yes/no decisions.

-As suggested, we reformatted Table 2 and 3, including only crude and final models, and stated p value as below or above 0.05. The objective of displaying both at the same table was to demonstrate that the adjustments had small effects on the results.

- It seems that you used stepwise procedures on the potentially confounding variables (or what do you mean by final model?). This is no good procedure, because we are not in the world of prediction. The aim of this manuscript is to investigate whether TSH is associated with markers of body composition - thus, to draw causal inference. For this it is better to define the model a priori to data analysis based on a Directed Acyclic Graph (DAG) and afterwards all models are run with the same confounder set.

As the study had a cross-sectional design, the objective was not to draw causal inference. In fact, we plan this for a future analysis of longitudinal data. This theme is still a challenge, even on longitudinal studies, where some authors have demonstrated causal relationship between TSH and BMI/waist circumference and others have not.

We pointed out the confounding variables, and the analysis was drawn not to compare models, but rather to group the variables as socio-demographic, comorbidities, health related conditions. In fact, it is not a stepwise model, and we did not try to build an algorithm. The analysis had an epidemiological basis, with the adjustment for the potential confounders.

In order to clarify, we rephrased the Statistical analysis: “All models were adjusted for the following covariates: Model 1- age (years) and sex (only for BMI and steatosis); Model 2- Model 1 plus race/skin color (white, black or mixed), education, TPOAb (negative/positive), diabetes (yes/no), cardiovascular disease (yes/no), and glomerular filtration rate; and Model 3 – Model 2 plus lifestyle behaviors (smoking, alcohol use, physical activity)” – Line 195-199.

- Stratified analyses: Did you test whether the interaction terms of smoking or TPOab with logTSH were significantly associated with markers of body composition? If not significant, stratified analyses are not useful.

Thank you for this important suggestion. We have tested the interaction terms of smoking and TPOAb with log TSH. The results of the interaction terms were not significant for BMI, BMI categorized. However, the interaction term of smoking and waist among women, and smoking and steatosis were borderline: p=0.08 for waist and p=0.06 for moderate/severe steatosis, suggesting a potential impact of these interactions. The interaction term of TPOAb and neck was significant (p=0.03) only among men. The interaction term of TPOAb and steatosis moderate/severe was also significant (p=0.01). We changed the Methods section and presented the results after the stratified analysis only when it was appropriate.

- Reviewer 2- Thank you for the valuable suggestions. Here are the answers:

- In methods CVs of laboratory measurements should be mentioned.

Thank you for this relevant suggestion.

Regarding the CVs of laboratory measurements, we included the reference 33: the following manuscript describes all of them.

33.LADWIG, R. et al. Variability in baseline laboratory measurements of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Braz J Med Biol Res [online]. 2016, vol.49, n.9, e5381. Epub Aug 01, 2016. ISSN 1414-431X.

We rewrote the manuscript including the information of CV: “Intra-assay coefficients of variation (CV) of the TSH, HDL cholesterol, triglycerides and A1c were, respectively, 3.32%, 2.0%,1.6%, 0.86%. Inter-assay CV for lower and higher control of TSH were 4.65 and 5.89, respectively” (Lines 133-136).

In Results Section Table 1 the sex-split results of the association of BMI with TSH is not reported, while for NC, WC and steatosis the results reported sex splitted and also adjusted foe sex in model 1!!!???? Please explain

If there is interaction between sex and TSH with BMI, all analysis should be sex splitted instead of adjusting for sex.

We did not show the results of BMI, categorical BMI and steatosis by sex, because the there was no evidence that sex modified the association between these adiposity markers and TSH levels through the analyses of interaction terms.

It is not clear all over the text (results and discussion), whether the association of TSH and BMI was assessed within 3 categories of BMI and this relationship was significant only in obese individuals or TSH was associated with obesity compared with other categories of normal and overweight ???

Thank you for the comment. We stated at the ‘Methods’ section that BMI was analyzed as a continuous variable, and also as a categorical variable: categorized as 18.5-25.0 kg/m2 (reference), ≥ 25- 29.9 kg/m2, and ≥ 30 kg/m2. The linear model with categorical BMI compared the reference group (normal weight) with overweight and with obesity, as depicted in Table 3. We tried to clarify this at the discussion:

Line 302-304- “In the analysis with BMI categories, this association was present only among participants with a BMI≥ 30 kg/m2 in comparison with normal weight category.”

Submitted filename: Rebuttal letter.docx

Click here for additional data file.

24 Jan 2020

Association between TSH levels within the reference range and adiposity markers at the baseline of the ELSA–Brasil study

PONE-D-19-24212R1

Dear Dr. Diniz,

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Academic Editor

PLOS ONE

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Comments to the Author

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Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for adressing all my questions and suggestions. I have no further comments.

**********

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

27 Jan 2020

PONE-D-19-24212R1

Association between TSH levels within the reference range and adiposity markers at the baseline of the ELSA–Brasil study

Dear Dr. Diniz:

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If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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