Trunk fat and leg fat in relation to free triiodothyronine in euthyroid postmenopausal women

in Endocrine Connections

Correspondence should be addressed to X Ma or Y Bao: maxiaojing@sjtu.edu.cn or yqbao@sjtu.edu.cn

*(X Nie and Y Xu contributed equally to this work)

Background

A high level of free triiodothyronine (FT3) within the reference range may be a potential metabolic risk marker. However, the relationship between different fat depots and FT3 has remained unclear.

Objective

We aimed to explore the relationships between segmental fat distribution and FT3 in euthyroid middle-aged and elderly men and postmenopausal women.

Methods

A total of 891 subjects (394 men and 497 women) were enrolled. A bioelectrical impedance analyzer was used to measure total, trunk, arm and leg fat mass (FM) and fat percentage (fat%). The leg fat mass to trunk fat mass ratio (LTR) was calculated to evaluate the relative distribution of leg fat compared with that of trunk fat. Thyroid hormones were measured by electrochemical luminescence immunoassay.

Results

FT3 in men did not change significantly with increases in LTR quartiles, while FT3 in women decreased significantly (P for trend = 0.004). In multivariate linear regression analysis, multiple metabolic and cardiovascular risk factors were adjusted. The LTR was negatively related to FT3 in women (P < 0.05). After further mutual adjustment for trunk fat and leg fat parameters, trunk FM and fat% were positively related to FT3, while leg FM and fat% were negatively related to FT3 in women (all P < 0.05).

Conclusions

In euthyroid postmenopausal women, trunk fat was positively correlated with FT3, whereas leg fat was negatively correlated with FT3. Our findings supported that a high level of FT3 within the reference range was related to adverse fat distribution.

Abstract

Background

A high level of free triiodothyronine (FT3) within the reference range may be a potential metabolic risk marker. However, the relationship between different fat depots and FT3 has remained unclear.

Objective

We aimed to explore the relationships between segmental fat distribution and FT3 in euthyroid middle-aged and elderly men and postmenopausal women.

Methods

A total of 891 subjects (394 men and 497 women) were enrolled. A bioelectrical impedance analyzer was used to measure total, trunk, arm and leg fat mass (FM) and fat percentage (fat%). The leg fat mass to trunk fat mass ratio (LTR) was calculated to evaluate the relative distribution of leg fat compared with that of trunk fat. Thyroid hormones were measured by electrochemical luminescence immunoassay.

Results

FT3 in men did not change significantly with increases in LTR quartiles, while FT3 in women decreased significantly (P for trend = 0.004). In multivariate linear regression analysis, multiple metabolic and cardiovascular risk factors were adjusted. The LTR was negatively related to FT3 in women (P < 0.05). After further mutual adjustment for trunk fat and leg fat parameters, trunk FM and fat% were positively related to FT3, while leg FM and fat% were negatively related to FT3 in women (all P < 0.05).

Conclusions

In euthyroid postmenopausal women, trunk fat was positively correlated with FT3, whereas leg fat was negatively correlated with FT3. Our findings supported that a high level of FT3 within the reference range was related to adverse fat distribution.

Introduction

Obesity is the key pathogenic factor of type 2 diabetes and cardiovascular disease (1). The disease risk associated with obesity is related not only to fat content but also to fat distribution. Abdominal obesity characterized by an ‘apple-shape’ significantly increases the risk of type 2 diabetes and cardiovascular disease (2). In contrast to that, a ‘pear-shape’ with most fat accumulating in the lower body is found to have metabolic and cardiovascular protective effects (3).

Free triiodothyronine (FT3) is the bioactive form of thyroxines. FT3 regulates basal metabolic rate, promotes fat decomposition and acts on the cardiovascular system through adrenergic signaling (4). Previous studies have found that a high level of FT3 within the reference range was positively related to BMI and waist circumference (5, 6). Non-alcoholic fatty liver disease (NAFLD) is one of the most common complications of obesity. In recent years, some studies found that a high level of FT3 within the reference range was related to the risk of NAFLD and multiple adverse metabolic and cardiovascular risk factors (5, 7, 8), which suggests that a high level of FT3 within the reference range may be a potential adverse metabolic risk marker. The middle-aged and elderly population is at high risk of metabolic and cardiovascular disease; thus, it is meaningful to explore the associations between different fat depots and FT3 in this age group.

BMI and waist circumference are simple indexes to evaluate obesity, but cannot be used to reflect the distribution of different fat depots. Segmental fat depots (trunk, arm and leg) measured by segmental bioelectrical impedance analysis (sBIA) are highly consistent with those measured by dual energy X-ray absorptiometry (9). sBIA can accurately evaluate body fat content with simple operation and lightweight size. None of the previous studies have focused on associations between segmental fat distribution and FT3. In the present study, we aimed to explore the relationship between segmental fat distribution and FT3 in euthyroid middle-aged and elderly men and postmenopausal women. Trunk fat and leg fat were found to have an inverse relationship with metabolic and cardiovascular disease in previous studies (3, 10, 11). The relationship between arm fat and cardiovascular metabolic risk was nonsignificant or weak (12, 13). Thus, we calculated the leg FM to trunk FM ratio (LTR) to study the relationship between a tendency of fat to accumulate in the legs rather than trunk and FT3.

Materials and methods

Study population

Men and postmenopausal women aged 50 years or older were recruited from Shanghai communities from October 2015 to July 2016. Postmenopausal status was defined as at least 1 year of amenorrhea in the absence of other medical conditions (14). The methods of population recruitment and data collection were described in our previous study (15). All participants provided informed consent and received questionnaires, a physical examination, laboratory tests and segmental fat measurements. The exclusion criteria included a history of thyroid disease with thyroxine supplement or anti-thyroid therapy; abnormal thyroid function; a history of diabetes or cardiovascular disease; moderate-to-severe anemia; malignancy or an intracranial mass lesion; severe kidney or liver dysfunction; acute infection; hypoalbuminemia; taking lipid-lowering drugs, hypotensive drugs, weight-loss pills, glucocorticoids, sex hormones, amiodarone or lithium. The study was approved by the Ethics Committee of the Shanghai Jiao Tong University Affiliated Sixth People’s Hospital and was carried out following the guidelines of the 1964 Declaration of Helsinki.

Anthropometric and biochemical assessments

Height, body weight and blood pressure were measured according to standard methods, which were described in our previous study (15). BMI was equal to the body weight (kg) divided by the height squared (m2). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were determined by the mean blood pressure from three measurements taken at 3-min intervals.

All subjects underwent a 75-g oral glucose tolerance test in the morning after an overnight fast of 10 h. The measurements of fasting plasma glucose (FPG), 2-h plasma glucose (2 h PG), glycated hemoglobin A1c (HbA1c), fasting insulin (FINS), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c) were performed according to methods described in our previous study (15). Estradiol (E2) was detected by chemiluminescent microparticle immunoassays on an Architect i2000SR analyzer (Abbott GmbH & Co. KG). The level of insulin resistance was evaluated by the homeostasis model assessment of insulin resistance (HOMA-IR) with the following formula: HOMA-IR = FINS (mU/L) × FPG (mmol/L)/22.5 (16).

A Cobas e601 analyzer (Roche Diagnostics GmbH) was used to measure FT3, free thyroxine (FT4) and thyroid-stimulating hormone (TSH). The reference range and intra- and interassay coefficients for FT3 were 3.10–6.80 pmol/L, <7.0 and <8.0%, respectively; for FT4, they were 12.00–22.00 pmol/L, <5.0 and <7.0%, respectively; and for TSH, they were 0.27–4.20 mIU/L, <3.0 and <8.0%, respectively.

Measurement of segmental fat distribution

An automatic bioelectrical impedance analyzer (TBF-418B; Tanita Corp., Tokyo, Japan) was used to measure total, trunk, arm and leg fat mass (FM) and fat percentage (fat%) according to a previously described method (15). To evaluate the tendency of fat accumulation in the legs rather than the trunk, the LTR was calculated as leg FM (kg) divided by trunk FM (kg).

Statistical analysis

The normality of the distribution of variables was evaluated by the Kolmogorov–Smirnov test. Normally distributed variables are expressed as the mean ± standard deviation. Variables with a skewed distribution are expressed as the median (interquartile range). For normally distributed variables, one-way ANOVA was used for trend analysis. For skewed variables, the Kruskal–Wallis H-test was used for trend analysis. Partial correlation analysis was used to explore the age- and BMI-adjusted relationships between fat parameters and thyroid hormones. Multivariate linear regression analysis was used to explore the relationship between segmental fat parameters and thyroid hormones. Considering close correlations among segmental fat parameters, multicollinearity was analyzed for each linear regression model. A variance inflation factor > 10 indicated serious multicollinearity. SPSS version 22.0 (SPSS, Inc.) statistical software was used for all data analyses. A two-tailed P value <0.05 was considered statistically significant.

Results

Clinical characteristics of the study participants

The study included 891 subjects with an age range of 50–81 years (mean age 61 ± 5 years). There were 394 men and 497 postmenopausal women. The medians of BMI, total FM and total fat% were 23.0 (21.2–25.1) kg/m2, 16.0 (12.2–20.1) kg, and 25.9 (20.0–32.3)%, respectively. The medians of FT3, FT4 and TSH were 4.93 (4.60–5.29) pmol/L, 16.55 (15.31–17.71) pmol/L and 2.17 (1.55–2.87) mIU/L, respectively.

Both men and women were divided into four groups according to LTR quartiles: Q1 < 0.58, Q2 0.58–0.63, Q3 0.64–0.73 and Q4 > 0.73 for men; and Q1 < 0.64, Q2 0.64–0.70, Q3 0.71–0.80 and Q4 > 0.80 for women (Table 1). In men, age, BMI, total FM, total fat%, DBP, FPG, FINS, HOMA-IR and E2 decreased significantly with increasing LTR quartiles (all P for trend < 0.05). In women, BMI, total FM, total fat%, SBP, FPG, HbA1c, FINS, HOMA-IR, TG and E2 decreased significantly with increasing LTR quartiles (all P for trend < 0.05). In both genders, HDL-c significantly increased with increasing LTR quartiles (all P for trend < 0.05).

Table 1

Clinical characteristics of subjects.

ParametersLTR quartilesP for trend
Q1Q2Q3Q4
Men
n98999998
 Age (years)63 (58–67)62 (57–66)61 (56–65)59 (56–65)0.026
 BMI (kg/m2)23.9 (22.4–25.5)24.4 (22.2–26.3)23.5 (22.2–25.0)22.0 (19.5–23.4)<0.001
 Total FM (kg)16.0 (12.9–18.7)15.4 (12.6–18.8)13.0 (11.0–15.3)8.7 (6.3–11.4)<0.001
 Total fat%23.1 (20.0–25.3)21.8 (19.5–24.0)18.7 (17.0–20.9)13.9 (10.9–16.4)<0.001
 SBP (mmHg)135 (122–148)134 (122–145)132 (123–144)130 (119–141)0.301
 DBP (mmHg)80 (74–88)81 (74–87)80 (74–86)77 (71–82)0.020
 FPG (mmol/L)5.7 (5.4–6.2)5.7 (5.4–6.0)5.8 (5.5–6.3)5.6 (5.3–6.1)0.044
 2 h PG (mmol/L)7.5 (5.8–9.7)7.4 (5.9–8.8)7.2 (5.8–8.3)7.1 (5.7–8.7)0.548
 HbA1c (%)5.7 (5.4–6.0)5.6 (5.4–5.9)5.6 (5.4–5.9)5.6 (5.4–5.8)0.614
 FINS (mU/L)8.8 (6.3–11.7)7.6 (5.8–11.5)8.3 (5.5–12.0)6.5 (4.7–9.1)0.001
 HOMA-IR2.2 (1.5–3.5)2.0 (1.4–3.1)2.1 (1.4–3.4)1.6 (1.1–2.4)0.001
 TC (mmol/L)5.3 ± 0.95.1 ± 0.95.2 ± 0.95.0 ± 0.80.102
 TG (mmol/L)1.6 (1.2–2.3)1.4 (1.0–2.3)1.5 (1.0–2.2)1.2 (0.9–2.0)0.105
 HDL-c (mmol/L)1.3 (1.1–1.5)1.2 (1.1–1.4)1.3 (1.1–1.4)1.4 (1.1–1.6)0.018
 LDL-c (mmol/L)3.3 ± 0.73.2 ± 0.83.2 ± 0.73.0 ± 0.70.051
 E2 (pmol/L)99.1 (80.8–125.7)113.8 (91.8–128.5)95.5 (84.4–110.1)99.1 (80.8–121.2)0.009
Women
n124126123124
 Age (years)60 (57–63)61 (56–65)61 (57–64)60 (57–63)0.355
 BMI (kg/m2)25.0 (23.1–27.0)23.4 (22.0–25.1)22.5 (21.0–24.4)20.2 (18.7–21.5)<0.001
 Total FM (kg)23.0 (20.0–27.0)19.6 (17.5–22.9)17.0 (14.9–20.0)12.4 (10.7–14.5)<0.001
 Total fat%36.5 (34.1–39.0)32.7 (30.9–35.5)30.4 (28.3–33.4)24.8 (22.1–26.7)<0.001
 SBP (mmHg)127 (115–140)128 (118–140)125 (116–137)121 (110–132)0.002
 DBP (mmHg)76 (71–83)76 (70–81)74 (68–82)73 (68–80)0.080
 FPG (mmol/L)5.8 (5.5–6.2)5.7 (5.4–6.2)5.7 (5.4–6.0)5.5 (5.1–6.0)<0.001
 2hPG (mmol/L)7.1 (6.1–9.2)7.1 (5.6–9.0)7.2 (6.1–8.5)6.8 (5.6–8.1)0.274
 HbA1c (%)5.7 (5.5–6.1)5.8 (5.6–6.0)5.7 (5.5–6.0)5.6 (5.4–5.9)0.049
 FINS (mU/L)11.3 (8.5–14.3)10.2 (7.3–14.1)8.3 (6.8–10.5)6.3 (4.8–7.7)<0.001
 HOMA-IR2.8 (2.2–3.9)2.6 (1.8–3.8)2.1 (1.7–2.7)1.6 (1.2–2.0)<0.001
 TC (mmol/L)5.7 ± 0.95.9 ± 1.05.6 ± 0.95.8 ± 1.00.974
 TG (mmol/L)1.4 (1.0–1.9)1.4 (1.0–2.0)1.3 (0.9–1.7)1.0 (0.8–1.4)<0.001
 HDL-c (mmol/L)1.4 (1.2–1.7)1.4 (1.3–1.7)1.6 (1.3–1.7)1.8 (1.5–2.0)<0.001
 LDL-c (mmol/L)3.6 ± 0.83.6 ± 0.93.4 ± 0.83.4 ± 0.90.073
 E2 (pmol/L)36.7 (18.4–47.7)38.5 (18.4–47.7)18.4 (18.4–44.1)18.4 (18.4–40.4)0.002

Data were means ± standard deviation or medians (interquartile range). Q1 < 0.58, Q2 0.58–0.63, Q3 0.64–0.73 and Q4 > 0.73 for men; Q1 < 0.64, Q2 0.64–0.70, Q3 0.71–0.80 and Q4 > 0.80 for women.

2 h PG, 2-h plasma glucose; BMI, body mass index; DBP, diastolic blood pressure; fat%, fat percentage; E2, estradiol; FINS, fasting insulin; FM, fat mass; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin A1c; HDL-c, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment-insulin resistance; LDL-c, low-density lipoprotein cholesterol; LTR, leg fat mass to trunk fat mass ratio; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride.

Table 2

Multivariate linear regression for thyroid hormones in women.

Independent variablesFT3FT4TSH
Model 1
 TotalFat mass0.021 (0.402)0.021 (0.407)−0.037 (−0.693)
Fat%0.050 (0.976)−0.003 (−0.063)−0.053 (−1.019)
 TrunkFat mass0.042 (0.818)0.031 (0.597)−0.041 (−0.783)
Fat%0.056 (1.111)−0.005 (−0.102)−0.050 (−0.968)
 ArmFat mass−0.003 (−0.063)0.008 (0.160)−0.022 (−0.428)
Fat%0.004 (0.069)−0.019 (−0.376)−0.025 (−0.489)
 LegFat mass−0.019 (−0.382)0.003 (0.050)−0.030 (−0.572)
Fat%−0.026 (−0.530)−0.048 (−0.970)−0.032 (−0.634)
 Leg fat mass to trunk fat mass ratio−0.116 (−2.464)a−0.032 (−0.667)0.073 (1.505)
Model 2
 TrunkFat mass0.303 (2.628)b0.146 (1.244)−0.072 (−0.609)
fat%0.218 (2.582)a0.097 (1.130)−0.067 (−0.768)
 LegFat mass−0.285 (−2.525)a−0.126 (−1.092)0.034 (0.291)
fat%−0.195 (−2.388)a−0.124 (−1.486)0.020 (0.236)

Data were expressed as standardized β (t). aP < 0.05, bP < 0.01.

Model 1 was adjusted for age, homeostasis model assessment-insulin resistance, diastolic blood pressure, low-density lipoprotein cholesterol and estradiol.

In model 2, all confounding factors included in model 1 were adjusted, in addition that trunk fat mass was adjusted for leg fat mass, trunk fat% was adjusted for leg fat%, leg fat mass was adjusted for trunk fat mass, leg fat% was adjusted for trunk fat%.

fat%, fat percentage; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone.

Changes of thyroid hormones in LTR quartiles

In men, FT3, FT4 and TSH did not significantly change with increasing LTR quartiles (all P > 0.05). In women, only FT3 significantly decreased with increasing LTR quartiles. The medians of FT3 from the Q1 to the Q4 group of LTR quartiles were 4.84 (4.56–5.19) pmol/L,4.78 (4.48–5.15) pmol/L, 4.74 (4.52–5.03) pmol/L and 4.64 (4.34–4.93) pmol/L (P for trend = 0.004). In women, FT4 and TSH did not change significantly with increasing LTR quartiles (all P > 0.05, Fig. 1).

Figure 1
Figure 1

Thyroid hormones with LTR quartiles. FT3 decreased significantly as LTR quartiles increased in women (P for trend = 0.004), but this trend was not significant in men. FT4 and TSH were not significantly changed as LTR quartiles increased in both men and women.

Citation: Endocrine Connections 8, 10; 10.1530/EC-19-0394

Partial correlation analysis of segmental fat parameters and thyroid hormones

In men and women, trunk FM was 7.5 (5.5–9.9) kg and 9.9 (7.4–12.1) kg, trunk fat% was 21.0 (16.5–25.3)% and 30.9 (25.8–35.1)%, arm FM was 1.0 (0.8–1.2) kg and 1.4 (1.2–1.9) kg, arm fat% was 1.5 (1.3–1.7)% and 2.6 (2.1–3.1)%, leg FM was 4.8 (3.8–5.9) kg and 6.8 (5.8–8.0) kg, leg fat% was 7.0 (6.1–8.0)% and 11.9 (11.1–12.8)%, and the LTR was 0.63 (0.58–0.73) and 0.70 (0.64–0.80), respectively.

Age and BMI were adjusted in the partial correlation analysis. In men, none of the fat parameters were related to FT3 (all P > 0.05). In women, total FM (r = 0.098, P = 0.030), total fat% (r = 0.138, P = 0.002), trunk FM (r = 0.124, P = 0.006) and trunk fat% (r = 0.132, P = 0.003) were positively related to FT3, while the LTR was negatively related to FT3 (r = −0.147, P = 0.001) (Fig. 2). In women, leg FM, leg fat%, arm FM and arm fat% were not related to FT3 (all P > 0.05). None of the segmental fat parameters were related to FT4 or TSH in both men and women (all P > 0.05).

Figure 2
Figure 2

Partial correlation analysis of total FM, trunk FM, LTR and FT3 in women. Age and BMI were adjusted.

Citation: Endocrine Connections 8, 10; 10.1530/EC-19-0394

Multivariate linear regression analysis for thyroid hormones

Multivariate linear regression analysis was used to explore the relationships between segmental fat parameters and thyroid hormones in women (Table 2). In model 1, age, HOMA-IR, DBP, LDL-c and E2 were adjusted. Only LTR was negatively related to FT3 (standardized β = −0.116, P < 0.05), while total fat and other segmental fat parameters were not related to FT3 (all P > 0.05). None of the segmental fat parameters were related to FT4 or TSH (all P > 0.05).

Considering a significant and negative relationship between LTR and FT3, trunk fat and leg fat might be confounding factors of one another. Thus, we further created model 2. In model 2, all confounding factors involved in model 1 were included. In addition, leg FM and trunk FM were mutually adjusted, and leg fat% and trunk fat% were mutually adjusted. Trunk FM and trunk fat% were both positively related to FT3 (standardized β = 0.303 and 0.218, respectively, all P < 0.05). Leg FM and leg fat% were both negatively related to FT3 (standardized β = −0.285 and −0.195, respectively, all P < 0.05).

Discussion

In the present study, we found that trunk fat accumulation was related to increased FT3 in euthyroid postmenopausal women, while increased leg fat accumulation was related to decreased FT3. These relationships were independent of multiple metabolic and cardiovascular risk factors. In men, none of the fat parameters were related to thyroid hormones.

Obesity is closely related to FT3. Previous studies have found that BMI and waist circumference were positively related to FT3 in the euthyroid population (5, 6). A Mendelian randomization study indicated that higher BMI or FM played a causal role in increasing FT3 levels (17). Simple obesity indexes, such as BMI have been widely used in these studies. There have been few studies of associations between precisely-measured fat distribution and FT3. Lambrinoudaki et al. measured abdominal subcutaneous fat and preperitoneal fat through ultrasonography in 194 euthyroid postmenopausal women. They found that FT3 was positively associated with subcutaneous fat and preperitoneal fat (18). sBIA directly measures the bioelectrical impedance of the trunk, arms and legs. Subsequently, FM and fat% of different fat depots can be calculated based on the bioelectrical impedance. The segmental fat distribution measured by sBIA was highly consistent with that measured by dual energy X-ray absorptiometry (r ≥ 0.95, P < 0.001) (9). Our findings in trunk fat were in consistent with Lambrinoudaki`s results. However, we further found a significant and negative relationship between FT3 and leg fat, while arm fat was not related to thyroid hormones. Moreover, our study found that there were no relationships between segmental fat distribution and thyroid hormones in men.

In this study, the LTR was calculated to reflect a tendency of fat accumulating in the legs rather than the trunk. In one previous study, Gavi et al. also calculated the limb fat to trunk fat ratio. Limb fat specifically referred to leg fat in their study. They found that the limb fat to trunk fat ratio was negatively related to insulin resistance and TG and positively related to HDL-c in a middle-aged and elderly population (19). The middle-aged and elderly population, especially postmenopausal women, is at high risk of metabolic and cardiovascular diseases (20). In our study, we also chose this high-risk population as study subjects. We found that the LTR was negatively related to FT3 in postmenopausal women. The correlation coefficient of total FM related to FT3 was weaker than that of trunk FM related to FT3 in the partial correlation analysis, and a negative relationship was found between the LTR and FT3. Thus, we conjectured that leg fat and trunk fat might be confounding factors of one another. In the multivariate linear regression analysis, trunk fat and leg fat parameters were mutually adjusted. We found that trunk fat parameters were positively related to FT3 and that leg fat parameters were negatively related to FT3. The result suggested that leg fat might alleviate the adverse effect between trunk fat and increased FT3. A sex difference existed in the relationship between segmental fat distribution and FT3 in our study, which might because women had a greater propensity to store fat in the lower body driven by the effects of sex hormones (21, 22, 23).

Because FT3 promotes adipose decomposition and increases the basal metabolic rate (4), a high level of FT3 in the obese population was considered a compensatory effect under the condition of energy surplus according to the conventional view (6). However, in recent years, some studies found that a high level of FT3 within the reference range is related to an increased risk of NAFLD (7, 8). In the Lifelines cohort study, the risk of NAFLD increased 1.34-fold for each 1 standard deviation increase in FT3 in the euthyroid population (7). In a euthyroid Chinese population, a high level of FT3 was an independent predictive factor of NAFLD (8). Roef et al. found that FT3 was positively related to multiple adverse metabolic and cardiovascular risk factors in a euthyroid middle-aged population (5). Although there is still a lack of evidence from prospective studies, a high level of FT3 within the reference range may be a potential adverse metabolic marker.

Trunk fat and leg fat have exhibited inverse relationships with type 2 diabetes, cardiovascular disease and multiple metabolic and cardiovascular risk factors (3, 10, 11, 24, 25). There are large discrepancies between trunk fat and leg fat in the uptake and release of free fatty acids (26). Trunk fat is more sensitive to lipolysis stimulus. Leg fat has a lower lipolysis rate and is the ‘storage pool’ of circulating ectopic lipids. Leg fat can stabilize free fatty acids that are released by trunk fat and thus decrease lipotoxicity (21). Trunk fat and leg fat also differ in the release of adipokines and inflammatory factors (11, 27, 28); however, few studies have explored the relationships between adipokines and FT3. Further research is needed to clarify the mechanisms underlying the correlations between trunk fat, leg fat and FT3.

Our study has some limitations. First, the study subjects were middle-aged and elderly; thus, the results may not be generalizable to other age groups. Second, the present study did not fully consider some information such as luteinizing hormone, follicle-stimulating hormone and selective estrogen receptor modulators. Third, due to the nature of the cross-sectional study, causality cannot be deduced. Further prospective studies are needed to elucidate the causal relationship between segmental fat distribution and FT3.

Conclusions

In euthyroid postmenopausal women, trunk fat is positively related to FT3, while leg fat is negatively related to FT3. Our findings supported that a high level of FT3 within the reference range was related to adverse fat distribution. Further studies are needed to clarify the causality and underlying mechanisms.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This work was supported by the National Key R&D Program of China (2016YFA0502003).

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    • Export Citation
  • 6

    De PergolaGCiampolilloAPaolottiSTrerotoliPGiorginoR. Free triiodothyronine and thyroid stimulating hormone are directly associated with waist circumference, independently of insulin resistance, metabolic parameters and blood pressure in overweight and obese women. Clinical Endocrinology 2007 265269. (https://doi.org/10.1111/j.1365-2265.2007.02874.x)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    van den BergEHvan Tienhoven-WindLJAminiMSchreuderTCFaberKNBlokzijlHDullaartRP. Higher free triiodothyronine is associated with non-alcoholic fatty liver disease in euthyroid subjects: the lifelines cohort study. Metabolism: Clinical and Experimental 2017 6271. (https://doi.org/10.1016/j.metabol.2016.11.002)

    • Search Google Scholar
    • Export Citation
  • 8

    LiuGZhengXGuanLJiangZLinHJiangQZhangNZhangYZhangXYuC Free triiodothyronine levels are positively associated with non-alcoholic fatty liver disease in euthyroid middle-aged subjects. Endocrine Research 2015 188193. (https://doi.org/10.3109/07435800.2014.987399)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    PietrobelliARubianoFSt-OngeMPHeymsfieldSB. New bioimpedance analysis system: improved phenotyping with whole-body analysis. European Journal of Clinical Nutrition 2004 14791484. (https://doi.org/10.1038/sj.ejcn.1601993)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    SnijderMBDekkerJMVisserMBouterLMStehouwerCDYudkinJSHeineRJNijpelsGSeidellJC & Hoorn study. Trunk fat and leg fat have independent and opposite associations with fasting and postload glucose levels: the Hoorn study. Diabetes Care 2004 372377. (https://doi.org/10.2337/diacare.27.2.372)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    WuHQiQYuZSunQWangJFrancoOHSunLLiHLiuYHuFB Independent and opposite associations of trunk and leg fat depots with adipokines, inflammatory markers, and metabolic syndrome in middle-aged and older Chinese men and women. Journal of Clinical Endocrinology & Metabolism 2010 43894398. (https://doi.org/10.1210/jc.2010-0181)

    • Search Google Scholar
    • Export Citation
  • 12

    Sanchez-LopezMOrtegaFBMoya-MartinezPLopez-MartinezSOrtiz-GaleanoIGomez-MarcosMASjostromMMartinez-VizcainoV. Leg fat might be more protective than arm fat in relation to lipid profile. European Journal of Nutrition 2013 489495. (https://doi.org/10.1007/s00394-012-0350-4)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    HuGBouchardCBrayGAGreenwayFLJohnsonWDNewtonRL JrRavussinERyanDHKatzmarzykPT. Trunk versus extremity adiposity and cardiometabolic risk factors in white and African American adults. Diabetes Care 2011 14151418. (https://doi.org/10.2337/dc10-2019)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    National Collaborating Centre for Women’s and Children’s Health. Menopause: Full guideline. Clinical guideline: methods evidence and recommendations. London, UK: National Institute for Health and Care Excellence (UK)2015. (available at: https://www.nice.org.uk/guidance/ng23/evidence/full-guideline-559549261)

    • Search Google Scholar
    • Export Citation
  • 15

    XuYMaXShenYGuCTangJBaoY. Role of hyperglycaemia in the relationship between serum osteocalcin levels and relative skeletal muscle index. Clinical Nutrition 2018 [epub]. (https://doi.org/10.1016/j.clnu.2018.11.025)

    • Search Google Scholar
    • Export Citation
  • 16

    MatthewsDRHoskerJPRudenskiASNaylorBATreacherDFTurnerRC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985 412419. (https://doi.org/10.1007/bf00280883)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17

    TaylorPNRichmondRDaviesNSayersAStevensonKWoltersdorfWTaylorAGroomANorthstoneKRingS Paradoxical relationship between body mass index and thyroid hormone levels: a study using Mendelian randomization. Journal of Clinical Endocrinology & Metabolism 2016 730738. (https://doi.org/10.1210/jc.2015-3505)

    • Search Google Scholar
    • Export Citation
  • 18

    LambrinoudakiIArmeniERizosDGeorgiopoulosGAthanasouliFTriantafyllouNPanoulisKAugouleaACreatsaMAlexandrouA Indices of adiposity and thyroid hormones in euthyroid postmenopausal women. European Journal of Endocrinology 2015 237245. (https://doi.org/10.1530/EJE-15-0141)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19

    GaviSFeinerJJMelendezMMMynarcikDCGelatoMCMcNurlanMA. Limb fat to trunk fat ratio in elderly persons is a strong determinant of insulin resistance and adiponectin levels. Journals of Gerontology. Series A Biological Sciences and Medical Sciences 2007 9971001. (https://doi.org/10.1093/gerona/62.9.997)

    • Search Google Scholar
    • Export Citation
  • 20

    AuroKJoensuuAFischerKKettunenJSaloPMattssonHNiironenMKaprioJErikssonJGLehtimakiT A metabolic view on menopause and ageing. Nature Communications 2014 4708. (https://doi.org/10.1038/ncomms5708)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    TchkoniaTThomouTZhuYKaragiannidesIPothoulakisCJensenMDKirklandJL. Mechanisms and metabolic implications of regional differences among fat depots. Cell Metabolism 2013 644656. (https://doi.org/10.1016/j.cmet.2013.03.008)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22

    KoutsariCAliAHMundiMSJensenMD. Storage of circulating free fatty acid in adipose tissue of postabsorptive humans: quantitative measures and implications for body fat distribution. Diabetes 2011 20322040. (https://doi.org/10.2337/db11-0154)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Rask-AndersenMKarlssonTEkWEJohanssonÅ. Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects. Nature Communications 2019 339. (https://doi.org/10.1038/s41467-018-08000-4)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    YanoYVongpatanasinWAyersCTurerAChandraACarnethonMRGreenlandPde LemosJANeelandIJ. Regional fat distribution and blood pressure level and variability: the Dallas Heart study. Hypertension 2016 576583. (https://doi.org/10.1161/HYPERTENSIONAHA.116.07876)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    LeeMChohACDemerathEWTowneBSiervogelRMCzerwinskiSA. Associations between trunk, leg and total body adiposity with arterial stiffness. American Journal of Hypertension 2012 11311137. (https://doi.org/10.1038/ajh.2012.92)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    EbbertJOJensenMD. Fat depots, free fatty acids, and dyslipidemia. Nutrients 2013 498508. (https://doi.org/10.3390/nu5020498)

  • 27

    PinnickKENevilleMJFieldingBAFraynKNKarpeFHodsonL. Gluteofemoral adipose tissue plays a major role in production of the lipokine palmitoleate in humans. Diabetes 2012 13991403. (https://doi.org/10.2337/db11-1810)

    • Search Google Scholar
    • Export Citation
  • 28

    AntonyBJonesGStannusOBlizzardLDingC. Body fat predicts an increase and limb muscle strength predicts a decrease in leptin in older adults over 2.6 years. Clinical Endocrinology 2013 652660. (https://doi.org/10.1111/cen.12101)

    • PubMed
    • Search Google Scholar
    • Export Citation

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    Thyroid hormones with LTR quartiles. FT3 decreased significantly as LTR quartiles increased in women (P for trend = 0.004), but this trend was not significant in men. FT4 and TSH were not significantly changed as LTR quartiles increased in both men and women.

  • View in gallery

    Partial correlation analysis of total FM, trunk FM, LTR and FT3 in women. Age and BMI were adjusted.

  • 1

    DaleCEFatemifarGPalmerTMWhiteJPrieto-MerinoDZabanehDEngmannJELShahTWongAWarrenHR Causal associations of adiposity and body fat distribution with coronary heart disease, stroke subtypes, and type 2 diabetes mellitus: a Mendelian randomization analysis. Circulation 2017 23732388. (https://doi.org/10.1161/CIRCULATIONAHA.116.026560)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    EmdinCAKheraAVNatarajanPKlarinDZekavatSMHsiaoAJKathiresanS. Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. JAMA 2017 626634. (https://doi.org/10.1001/jama.2016.21042)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    VasanSKOsmondCCanoyDChristodoulidesCNevilleMJDi GravioCFallCHDKarpeF. Comparison of regional fat measurements by dual-energy X-ray absorptiometry and conventional anthropometry and their association with markers of diabetes and cardiovascular disease risk. International Journal of Obesity 2018 850857. (https://doi.org/10.1038/ijo.2017.289)

    • Search Google Scholar
    • Export Citation
  • 4

    MullurRLiuYYBrentGA. Thyroid hormone regulation of metabolism. Physiological Reviews 2014 355382. (https://doi.org/10.1152/physrev.00030.2013)

  • 5

    RoefGLRietzschelERVan DaeleCMTaesYEDe BuyzereMLGillebertTCKaufmanJM. Triiodothyronine and free thyroxine levels are differentially associated with metabolic profile and adiposity-related cardiovascular risk markers in euthyroid middle-aged subjects. Thyroid 2014 223231. (https://doi.org/10.1089/thy.2013.0314)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    De PergolaGCiampolilloAPaolottiSTrerotoliPGiorginoR. Free triiodothyronine and thyroid stimulating hormone are directly associated with waist circumference, independently of insulin resistance, metabolic parameters and blood pressure in overweight and obese women. Clinical Endocrinology 2007 265269. (https://doi.org/10.1111/j.1365-2265.2007.02874.x)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    van den BergEHvan Tienhoven-WindLJAminiMSchreuderTCFaberKNBlokzijlHDullaartRP. Higher free triiodothyronine is associated with non-alcoholic fatty liver disease in euthyroid subjects: the lifelines cohort study. Metabolism: Clinical and Experimental 2017 6271. (https://doi.org/10.1016/j.metabol.2016.11.002)

    • Search Google Scholar
    • Export Citation
  • 8

    LiuGZhengXGuanLJiangZLinHJiangQZhangNZhangYZhangXYuC Free triiodothyronine levels are positively associated with non-alcoholic fatty liver disease in euthyroid middle-aged subjects. Endocrine Research 2015 188193. (https://doi.org/10.3109/07435800.2014.987399)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    PietrobelliARubianoFSt-OngeMPHeymsfieldSB. New bioimpedance analysis system: improved phenotyping with whole-body analysis. European Journal of Clinical Nutrition 2004 14791484. (https://doi.org/10.1038/sj.ejcn.1601993)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    SnijderMBDekkerJMVisserMBouterLMStehouwerCDYudkinJSHeineRJNijpelsGSeidellJC & Hoorn study. Trunk fat and leg fat have independent and opposite associations with fasting and postload glucose levels: the Hoorn study. Diabetes Care 2004 372377. (https://doi.org/10.2337/diacare.27.2.372)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    WuHQiQYuZSunQWangJFrancoOHSunLLiHLiuYHuFB Independent and opposite associations of trunk and leg fat depots with adipokines, inflammatory markers, and metabolic syndrome in middle-aged and older Chinese men and women. Journal of Clinical Endocrinology & Metabolism 2010 43894398. (https://doi.org/10.1210/jc.2010-0181)

    • Search Google Scholar
    • Export Citation
  • 12

    Sanchez-LopezMOrtegaFBMoya-MartinezPLopez-MartinezSOrtiz-GaleanoIGomez-MarcosMASjostromMMartinez-VizcainoV. Leg fat might be more protective than arm fat in relation to lipid profile. European Journal of Nutrition 2013 489495. (https://doi.org/10.1007/s00394-012-0350-4)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    HuGBouchardCBrayGAGreenwayFLJohnsonWDNewtonRL JrRavussinERyanDHKatzmarzykPT. Trunk versus extremity adiposity and cardiometabolic risk factors in white and African American adults. Diabetes Care 2011 14151418. (https://doi.org/10.2337/dc10-2019)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    National Collaborating Centre for Women’s and Children’s Health. Menopause: Full guideline. Clinical guideline: methods evidence and recommendations. London, UK: National Institute for Health and Care Excellence (UK)2015. (available at: https://www.nice.org.uk/guidance/ng23/evidence/full-guideline-559549261)

    • Search Google Scholar
    • Export Citation
  • 15

    XuYMaXShenYGuCTangJBaoY. Role of hyperglycaemia in the relationship between serum osteocalcin levels and relative skeletal muscle index. Clinical Nutrition 2018 [epub]. (https://doi.org/10.1016/j.clnu.2018.11.025)

    • Search Google Scholar
    • Export Citation
  • 16

    MatthewsDRHoskerJPRudenskiASNaylorBATreacherDFTurnerRC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985 412419. (https://doi.org/10.1007/bf00280883)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17

    TaylorPNRichmondRDaviesNSayersAStevensonKWoltersdorfWTaylorAGroomANorthstoneKRingS Paradoxical relationship between body mass index and thyroid hormone levels: a study using Mendelian randomization. Journal of Clinical Endocrinology & Metabolism 2016 730738. (https://doi.org/10.1210/jc.2015-3505)

    • Search Google Scholar
    • Export Citation
  • 18

    LambrinoudakiIArmeniERizosDGeorgiopoulosGAthanasouliFTriantafyllouNPanoulisKAugouleaACreatsaMAlexandrouA Indices of adiposity and thyroid hormones in euthyroid postmenopausal women. European Journal of Endocrinology 2015 237245. (https://doi.org/10.1530/EJE-15-0141)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19

    GaviSFeinerJJMelendezMMMynarcikDCGelatoMCMcNurlanMA. Limb fat to trunk fat ratio in elderly persons is a strong determinant of insulin resistance and adiponectin levels. Journals of Gerontology. Series A Biological Sciences and Medical Sciences 2007 9971001. (https://doi.org/10.1093/gerona/62.9.997)

    • Search Google Scholar
    • Export Citation
  • 20

    AuroKJoensuuAFischerKKettunenJSaloPMattssonHNiironenMKaprioJErikssonJGLehtimakiT A metabolic view on menopause and ageing. Nature Communications 2014 4708. (https://doi.org/10.1038/ncomms5708)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    TchkoniaTThomouTZhuYKaragiannidesIPothoulakisCJensenMDKirklandJL. Mechanisms and metabolic implications of regional differences among fat depots. Cell Metabolism 2013 644656. (https://doi.org/10.1016/j.cmet.2013.03.008)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22

    KoutsariCAliAHMundiMSJensenMD. Storage of circulating free fatty acid in adipose tissue of postabsorptive humans: quantitative measures and implications for body fat distribution. Diabetes 2011 20322040. (https://doi.org/10.2337/db11-0154)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Rask-AndersenMKarlssonTEkWEJohanssonÅ. Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects. Nature Communications 2019 339. (https://doi.org/10.1038/s41467-018-08000-4)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    YanoYVongpatanasinWAyersCTurerAChandraACarnethonMRGreenlandPde LemosJANeelandIJ. Regional fat distribution and blood pressure level and variability: the Dallas Heart study. Hypertension 2016 576583. (https://doi.org/10.1161/HYPERTENSIONAHA.116.07876)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    LeeMChohACDemerathEWTowneBSiervogelRMCzerwinskiSA. Associations between trunk, leg and total body adiposity with arterial stiffness. American Journal of Hypertension 2012 11311137. (https://doi.org/10.1038/ajh.2012.92)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    EbbertJOJensenMD. Fat depots, free fatty acids, and dyslipidemia. Nutrients 2013 498508. (https://doi.org/10.3390/nu5020498)

  • 27

    PinnickKENevilleMJFieldingBAFraynKNKarpeFHodsonL. Gluteofemoral adipose tissue plays a major role in production of the lipokine palmitoleate in humans. Diabetes 2012 13991403. (https://doi.org/10.2337/db11-1810)

    • Search Google Scholar
    • Export Citation
  • 28

    AntonyBJonesGStannusOBlizzardLDingC. Body fat predicts an increase and limb muscle strength predicts a decrease in leptin in older adults over 2.6 years. Clinical Endocrinology 2013 652660. (https://doi.org/10.1111/cen.12101)

    • PubMed
    • Search Google Scholar
    • Export Citation