Abstract
Background: Studies assessing thyroid hormones in metabolic syndrome (MetS) patients are contradictory. Also, the effect of MetS on thyroid function over time is not yet evaluated. This study investigated the prevalence and incidence of thyroid dysfunction (TD) as well as time trends of thyroid hormones in subjects with and without MetS, during a 10-year follow-up in Tehranian adult population. Methods: This is a prospective cohort study conducted in the framework of Tehran Thyroid Study on 5,786 subjects aged ≥20 years: 4,905 eligible participants entered the study after excluding those with corticosteroid or radioactive iodine use, pregnancy, thyrotropin (TSH) <0.1 and >10 mU/L, and missing data. Physical examinations were performed and serum concentrations of TSH, free thyroxine (FT4), thyroid peroxidase antibody (TPOAb), fasting plasma glucose, insulin, and lipid profile were assessed at baseline and 3-year intervals during the follow-up. MetS was defined according to the Joint Interim Statement Definition. Results: At baseline, there were no difference in median serum concentrations of FT4 and TSH between MetS and non-MetS group after adjusting for age, sex, BMI, smoking, and TPOAb positivity. Although there was higher risk of overt (42%) and subclinical hypothyroidism (16%) in MetS compared with non-MetS subjects, no significant difference was observed in adjusted ORs for any TD between 2 groups. There were also no significant differences in time trends of TSH, FT4, TPOAb positivity, and incidence rates of TDs between MetS and non-MetS groups during 10 years, after adjustment for age, sex, BMI, smoking status, and TPOAb positivity. Conclusion: MetS is not associated with thyroid hypofunction considering other important confounders such as age, sex, smoking, BMI, and TPOAb positivity. There is also no difference in the trend of thyroid hormones and incidence of TD between MetS and non-MetS subjects during a 10-year follow-up.
Introduction
Metabolic syndrome (MetS), a pathologic condition characterized by a cluster of metabolic abnormalities [1, 2], has been known as one of the causes of morbidity and mortality [3-7]. About one quarter of the world’s population is estimated to be affected by MetS [5, 7, 8].
Thyroid dysfunction (TD) is also a common endocrine disorder which can disturb lipid and glucose metabolism, blood pressure, and body weight, and it has been confirmed to be an independent risk factor for atherosclerotic cardiovascular disease [9, 10]. On the other hand, metabolic syndrome (MetS) might be associated with risk of developing thyroid hypofunction [2, 11]. Additionally, a prospective cohort study demonstrated an increased risk of developing subclinical hypothyroidism among participants with MetS in comparison with their counterparts without MetS [12]. There are also reports on higher thyrotropin (TSH) values in patients with MetS compared with non-MetS subjects [1]. Therefore, due to the overlap between pathogenic mechanisms of MetS and TD, coexistence of both diseases in an individual may exaggerate their common metabolic abnormalities, especially in overt hypothyroid patients [13] and may consequently promote an increased atherosclerotic cardiovascular disease risk [14].
Studies assessing thyroid hormones in MetS patients are cross sectional and contradictory; likewise, cohort studies on this issue are lacking. Therefore, more studies investigating thyroid hormones in patients with MetS are needed to provide evidence about the overlap of 2 diseases and tell us whether assessing thyroid function in MetS patients is crucial or not. Findings may also help clinicians for the better management of MetS patients and consequently decreasing cardiovascular morbidity and mortality due to MetS especially when accompanied with TD.
In this study, we aimed to assess the potential relationship between MetS and TD by determining the prevalence and odds ratio of TDs among individuals with and without MetS as well as evaluating the incidence of TD and trend of thyroid hormones according to the MetS group, during a 10 year follow-up in an iodine sufficient population.
Materials and Methods
Study Design
TTS is a population-based cohort study conducted in the framework of Tehran Lipid and Glucose Study (TLGS) [15]. TLGS is an ongoing multistage population-based survey of noncommunicable diseases among civilian population of Tehran, the capital of Iran which is initiated in 1997 and designed to continue for at least 20 years with 3-year follow-up intervals [16].
Study Population
At the beginning, a total sample of 15,005 individuals aged 3–69 years recruited in the TLGS from the residents under the coverage of 3 medical health centers in district No. 13 in Tehran using a multistage stratified cluster random sampling technique. From a total of 10,368 individuals aged ≥20 years, we selected 5,786 individuals who were assessed at baseline and 3-year intervals during 9.73 years of follow-up between 1997 and 2008 to participate in the TTS. In this study we excluded those participants with following characteristics: history of thyroid surgery (n = 103), use of corticosteroids (n = 138) or radioactive iodine (n = 2), pregnancy (n = 145), TSH <0.1 and TSH >10 µ/L (n = 279) as well as those with incomplete laboratory or missing data (n = 211). Subsequently, 4,905 eligible participants remained to enter the current study (Fig. 1).
Participants’ flow diagram. TSH, thyroid-stimulating hormone.
Citation: European Thyroid Journal 10, 5; 10.1159/000512665
Medical History and Clinical Examination
At the first visit, after full explanation of the study procedures by trained staff, written informed consent was obtained from all study participants. Fasting Blood samples were obtained and participants underwent a total interview and physical examination at baseline and 3-year intervals. The details of getting medical history and physical examination are given elsewhere [16].
Laboratory Measurements
After an overnight fast of 8–12 h, blood samples were obtained from all subjects between 7:00 and 9:00 a.m. at baseline and at 3-year intervals. The details of laboratory measurements were presented in the previous publications.
Definitions
TDs were defined based on TSH (0.32–5.06 µ/L) and free thyroxin (FT4) (0.91–1.55 ng/dL) reference ranges previously calculated in the study population as the representative sample of Tehranian population [17]. TPOAB positivity is considered as serum thyroid peroxidase antibody (TPOAb) values ≥35 in women and ≥32 in men population-specific reference [18].
We considered MetS according to the Joint Interim Statement (JIS) statement for the Iranian population [19]. Diagnosis requires the presence of at least 3 of the following 5 findings (19): (i) WC >95 cm in both sexes; (ii) serum TGs ≥150 mg/dL or on specific treatment; (iii) HDL-C <40 mg/dL in males and <50 mg/dL in females or on specific treatment; (iv) systolic blood pressure ≥130 mm Hg and/or diastolic blood pressure ≥85 mm Hg or on specific treatment for previously diagnosed hypertension; and (v) FPG ≥100 mg/dL or on treatment for diabetes.
Statistical Analysis
Baseline demographic, thyroid hormone levels, and thyroid disorders are described as median (IQR) and frequency (percentage %) and also compared between groups with and without MetS (with and without adjustments) by linear regression analysis, multinomial logistic regression analysis, and logistic regression analysis. To impute incomplete variables in this longitudinal study, the fully conditional specification technique was used. In the mice package, 2l.pan and 2l.bin methods were used for continuous and binary variables respectively [20].
As observations for each individual are correlated during the follow-ups, secular longitudinal trends of TSH (µ/L), FT4 (ng/dL) and TPOAb positivity were measured at baseline and at 3 subsequent follow-up visits, and the data were analyzed by using the generalized estimation equation (GEE) method. In this population-averaged modeling of repeated data, instead of assuming that data were generated from a certain distribution, we used moment assumptions to estimate the best β to describe the relationship between covariates and response [21].
Marginal mean (95% CI) and marginal probability (95% CI) were also estimated. Models for examination of time trends were fitted separately for TSH, FT4, and TPOAb positivity, and p values for trend were reported in each of them. This model included the following predictors: time (follow-up years), MetS status, and an interaction term of these 2 (follow-up years × MetS status) and was adjusted for age, sex, BMI, and smoking. This interaction shows how the effect of MetS status on thyroid functions changed over time. In this study all variables that vary during the follow-up considered as external time-varying covariates in GEE model.
To analyze the person-years incidence rate of thyroid disorders we included participant without thyroid disorders and the following formula was used:
(Number of new events of the condition [cases] during the study time)/(Sum of person – time [person × year] at risk among the study participants).
For the censored or lost to follow-up subjects, the time to failure (development of thyroid disorders) was the time between the first and last observation dates. All analyses were performed using R.4.0.2 and IBM SPSS (version 23; SPSS Inc., Chicago, IL, USA). Two-tailed p values <0.05 were considered significant.
Results
Baseline demographic, clinical, and biochemical characteristics of the study population based on their MetS status are depicted in Table 1. Among 4,905 participants, 1,584 (32%) had MetS based on the JIS definition. The study population had a mean age of 40.4 ± 14.2 years including 2,753 (56%) females. The mean age (48.8 ± 12.9 vs. 36.3 ± 13.1, p < 0.001) and mean BMI (29.5 ± 4.1, vs. 25.1 ± 4.1, p < 0001) of the MetS group were significantly higher than non-MetS group. The frequency of female subjects in non-MetS group (58%) were significantly higher than MetS group (52%). At baseline, although there were significant differences in serum TSH and FT4 values between MetS and non-MetS groups in the crude analysis, in linear regression analysis, after adjustment for age, sex, smoking, BMI, and TPOAb positivity the difference did not remain significant. Adjusted mean differences for TSH and FT4 values based on the logarithmic scale were 0.01 (95% CI: −0.04, 0.07) and 0.007 (95% CI: −0.003, 0.017), respectively.
Baseline characteristics and the prevalence of thyroid disorders in total population and those with and without MetS
The prevalence of overt hypothyroidism was higher in the MetS group compared with the non-MetS group (1.4 vs. 0.7%, p = 0.02). There was 16 and 42% higher risk of subclinical and overt hypothyroidism in MetS compared with non-MetS subjects, respectively, considering age, sex, smoking, BMI, and TPOAb positivity; however, the adjusted odds ratios were not significant (Table 1). No difference was also found in odds ratio of hyperthyroidism between MetS and non-MetS group. The prevalence of TPOAb positivity was lower in the MetS group compared with non-MetS only after adjustment for sex, age, smoking, and BMI (31% lower odds in MetS subjects).
The time trends of thyroid hormones and TPOAb according to the MetS group using GEE analysis are shown in Table 2. After adjustment for BMI, age, sex, smoking status, and TPOAb positivity, no statistically significant difference was found in the trends of thyroid hormones between the 2 study groups. Overall estimated marginal mean changes of TSH, TPOAb levels, and prevalence of TPOAb positivity were not significantly different based on the MetS group throughout the follow-up. Figure 2 shows the time trends of TSH, FT4, and TPOAb positivity in MetS and non-MetS groups.
Time trend in thyroid hormones according to MetS groups using GEE
Estimated marginal mean and 95% CI. TSH (a), FT4 (b) and TPOAb (c), within follow-ups among the participants with and without MetS. TSH, thyroid-stimulating hormone; FT4, free thyroxin; MetS, metabolic syndrome; TPOAb, thyroid peroxidase antibody.
Citation: European Thyroid Journal 10, 5; 10.1159/000512665
Table 3 presents incidence of thyroid disorders per 1,000 person-years according to the MetS group. Total incidence rate of TD (per 1,000 person-years) were as follows: TPOAb positivity (6.58, 95% CI: 5.77, 7.48), subclinical hypothyroidism (11.83, 95% CI: 10.74, 13.00), overt hypothyroidism (2.44, 95% CI: 1.97, 2.99), subclinical hyperthyroidism (1.65, 95% CI: 1.27, 2.11), and overt hyperthyroidism (1.26, 95% CI: 0.93, 1.67). There were no remarkable differences between incidence rates of TDs between 2 study groups during the follow-up. The incidence rate ratio (95% CI) of TPOAb positivity (0.76 [0.56, 1.01]), subclinical hypothyroidism (0.83 [0.66, 1.02]), overt hypothyroidism (0.72 [0.43, 1.15]), subclinical hyperthyroidism (1.66 [0.97, 2.81]), and overt hyperthyroidism (1.12 [0.59, 2.11]) demonstrated no difference between the 2 groups.
Incidence of thyroid disorders per 1,000 person-years according to the MetS groups at baseline
We also conducted sex split analyses and considered menopause as another confounding factor which resulted in no differences in the study findings (data not shown). For comparing the trends of thyroid hormones in MetS and non-MetS groups, we also performed a sensitivity analysis on total population without excluding TDs and also on euthyroid subjects and the similar results were obtained.
Discussion
This prospective cohort study indicated no differences in time trends of thyroid hormones and incidence of TD between MetS and non-MetS subjects during 10 years follow-up. Although significant differences were found in serum TSH and FT4 values and the prevalence of overt hypothyroidism between MetS and non-MetS groups at baseline, the significance was disappeared after further adjustment for age, sex, BMI, smoking, and TPOAb positivity. MetS subjects have lower frequency of TPOAb positivity than non-MetS subjects.
Thyroid hormone actions lead to specific effects that influence endpoints regarding body adiposity, glucose or lipid levels, and blood pressure. In this way, all 4 features of MetS may be influenced by TH levels [22, 23]. Many studies disputed the effect of thyroid hormones on MetS parameters, mostly in favor of association of higher serum TSH and lower serum FT4 levels, even within the reference range, with metabolic parameters or MetS as a whole entity [13, 24-26]; however, there are reports which found no association between thyroid hormones and metabolic parameters [13, 27, 28].
Although thyroid hormones may affect each of the components of MetS via several mechanisms, based on the current evidence, the relation is not necessarily unidirectional since target tissues of thyroid hormones may also be involved with thyroid function and more studies are needed in this regard to find cause-and-effect relationships. The hypothesis has come from the evidences indicating the effect of some components of MetS on thyroid hormones especially obesity and diabetes.
Although weight gain is often regarded to be secondary to hypothyroidism (8), until recently, more novel views have been raised suggesting that thyroid disorders could be secondary to obesity [29-32]. The possible explanation may be due to chronic inflammation status caused by leptin, cytokines, and other inflammatory markers produced by over-loading adipose tissue [32], which may inhibit the mRNA expression of sodium/iodide symporter and disturb iodide uptake activity in thyroid cells [33, 34] or modulate the expression and activity of deiodinases [35, 36]. Nevertheless, the etiology for the correlation of obesity and hypothyroidism still needs to be further elucidated.
A number of studies have suggested that serum concentrations of thyroid hormones might be altered in diabetic patients [37]. T2DM has been shown to have a negative association with serum concentration of TSH [37], and it has been demonstrated that the nocturnal TSH peak was abolished in poorly controlled diabetes as TSH response to TRH was disrupted [38]. In contrast, other studies showed that higher serum TSH and lower serum FT4 levels were associated with increased insulin resistance markers [39, 40] and elevated blood glucose levels [41], due to contribution of pathological pathways connecting diabetes and TD including biochemical, genetic, and hormonal malfunctions [37, 42]. The most probable mechanism leading to TD in diabetic patients could be attributed to insulin resistance [37, 43] which is reported in both hyperthyroidism and hypothyroidism [44].
Despite much evidence regarding the effect of thyroid hormones on MetS, there are limited studies exploring the reverse causation, that is, the effect of MetS on thyroid function. These studies, mostly hampered by cross-sectional design, showed an association with TD especially overt or subclinical hypothyroidism [11, 45-48]. In the current survey, crude analysis showed similar results; however, after adjusting important cofounders, for example, age, sex, BMI, and TPOAb positivity, no difference was found in the prevalence and risk of hypothyroidism between MetS and non-MetS subjects. In contrast to most previous reports, the current study, which privileged with stronger population-based cohort design, larger sample size and adjusting for important confounders, would suggest no association.
The current study showed no differences in time trends of TSH, FT4, and TPOAb positivity between MetS and non-MetS groups during 10 years of follow-up. To the best of our knowledge, the trend of thyroid hormones in MetS has not yet been investigated in another study. We suggest the hypothesis that the effect of MetS on HPA axis and thyroid hormones might reach the balance within the early months of MetS development and after MetS stabilization; therefore, the duration and cumulative effect of MetS might have no effect on TD. We also found no differences in the incidence of TD between individuals with MetS and non-MetS over time. Only 1 prospective study on 66,822 subjects indicated that the incidence rate of subclinical hypothyroidism was significantly higher in MetS patients rather than those without MetS, through an average follow-up of 4.2 years. They showed that MetS patients were at a 21% excess risk of developing subclinical hypothyroidism compared with non-MetS subjects [12].
Strength of the present study includes the large-size population-based cohort design in an iodine-sufficient population [49] with relatively long follow-up duration as well as appropriate exclusion criteria and adjusted confounding factors in the analysis. Moreover, evaluation of both serum TSH and FT4 as well as TPOAb is another strength of the study. However, differences in cutoff values for WC in MetS definition might hamper the generalizability of findings to other populations. Coexistence of MetS and TD may aggravate their common metabolic features which might strongly impact the health of populations especially in hypothyroid elderly subjects regarding cardiovascular and metabolic risk factors.
We conclude that MetS is not associated with thyroid hypofunction considering other important confounders such as age, sex, smoking, BMI, and TPOAb positivity. Also, there is no cumulative effect of MetS over time on thyroid gland to cause any difference in trends of thyroid hormones and incidence of TD between those with and without MetS.
Acknowledgements
The authors would like to express their appreciation and gratitude to the support of Research Institute for Endocrine Sciences (RIES).
Statement of Ethics
The present study has been conducted based on the principles of the Declaration of Helsinki and provided informed written consent of all subjects before participation in the survey. It has been performed with the approval of Ethics Human Research Review Committee of the Endocrine Research Center, Shahid Beheshti University (ID number: IR.SBMU.ENDOCRINE.REC.1398.100).
Conflict of Interest Statement
There is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
Funding Sources
This research did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector.
Author Contributions
Mehran L.: designing the protocol and writing the manuscript (MD, PhD); Azizi F.: study supervisor and writing the manuscript (MD, endocrinologist); Amouzegar A.: searching and writing the manuscript (endocrinologist); Abdi H.: writing the manuscript (MD, endocrinologist); Delbari N.: data gathering and writing (MD, MPH); Madreseh E.: data analysis and writing the manuscript (Statisticians, PhD); Tohidi M.: laboratory supervisor and writing (MD, pathologist); Mansournia M.A. (MD, PhD): consultant in statistical methodology and analysis.
Footnotes
verified
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