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Exploring the association between triglyceride-glucose index and thyroid function

European Journal of Medical Research volume 28, Article number: 508 (2023) Cite this article

A Correction to this article was published on 24 January 2024

This article has been updated

Abstract

Background

Thyroid dysfunction is associated with abnormal glucose-insulin homeostasis, and the triglyceride-glucose (TyG) index has been recommended as a convenient surrogate of insulin resistance (IR). This study aimed to investigate the relationship between TyG and thyroid function in the US population.

Methods

 We analyzed data from the National Health and Nutrition Examination Survey (NHANES) conducted from 2007 to 2012 in a cross-sectional manner. Aside from conventional thyroid parameters, our study evaluated the central sensitivity to thyroid hormones (THs) using the thyroid feedback quantile-based index (TFQI), thyrotropin resistance index (TT4RI), and thyrotropin index (TSHI). To evaluate peripheral sensitivity to THs, we calculated the ratio of free triiodothyronine (FT3) to free thyroxine (FT4) and the sum activity of peripheral deiodinases (SPINA-GD). In the 1848 adults, multivariable linear regression, subgroup, and interaction analyses were employed to estimate the association between TyG and thyroid parameters. The nonlinear relationship was addressed by smooth curve fittings and generalized additive models.

Results

After adjusting covariates, we demonstrated a significant negative association between TyG and FT4 (β = − 0.57, p < 0.001), and a positive relationship between TyG and thyroid-stimulating hormone (β = 0.34, p = 0.037), as well as TgAb (β = 17.06, p = 0.005). Subgroup analysis indicated that the association between TyG and TgAb was more pronounced in the female subjects (β = 32.39, p < 0.001, p for interaction = 0.021). We also confirmed an inverse correlation between TyG and central sensitivity to THs, as assessed by TSHI and TT4RI (βTSHI = 0.12, p < 0.001; βTT4RI = 2.54, p = 0.023). In terms of peripheral sensitivity to THs, we found a significant positive correlation between TyG and FT3/FT4 (β = 0.03, p = 0.004), and SPINA-GD (β = 2.93, p = 0.004).

Conclusion

The present study established a noteworthy association between TyG and thyroid parameters, indicating a strong link between IR and thyroid dysfunction. Further investigations are warranted to validate these results.

Introduction

Insulin resistance (IR) is a condition in which tissues are not responding properly to insulin [1], and it has been identified as a major cause of numerous diseases, including type 2 diabetes (T2DM), cardiovascular diseases (CVD), polycystic ovary syndrome (PCOS), and dyslipidemia [2,3,4]. Currently, the hyperinsulinemic-euglycemic clamp (HEC) technique is accepted as the “gold standard” for measuring IR [5], nevertheless, this method requires a great deal of time, effort, and expense, which limits its clinical applications [6]. Therefore, researchers are dedicated to developing the surrogate indexes of IR, including homeostatic model assessment of IR (HOMA-IR) [7], metabolic score for IR (METS-IR) [8], quantitative insulin sensitivity check index (QUICKI) [5], and Matsuda index [9]. In recent studies, the triglyceride-glucose (TyG) index, determined from fasting plasma glucose (FPG) and triglycerides (TG), has been proposed as a convenient and reliable method to evaluate peripheral IR [10], as well as a potential risk marker of various diseases [11,12,13,14].

Thyroid hormones (THs) are crucial for the regulation of multiple metabolic processes [15], and it has been suggested that thyroid dysfunction can contribute to abnormal glucose homeostasis [16]. It is known that overproduction of THs can affect the alternation of insulin signaling and glucose metabolism, and clinical and experimental hyperthyroidism often coincides with impaired glucose tolerance [16]. Previous studies have also revealed the tight linkage between hypothyroidism and metabolic disorders [17, 18], and even the modest increases in thyroid-stimulating hormone (TSH) levels are correlated with the presence of IR [19]. Moreover, it has also been reported that IR is associated with low-normal thyroid function, indicated by low levels of free thyroxine (FT4) and/or high concentrations of TSH [20, 21]. However, the research investigating the relationship between THs and IR had conflicting results.

Only a few studies have investigated the relationship between TyG and thyroid parameters, which used only FT4 and TSH levels to assess thyroid function [22, 23]. Thyroid antibodies were not involved in this research, not to mention sensitivity to thyroid hormone indices, which are considered to reveal the complex correlation between THs [24]. In this study, the association between TyG and thyroid parameters was investigated in a nationally representative sample of adults from the United States (US) using the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2012.

Materials and methods

Study population

In this study, data were obtained from the NHANES, a cross-sectional survey conducted biennially by the Centers for Disease Control and Prevention to assess US citizens’ nutrition and health conditions. Demographic information, dietary information, physical examinations, laboratory data, and multiple questionnaires are among the main sections of the database. The Institutional Review Board (IRB) of the National Center for Health Statistics (NCHS) has approved the research protocol of the NHANES before interviews or data collection begins, and the participants provided written informed consent. It is worth mentioning that this analysis exclusively relied on publicly accessible data, and no ethical approval was needed.

Three waves of the NHANES were pooled for this study, including NHANES 2007–2008 (‘‘E’’ data), NHANES 2009–2010 (‘‘F’’ data), and NHANES 2011–2012 (‘‘G’’ data). The present study utilized NHANES operation manuals to access variables, which were available on the NHANES website (https://www.cdc.gov/nchs/nhanes/). We included participants who did not have a pregnancy, were at least 18 years old, and had laboratory data regarding TyG and thyroid function (Fig. 1). Furthermore, to avoid potential bias, the subjects suffering from diabetes or prediabetes [including impaired fasting glucose (IFG), and impaired glucose Subjects tolerance (IGT)] were excluded.

Fig. 1
figure 1

Study flowchart. NHANES, National Health and Nutrition Examination Survey; TyG, Triglyceride-glucose

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Measurement of TyG and thyroid outcomes

TyG was calculated by the formula of Ln[TG (mg/dL) × FPG (mg/dL)/2] [25]. In the present study, thyroid function parameters were assessed including free triiodothyronine (FT3), FT4, TSH, anti-thyroglobulin antibody (TgAb), and anti-thyroperoxidase antibody (TPOAb), and the NHANES Laboratory/Medical Technologists Procedures Manual (LPM) provides detailed specimen collection and processing instructions [26]. According to the “Thyroid profile” section in the laboratory data obtained from NHANES 2007–2012, the measurement of FT3 (reference range, 2.5–3.9 pg/mL) and FT4 (reference range, 0.6–1.6 ng/dL) involved a competitive two-step enzyme immunoassay, wherein specific antibodies were introduced to the sample and subsequently combined with the chemiluminescent substrate Lumi-Phos™ 530. The resulting sample was then measured using a luminometer, with results determined through reference to a stored, multi-point calibration curve. Notably, the stripping agent was not utilized for the FT3 and FT4 tests. TSH (reference range, 0.34–5.6 µIU/mL), on the other hand, was measured using a two-site, immune-enzymatic ("sandwich") assay of the third generation, and its concentration was determined using a multi-point calibration curve, with the addition of Lumi-Phos™ 530. Furthermore, the sequential two-step immunoenzymatic "sandwich" assay was utilized to measure TPOAb and TgAb, with reference ranges of 0–9.0 IU/mL and 0–4.0 IU/mL respectively [26].

The central sensitivity to THs was evaluated using three indices, namely thyrotropin index (TSHI), thyrotropin thyroxine resistance index (TT4RI), and thyroid feedback quantile-based index (TFQI), which were calculated according to previous studies [27], [28]:

$$ {\text{TSHI}} = {\text{ln TSH }}\left( {{\text{mIU}}/{\text{L}}} \right)\, + \,0.{1345 }*{\text{ FT4 }}\left( {{\text{pmol}}/{\text{L}}} \right) $$
$$ {\text{TT4RI}}\, = \,{\text{FT4 }}\left( {{\text{pmol}}/{\text{L}}} \right)\, \times \,{\text{TSH }}\left( {{\text{mIU}}/{\text{L}}} \right) $$

Based on FT4 and TSH quantiles transformed by empirical cumulative distribution functions (cdf), TFQI was calculated as follows [29]:

$$ {\text{TFQI}}\, = \,{\text{cdf FT4 }}\left( {{\text{pmol}}/{\text{L}}} \right) - \left[ {{1} - {\text{cdf TSH }}\left( {{\text{mIU}}/{\text{L}}} \right)} \right] $$

A higher value of TSHI or TT4RI indicates a lower central sensitivity to THs. As for TFQI, a negative value indicates greater pituitary sensitivity to THs, a positive value indicates less sensitivity and a value of 0 indicates normal sensitivity.

As for peripheral sensitivity to THs, the FT3/FT4 ratio and the sum activity of peripheral deiodinases (SPINA-GD) were applied according to the equation:

FT3/FT4 ratio = FT3 (pmol/L)/FT4 (pmol/L) [30]. An increased FT3/FT4 ratio indicates a higher activity of peripheral THs.

SPINA-GD = β31 (KM1 + [FT4]) (1 + K30[TBG]) [FT3]/α31[FT4] [31]. β31: Clearance exponent for T3; KM1: Dissociation constant of type1 1 deiodinase; K30: Dissociation constant of T3 at thyroxine-binding globulin; TBG: Standard concentration of thyroxine-binding globulin; α31: Dilution factor for triiodothyroine.

The secretory capacity of the thyroid gland (SPINA-GT) = βT (DT + [TSH]) (1 + K41[TBG] + K42[TBPA]) [FT4]/αT[TSH] [31]. βT: Clearance exponent for T4; DT: EC50 for TSH; K41: Dissociation constant of T4 at thyroxine-binding globulin; TBG: Standard concentration of thyroxine-binding globulin; TBPA: Standard transthyretin concentration; K42: Dissociation constant of T4 at transthyretin; αT: Dilution factor for thyroxine.

Thyroid autoimmunity was defined as TPOAb titers exceeding 9.0 IU/mL and/or TgAb titers exceeding 4.0 IU/mL [32].

Collect of demographics and social characteristics

The information regarding age, gender, race/ethnicity, education, marital status, poverty-to-income ratio (PIR), medical history (including diabetes, hypertension, and congestive heart failure), tobacco consumption, and alcohol use were collected using a standardized questionnaire. Education levels were classified into three groups: less than high school diploma, high school diploma, and more than high school diploma, and the socioeconomic status of the participants was evaluated using PIR, categorized as 0–1 and > 1. Tobacco use was classified into three categories proposed by NHANES: never, former, and current status. Participants who were never smokers reported smoking less than 100 cigarettes in their lives, whereas former smokers were defined as having smoked more than 100 cigarettes in life but not currently. Similarly, alcohol consumption was coded into never, former, and current conditions, with current alcohol users further divided into three categories: (1) heavy alcohol users (≥ 3 drinks per day or binge drinking [≥ 4 drinks on the same occasion] ≥ 5 days per month for females, ≥ 4 drinks per day or binge drinking [≥ 5 drinks on the same occasion] ≥ 5 days per month for males; (2) moderate alcohol users (≥ 2 drinks per day for females, ≥ 3 drinks per day for males, or binge drinking ≥ 2 days per month); and (3) mild alcohol users were the ones who drunk currently but did not meet heavy and moderate alcohol standards [33].

Body mass index (BMI), waist circumference (WC), and blood pressure (BP) were measured by certified technicians in Mobile Examination Centers (MECs). BMI was calculated using the following formula: BMI = body weight (kg)/height2 (m2). WC was assessed using an inelastic ruler with a minimum scale of one millimeter. Measurements were taken after a regular exhalation, while the participant stood in a natural position with legs positioned approximately 25–30 cm apart. The ruler was positioned at the midpoint of the connecting line between the upper edge of the iliac crest and the lower edge of the 12th rib, which typically represents the narrowest point of the waist. A horizontal circumference measurement was then obtained by encircling the abdomen. The final measurement was rounded to the nearest 0.1 cm [34]. Furthermore, a gender-specific cut-off of waist circumference (102 cm for males, 88 cm for females) was then involved in the subgroup analysis [35]. Following a minimum of 5 min of rest at the MEC, BP was assessed in a seated posture using a standardized mercury sphygmomanometer [36]. Furthermore, physical activity (PA) was calculated based on five categories: vigorous work activity/vigorous recreational activity, moderate work activity/moderate recreational activity, and walking/bicycle. The weekly metabolic equivalent (MET) was calculated using the NHANES recommended MET scores of 8 points for vigorous work activity/vigorous recreational activities and 4 points for moderate work activity/moderate recreational activities and walking/bicycle. Additionally, the weekly MET minutes were documented as low, intermediate, and high levels [37, 38].

Measurement of laboratory indices

Subjects who fasted for a minimum of 8 h but less than 24 h had their levels of FPG, fasting blood insulin (FBI), total cholesterol (TC), TG, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) estimated, which were recorded by well-trained technicians utilizing standardized laboratory methods. FPG was measured using the hexokinase assay, and FBI was determined through a two-site enzyme immunoassay. Notably, FPG and FBI were measured in the morning examination session only. HDL-C, LDL-C, TC, and TG were determined in serum using a nephelometric immunoassay on the Roche Modular P chemistry analyzer.

According to “Standard Biochemistry Profile” in laboratory data from NHANES 2007–2012, the LX20 employs an enzymatic rate technique for the quantification of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activity in serum or plasma. The concentration of blood urea nitrogen (BUN) was determined utilizing the enzymatic conductivity rate method. Creatinine was measured using a Jaffe rate method (kinetic alkaline picrate). The quantification of uric acid was conducted through a timed endpoint methodology.

Hemoglobin was measured using a single-beam photometer, while glycohemoglobin (HbA1c) measurements were performed on the A1c G7 HPLC Glycohemoglobin Analyzer. Moreover, urine iodine concentration (UIC) was measured by ICP-DRC-MS (Inductively Coupled Plasma Dynamic Reaction Cell Mass Spectroscopy) to determine iodine conditions in participants, which was classified as < 99 g/L, 99–199 g/L, and > 199 g/L in this analysis [39]. All laboratory measurements complied with the standardization and certification program. Further comprehensive details regarding the analyzers and methodologies utilized can be obtained from the laboratory method file accessible on the NHANES website [https://www.cdc.gov/nchs/nhanes/ (accessed date: 11 September 2023)].

Definition of metabolic syndrome

Metabolic Syndrome (MetS) was defined following the updated criteria established by the National Cholesterol Education Program/Adult Treatment Panel III (NCEP-ATP III) for the American population. Specifically, individuals were classified as having MetS if they met three or more of the following components: WC ≥ 102 cm for males or ≥ 88 cm for females; BP ≥ 130/85 mmHg or receiving treatment with anti-hypertensive medications; FPG ≥ 5.6 mmol/L or receiving medications for diabetes management; TG levels ≥ 150 mg/dL or receiving medications for lipid abnormalities; and HDL levels < 40 mg/dL for males or < 50 mg/dL for females, or receiving medications for lipid abnormalities [40].

Statistical analysis

In this analysis, continuous baseline variables were summarized using the mean ± standard deviation (SD) and compared using a one-way analysis of variance (ANOVA) analysis. Categorical variables were summarized by percentage and tested using Chi-squared statistics. The association between TyG and thyroid function was investigated using multivariable linear and logistic regression analyses. Then we performed interaction and subgroup analyses according to gender and WC category. The initial analysis (model 1) was conducted without any adjustments, while subsequent analyses involved iterative adjustments for covariates that were deemed clinically significant. Model 2 adjusted for age, gender, and race/ethnicity. Model 3 added education, PIR, alcohol use, smoke, WC, systolic blood pressure, diastolic blood pressure, physical activity level, hypertension, CVD, MetS, FBI, HbA1c, AST, ALT, BUN, HDL-C, LDL-C, TC, creatinine, uric acid, hemoglobin, and urine iodine. Moreover, the current study used smooth curve fittings and generalized additive models (GAM) to address the nonlinear relationship, and a recursive algorithm was further applied to assess inflection points. All data was analyzed using the statistical software packages R 4.3.0 (http://www.R-project.org) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA), with a two-tailed p-value < 0.05 considered statistically significant.

Result

Baseline characteristics of participants

The clinical and laboratory characteristics of the subjects are shown in Table 1. Based on NHANES 2007–2012, this study included 1848 participants (range: 18–80 years), of whom 966 (52.57%) were males and 882 (47.73%) were females, with a mean age of 41.44 ± 16.81 years. The mean level of TyG was 8.43 ± 0.55.

Table 1 Baseline characteristics of the NHANES (2007–2012) study population in TyG tertiles
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Association between TyG index and thyroid function

As demonstrated in Fig. 2, the TyG index was significantly negatively and positively associated with FT4 and TSH, respectively. The negative association between TyG index and FT4 was present in the unadjusted model [β = − 0.44, 95% confidence interval (CI): − 0.62 to − 0.26, p < 0.001], model 2 (β = − 0.46, 95% CI: − 0.64 to − 0.28, p < 0.001), and model 3 (β = − 0.57, 95% CI: − 0.79 to − 0.34, p < 0.001). Similarly, the result of weighted multivariable linear regression analysis indicated a significant positive relationship between TyG index and TSH in the unadjusted model (β = 0.29, 95% CI: 0.04 to 0.54, p = 0.023), model 2 (β = 0.34, 95% CI: 0.09 to 0.59, p = 0.008), and model 3 (β = 0.34, 95% CI: 0.02 to 0.66, p = 0.037). Furthermore, Fig. 2 also indicated that TyG index was also significantly associated with TgAb in the unadjusted model (β = 14.78, 95% CI: 5.55 to 24.00, p = 0.002), model 2 (β = 15.48, 95% CI: 6.13 to 24.83, p = 0.001), and model 3 (β = 17.06, 95% CI: 5.23 to 28.89, p = 0.005). In the subgroup analysis (Fig. 3), gender and WC didn’t play an interactive role in the relationship between TyG and FT4, and TSH, while the significant association between TyG index and TgAb was more pronounced in the female subjects (β = 32.39, 95% CI: 15.02 to 49.76, p < 0.001, p for interaction = 0.021). Whereas, there was no significant association between TyG index and the levels of FT3 and TPOAb, as indicated in Fig. 2.

Fig. 2
figure 2

The association between TyG index and thyroid parameters. A Model 1: no covariates were adjusted. B Model 2: age, gender, and race/ethnicity were adjusted. C Model 3: age, gender, race/ethnicity, education, poverty-to-income ratio, alcohol use, smoke, waist circumference, systolic blood pressure, diastolic blood pressure, physical activity level, hypertension, cardiovascular disease, metabolic syndrome, fasting blood insulin, glycohemoglobin, aspartate aminotransferase, alanine aminotransferase, blood urea nitrogen, high density lipoprotein, low density lipoprotein, total cholesterol, creatinine, uric acid, hemoglobin, and urine iodin were adjusted. The solid red line represents the smooth curve fit between TyG index and FT4 (D), as well as TSH (E). Blue bands represent the 95% confidence interval from the fit. TyG, Triglyceride-glucose; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; TgAb, anti-thyroglobulin antibody; TPOAb, anti-thyroperoxidase antibody; TSHI, thyrotropin index; TT4RI, thyrotropin thyroxine resistance index; TFQI, thyroid feedback quantile-based index

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Fig. 3
figure 3

Subgroup analysis stratified by gender and WC. In the subgroup analysis of gender (A), age, race/ethnicity, education, poverty-to-income ratio, alcohol use, smoke, waist circumference, systolic blood pressure, diastolic blood pressure, physical activity level, hypertension, cardiovascular disease, metabolic syndrome, fasting blood insulin, glycohemoglobin, aspartate aminotransferase, alanine aminotransferase, blood urea nitrogen, high density lipoprotein, low density lipoprotein, total cholesterol, creatinine, uric acid, hemoglobin, and urine iodin were adjusted. In the subgroup analysis of WC (B), age, gender, race/ethnicity, education, poverty-to-income ratio, alcohol use, smoke, systolic blood pressure, diastolic blood pressure, physical activity level, hypertension, cardiovascular disease, metabolic syndrome, fasting blood insulin, glycohemoglobin, aspartate aminotransferase, alanine aminotransferase, blood urea nitrogen, high density lipoprotein, low density lipoprotein, total cholesterol, creatinine, uric acid, hemoglobin, and urine iodin were adjusted. TyG, Triglyceride-glucose; WC, waist circumference; FT4, free thyroxine; TSH, thyroid-stimulating hormone; TgAb, anti-thyroglobulin antibody; TSHI, thyrotropin index; TT4RI, thyrotropin thyroxine resistance index

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Association between TyG index and sensitivity to thyroid hormone indices

A significant negative correlation was observed between TyG index and central sensitivity to THs, as assessed by TSHI and TT4RI. As shown on Fig. 2 and Additional file 1: Figure S1, TyG index showed a positive association with TSHI and TT4RI, and the significant association remained in the unadjusted model (βTSHI = 0.10, 95% CI: 0.04 to 0.15, p = 0.001; βTT4RI = 1.93, 95% CI: 0.21 to 3.66, p = 0.028), model 2 (βTSHI = 0.10, 95% CI: 0.05 to 0.16, p < 0.001; βTT4RI = 2.25, 95% CI: 0.50 to 4.00, p = 0.012), and model 3 (βTSHI = 0.12, 95% CI: 0.05 to 0.19, p < 0.001; βTT4RI = 2.54, 95% CI: 0.35 to 4.74, p = 0.023). However, after adjusting for confounders, there was no significant relationship between TyG index and TFQI. In terms of peripheral sensitivity to thyroid hormones, as demonstrated in Fig. 2 and Additional file 1: Figure S1, the present study found a significant positive correlation between TyG index and FT3/FT4 ratio in the unadjusted model (β = 0.03, 95% CI: 0.02 to 0.05, p < 0.001), model 2 (β = 0.03, 95% CI: 0.02 to 0.05, p < 0.001), and model 3 (β = 0.03, 95% CI: 0.01 to 0.05, p = 0.004). Moreover, TyG index and SPINA-GD also showed a significant relationship: unadjusted model (β = 3.24, 95% CI: 1.68 to 4.80, p < 0.001), model 2 (β = 3.06, 95% CI: 1.48 to 4.64, p < 0.001), model 3 (β = 2.93, 95% CI: 0.94 to 4.91, p = 0.004).

Discussion

In the present study, a nationally representative sample of the American population was investigated to determine the relationship between TyG index and thyroid function. In the 1848 adult participants, we noted a significant association between TyG index and FT4, TSH, and TgAb, as well as the sensitivity to thyroid hormone indices throughout multiple analyses. Subgroup analysis indicated that a positive association between TyG index and TgAb existed in the female participants.

It is well known that THs and insulin signaling are interconnected, with abnormalities in one that can lead to dysregulation of the other [41]. In line with our results, a cohort study conducted by Amouzegar et al. of 2758 euthyroid participants from the Tehran Thyroid Study (TTS) to investigate the relationship between IR, as assessed by HOMA-IR, and thyroid function and indicated that low FT4 levels were independently associated with IR [42]. Likewise, an investigation of 6241 euthyroid subjects found that IR was significantly related to low normal FT4 concentrations after adjusting for age, gender, metabolic, and lifestyle factors [43]. Our work was also in agreement with those of Choi et al., who observed an inverse relationship between TyG index and FT4 after adjusting for confounders based on the Korean National Health and Nutritional Examination Survey (KNHANES) 2015 [23]. Even though T4 was regarded as a prohormone for biologically active T3, the present study showed a close relationship between TyG index and FT4, rather than FT3 [44]. It seemed logical when considering that FT4 might be a more reliable indicator of tissue thyroid status for its core mediative effect in peripheral thyroid activity and local thyroid regulatory mechanisms and that FT4 has a more efficient non-genomic capacity to drive metabolism for it is independent of nuclear receptor binding [16, 20, 45].

Research regarding the relationship between IR and TSH levels has produced conflicting results. In a cross-sectional study of a Netherlands population, Roos et al. found that the HOMA-IR index and TSH concentrations were not significantly related after further adjustment for confounders [20]. Similarly, Ruhla et al. also observed a weak relationship between TSH levels and IR, which was no longer significant after excluding the individuals suffering impaired glucose tolerance (IGT) [46]. The results of the investigation by Amouzegar et al. indicated that, despite a significant positive association observed in linear regression analysis for serum TSH levels and IR, it was not significant in the logistic regression model [42]. Inversely, in a retrospective study performed by Zhu et al., TSH was independently associated with IR, and a significant correlation was prominent in the participants with diabetes and high HbA1c levels [47]. Likewise, Ambrosi et al. also observed higher TSH levels in the obese subjects with IR, and that TSH was positively and negatively associated with HOMA-IR index and QUICKI index, respectively [48]. After adjusting for confounding factors, the current investigation revealed a significant positive relationship between TyG index and TSH, which was consistent with previous studies conducted on Korean cohorts [22, 23]. Such discrepancies may have been due to the application of different inclusion criteria, study designs, ethnicity, and assay of laboratory indices. Furthermore, these divergences may also be attributed to the application of different surrogate IR indexes. Unlike the more popular HOMA-IR index, which is primarily used to estimate hepatic IR, TyG index is considered the reliable indicator of peripheral IR [10, 49].

In general, our results, along with other studies suggested a close link between IR and low normal thyroid function, overt or subclinical hypothyroidism [20, 21, 43, 46, 50, 51]. In addition, this investigation also showed a threshold effect and a saturation effect of the association between FT4, TSH, and TyG index, respectively, suggesting that hyper- and hypothyroidism might both be associated with IR, but through different mechanisms. Moreover, the central and peripheral mechanisms should be taken into account in light of the significant associations observed between the TyG index and TSHI, TT4RI, FT3/FT4 ratio, and SPNIA-GD. In instances of hypo- and hyperthyroidism, THs possess the capacity to regulate insulin secretion both directly and indirectly, through the suppression of glucose-induced insulin secretion and the reduction of β-cell responsiveness [52, 53]. There is evidence that hypothyroidism can reduce glucose absorption from the gastrointestinal tract, increase insulin levels, and suppress hepatic glucose production [16, 54]. Whereas, as a result of hyperthyroidism, glucose depletion increases substantially, increasing insulin demand in peripheral tissues, which is generated by insulin-stimulated glucose oxidation, and ultimately leads to a disruption in hepatic insulin sensitivity [16, 55]. Furthermore, in a hyperthyroid state, hepatic gluconeogenesis is augmented through canonical transcription-mediated THs signaling, which activates gluconeogenic-drive proteins and glucose transporters, or through secondary effects on hepatocytes, resulting in reduced hepatic insulin sensitivity [56, 57].

The findings presented in this research can be elucidated through various mechanisms. It has been reported that THs play an essential role in regulating the expression and activation of proteins related to IR, such as β2 adrenergic receptor (β2AR) and peroxisome proliferator-activated receptor (PPAR-γ) [58]. In addition, THs also increase glucose transport and glycolysis by upregulating glucose transporter type-4 (GLUT-4) and phosphoglycerate kinase genes, which further work synergistically with insulin to promote glucose disposal and utilization in peripheral tissues [59,60,61,62,63]. Moreover, it is plausible that the mechanism may involve a synergistic interaction between THs and other endocrine factors, such as catecholamine, adiponectin, and ghrelin, whose imbalances could potentially result in enhanced insulin sensitivity [64,65,66,67].

The findings of this study should be considered in the context of certain limitations. First, the causal relationship between thyroid parameters and IR could not be established for the cross-sectional design of NHANES, thus, a cohort study with a larger sample size and longer observation periods, along with a Mendelian randomization investigation is warranted. Furthermore, the data about medical history, PIR, education, marital status, tobacco consumption, and alcohol use were acquired through self-reported questionnaires, thereby rendering the possibility of recall bias and other potential inaccuracies. Ultimately, the TyG index is considered a noteworthy parameter with extensive biological implications that can be impacted by various endocrine factors. Unfortunately, these endocrine confounders, such as catecholamine, adiponectin, and ghrelin, were not included in NHANES.

Conclusion

In conclusion, the current investigation, which employed a nationally representative sample of the adult population in the US, has established a strong association between the TyG index and thyroid parameters, including FT4, TSH, TgAb, TT4RI, TSHI, FT3/FT4 ratio and SPINA-GD. These findings indicate a noteworthy correlation between IR and thyroid dysfunction, necessitating additional causal investigations to validate this inference.

Data availability

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

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References

  1. Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuniga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):122. https://doi.org/10.1186/s12933-018-0762-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lee JW, Gu HO, Jung Y, Jung Y, Seo SY, Hong JH, Hong IS, Lee DH, Kim OH, Oh BC. Candesartan, an angiotensin-II receptor blocker, ameliorates insulin resistance and hepatosteatosis by reducing intracellular calcium overload and lipid accumulation. Exp Mol Med. 2023. https://doi.org/10.1038/s12276-023-00982-6.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kakoly NS, Khomami MB, Joham AE, Cooray SD, Misso ML, Norman RJ, Harrison CL, Ranasinha S, Teede HJ, Moran LJ. Ethnicity, obesity and the prevalence of impaired glucose tolerance and type 2 diabetes in PCOS: a systematic review and meta-regression. Hum Reprod Update. 2018;24(4):455–67. https://doi.org/10.1093/humupd/dmy007.

    Article  CAS  PubMed  Google Scholar 

  4. Ausk KJ, Boyko EJ, Ioannou GN. Insulin resistance predicts mortality in nondiabetic individuals in the US. Diabetes Care. 2010;33(6):1179–85. https://doi.org/10.2337/dc09-2110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. 2008;294(1):E15-26. https://doi.org/10.1152/ajpendo.00645.2007.

    Article  CAS  PubMed  Google Scholar 

  6. Li M, Zhan A, Huang X, Hu L, Zhou W, Wang T, Zhu L, Bao H, Cheng X. Positive association between triglyceride glucose index and arterial stiffness in hypertensive patients: the China H-type Hypertension Registry Study. Cardiovasc Diabetol. 2020;19(1):139. https://doi.org/10.1186/s12933-020-01124-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27(6):1487–95. https://doi.org/10.2337/diacare.27.6.1487.

    Article  PubMed  Google Scholar 

  8. Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, Sanchez-Lazaro D, Meza-Oviedo D, Vargas-Vazquez A, Campos OA, Sevilla-Gonzalez M, Martagon AJ, Hernandez LM, Mehta R, Caballeros-Barragan CR, Aguilar-Salinas CA. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur J Endocrinol. 2018;178(5):533–44. https://doi.org/10.1530/EJE-17-0883.

    Article  CAS  PubMed  Google Scholar 

  9. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999;22(9):1462–70. https://doi.org/10.2337/diacare.22.9.1462.

    Article  CAS  PubMed  Google Scholar 

  10. Li Z, He Y, Wang S, Li L, Yang R, Liu Y, Cheng Q, Yu L, Zheng Y, Zheng H, Gao S, Yu C. Association between triglyceride glucose index and carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a RCSCD-TCM study in China. Cardiovasc Diabetol. 2022;21(1):38. https://doi.org/10.1186/s12933-022-01470-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Yan F, Yan S, Wang J, Cui Y, Chen F, Fang F, Cui W. Association between triglyceride glucose index and risk of cerebrovascular disease: systematic review and meta-analysis. Cardiovasc Diabetol. 2022;21(1):226. https://doi.org/10.1186/s12933-022-01664-9.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Huang R, Wang Z, Chen J, Bao X, Xu N, Guo S, Gu R, Wang W, Wei Z, Wang L. Prognostic value of triglyceride glucose (TyG) index in patients with acute decompensated heart failure. Cardiovasc Diabetol. 2022;21(1):88. https://doi.org/10.1186/s12933-022-01507-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Park B, Lee HS, Lee YJ. Triglyceride glucose (TyG) index as a predictor of incident type 2 diabetes among nonobese adults: a 12-year longitudinal study of the Korean Genome and Epidemiology Study cohort. Transl Res. 2021;228:42–51. https://doi.org/10.1016/j.trsl.2020.08.003.

    Article  CAS  PubMed  Google Scholar 

  14. Gao JW, Hao QY, Gao M, Zhang K, Li XZ, Wang JF, Vuitton DA, Zhang SL, Liu PM. Triglyceride-glucose index in the development of peripheral artery disease: findings from the Atherosclerosis Risk in Communities (ARIC) Study. Cardiovasc Diabetol. 2021;20(1):126. https://doi.org/10.1186/s12933-021-01319-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. van den Beld AW, Kaufman JM, Zillikens MC, Lamberts S, Egan JM, van der Lely AJ. The physiology of endocrine systems with ageing. Lancet Diabetes Endocrinol. 2018;6(8):647–58. https://doi.org/10.1016/S2213-8587(18)30026-3.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Martinez B, Ortiz RM. Thyroid Hormone Regulation and Insulin Resistance: Insights From Animals Naturally Adapted to Fasting. Physiology (Bethesda). 2017;32(2):141–51. https://doi.org/10.1152/physiol.00018.2016.

    Article  CAS  PubMed  Google Scholar 

  17. Hage M, Zantout MS, Azar ST. Thyroid disorders and diabetes mellitus. J Thyroid Res. 2011;2011: 439463. https://doi.org/10.4061/2011/439463.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kalra S, Aggarwal S, Khandelwal D. Thyroid Dysfunction and Type 2 Diabetes Mellitus: Screening Strategies and Implications for Management. Diabetes Ther. 2019;10(6):2035–44. https://doi.org/10.1007/s13300-019-00700-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Vyakaranam S, Vanaparthy S, Nori S, Palarapu S, Bhongir AV. Study of Insulin Resistance in Subclinical Hypothyroidism. Int J Health Sci Res. 2014;4(9):147–53.

    PubMed  PubMed Central  Google Scholar 

  20. Roos A, Bakker SJ, Links TP, Gans RO, Wolffenbuttel BH. Thyroid function is associated with components of the metabolic syndrome in euthyroid subjects. J Clin Endocrinol Metab. 2007;92(2):491–6. https://doi.org/10.1210/jc.2006-1718.

    Article  CAS  PubMed  Google Scholar 

  21. Garduno-Garcia JJ, Alvirde-Garcia U, Lopez-Carrasco G, Padilla MM, Mehta R, Arellano-Campos O, Choza R, Sauque L, Garay-Sevilla ME, Malacara JM, Gomez-Perez FJ, Aguilar-Salinas CA. TSH and free thyroxine concentrations are associated with differing metabolic markers in euthyroid subjects. Eur J Endocrinol. 2010;163(2):273–8. https://doi.org/10.1530/EJE-10-0312.

    Article  CAS  PubMed  Google Scholar 

  22. Choi YM, Kim MK, Kwak MK, Kim D, Hong EG. Association between thyroid hormones and insulin resistance indices based on the Korean National Health and Nutrition Examination Survey. Sci Rep. 2021;11(1):21738. https://doi.org/10.1038/s41598-021-01101-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Choi W, Park JY, Hong AR, Yoon JH, Kim HK, Kang HC. Association between triglyceride-glucose index and thyroid function in euthyroid adults: The Korea National Health and Nutritional Examination Survey 2015. PLoS ONE. 2021;16(7): e0254630. https://doi.org/10.1371/journal.pone.0254630.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yang S, Lai S, Wang Z, Liu A, Wang W, Guan H. Thyroid Feedback Quantile-based Index correlates strongly to renal function in euthyroid individuals. Ann Med. 2021;53(1):1945–55. https://doi.org/10.1080/07853890.2021.1993324.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. F. Guerrero-Romero, L. E. Simental-Mendia, M. Gonzalez-Ortiz, E. Martinez-Abundis, M. G. Ramos-Zavala, S. O. Hernandez-Gonzalez, O. Jacques-Camarena and M. Rodriguez-Moran: The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab, 95(7), 3347–51 (2010) doi:https://doi.org/10.1210/jc.2010-0288

  26. Liu N, Ma F, Feng Y, Ma X. The Association between the Dietary Inflammatory Index and Thyroid Function in US Adult Males. Nutrients. 2021. https://doi.org/10.3390/nu13103330.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Jostel A, Ryder WD, Shalet SM. The use of thyroid function tests in the diagnosis of hypopituitarism: definition and evaluation of the TSH Index. Clin Endocrinol (Oxf). 2009;71(4):529–34. https://doi.org/10.1111/j.1365-2265.2009.03534.x.

    Article  CAS  PubMed  Google Scholar 

  28. Yagi H, Pohlenz J, Hayashi Y, Sakurai A, Refetoff S. Resistance to thyroid hormone caused by two mutant thyroid hormone receptors beta, R243Q and R243W, with marked impairment of function that cannot be explained by altered in vitro 3,5,3’-triiodothyroinine binding affinity. J Clin Endocrinol Metab. 1997;82(5):1608–14. https://doi.org/10.1210/jcem.82.5.3945.

    Article  CAS  PubMed  Google Scholar 

  29. Laclaustra M, Moreno-Franco B, Lou-Bonafonte JM, Mateo-Gallego R, Casasnovas JA, Guallar-Castillon P, Cenarro A, Civeira F. Impaired sensitivity to thyroid hormones is associated with diabetes and metabolic syndrome. Diabetes Care. 2019;42(2):303–10. https://doi.org/10.2337/dc18-1410.

    Article  CAS  PubMed  Google Scholar 

  30. Nie X, Ma X, Xu Y, Shen Y, Wang Y, Bao Y. Increased serum adipocyte fatty acid-binding protein levels are associated with decreased sensitivity to thyroid hormones in the euthyroid population. Thyroid. 2020;30(12):1718–23. https://doi.org/10.1089/thy.2020.0011.

    Article  CAS  PubMed  Google Scholar 

  31. Dietrich JW, Landgrafe-Mende G, Wiora E, Chatzitomaris A, Klein HH, Midgley JE, Hoermann R. Calculated Parameters of Thyroid Homeostasis: Emerging Tools for Differential Diagnosis and Clinical Research. Front Endocrinol Lausanne. 2016;7:57. https://doi.org/10.3389/fendo.2016.00057.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yehuda M, Wang CH, Pak Y, Chiu KC, Gianoukakis AG. Parity and Risk of Thyroid Autoimmunity Based on the NHANES (2001-2002, 2007-2008, 2009-2010, and 2011-2012). J Clin Endocrinol Metab. 2017;102(9):3437–42. https://doi.org/10.1210/jc.2017-00290.

    Article  PubMed  Google Scholar 

  33. Rattan P, Penrice DD, Ahn JC, Ferrer A, Patnaik M, Shah VH, Kamath PS, Mangaonkar AA, Simonetto DA. Inverse Association of Telomere Length With Liver Disease and Mortality in the US Population. Hepatol Commun. 2022;6(2):399–410. https://doi.org/10.1002/hep4.1803.

    Article  CAS  PubMed  Google Scholar 

  34. Bawadi H, Abouwatfa M, Alsaeed S, Kerkadi A, Shi Z. Body Shape Index Is a Stronger Predictor of Diabetes. Nutrients. 2019. https://doi.org/10.3390/nu11051018.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SJ, Spertus JA, Costa F. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735–52. https://doi.org/10.1161/CIRCULATIONAHA.105.169404.

    Article  PubMed  Google Scholar 

  36. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JJ, Jones DW, Materson BJ, Oparil S, Wright JJ, Roccella EJ. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–72. https://doi.org/10.1001/jama.289.19.2560.

    Article  CAS  PubMed  Google Scholar 

  37. Lau E, Neves JS, Ferreira-Magalhaes M, Carvalho D, Freitas P. Probiotic Ingestion, Obesity, and Metabolic-Related Disorders: Results from NHANES, 1999–2014. Nutrients. 2019. https://doi.org/10.3390/nu11071482.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Liu X, Gao W, Yang J, Mao G, Lu H, Xing W. Association between probiotic, prebiotic, and yogurt consumption and chronic kidney disease: The NHANES 2010–2020. Front Nutr. 2022;9:1058238. https://doi.org/10.3389/fnut.2022.1058238.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zimmermann MB, Boelaert K. Iodine deficiency and thyroid disorders. Lancet Diabetes Endocrinol. 2015;3(4):286–95. https://doi.org/10.1016/S2213-8587(14)70225-6.

    Article  CAS  PubMed  Google Scholar 

  40. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SJ, Spertus JA, Fernando C. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement: Executive Summary. Crit Pathw Cardiol. 2005;4(4):198–203. https://doi.org/10.1097/00132577-200512000-00018.

    Article  PubMed  Google Scholar 

  41. Fernandez-Real JM, Lopez-Bermejo A, Castro A, Casamitjana R, Ricart W. Thyroid function is intrinsically linked to insulin sensitivity and endothelium-dependent vasodilation in healthy euthyroid subjects. J Clin Endocrinol Metab. 2006;91(9):3337–43. https://doi.org/10.1210/jc.2006-0841.

    Article  CAS  PubMed  Google Scholar 

  42. Amouzegar A, Kazemian E, Gharibzadeh S, Mehran L, Tohidi M, Azizi F. Association between thyroid hormones, thyroid antibodies and insulin resistance in euthyroid individuals: a population-based cohort. Diabetes Metab. 2015;41(6):480–8. https://doi.org/10.1016/j.diabet.2015.04.004.

    Article  CAS  PubMed  Google Scholar 

  43. Shin JA, Mo EY, Kim ES, Moon SD, Han JH. Association between lower normal free thyroxine concentrations and obesity phenotype in healthy euthyroid subjects. Int J Endocrinol. 2014;2014: 104318. https://doi.org/10.1155/2014/104318.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Davis PJ, Leonard JL, Davis FB. Mechanisms of nongenomic actions of thyroid hormone. Front Neuroendocrinol. 2008;29(2):211–8. https://doi.org/10.1016/j.yfrne.2007.09.003.

    Article  CAS  PubMed  Google Scholar 

  45. Brent GA. Mechanisms of thyroid hormone action. J Clin Invest. 2012;122(9):3035–43. https://doi.org/10.1172/JCI60047.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ruhla S, Weickert MO, Arafat AM, Osterhoff M, Isken F, Spranger J, Schofl C, Pfeiffer AF, Mohlig M. A high normal TSH is associated with the metabolic syndrome. Clin Endocrinol (Oxf). 2010;72(5):696–701. https://doi.org/10.1111/j.1365-2265.2009.03698.x.

    Article  CAS  PubMed  Google Scholar 

  47. Zhu P, Liu X, Mao X. Thyroid-Stimulating Hormone Levels Are Positively Associated with Insulin Resistance. Med Sci Monit. 2018;24:342–7. https://doi.org/10.12659/msm.905774.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ambrosi B, Masserini B, Iorio L, Delnevo A, Malavazos AE, Morricone L, Sburlati LF, Orsi E. Relationship of thyroid function with body mass index and insulin-resistance in euthyroid obese subjects. J Endocrinol Invest. 2010;33(9):640–3. https://doi.org/10.1007/BF03346663.

    Article  CAS  PubMed  Google Scholar 

  49. Nam KW, Kwon HM, Jeong HY, Park JH, Kwon H, Jeong SM. High triglyceride-glucose index is associated with subclinical cerebral small vessel disease in a healthy population: a cross-sectional study. Cardiovasc Diabetol. 2020;19(1):53. https://doi.org/10.1186/s12933-020-01031-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Muscogiuri G, Sorice GP, Mezza T, Prioletta A, Lassandro AP, Pirronti T, Della CS, Pontecorvi A, Giaccari A. High-normal TSH values in obesity: is it insulin resistance or adipose tissue’s guilt? Obesity (Silver Spring). 2013;21(1):101–6. https://doi.org/10.1002/oby.20240.

    Article  CAS  PubMed  Google Scholar 

  51. Gourmelon R, Donadio-Andrei S, Chikh K, Rabilloud M, Kuczewski E, Gauchez AS, Charrie A, Brard PY, Andreani R, Bourre JC, Waterlot C, Guedel D, Mayer A, Disse E, Thivolet C, Boullay HD, Falandry C, Gilbert T, Francois-Joubert A, Vignoles A, Ronin C, Bonnefoy M. Subclinical hypothyroidism: is it really subclinical with aging? Aging Dis. 2019;10(3):520–9. https://doi.org/10.14336/AD.2018.0817.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Mitrou P, Raptis SA, Dimitriadis G. Insulin action in hyperthyroidism: a focus on muscle and adipose tissue. Endocr Rev. 2010;31(5):663–79. https://doi.org/10.1210/er.2009-0046.

    Article  CAS  PubMed  Google Scholar 

  53. Stanicka S, Vondra K, Pelikanova T, Vlcek P, Hill M, Zamrazil V. Insulin sensitivity and counter-regulatory hormones in hypothyroidism and during thyroid hormone replacement therapy. Clin Chem Lab Med. 2005;43(7):715–20. https://doi.org/10.1515/CCLM.2005.121.

    Article  CAS  PubMed  Google Scholar 

  54. Daher R, Yazbeck T, Jaoude JB, Abboud B. Consequences of dysthyroidism on the digestive tract and viscera. World J Gastroenterol. 2009;15(23):2834–8. https://doi.org/10.3748/wjg.15.2834.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Cavallo-Perin P, Bruno A, Boine L, Cassader M, Lenti G, Pagano G. Insulin resistance in Graves’ disease: a quantitative in-vivo evaluation. Eur J Clin Invest. 1988;18(6):607–13. https://doi.org/10.1111/j.1365-2362.1988.tb01275.x.

    Article  CAS  PubMed  Google Scholar 

  56. Mokuno T, Uchimura K, Hayashi R, Hayakawa N, Makino M, Nagata M, Kakizawa H, Sawai Y, Kotake M, Oda N, Nakai A, Nagasaka A, Itoh M. Glucose transporter 2 concentrations in hyper- and hypothyroid rat livers. J Endocrinol. 1999;160(2):285–9. https://doi.org/10.1677/joe.0.1600285.

    Article  CAS  PubMed  Google Scholar 

  57. Weinstein SP, O’Boyle E, Fisher M, Haber RS. Regulation of GLUT2 glucose transporter expression in liver by thyroid hormone: evidence for hormonal regulation of the hepatic glucose transport system. Endocrinology. 1994;135(2):649–54. https://doi.org/10.1210/endo.135.2.8033812.

    Article  CAS  PubMed  Google Scholar 

  58. Frederiksen L, Brodbaek K, Fenger M, Jorgensen T, Borch-Johnsen K, Madsbad S, Urhammer SA. Comment: studies of the Pro12Ala polymorphism of the PPAR-gamma gene in the Danish MONICA cohort: homozygosity of the Ala allele confers a decreased risk of the insulin resistance syndrome. J Clin Endocrinol Metab. 2002;87(8):3989–92. https://doi.org/10.1210/jcem.87.8.8732.

    Article  CAS  PubMed  Google Scholar 

  59. Raboudi N, Arem R, Jones RH, Chap Z, Pena J, Chou J, Field JB. Fasting and postabsorptive hepatic glucose and insulin metabolism in hyperthyroidism. Am J Physiol. 1989;256(1 Pt 1):E159–66. https://doi.org/10.1152/ajpendo.1989.256.1.E159.

    Article  CAS  PubMed  Google Scholar 

  60. Cettour-Rose P, Theander-Carrillo C, Asensio C, Klein M, Visser TJ, Burger AG, Meier CA, Rohner-Jeanrenaud F. Hypothyroidism in rats decreases peripheral glucose utilisation, a defect partially corrected by central leptin infusion. Diabetologia. 2005;48(4):624–33. https://doi.org/10.1007/s00125-005-1696-4.

    Article  CAS  PubMed  Google Scholar 

  61. Clement K, Viguerie N, Diehn M, Alizadeh A, Barbe P, Thalamas C, Storey JD, Brown PO, Barsh GS, Langin D. In vivo regulation of human skeletal muscle gene expression by thyroid hormone. Genome Res. 2002;12(2):281–91. https://doi.org/10.1101/gr.207702.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Viguerie N, Millet L, Avizou S, Vidal H, Larrouy D, Langin D. Regulation of human adipocyte gene expression by thyroid hormone. J Clin Endocrinol Metab. 2002;87(2):630–4. https://doi.org/10.1210/jcem.87.2.8200.

    Article  CAS  PubMed  Google Scholar 

  63. Brenta G. Why can insulin resistance be a natural consequence of thyroid dysfunction? J Thyroid Res. 2011;2011: 152850. https://doi.org/10.4061/2011/152850.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Liu YY, Schultz JJ, Brent GA. A thyroid hormone receptor alpha gene mutation (P398H) is associated with visceral adiposity and impaired catecholamine-stimulated lipolysis in mice. J Biol Chem. 2003;278(40):38913–20. https://doi.org/10.1074/jbc.M306120200.

    Article  CAS  PubMed  Google Scholar 

  65. Pontikides N, Krassas GE. Basic endocrine products of adipose tissue in states of thyroid dysfunction. Thyroid. 2007;17(5):421–31. https://doi.org/10.1089/thy.2007.0016.

    Article  CAS  PubMed  Google Scholar 

  66. Agbaht K, Erdogan MF, Emral R, Baskal N, Gullu S. Circulating glucagon to ghrelin ratio as a determinant of insulin resistance in hyperthyroidism. Endocrine. 2014;45(1):106–13. https://doi.org/10.1007/s12020-013-9951-9.

    Article  CAS  PubMed  Google Scholar 

  67. Zhou Y, Yang Y, Zhou T, Li B, Wang Z. Adiponectin and thyroid cancer: insight into the association between adiponectin and obesity. Aging Dis. 2021;12(2):597–613. https://doi.org/10.14336/AD.2020.0919.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the participants and staff of NHANES. Thanks to Jing Zhang (Shanghai Tongren Hospital) for his work in the NHANES database. His outstanding work, nhanesR package and webpage, makes it easier for us to explore the NHANES database.

Funding

This work was supported by grants from the Social Development Fund of Jiangsu Province (BE2022819), the Project for Improving Head of Department of Jiangsu Province of Chinese Medicine (Y2021ZR03) and the Natural Science Foundation of Nanjing University of Chinese Medicine (XZR2021012).

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Author notes

  1. Hui Cheng and Yanyan Hu contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, No.155, Hanzhong Road, Qinhuai District, Nanjing, 210029, Jiangsu, People’s Republic of China

    Hui Cheng, Haoran Zhao, Guowei Zhou, Gaoyuan Wang & Chaoqun Ma

  2. Nursing College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China

    Yanyan Hu

  3. Outpatient Department, Nanjing Hospital Affiliated to Nanjing University of Chinese Medicine, The Second Hospital of Nanjing, No.1, Zhongfu Road, Gulou District, Nanjing, 210003, Jiangsu, People’s Republic of China

    Yan Xu

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Contributions

HC, YYH and GWZ contributed to the concept and designed of this study; GYW, YX and CQM organized and supervised this study; HC, YYH, HRZ and GWZ were responsible for the statistical analysis and for writing the report. HRZ and GWZ interpreted the results. YX and CQM are the correspondent authors of this report. All authors contributed to the acquisition, analysis, and interpretation of data. HC, YYH and GWZ drafted the manuscript. HC and YYH contributed equally to this work and should be considered as co-first authors.

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Correspondence to Chaoqun Ma or Yan Xu.

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The original online version of this article was revised: The affiliation details of the co-author, Yanyan-Hu was incorrectly given as '1,2' but should have been '2'.

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Additional file 1.

The association between TyG index and thyroid parameters.

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Cheng, H., Hu, Y., Zhao, H. et al. Exploring the association between triglyceride-glucose index and thyroid function. Eur J Med Res 28, 508 (2023). https://doi.org/10.1186/s40001-023-01501-z

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