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Associations between indicators of lipid and glucose metabolism and hypothyroidism

Lipids in Health and Disease volume 24, Article number: 58 (2025) Cite this article

Abstract

Background

Hypothyroidism, a prevalent thyroid hormone abnormality identified by biochemical indicators, is prone to serious consequences because of its insidious clinical manifestations and easy underdiagnosis. This research aimed to elucidate the relationships between indicators of lipid and glucose metabolism and hypothyroidism and to assess the value of metabolic indicators for hypothyroidism.

Methods

Prevalence surveys were conducted utilizing information from 3254 NHANES individuals who passed screening between 2007 and 2012. Comparisons of baseline characteristics, assessment of logistic regression and subgroup analyses, visualization of restricted cubic splines curves, and validation of causal mediation analyses were performed to obtain a comprehensive view of the relationships between indicators of lipid and glucose metabolism and hypothyroidism.

Results

Lipid and glucose metabolism indicators, especially the unconventional parameters triglyceride‒glucose index (TyG) and remnant cholesterol (RC) and the conventional parameter triglyceride (TG), exhibited robust positive relationships with hypothyroidism and served as crucial mediators in the pathways by which hypothyroidism affects health outcomes. Indicators were varying suggestive for hypothyroidism in distinct populations, with TyG being relatively more valuable.

Conclusions

Indicators of lipid and glucose metabolism (TyG, TG, and RC) were intimately associated with hypothyroidism, with potential applications in the assessment and management of hypothyroidism.

Introduction

Hypothyroidism is a common endocrine disorder worldwide, with wide variations among populations of different ages, sexes, races, genetic factors, etc [1]. It is a common thyroid hormone (TH) anomaly with a broad spectrum of clinical manifestations and no distinct symptoms, which may trigger major health repercussions if not recognized and treated promptly [2]. The medical definition of hypothyroidism depends on statistical reference ranges for pertinent biochemical variables, affecting up to 5% of the population at large, with an additional 5% going undiagnosed, with easily overlooked symptoms such as modest weight gain, fatigue, constipation, and memory loss [3]. Untreated or improperly treated hypothyroidism contributes to systemic symptoms, particularly cardiovascular disease [4]. Hypothyroidism impacts cardiac contractility, resulting in decreased cardiac output and heart rate, which may culminate in heart failure [5]. Additionally, it can induce arrhythmias, including sinus bradycardia, atrioventricular block, and atrial fibrillation [6]. Hypothyroidism is closely associated with diabetes mellitus, renal dysfunction, and cancer progression along with mortality, indicating the complexity of the molecular mechanisms regulated by THs [7,8,9].

THs influence lipid generation, transportation, and metabolism through specialized effects on the liver and adipose tissue, and hypothyroidism is correlated with dyslipidemia and increased total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels. Through multiple mechanisms, including indirect effects on the metabolism of LDL-C synthesis-limiting enzymes and adipokines, direct effects on circulating levels of apolipoproteins, and probably even through the regulation of hepatic bile acid synthesis and secretion, THs influence the synthesis and metabolism of LDL-C [10,11,12,13]. Hypothyroidism can alter triglyceride (TG) metabolism by modulating circulating apolipoprotein E levels and the function of high-density lipoprotein particles, particularly by modulating reverse cholesterol transport [14]. As a result, serum or intracellular lipid metabolism is disrupted and various kinds of pathological mechanisms lead to the development of hyperlipidaemia in hypothyroidism patients [10]. Owing to its profound effects on lipid metabolism, hypothyroidism might also be involved in insulin resistance (IR), metabolic syndrome, and increased risk of cardiovascular disease, and diverse factors complicate the analysis of the implications [15]. Hypothyroidism impairs glucose metabolism, resulting in lower glucose uptake, gluconeogenesis, and glycogenolysis, all of which are related to diabetes [16, 17]. By modulating target organs, including the liver, adipose tissue, and pancreatic β-cells via a range of central and peripheral routes, THs have profound effects on the regulation of glucose homeostasis and lipid metabolism [18]. For example, thyroid stimulating hormone (TSH) stimulates leptin secretion and decreases insulin synthesis and secretion, both of which are elevated in hypothyroid individuals, and triiodothyronine affects insulin signalling, all of which can ultimately induce metabolic disorders [19]. To minimize serious adverse effects on multiple organ systems, screening, diagnosis, and treatment must be performed in a timely and accurate manner.

Dyslipidemia is an alteration in the composition of blood lipids, and plasma concentrations of TG, TC, LDL-C, and high-density lipoprotein cholesterol (HDL-C) are typically measured, enabling physicians to quantify lipid-related health risks [20]. Unconventional lipid parameters derived from the preceding parameters, including non-high-density lipoprotein cholesterol (non-HDL-C), remnant cholesterol (RC), the non-HDL-C to HDL-C ratio (NHHR), and other ratios, have decent application value and are typically superior to traditional parameters [21]. The metabolic score for insulin resistance (METS-IR) is an innovative indicator for measuring insulin sensitivity and cardiometabolic risk, which is highly related to visceral obesity and diabetes risk [22,23,24]. Similarly, the triglyceride‒glucose index (TyG), a biomarker that identifies IR, and the atherogenic index of plasma (AIP), a favourable predictor of cardiovascular hazard, are inextricably associated with many metabolic diseases [25,26,27]. Detecting metabolic abnormalities with straightforward clinical indicators can contribute to prevention, early management, and risk reduction for metabolic-related diseases. Alterations in the lipid profile are a precursor to the development of cardiovascular disease, with hypothyroidism acting as a risk factor [28]. TSH levels are correlated with TC and LDL levels, indicating the integral involvement of the pituitary-thyroid-heart axis in lipid metabolism [29]. Significant variations in METS-IR across populations underscore its validity in recognizing metabolic derangements in hypothyroidism [30]. TyG, a convenient surrogate for IR, is strongly associated with thyroid dysfunction and hormone sensitivity [31].

The relationships between thyroid function and lipid and glucose metabolism are self-evident; however, there is a paucity of knowledge concerning the associations between metabolic indicators and hypothyroidism. The determination of thyroid function relies on laboratory indicators such as free triiodothyronine (FT3) and free thyroxine (FT4), which are often excluded from routine physical examinations, making patients with hypothyroidism prone to being overlooked [32]. More research is undoubtedly needed to assist in and support the assessment and management of hypothyroidism, additionally decreasing the clinical burden [33]. This study aimed to examine the relationships between hypothyroidism and lipid and glucose indicators, which were brief and representative, compare them horizontally, and identify those with greater application value. It was intended to demonstrate that metabolic indicators can play a suggestive and supportive role and be used as signals for hypothyroidism, providing invaluable scientific evidence for public health strategies and disease management.

Methods

Study design and population screening

Committed to obtaining information on public wellness and nutritional status, the National Health and Nutrition Examination Survey (NHANES) is a gigantic, continuous prevalence survey carried out biennially. The findings are rendered publicly accessible on the official website and are intended to be utilized in the assessment, development, and safeguarding of policies aimed at preventing disease, extending life, and promoting psychological and physical wellness. Data from three cycles (2007–2012), totalling 30,442 respondents, were utilized in this study, 13,605 of which were removed owing to missing baseline values. Given the dramatic effect of pregnancy on metabolic status and hormone levels, this group of participants was excluded, as were those who lacked routine thyroid function and lipid and glucose parameters. Familial hypercholesterolemia (FH) may have an impact on outcomes; however, the NHANES database does not include genetic testing, family history, personal history of peripheral artery disease, or significant physical findings necessary for the classification of FH. We utilized a modified version that awards ratings depending on LDL-C levels [34, 35]. People with extremely high LDL-C levels (> 330 mg/dL) who might be classified as having definite FH on the basis of available data were removed. Receiving lipid-lowering medication might have a considerable influence on metabolic indicators; thus, this group was eliminated, with 3,254 people ultimately enrolled, and the screening flowchart was shown in Fig. 1.

Fig. 1
figure 1

Flowchart of participants screening technique from NHANES 2007–2012

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Diagnostic criteria for hypothyroidism

The Clinical Practice Guidelines for Hypothyroidism in Adults, sponsored by the American Association of Clinical Endocrinologists and the American Thyroid Association, set the normal upper limit for TSH at 4.5 mIU/L [36, 37]. Hypothyroidism encompasses both subclinical and overt hypothyroidism and was accordingly defined as a laboratory finding of an elevated TSH above the normal range, an FT4 in the normal range (9–25 pmol/L) or lower, and receiving some kind of thyroid hormone replacement therapy (levothyroxine, liothyronine, or other) [38, 39]. The NHANES laboratory data were subjected to a comprehensive quality assurance examination, and the laboratory analyses were monitored, with duplicate tests performed on 5% of all the samples. As a result, it is reasonable to conclude that all participants were assessed similarly.

Crowd-related data collection

Meticulous accounting was carried out on the population’s baseline characteristics, including its structure and current status. A history of physician-diagnosed hypertension, a mean systolic blood pressure of at least 140 mmHg or a mean diastolic blood pressure of at least 90 mmHg on three measurements, or intake of antihypertensive medication were considered indicators of hypertension [40]. A prior episode of diagnosis, a fasting plasma glucose (FPG) level over 7.0 mmol/L, a glycosylated haemoglobin level over 6.5%, or the use of hypoglycemic drugs were the criteria for the diagnosis of diabetes mellitus [41]. On the basis of population self-reports, the presence of cardiovascular disease—which includes congestive heart failure, coronary heart disease, angina pectoris, myocardial infarction, and stroke—was identified [42]. Twelve drinks per year was the cut-off for either alcohol intake or nonconsumption, while smoking status was defined as smoking more than 100 cigarettes. Laboratory data were utilized to measure pertinent indicators, such as the glucose indicator FPG, and a variety of lipid indicators, such as TG and TC, in blood samples. The formulas for calculating non-HDL-C, RC, NHHR, METS-IR, TyG, and AIP were identical to those in previous article descriptions [21, 24, 26, 43, 44].

Statistical analysis

In response to the characteristics of the NHANES multistage probability sampling design, the analysis used complex sampling weights to comprehensively describe the entire population. The Wilcoxon rank-sum test for complex survey samples was employed for nonnormally distributed continuous variables, described by the median and interquartile range (IQR); the t-test adapted for complex survey samples was employed for normally distributed variables, described by the mean ± standard deviation (SD); and the Rao‒Scott chi-square test was employed for weighted percentages of categorical variables. Lipid and glucose metabolism indicators, which varied substantially in the hypothyroidism group, were selected to demonstrate their application potential for hypothyroidism via logistic regression models. To verify stability and investigate the relationships, models adjusted for various covariables were constructed, and quartiles for noteworthy indicators were computed. Restricted cubic splines (RCSs) were set up with four knots at the 5th, 35th, 65th, and 95th centiles for model fitting to visualize the relationships between indicators and hypothyroidism, followed by subgroup analyses to investigate specific populations. Furthermore, a causal mediation study was conducted between hypothyroidism and cardiovascular disease, hypertension, and diabetes mellitus, with the bootstrap technique used to calculate confidence intervals for the mediating impact. Statistical significance was determined by considering P < 0.05 in all analyses, which were performed with R studio (version 4.2.2), EmpowerStats (http://www.empowerstats.com), and MSTATA software (www.mstata.com).

Results

Baseline characteristics of the participant group

Table 1 displayed the demographic and medical details of 3254 subjects (280 of whom had hypothyroidism and 2974 of whom did not) representing a population of approximately 280 million Americans. Baseline parameters, including sex, age, race, hypertension, diabetes mellitus, and cardiovascular disease had profound relationships with hypothyroidism (P < 0.05). Individuals with hypothyroidism had a greater frequency of comorbidities than those without hypothyroidism. Between-group comparisons of the presence of hypothyroidism revealed substantial variations in thyroid function and several lipid and glucose metabolism indicators (P < 0.05), especially nonroutine parameters, implying that metabolic disorders do have some exploratory value. These findings highlight the importance of considering demographic and clinical factors in the assessment, and specific indicators were selected for further investigations.

Table 1 Demographic and medical details of the study participants
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1 Chi-squared test with Rao & Scott’s second-order correction.

2 T-test adapted to complex survey samples.

3 Wilcoxon rank-sum test for complex survey samples.

Associations between hypothyroidism and indicators of lipid and glucose metabolism

Table 2 displayed the logistic regression findings for hypothyroidism with the indicators (TyG, TG, RC, TC, HDL, and non-HDL-C) identified as significantly different between the groups in Table 1. The results of the logistic regression of TC, HDL-C, and non-HDL-C suggested that the associations between these indicators and hypothyroidism were not remarkable and did not require further analysis. However, for TyG, TG, and RC, unadjusted models revealed significant positive relationships between hypothyroidism and each indicator (P < 0.001), with models 2 and 3 retaining solid positive associations after controlling for key demographic characteristics, reflecting the specificity of the indicators in capturing the metabolic characteristics of hypothyroidism. To explicitly demonstrate the variability of the associations with hypothyroidism across intervals, the indicators of interest were categorized by quartiles. The prevalence of hypothyroidism increases incrementally with elevated levels of lipid and glucose metabolism indicators, with a certain tendency to increase. For each indicator, the likelihood of prevalence was markedly greater in the highest quartile than in the lowest quartile, with odd ratios (ORs) and 95% confidence intervals (CIs) after sufficient adjustment of TyG: 1.95 (1.24, 3.07); TG: 1.62 (1.07, 2.45); RC: 1.66 (1.09, 2.52). Unlike TyG, whose effects were significantly different across all intervals, TG and RC showed variability only at higher levels. These findings suggested that indicators, particularly TyG, have potential clinical applications, in the assessment and management of hypothyroidism.

Table 2 Associations between indicators of lipid and glucose metabolism and hypothyroidism
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The flexible modelling features of RCS were utilized to graphically identify the nonlinear associations between hypothyroidism and lipid and glucose metabolism indicators. As demonstrated in Fig. 2A, B, and C, the nonlinear relationships between the prevalence of hypothyroidism and TyG, TG, and RC were consistently statistically significant (P < 0.05), but the strength of the nonlinearity was relatively weak, and the nonlinear component was not dominant (P-Nonlinear > 0.05). With the inflection point established as the median of the indicator in the overall population, as the indicator increased, the OR of hypothyroidism first underwent a phase of steadily increasing change, flattened (for TyG) or declined slightly near the inflection point (for TG and RC), and then continued to rise, a result consistent with logistic regression on the indicator’s quartiles.

Fig. 2
figure 2

Restricted cubic splines visualized the associations between indicators of lipid and glucose metabolism and hypothyroidism. TyG (A), TG (B), and RC (C). The Y-axis represents the OR of hypothyroidism for any value of indicator compared to individuals with the reference value (50th percentile) of indicator. All adjusted for gender, age, race, educational level, marital status, PIR, BMI, hypertension, diabetes mellitus, cardiovascular disease, alcohol consumption, and smoking status

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Subgroup analyses revealing potential variations among populations

To investigate the disparities between TyG, TG, RC, and hypothyroidism among distinct populations, subgroup analyses were conducted, and the various degrees of association were vividly depicted in the form of forest plots, as shown in Fig. 3A, B, and C. Since the effect value was not significant, it was scaled up by a factor of 10 using TG/10 and RC/10. Sex, age, tobacco and alcohol usage, and common diseases had no significant effect on TyG, TG, or RC (P for interaction > 0.05), implying that these factors had independent effects on hypothyroidism. In between-group comparisons of age, increases in indicators were consistently associated with the prevalence of hypothyroidism, albeit to varying degrees. Elevations in TyG, TG, and RC were more suggestive of hypothyroidism in those without common diseases, nonalcoholics, and nonsmokers.

Fig. 3
figure 3

Subgroup analyses for the relationships of TyG (A), TG/10 (B), and RC/10 (C) with hypothyroidism

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Mediation analyses associated with hypothyroidism

The constructed form and pathway of the causal mediation study were animated in Fig. 4. Through utilizing mediation analysis, hypothyroidism was designated as the independent variable to investigate how hypothyroidism shapes health outcomes (which incorporates cardiovascular disease, hypertension, and diabetes mellitus) and whether metabolic indicators (TyG, TG, and RC) serve a mediating role. Hypothyroidism had substantial overall effects on health outcomes (P < 0.001), as did indirect effects via indicators (P < 0.001). Table 3 detailed the relevant effect coefficients and mediation ratios. Compared with 17.2%, 19.6%, and 57.1% for TyG, and 10.0%, 12.0%, and 26.0% for TG, the proportions of RC mediating the associations between hypothyroidism and cardiovascular disease, hypertension, and diabetes mellitus were 9.9%, 11.9%, and 25.7%, respectively. The apparent mediating impact implied that metabolic indicators might explain the potential association between hypothyroidism and health outcomes, inversely demonstrating that changes in metabolic markers can be utilized for hypothyroidism, with the TyG score outperforming in comparison.

Fig. 4
figure 4

Mediation construction associated with hypothyroidism

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Table 3 Mediation analyses of TyG, TG, and RC for independent variable hypothyroidism
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Discussion

The findings of this prevalence survey of 3,254 participants revealed substantial positive associations between hypothyroidism and indicators of lipid and glucose metabolism, with the prevalence of hypothyroidism increasing dramatically as the parameters rose. Using logistic regression to select meaningful indicators (TyG, TG, and RC), consistent and stable associations were observed in all partially and fully adjusted models, particularly in the top quartile group. This situation reflected differences in the association with the prevalence of hypothyroidism across the various intervals of the metabolic indicator distribution, suggesting possible nonlinear relationships and emphasizing the importance of paying special attention to groups with higher indicators in practical terms. The RCS straightforwardly and persuasively displayed a nonlinear association, which was not dominant, which was consistent with the findings of the logistic regression. Subgroup analyses were then conducted to investigate the population variations. Causal mediation studies revealed that metabolic indicators play a paramount mediating role in the routes by which hypothyroidism impacts health outcomes. These findings demonstrate the relationships and suggestive nature of lipid and glucose metabolism indicators for hypothyroidism, highlighting the importance of focusing on metabolic disorders and the role of referencing and utilizing simple parameters in the assessment and management of hypothyroidism, as well as providing a foundation for diagnosis, monitoring, and control in future clinical practice.

Individualizing and precisely diagnosing hypothyroidism is challenging due to the broad definition of reference ranges and the insidious onset of the disease in most cases, and numerous studies have explored the disease modifications associated with hypothyroidism [45]. Since TH receptors are present in myocardial and vascular endothelial tissues, hypothyroidism influences cardiovascular function by altering ion channel activity, signalling pathways, and the autonomic and renin‒angiotensin‒aldosterone systems [46]. TH regulates the metabolic rate in the body, alters adrenergic system activity, impacts peripheral vascular resistance, and is closely associated with aging; thus, hypothyroidism frequently results in metabolic disorders, the development of coronary heart disease, and osteoporosis [47]. TH imbalance impacts the aminergic system, resulting in mood disorders such as depression and anxiety, with hypothyroidism being one of the primary causes of refractory depression [48]. Hypothyroidism can alter Alzheimer’s disease neuropathology and development via a variety of mechanistic pathways, including beta-amyloid formation, tau protein phosphorylation, oxidative stress, mitochondrial dysfunction, and autophagy dysfunction [49].

Studies have shown that TH acts on various target peripheral tissues through multiple mechanisms and is inextricably related to metabolism. TH has a straightforward effect on adenosine triphosphate utilization and mitochondrial function, regulates the transcription of specific genes and posttranslational modifications of proteins, influences the concentration of metabolites such as HDL-C and LDL-C, and affects nutrient metabolic pathways that stimulate the synthesis and degradation of energy reserves such as lipids [50]. The hypothalamic‒pituitary‒thyroid axis additionally affects energy homeostasis, with leptin being a key regulator, that influences lipid metabolism [51, 52]. Jonklaas’s extensive description and assessment of hypothyroidism and changes in lipid parameters demonstrated that dyslipidemia occurs in people with both subclinical and overt hypothyroidism and that TH treatment reverses these abnormalities and minimizes the risk of metabolic and cardiovascular repercussions [15]. Pancreatic beta cell formation and function, gastrointestinal motility, liver and skeletal muscle gene expression, and sympathetic pathways are all impacted by TH, influencing glucose metabolism [53]. Furthermore, nonclassical effects of TH on the insulin signalling pathway, such as stimulation of the critical mediators phosphatidylinositol 3-kinase and protein kinase B, may induce various physiological responses [54]. According to Vemula et al., hypothyroidism leads to elevated thyroid stimulating hormones, which increase leptin while causing increased expression of glucose-6-phosphate and phosphoenolpyruvate carboxykinase stimulating hepatic glucose production, and causing IR, in addition to decreasing insulin secretion and synthesis to increase blood glucose levels, all of which ultimately predispose individuals to diabetes mellitus [55]. Given the robust associations between the occurrence of lipid and glucose metabolism disorders and hypothyroidism, which is diagnosed by specific laboratory indicators, it is imperative to explore the relationships between brief, meaningful, and representative metabolic indicators and hypothyroidism. Assessment of hypothyroidism via novel clinically relevant markers is beneficial [56]. To better serve clinical work and minimize hypothyroidism neglect and underdiagnosis, identifying the optimal metabolic indicators is highly practical.

Moreover, considerable investigations have looked into the associations between various diseases and lipid and glucose metabolism-related indicators. Sheng et al. demonstrated that RC/HDL-C was the optimal marker for predicting diabetes risk and that different nonconventional lipid parameters had distinct long- and short-term predictive values [21]. Hou et al. evaluated the relationships between various IR substitutes and all-cause mortality in subjects with coronary heart disease combined with hypertension to determine reliable predictors thereof [41]. Zou et al. systematically assessed the diagnostic performance of a series of straightforward, cost-effective, and noninvasive scoring systems for metabolic dysfunction-associated fatty liver disease and reported that regional and ethnographic differences affect the predictive ability of these systems [57]. Xiao et al. explored the associations of the systemic immune inflammation index with estimated pulse wave velocity, AIP, TyG, TG, HDL-C, and FPG and revealed a curvilinear relationship with cardiovascular disease prevalence [58]. The article by Yin et al. probed the nonlinear association of AIP with IR and type 2 diabetes, implying that AIP ought to be maintained at a low degree to circumvent the concomitant risks [44]. These findings are instructive in aggregating multiple metabolism-related parameters for comparison and in better assessing their utility in disease management. For hypothyroidism, the integration of novel and unconventional lipid and glucose metabolism indicators would be more comprehensive, systematic, and innovative.

On the basis of the above, a detailed list of routinely tested indicators was first organized in our study, and topical lipid metabolism and glucose metabolism parameters were calculated via formulas, and all were compared cross-sectionally. In the between-group comparisons of hypothyroidism, the initial screening was performed to identify indicators with substantial differences that warranted further investigation, at which point it was found that some of the indicators had failed. After comparison, relatively optimal indicators (TyG, TG, and RC) were derived, and hypothyroidism was proven to be associated with metabolic indicators, as supported by relevant papers. Insulin sensitivity is impacted by TH, which controls the gene expression implicated in insulin synergism and the activation of IR-related proteins, as manifested by the TyG index [31]. In the hypothyroid state, polyunsaturated fatty acids fail to induce thyroid hormone receptors but stimulate peroxisome proliferator-activated receptor expression, leading to persistent hypertriglyceridaemia and hence the significance of TG [19]. RC, a highly atherogenic substance, is tied to thyroid system equilibrium, which in turn leads to decreased central and peripheral sensitivity to TH at high levels [59]. Logistic regression and RCS revealed a nonlinear relationship that was not dominant, suggesting the predictability and stability of the results. Subgroup analyses indicated that the metabolic indicators had meaningful application value for hypothyroidism at younger ages, in the absence of hypertension, diabetes mellitus, cardiovascular disease, and tobacco and alcohol addiction, which can be applied to clinical scenarios. The mediating role of metabolic indicators explains potential pathways by which hypothyroidism affects health outcomes, which is consistent with the above discussion. Therefore, from a clinical perspective, the prevalence of hypothyroidism substantially increases as metabolic indicators rise, suggesting that physicians can speculate on the likelihood of hypothyroidism by higher metabolic parameters to assist in professional judgement. Pathophysiological mechanisms underpin and explain the preponderance of dominant indicators. The regulatory role of THs in TG is firmly established, whereas their effect on HDL-C is complex. Hypothyroidism slows HDL particle synthesis and reduces circulating concentrations of HDL-C via homocysteine, but it may be elevated due to the inhibitory effects of cholesteryl ester transfer proteins, which have opposing effects [60, 61]. The instability of HDL-C-related indicators was evident, as was the predominance of TG-related parameters, which was highly congruent with the results. Hypothyroidism, as a disease related to both lipid and glucose metabolism, is clearly connected to IR, and TyG and RC aptly capture the key features of metabolism that accentuate distinctions, with TyG synthesizing critical points in particular [62, 63].

The huge volume, comprehensiveness, rigor, and standardization of the data provided by the NHANES database allowed for a significant reduction in the potential deviation present in this study, and adequate covariable adjustment improved the reliability of the statistical results. The innovative cross-sectional comparison was a greater dimension than previous analyses of the relationship between a particular metabolic indicator and hypothyroidism, taking advantage of the data’s breadth. Unlike homogeneous studies, characteristic and unconventional indicators of lipid and glucose metabolism were included to study their associations with hypothyroidism. After validating stable associations, subgroup analyses delved into exploring the causes of slight discrepancies in primary indicator findings among populations, and mediation studies demonstrated the potential application of the indicators. Given that THs alter metabolic function, for the first time, the vision of hypothyroidism prevalence falls on easy-to-access biochemical indicators, and a novel tool is suggested to aid in the screening of hypothyroidism. However, the limitations of this study must be mentioned. As a time-specific investigation, clear causal associations cannot be ascertained, and more prospective studies and mechanistic explorations are warranted to fill this gap. The application of reference ranges for thyroid function is highly controversial, and the definition of hypothyroidism is challenging in practice, which can bias the screening of hypothyroid populations. The limitations of the NHANES data give rise to a series of issues, such as the fact that laboratory data were collected only once, leading to erratic blood-related results, and recall bias was inevitable when incorporating disease data from interviews and self-reports during screening and analysis. Although the selection of covariables was adequately considered, possible confounders such as genetic and environmental factors remained, while the robust relationships between hypothyroidism and indicators of lipid and glucose metabolism were not easily compromised. The choice of metabolic indicators was extensive, and only representative and prevalent indicators were cherry-picked; there may be superior parameters waiting to be mined and evaluated. Attention, reference, and utilization of parameters related to metabolic disorders play considerable roles in hypothyroidism. The associations between metabolism and illness are manifold, and diverse potential pathways, mechanisms of influence, and clinical significance have led to a broad and deep exploration. The applicability of metabolic indicators to diseases other than hypothyroidism is temporarily vacant. It is crucial to capture the metabolic characterization to better serve the clinic and provide a valuable basis for health management.

Conclusions

This study demonstrated strong associations between indicators of lipid and glucose metabolism and hypothyroidism, confirming stable positive relationships and yielding parameters with considerable application value through cross-sectional comparisons, which could contribute to future clinical endeavors. The potential value of elevated metabolic indicators for hypothyroidism is noteworthy, and further prospective studies are warranted.

Data availability

Access to the data used in this study is publicly available from the NHANES database.

Abbreviations

TH:

Thyroid Hormone

IR:

Insulin Resistance

TG:

Triglyceride

TC:

Total Cholesterol

LDL-C:

Low-Density Lipoprotein Cholesterol

HDL-C:

High-Density Lipoprotein Cholesterol

Non-HDL-C:

Non-High-Density Lipoprotein Cholesterol

RC:

Remnant Cholesterol

NHHR:

Non-HDL-C to HDL-C Ratio

METS-IR:

Metabolic Score for Insulin Resistance

TyG:

Triglyceride‒Glucose Index

AIP:

Atherogenic Index of Plasma

TSH:

Thyroid Stimulating Hormone

FT3:

Free Triiodothyronine

FT4:

Free Thyroxine

NHANES:

National Health and Nutrition Examination Survey

FH:

Familial Hypercholesterolemia

FPG:

Fasting Plasma Glucose

IQR:

Interquartile Range

SD:

Standard Deviation

ROC:

Receiver Operating Characteristic

AUC:

Area Under the Curve

RCS:

Restricted Cubic Spline

CI:

Confidence Interval

OR:

Odd Ratio

PIR:

Ratio of Household Income to Poverty

BMI:

Body Mass Index

TGAb:

Thyroglobulin Antibodies

TPOAb:

Thyroid Peroxidase Antibodies

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Authors and Affiliations

  1. Department of General Surgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China

    Xinyi Huang, Hengzheng Cheng, Shuting Wang, Laifu Deng & Jiaxiu Li

  2. Department of Thyroid and Breast Surgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China

    An Qin, Chunqiang Chu & Xiao Liu

  3. Wuxi Medical Center, Nanjing Medical University, Wuxi, China

    Xinyi Huang, Hengzheng Cheng, Shuting Wang, Laifu Deng, Jiaxiu Li, An Qin, Chunqiang Chu & Xiao Liu

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Contributions

XYH, HZC: study design, drafting, and plotting. STW, LFD, and JXL: gathering, organizing, and analysing. AQ, CQC: interpreting. XL: reviewing. XYH: writing. All the authors reviewed and approved the final paper.

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Correspondence to Xiao Liu.

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Huang, X., Cheng, H., Wang, S. et al. Associations between indicators of lipid and glucose metabolism and hypothyroidism. Lipids Health Dis 24, 58 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02457-1

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X