Association of Thyroid Dysfunction With Dyslipidemia and Metabolic Risk in the Health Examination Population: A Retrospective Analysis

Summary

This study explores the links between thyroid dysfunction, dyslipidemia, and other metabolic risks among people undergoing health examinations.

Abstract

This study aims to investigate the associative relationships between thyroid dysfunction, dyslipidemia, and other metabolic risks among people undergoing health examinations. 305 individuals undergoing routine health examinations were enrolled (June 2023–December 2025). After exclusions, 300 participants were included in the final analysis. Core observation indicators included the thyroid function indices thyroid-stimulating hormone (TSH), free triiodothyronine (FT3) and free thyroxine (FT4); lipid profile parameters covering total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C); and metabolic markers such as fasting blood glucose (FBG), waist circumference (WC) and blood pressure (BP). Participants were grouped into euthyroid, hyperthyroid, and hypothyroid cohorts by standard thyroid diagnostic criteria. Pearson correlation and multivariate logistic regression analyses were performed to examine the associations between indicators and risk factors for thyroid dysfunction. Of 300 participants, 224 (74.67%) were euthyroid, 32 (10.67%) hyperthyroid, and 44 (14.66%) hypothyroid. One-way ANOVA demonstrated that participants with hyperthyroidism had lower TSH, higher FT3/FT4, reduced TC/TG/LDL-C, elevated HDL-C, higher FBG, lower WC/diastolic blood pressure (DBP), and mild systolic blood pressure (SBP) rise (all p < 0.05) than euthyroidism; hypothyroidism showed the opposite (all p. < 0.05). Pearson correlation analysis showed TSH positively correlated with TC, TG, LDL-C, FBG, WC, SBP, and DBP, and negatively correlated with HDL-C. FT3 and FT4 were negatively correlated with TC, TG, LDL-C, and positively correlated with HDL-C. Both markers had negative correlations with WC and DBP, with FT4 showing a moderate correlation with WC. Multivariate logistic regression confirmed independent associations of dyslipidemia and metabolic abnormalities with thyroid dysfunction in health examination populations. Thyroid dysfunction is closely associated with lipid and metabolic disturbances, and these indicators provide a valuable reference for population screening for chronic diseases. This study was limited by its single-center cross-sectional design, small sample size, and insufficient stratification of thyroid dysfunction subtypes.

Introduction

As population aging accelerates across China and residents’ lifestyles undergo profound shifts, chronic noncommunicable diseases have emerged as the primary public health threat to the health examination population. Among these conditions, the incidence of thyroid dysfunction and dyslipidemia has been on a steady rise, with their comorbidity becoming an increasingly prominent issue that places a heavy burden on chronic disease prevention and control at the health examination population level¹. The thyroid, a vital endocrine gland in the human body, secretes thyroid hormones that regulate the entire process of material and energy metabolism, exerting precise regulatory effects on lipid synthesis, breakdown, and transport. Thyroid-stimulating hormone (TSH), as the core regulatory index of thyroid function, sees its level fluctuations directly affect the secretory balance of thyroid hormones, which in turn may trigger systemic metabolic disorders². Dyslipidemia, characterized primarily by elevated total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and reduced high-density lipoprotein cholesterol (HDL-C), stands as a core risk factor for cardiovascular diseases such as atherosclerosis and coronary heart disease, and also serves as a key external manifestation of systemic metabolic imbalance³. In recent years, studies have confirmed^4,5 that thyroid dysfunction and dyslipidemia do not exist in isolation; instead, they may interact through common metabolic pathways, forming a complex network of metabolic disorders. Yet the specific mechanisms and quantitative relationships underlying their association in the health examination population remain unclear.

Thyroid dysfunction remains highly prevalent worldwide, with the notable features of a high incidence in the health examination population and insidious onset. According to recent epidemiological survey data⁶, the overall prevalence of thyroid dysfunction in the adult health examination population in China has exceeded 20%. Of this figure, hypothyroidism accounts for approximately 14% and hyperthyroidism for around 10%, with the prevalence rising with age, climbing to over 35% in the elderly population aged sixty and above⁷. In patients with hypothyroidism, insufficient thyroid hormone secretion slows down the rate of lipid metabolism, leading to increased cholesterol synthesis and reduced decomposition, which easily induces hypercholesterolemia. In contrast, excessive thyroid hormone in hyperthyroidism patients accelerates lipid breakdown and excretion, potentially resulting in decreased blood lipid levels^8,9. At the same time, the prevalence of dyslipidemia in the Chinese health examination population has surpassed 40%. This condition is closely linked to unhealthy dietary patterns, physical inactivity, obesity, and other contributing factors^10,11. Notably, the comorbidity rate of thyroid dysfunction and dyslipidemia has risen year by year. Individuals with this comorbidity face a significantly increased risk of concurrent metabolic abnormalities such as elevated blood glucose and abdominal obesity, which further raises their susceptibility to cardiovascular diseases.

In recent years, scholars at home and abroad have conducted numerous studies on the association between thyroid function and lipid metabolism, yet their conclusions remain controversial, and targeted research in general health screening populations remains scarce. Some studies have indicated¹² that TSH levels are positively correlated with TC and LDL-C levels and negatively correlated with HDL-C levels, and that hypothyroidism acts as an independent risk factor for dyslipidemia, while the association between hyperthyroidism and dyslipidemia is relatively weak. However, other research has pointed out that TG levels are significantly elevated in hyperthyroidism patients and positively correlated with thyroid hormone levels, a phenomenon that may be related to individual metabolic differences, dietary structures, and other factors^13,14. In terms of metabolic risk, thyroid hormones can regulate blood glucose metabolism by modulating insulin sensitivity. Patients with hypothyroidism have a higher incidence of insulin resistance, which easily leads to elevated fasting blood glucose (FBG), whereas hyperthyroidism patients may experience blood glucose fluctuations due to an accelerated rate of energy metabolism^15,16. Most existing studies, however, have focused on hospital-based patients, with limited representativeness of the sample. They also fail to fully incorporate characteristics such as age and living habits of the health examination population, making it difficult to reflect the real situation of general health screening populations¹⁷.

At present, chronic disease management in Chinese communities is primarily centered on single-disease care, and the comprehensive management strategies for populations with comorbid thyroid dysfunction and dyslipidemia are still inadequate, lacking precise data support and individualized intervention plans. As population aging worsens in China, factors such as altered body composition in the elderly (e.g., increased visceral fat) and exposure to environmental pollutants have further exacerbated the risk of disrupted lipid metabolic homeostasis and thyroid dysfunction. Regrettably, the existing health management system has not yet taken these influencing factors into account¹⁸. Health screening, as an important tool for early detection of chronic diseases, enables simultaneous measurement of thyroid function, blood lipids, and metabolic indicators, providing a solid sample base for research on the association between these conditions. By analyzing the degree of correlation between thyroid dysfunction and blood lipid, metabolic indicators in health screening populations, and identifying the correlations among various indicators as well as independent risk factors, this research can offer a scientific basis for the early screening, risk assessment, and comprehensive intervention of thyroid dysfunction and dyslipidemia in health examination populations.

Based on physical examination data from the Navy Qingdao Special Service Recuperation Center's health examination population, this study selected 300 health examination participants as research subjects, focusing on the association among thyroid dysfunction, dyslipidemia, and metabolic abnormalities. It aims to fill the research gap on these comorbidities at the population level of health examinations and to enrich research on the correlation between thyroid dysfunction and metabolic disorders, thereby providing theoretical and practical evidence to optimize comprehensive chronic disease management strategies and improve the health status of residents undergoing health examinations. Meanwhile, the research findings targeting health screening populations can help clinicians more accurately identify high-risk groups for thyroid dysfunction, formulate individualized screening and intervention plans, and reduce the incidence of metabolism-related complications.

Protocol

This study was conducted in strict accordance with the principles of the Declaration of Helsinki and was approved by Qingdao Special Servicemen Recuperation Center of PLA Navy Ethics Committee (Approval Number: QDTLLL2023-024). Written informed consent was obtained from all participants before enrollment.

Participant recruitment and study design
A total of 305 residents who underwent routine health examinations at the Navy Qingdao Special Service Recuperation Center between June 2023 and December 2025 were initially enrolled. After screening and application of exclusion criteria, 300 participants were included in the final analysis. Participants were categorized into euthyroid, hyperthyroid, and hypothyroid groups according to standard diagnostic criteria for thyroid dysfunction (Figure 1).

Eligible participants were permanent residents aged 18–80 years who had resided at the recuperation center for at least six months. All participants completed comprehensive physical examinations¹⁹, and complete clinical and laboratory data were obtained. Individuals with acute infection, severe trauma, postoperative recovery status, or other acute conditions potentially affecting thyroid or metabolic indicators at the time of examination were excluded.

Participants were excluded if they had confirmed organic thyroid diseases, including thyroid nodules (≥1 cm with malignant imaging features), thyroiditis, or thyroid tumors²⁰. Subjects who had received thyroid hormone replacement therapy, antithyroid medications, or other drugs affecting thyroid function within the previous three months were also excluded. Additional exclusion criteria included diabetes mellitus, Cushing’s syndrome, Addison’s disease, severe obesity (body mass index [BMI], calculated as weight (kg)/height (m)², BMI ≥35 kg/m²), cachexia, bariatric or metabolic surgery within six months, severe hepatic, renal, or biliary diseases, and recent use of lipid-lowering drugs, glucocorticoids, contraceptives, or other medications influencing lipid metabolism. All medication histories were verified through medical records rather than self-report. Participants with severe cardiovascular diseases, hematological disorders, recent myocardial infarction or cerebral infarction, pregnancy, lactation, major surgery, severe trauma, acute infection, chronic heavy alcohol consumption, chronic smoking, mental disorders, or cognitive dysfunction¹⁷ were also excluded.

The date of physical examination was used as the baseline, on which all core indicators were measured at the Navy Qingdao Special Service Recuperation Center. Raw data regarding general characteristics, thyroid function, blood lipid profiles, and metabolism-related parameters were synchronously extracted. Data were independently entered by two investigators and cross-verified to ensure accuracy.

Outcome measures
Venous blood samples were collected from all participants in the early morning on the day of the physical examination, after fasting. All participants were required to fast overnight for 8–12 h before blood collection, abstain from alcohol, high-fat diets, and strenuous exercise for 24 h before sampling, and consume no beverages other than water for at least 4 h before blood collection. All blood samples were collected centrally between 7:30 a.m. and 9:30 a.m. to minimize circadian variation. Peripheral venous blood (5 mL) was collected from each participant under standard sterile conditions and placed into nonanticoagulant serum tubes. The samples were allowed to clot at room temperature for 30 min, then centrifuged at 1,500 × g. for 10 min to separate the serum. Serum samples were analyzed within 2 h after centrifugation to minimize potential analytical variation.

All serum samples were analyzed for thyroid function within 2 h after serum separation. Thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) levels were measured using an automated chemiluminescence immunoassay analyzer and matched commercial assay kits. The reference ranges adopted in this study were as follows: TSH, 0.27–4.2 mIU/L; FT3, 3.1–6.8 pmol/L; and FT4, 12.0–22.0 pmol/L. All assays were performed in accordance with the manufacturer's standardized operating procedures, including instrument startup, calibration, quality control, and sample loading protocols, to ensure the stability and repeatability of the measurements.

Euthyroidism was defined as normal serum TSH, FT3, and FT4 levels in the absence of clinical thyroid dysfunction, a history of thyroid disease, or thyroid-related medication use²¹. Hyperthyroidism was classified into clinical and subclinical types. Clinical hyperthyroidism was defined as elevated FT3 and/or FT4 levels accompanied by suppressed TSH levels. Subclinical hyperthyroidism was defined as normal FT3 and FT4 levels with isolated TSH suppression accompanied by typical hyperthyroid manifestations after exclusion of transient thyroid dysfunction.

Hypothyroidism was classified into clinical and subclinical types. Clinical hypothyroidism was defined as decreased FT3 and/or FT4 levels accompanied by elevated TSH levels. Subclinical hypothyroidism was defined as normal FT3 and FT4 levels with isolated TSH elevation after exclusion of temporary TSH fluctuations caused by external interfering factors.

All serum specimens were analyzed for lipid and glucose levels within 2 h after serum separation using an automated biochemical analyzer and matched reagent kits. Blood lipid parameters, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), were measured according to the manufacturer's standard operating protocols. Daily instrument calibration and routine quality control procedures were performed before sample analysis to ensure the accuracy and stability of the biochemical measurements. According to the Chinese Guidelines for Lipid Management (2023 Edition)²², a normal lipid profile was defined as all lipid parameters falling within their respective reference ranges, whereas dyslipidemia was diagnosed when one or more lipid indicators exceeded their respective normal ranges.

Fasting blood glucose (FBG) levels were determined using the hexokinase method with the same automated biochemical analyzer and supporting assay kit. Waist circumference (WC) measurements were performed by uniformly trained medical personnel using standardized procedures. Participants were instructed to stand upright with their feet shoulder-width apart and their abdominal muscles fully relaxed. Waist circumference was measured at the horizontal level of the umbilicus using a flexible medical measuring tape with an accuracy of 0.1 cm. The tape was positioned closely against the skin without compressing the subcutaneous tissues. Two independent measurements were obtained, and the average of the two valid measurements was recorded for analysis. Before blood pressure measurement, participants rested quietly in a seated position for 5 min. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured on the right upper arm using an electronic sphygmomanometer, with the arm maintained at heart level throughout the procedure. Three consecutive measurements were obtained at 1-min intervals, and the average of the final two measurements was used for statistical analysis.

According to the Chinese Guidelines for Bariatric and Metabolic Surgery (2024 Edition)²³, metabolic abnormality was defined as the presence of three or more of the following conditions: central obesity identified by elevated waist circumference (WC), impaired glucose regulation, elevated blood pressure (BP), increased triglyceride (TG) levels, and decreased high-density lipoprotein cholesterol (HDL-C) levels.

Sample size calculation
As this study was retrospective in design, no a priori sample size calculation was performed. The final study population consisted of 300 participants who met the predefined inclusion and exclusion criteria.

Post hoc statistical power analysis was performed using statistical software. Based on a previously published single-center study²⁴, serum triglyceride (TG) levels were reported as 84.07 ± 3.12 mg/dL in patients with hyperthyroidism and 91.6 ± 9.14 mg/dL in control subjects, corresponding to a Cohen’s d effect size of 0.95. Using a two-sided significance level of α = 0.05 and statistical power of 80% (β = 0.20), the minimum required sample size was calculated as 29 participants per group.

In the present study, 224 participants were included in the euthyroid group, 32 participants in the hyperthyroid group, and 44 participants in the hypothyroid group, all of which exceeded the calculated minimum sample size requirement. These sample sizes ensured statistical power greater than 80% for the primary analyses. To further assess the robustness of the analysis, the minimum detectable effect size for TG was calculated as 0.52, which was lower than the observed effect size of 0.594. These findings indicated that the present study had adequate sensitivity to detect clinically relevant differences among groups.

Statistical analysis
Potential confounding factors in baseline characteristics were controlled using logistic regression analysis. Data normality was assessed using the Shapiro–Wilk test, and homogeneity of variances was evaluated using Levene’s test. Continuous variables with normal distributions and homogeneous variances were expressed as mean ± standard deviation (SD). Comparisons among groups were performed using one-way analysis of variance (ANOVA), followed by Bonferroni correction for multiple comparisons. Nonnormally distributed variables were expressed as median (interquartile range) and analyzed using the Kruskal–Wallis H test. Categorical variables were presented as numbers (percentages) and compared using the chi-square test. Correlations among thyroid function indicators, lipid profiles, and metabolic parameters were evaluated using Pearson or Spearman rank correlation analyses as appropriate. Variables with p < 0.05 in the univariate analysis were included in multivariate logistic regression models to identify independent risk factors for thyroid dysfunction. All statistical tests were two-sided, and p. < 0.05 was considered statistically significant.

Results

Disease distribution
Among the 300 participants included in the study, 76 participants were diagnosed with thyroid dysfunction, accounting for 25.33% of the total study population. This subgroup included 32 cases of hyperthyroidism (10.67%) and 44 cases of hypothyroidism (14.67%). In addition, dyslipidemia was identified in 120 participants (40.00%), whereas metabolic abnormality was detected in 130 participants (43.33%) (Table 1).

Baseline characteristics
Comparisons of baseline characteristics among the euthyroid, hyperthyroid, and hypothyroid groups demonstrated statistically significant differences in age and body mass index (BMI) (both p < 0.001). Participants in the hypothyroid group were younger and exhibited a higher mean BMI compared with those in the euthyroid group. No statistically significant differences were observed among the three groups with respect to sex distribution, prevalence of hypertension and diabetes mellitus, smoking status, alcohol consumption, exercise frequency, dietary habits, family history of thyroid disease, iodine nutritional status, prevalent cardiovascular disease, or lipid-lowering medication use (all p. > 0.05) (Table 2).

Thyroid function indices
As shown in Table 3, serum thyroid-stimulating hormone (TSH) levels were significantly lower in the hyperthyroid group than in the euthyroid group (p < 0.05), whereas free triiodothyronine (FT3) and free thyroxine (FT4) levels were significantly increased (both p < 0.05). In contrast, participants in the hypothyroid group demonstrated significantly elevated TSH levels and significantly reduced FT3 and FT4 levels compared with the euthyroid group (all p. < 0.05).

Blood lipid indices
The lipid profiles of participants differed significantly according to thyroid function status, as shown in Table 4. Compared with the euthyroid group, the hyperthyroid group exhibited significantly lower total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) levels, whereas high-density lipoprotein cholesterol (HDL-C) levels were significantly increased (all p < 0.05). Conversely, the hypothyroid group demonstrated significantly elevated TC, TG, and LDL-C levels, accompanied by significantly reduced HDL-C levels compared with the euthyroid group (all p. < 0.05). These findings suggest a close association between thyroid dysfunction and abnormal lipid metabolism.

Metabolic function indices
As presented in Table 5, fasting blood glucose (FBG) and systolic blood pressure (SBP) levels were mildly elevated in the hyperthyroid group compared with the euthyroid group, whereas waist circumference (WC) and diastolic blood pressure (DBP) levels were significantly reduced (all p < 0.05). In contrast, participants in the hypothyroid group exhibited significantly increased FBG, WC, SBP, and DBP levels compared with those in the euthyroid group (all p. < 0.05). These findings indicate that thyroid dysfunction is associated with systemic metabolic disturbances and abnormalities in glucose metabolism, blood pressure regulation, and body composition.

Correlation analysis
Correlation analyses are summarized in Figure 2. Serum TSH levels demonstrated strong positive correlations with TC, TG, and LDL-C levels (r > 0.7), moderate positive correlations with FBG, WC, SBP, and DBP levels (0.4 < r ≤ 0.7), and a weak negative correlation with HDL-C levels (|r| < 0.4). In contrast, FT3 and FT4 levels showed moderate negative correlations with TC, TG, and LDL-C levels (0.4 < |r| ≤ 0.7) and moderate positive correlations with HDL-C levels (0.4 < r ≤ 0.7). FT3 levels demonstrated weak negative correlations with WC and DBP levels (|r| < 0.4). FT4 levels were moderately negatively correlated with WC (r = -0.45) and weakly negatively correlated with DBP (r. = -0.35). These findings suggest that abnormal thyroid hormone levels may be associated with disturbances in lipid metabolism and metabolic homeostasis.

Multivariate logistic regression analysis of independent risk factors for thyroid dysfunction
Multivariate logistic regression analysis was performed after adjustment for age and BMI (Figure 3). Dyslipidemia was identified as an independent risk factor for thyroid dysfunction (B = 0.742, p < 0.001, odds ratio [OR] = 2.10, 95% confidence interval [CI]: 1.69–2.61). Participants with dyslipidemia exhibited a 2.10-fold increased risk of thyroid dysfunction compared with participants without dyslipidemia. Metabolic abnormality was also identified as an independent risk factor for thyroid dysfunction (B = 0.588, p. < 0.001, OR = 1.80, 95% CI: 1.52–2.14). Participants with metabolic abnormality demonstrated a 1.80-fold higher risk of thyroid dysfunction than those with normal metabolic status.

DATA AVAILABILITY:
All data generated or analyzed during this study are included in this article. The data supporting the findings of this study are provided as Supplemental File 1.

Figure 1: Study flow chart. 305 patients were initially screened in this study. After exclusions, 300 cases were included: 224 in the euthyroid group, 32 in the hyperthyroid group, and 44 in the hypothyroid group. Abbreviations: TC = total cholesterol; TG = triglyceride; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; FBG = fasting blood glucose; WC = waist circumference; SBP = systolic blood pressure; DBP = diastolic blood pressure; TSH = thyroid-stimulating hormone; FT3 = free triiodothyronine; FT4 = free thyroxine. Please click here to view a larger version of this figure.

Figure 2: Correlation analysis between various indicators. This heatmap displays the Pearson correlation coefficients between thyroid function parameters (TSH, FT3, FT4) and metabolic indicators (TC, TG, HDL-C, LDL-C, FBG, WC, SBP, DBP). The color gradient from green to purple represents correlation coefficients ranging from -1.0 to 1.0, where green indicates negative correlations, purple indicates positive correlations, and the shade intensity reflects the magnitude of the correlation coefficient. The correlation coefficient for each pair of variables is shown within the corresponding cell. Please click here to view a larger version of this figure.

Figure 3: Forest plot of independent risk factors for thyroid dysfunction. This forest plot presents the results of a multivariate logistic regression analysis evaluating the associations between different indicators and the outcome of interest. The horizontal axis displays the OR with 95% CI. Each indicator is represented by a point estimate (OR value) and a horizontal line (95% CI). The vertical dashed line at OR = 1.0 indicates no effect. An OR > 1.0 with a 95% CI not crossing 1 indicates a significant positive association with the outcome, while an OR < 1.0 with a 95% CI not crossing 1 indicates a significant negative association. The table on the left lists the indicators, p.-values, OR values, and corresponding 95% CIs. Abbreviations: OR = odds ratio; CI = confidence interval; BMI = body mass index. Please click here to view a larger version of this figure.

Indicators	Thyroid dysfunction n (%)	Hyperthyroidism n (%)	Hypothyroidism n (%)	Dyslipidemia n (%)	Metabolic abnormalities n (%)
Total (n = 300)	76 (25.33)	32 (10.67)	44 (14.67)	120 (40.00)	130 (43.33)

Table 1: Distribution characteristics of thyroid function, dyslipidemia, and metabolic abnormalities. This table presents the distribution of thyroid dysfunction (including hyperthyroidism and hypothyroidism), dyslipidemia, and metabolic abnormalities among the total study population (n = 300). Data are expressed as the number of cases (n) and corresponding percentages (%). Thyroid dysfunction was defined as the presence of either hyperthyroidism or hypothyroidism. Dyslipidemia and metabolic abnormalities were diagnosed according to predefined clinical criteria.

Indicators	Euthyroid group (n = 224)	Hyperthyroid group (n = 32)	Hypothyroid group (n = 44)	p	OR	95% CI for OR
Age (years,mean ± SD)	53.04 ± 3.07	53.69 ± 3.14	49.86 ± 4.28	< 0.001	0.77	0.69, 0.85
Gender n (%)				0.945	1.02	0.53, 1.97
Male	98 (43.75)	14 (43.75)	19 (43.18)
Female	126 (56.25)	18 (56.25)	25 (56.82)
BMI (kg/m2, mean ± SD)	23.88 ± 1.08	23.78 ± 1.36	25.64 ± 1.24	< 0.001	3.36	2.51, 5.27
Hypertension n (%)	65 (29.02)	8 (25.00)	16 (36.36)	0.333	0.72	0.36, 1.41
Diabetes n (%)	21 (9.38)	4 (12.50)	7 (15.91)	0.201	0.55	0.22, 1.38
Smoking n (%)	51 (22.77)	7 (21.88)	12 (27.27)	0.52	0.79	0.38, 1.64
Drinking n (%)	48 (21.43)	6 (18.75)	11 (25.00)	0.602	0.82	0.39, 1.74
Exercise frequency				0.235	1.2	0.89, 1.64
≥ 3 times per week n (%)	68 (30.36)	11 (34.38)	10 (22.73)
1–2 times per week n (%)	82 (36.61)	12 (37.50)	15 (34.09)
Occasionally n (%)	39 (17.41)	5 (15.63)	11 (25.00)
Never n (%)	35 (15.63)	4 (12.50)	8 (18.18)
Dietary habits				0.875	1.03	0.69, 1.55
Light diet n (%)	85 (37.95)	13 (40.63)	15 (34.09)
Salty diet n (%)	79 (35.27)	10 (31.25)	18 (40.91)
Oily diet n (%)	60 (26.79)	9 (28.13)	11 (25.00)
Family history of thyroid disease n (%)	17 (7.59)	3 (9.38)	5 (11.36)	0.408	1.56	0.54, 4.48
Iodine nutritional status n (%)				0.561	1.409	0.443, 4.479
Adequate iodine	186 (83.0)	24 (75.0)	33 (75.0)
Iodine deficiency	22 (9.8)	5 (15.6)	7 (15.9)
Excessive iodine	16 (7.2)	3 (9.4)	4 (9.1)
Prevalent cardiovascular disease n (%)	32 (14.3)	3 (9.4)	9 (20.5)	0.301	1.543	0.678, 3.512
Lipid-lowering medication use n (%)	25 (11.2)	2 (6.3)	8 (18.2)	0.2	1.769	0.740, 4.229

Table 2: Baseline clinical data. This table summarizes the baseline demographic, clinical, and lifestyle characteristics of participants stratified by thyroid function status: euthyroid (n = 224), hyperthyroid (n = 32), and hypothyroid (n = 44). Continuous variables (age, BMI) are presented as mean ± SD. Categorical variables (gender, hypertension, diabetes, smoking, drinking, exercise frequency, dietary habits, family history of thyroid disease, iodine nutritional status, prevalent cardiovascular disease, and lipid-lowering medication use) are expressed as the number of cases and corresponding percentages (n, %). Between-group comparisons were performed using appropriate statistical tests (ANOVA for continuous variables, χ² test for categorical variables), and the resulting p.-values, OR, and 95% CI for the association of each indicator with thyroid dysfunction are reported. Abbreviations: OR = odds ratio; CI = confidence interval; BMI = body mass index; SD = standard deviation.

Indicators	Euthyroid group (n = 224)	Hyperthyroid group (n = 32)	Hypothyroid group (n = 44)	p	Effect size (η²)
TSH (mIU/L)	2.13 ± 0.31	0.18 ± 0.09a	8.80 ± 0.96a	< 0.001	0.969
FT3 (pmol/L)	4.41 ± 0.53	8.86 ± 0.94a	2.92 ± 0.26a	< 0.001	0.884
FT4 (pmol/L)	16.38 ± 1.16	28.76 ± 0.93a	10.22 ± 0.65a	< 0.001	0.95

Table 3: Comparison of Thyroid Function Indicators (mean ± SD). All thyroid function parameters are presented as mean ± SD. One-way analysis of variance was used for intergroup comparison. The partial eta-squared (η²) was calculated to reflect the effect size. ^ap. < 0.05 versus the euthyroid group. Abbreviations: TSH = thyroid-stimulating hormone; FT3 = free triiodothyronine; FT4 = free thyroxine.

Indicators	Euthyroid group (n = 224)	Hyperthyroid group (n = 32)	Hypothyroid group (n = 44)	p	Effect size (η²)
TC (mmol/L)	4.83 ± 0.32	3.85 ± 0.32a	6.15 ± 0.32a	< 0.001	0.78
TG (mmol/L)	1.48 ± 0.26	0.97 ± 0.25a	2.17 ± 0.25a	< 0.001	0.594
HDL-C (mmol/L)	1.18 ± 0.26	1.39 ± 0.25a	0.87 ± 0.25a	< 0.001	0.218
LDL-C (mmol/L)	2.74 ± 0.31	2.15 ± 0.32a	3.75 ± 0.32a	< 0.001	0.653

Table 4: Comparison of Blood Lipid Indicators (mean ± SD). Data are expressed as mean ± SD. One-way ANOVA was applied for intergroup comparisons, and partial eta-squared (η²) was calculated as the effect size. ^ap. < 0.05 versus the euthyroid group. Abbreviations: TC = total cholesterol; TG = triglyceride; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol.

Indicators	Euthyroid group (n = 224)	Hyperthyroid group (n = 32)	Hypothyroid group (n = 44)	p	Effect size (η²)
FBG (mmol/L)	5.34 ± 0.31	5.55 ± 0.32a	6.15 ± 0.32a	< 0.001	0.458
WC (cm)	84.35 ± 3.11	79.5 ± 3.18a	89.50 ± 3.19a	< 0.001	0.395
SBP (mmHg)	125.37 ± 3.11	127.50 ± 3.18a	134.50 ± 3.19a	< 0.001	0.514
DBP (mmHg)	77.35 ± 3.11	73.50 ± 3.18a	81.50 ± 3.19a	< 0.001	0.295

Table 5: Comparison of Metabolic Function Indicators (mean ± SD). All data are presented as mean ± SD. One-way ANOVA was used for intergroup comparisons, and partial eta-squared (η²) was calculated to assess the effect size. ^ap. < 0.05 versus the euthyroid group. Abbreviations: FBG = fasting blood glucose; WC = waist circumference; SBP = systolic blood pressure; DBP = diastolic blood pressure.

Supplemental File 1: Raw data used for the analysis of thyroid dysfunction, dyslipidemia, and metabolic risk factors. Please click here to download this file.

Discussion

A total of 300 residents undergoing routine physical examinations were enrolled in this study; 74.67% had normal thyroid function, 10.67% were diagnosed with hyperthyroidism, and 14.66% with hypothyroidism. These figures indicate that thyroid dysfunction has a relatively high prevalence in the general health examination population, making it a notable disease category that cannot be overlooked in the prevention and control of chronic diseases at the health examination population level. Results of univariate analysis showed that the hyperthyroidism group had significantly lower TSH levels yet markedly elevated FT3 and FT4 levels compared with the euthyroid group; the hypothyroidism group, by contrast, exhibited a completely opposite trend in these indices, a finding that aligns with the typical pathophysiological characteristics of thyroid dysfunction. In terms of lipid profiles, the hyperthyroidism group showed reduced TC, TG, and LDL-C levels, with elevated HDL-C, whereas the hypothyroidism group showed increased TC, TG, and LDL-C levels and decreased HDL-C. Analysis of metabolism-related indices revealed that the hyperthyroidism group had a slightly higher FBG level, reduced WC and DBP levels, and a mild rise in SBP relative to the euthyroid group; in stark contrast, all four indices (FBG, WC, SBP, and DBP) were significantly elevated in the hypothyroidism group. Pearson correlation analysis further verified that abnormal fluctuations in thyroid hormone levels were associated with aberrations in blood lipid and metabolic indices. Ultimately, multivariate logistic regression analysis confirmed that dyslipidemia and metabolic abnormality may act as independent risk factors for the development of thyroid dysfunction, which further validates the close correlations among these three conditions. The association between thyroid dysfunction, dyslipidemia, and metabolic risk is likely closely linked to thyroid hormones' central regulatory role in systemic metabolism. As key metabolic regulatory hormones in the human body, abnormal secretion of thyroid hormones directly impairs lipid and glucose metabolism and cardiovascular function, thereby triggering lipid dysregulation and metabolic abnormalities. This is presumably the core mechanism underlying the correlations among the three conditions mentioned above.

In hypothyroidism, elevated TSH levels, accompanied by reduced FT3 and FT4 levels, weaken the stimulatory effect of thyroid hormones on lipid metabolism, leading to increased lipogenesis and decreased lipolysis. This, in turn, causes the accumulation of TC, TG, and LDL-C in the body, while inhibiting the synthesis and metabolism of HDL-C, resulting in a decline in HDL-C levels and, ultimately, the development of hyperlipidemia^25,26. In terms of glucose metabolism, insufficient thyroid hormone secretion may impair insulin sensitivity and reduce the uptake and utilization of glucose by peripheral tissues, thus contributing to elevated FBG levels²⁷. Meanwhile, a slowed metabolic rate may lead to reduced energy expenditure and fat accumulation, further increasing WC. Given the close association between obesity and elevated BP, these changes ultimately result in a rise in both SBP and DBP levels, forming a vicious cycle of metabolic dysfunction²⁸. The mechanism underlying the association between hyperthyroidism and dyslipidemia/metabolic abnormality is presumably the reverse of that in hypothyroidism: in hyperthyroidism, elevated FT3 and FT4 levels with reduced TSH levels enhance the promotional effect of thyroid hormones on lipid metabolism, accelerating lipolysis and fat oxidation while inhibiting hepatic cholesterol synthesis. This consequently leads to decreased TC, TG, and LDL-C levels, and as a cardioprotective cholesterol, HDL-C undergoes accelerated metabolism, which may contribute to its elevated level^29,30. In hyperthyroidism, accelerated systemic metabolism may stimulate insulin secretion, leading to a slight short-term rise in FBG levels. At the same time, increased energy expenditure may result in weight loss and reduced WC, while the excitatory effect of thyroid hormones on the cardiovascular system may cause tachycardia and vasoconstriction, thereby leading to a mild elevation in SBP and a decline in DBP. This pattern of hemodynamic change may be associated with increased sympathetic nerve activity in hyperthyroidism, yet the specific underlying mechanism remains to be further investigated^31,32.

In a retrospective study conducted in Saudi Arabia³³, patients with subclinical hypothyroidism presented with elevated TC levels and reduced HDL-C levels, a finding that is largely consistent with the results of the present study. Another study focusing on patients with hyperthyroidism³⁴ reported decreased TC, LDL-C, and TG levels in hyperthyroid patients compared with healthy individuals, which also aligns with the research outcomes. In contrast, a controlled study³⁵ found that only LDL-C levels were significantly elevated in elderly patients with subclinical hypothyroidism, with no obvious abnormalities in TC, TG, and HDL-C levels. This finding differs from our study, in which multiple lipid indices were altered in the hypothyroidism group, suggesting that age may be an important modifier of the association between hypothyroidism and dyslipidemia. Notably, the present study did not place primary emphasis on analyzing the impact of age stratification.

This study innovatively established a comprehensive indicator system encompassing core parameters of thyroid function, blood lipids, and metabolism. It not only explored the overall associations among thyroid dysfunction, dyslipidemia, and metabolic abnormalities, but also quantified the strength of correlations between individual indicators using correlation analysis. The findings are therefore more scientific and targeted, and can provide accurate references for disease screening and intervention.

The present findings carry practical implications for clinical practice, preventive medicine, and public health management. Combined detection of thyroid function can be applied to routine metabolic risk assessment, serving as an effective auxiliary tool for clinical screening of metabolic disorders. Early identification of coexisting thyroid and metabolic abnormalities also facilitates timely intervention for metabolic syndrome and improves the efficiency of disease prevention in at-risk populations. Furthermore, integrating thyroid and metabolic indicator assessment into regular health check-ups is conducive to optimizing the chronic disease screening system for community residents and delivering greater public health benefits. In the context of personalized intervention, individual thyroid-metabolic profiles can be used to formulate targeted management strategies that advance individualized care for people with metabolic and thyroid-related disorders.

Although this study has yielded certain research findings, it still has several limitations that need to be addressed in subsequent research. First and foremost, this was a retrospective study that explored the correlations among the three conditions solely based on cross-sectional physical examination data; such a design cannot confirm a causal relationship between them, nor can it capture the dynamic changes in thyroid dysfunction, dyslipidemia, and metabolic abnormality, which may compromise the accuracy of the study conclusions. Second, this study did not conduct a subgroup analysis of subclinical and overt thyroid dysfunction, failing to compare differences in dyslipidemia and metabolic risk characteristics between the two types of thyroid abnormalities, thereby weakening the novelty and depth of the research results. Third, the study had a relatively small sample size and recruited research subjects exclusively from a single physical examination population; this limits the representativeness of the sample and potentially generalizability to the entire health examination population. In particular, the correlation characteristics may vary among health examination populations with different geographical backgrounds, age structures, and living habits. Fourth, although general demographic data for participants were collected during the study, several potential confounding factors were not fully considered, which could, in turn, affect the accuracy of the study outcomes. Finally, this study investigated only the associations between thyroid dysfunction and dyslipidemia or metabolic risk, without an in-depth exploration of the underlying molecular mechanisms; it also failed to analyze the differential impacts of varying severities of thyroid dysfunction on blood lipid and metabolic indices, indicating that the depth of the research needs to be further enhanced.

To address the aforementioned limitations, prospective cohort studies should be conducted. The sample size will be expanded and the follow-up period extended to track dynamic changes in thyroid function, blood lipids, and metabolic indices among participants, thereby clarifying the causal relationships among the three conditions. The research scope will be broadened to include participants from multiple communities of different types across various regions, thereby improving the representativeness and generalizability of the study results. Moreover, the impacts of various confounding factors will be fully considered, additional potential confounders will be incorporated into the study design, and more rigorous statistical methods will be adopted to control for confounding effects, thereby enhancing the accuracy of the research findings. Additionally, in-depth investigations into the molecular mechanisms underlying the associations between thyroid dysfunction and dyslipidemia or metabolic risk should be carried out; meanwhile, the differential effects of thyroid dysfunction with different severities and disease durations on blood lipid and metabolic indices will be analyzed, aiming to provide more in-depth theoretical support for the precise intervention of these diseases.

There is a strong association between thyroid dysfunction and dyslipidemia or other metabolic abnormalities in the physical examination population. Dyslipidemia and metabolic abnormalities may serve as independent risk factors for the development of thyroid dysfunction, and blood lipids, as well as metabolism-related indices, can serve as reference indicators for screening and intervention for thyroid dysfunction. Nevertheless, the study results still need to be further verified and refined by multi-center, large-sample, and prospective studies.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Automatic biochemistry analyzer	Roche Diagnostics GmbH	cobas 8000	Lipid profiles including TC, TG, HDL-C, LDL-C, and FBG were determined.
Automatic chemiluminescence immunoassay analyzer	Roche Diagnostics GmbH	cobas 6000 e 601	Thyroid function indicators including TSH, FT3 and FT4 were measured.
Centrifuge	Shanghai Anting Scientific Instrument Factory	LXJ-IIB	Centrifuge the blood sample to separate serum.
Electronic sphygmomanometer	A&D Electronics (Shenzhen) Co.,Ltd.	TM-2656VP	Measure blood pressure.
FT3 assay kit	Abbott Laboratories	03P6030	Measure FT3
FT4 assay kit	Abbott Laboratories	03P6040	Measure FT4
Glucose assay kit	Roche Diagnostics	04718667190	Measure FBG
HDL-C kit	Roche Diagnostics	04718900190	Measure HDL-C
LDL-C kit	Roche Diagnostics	04718918190	Measure LDL-C
Professional medical soft tape	Shanghai Yiren Medical Equipment Co., Ltd.	YC-R01	Measure WC
SPSS software	IBM	SPSS 27.0	Statistical analysis
Statistical software	Heinrich - Heine - Universität Düsseldorf	G*Power 3.1.9.7	Sample size calculation
TC kit	Roche Diagnostics	04718888190	Measure TC
TG kit	Roche Diagnostics	04718896190	Measure TG
TSH assay kit	Abbott Laboratories	03P6020	Measure TSH