Association between Noise Exposure and Hyperthyroidism Risk: A Retrospective Case-Control Study

Abstract

Background:

Environmental and lifestyle factors may influence hyperthyroidism prevalence. This research sought to explore the association between noise exposure and the risk of hyperthyroidism.

Methods:

This retrospective case–control study was conducted in two hospitals in China between January 2022 and December 2023. Among the 128 participants enrolled, 64 were hyperthyroidism patients (the hyperthyroidism group), and 64 had normal thyroid function (the control group). The variables compared between the groups included body mass index (BMI), residence areas (urban/rural), average noise levels, noise compliance rates, iodized salt intake, and anxiety levels (Self-Rating Anxiety Scale, SAS). Multivariable logistic regression was performed to identify the risk factors for hyperthyroidism.

Results:

The hyperthyroidism group showed significantly higher residential noise level, SAS score, excessive iodized salt intake rate, and urban residential rate compared with the control group (P < 0.05). The hyperthyroidism group also showed a lower noise compliance rate and BMI compared with the control group (P < 0.05). Urban participants exhibited higher noise levels, excessive iodized salt intake rate, hyperthyroidism incidence, SAS score, and lower noise compliance rates compared with the rural participants (P < 0.05). Logistic regression analysis revealed that high noise level (OR = 1.103, 95% CI 1.024–1.187) and high anxiety level (OR = 1.292, 95% CI 1.136–1.329) are risk factors for hyperthyroidism. High noise compliance rate (OR = 0.787, 95% CI 0.060–0.845) and rural residence (OR = 0.643, 95% CI 0.078–0.829) are protective factors for hyperthyroidism.

Conclusion:

This study underscored noise exposure and anxiety as modifiable risk factors for hyperthyroidism. Strict environmental noise regulations and enhanced public health education are necessary to reduce the risk of hyperthyroidism.

KEY MESSAGES

• (1)A high noise level, particularly in urban areas, is associated with an increased risk of hyperthyroidism.
• (2)Elevated anxiety level is a risk factor for hyperthyroidism.
• (3)Good noise compliance can reduce the risk of hyperthyroidism, which highlights the need for strict environmental noise management.

INTRODUCTION

Hyperthyroidism presents a crucial health concern worldwide; it is characterized by an overproduction of thyroid hormones, which can potentially result in a host of metabolic, cardiovascular, and neuromuscular complications.[1,2] Despite considerable advancements in the comprehension of its pathophysiology, the environmental determinants of hyperthyroidism remain insufficiently explored.[3,4]

Environmental noise is increasingly being recognized as a prevalent and persistent stressor with potential adverse effects on various aspects of human health.[5,6] Chronic noise exposure is linked to adverse cardiovascular outcomes, sleep disturbances, and mental health conditions, such as anxiety and depression.[7] The physiological stress response induced by noise is primarily mediated through the activation of the hypothalamic–pituitary–adrenal axis, leading to increased cortisol levels. Prolonged activation of this stress pathway disrupts normal endocrine functioning, including thyroid hormone regulation, indicating a potential connection between noise exposure and thyroid disorders.[8,9]

Urban environments expose residents to high levels of ambient noise due to increased traffic, industrial activities, and densely populated living conditions.[10,11] The concept of ‘urban noise syndrome’ encapsulates chronic exposure to these elevated noise levels, which can contribute to a continuum of stress responses adversely affecting thyroid health.[12,13] However, urban settings also present other environmental and lifestyle factors, such as dietary habits and work pressures, that can influence thyroid function.

Prior studies have extensively reported the risk posed by iodine intake and autoimmune factors on thyroid function, yet the influence of noise pollution remains underexplored.[14] This research aimed to address this knowledge gap by investigating the association between noise pollution and the incidence of hyperthyroidism, with a focus on contrasting urban and rural populations.

MATERIALS AND METHODS

Study Design

This study is a retrospective case-control study designed to investigate the association between noise pollution and the risk of hyperthyroidism. The initial pool consisted of 150 patients who visited Sir Run Run Hospital of Nanjing Medical University and Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University) between January 2022 and December 2023.

The sample size was calculated using G*Power software (version 3.1). Assuming a significance level of 0.05, a power of 0.80, and a moderate effect size (Cohen’s d = 0.5), we estimated a minimum sample size of 54 participants for each group. To account for potential dropouts or exclusions, we finally selected 128 participants for two groups.

In accordance with the inclusion and exclusion criteria, 64 patients diagnosed with hyperthyroidism were included in the hyperthyroidism group. Another 64 patients (primary disease: 21 hypertension, 18 diabetes, 16 dyslipidemia, and nine other diseases) with normal thyroid function who visited other internal medicine departments during the same period were included in the control group. The participants were further categorized based on their residential locations into an urban group (n = 79) and a rural group (n = 49) to examine the urban–rural differences in noise exposure.

The study was approved by the Hospital Ethics Committee and all participants gave their informed consent.

Inclusion and Exclusion Criteria

Inclusion criteria: (1) Patients in the hyperthyroidism group met the diagnostic standards[15] for hyperthyroidism, demonstrated diffuse enlargement of the thyroid gland via ultrasound assessments, and showed a considerable increase in blood flow distribution, as observed through color Doppler flow imaging. (2) patients in the control group had normal thyroid function test results, (3) all patients aged 18 years or older, (4) all patients had not taken any medications affecting liver or thyroid function within 3 months before the study, and (5) all patients had complete clinical data.

Exclusion criteria: (1) patients with thyroid adenomas, subacute thyroiditis, parathyroid diseases, or other endocrine disorders; (2) patients with cardiovascular diseases or liver/biliary system diseases; (3) malignant tumors; (4) pregnant women; (5) occupational noise level exceeding 85 dB(A) for 8 h.

Noise data Collection

Residential noise level

To accurately assess the noise exposure of patients, we collected and analyzed noise level data from the residential areas of the subjects over the past 5 years. Specifically, we used the historical noise monitoring data provided by the Nanjing and Changsha Ecological Environment Bureaus to conduct an in-depth study of the average noise levels in the residential areas of the participants. Monitoring was performed once per quarter, with each monitoring session lasting for 24 consecutive hours. Both cities have 20 such monitoring points each. By matching the residential addresses of the participants with the nearest noise monitoring stations, we estimated their long-term noise exposure levels relatively accurately.

In rural settings, where direct noise monitoring data were scarce, noise levels were estimated based on comparable regions documented by environmental protection agencies. These regions were identified as having similar geographical, demographic, and socioeconomic characteristics. As an example, consider a participant who lived in a village on the outskirts of Nanjing over the past five years, where direct noise monitoring data are unavailable. To estimate their noise exposure, we selected another plain area village within the same province as a reference. This reference village shares similar geographic features with the considered village—flat open land primarily used for agricultural production, low population density with an aging population, and main economic activities, such as rice cultivation and small poultry farming. Based on this information, we estimated that the participant’s long-term noise exposure levels would be similar to those of the reference village.

A Benetech GM1351 sound level meter (Shenzhen Jumaoyuan Science and Technology Co., Ltd., Shenzhen, China), which has a measurement range of 30 dB to 130 dB and an accuracy of ±1.5 dB, was used as a reference standard for noise measurement accuracy.

Noise compliance rate

The World Health Organization (WHO) Environmental Noise Guidelines define permissible noise levels for residential zones as follows:[16] 55 dB(A) during the day and 45 dB(A) at night. The noise level in a location was considered compliant if it did not exceed these limits for at least 90% of the monitoring time. Any noise levels exceeding these standards for more than 10% of the monitoring time were considered noncompliant.

General information

The general information of patients was compiled from the medical record system and supplemented with data from a standardized questionnaire interviewed at patients’ first visit. The general information included age, gender, ethnic groups, body mass index (BMI), lifestyle (smoking or drinking history), primary disease, place of residence, noise exposure level, noise compliance rate, and iodized salt intake (based on patients’ self-reported dietary habits, iodized salt intake exceeding the WHO recommended 150 μg/day was considered excessive intake).

Anxiety Level

The Self-Rating Anxiety Scale (SAS) was employed to assess the anxiety levels of patients. This scale consists of 20 items, focusing on common symptoms of anxiety. The respondents rated each item on a 4-point scale. The raw scores for the 20 items were summed and then multiplied by 1.25 to get a standard score. A standard score of less than 40 is considered normal, 40–49 represents mild anxiety, 50–59 implies moderate anxiety, and 60 or above corresponds to severe anxiety. The Chinese version of SAS had Cronbach’s α of 0.834.[17]

Statistical Methods

Data analysis was conducted using SPSS 29.0 (SPSS Inc., Chicago, IL, USA). Categorical variables were presented as (n[%] and analyzed through chi-squared test. When sample sizes were ≥ 40 and theoretical frequencies (T) ≥ 5, the standard formula was applied. When the sample size was ≥ 40 but 1 ≤ T < 5, a chi-squared test with a correction formula was employed. When sample sizes < 40 or T < 1, Fisher’s exact probability test was used. Normally distributed continuous variables are presented as mean, and standard deviation, and independent t-tests were conducted to compare differences between groups. Variables that showed statistical significance (P < 0.05) between the control and hyperthyroidism groups were incorporated as independent variables in the multivariable logistic regression analysis.

All figures and tables were created using GraphPad Prism 9.0 (GraphPad Software, San Diego, CA, USA).

RESULTS

General data

No significant differences were found in terms of gender distribution, age, smoking history, or drinking history (P > 0.05). However, the hyperthyroidism group exhibited a significantly lower BMI compared with the control group (t = 2.820, P = 0.006). The proportion of urban residents (χ² = 9.556, P = 0.002) and excessive iodized salt intake (χ² = 7.047, P = 0.008) in the hyperthyroidism group were higher than those in the control group. In addition, the hyperthyroidism group had higher noise levels (t = 4.372, P < 0.001) and lower noise compliance rates (χ²= 6.425, P = 0.011) than the control group [Table 1].

Table 1.

Comparison of general data between the control and hyperthyroidism groups

General data	Control group (n = 64)	Hyperthyroidism group (n = 64)	t/χ2	P
Gender			0.293	0.588
- Male	27 (42.19%)	24 (37.5%)
- Female	37 (57.81%)	40 (62.50%)
Age	55.92 ± 6.31	54.85 ± 5.24	1.047	0.297
BMI (kg/m2)	23.21 ± 2.86	21.83 ± 2.64	2.820	0.006
Smoking history	11 (17.19%)	8 (12.5%)	0.556	0.456
Drinking history	15 (23.44%)	13 (20.31%)	0.183	0.669
Ethnic group (Han Chinese)	61 (95.31%)	62 (96.88%)	0.000	1.000
Place of residence			9.556	0.002
- Rural	33 (51.56%)	16 (25.00%)
- Urban	31 (48.44%)	48 (75.00%)
Noise level (dB(A))	52.74 ± 6.46	58.77 ± 8.94	4.372	<0.001
Noise compliance rate	52 (81.25%)	39 (60.94%)	6.425	0.011
Excessive iodized salt intake	23 (35.94%)	38 (59.38%)	7.047	0.008

Notes: BMI, body mass index; excessive iodized salt intake: daily iodized salt intake exceeds 150 μg.

Anxiety level between the control and hyperthyroidism groups

The SAS scores of the two groups are shown in [Figure 1]. The hyperthyroidism group had a significantly higher SAS score than the control group (P < 0.001).

Figure 1.

Comparison of SAS scores between the control and hyperthyroidism groups. Note: SAS, Self-Rating Anxiety Scale; *** P < 0.001.

Residential noise and physiological indicators of urban and rural groups

Table 2 shows the comparison of residential noise and physiological indicators between the urban and rural groups. The noise level in the urban group was significantly higher than that in the rural group (t = 3.991, P < 0.001). The noise compliance rate of urban residents was lower than that of rural residents (χ² = 6.114, P = 0.013). The urban group had a higher rate of excessive iodized salt intake (χ² = 5.348, P = 0.021) and hyperthyroidism prevalence (χ² = 5.588, P = 0.018) than the rural group. Furthermore, the urban group displayed significantly higher SAS scores than the rural group (t = 12.145, P < 0.001). These findings suggest that urban environments are associated with poor noise compliance, high anxiety levels, high rates of excessive iodized salt intake, and hyperthyroidism prevalence.

Table 2.

Residential noise and physiological indicators of urban and rural groups

Parameters	Urban group (n = 79)	Rural group (n = 49)	t/χ2	P
Noise level (dB(A))	56.41 ± 6.59	51.74 ± 6.17	3.991	<0.001
Noise compliance rate	50 (63.29%)	41 (83.67%)	6.114	0.013
Excessive iodized salt intake	44 (55.70%)	17 (34.69%)	5.348	0.021
Hyperthyroidism	46 (58.23%)	18 (36.73%)	5.588	0.018
SAS score	52.79 ± 4.87	41.26 ± 5.74	12.145	<0.001

Note: SAS, Self-Rating Anxiety Scale.

Multivariable logistic regression analysis for hyperthyroidism

A logistic regression analysis was conducted with hyperthyroidism status (hyperthyroidism = 1, nonhyperthyroidism = 0) as the dependent variable. The independent variables included in the model were as follows: BMI (kg/m²), place of residence (rural = 1, urban = 0), noise level (in dBA), noise compliance rate (compliance = 1, noncompliance = 0), excessive iodized salt intake (yes = 1, no = 0) and anxiety level (SAS score). Table 3 presents the detailed results.

Table 3.

Multivariable logistic regression analysis for hyperthyroidism

Independent variable	Coefficient	SE	Wald	P	OR	95% CI
BMI	−0.125	0.106	1.124	0.236	1.202	0.717–1.086
Place of residence (rural)	−0.082	0.065	1.591	0.023	0.643	0.078–0.829
Noise level	0.098	0.038	6.651	0.009	1.103	1.024–1.187
Noise compliance rate	−0.092	0.075	1.504	0.027	0.787	0.060–0.845
Excessive iodized salt intake	0.090	0.071	1.607	0.073	0.725	0.909–8.542
SAS	0.206	0.040	26.520	<0.001	1.292	1.136–1.329

Notes: BMI, body mass index; SAS, Self-Rating Anxiety Scale; SE, standard error; OR, odds ratio; CI, confidence interval.

Rural residence (odds ratio [OR] = 0.643, 95% CI 0.078–0.829) and high noise compliance rate (OR = 0.787, 95% CI 0.060–0.845) are protective factors for hyperthyroidism. High noise level (OR = 1.103, 95% CI 1.024–1.187) and high anxiety level (OR = 1.292, 95% CI 1.136–1.329) are risk factors for hyperthyroidism. The BMI and excessive iodized salt intake had potential associations with hyperthyroidism, but the P values did not reach statistical significance.

DISCUSSION

The results of this study offer compelling insights into the association between environmental noise exposure and the prevalence of hyperthyroidism in urban and rural populations. The data underscore a variety of environmental and lifestyle factors that can potentially contribute to the development of hyperthyroidism, which prompts a re-evaluation of existing paradigms related to hyperthyroidism risks.

Noise exposure represents predominant common environmental factors affecting residents’ health outcomes.[18,19] Hahad et al.[20] demonstrated that long-time exposure to traffic noise could increase the risk of cardiovascular diseases, which is likely mediated through stress-induced pathways. The relationship between noise exposure and health often converges on stress-related pathways.[21] Similarly, studies by Muentabutr et al.[22] and Senerth et al.[23] revealed that noise exposure is linked to elevated levels of cortisol, which can influence metabolic activities and immune responses. Our results demonstrated that the noise level and SAS score in the hyperthyroidism group were significantly higher than those in the control group, which is consistent with this theory.

Urban environments are synonymous with high noise levels due to traffic, industrial activities, and social gatherings, which potentially contribute to heightened stress responses and subsequent endocrine disruptions.[24] A study of Wang et al.[25] highlighted how urban environments limit opportunities for stress-relieving activities, which exacerbates stress-induced health concerns. A large-scale epidemiological study by Yu et al.[26] indicated that urban areas exhibited a high incidence of hyperthyroidism, which was possibly due to increased exposure to pollutants, including noise and air pollution. In accordance with this understanding, our findings showed that the hyperthyroidism group showed a higher urban residential rate compared with the control group, and the urban group had higher noise levels, SAS scores, and hyperthyroidism incidence than the rural group, which suggests that the onset of hyperthyroidism may be related to noise and anxiety level.

Moreover, studies reported the association between urban lifestyle practices and thyroid alterations.[27] In China, the use of iodized salt is promoted uniformly across urban and rural areas.[28] However, due to socioeconomic factors, rural residents usually consumed more noniodized salt than urban residents. Our finding showed that the hyperthyroidism group had a higher excessive iodized salt intake rate than the control group, which aligns with the results of previous research.[14]

Furthermore, noise compliance rates were significantly lower in the hyperthyroid group and urban residents, which emphasizes the importance of regulatory frameworks in environmental health. Environmental adaptations and technological interventions are important for managing urban noise pollution. Sound-proofing residential areas, utilizing noise barriers in traffic-heavy zones, and enforcing stricter control over industrial noise emissions can contribute considerably to reducing noise levels and improving public health outcomes.

Multivariable analysis offered additional context. High noise and anxiety levels were risk factors for hyperthyroidism, while high noise compliance rate and rural residence were protective factors for hyperthyroidism. This outcome supports our conclusion that comprehensive public health strategies are necessary for noise control and the health of residents.

Limitations

While this study offers valuable insights into the correlation between environmental noise and hyperthyroidism, several limitations must be recognized. The retrospective design inherently limited causal inference and possibly introduced biases, such as recall bias. Our analysis did not account for potential confounders, including socioeconomic status, urbanization levels, or exposure to other environmental toxins (e.g., air pollution), which can influence living conditions and health outcomes. Moreover, reverse causality is a possibility; individuals with hyperthyroidism may develop altered dietary habits or increased sensitivity to environmental stressors. Our study only investigated the environmental noise level in residential areas and did not cover people who live in industrial zones. Future studies may consider including other environments to explore broader impacts. Additionally, in the multivariate logistic regression, some indicators had large standard errors or wide confidence intervals, which may be due to the small sample size introduced. Larger samples or case–control studies are needed for further validation. Furthermore, future research should strive to address these limitations through prospective studies and employ precise noise measurement methods and objective health outcome indicators.

CONCLUSION

This study reinforces the negative effect of noise level and lifestyle on thyroid function. High noise and anxiety levels are risk factors for hyperthyroidism, while high noise compliance rates and living in rural areas are protective factors for hyperthyroidism. Strict noise management policies should be developed to mitigate the adverse effects of environmental noise on thyroid health.

Author Contributions

M Gao and LY Liu: Conceptualization, responsible for the entire work from conception to publication; M Gao and L Xu: Writing, original draft, editing, and final approval of the manuscript; LY Liu and JN Yi: Data curation and review; L Xu and JN Yi: Formal analysis.

Availability of Data and Materials

The datasets used during the present study are available from the corresponding authors upon reasonable request.

Ethics Approval and Consent to Participate

The study was approved by the Ethics Committee of Sir Run Run Hospital of Nanjing Medical University (Approval No. 2024-SR-061) and the Ethics Committee of Hunan Provincial People’s Hospital (IRB Approval No. 2024-411) and adhered to the research principles outlined in the Declaration of Helsinki. All participants have given informed consent.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding Statement

None.