Assessment of noise-induced hearing loss based on frequency analysis and its association with metabolic syndrome: a case study in a petrochemical industry

Abstract

With the global expansion of industrial sectors, noise has emerged as a significant harmful physical agent in workplaces. Besides auditory effects, high-level noise exposure can cause non-auditory consequences, especially at high sound intensities. These effects may include metabolic disturbances such as Metabolic Syndrome (MetS). This study aimed to examine the relationship between noise-induced hearing loss (NIHL) at different sound frequencies and the occurrence of MetS and its defining factors. This cross-sectional descriptive-analytical study was conducted on 1,142 male employees in a petrochemical industry. Fasting blood glucose, triglycerides, and HDL cholesterol were measured from serum samples, and pure-tone audiometry was performed to assess hearing thresholds at 500, 1000, 2000, 3000, 4000, 6000, and 8000 Hz. MetS was defined based on the NCEP-ATP III criteria. Data were analyzed using logistic regression models, with MetS as the dependent variable and NIHL as the predictor, adjusting for age, work experience, BMI, and blood pressure. Findings showed the prevalence of MetS and NIHL was 6.92% and 22.24%, respectively. mean hearing threshold across the tested frequencies significantly higher in individuals with MetS than in those without (p < 0.05), and this difference was more pronounced at higher frequencies. Statistical analysis indicated that NIHL was associated with higher odds of MetS (OR = 1.35, 95% CI: 1.27–1.44; p < 0.05), although the strength of this association decreased with increasing frequency. The greatest NIHL effect was seen in raising triglyceride (TG) levels, while its effect on lowering HDL levels was not statistically significant (p > 0.05). Among demographic variables, higher BMI and education level were associated with higher odds of MetS. Overall, the results show that frequency-specific patterns of NIHL were associated with a higher likelihood of MetS. Therefore, controlling noise and addressing its non-auditory effects is essential in industrial settings.

Introduction

Studies have shown that in some petrochemical industries, noise levels commonly exceed recommended standard limits, which may be attributed to the use of heavy machinery and complex production processes. Operations such as distillation, pumping, and chemical reactions—especially when conducted at large scales are associated with elevated sound pressure levels¹. Major sources of noise in these industries include compressors, pumps, turbines, and blowers^2,3.These devices, due to their high workload and the nature of the processes for which they are designed, typically generate high sound pressure levels. Moreover, mechanical processes such as crushing and mixing of materials can also be significant sources of noise^2,4.

One of the major consequences of excessive noise exposure is damage to the sensory-neural system in the inner ear, which may be associated with partial or permanent hearing loss (deafness) in one or both ears⁵. Nowadays, hearing loss is considered a serious health issue in various industries, including the petrochemical sector¹, as it not only restricts social interactions but also adversely affects health, well-being, daily functioning, cognitive and emotional status, quality of life, and work productivity⁶. According to some studies, nearly half a billion people worldwide suffer from hearing loss⁷, with noise-induced hearing loss (NIHL) recognized as a serious issue and one of the most prevalent occupational disorders globally, accounting for 7% to 21% of all hearing loss cases⁸. In the United States, approximately 16% to 24% of hearing loss cases are attributed to occupational noise exposure^9,10, while World Health Organization (WHO) statistics indicate that 16% of hearing loss in adults is due to occupational noise exposure⁹.

Additionally, various studies have shown that the prevalence and severity of NIHL can vary based on work experience and the frequency of exposure. This condition often develops gradually over years of continuous or intermittent noise exposure, initially affecting high-frequency HT, and with prolonged exposure, extending to lower frequencies as well^11,12.

However, recent research has reported that noise exposure beyond recommended standards has been associated with non-auditory effects. According to studies, noise-induced stress has been linked to elevated blood pressure^13,14, is associated with metabolic and cardiovascular disorders^15,16, is associated with alterations in glucose levels and insulin regulation¹⁷, has been associated with obesity¹⁸, and result in increased waist circumference and higher body mass index (BMI)¹⁹.

Arlien-Søborg and colleagues also found that occupational noise exposure is associated with increased triglyceride levels and decreased cholesterol levels^20,21. Therefore, it can be suggested that environmental risk factors such as noise may be associated with the occurrence of metabolic syndrome (MetS) by affecting inflammatory pathways^22–24.

According to the ATP III guidelines, MetS can be diagnosed when an individual presents with three or more of the following five criteria: abdominal obesity, elevated triglycerides (TG), reduced high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia. These components are known to be associated with increased mortality due to cardiovascular diseases (CVD), coronary heart disease (CHD), and type 2 diabetes^20,25. Given the adverse effects of noise on some MetS components, environmental noise can be considered a potential risk factor for the development of MetS¹⁴. In fact, MetS has been reported to be associated with hearing outcomes through mechanisms such as vascular and neural dysfunction, which can lead to degeneration of inner ear tissues. As a result, thorough investigation of the effects of MetS on hearing health is particularly important, especially among populations at risk of metabolic disorders. However, findings regarding these associations have varied significantly across studies²⁶. Recent genetic and epidemiological studies have also highlighted that chronic inflammation, oxidative stress, and vascular impairments may represent shared biological pathways linking metabolic dysfunction to hearing impairment, potentially explaining part of the co-occurrence observed between these conditions²⁷.

The global prevalence of MetS is reported to range from 10% to 50%, with an estimated prevalence of 34.7% in Iran. The syndrome is more prevalent among urban populations, women, and individuals aged 55–64, compared to rural populations, men, and other age groups^28–30. Therefore, considering the prevalence in Iran, it can be classified as moderate to high^31–33.

With the expansion of industrialization and urbanization, exposure to occupational hazards, particularly noise pollution, has increased, and noise-related occupational injuries have also become more prevalent. The association between noise exposure and certain components of MetS, along with the non-auditory effects of noise, highlights the critical importance of MetS management in industrial settings. Given the limited number of studies exploring the association between noise and MetS especially in diverse populations²⁵ and considering the significance of the observed association between MetS and NIHL, the objective of this study is to investigate noise-induced hearing loss, derived from audiometric thresholds across tested frequencies, and its association with MetS in a petrochemical industry.

Materials and methods

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Abadan University of Medical Sciences (Ethics approval code: IR.ABADANUMS.REC.1403.023). All procedures were performed in accordance with relevant guidelines and regulations, including the Declaration of Helsinki. All participants were fully informed about the study objectives and procedures, and written informed consent was obtained from each participant prior to enrollment. Participation was entirely voluntary, and individuals had the right to withdraw from the study at any time without any consequences.

Participants

This descriptive-analytical cross-sectional study was conducted on 1,185 male employees working in the Mahshahr Petrochemical Industrial Zone. Demographic variables, including marital status, educational level, work experience (in years), and physical activity (yes/no), as well as the inclusion and exclusion criteria, were obtained from participant-completed questionnaires. The inclusion criteria were a minimum of one year of employment in the current job position, willingness to participate in the study, no history of ear diseases or ear surgeries, and no use of ototoxic medications. The exclusion criteria included a known history of congenital disorders, heart failure, kidney disease, secondary hypertension, and use of medications that could interfere with the variables under study. To verify the accuracy of the data provided by participants, information from a randomly selected subset of questionnaires was compared with data from their occupational health records. For physical measurements, height was measured using a stadiometer with 1 mm precision; weight was measured with a mechanical scale accurate to 100 g; BMI was then calculated from measured height and weight (kg/m²); blood pressure was measured using a mercury sphygmomanometer; and waist circumference was measured using a flexible tape. Blood pressure was recorded in the seated position as the average of two measurements taken from the right arm after at least five minutes of rest, using a mercury sphygmomanometer. Waist circumference was measured at the narrowest point above the navel while the participant was at the end of a normal exhalation. After recording demographic data, the participants underwent clinical examination and audiometric assessment. They were then instructed to fast for 12–14 h before visiting the laboratory for venous blood sampling to assess paraclinical variables.

Audiological examination

To identify individuals exposed to noise, the Time-Weighted Average (TWA) noise exposure level was calculated in accordance with ISO 9612:2009 guidelines³⁴. Noise level measurements were conducted using a TES-1358 sound level meter (manufactured in Taiwan). The equivalent continuous sound pressure level (Leq) was calculated based on measured sound pressure levels and subsequently normalized to an 8-hour reference period to obtain the TWA. Workers whose occupational noise exposure exceeded 85 dB(A) were classified as noise-exposed individuals^34,35. Pure-tone audiometry tests were performed by a licensed audiologist in sound-treated rooms (with background noise levels below 30 dB) for both ears. Air conduction hearing thresholds (HT) were measured for the left and right ears at frequencies of 500, 1000, 2000, 3000, 4000, 6000, and 8000 Hz, with presentation levels ranging from 10 to 120 dB HL³⁵. Audiometric assessments were conducted at the occupational health unit using a DANPLEX-AS54 audiometer, which had been previously calibrated. To minimize measurement error, all audiologists followed a standardized procedure: “present the tone, reduce by 10 dB if heard, and increase by 5 dB if not heard.” All tests were conducted in accordance with ANSI S3.1–1991 standards. Participants were required to abstain from noise exposure for at least 14 h prior to the hearing test^36,37.

Age-corrected hearing thresholds were derived according to ISO 7029:2017³⁸. Noise-induced hearing loss (NIHL) was quantified as the mean of the age-adjusted thresholds across all tested frequencies. For descriptive purposes, NIHL was categorized using a cutoff of 25 dB to stratify participants into two groups (≥ 25 dB and < 25 dB). This computational approach follows the framework described in the NIOSH Criteria Document for Occupational Noise Exposure (NIOSH, 98–126), in which threshold elevation after age adjustment is used as an indicator of noise-related auditory impairment. For classification purposes, NIHL was determined separately for the right and left ears, and cases in which both ears met the NIHL criterion were categorized as bilateral NIHL³⁹.

Definition of metabolic syndrome

The criteria for assessing MetS were based on the guidelines proposed by the National Cholesterol Education Program—Adult Treatment Panel III (NCEP-ATP III). According to this standard, any individual exhibiting three or more of the diagnostic components of metabolic syndrome, including waist circumference, triglycerides, HDL-C, blood pressure, and fasting glucose as defined in Table 1, was considered to have MetS^40,41.

Table 1.

Criteria for clinical diagnosis of MetS by National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III).

Elevated waist circumference	≥ 90 cm in men
	≥ 85 cm in women
Elevated triglyceride	≥ 150 mg/dL (1.7 mmol/L)
Reduced HDL-C	≤ 40 mg/dL (1.03 mmol/L) in men
	≤ 50 mg/dL (1.3 mmol/L) in women
Elevated blood pressure	≥ 130 mmHg systolic pressure ≥ 85 mmHg diastolic pressure
Elevated fasting glucose	≥ 100 mg/dL

Statistical analysis

The Kolmogorov–Smirnov test was used to assess the distribution and normality of the variables. In cases where variables were not normally distributed, non-parametric tests were applied. For data analysis, various statistical tests were employed, including the independent t-test for comparing the means of two independent groups, the Mann–Whitney U test for comparing means in two independent groups without a normal distribution, the chi-square test for examining the association between two categorical variables, and logistic regression to predict the odds ratio (OR) of the binary dependent variable based on the independent variables. In the logistic regression analysis, three sets of models were applied. First, univariable models were performed in which metabolic syndrome served as the dependent variable and individual predictors, including NIHL and age-corrected hearing thresholds at tested frequencies, were entered separately. Second, additional univariable models were conducted in which lipid profile parameters were treated as dependent variables and NIHL was examined as the independent predictor. Finally, a multivariable logistic regression model was constructed with metabolic syndrome as the dependent variable, NIHLt as the primary independent variable, and age, marital status, education, work experience, physical activity, and BMI included as potential confounders. All analyses were performed using SPSS software version 25, with a significance level set at 0.05.

Results

Taking into account the exclusion criteria, 43 individuals were excluded from the study, and a total of 1,142 male participants were included in the final analysis. The mean age and work experience of the participants were 37.45 ± 9.28 and 10.59 ± 7.13 years, respectively. Among the participants, 66.2% were married, and 64.62% had a high school diploma or lower educational attainment. Moreover, more than half of the individuals (59.72%) did not follow a regular exercise program.

Table 2 presents the means and frequencies of demographic variables and biochemical parameters assessed in this study, comparing participants with MetS and those without it (nMetS), along with the statistical significance of differences between the two groups. The analysis of demographic variables revealed statistically significant differences in educational level, work experience, and body mass index (BMI) between the two groups. Specifically, the mean work experience was higher in the MetS group compared to the nMetS group (12.23 ± 6.48 vs. 10.47 ± 7.16 years, respectively). Similarly, the BMI score was significantly higher in the MetS group than in the nMetS group (29.09 ± 3.05 vs. 24.32 ± 3.81, respectively).

Table 2.

Baseline characteristics of participants with and without MetS.

Variables	Total N or mean ± SD	Participants without MetS (n = 1063)	Participants with MetS (n = 79)	P-value
Age (years)	37.45 ± 9.28	37.32 ± 9.23	39.16 ± 9.84	0.088
Marital
Single	386(33.8%)	357(92.49%)	29(7.51%)	0.571
Married	756(66.2%)	706(93.39%)	50(6.61%)
Education
High school diploma or lower	738(64.62%)	703(95.26%)	35(4.74%)	0.001
Associate degree	149(13.05%)	132(88.59%)	17(11.41%)
Bachelor’s degree	213(18.65%)	189(88.73%)	24(11.27%)
Master’s degree or higher	42(3.68%)	39(92.86%)	3(7.14%)
Work experience	10.59 ± 7.13	10.47 ± 7.16	12.23 ± 6.48	0.023
Physical activity
Yes	460(40.28%)	428(93.04%)	32(6.96%)	0.966
No	682(59.72%)	635(93.11%)	47(6.89%)
WC (cm)	91.65 ± 14.9	90.4 ± 14.37	108.39 ± 11.5	< 0.001
FBS (mg/dL)	96.83 ± 46.66	93.87 ± 41.98	136.54 ± 78.3	< 0.001
HDL (mg/dL)	43.61 ± 4.4	43.6 ± 4.36	43.71 ± 4.94	0.833
TG (mg/dL)	164.46 ± 81.01	159.71 ± 79.32	228.18 ± 76.91	< 0.001
Hypertension (%)	118(10.33%)	82(69.49)	36(30.51)	< 0.001
Systolic BP (mmHg)	112.33 ± 11.1	111.66 ± 10.39	121.46 ± 15.51	< 0.001
Diastolic BP (mmHg)	73.51 ± 6.78	73.31 ± 6.61	76.2 ± 8.41	0.004
BMI
Total (kg/m2)	24.65 ± 3.95	24.32 ± 3.81	29.09 ± 3.05	< 0.001
Underweight	52(4.55%)	52(100%)	0(0%)	< 0.001
Healthy Weight	578(50.61%)	571(98.79%)	7(1.21%)
Overweight	419(36.69%)	368(87.83%)	51(12.17%)
Obesity	93(8.14%)	72(77.42%)	21(22.58%)

Note: SD= Standard deviation; WC= waist circumference; FBS= fasting blood sugar; HDL = high-density lipoprotein; TG = Triglyceride; BMI = Body mass index; BP= blood Pressure, Statistical tests used: independent t-test for continuous variables and chi-square(χ2) test for categorical variables.

In this study, 79 individuals (6.92%) were identified as meeting the criteria for MetS. Among the MetS diagnostic criteria, hypertriglyceridemia was the most prevalent disorder, observed in 537 individuals (47.02%), while hypertension was the least prevalent, detected in 118 individuals (10.33%). Further analysis of the MetS criteria showed that all variables, except for HDL, exhibited statistically significant differences between the MetS and nMetS groups (P < 0.05), with higher values observed in the MetS group. No significant difference was found in HDL levels between the two groups (P > 0.05).

The results of the NIHL assessment showed that 254 individuals (22.24%) had NIHL values of ≥ 25 dB, with 168 cases (14.71%) in the left ear and 81 cases (7.09%) in the right ear. These findings are presented in Fig. 1. Table 3 displays the differences in NIHL between the MetS and nMetS groups, broken down by ear (left and right) and Hearing thresholds across various frequencies, along with the statistical significance of these differences. The results of this study indicate that the NIHL in all general conditions, separated by left and right ear, and hearing thresholds at different frequencies was significantly different between the MetS and nMetS groups (P < 0.05). In all these conditions, the NIHL in the MetS group was higher than in the nMetS group. The difference in mean hearing threshold between the two groups increased with increasing frequency. Figure 2 shows the mean difference (± SD) in hearing thresholds at each test frequency between participants with MetS and nMetS.

Fig. 1.

Prevalence of NIHL ≥ 25 dB, MetS and its components.

Table 3.

NIHL at different frequencies in participants with and without MetS.

Variables	Mean ± SD			Mean difference	95% Confidence Interval of the difference		P-value
	Total	Participants without MetS (n = 1063)	Participants with MetS (n = 79)		Lower	Upper
NIHL-Total	24.94 ± 6.81	23.66 ± 4.14	42.18 ± 11.03	18.52 ± 1.25	16.03	21.00	< 0.001
NIHL-Left	27.08 ± 8.25	25.76 ± 6.18	44.87 ± 11.48	19.11 ± 1.31	16.52	21.71	< 0.001
NIHL-Right	26.2 ± 8.39	24.87 ± 6.19	44.03 ± 12.74	19.16 ± 1.45	16.28	22.03	< 0.001
HT 500 Hz	20.42 ± 1.84	20.28 ± 1.22	22.37 ± 5.02	2.09 ± 0.57	0.96	3.22	< 0.001
HT 1000 Hz	21.07 ± 4.21	20.65 ± 3.31	26.7 ± 8.74	6.05 ± 0.99	4.08	8.01	< 0.001
HT 2000 Hz	20.9 ± 3.7	20.44 ± 2.08	27.14 ± 9.94	6.70 ± 1.12	4.47	8.93	< 0.001
HT 3000 HZ	24.61 ± 9.75	22.93 ± 5.85	47.32 ± 19.1	24.39 ± 2.16	20.10	28.68	< 0.001
HT 4000 HZ	29.46 ± 12.72	27.37 ± 8.92	57.48 ± 20.74	30.11 ± 2.35	25.43	34.78	< 0.001
HT 6000 HZ	29.05 ± 13.1	26.89 ± 9.57	58.14 ± 18.5	31.25 ± 2.1	27.07	35.43	< 0.001
HT 8000 Hz	28.16 ± 13.47	26.04 ± 9.82	56.65 ± 21.43	30.60 ± 2.43	25.77	35.44	< 0.001

Note: SD= Standard deviation; NIHL=Noise induced hearing loss; n=Number, HT: Hearing Threshold; P value for Independent Samples T Test.

Fig. 2.

Mean difference in hearing thresholds across test frequencies between MetS and nMetS groups.

Table 4 presents the results of the multivariable logistic regression model examining demographic variables and NIHL in relation to metabolic syndrome. The findings indicate that higher NIHL values were associated with higher odds of MetS (OR = 1.35; 95% CI: 1.27–1.44). Age, marital status, work experience, and physical activity were not significantly associated with the odds of MetS (P > 0.05). However, higher BMI and educational level were significantly associated with higher odds of MetS. Specifically, each unit increase in BMI was associated with 11.1% higher odds of MetS (OR = 1.11; 95% CI: 1.02–1.22). Moreover, higher educational level was associated with 2.6-fold higher odds of MetS (OR = 2.6; 95% CI: 1.67–4.02). Using a high school diploma as the reference group, the associate degree level was not significantly associated with the odds of MetS, whereas both bachelor’s and master’s degrees were associated with significantly higher odds of MetS (OR = 9.21 and OR = 7.86, respectively).

Table 4.

Results of logistic regression analysis of MetS influenced by NIHL and demographic variables.

Variables	P-value	OR	95% CI for OR
			Lowest	Highest
NIHL	< 0.001	1.351	1.272	1.436
Age	0.763	1.017	0.909	1.139
Marital status	0.495	0.698	0.249	1.960
Education	< 0.001	2.596	1.675	4.024
High school diploma or lower	ref	ref	ref	ref
Associate degree	0.073	3.008	0.904	10.010
Bachelor degree	< 0.001	9.206	3.387	25.027
Master’s degree or higher	0.038	7.857	1.126	54.813
Work experience	0.429	1.069	0.906	1.262
Physical activity	0.118	0.541	0.251	1.168
BMI	0.021	1.111	1.016	1.216

BMI = Body Mass Index, NIHL = Noise-Induced Hearing Loss, CI = Confidence Interval, OR = Odds Ratio.

Table 5 presents the results of univariable logistic regression models in which metabolic syndrome and each of its diagnostic components were treated as dependent variables, with NIHL entered as the independent predictor. The results show that higher NIHL values were associated with higher odds of MetS and its components. Specifically, NIHL was associated with 28.9% higher odds of MetS (OR = 1.289, 95% CI: 1.237–1.343). Among the MetS components, NIHL showed the strongest and weakest associations with hypertriglyceridemia (32% higher odds, OR = 1.32, 95% CI: 1.256–1.388) and abnormal HDL levels (4.2% higher odds, OR = 1.042, 95% CI: 1.02–1.065), respectively. Figure 3 displays the overall mean hearing thresholds across test frequencies, comparing the MetS and nMetS groups as well as groups with normal and abnormal levels of HDL, WC, FBS, TG, and BP.

Table 5.

Results of NIHL effect on increased OR of MetS and each of its components.

Dependent variable	P-value	OR	95% CI for OR
			Lowest	Highest
MetS	< 0.001	1.289	1.237	1.343
WC	< 0.001	1.185	1.151	1.221
FBS	< 0.001	1.156	1.128	1.185
HDL	< 0.001	1.042	1.02	1.065
TG	< 0.001	1.32	1.256	1.388
BP	< 0.001	1.078	1.056	1.1

FBS = Fasting Blood Sugar, HDL = High-Density Lipoprotein, TG = Triglycerides, NIHL = Noise-Induced Hearing Loss, CI = Confidence Interval, OR = Odds Ratio.

Fig. 3.

Comparison of overall mean hearing thresholds across test frequencies between MetS and nMetS groups, as well as between groups with normal and abnormal MetS indicators.

Table 6 presents the univariable logistic regression results in which metabolic syndrome was treated as the dependent variable and NIHL as well as age-corrected hearing thresholds at the tested frequencies were included as independent variables. The results showed that the hearing threshold at all frequencies, as well as the overall NIHL, were associated with higher odds of MetS.

Table 6.

Logistic Regression Results for the association between NIHL and Hearing Thresholds at Each Frequency on Increasing the OR of MetS.

Variables		B	S.E.	P-Value	OR	95% CI for OR
Independent variable	Dependent variable					Lowest	Highest
NIHL	MetS	0.254	0.021	< 0.001	1.289	1.237	1.343
NIHL-Right		0.177	0.015	< 0.001	1.194	1.159	1.23
NIHL-Left		0.188	0.015	< 0.001	1.207	1.172	1.244
HT 500 Hz		0.296	0.049	< 0.001	1.344	1.222	1.48
HT 1000 Hz		0.147	0.02	< 0.001	1.159	1.115	1.205
HT 2000 Hz		0.224	0.028	< 0.001	1.251	1.184	1.322
HT 3000 HZ		0.144	0.012	< 0.001	1.155	1.129	1.182
HT 4000 HZ		0.12	0.01	< 0.001	1.127	1.106	1.149
HT 6000 HZ		0.115	0.009	< 0.001	1.122	1.102	1.142
HT 8000 Hz		0.1	0.008	< 0.001	1.105	1.088	1.123

FBS = Fasting blood sugar, HDL = High-density lipoprotein, TG = Triglycerides, NIHL = Noise-induced hearing loss, CI = Confidence interval, OR = Odds ratio, HT: Hearing Threshold.

The strongest association was observed at 500 Hz, with 34.4% higher odds of MetS (OR = 1.344, 95% CI: 1.222–1.480). In contrast, the weakest association was observed at 8000 Hz, with 10.5% higher odds of MetS (OR = 1.105, 95% CI: 1.088–1.238).

Discussion

The development of process industries, particularly in southern Iran’s petrochemical sector, has significantly increased workers’ exposure to harmful environmental factors, including industrial noise. As a physical hazard, noise not only affects the auditory system but can also induce numerous non-auditory effects, such as metabolic disorders - impacts that are frequently overlooked⁴². Exposure to high-intensity noise has been associated with chronic stress, which is directly associated with elevated levels of stress hormones (e.g., cortisol). these hormones are thought to be involved in metabolic processes including lipolysis, glucose metabolism, and blood pressure regulation^42,43.

In this context, Gupta et al.‘s study demonstrated that occupational exposure to high noise levels is associated with significant non-auditory health outcomes, including metabolic disorders such as elevated triglyceride and total cholesterol levels⁴⁴. Similarly, Kamp et al. reported that environmental noise exposure has been associated with metabolic disturbances including diabetes and obesity⁴⁵. However, limited research exists regarding the frequency-specific effects of noise on lipid profiles, underscoring the need for more comprehensive investigations in this field²⁵. The present study represents one of the few investigations examining MetS diagnostic criteria and the association between NIHL and MetS. Our statistical analyses revealed that among the five MetS diagnostic criteria evaluated, hypertriglyceridemia showed the highest prevalence while hypertension demonstrated the lowest prevalence. These findings align with results from Dehaghi et al.‘s study on MetS frequency distribution⁴⁶.

In contrast, although the findings of Rashnuodi et al. are consistent with the present study regarding the prevalence of hypertriglyceridemia, they contradict it in identifying the condition with the lowest prevalence; their study indicated that the least common metabolic disorder was related to FBS¹³. Comparing the prevalence of MetS in Iran with other countries such as Finland, Germany, the United Kingdom, and Italy can provide a broader perspective on the status of this syndrome at the international level. According to available data, the prevalence of MetS in Iran is reported to be approximately 9.6%, which is lower than that reported in Finland (65.4%), Germany (12.9%), the United Kingdom (18.26%), and Italy (17.69%)^46–51. This variation in prevalence rates may be influenced by various factors, including cultural, social, and economic differences, lifestyle, and dietary habits, levels of physical activity, and health and medical policies in each country. For instance, in European countries, high urbanization rates, sedentary lifestyles, high-calorie dietary patterns, and work-related stress levels may play a significant role in increasing the prevalence of this syndrome. Conversely, although the prevalence of MetS in Iran is on the rise, it remains lower due to climatic differences, specific dietary patterns, and certain public health intervention^29,52,53. The differing results across these studies may be attributed to the metabolic syndrome phenotype, which is the outcome of multiple underlying mechanisms and the interaction between genetic, environmental, and behavioral factors¹³.

An analysis of the MetS assessment criteria indicates that only the HDL criterion did not show a significant difference in mean values between the two groups, whereas the other criteria exhibited significant mean differences, with higher values observed in the MetS group compared to the nMetS group. These results are consistent with the findings of Ford et al., who investigated the prevalence of MetS among adults in the United States. Their study demonstrated that there were significant mean differences between MetS prevalence and various indicators such as blood glucose, blood pressure, and waist circumference, while no significant mean difference was found for HDL⁵⁴. Similarly, the results of studies by Amiri et al⁴⁶. and Arabian et al⁵⁵. showed that among the MetS assessment criteria, there was no significant mean difference in HDL levels between the MetS and nMetS groups. However, the mean values of other variables such as hypertriglyceridemia, blood glucose, and waist circumference differed significantly, with higher values in the MetS group. One possible explanation for this finding is that changes in HDL levels are more strongly influenced by genetic factors and lifestyle elements such as physical activity and diet. Therefore, exposure to noise may have a lesser impact on HDL levels⁵⁶. Additionally, several genetic and pathophysiological studies have proposed that chronic inflammation, oxidative stress, and vascular alterations may act as shared biological mechanisms underlying both metabolic abnormalities and auditory dysfunction, which could help explain the co-occurrence of MetS with hearing impairment²⁷.

In this study, the NIHL was significantly different between the MetS and nMetS groups under all examined conditions, with the MetS group exhibiting significantly higher levels than the nMetS group. In line with these findings, the results of studies by Li et al⁵⁷. and Yu et al²⁰., which investigated the impact of noise exposure on metabolic indicators, demonstrate a direct relationship between sound pressure levels and increases in blood pressure and blood glucose — both of which are among the assessment criteria for MetS — while no significant effect on HDL was observed. Furthermore, the study by Huang et al¹⁴. reported an increased risk of hypertriglyceridemia and abdominal obesity in individuals exposed to moderate and high-intensity noise, which is consistent with the results obtained in the present research.

An analysis of the mean hearing thresholds in the MetS and nMetS groups indicates that as the audiometric test frequencies increased, the mean difference between the two groups became more pronounced. The greatest difference was observed at frequencies above 3 kHz. Higher hearing thresholds at all frequencies were associated with MetS; however, despite larger threshold differences at higher frequencies, the strength of the association with MetS was greater at lower frequencies. Specifically, the strongest association was observed at 500 Hz (34.4% higher odds), while the weakest association was observed at 8000 Hz (10.5% higher odds).

The findings of this study are consistent with previous research, such as the study by Khosravipour et al., which showed that exposure to frequencies of 4 and 8 kHz has been associated with higher prevalence of MetS. Their results also indicated that lower frequencies, particularly 500 Hz, showed a stronger association with MetS⁵⁸. In the study by Amiri et al., which examined the association between noise exposure and different frequencies and the odds of MetS, it was shown that as noise frequency increases, the OR gradually decreases. Their analysis revealed that the highest OR was observed at 500 Hz (1.423) and the lowest at 8000 Hz (1.204). This pattern of decreasing OR with increasing frequency aligns with the findings of the present study, suggesting that lower-frequency hearing thresholds were more strongly associated with the odds of MetS⁴⁶.

The results of the logistic regression analysis show that higher NIHL values were associated with approximately 28.9% higher odds of MetS. Among the MetS assessment criteria, NIHL showed the strongest and weakest associations with hypertriglyceridemia (OR = 1.32) and abnormal HDL levels (OR = 1.042), respectively.

One possible explanation for this is the higher penetrability of low-frequency sounds (such as 500 Hz), which are suggested to have deeper physiological effects on the body.

Such sounds are capable of affecting various bodily systems and may trigger mechanisms such as chronic stress. Chronic stress is associated with elevated levels of hormones such as cortisol, which play a role in regulating blood pressure, lipid metabolism, and glucose metabolism. these mechanisms have been associated with metabolic disturbances and MetS^42,43.

The analysis indicates that among the demographic variables examined, education level, work experience, and BMI showed significant differences between the MetS and nMetS groups. In line with this, the assessment of the OR for demographic variables revealed that only higher BMI and education level were significantly associated with the odds of MetS. Specifically, higher education level and BMI were associated with higher odds of MetS. These findings are consistent with those reported by Arlene-Sabourk et al., who identified higher BMI and lower education levels as influential variables associated with MetS. They highlighted the significant effects of BMI and lower education levels on lipid profile changes and confirmed the role of these factors in metabolic abnormalities related to MetS²¹. Similarly, the results of Huang et al. emphasized the role of demographic variables in the prevalence of MetS, showing that higher BMI is associated with a MetS¹⁴. Supporting the demographic results of the present study, Afshari et al. also demonstrated that demographic variables such as BMI are associated with MetS. In their study, higher BMI was associated with higher odds of MetS (OR = 1.30), and the odds were also higher among shift workers⁵⁹.

Regarding the inverse relationship between education level and the odds of MetS, one possible explanation is the insufficient physical activity among individuals with higher education levels due to the sedentary nature of their occupations. This has also been confirmed in the study by Ping and Oshio, whose findings showed that individuals with higher education levels, compared to those with lower education, have higher odds of dyslipidemia and cardiovascular disease. These findings may be attributed to a sedentary lifestyle, poor dietary habits, and occupational stress⁶⁰. In some studies, however, this relationship has been reported as direct, with authors suggesting that higher education can change individuals’ attitudes toward the awareness and management of metabolic disorders through healthier diets and greater physical activity^61–63. The inconsistency with these studies may be due to insufficient education of individuals⁶¹, the nature of their jobs, or demographic characteristics of the studied population, such as ethnicity, culture, age, and other factors⁶².

Limitations

Despite the significant findings of this study, there are several limitations that should be considered and addressed in future research. Gender was not included as a variable. Individual dosimetric noise measurements were not available. Smoking status was not collected as part of the study protocol.

Author contributions

A.A: Writing – original draft, Methodology, Formal analysis. E.S: Investigation, Writing – review & editing. A.K: Conceptualization, Supervision. P.R: Writing – original draft, Methodology, Data curation.

Funding

This study was registered and financially supported by the Research Committee of Abadan University of Medical Sciences under project number IR.ABADANUMS.REC.1403.023. The authors would like to express their sincere gratitude to all individuals who kindly contributed to the completion of this research.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval

This study was reviewed and approved by the Ethics Committee of Abadan University of Medical Sciences (Approval Code: IR.ABADANUMS.REC.1403.023).