Abstract
Background:
Noise-induced hearing loss remains a common occupational disease. Although hazardous noise is primary cause, metabolic factors like dyslipidaemia may contribute, yet comparative evidence across diverse occupational settings remains limited.
Objective:
This study aimed to compare hearing thresholds and serum lipid profiles between healthcare employees and automotive industry workers and to assess the relative contributions of occupational noise exposure and dyslipidaemia to auditory outcomes.
Methods:
In this cross-sectional study, 9965 employees (5698 automotive workers and 4267 healthcare employees) were evaluated. Pure-tone audiometry was performed in accordance with International Organization for Standardization 8253-1 standards, and fasting lipid panels were analysed using standardised protocols. Occupational noise exposure was characterised using representative workplace monitoring data. Healthcare staff experienced the equivalent continuous sound level (LAeq) 60–70 dBA, exceeding WHO comfort recommendations. Automotive workers were exposed to LAeq 75–85 dBA, above long-term exposure limits. Comparative analyses were conducted using independent-samples t-tests and chi-square tests. Multivariable regression models were applied to identify independent predictors of hearing thresholds.
Results:
Despite their higher noise exposure, automotive workers demonstrated significantly better auditory thresholds than healthcare employees (mean binaural threshold: 8.2 ± 6.8 dB vs. 10.7 ± 8.0 dB; P < 0.001). Occupation type was an independent predictor of hearing status. Lipid parameters (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, non-HDL cholesterol and triglycerides) showed only weak and clinically negligible associations with auditory thresholds.
Conclusions:
Hearing outcomes were more strongly influenced by occupational setting and preventive frameworks than by noise intensity or lipid status. Structured hearing conservation programs in the automotive sector showed protective effects, contrasting with limited measures in healthcare workplaces. These findings highlight the need to extend hearing conservation strategies beyond other sectors.
Keywords: audiometry, automotive industry, dyslipidaemia, healthcare workers, noise-induced hearing loss, occupational exposure
KEY MESSAGES
-
(1)
Occupational determinants: Workplace category and preventive measures shaped hearing outcomes more strongly than noise intensity or lipid profile.
-
(2)
Lipid associations: Total and non-HDL cholesterol showed weak but consistent relationships with hearing thresholds.
-
(3)
Noise-health paradox: Healthcare workers exhibited poor hearing despite low measured noise, reflecting absent conservation programs.
-
(4)
Preventive strategies: Expanding structured hearing-conservation programs to nonindustrial sectors is essential to mitigate occupational hearing loss.
Introduction
Hearing is essential for effective communication, social participation, cognitive health and emotional well-being. It enables individuals to engage with their environment, interpret auditory signals and enjoy music and conversation. Impairment of auditory function disrupts daily communication, reduces productivity, contributes to cognitive decline and diminishes quality of life. The detrimental effects of occupational noise exposure on hearing health have been extensively documented over the past decades.[1,2]
Epidemiological studies identified hearing loss as the third most prevalent chronic health condition in the United States, following hypertension and arthritis.[3,4] Noise-induced hearing loss (NIHL) is one of the most common occupational diseases, significantly impairing communication; reducing functional capacity and adversely influencing cognitive performance, emotional stability, quality of life and work productivity.[5] Globally, approximately 500 million people were reported to be living with disabling hearing loss in 2015, and up to 1.3 billion were estimated to be at risk of NIHL.[6,7] According to the World Health Organization (WHO), around 10% of the world’s population is routinely exposed to hazardous noise levels, and 5.3% already experience some degree of hearing impairment.[8] NIHL is considered the most common occupational disease worldwide, affecting over 10% of workers in developed countries[9] and accounting for an estimated 7–21% of all hearing loss cases globally.[10,11,12]
Despite these well-established facts, uncertainties remain regarding the roles of individual susceptibility and nonauditory risk factors. Although occupational noise exposure above 85 dBA is the principal driver of auditory damage, additional hazards such as ototoxic chemicals, heavy metals, nanoplastics and airborne particulates may exacerbate cochlear injury.[11,13] Moreover, metabolic disorders, including dyslipidaemia and cardiovascular risk factors, may impair cochlear microcirculation and increase vulnerability to hearing loss.
Epidemiological data also reveal striking variations across industries. Studies from the United States, Norway and China documented high prevalence rates of hearing loss in traditionally noisy industries and in agriculture, service sectors and healthcare settings.[14,15,16,17] In automotive manufacturing, noise levels in final assembly were reported to be an equivalent continuous sound level (average LAeq) of 75–85 A-weighted decibels or dB (A), (L10: 80–90; L50: 76–82; L90: 70–75), which exceed long-term occupational exposure recommendations. By contrast, healthcare professionals typically experience LAeq 60–70 dBA (L10: 65–75; L50: 60–65; L90: 50–60), primarily from alarms, conversations and foot traffic. Although these levels are below traditional regulatory action limits, they substantially exceed WHO comfort guidelines for hospitals (≤35–40 dBA), suggesting that chronic low-to-moderate exposure may also contribute to auditory fatigue and cumulative risk.[18,19]
Inconsistencies exist in reported associations between occupational noise exposure and hearing impairment. For example, a study in the automotive industry found no significant correlation,[20] whereas another in Wuhan reported high-frequency hearing loss in 25% of workers and speech-frequency impairment in 6.41%.[21] Additional factors—including age, sex, elevated blood pressure, smoking, unsafe behaviour and limited use of hearing protection—have also been implicated.[22,23] These discrepancies highlight the need to investigate other determinants, such as biological and metabolic factors, that may influence hearing vulnerability.
In this context, this study examines the potential interplay between occupational noise exposure and metabolic risk, with a focus on serum lipid profiles. By comparing healthcare workers (low-to-moderate noise, limited preventive frameworks) with automotive employees (high noise, structured hearing conservation programs), this work was designed to: (1) evaluate independent associations between serum lipid components and hearing thresholds, (2) assess whether occupational setting modifies these associations, (3) explore differences across speech and high-frequency ranges and (4) identify which lipid parameters, if any, show the strongest correlation with hearing loss.
MATERIALS AND METHODS
Study Design and Participants
This cross-sectional study was conducted in 2019 to investigate the relationship between serum lipid profiles and hearing thresholds in two occupational groups with distinct noise exposure patterns. Group A consisted of healthcare personnel employed at our institution,[24] and Group B comprised industrial workers from a large automobile manufacturing company.
A total of 9965 individuals were enrolled, including 4267 healthcare employees (1626 men and 2641 women) and 5698 male industrial workers. A total of 10,273 individuals initially enrolled in the study. Among them, 308 (3.0%) participants were excluded because of incomplete audiometric data, incomplete biochemical measurements or failing to meet predefined exclusion criteria such as a history of ear surgery, chronic ear disease, ototoxic medication use or significant non-occupational noise exposure. The final analytic sample consisted of 9965 participants. All participants provided written informed consent prior to enrolment. All employees were allowed to enrol voluntarily without initial restrictions. For analytical purposes, eligibility was determined afterwards using predefined inclusion and exclusion criteria. Each participant underwent all assessments in a single day at designated examination centres. Standardised procedures were applied by trained medical personnel, and data quality was monitored by an independent assurance team.
Inclusion and Exclusion Criteria
Eligible participants were full-time employees aged 18 years or older from the healthcare and automotive sectors. Inclusion criteria included at least 1 year of job experience, no acute ear infection and completion of audiometric and biochemical tests with written informed consent.
Exclusion criteria were a history of ear surgery or chronic ear disease, use of ototoxic drugs, head trauma or significant non-occupational noise exposure. Only participants meeting these conditions were included in the analysis.
Occupational Noise Exposure Assessment
Noise exposure levels for each occupational group were characterised using available workplace monitoring data and prior validated surveys. In healthcare environments, typical background levels were LAeq 60–70 dBA, with percentile distributions of L10: 65–75 dBA, L50: 60–65 dBA and L90: 50–60 dBA, mainly arising from alarms, conversations and foot traffic, respectively. These values exceed WHO environmental noise recommendations for healthcare facilities (≤35–40 dBA) (World Health Organization, Guidelines for Community Noise, 1999).[25]
In the automotive final assembly line, workers were exposed to substantially higher continuous noise, with LAeq 75–85 dBA, L10: 80–90 dBA, L50: 76–82 dBA and L90: 70–75 dBA, primarily generated by machinery and tools. Although these values are lower than those in pressing or welding areas, they remain above recommended long-term occupational exposure limits (≥85 dBA for 8 hours).[26] These exposure data were used descriptively to contextualise observed differences in hearing outcomes between the two groups.
Audiometric Assessment
Pure-tone air-conduction audiometry was conducted in accordance with ISO 8253-1:2010 standards.[27] Thresholds were measured at 250, 500, 1000, 2000, 4000 and 8000 Hz in both ears using a calibrated Madsen Xeta audiometer (Denmark) with TDH-39P headphones. All tests were performed in soundproof booths with ambient noise levels <25 dB(A) by certified audiologists. Thresholds were recorded in 5 dB increments.
Definition of Hearing Loss
Hearing loss was defined using the average of air-conduction thresholds at 500, 1000, 2000 and 4000 Hz. Binaural thresholds were calculated as: binaural hearing threshold = (better ear × 5 + worse ear) ÷ 6.
Hearing was classified as normal (<16 dB), mild (16–25 dB), moderate (26–30 dB), moderately severe (31–50 dB), severe (51–70 dB) or profound (>70 dB) in accordance with the criteria recommended by the National Institute for Occupational Safety and Health.[26] For analytic purposes, a dichotomous classification was also applied (≤25 dB vs. >25 dB).
High-frequency hearing loss was defined as the mean threshold at 2000–8000 Hz, and low-frequency loss was defined as the mean threshold at 250–1000 Hz. Both were categorised using the same severity cut-offs.
Covariates and Clinical Measurements
Demographic, medical and occupational information was obtained from standardised occupational health examination forms routinely used in annual surveillance programs. These forms are part of the national mandatory occupational health system and are not considered research questionnaires. Therefore, conventional psychometric indices such as Cronbach’s alpha or construct validity do not apply, as the data represent objectively recorded clinical and administrative variables rather than self-reported, scale-based responses.
Lipid Profile
Fasting serum total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) were measured using enzymatic methods in accredited laboratories.
Anthropometry
Weight was measured with a SECA digital scale (±0.1 kg) and height with a SECA stadiometer (±1 mm). Body mass index (BMI) was calculated as weight/height2 (kg/m2) and classified in accordance with WHO criteria.
BMI Grouping Criteria
BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2). The participants were categorised according to the WHO classification[28]:
Underweight: <18.5 kg/m2
Normal weight: 18.5–24.9 kg/m2
Overweight: 25.0–29.9 kg/m2
Obesity: ≥30.0 kg/m2
Blood Pressure
Blood pressure was measured three times in a seated position after 15 minutes rest using a calibrated mercury sphygmomanometer. Measurements were taken at baseline, 30 minutes later and 2 hours after the second reading. The average of the three values was used for analysis.
Smoking History Assessment
Data on smoking, including current and past smoking status, number of cigarettes smoked per day and duration in years, were obtained using a data collection form. From these variables, the pack-year index (packs/day × years smoked) was calculated and used for analysis.
Statistical Analysis
Descriptive statistics were presented as mean ± standard deviation for continuous variables and as frequency (percentage) for categorical variables. Differences between the two occupational groups were analysed using independent-samples t-tests for continuous variables, and categorical variables were compared using the chi-square test. Associations between quantitative variables were examined using Spearman’s rank correlation. Multivariate logistic regression analysis was performed to identify independent predictors of hearing loss (>25 dB). A P-value <0.05 was considered statistically significant. Statistical analyses were conducted using SPSS version 26.0 (IBM Corp., Armonk, NY, USA).
Ethical Considerations
The study protocol was reviewed and approved by the Ethics Committee (Approval ID: IR.TUMS.FNM.REC.1399.227). All participants provided informed written consent. Data confidentiality was strictly maintained, and the procedures adhered to the Declaration of Helsinki and institutional ethical guidelines.[29]
RESULTS
A total of 9965 participants were included in the study, comprising 5698 male automotive workers (57.2%) and 4267 healthcare employees (38.5% male, 61.5% female). The mean age of the study population was 38.2 ± 6.7 years (range: 18–72 years). The largest proportion of participants belonged to the age group of 30–40 years (57.4%), followed by 40–50 years (26.9%). Healthcare employees were significantly older than automotive workers (38.8 ± 8.6 vs. 37.8 ± 4.6 years, P < 0.001). The mean height was 170.9 ± 9.3 cm, and the mean weight was 78.4 ± 13.8 kg. According to BMI (26.8 ± 4.3 kg/m2), the majority were overweight (41.9%), and approximately 20% were obese. Healthcare employees had a slightly higher BMI than automotive workers (27.0 vs. 26.7 kg/m2, P = 0.021). Smoking exposure was generally low in the study population, with most participants classified as nonsmokers or light smokers according to the pack-year distribution. The demographic and metabolic characteristics of the participants are summarised in Table 1.
Table 1.
Participants’ demographic information, blood lipid profile including blood pressure and smoking history, separated into two occupational groups and all together
| Total (n = 9965) | Female(n = 2641) | Male (n = 7324) | P-value | Healthcare employees (n = 4267) | Automotive workers (n = 5698) | P-value | ||
|---|---|---|---|---|---|---|---|---|
| Age (year) mean ± SD | 38.2 ± 6.7 | 38.2 ± 8.3 | 38.2 ± 6.0 | 0.890 | 38.8 ± 8.6 | 37.8 ± 4.6 | <0.001 | |
| Age groups n (%) | 18–30 | 1027 (10.3) | 468 (17.7) | 559 (7.6) | <0.001 | 722 (16.9) | 305 (5.4) | <0.001 |
| 30–40 | 5723 (57.4) | 1052 (39.8) | 4671 (63.8) | 1632 (38.2) | 4091 (71.8) | |||
| 40–50 | 2678 (26.9) | 878 (33.2) | 1800 (24.6) | 1429 (33.5) | 1249 (21.9) | |||
| >50 | 535 (5.4) | 241 (9.1) | 294 (4.0) | 482 (11.3) | 53 (0.9) | |||
| Height, cm mean ± SD | 170.9 ± 9.3 | 159.6 ± 5.8 | 175.1 ± 6.4 | <0.001 | 164.9 ± 9.1 | 175.6 ± 6.3 | <0.001 | |
| Weight, kg mean ± SD | 78.4 ± 13.8 | 67.6 ± 11.7 | 82.3 ± 12.3 | <0.001 | 73.6 ± 14.5 | 82.1 ± 12.0 | <0.001 | |
| BMI * kg/m2 mean ± SD | 26.8 ± 4.3 | 26.6 ± 4.6 | 26.9 ± 4.2 | <0.001 | 27.0 ± 4.3 | 26.7 ± 4.3 | 0.021 | |
| BMI classification n (%) | underweight | 117 (1.2) | 34 (1.3) | 83 (1.1) | <0.001 | 44 (1.0) | 73 (1.3) | 0.226 |
| healthy weight | 3307 (33.2) | 1075 (40.7) | 2232 (30.5) | 1437 (33.7) | 1870 (32.8) | |||
| overweight | 2179 (41.9) | 981 (37.1) | 3198 (43.7) | 1867 (43.8) | 2312 (40.6) | |||
| obesity | 2021 (20.3) | 551 (20.9) | 1470 (20.1) | 918 (21.5) | 1103 (19.4) | |||
| Work experience (year) mean ± SD | 12.5 ± 6.1 | 11.7 ± 7.7 | 12.8 ± 5.4 | <0.001 | 12.1 ± 8.1 | 12.8 ± 4.2 | <0.001 | |
| Systolic blood pressure (mmHg) mean ± SD | 113.1 ± 13.8 | 109.7±14.1 | 114.4 ± 13.5 | <0.001 | 113.0 ± 14.8 | 113.2 ± 13.0 | 0.517 | |
| Diastolic blood pressure (mmHg) mean ± SD | 74.5 ± 9.5 | 73.0 ± 9.0 | 75.1 ± 9.6 | <0.001 | 74.8 ± 9.3 | 74.4 ± 9.7 | 0.013 | |
| Mean arterial blood pressure (mmHg) mean ± SD | 87.4 ± 10.1 | 85.2 ± 9.8 | 88.2 ± 10.1 | <0.001 | 87.5 ± 10.2 | 87.3 ± 10.1 | 0.190 | |
| Cholesterol mg/dL mean ± SD | 172.9 ± 33.1 | 179.7 (39.4) | 170.0± 29.3 | <0.001 | 181.0 ± 39.8 | 165.2 ± 22.0 | <0.001 | |
| HDL* mg/dL mean ± SD | 45.5 ± 8.9 | 48.9 ± 10.6 | 44.3 ± 8.0 | <0.001 | 46.9 ± 10.2 | 44.4 ± 7.7 | <0.001 | |
| LDL* mg/dL mean ± SD | 100.0 ± 23.5 | 106.3 ± 53.0 | 130.0± 57.8 | <0.001 | 122.4 ± 69.9 | 122.9 ± 40.9 | <0.001 | |
| TG* mg/dL mean ± SD | 122.6 ± 57.4 | 104.5 ± 26.2 | 98.2 ± 22.1 | <0.001 | 104.7 ± 26.6 | 95.8 ± 19.5 | <0.001 | |
| LDL/HDL mean ± SD | 2.3 ± 0.6 | 2.2 ± 0.7 | 2.3 ± 0.6 | <0.001 | 2.3 ± 0.7 | 2.2 ± 0.6 | <0.001 | |
| Chol/HDL mean ± SD | 3.9 ± 0.9 | 3.8 ± 1.0 | 4.0 ± 0.9 | <0.001 | 4.0 ± 1.0 | 3.9 ± 0.8 | <0.001 | |
| TG/HDL mean ± SD | 2.8 ± 1.6 | 2.3 ± 1.3 | 3.0 ± 1.6 | <0.001 | 2.8 ± 1.9 | 2.8 ± 1.1 | <0.001 | |
| Non-HDL Cholesterol mg/dL | 127.7 ± 31.6 | 179.7 ± 39.5 | 126.3 (28.1) | <0.001 | 134.1 ± 38.3 | 121.5 ± 21.4 | <0.001 | |
| Smoking status n (%) | Current smoker | 2082 (20.9) | 258 (9.8) | 1824 (24.9) | <0.001 | 864 (20.2) | 1218 (21.4) | 0.179 |
| Ex-smoker | 1402 (14.1) | 537 (20.3) | 865 (11.8) | <0.001 | 1238 (29.0) | 164 (2.9) | <0.001 | |
| Pack-Year | 0.85 (3.3) | 0.06 (0.6) | 1.1 (3.7) | <0.001 | 0.65 (3.1) | 1.0 (3.4) | <0.001 | |
Notes: BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LAeq, equivalent continuous sound level; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides.
Biochemical analysis revealed a mean serum cholesterol of 172.9 ± 33.1 mg/dL, triglycerides of 122.6 ± 57.4 mg/dL, LDL-C of 100.0 ± 23.5 mg/dL and HDL-C of 45.5 ± 8.9 mg/dL. Derived ratios included LDL/HDL (2.3 ± 0.6), Chol/HDL (3.9 ± 0.9) and TG/HDL (2.8 ± 1.6). Healthcare employees exhibited significantly higher total cholesterol and non-HDL cholesterol compared with automotive workers, whereas automotive workers had lower HDL-C values.
Occupational noise exposure differed substantially between the two groups as described in Methods Section, with healthcare employees exposed to moderate continuous noise levels and automotive workers exposed to high industrial noise. The distribution of right- and left-ear hearing thresholds across occupational groups is presented in 234.
Table 2.
Hearing loss characteristics (thresholds and rankings) by occupation — left ear
| Left ear | ||||||||
|---|---|---|---|---|---|---|---|---|
|
|
||||||||
| Total(n = 9965) | Female(n = 2641) | Male(n = 7324) | P-value | Healthcare employees (n = 4267) | Automotive workers (n = 5698) | P-value | ||
| Hearing threshold dB mean ± SD | 12.1 ± 8.4 | 12.4 ± 8.1 | 12.1 ± 8.5 | <0.001 | 13.4 ± 9.2 | 11.2 ± 7.5 | <0.001 | |
| Hearing loss classification n (%) | None <16 dB | 7859 (78.9) | 2028 (76.8) | 5831 (79.6) | <0.001 | 3067 (71.9) | 4792 (84.1) | <0.001 |
| Slight 16–25 | 1581 (15.9) | 490 (18.6) | 1091 (14.9) | 885 (20.7) | 696 (12.2) | |||
| Mild 26–30 | 236 (2.4) | 62 (2.3) | 174 (2.4) | 143 (3.4) | 93 (1.6) | |||
| Moderate 31–50 | 218 (2.2) | 45 (1.7) | 173 (2.4) | 129 (3.0) | 89 (1.6) | |||
| Severe 51–70 | 49 (0.5) | 9 (0.3) | 40 (0.5) | 30 (0.7) | 19 (0.3) | |||
| Profound >70 dB | 22 (0.2) | 7 (0.3) | 15 (0.2) | 13 (0.3) | 9 (0.2) | |||
| Normal hearing loss <25 dB n (%) | 9360 (93.9) | 2493 (94.4) | 6867 (93.8) | 0.241 | 3903 (91.5) | 5457 (95.8) | <0.001 | |
| 1410 (86.7) | ||||||||
| Abnormal hearing loss ≥25 dB n (%) | 605 (6.1) | 148 (5.6) | 457 (6.2) | 364 (8.5) | 241 (4.2) | |||
| 216 (13.3) | ||||||||
| Low frequency hearing loss ≥25dB n (%) | 328 (3.3) | 133 (5.0) | 195 (2.7) | <0.001 | 236 (5.5) | 92 (1.6) | <0.001 | |
| 103 (6.3) | ||||||||
| High frequency hearing loss ≥25 dB n (%) | 1403 (14.1) | 270 (10.2) | 1133 (15.5) | <0.001 | 664 (15.6) | 739 (13.0) | <0.001 | |
| 394 (24.2) | ||||||||
| Notch 4000 Hz hearing loss ≥25 dB n (%) | z | 387 (14.7) | 1923 (26.3) | <0.001 | 960 (22.5) | 1350 (23.7) | 0.162 | |
| 573 (35.2) | <0.001 | |||||||
Table 3.
Hearing loss characteristics (thresholds and rankings) by occupation — right ear
| Right ear | Left earVs. right ear |
||||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|
|
|||||||
| Total (n = 9965) |
Female (n = 2641) |
Male (n = 7324) |
P-value | Healthcare employees (n = 4267) | Automotive workers (n = 5698) | P-value | |||
| Hearing threshold dB mean ± SD | 12.2 ± 8.2 | 13.2 ± 8.1 | 11.9 ± 8.2 | <0.001 | 13.9 ± 8.8 | 11.0 ± 7.5 | <0.001 | <0.001 | |
| Hearing loss classification n (%) | None <16 dB | 7875 (79.0) | 1939 (73.4) | 5936 (81.0) | <0.001 | 3002 (70.4) | 4873 (85.5) | <0.001 | <0.001 |
| Slight 16–25 | 1589 (15.9) | 564 (21.4) | 1025 (14.0) | 953 (22.3) | 636 (11.2) | ||||
| Mild 26–30 | 215 (2.2) | 63 (2.4) | 152 (2.1) | 138 (3.2) | 77 (1.4) | ||||
| Moderate 31–50 | 222 (2.2) | 58 (2.2) | 164 (2.2) | 137 (3.2) | 85 (1.5) | ||||
| Severe 51–70 | 44 (0.4) | 11 (0.4) | 33 (0.5) | 26 (0.6) | 18 (0.3) | ||||
| Profound >70 dB | 20 (0.2) | 6 (0.2) | 14 (0.2) | 11 (0.3) | 9 (0.2) | ||||
| Normal hearing loss <25 dB n (%) | 9379 (94.1) | 2476 (93.8) | 6903 (94.3) | 0.350 | 3903 (91.5) | 5476 (96.1) | <0.001 | <0.001 | |
| 1427 (87.8) | |||||||||
| Abnormal hearing loss ≥25 dB n (%) | 586 (5.9) | 165 (6.2) | 421 (5.7) | 364 (8.5) | 222 (3.9) | ||||
| 199 (12.2) | |||||||||
| Low frequency hearing loss ≥25 dB n (%) | 357 (3.6) | 163 (6.2) | 194 (2.6) | <0.001 | 272 (6.4) | 85 (1.5) | <0.001 | <0.001 | |
| 109 (6.7) | |||||||||
| High frequency Hearing loss ≥25 dB n (%) | 1349 (13.5) | 291 (11.0) | 1058 (14.4) | <0.001 | 666 (15.6) | 683 (12.0) | <0.001 | <0.001 | |
| 375 (23.1) | |||||||||
| Notch 4000 Hz Hearing loss ≥25 dB n (%) | 2086 (20.9) | 383 (14.5) | 1703 (23.3) | <0.001 | 909 (21.3) | 1177 (20.7) | 0.432 | <0.001 | |
| 526 (32.3) | <0.001 | ||||||||
Table 4.
High- and low-frequency hearing loss at 4000 Hz for left and right ears by occupational group
| Both ears | ||||||||
|---|---|---|---|---|---|---|---|---|
|
|
||||||||
| Total (n = 9965) |
Female (n = 2641) |
Male (n = 7324) |
P-value | Healthcare employees (n = 4267) | Automotive workers (n = 5698) | P-value | ||
| Hearing threshold dB mean ± SD | 9.2±7.4 | 10.0±7.3 | 9.0±7.4 | <0.001 | 10.7±8.0 | 8.2±6.8 | <0.001 | |
| Hearing loss classification N (%) | None <16 dB | 8619 (86.5) | 2217 (83.9) | 6402 (87.4) | <0.001 | 3435 (80.5) | 5184 (91.0) | <0.001 |
| Slight 16–25 dB | 1015 (10.2) | 336 (12.7) | 679 (9.3) | 620 (14.5) | 395 (6.9) | |||
| Mild 26–30 dB | 179 (1.8) | 56 (2.1) | 123 (1.7) | 120 (2.8) | 59 (1.0) | |||
| Moderate 31–50 dB | 134 (1.3) | 28 (1.1) | 106 (1.4) | 83 (1.9) | 51 (0.9) | |||
| Severe 51–70 dB | 17 (0.2) | 3 (0.1) | 14 (0.2) | 8 (0.2) | 9 (0.2) | |||
| Profound >70 dB | 1 (0.0) | 1 (0.0) | 0 | 1 (0.0) | 0 | |||
| Normal (hearing loss) <25 dB | 9630 (96.6) | 2551 (96.6) | 7079 (96.7) | 0.878 | 4053 (95.0) | 5577 (97.9) | <0.001 | |
| 1502 (92.4) | ||||||||
| Abnormal (hearing loss) ≥25 dB | 335 (3.4) | 90 (3.4) | 245 (3.3) | 214 (5.0) | 121 (2.1) | |||
| 124 (7.6) | ||||||||
| Low frequency hearing loss ≥25 dB | 159 (1.6) | 70 (2.7) | 89 (1.2) | <0.001 | 121 (2.8) | 38 (0.7) | <0.001 | |
| 51 (3.1) | ||||||||
| High frequency hearing loss ≥25 dB | 827 (8.3) | 151 (5.7) | 676 (9.2) | <0.001 | 412 (9.7) | 415 (7.3) | <0.001 | |
| 261 (16.1) | ||||||||
| Notch 4000 Hz hearing loss ≥25 dB | 1113 (11.2) | 153 (5.8) | 960 (13.1) | <0.001 | 492 (11.5) | 621 (10.9) | 0.322 | |
| 339 (20.8) | <0.001 | |||||||
Audiometric evaluation showed mean thresholds of 12.1 ± 8.4 dB in the left ear and 12.2 ± 8.2 dB in the right ear, with a bilateral average of 9.2 ± 7.4 dB. Overall, 86.5% of the participants had normal hearing (<16 dB), 10.2% had slight loss, 1.8% had mild, 1.3% had moderate and fewer than 1% had severe to profound impairment. At a cut-off of ≥25 dB, 3.4% of the participants were classified as having bilateral hearing loss. Frequency-specific analysis revealed low-frequency (250–1000 Hz) hearing loss in 1.6% of the participants and high-frequency (2000–8000 Hz) loss in 8.3%. A 4000 Hz notch, typical of NIHL, was detected in 11.2% of the participants. Detailed classifications of hearing loss severity across the groups are shown in 234.
Hearing loss prevalence was consistently higher among healthcare employees than automotive workers across bilateral, low-frequency and high-frequency measures (all P < 0.001). For example, abnormal bilateral hearing loss (≥25 dB) was observed in 5.0% of healthcare employees compared with 2.1% of automotive workers. Although the overall prevalence of the 4000 Hz notch did not differ significantly between these occupational groups, subgroup analysis limited to male participants revealed a higher frequency of notching among healthcare employees. Left-ear thresholds were marginally higher at 4000 Hz compared with the right ear, and bilateral measures showed strong concordance (Spearman’s ρ = 0.77).
Subgroup Analysis
In the male subgroup (n = 7324), significant differences were observed between healthcare workers and automotive workers. Healthcare workers showed higher mean hearing thresholds at low and high frequencies compared with automotive workers (P < 0.001). The prevalence of abnormal hearing loss (>25 dB) was 7.4% among healthcare workers and 5.7% among automotive workers. No significant difference in age or BMI was found between the males in the two occupational groups (P > 0.05). However, serum cholesterol and triglyceride levels were significantly higher among healthcare workers (both P < 0.001). These findings indicate that despite their lower occupational noise exposure, healthcare workers demonstrated poorer auditory outcomes, likely related to non-noise factors.
Correlation analyses demonstrated weak but significant associations between bilateral thresholds and lipid parameters. Total cholesterol (ρ = 0.092, P < 0.001), triglycerides (ρ = 0.052, P < 0.001), LDL-C (ρ = 0.051, P < 0.001) and non-HDL cholesterol (ρ = 0.096, P < 0.001) were all positively correlated with poor hearing, with the strongest associations observed for low-frequency thresholds with total cholesterol (ρ = 0.116, P < 0.001) and non-HDL cholesterol (ρ = 0.129, P < 0.001). HDL-C showed weak or nonsignificant relationships across frequency ranges [Table 5].
Table 5.
Correlation between lipid profile values and bilateral hearing loss indicators
| Both ears hearing loss | |||||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||
| Total hearing threshold mean | Low frequency hearing threshold | High frequency hearing threshold | Notch 4000 Hz hearing threshold | ||||||
|
|
|
|
|
||||||
| ρ | P-value | ρ | P-value | ρ | P-value | ρ | P-value | ||
| Lipid Profiles | Cholesterol mg/dL | 0.092 | <0.001 | 0.116 | <0.001 | 0.089 | <0.001 | 0.058 | <0.001 |
| TG mg/dL | 0.052 | <0.001 | −0.011 | 0.316 | 0.093 | <0.001 | 0.068 | <0.001 | |
| HDL mg/dL | 0.053 | <0.001 | 0.057 | <0.001 | 0.076 | <0.001 | 0.038 | <0.001 | |
| LDL mg/dL | 0.026 | 0.016 | 0.041 | <0.001 | 0.037 | <0.001 | 0.015 | 0.154 | |
| LDL/HDL | 0.051 | <0.001 | 0.094 | <0.001 | 0.039 | <0.001 | 0.025 | 0.021 | |
| Chol/HDL | 0.014 | 0.180 | 0.040 | <0.001 | −0.025 | 0.012 | <0.001 | 0.996 | |
| TG/HDL | 0.060 | <0.001 | −0.009 | 0.401 | 0.096 | <0.001 | 0.077 | <0.001 | |
| Non-HDL Cholesterol | 0.096 | <0.001 | 0.129 | <0.001 | 0.080 | <0.001 | 0.055 | <0.001 | |
Notes: ρ: spearman correlation coefficient; HDL, high-density lipoprotein; LDL, low-density lipoprotein, TG, triglyceride.
Multivariate regression analysis adjusted for age, sex and BMI confirmed occupation as an independent predictor of hearing thresholds. Automotive workers demonstrated significantly lower average thresholds compared with healthcare employees (B = –3.07, 95% CI −3.46 to −2.67, P < 0.001). Age (B = 0.25 per year, P < 0.001), female sex (B = 1.39, P < 0.001) and BMI (B = 0.06 per unit, P = 0.001) were also significant predictors of worse hearing. Table 6 presents the multivariable linear regression model evaluating independent predictors of bilateral hearing thresholds.
Table 6.
Linear regression model of bilateral hearing loss and its relationship with occupation R 2 = 0.087
| Dependent variable: both hearing threshold | |||||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| Unstandardised coefficients |
Standardised coefficients beta | t | P-value | 95.0% Confidence interval for β |
|||
| β | Std. error | Lower bound | Upper bound | ||||
| Occupational group | −3.066 | 0.202 | −0.205 | −15.150 | <0.001 | −3.463 | −2.669 |
| Age year | 0.246 | 0.011 | 0.223 | 22.495 | <0.001 | 0.225 | 0.268 |
| Sex (Female/Male) | 1.393 | 0.225 | 0.084 | 6.199 | <0.001 | 0.953 | 1.834 |
| BMI kg/m2 | 0.057 | 0.017 | 0.033 | 3.336 | 0.001 | 0.023 | 0.090 |
| (Constant) | 2.113 | 0.641 | - | 3.294 | 0.001 | 0.855 | 3.370 |
Notes: Adjustments: age, sex and body mass index; B, unstandardised coefficient; BMI, body mass index; CI, confidence interval; dB, decibel; SE, standard error, β, Standardised coefficient.
Despite the higher measured noise exposure in automotive assembly lines, workers in this group had better auditory outcomes than healthcare employees. This paradox highlights the protective role of structured hearing conservation programs commonly implemented in industrial settings and underscores the absence of comparable preventive strategies in healthcare environments, where even moderate but chronic noise exposure may result in measurable auditory decline.
DISCUSSION
This cross-sectional study investigated the prevalence of hearing loss and its potential association with serum lipid profiles among two distinct occupational groups—healthcare employees and automotive industry workers—exposed to markedly different noise environments. Contrary to expectations, healthcare employees, despite experiencing lower measured occupational noise (LAeq 60–70 dBA), exhibited significantly worse bilateral, low-frequency and high-frequency hearing outcomes compared with automotive workers, who were exposed to higher noise levels (LAeq 75–85 dBA).
Age is a well-established determinant of auditory decline.[30] Within the study cohort, most of the participants (94%) were younger than 50 years, with over half (57.7%) aged 30–40 years, minimising age-related confounding.[31] Although healthcare workers were marginally older than industrial workers, regression analyses confirmed that occupation remained an independent predictor of hearing loss after adjustment. Similarly, sex distribution differed between groups as follows: the automotive group included only men, whilst healthcare workers included men and women. Despite sex-adjusted analyses, gender-related susceptibility cannot be fully excluded because sex differences in NIHL have been reported.
Both groups demonstrated a high prevalence of metabolic risk, with a mean BMI in the overweight range and approximately one-fifth classified as obese. BMI was slightly higher among healthcare workers, likely influenced by sex distribution,[32] but regression models did not support BMI as an independent predictor of hearing impairment. This finding aligns with prior research reporting weak or inconsistent associations between metabolic risk factors and auditory function. Cholesterol and non-HDL cholesterol showed small but statistically significant correlations with low-frequency thresholds; however, the effect sizes were negligible (ρ < 0.13) and not clinically meaningful. These findings are consistent with previous studies reporting weak correlations between dyslipidaemia and hearing loss.[33] In the adjusted analyses, lipid parameters were not independent predictors once occupation was considered, highlighting that occupational factors outweigh metabolic influences.
NIHL is well documented as a major occupational hazard, especially in high-noise industries.[34] The study findings indicate that automotive workers—despite their higher exposure—had better hearing outcomes than healthcare workers. Several explanations warrant consideration. Firstly, the ‘Healthy Worker Effect’[35] likely contributed, as automotive companies routinely conduct pre-employment medical screenings, including audiometry, excluding individuals with preexisting hearing deficits. Furthermore, structured hearing conservation programs—including annual audiometric surveillance, exposure monitoring, engineering controls and mandatory personal protective equipment—are widely implemented in industrial settings, mitigating NIHL risk.
By contrast, healthcare settings, despite their lower average noise levels, lack systematic hearing protection measures. Hospital staff are regularly exposed to alarms, conversations and footstep noise exceeding WHO comfort guidelines for healthcare facilities (≤35–40 dBA). These exposures, though moderate, are chronic and continuous, contributing to cumulative auditory fatigue. The absence of pre-employment hearing screening, regular audiometry or structured hearing conservation programs likely explains the unexpectedly higher prevalence of hearing loss among healthcare workers.
Although the multivariable regression model demonstrated a low explanatory power (R 2 = 0.087), this finding is consistent with cross-sectional epidemiological studies in which hearing threshold variability is influenced by numerous unmeasured biological, environmental and behavioural factors. Hearing outcomes are affected by genetic susceptibility, long-term cumulative noise exposure, ototoxic agents, metabolic health, cardiovascular status, lifestyle behaviours and use of hearing protection—many of which were not captured in the present dataset. Therefore, the primary aim of our model is not to achieve high predictive accuracy but to identify independent associations between serum lipid components and hearing thresholds. The observed R 2 value does not undermine the validity of these associations but highlights the multifactorial nature of hearing loss.
Overall, these results emphasise that occupational hearing outcomes cannot be explained by noise intensity alone. Preventive frameworks, workforce selection and ongoing monitoring substantially shape auditory health. The study’s findings are consistent with international evidence indicating that structured hearing conservation programs remain the cornerstone of NIHL prevention. Whilst metabolic factors such as dyslipidaemia may contribute modestly, occupation type and the presence or absence of preventive interventions emerged as the strongest determinants of hearing loss in our cohort.
CONCLUSION
This study demonstrates that occupation-related factors, particularly the presence or absence of structured hearing conservation programs, are stronger determinants of hearing health than measured workplace noise levels alone. Despite their chronic exposure to LAeq 75–85 dBA, automotive workers exhibited better hearing outcomes than healthcare employees who were exposed to lower noise intensities but lacked preventive frameworks. These results highlight the paradox that nonindustrial sectors, often overlooked in occupational safety policy, may face unrecognised risks of hearing impairment.
Although serum lipid parameters, particularly total and non-HDL cholesterol, showed weak associations with low-frequency thresholds, they did not appear as independent predictors of hearing loss in the adjusted models. This finding suggests that metabolic influences may play a secondary role, and occupational setting and systematic protective measures remain primary.
Future occupational health strategies should extend beyond high-noise industries to include nonindustrial workplaces such as healthcare. Implementation of pre-employment screening, routine audiometric surveillance and educational interventions is essential to address this overlooked risk. By recognising that noise exposure is not the sole determinant of occupational hearing loss, policy and practice can be aligned to protect hearing health across diverse work environments.
Availability of Data and Materials
Data are subject to third-party restrictions.
Author Contributions
Concept and Study Design: Hamidreza Pouragha, Ramin Mehrdad
Intellectual Content: Gholamreza Pouryaghoub, Ramin Mehrdad
Literature Review: Hamidreza Pouragha
Clinical/Experimental Work: Gholamreza Pouryaghoub, Ramin Mehrdad
Data Collection: Mahsa Naserpour
Statistical Analysis: Hamidreza Pouragha, Ramin Mehrdad
Manuscript Drafting: Hamidreza Pouragha
Manuscript Editing: Mahsa Naserpour, Ramin Mehrdad
Final Review: All authors
Ethics Approval and Consent to Participate
Ethics Approval: Approved by the Ethics Committee of Tehran University of Medical Sciences (IR.TUMS.FNM.REC.1399.227).
Informed Consent: Written informed consent was obtained from all participants.
Conflict of Interest
The authors declare no conflict of interest.
Acknowledgement
The authors thank the Tehran University of Medical Sciences Employees’ Cohort (TEC) Study.
Funding Statement
This study did not receive any specific financial support from public, commercial, or non-profit funding agencies.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are subject to third-party restrictions.