GRAPHICAL ABSTRACT

INTRODUCTION

Exposure to industrial noise is a widespread occupational hazard across multiple industries, with approximately 22 million employees in the United States exposed to harmful noise levels annually. Prolonged exposure may result in noise-induced hearing loss and communication difficulties that can impair social relationships. Beyond auditory sequelae, noise exposure has also been associated with tinnitus, cardiovascular disorders, cognitive decline, and poor mental health outcomes [1]. When noise levels exceed the body’s adaptive capacity, adverse physiological responses may occur [2]. The National Institute for Occupational Safety and Health recommends a permissible exposure limit of 85 A-weighted decibels (dB[A]) averaged over an 8-hour time-weighted period [1].

Dyslipidemia refers to abnormalities in lipid parameters, including triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). It may result from dietary patterns, tobacco exposure, or genetic predisposition and is a well-established etiological factor in the development of cardiovascular disease (CVD) [3]. CVD encompasses a heterogeneous group of disorders, including cerebrovascular disease, hypertension, heart failure, peripheral artery disease, congenital heart disease, rheumatic heart disease, and coronary heart disease [4]. Conventional cardiovascular risk factors, including hypertension [4], smoking [5], and diabetes mellitus [6], have been extensively investigated. More recently, environmental noise pollution has increasingly been studied as a potential contributor to CVD [7].

Epidemiological evidence suggests that environmental noise from sources such as road traffic, aircraft, and railways is associated with an increased burden of CVD and cardiovascular morbidity [8]. Similarly, several epidemiological studies have reported associations between chronic noise exposure and alterations in cardiovascular function, particularly heart rate and blood pressure [9–11]. However, these studies did not evaluate changes in lipid profiles, and many were limited to single-sex cohorts [12] or focused only on the acute effects of noise exposure [13].

Conversely, other studies have failed to demonstrate significant associations, often because of methodological limitations such as reliance on self-reported noise exposure [14] or restricted participant demographics [15]. For example, Powazka [16] reported an association between occupational noise exposure and systolic blood pressure (SBP).

Despite these mixed findings, the association between occupational noise exposure and cardiovascular health outcomes remains inconclusive. Notably, no previous study has simultaneously evaluated both environmental and personal noise measurements in relation to cardiovascular function and lipid profile. Therefore, this study aimed to compare cardiovascular function between workers exposed and unexposed to occupational noise and to compare lipid profiles between these groups.

METHODS

Aim, Design, and Setting

This comparative cross-sectional study was conducted to (1) compare cardiovascular function between workers exposed and unexposed to occupational noise and (2) compare lipid profiles between these groups. The study was conducted in a denim garment manufacturing facility located in the 1st Industrial Area of Ismailia Governorate, Egypt. Data were collected between January 2024 and June 2024. The facility employed approximately 3000 workers, including 2600 production workers exposed to high noise levels greater than 85 dB(A) and 400 administrative workers exposed to lower noise levels below 85 dB(A).

Participants

The study population comprised two groups. Group A included workers exposed to occupational noise ≥85 dB(A) for at least 3 years [16], and Group B included workers exposed to noise <85 dB(A), defined as environments in which verbal communication was possible without raising one’s voice [14]. Eligible participants were 18–60 years of age and met the exposure criteria for their respective groups. Workers with pre-existing CVD or dyslipidemia identified during the initial medical examination, thyroid dysfunction, or chronic renal failure/nephrotic syndrome were excluded.

The sample size was calculated for the two-sample t-test comparing two independent means at a 95% confidence level and 80% power, assuming a standard deviation of 0.7 mmol/L and a mean total cholesterol difference of 0.2 mmol/L between groups [17]. This yielded a minimum sample size of 193 participants per group; after adding 10% for non-response, the required sample size was 213 participants per group, for a total sample of 426. Systematic random sampling was performed using lists obtained from the human resources department. For the exposed group (n=2600), the sampling interval was 13, with a random start at 10; for the unexposed group (n=400), the interval was 2, with a random start at 7.

Exposure Assessment

The independent variable was occupational noise exposure, defined as exposure to 85 dB(A) or higher for at least 3 years. Dependent variables included cardiovascular function, CVD, and dyslipidemia. Cardiovascular function was assessed using diastolic blood pressure (DBP) and SBP (normal, <120/80 mmHg) [18], heart rate (60–100 bpm) [19], absence of CVD, and absence of electrocardiographic (ECG) abnormalities, including ventricular repolarization disorders, axis deviations, conduction disturbances, arrhythmias, ventricular hypertrophy, ischemic changes, and evidence of previous myocardial necrosis. CVD was defined as stroke, coronary heart disease, hypertension, or congestive heart failure [4]. Dyslipidemia was defined as the presence of one or more of the following: LDL-C ≥4.1 mmol/L, total cholesterol ≥6.2 mmol/L, HDL-C <1.0 mmol/L, or triglycerides ≥2.3 mmol/L [20].

Data were collected using a predesigned interview questionnaire, clinical examinations, ECG records, and laboratory results. The questionnaire collected information on socio-demographic characteristics, occupational history, including duration of work, use of personal protective equipment (PPE), noise exposure, medical history, and factors predisposing participants to cardiovascular pathology, including body mass index (BMI), smoking status, and physical activity level. Smoking status was classified as current, former, or never smoker [21]. BMI was calculated as body mass in kilograms divided by height in meters squared and classified according to the World Health Organization 2010 anthropometric classification scheme [22]. Physical activity during the previous 30 days was classified as inactive, moderate, or active on the basis of frequency and intensity [21].

Outcome Measurements

Cardiovascular assessment included a 12-lead ECG performed for all participants. Participants with ECG abnormalities were referred for echocardiography at Suez Canal University Hospitals. Arterial blood pressure was measured by manual auscultation using a calibrated mercury-column sphygmomanometer according to Centers for Disease Control and Prevention 2023 guidelines [23]. Heart rate and rhythm were assessed from the radial pulse over 1 minute. Mean arterial pressure (MAP) was calculated as [1/3(SBP–DBP)+DBP], whereas pulse pressure (PP) was calculated as SBP–DBP and interpreted according to Homan et al. [24]. Hypertension was defined as systolic arterial pressure ≥140 mmHg and/or diastolic arterial pressure ≥90 mmHg, consistent with conventional diagnostic thresholds. Self-reported CVD included heart disease, myocardial infarction, stroke, or hypertension.

Blood samples were collected by antecubital venipuncture under aseptic conditions. Laboratory analyses included LDL-C, total cholesterol, triglycerides, and HDL-C. Triglycerides and total cholesterol were measured using enzymatic colorimetric methods [25], and HDL-C and LDL-C were analyzed using the precipitation method (Spectrum Diagnostics, Obour City, Egypt). Noise exposure was assessed using both environmental measurements obtained from administrative records and personal measurements conducted with an integrated sound level meter (CEL-600 Series; Casella, Bedford, UK) according to ISO 9612: 2009 standards. Hearing impairment was defined as self-reported difficulty hearing, significant hearing difficulty, or deafness.

Covariates

Participants were randomly selected from production (exposed) and administrative (control) departments with similar age and sex distributions to minimize selection bias. Information bias was reduced through the use of standardized questionnaires and calibrated instruments, and all assessors were trained before data collection. Measurement bias was controlled by applying identical procedures in both groups. Confounding was minimized by recording potential confounders, including age, BMI, smoking status, and employment duration, and adjusting for them in the statistical analysis.

Statistical Analysis

Data were entered using Microsoft Excel 365 (Microsoft, Redmond, WA, USA) and analyzed with SPSS version 26 (IBM Corp., Armonk, NY, USA). Normality was assessed using the Kolmogorov–Smirnov test. Qualitative variables were expressed as frequencies and percentages and compared using the chi-square or Fisher exact test. Quantitative variables were expressed as mean±standard deviation or median (minimum–maximum) and compared using the Mann–Whitney U-test, as appropriate. Spearman correlation analysis was used to assess associations between quantitative variables, whereas multiple logistic regression analysis was used to identify independent predictors of cardiovascular outcomes and dyslipidemia. Statistical significance was set at p-value <0.05. Missing data were checked before analysis; participants with incomplete laboratory or ECG data (<5% of the total sample) were excluded because this small proportion was unlikely to bias the results.

Ethics Statement

Ethical approval for this study was obtained from Faculty of Medicine Suez Canal University Ethical Committee (approval No. 5446/2023). Written informed consent was obtained from all participants before data collection. Confidentiality of personal data was strictly maintained throughout the study.

RESULTS

Because of difficulty drawing blood samples, 3 participants in the noise-unexposed group dropped out. The mean ages of the noise-exposed (n=213) and unexposed groups (n=210) were 34.5±9.8 years and 33.3±7.7 years, respectively. Females accounted for 67.6% of the noise-exposed group and 66.2% of the control group. Most participants were married. University graduates accounted for 23.0% and 21.9% of the noise-exposed and unexposed groups, respectively. Current smokers accounted for 10.8% and 7.6% of the exposed and unexposed groups, respectively. Among noise-exposed participants, 69.0% were physically inactive, compared with 59.5% of noise-unexposed participants; this difference was not statistically significant. No statistically significant differences in personal characteristics were observed between the two groups. Occupational characteristics showed that duration of work and duration of noise exposure ranged from 3 years to 26 years, without significant differences between the two groups. Only 0.9% of the exposed group regularly used PPE, whereas 92.0% did not use PPE at all (Table 1).

Environmental and personal noise measurements were significantly higher among exposed workers. Hearing impairment was evaluated subjectively; 3.3% of exposed participants reported hearing impairment compared with 1.0% of unexposed participants (Table 1). In the noise-exposed group, the most frequent ECG abnormalities were P mitrale (51.5%), right bundle branch block (18.6%), P pulmonale, and left ventricular hypertrophy (LVH; 6.3%). In the noise-unexposed group, the most frequent abnormalities were P mitrale and LVH (22.9%), premature atrial contractions, and P pulmonale (12.0%). Noise-exposed workers had significantly higher SBP, PP, prevalence of ECG abnormalities (30.0 vs. 8.1%), total cholesterol, LDL-C, triglycerides, and dyslipidemia (59.2 vs. 48.6%). Hypertension and hypercholesterolemia were more frequent in the noise-exposed group, whereas DBP, MAP, heart rate, HDL-C, and self-reported CVD did not differ significantly between groups (Table 2).

Total cholesterol had a significant moderate positive correlation with age and duration of noise exposure and a weak positive correlation with SBP, PP, and duration of work. LDL-C had significant weak positive correlations with age, SBP, PP, and personal noise measurements, and a moderate positive correlation with duration of work and duration of noise exposure. Triglycerides had significant weak positive correlations with age, SBP, MAP, PP, duration of work, and duration of noise exposure (Table 3).

Workers with ECG abnormalities were significantly older and had longer work duration and occupational noise exposure than those with normal ECG findings. They also had higher SBP, total cholesterol, and LDL-C levels. In addition, ECG abnormalities were more frequent among current smokers, physically inactive workers, workers who did not regularly use PPE, and those with hearing impairment. Hypertension and dyslipidemia were also significantly more prevalent among workers with ECG abnormalities. No significant differences in DBP, HDL-C, or triglyceride levels were observed between workers with and without ECG abnormalities (Supplemental Material 1). Workers with dyslipidemia had significantly higher BMI, higher environmental noise levels, and higher personal noise exposure than workers with normal lipid profiles (Supplemental Material 2).

In the binary logistic regression model using ECG abnormalities as the dependent variable, age, duration of noise exposure, personal noise measurements, and SBP were significant direct predictors of ECG abnormalities, whereas non-smoking status was a significant protective factor (Table 4). In the binary logistic regression model using dyslipidemia as the dependent variable, duration of noise exposure and BMI were significant direct predictors of dyslipidemia (Table 5).

DISCUSSION

This comparative cross-sectional study compared cardiovascular function and lipid profiles between workers exposed and unexposed to occupational noise. The principal findings were that occupational noise exposure was associated with elevated SBP, increased PP, and a higher prevalence of hypertension. Occupational noise exposure was also associated with higher total cholesterol, LDL-C, triglyceride levels, and dyslipidemia. Moreover, occupational noise exposure independently predicted ECG abnormalities.

In the present study, noise-exposed workers demonstrated a higher prevalence of hypertension, as well as elevated SBP and PP. One possible biological mechanism underlying this association is that noise acts as a neuropsychological stressor that activates the sympathetic nervous system and hypothalamic-pituitary-adrenal axis, thereby increasing cortisol, adrenaline, and noradrenaline levels. These physiological changes may increase vascular tone and blood pressure. In addition, noise exposure may promote oxidative stress, endothelial dysfunction, and inflammation, thereby contributing to hypertension, dyslipidemia, and CVD [7,26].

A Chinese cross-sectional study reported a higher prevalence of hypertension among workers with greater occupational noise exposure, particularly among males 30–45 years of age or those with more than 10 years of exposure [9]. However, the reported prevalence (13.64%) was lower than that observed in our study, likely because of differences in exposure assessment and sex distribution, as males accounted for 93.7% of their sample [9]. In contrast, our study used multiple logistic regression analysis to adjust for major measured confounders, thereby providing a more robust estimate of the association between occupational noise exposure and hypertension, although residual confounding from unmeasured occupational factors cannot be excluded.

Similarly, a Chinese cross-sectional study involving automotive lighting workers reported significantly higher SBP and DBP among noise-exposed workers than among controls. Occupational noise exposure ranged from 65.4 dB(A) to 95.8 dB(A) across 36 job posts, and significant differences were observed in both SBP and DBP, highlighting the potential cardiovascular effects of occupational noise exposure [27].

Meanwhile, a case-control study among male automobile workers found that occupational noise exposure above 80 dB(A) was associated with hypertension, although a paradoxical non-linear trend was observed, with increased odds below 80 dB(A) and reduced odds above this threshold [28]. This finding may reflect confounding related to PPE use, lifestyle factors, or healthy-worker effects. Additional limitations included potential recall bias and inadequate adjustment for modifiers such as age, sex, and metabolic risk factors, thereby limiting the generalizability of the findings.

Supporting a possible long-term association, the Guangzhou Biobank Cohort Study reported a significant association between occupational noise exposure and hypertension. Workers with a history of occupational noise exposure had a higher prevalence of hypertension (44.6%) [29]. However, the cohort relied on self-reported exposure history, introducing potential misclassification bias. In addition, although the study quantified hypertension prevalence, it did not adequately evaluate dose-response relationships or distinguish workplace noise from environmental noise exposure.

Furthermore, observational epidemiological studies have been synthesized in meta-analyses evaluating the relationship between various types of noise exposure and hypertension. These analyses suggested that noise exposure may represent a risk factor for hypertension and demonstrated a positive dose-response relationship [30]. Individuals exposed to noisy environments had a 62% higher risk of hypertension. Reported limitations of previous studies included heterogeneous study designs, self-reported exposure assessment, and inadequate adjustment for confounders. In contrast, our study incorporated both personal and environmental noise measurements in an industrial workforce and adjusted for exposure duration and demographic characteristics.

The present study extends prior work by combining personal and environmental noise measurements and by assessing blood pressure, ECG abnormalities, and lipid profiles in the same workforce. The study also addressed the sex imbalance commonly observed in prior research by including a predominantly female and relatively young workforce. In addition, multiple logistic regression analysis was used to adjust for confounders, including age, BMI, smoking status, and duration of exposure. Furthermore, the study provides additional data regarding PP, ECG abnormalities, and lipid profile changes, highlighting the broader cardiovascular effects of occupational noise exposure.

Although the present study adjusted for major confounders, including age, BMI, smoking status, and employment duration, residual confounding from other occupational exposures cannot be completely excluded. Workers in garment manufacturing settings may also be exposed to heat stress, cotton dust, ergonomic strain, physical workload, shift-related stress, and potential chemical exposure from dyes or finishing processes, all of which may influence blood pressure, lipid profile, and cardiovascular findings. These exposures were not quantitatively measured in the current study and should therefore be considered in future investigations using more comprehensive occupational exposure assessment models.

In the present study, SBP was positively correlated with age, duration of work, and duration of noise exposure. These findings are consistent with previous studies demonstrating a cumulative association between hypertension and cumulative occupational noise exposure [9,31]. Because age and duration of work may confound the relationship between hypertension and occupational noise exposure, the two study groups were matched for these variables, and multiple regression analysis was performed to adjust for their potential effects.

The current study also found that occupational noise exposure was associated with a high prevalence of dyslipidemia (59.2%). Total cholesterol, LDL-C, and triglyceride levels were higher among noise-exposed workers. Although the biological mechanisms underlying this potential association remain unclear, one possible explanation is that occupational noise exposure may contribute to sleep disturbance, which has been associated with adverse lipid profiles [32].

A comparative Chinese study evaluating the effects of noise exposure among workers in an automotive lighting company reported significantly elevated total cholesterol, HDL-C, and LDL-C levels among workers exposed to approximately 90 dB of noise [27]. However, unlike our study, that study did not clearly specify whether noise exposure was measured individually or whether analyses were adjusted for important confounders such as age, diet, BMI, or physical activity.

Similarly, a case-control study investigating the association between occupational noise exposure and hypertension in an automobile plant reported significantly elevated triglyceride, total cholesterol, and LDL-C levels among individuals exposed to noise levels >80 dB(A) [28]. Unlike our findings, HDL-C levels were lower among workers exposed to >75.9 dB(A), possibly because of differences in measurement methods, metabolic factors, physical activity, or shift work, none of which were adequately addressed in that study. In addition, that study focused exclusively on male automotive workers, whereas our study included a more diverse workforce, thereby helping address the underrepresentation of female cardiovascular responses to occupational noise exposure.

The Guangzhou Biobank Cohort Study also demonstrated a substantial association between occupational noise exposure and dyslipidemia, reporting a dyslipidemia prevalence of 52.3% among noise-exposed workers [29]. Although the cohort design provided stronger evidence than cross-sectional studies, reliance on self-reported occupational noise exposure introduced potential recall and misclassification bias. Furthermore, heterogeneous occupational histories limited the ability to attribute findings specifically to current occupational exposure. In contrast, the present study evaluated currently employed industrial workers with measured workplace noise exposure.

A systematic review and meta-analysis by Sivakumaran et al. [33] reported moderate evidence linking chronic noise exposure to adverse metabolic outcomes, particularly elevated total cholesterol and LDL-C levels. However, heterogeneity in study design and limited adjustment for confounders reduced the generalizability of those findings. Our study contributes additional evidence by evaluating a defined industrial setting and assessing both lipid abnormalities and cardiovascular markers, including blood pressure and ECG abnormalities.

In the current study, the prevalence of ECG abnormalities was significantly higher among noise-exposed workers (30.0%). Multiple logistic regression analysis further identified noise level and duration of exposure as significant independent predictors of ECG abnormalities. Consistent with these findings, Naderyan Fe’li et al. [26] suggested that noise exposure acts as a potent stressor that increases corticosterone levels. This mechanism may contribute to cardiovascular dysfunction through activation of the endocrine and sympathetic nervous systems. In addition, hypertension is a well-established risk factor for cardiovascular abnormalities [34].

Similarly, a meta-analysis reported that noise-exposed workers had a 2.27-fold higher risk of ECG abnormalities than controls [35]. However, the included studies demonstrated substantial methodological heterogeneity, frequently lacking standardized ECG assessment, precise exposure measurements, or adequate adjustment for confounding variables. Our study provides additional insight by characterizing common ECG findings and linking ECG abnormalities to both noise intensity and duration of exposure.

Nonetheless, a cross-sectional study investigating occupational noise exposure and dyslipidemia reported a 26.42% prevalence of ECG abnormalities among exposed workers, although the difference compared with unexposed workers was not statistically significant [20]. Limitations of that study included a small sample size, lack of confounder adjustment, and insufficient detail regarding exposure characteristics and cardiovascular markers. In contrast, our study evaluated blood pressure, PP, and lipid profile in addition to ECG findings, thereby providing a more comprehensive cardiovascular assessment.

The present study is limited by its cross-sectional design, which precludes causal inference, and by the minimal use of PPE among workers (<1.0%), which prevented evaluation of its potential protective role. Strengths of the study include the use of both personal and environmental noise measurements, inclusion of a younger and predominantly female workforce, and comprehensive assessment of multiple cardiovascular outcomes, including blood pressure, PP, ECG abnormalities, and lipid profile. This broader assessment provides a more integrative understanding of the potential contribution of occupational noise exposure to early cardiovascular dysfunction.

The findings may be cautiously generalized to workers in similar garment manufacturing environments in which occupational noise levels frequently exceed 85 dB(A). However, because the study was conducted in a single denim garment factory in Ismailia, Egypt, the findings may not fully represent workers in other industrial sectors with different noise spectra, exposure durations, or demographic characteristics.

In conclusion, chronic occupational noise exposure was associated with elevated blood pressure, dyslipidemia, and ECG abnormalities. Longer duration and greater intensity of noise exposure were also associated with higher cardiovascular risk indicators. These findings suggest the need for improved workplace noise control, regular cardiovascular screening, and increased use of PPE.

Supplemental Materials

Notes

Data Availability

Data are available upon reasonable request.

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

None.

Author Contributions

Conceptualization: Ibraheem RB, Fahim AE, Gaafar SM, Mishriky AM. Data curation: Ibraheem RB, Nada FA. Formal analysis: Ibraheem RB, Mishriky AM. Funding acquisition: None. Methodology: Ibraheem RB, Fahim AE, Gaafar SM, Mishriky AM. Project administration: Ibraheem RB, Fahim AE, Gaafar SM, Mishriky AM. Visualization: Ibraheem RB. Writing – original draft: Ibraheem RB. Writing – review & editing: Fahim AE, Gaafar SM, Nada FA, Mishriky AM.

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