Relationship Between Work-Related Noise Exposure and Cognitive Impairment: A Cross-Sectional Study in China

Abstract

Background:

Noise exposure and the risk of cognitive impairment are currently major public health issues.

Objective:

This study aimed to analyze the relationship between noise exposure and early impairment of cognitive function from the perspective of occupational epidemiology and to provide evidence for the long-term prevention and treatment of dementia in the context of aging.

Methods:

This study was conducted in China between May and August 2021. The independent variables were the type of hazardous factors, duration of noise exposure, perceived noise intensity, and cumulative noise exposure (CNE). The dependent variable was cognitive function, which was measured using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Multiple linear and logistic regression were used to analyze the relationship between noise exposure and cognitive function and to establish an effect curve.

Results:

The detection rates of cognitive dysfunction using the MMSE and MoCA were 1.1% and 36.2%, respectively. The predicted MMSE and MoCA scores showed a downward trend within the CNE value ranging from 90–140 dB.time. Each unit increase in CNE decreased cognitive function scores by 0.025 (0.037, 0.013) and 0.020 (0.037, 0.003) points,respectively.

Conclusions:

From the perspective of occupational epidemiology, these findings reveal a potential link between long-term noise exposure and early cognitive impairment.

Keywords

Alzheimer’s disease cognitive impairment cumulative dose noise occupational exposure

INTRODUCTION

Dementia is a chronic mental retardation syndrome characterized by cognitive degeneration and mental decline, accompanied by personality changes, and language and thinking disorders. According to some reports, more than 57.4 million people globally suffered from dementia in 2019, with the number expected to reach 150 million by 2050 [1]. The prevalence of dementia ranked first in Turkey and Brazil, and mainland China had the largest number of patients with dementia [2]. Taking Alzheimer’s disease (AD) as an example, the latest statistics in the United States showed that more than 6.5 million people over 65 years suffer from AD, which could increase to 13.8 million by the mid-century [3]. With the increasing aging population globally, the number of people with dementia continues to increase. As there is no definite drug to treat dementia, identifying risk factors and related pathogenic mechanisms is an important step toward preventing dementia [4].

Previous studies have suggested that noise exposure may increase the risk of cognitive impairment and dementia. Experimental studies have indicated that noise exposure may lead to learning and memory impairment and other related neuropathological changes in brain tissue [5, 6]. Jafari et al. also reported the effects of noise exposure during pregnancy and prenatal period on gender, learning and memory, and neuropathological changes in animals and their offspring [7]. Other similar studies have mainly focused on the effects of traffic noise (such as urban traffic noise [8], railway noise [9]), and white noise [10] on the behavior, learning, and memory functions of rodents. Notably, under different noise exposure patterns, changes such as amyloid-β (Aβ) deposition, tau protein phosphorylation, decreased neurogenesis, and apoptosis have been observed in the hippocampus [11 –14].

Regarding the molecular mechanisms of noise-induced cognitive degradation, several conjectures have been put forward, including environment-genetic interactions, noise-induced psychological stress, neuroexcitotoxicity, the microbiota-gut-brain axis, and neuroinflammation [15 –18].

Human trials have found significant effects of noise exposure on short-term learning, memory, and work performance [19 –21]. Cross-sectional studies have shown that noise exposure has negative effects on attention [22] and event-related potentials [23] in adults, and similar changes have been demonstrated in school-age children [24], women [25], older adults [26], and occupational populations [27, 28]. In addition, previous studies found that traffic noise had certain effects on children’s behavior, learning, and long-term memory development [29]; however, for cognitive function, Clark et al. reported no evidence for the damaging effects of traffic and railway noise in children [30]. Epidemiological studies on noise exposure and the risk of cognitive impairment or dementia are scarce. Relevant studies have briefly mentioned the possible effects of traffic and community noise on urban air pollutants, such as NO_x, PM_2.5, PM₁₀, and the risk of dementia. Urban traffic noise exposure has been reported to be associated with mild cognitive impairment (MCI) [31]. Weuve et al. also found that for every 10 dB increase in traffic noise, the probability of MCI and AD increased by 36% and 29%, respectively [32]. A longitudinal study conducted in Madrid reported that traffic noise exposure may exacerbate dementia symptoms [33]. However, several subsequent studies found no relationship between dementia-related outcome events and traffic noise after the inclusion of air pollutants [34, 35]. In addition, those studies focused on air pollutants, green, and the risk of dementia also tended to believe that there was no relationship between environmental noise and cognitive impairment and that air pollutants were the main sources [36, 37]. A dose-response meta-analysis conducted by our team showed that noise might be a specific risk factor for dementia; however, the current evidence remains weak [38].

However, there are unresolved problems in existing epidemiological studies on the risk of noise and dementia, which are mainly reflected in the following aspects [38, 39]: 1) there have been few confirmatory studies focused on noise and dementia. Environmental noise is usually mentioned in studies focused on air pollution and the risk of AD and other dementias, while some studies have suggested that the effects of noise and air pollutants cannot be accurately distinguished. 2) Exposure assessment was not sufficiently precise. Most studies have used noise prediction models to assess environmental noise. Similar models made the studies more convenient; however, there were still uncertain biases in assessing cumulative exposure that may lead to dementia. Changes in factors such as population migration, traffic changes, and additional occupational exposure are often inevitable for decades. 3) Previous studies have mainly focused on the general population and environmental noise, and few studies have paid attention to occupational populations and noise with higher exposure intensities and more typical exposure characteristics. Noise exposure in the workplace is a pervasive global problem. Besides noise-induced hearing loss, the non-auditory effects of noise have also emerged as a key concern of contemporary research and attention. Exposure period of noise in the occupational population can be as long as 20 to30 years or even the entire work life. Dementia is a chronic progressive disease, and its cognitive degeneration process also can last for 20 to 30 years or even longer. It is worth exploring whether there is a potential relationship between noise exposure and dementia.

This study aimed to analyze the risk of work-related noise exposure and early impairment of cognitive function and try to establish a dose-effect curve between noise exposure and cognitive function with a survey in an occupational health surveillance cohort. The conclusions of this study could provide scientific evidence for the long-term prevention and treatment of dementia in the context of aging.

METHODS

Study population

The professional population of a large machinery and equipment manufacturing enterprise in western China was selected as the research object of this study. The research object comes from 4 departments, including Administration Department, Auxiliary Materials Department, General Assembly Department 1 and General Assembly Department 2. Based on the different department settings and the number of people in each department, a stratified random sampling method was adopted to select occupational groups. This research was conducted from May to August 2021.

The inclusion criteria were as follows: 1) workers in the front-line and auxiliary production departments; 2) aged over 18 years, with working experience of not less than 6 months; 3) no serious trauma, physical disability, or sensory defect; 4) smooth communication and no language barriers; and 5) voluntary participation in this study under the premise of being fully informed. The exclusion criteria includes 1) a history of exposure to heavy metal elements such as manganese, lead, and copper; 2) previously or currently taking drugs that may cause temporary or permanent neuropsychiatric symptoms; 3) secondary neuropsychiatric symptoms caused by trauma or surgery; 4) schizophrenia, drug abuse, or mental retardation; 5) history of encephalitis or meningitis; and 6) suffering from other neurological diseases that may lead to cognitive dysfunction, such as epilepsy, cerebral infarction, or stroke.

A total of 710 workers were enrolled in the study through sampling, and 74 were excluded based on the exclusion criteria. During the implementation, 22 participants failed to complete the cognitive function tests (withdrawal from the cognitive test). A total of 614 participants met the requirements, and a flow diagram of the study participants is shown in Fig. 1.

Fig. 1

Flow diagram of the study participants.

This study was approved by the Ethics Committee of West China School of Public Health/West China Fourth Hospital, Sichuan University (HXSY-EC-2021035). We confirmed that this research were performed in accordance with the relevant guidelines and regulations of the Declaration of Helsinki. The investigators explained the purpose and specific circumstances of this study to the participants during the investigation process. All participants were informed and voluntarily participated in the study (signed informed consent).

Data collection

Demographic and occupational features

A self-designed questionnaire was administered to collect the demographic and occupational characteristics of the study participants. Demographic information included age, sex, marital status, education, and living status. Occupational information included jobs, daily exposure time, monthly income, job changes and the corresponding length of service. The types of hazardous factors came from the baseline data of the health surveillance cohort. The investigators guided the participants to complete a self-administered questionnaire.

Measurement of equivalent noise pressure level

A handheld sound level meter (SVANTEK SV 971, Warszawa, Poland) and a personal noise dosimeter (SVANTEK SV 104IS, Warszawa, Poland) were used for the measurements. All sound level meters adopted an A-weighting network, a 3 dB exchange rate, and a slow gear (SLOW). The spectrum used 1/1 octave, filter set to Z level, and detector set to “Linear”. The sound level meter was calibrated using a sound calibrator (SVANTEK SV34, 114 dB, 1000 Hz, Warszawa, Poland). Noise measurements and calculations were carried out according to national standards. Jobs with complex noise conditions were measured using individual sampling, and the sampling period covered the entire working day as much as possible. Fixed-point sampling was used as an auxiliary measurement method for positions in a simple noise environment.

Calculation of cumulative noise exposure

According to the equal-energy principle of noise, the cumulative noise exposure (CNE) of different objects was calculated based on the noise intensity at different positions, with the working duration (cumulative days) as the weight [40]. Owing to the job change and internal job transfer of some participants, detailed career history and changes in career information were collected during the questionnaire survey, including positions, job descriptions, and employment duration. Those with incomplete or missing information were confirmed through face-to-face interviews or telephone visits. CNE was estimated using the following formula: $CNE = 10 lg (\sum_{i = 1}^{n} T_{i} 10^{0.1 {(L_{EX, 8 h})}_{i}})$

Note: CNE, cumulative noise exposure (dB.time); L _{EX,8h
_i}, noise equivalent sound level of job i in 8 h working day (dB(A)); T _i, duration of job i (days); n, the total number of jobs.

Cognitive function test

The Chinese version of Mini-Mental State Examination (MMSE) [41] and Montreal Cognitive Assessment (MoCA) [42] were used to assess the cognitive function of the participants. The total scores of MMSE and MoCA are 30 points. Criteria of cognitive dysfunction: 1) different cutoff values of the MMSE are used to define cognitive dysfunction based on educational levels. These include illiteracy (≤17), primary school (≤20), middle school (≤22), and undergraduate (≤23) [43]. The MoCA score is also related to the years of education. If the years of education ≤12 years, the total score will be increased by 1 point. The MoCA score higher than 26 is considered normal [42]. In the statistical section, the raw scores of MMSE and MoCA were used for analysis (Tables 2 to 5).

Statistical analysis

Statistical analyses were performed using STATA 14.0 software. The analysis methods included the following: 1) statistical description, which describes the distribution and composition of the demographic, occupational, and noise exposure characteristics of the research objects. 2) Statistical inference: The Shapiro–Wilk normality test was used to judge the overall distribution of cognitive function scores, which was considered statistically significant at p < 0.05. The Kruskal–Wallis test was used to compare the cognitive function scores of the subgroups with different demographic, occupational, and noise exposure characteristics. Differences between subgroups were considered statistically significant at p < 0.05. Multiple linear and logistic regression were used to develop the multivariate model of cognitive function and to establish a dose-effect and dose-response curve between noise exposure and cognitive function. The variables were considered to be statistically significant at p < 0.05.

RESULTS

Basic characteristics of the participants

The age of the 614 participants ranged from 19 to 59 years, with an average age of 34.2±9.93 years. Men accounted for 95.9% (589/614) and married individuals accounted for 63.7% (391/614). A total of 64.3% of participants had a college degree or above, 16.4% lived alone, and 84.7% had a monthly income of ¥ 2500–8000. The occupational hazards that participants were exposed to included noise, dust, vibration, high temperature, X-rays, and organic solvents. The exposure rate to noise was 75.6% (464/614), and the exposure rates to dust, vibration, and high temperature exceeded 20%. A total of 1.3% (8/614) of the study participants were exposed to X-rays.

The average MMSE score was 28.3±1.70, and the average MoCA score was 25.6±2.56. The Shapiro–Wilk normality test showed that the MMSE and MoCA scores were right-skewed (p < 0.05). The results (Table 1) present the detection rate of cognitive dysfunction in the MMSE as 1.1% (7/614) and the detection rate in the MoCA as 36.2% (222/614).

Table 1

Cognitive function of the included study participants

Scale	Participants	Cognitive score				Cognitive dysfunction
		Mean	Min	Median	Max	N	Rate (%)
MMSE	614	28.3±1.70	19	29	30	7	1.1
MoCA	614	25.6±2.56	14	26	30	222	36.2

MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment.

Cognitive function of different subgroups

The results of the Kruskal–Wallis test showed that there were differences in MMSE and MoCA scores at different ages (H = 80.92, p < 0.05, and H = 109.01, p < 0.05), education (H = 40.47, p < 0.05, and H = 101.42, p < 0.05), and marital status (H = 22.10, p < 0.05, and H = 44.55, p < 0.05). There were significant differences in the MoCA scores between the monthly income (H = 15.441, p < 0.05) subgroups. No significant differences were found in the MMSE and MoCA scores between the different sex and living status subgroups (Table 2).

Table 2

Cognitive function in different demographic subgroups

Variable	N (%)	MMSE	p	MoCA	p
Age (y)
<25	111 (18.1)	28.8±1.13		26.7±1.75
25	252 (41.0)	28.7±1.32		26.4±2.04
35	116 (18.9)	28.1±1.88		25.0±2.54
45	135 (22.0)	27.2±2.00	<0.001	23.8±2.92	<0.001
Sex
Male	589 (95.9)	28.3±1.70		25.6±2.57
Female	25 (4.1)	28.2±1.60	0.540	25.9±2.53	0.470
Education
Junior high school or below	39 (6.4)	27.1±2.10		22.8±2.86
High school	180 (29.3)	27.9±1.74		24.6±2.43
Bachelor degree or above	395 (64.3)	28.6±1.50	<0.001	26.3±2.22	<0.001
Marital status
Unmarried or single	223 (36.3)	28.7±1.39		26.5±2.00
Married	391 (63.7)	28.1±1.81	<0.001	25.1±2.70	<0.001
Living status
Live alone	101 (16.4)	28.2±1.93		25.5±2.91
Live with family	513 (83.6)	28.3±1.61	0.958	25.6±2.45	0.743
Monthly income (¥)
<2500	43 (7.0)	28.0±1.57		24.9±2.46
2500	249 (40.6)	28.2±1.73		25.2±2.52
5000	271 (44.1)	28.3±1.73		25.9±2.67
8000	37 (6.0)	28.8±1.19		26.1±1.67
10000	14 (2.3)	28.4±1.22	0.438	25.9±2.03	0.004

MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment.

Compared with the control group, the vibration, high-temperature, and organic solvent exposure groups had significant differences in MoCA scores (p < 0.05), and the dust exposure group had a difference in MMSE score (H = 4.38, p < 0.05) (Table 3). The difference in MMSE scores between the noise exposure and control groups was close to the significance level (p = 0.057). There were significant differences in the MMSE (H = 68.34, p < 0.05) and MoCA (H = 89.02, p < 0.05) scores for the different durations of noise exposure. No significant difference was found in the MMSE and MoCA scores of the differently perceived noise intensity subgroups; however, the p values were close to the significance level (p = 0.080 and p = 0.077) (Table 4).

Table 3

Cognitive function in different occupational exposure subgroups

Variable	N (%)	MMSE	p	MoCA	p
Noise
No	150(24.4)	28.5±1.69		25.8±2.30
Yes	464(75.6)	28.2±1.70	0.057	25.5±2.64	0.210
Dust
No	476(77.5)	28.2±1.76		25.5±2.59
Yes	138(22.5)	28.6±1.40	0.036	25.9±2.44	0.114
Vibration
No	412(67.1)	28.4±1.55		25.8±2.32
Yes	202(32.9)	28.1±1.95	0.432	25.1±2.95	0.009
High temperature
No	469(76.4)	28.3±1.76		25.8±2.59
Yes	145(23.6)	28.2±1.48	0.261	24.9±2.33	<0.001
X-ray
No	606(98.7)	28.3±1.70		25.6±2.57
Yes	8(1.3)	28.5±1.51	0.792	26.4±2.20	0.435
Organic solvents
No	520(84.7)	28.3±1.64		25.7±2.54
Yes	94(15.3)	28.1±1.96	0.451	25.0±2.62	0.021

MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment

Table 4

Cognitive function in different noise exposure characteristic subgroups

Variable	N (%)	MMSE	p	MoCA	p
Duration of noise exposure (y)
<10	373 (60.7)	28.7±1.34		26.4±2.04
10	119 (19.4)	28.0±1.85		24.7±2.68
20	79 (12.9)	27.5±1.92		24.3±2.89
30	43 (7)	27.0±2.21	<0.001	23.5±2.91	<0.001
Perceived noise intensity
Hardly	76 (12.4)	28.3±1.84		26.0±2.13
Slightly noisy	173 (28.2)	28.5±1.55		25.7±2.53
Noisy	120 (19.5)	28.3±1.69		25.3±2.48
Very noisy	165 (26.9)	28.2±1.77		25.8±2.74
Extremely noisy	80 (13.0)	28.1±1.39	0.080	25.4±2.40	0.077

MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment.

Work-related cumulative noise exposure of participants

The range of CNE was 86.6 to 136.1 dB.time, and the average value was 117.5±11.35 dB.time (Fig. 2). Taking the 8 h noise-equivalent sound levels of 70 dB (A) and 90 dB (A) and the cumulative exposure dose of 6000 days (30 years of continuous work, 200 days per year) as the cut-off point, CNE was divided into low-dose, medium-dose, and high-dose groups. The results showed that 56.5% (347/614) of the participants were in the medium-dose group and 23.3% (143/614) were in the high-dose group (Supplementary Table 1). The downward trend of cognitive scores with age in different CNE group is shown in Supplementary Figure 1.

Fig. 2

Distribution of work-related cumulative noise exposure of participants.

Regression analysis of noise exposure and cognitive function

In the baseline model (Model 1) of MMSE and MoCA, age and education were both statistically significant (p < 0.05). Model 2 included occupational hazard factors, and the results showed that noise was statistically significant in both MMSE and MoCA (p < 0.05). Model 3 included the duration of noise exposure and perceived noise intensity, and the results showed that perceived noise intensity was statistically significant in both the MMSE and MoCA (p < 0.05), and the duration of noise exposure was close to the significance test level (p = 0.068 and p = 0.074). Model 4 included CNE, and the results showed that CNE was statistically significant in both the MMSE and MoCA (p < 0.05). For every unit increase in CNE (dB.time), the corresponding scores in MMSE and MoCA decreased by 0.025 (0.037, 0.013) and 0.020 (0.037, 0.003) points, respectively (Table 5).

Table 5

Regression of occupation-related noise exposure and cognitive function

Variable	MMSE		MoCA
	β (95% CI)	p	β (95% CI)	p
Model 1	F = 14.0, R² = 0.13	<0.001	F = 29.0, R² = 0.23	<0.001
Age	–0.511 (–0.686, –0.336)	<0.001	–0.699 (–0.950, –0.449)	<0.001
Sex	–0.156 (–0.783, 0.471)	0.625	0.238 (–0.661, 1.137)	0.603
Education	0.265 (0.021, 0.508)	0.033	0.991 (0.642, 1.339)	<0.001
Marital status	0.103 (–0.251, 0.458)	0.568	–0.238 (–0.746, 0.270)	0.357
Living status	0.270 (–0.089, 0.630)	0.140	0.593 (0.078, 1.108)	0.024
Monthly income	0.125 (–0.048, 0.298)	0.156	0.279 (0.031, 0.526)	0.027
Model 2	F = 8.0, R² = 0.14	<0.001	F = 15.2, R² = 0.24	<0.001
Age	–0.505 (–0.682, –0.329)	<0.001	–0.716 (–0.969, –0.463)	<0.001
Sex	–0.187 (–0.832, 0.459)	0.570	0.091 (–0.836, 1.018)	0.847
Education	0.303 (0.053, 0.553)	0.018	0.943 (0.584, 1.302)	<0.001
Marital status	0.098 (–0.259, 0.454)	0.591	–0.219 (–0.731, 0.293)	0.401
Living status	0.254 (–0.105, 0.614)	0.165	0.603 (0.087, 1.120)	0.022
Monthly income	0.172 (–0.013, 0.358)	0.069	0.253 (–0.013, 0.520)	0.062
Noise	–0.411 (–0.730, –0.092)	0.012	–0.445 (–0.904, 0.014)	0.057
Dust	0.149 (–0.180, 0.477)	0.375	–0.045 (–0.517, 0.427)	0.851
Vibration	–0.085 (–0.371, 0.202)	0.562	–0.216 (–0.629, 0.195)	0.303
High temperature	0.391 (0.028, 0.754)	0.035	–0.044 (–0.566, 0.478)	0.868
X-ray	–0.086 (–1.052, 1.223)	0.883	0.210 (–1.424, 1.844)	0.801
Organic solvents	–0.243 (–0.649, 0.162)	0.239	–0.208 (–0.791, 0.375)	0.485
Model 3	F = 12.49, R² = 0.15	<0.001	F = 22.3, R² = 0.24	<0.001
Age	–0.368 (–0.600, –0.135)	0.002	–0.512 (–0.853, –0.172)	0.003
Sex	–0.332 (–0.961, 0.297)	0.300	0.142 (–0.778, 1.063)	0.761
Education	0.304 (0.058, 0.550)	0.016	0.983 (0.623, 1.342)	<0.001
Marital status	0.080 (–0.274, 0.435)	0.657	–0.308 (–0.827, 0.211)	0.244
Living status	0.327 (–0.028, 0.683)	0.071	0.660 (0.139, 1.180)	0.013
Monthly income	0.133 (–0.041, 0.307)	0.134	0.316 (0.061, 0.571)	0.015
Duration of noise exposure	–0.202 (–0.421, 0.015)	0.068	–0.291 (–0.610, 0.028)	0.074
Perceived noise intensity	–0.186 (–0.294, –0.077)	0.001	–0.182 (–0.342, 0.024)	0.025
Model 4	F = 14.75, R² = 0.15	<0.001	F = 25.78, R² = 0.24	<0.001
Age	–0.436 (–0.612, –0.259)	<0.001	–0.640 (–0.895, –0.385)	<0.001
Sex	–0.198 (–0.818, 0.420)	0.529	0.204 (–0.692, 1.100)	0.655
Education	0.324 (0.082, 0.565)	0.009	1.038 (0.688, 1.387)	<0.001
Marital status	0.146 (–0.04, 0.497)	0.412	–0.204 (–0.711, 0.303)	0.429
Living status	0.259 (–0.095, 0.614)	0.152	0.584 (0.071, 1.097)	0.026
Monthly income	0.200 (0.026, 0.375)	0.024	0.339 (0.087, 0.591)	0.009
CNE	–0.025 (–0.037, –0.013)	<0.001	–0.020 (–0.037, –0.003)	0.025

Model 1 is the baseline model; Models 2 to 4 adjusted for age, sex, education, marital status, living status and monthly income. CNE, cumulative noise exposure.

Dose-effect curves of CNE and cognitive function were established using linear regression. After adjusting for demographic characteristics, the results showed that within the CNE value range of 90–140 dB.time, the expected values of the MMSE and MoCA scores showed a downward trend (Fig. 3, p < 0.05). In order to establish the dose-response curves of CNE and the low-score detection rate, the MMSE and MoCA scores were divided into the low score and normal score groups with cutoff values of 27 points (≤27) and 26 points (≤26), respectively. The dose-response curves were established using logistic regression. After adjusting for demographic characteristics, the results showed that the expected rate of low MMSE scores increased from 5.8% to 37.6%, and the expected rate of MoCA scores increased from 16.1% to 48.1% within the CNE value ranging from 90–140 dB.time (Fig. 3, Supplementary Table 2, p < 0.05). The MMSE and MoCA low-score rate matrix for noise intensity and duration of exposure is shown in Supplementary Tables 3 and 4.

Fig. 3

Dose-effect diagram of cumulative noise exposure and cognitive function. A, B) In the CNE value range of 90–140 dB.time, the MMSE and MoCA scores showed a downward trend with an increase in CNE. C, D) In the CNE value range of 90–140 dB.time, the low-score rates of MMSE and MoCA showed an upward trend with an increase in CNE. MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; CNE, cumulative noise exposure.

DISCUSSION

This study focused on the risk of work-related noise exposure and cognitive impairment in the occupational population. The findings revealed that the detection rates of cognitive dysfunction in the MMSE and MoCA were 1.1% and 36.2%, respectively. The difference between the MMSE and MoCA detection rates may be due to the different emphases of the two scales. It is generally believed that the MMSE focuses on short-term memory and language function, while the MoCA focuses on executive function and visuospatial ability [44 –46].

The Kruskal–Wallis test showed that age and education level were related to both MMSE and MoCA scores, which was further corroborated by regression analysis. However, there is evidence that age-related factors, education, and lifestyle have all been linked to MCI and dementia [47]. Age-related factors linked to biological aging, and educational level reflects disparities in cognitive reserve; the process of education may also promote the development of reserve through mechanisms such as increased dendritic branching [48].

Previous studies have also reported varying degrees of association between noise (weak evidence), organic solvents (strong evidence), silica (strong evidence), vibratory tools (weak evidence), radiation (weak evidence), and the risk of AD and other dementias [49]. In this study, occupational exposure history of all the workers were investigated, including noise, dust, vibration, high temperature, X-ray, and organic solvents. Apart from noise, no consistent or stable statistical associations between occupational exposure and cognitive function were found. Different noise assessment metrics include whether exposed, duration of noise exposure, and perceived noise intensity were statistically related to both MMSE and MoCA scores.

However, occupational exposure and cognitive degeneration are both long-term and gradual process. How to accurately assess cumulative exposure is an important part in the etiology of neurodegenerative diseases. In this study, CNE index was proposed based on occupational history and exposure period (days) to assess work-related cumulative noise dose. The results showed that the CNE was range from 86.6 to 136.1 dB.time. 56.5% of the participants were in the medium-dose group and 23.3% were in the high-dose group.

Regression analysis revealed that cognitive function was correlated with perceived noise intensity, and there was a borderline correlation with the duration of noise exposure. For every unit increase in CNE (dB.time), the corresponding MMSE and MoCA scores decreased by 0.025 and 0.020 points, respectively. The dose-response curve also showed that within the CNE range of 90–140 dB.time, the MMSE and MoCA scores showed a downward trend with increasing CNE and an upward trend in the low-score detection rates.

The findings of this study support the potential relationship between noise exposure and cognitive impairment from the perspective of occupational epidemiology and are consistent with the findings about environmental noise. A study on residential noise revealed a relationship between noise exposure and overall cognitive impairment in older women [26]. Tzivian et al. also found that traffic noise exposure was associated with cognitive decline and that there may be a synergistic effect between air pollutants and traffic noise [50, 51]. However, opposing views exist. Tyas et al. revealed that excessive occupational noise exposure reduced the risk of AD; however, the study did not mention the noise level, dose, or how to conduct the noise assessment [52].

It is well known that noise, as an external stressor, can cause systemic injury beyond just the auditory system. There is a view that noise-induced hearing loss causes cognitive deterioration rather than noise [39]. On the other hand, several studies have found that even in low-intensity noise environments, such as community and road traffic noise, changes in memory, reaction ability, and mood of participants were still observed [39]. Similarly, the noise level in those experimental studies was not high enough to cause hearing loss; however, it still affects neurological function [53, 54]. In summary, these issues require further study.

This study had some limitations: 1) the study group came from an occupational health surveillance cohort; however, cognitive function was only cross-sectional baseline data. Follow-up data were not collected based on the limited duration of the study. 2) The outcome variable was early impairment of cognitive function, and disease outcomes such as MCI and dementia could not be tracked. Research prospects: 1) Continue to carry out longitudinal follow-up and further study the timing and effect characteristics of noise exposure on the induction and long-term progression of dementia. 2) Analyze the role of noise-induced hearing loss in the association between noise and cognitive impairment and its underlying mechanisms.

Conclusions

From the perspective of occupational epidemiology, this study reveals a potential link between long-term noise exposure and early cognitive impairment, and provided epidemiological evidence for the study of the etiology of neurodegenerative dementia. Simultaneously, taking noise and cognitive function as meeting points will open up new ideas and directions for the etiology, mechanism, prevention, and treatment of neurodegenerative diseases from an occupational health perspective.

AUTHOR CONTRIBUTIONS

Lei Huang (Investigation; Methodology; Writing – original draft); Jingxuan Ma (Investigation); Fugui Jiang (Writing – review & editing); Shushan Zhang (Writing – review & editing); Yajia Lan (Methodology; Writing – review & editing); Yang Zhang (Investigation; Methodology; Writing – original draft; Writing – review & editing).

Footnotes

ACKNOWLEDGMENTS

We would like to thank all the participants in this study, and all those who provided help with this study. We also thank Editage for English language editing.

FUNDING

This study was supported by Sichuan Science and Technology Program of the Science and Technology Department of Sichuan Province (No. 2023NSFSC1736).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The datasets of the current study are available from the corresponding author upon reasonable request.

The supplementary material is available in the electronic version of this article: .

References

GBD 2019 Dementia Forecasting Collaborators (2022) Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 7, e105–e125.

GBD 2016 Dementia Collaborators (2019) Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 18, 88–106.

Alzheimer’s Association (2022) 2022 Alzheimer’s disease facts and figures. Alzheimers Dement 18, 700–789.

Livingston

, Huntley

, Sommerlad

, Ames

, Ballard

, Banerjee

, Brayne

, Burns

, Cohen-Mansfield

, Cooper

, Costafreda

, Dias

, Fox

, Gitlin

, Howard

, Kales

, Kivimäki

, Larson

, Ogunniyi

, Orgeta

, Ritchie

, Rockwood

, Sampson

, Samus

, Schneider

, Selbæk

, Teri

, Mukadam

(2020) Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396, 413–446.

Cui

, Wu

, She

(2009) Effects of chronic noise exposure on spatial learning and memory of rats in relation to neurotransmitters and NMDAR2B alteration in the hippocampus. J Occup Health 51, 152–158.

Manukyan

, Grigoryan

, Hunanyan

, Harutyunyan

, Manukyan

, Mkrtchyan

, Melkonyan

(2020) Alfa2-adrenoblockers attenuate the elevated plasma cholesterol, anxiety levels and restore impaired spatial memory of rats under the chronic noise exposure. Sci Total Environ 740, 140390.

Jafari

, Kolb

, Mohajerani

(2020) Noise exposure accelerates the risk of cognitive impairment and Alzheimer’s disease: Adulthood, gestational, and prenatal mechanistic evidence from animal studies. Neurosci Biobehav Rev 117, 110–128.

Jafari

, Kolb

, Mohajerani

(2018) Chronic traffic noise stress accelerates brain impairment and cognitive decline in mice. Exp Neurol 308, 1–12.

, Liu

, Xu

, Kim

(2019) Effects of combined traffic noise on the synaptic ultrastructure and expressions of p-CaMKII and NMDAR1 in the hippocampus of young SD rats. Environ Sci Pollut Res Int 26, 22030–22039.

10.

Uran

, Aon-Bertolino

, Caceres

, Capani

, Guelman

(2012) Rat hippocampal alterations could underlie behavioral abnormalities induced by exposure to moderate noise levels. Brain Res 1471, 1–12.

11.

Cui

, Li

, Gai

, She

, Zhang

, Xu

, Chen

, An

, Ma

, Wang

(2015) Chronic noise exposure acts cumulatively to exacerbate Alzheimer’s disease-like amyloid-β pathology and neuroinflammation in the rat hippocampus. Sci Rep 5, 12943.

12.

Park

, Kim

, Yeo

, Park

(2018) Cognitive decline and increased hippocampal p-tau expression in mice with hearing loss. Behav Brain Res 342, 19–26.

13.

Shukla

, Roy

, Kaur

, Nayak

, Mani

, Shukla

, Kapoor

(2019) Attenuation of adverse effects of noise induced hearing loss on adult neurogenesis and memory in rats by intervention with Adenosine A(2A) receptor agonist. Brain Res Bull 147, 47–57.

14.

Zheng

, She

, Wang

, Zhu

, Fu

, Ma

, Yang

, Gao

, Li

, Wu

, Cui

(2022) Around-the-clock noise induces AD-like neuropathology by disrupting autophagy flux homeostasis. Cells 11, 2742.

15.

, Su

, Zhai

, Chi

, She

, Gao

, Wang

, Yang

, Wang

, Cui

(2019) Proteomes analysis reveals the involvement of autophagy in AD-like neuropathology induced by noise exposure and ApoE4. Environ Res 176, 108537.

16.

, Li

, Chi

, Yang

, She

, Wang

, Gao

, Ma

, Zhang

, Cui

(2020) Transcriptome analysis of the hippocampus in environmental noise-exposed SAMP8 mice reveals regulatory pathways associated with Alzheimer’s disease neuropathology. Environ Health Prev Med 25, 3.

17.

Chi

, Cao

, Zhang

, Su

, Yang

, Li

, She

, Wang

, Gao

, Ma

, Zheng

, Li

, Cui

(2021) Environmental noise stress disturbs commensal microbiota homeostasis and induces oxi-inflammmation and AD-like neuropathology through epithelial barrier disruption in the EOAD mouse model. J Neuroinflammation 18, 9.

18.

Sun

, Lin

, Yi

, Zhang

, Liu

, Fu

, Sun

, Li

, Jiao

, Tian

, Xu

, Tseng

, Lin

(2021) Aircraft noise, like heat stress, causes cognitive impairments via similar mechanisms in male mice. Chemosphere 274, 129739.

19.

Jahncke

, Hygge

, Halin

, Green

, Dimberg

(2011) Open-plan office noise: Cognitive performance and restoration. J Environ Psychol 31, 373–382.

20.

Golmohammadi

, Darvishi

, Faradmal

, Poorolajal

, Aliabadi

(2019) Attention and short-term memory during occupational noise exposure considering task difficulty. Applied Acoustics 158, UNSP 107065.

21.

Wright

, Peters

, Ettinger

, Kuipers

, Kumari

(2016) Moderators of noise-induced cognitive change in healthy adults. Noise Health 18, 117–132.

22.

Tomei

, Tecchio

, Zappasodi

, Ercolani

, Moffa

, Chiovenda

, Ciarrocca

(2006) Exposure to traffic noise and effects on attention. Ann Ig 18, 507–519.

23.

Chiovenda

, Pasqualetti

, Zappasodi

, Ercolani

, Milazzo

, Tomei

, Capozzella

, Tomei

, Rossini

, Tecchio

(2007) Environmental noise-exposed workers: Event-related potentials, neuropsychological and mood assessment. Int J Psychophysiol 65, 228–237.

24.

van Kempen

, van Kamp

, Lebret

, Lammers

, Emmen

, Stansfeld

(2010) Neurobehavioral effects of transportation noise in primary schoolchildren: A cross-sectional study. Environ Health 9, 25.

25.

Gomes

, Martinho Pimenta

, Castelo Branco

(1999) Effects of occupational exposure to low frequency noise on cognition. Aviat Space Environ Med 70, A115–118.

26.

Fuks

, Wigmann

, Altug

, Schikowski

(2019) Road traffic noise at the residence, annoyance, and cognitive function in elderly women. Int J Environ Res Public Health 16, 1790.

27.

Irgens-Hansen

, Gundersen

, Sunde

, Baste

, Harris

, Bråtveit

, Moen

(2015) Noise exposure and cognitive performance: A study on personnel on board Royal Norwegian Navy vessels. Noise Health 17, 320–327.

28.

Zeydabadi

, Askari

, Vakili

, Mirmohammadi

, Ghovveh

, Mehrparvar

(2019) The effect of industrial noise exposure on attention, reaction time, and memory. Int Arch Occup Environ Health 92, 111–116.

29.

Foraster

, Esnaola

, López-Vicente

, Rivas

, Álvarez-Pedrerol

, Persavento

, Sebastian-Galles

, Pujol

, Dadvand

, Sunyer

(2022) Exposure to road traffic noise and cognitive development in schoolchildren in Barcelona, Spain: A population-based cohort study. PLoS Med 19, e1004001.

30.

Clark

, Paunovic

(2018) WHO Environmental Noise Guidelines for the European Region: A systematic review on environmental noise and cognition. Int J Environ Res Public Health 15, 285.

31.

Tzivian

, Dlugaj

, Winkler

, Weinmayr

, Hennig

, Fuks

, Vossoughi

, Schikowski

, Weimar

, Erbel

, Jöckel

, Moebus

, Hoffmann

(2016) Long-term air pollution and traffic noise exposures and mild cognitive impairment in older adults: A cross-sectional analysis of the Heinz Nixdorf Recall Study. Environ Health Perspect 124, 1361–1368.

32.

Weuve

, D’Souza

, Beck

, Evans

, Kaufman

, Rajan

, de Leon

CFM

, Adar

(2021) Long-term community noise exposure in relation to dementia, cognition, and cognitive decline in older adults. Alzheimers Dement 17, 525–533.

33.

Linares

, Culqui

, Carmona

, Ortiz

, Díaz

(2017) Short-term association between environmental factors and hospital admissions due to dementia in Madrid. Environ Res 152, 214–220.

34.

Culqui

, Linares

, Ortiz

, Carmona

, Díaz

(2017) Association between environmental factors and emergency hospital admissions due to Alzheimer’s disease in Madrid. Sci Total Environ 592, 451–457.

35.

Carey

, Anderson

, Atkinson

, Beevers

, Cook

, Strachan

, Dajnak

, Gulliver

, Kelly

(2018) Are noise and air pollution related to the incidence of dementia? A cohort study in London, England. BMJ Open 8, e022404.

36.

Crous-Bou

, Gascon

, Gispert

, Cirach

, Sánchez-Benavides

, Falcon

, Arenaza-Urquijo

, Gotsens

, Fauria

, Sunyer

, Nieuwenhuijsen

, Luis Molinuevo

(2020) Impact of urban environmental exposures on cognitive performance and brain structure of healthy individuals at risk for Alzheimer’s dementia. Environ Int 138, 105546.

37.

Andersson

, Oudin

, Sundström

, Forsberg

, Adolfsson

, Nordin

(2018) Road traffic noise, air pollution, and risk of dementia - results from the Betula project. Environ Res 166, 334–339.

38.

Meng

, Zhang

, Jiang

, Sha

, Lan

, Huang

(2022) Chronic noise exposure and risk of dementia: A systematic review and dose-response meta-analysis. Front Public Health 10, 832881.

39.

Huang

, Zhang

, Wang

, Lan

(2021) Relationship between chronic noise exposure, cognitive impairment, and degenerative dementia: Update on the experimental and epidemiological evidence and prospects for further research. J Alzheimers Dis 79, 1409–1427.

40.

Jing

, Jing-xuan

, Yong-wei

, Ya-jia

, Lei

(2023) Preliminary discussion and application of occupation-related cumulative noise exposure assessment method. Mod Prev Med 50, 2721–2725.

41.

Zhengyu

, Mingyuan

(1989) Application of the Chinese version of Mini-Mental State Examination (MMSE). Shanghai Arch Psychiatry 7, 4.

42.

Wei

, Luning

(2007) Application of Montreal Cognitive Assessment Scale in screening patients with mild cognitive impairment. Chinese J Intern Med 46, 3.

43.

Neuropsychological and Behavioral Neurology Group (2019) Expert consensus on clinical application of commonly used neuropsychological cognitive assessment scales. Chinese J Neurol 52, 11.

44.

Siqueira

GSA

, Hagemann

PMS

, Coelho

, Santos

FHD

, Bertolucci

PHF

(2019) Can MoCA and MMSE be interchangeable cognitive screening tools? A systematic review. Gerontologist 59, e743–e763.

45.

Larner

(2012) Screening utility of the Montreal Cognitive Assessment (MoCA): In place of–or as well as–the MMSE? Int Psychogeriatr 24, 391–396.

46.

Dong

, Lee

, Basri

, Collinson

, Merchant

, Venketasubramanian

, Chen

(2012) The Montreal Cognitive Assessment is superior to the Mini-Mental State Examination in detecting patients at higher risk of dementia. Int Psychogeriatr 24, 1749–1755.

47.

Baumgart

, Snyder

, Carrillo

, Fazio

, Kim

, Johns

(2015) Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective. Alzheimers Dement 11, 718–726.

48.

Hugo

, Ganguli

(2014) Dementia and cognitive impairment: Epidemiology, diagnosis, and treatment. Clin Geriatr Med 30, 421–442.

49.

Killin

, Starr

, Shiue

, Russ

(2016) Environmental risk factors for dementia: A systematic review. BMC Geriatr 16, 175.

50.

Tzivian

, Dlugaj

, Winkler

, Hennig

, Fuks

, Sugiri

, Schikowski

, Jakobs

, Erbel

, Jöckel

, Moebus

, Hoffmann

, Weimar

(2016) Long-term air pollution and traffic noise exposures and cognitive function:A cross-sectional analysis of the Heinz Nixdorf Recall study. J Toxicol Environ Health A 79, 1057–1069.

51.

Tzivian

, Jokisch

, Winkler

, Weimar

, Hennig

, Sugiri

, Soppa

, Dragano

, Erbel

, Jöckel

, Moebus

, Hoffmann

(2017) Associations of long-term exposure to air pollution and road traffic noise with cognitive function-An analysis of effect measure modification. Environ Int 103, 30–38.

52.

Tyas

, Manfreda

, Strain

, Montgomery

(2001) Risk factors for Alzheimer’s disease: A population-based, longitudinal study in Manitoba, Canada. Int J Epidemiol 30, 590–597.

53.

, Qin

(2018) Influences of combined traffic noise on the ability of learning and memory in mice. Noise Health 20, 9–15.

54.

Cheng

, Wang

, Huang

, Liao

(2016) The hippocampus may be more susceptible to environmental noise than the auditory cortex. Hear Res 333, 93–97.