Abstract
The link between hearing impairment and air pollution has not been established, and the moderating effect of a healthy diet has never been investigated before. The purpose of this study was to investigate the association between air pollution and hearing impairment in British adults aged 37–73 years, and whether the association was modified by a healthy diet. We performed a cross-sectional population-based study with 158,811 participants who provided data from United Kingdom Biobank. A multivariate logistic regression model was used to investigate the link between air pollution and hearing impairment. Subgroup and effect modification analyses were carried out according to healthy diet scores, gender, and age. In the fully adjusted model, we found that exposure to PM10, NOX, and NO2 was associated with hearing impairment [PM10: odds ratio (OR) = 1.15, 95% confidence interval (95% CI) 1.02–1.30, P = 0.023; NOX: OR = 1.02, 95% CI 1.00–1.03, P = 0.040; NO2: OR = 1.03, 95% CI 1.01–1.06, P = 0.044], while PM2.5 and PM2.5 absorbance did not show similar associations. We discovered an interactive effect of age and air pollution on hearing impairment, but a healthy diet did not. The findings suggested that exposure to PM10, NOX and NO2 was linked to hearing impairment in British adults, whereas PM2.5 and PM2.5 absorbance did not show similar associations. These may help researchers focus more on the impact of air pollution on hearing impairment and provide a basis for developing effective prevention strategies.
Introduction
Hearing impairment is one of the most common age-related chronic health problems (). The rate of clinically significant hearing impairment is doubling approximately every decade (; ). Hearing impairment has been reported to be the second most prevalent disorder and the dominant cause of years lived with disability among global non-infectious diseases (). In contrast with normal hearing adults of the same age, those with hearing impairment have a greater incidence of hospitalization (), death (), falls (), cardiovascular disorders (), depression (), and dementia (). Consequently, hearing impairment causes a huge burden on the emotional and physical wellbeing of individuals (). It is predicted that one-fifth of the population of the United Kingdom will suffer from hearing impairment by 2035 (). Accordingly, the key is to prevent hearing impairment. Hearing impairment is caused by a combination of hereditary and environmental factors (). The identification of modifiable risk factors is critical to provide the basis for preventive strategies.
Global trends in urbanization and industrialization have led to a growing problem of air pollution (), which has become the main public health issue across the world (). Of note, growing evidence demonstrates that air pollution exposure is not only connected with respiratory disorders, such as lung cancer (), but also with cardiovascular diseases (; ), inflammatory diseases (), diabetes (), and neurodegenerative diseases (). Besides, the main environmental risk factor for human death is air pollution (). Lately, there have been reports that air pollution may impact hearing health, but available data is limited. A recent study () found that participants exposed to fine particulate matter (PM2.5: particulate matter ≤ 2.5 μm in diameter) and nitrogen dioxide (NO2) had a substantially increased risk of sudden sensorineural hearing loss (SSNHL). Another study () showed that increased concentrations of NO2 were linked to a higher risk of sensorineural hearing loss, while in a nested case-control study (), SSNHL was associated with NO2 exposure, but particulate matter with a diameter of 10 μm or less (PM10) was not associated with SSNHL. Similarly, another study () also found no association between PM10 and number of SSNHL patient. Although these studies explored the association of air pollution with sensorineural hearing loss, the results remained controversial.
A healthy diet might preserve hearing (; , ), as described by their role in preventing chronic illnesses (). A healthy diet includes multiple components that support antioxidant function and protect against free radical damage (), thereby regulating oxidative stress and delaying mitochondrial dysfunction (). In addition, a healthy diet might be beneficial to hearing impairment by protecting microvascular and macrovascular damage to cochlear blood flow (; ), providing the essential nutrients for an adequate cochlear blood supply (), and reducing inflammation (). According to previous research, dietary patterns could modify the relationship between air pollution and health-related outcomes, such as cardiovascular disease mortality risk () and cognitive function (). However, the moderating effect of a healthy diet on the link between hearing impairment and air pollution has not been investigated before. Therefore, in this cross-sectional study, we aimed to explore the link between air pollution and hearing impairment and to analyze whether a healthy diet has moderating effects on this link.
Materials and Methods
Study Subjects
The United Kingdom Biobank is an international and accessible data resource1 containing data on more than half a million people aged from 37 to 73 years (99.5% were between 40 and 69 years) in England, Scotland, and Wales (). Adults living within a 25-mile radius of one of 22 Biobank Assessment Centers in the United Kingdom were invited by email to join the United Kingdom Biobank between 2006 and 2010, achieving a response rate of approximately 5.5% (). Participants completed a computer touch screen questionnaire (which included questions on topics such as population, health, lifestyle, environment as well as medical history, etc.) and underwent physical measurements, including a hearing test. Written informed consent was signed by all the participants. The research was carried out with the general approval of the National Health Service and the National Research Ethics Service. The subjects of the current study were all those participants for whom data on both air pollution measures and hearing test results were available.
Hearing Test
The speech-in-noise hearing test (i.e., digit triplet test, DTT) of the United Kingdom Biobank provided participants with 15 groups of English monosyllabic numbers to evaluate the listening thresholds (i.e., signal-to-noise ratio) at different sound levels.2 Each ear was examined separately, in the order that the participants were allocated at random. Participants first wore circumaural headphones and selected the most comfortable volume. Then, they started the speech-in-noise hearing test to identify and type the three numbers they had heard by touching the screen interface. The noise level of the subsequent triple would increase if the triplet was correctly recognized; otherwise, it would reduce. The speech reception threshold (SRT) was defined as the signal-to-noise ratio of correctly understanding half of the presented speech. The SRT ranged from −12 to +8 dB, with a lower score representing better performance. Based on the cutoff point established by , the better performance ear was chosen for this study, and participants were divided into normal (SRT < −5.5 dB) and hearing impairment (SRT ≥ −5.5 dB) groups.
The DTT shows a very good correlation with the pure tone hearing test (r = 0.77) (), so it can be considered as a measure of hearing impairment (). There are some advantages to the DTT, for example, there is no need for a sound booth and the test can be delivered via the internet (). The most common hearing complaint is difficulty in hearing over background noise (), so the speech-in-noise hearing test used to evaluate hearing function represents an ecologically effective as well as objective hearing indicator ().
Measures of Air Pollution
The air pollution data recorded in the United Kingdom Biobank were from the Small Area Health Statistics Unit,3 a part of the BioShaRE-EU Environmental Determinants of Health Project.4 The Land Use Regression model was applied to assess air pollution in 2010 by modeling at each residential address of the participants, which was developed as part of the European Study of Cohorts for Air Pollution Effects.5 The Land Use Regression model used to calculate the spatial distribution of air pollutants was based on geographic predictors such as traffic, land use, and topography in the geographical information system. In this study, the air pollutants assessed were PM2.5, PM10, PM2.5 absorbance, NOX, and NO2, of which all were annual average concentrations in μg/m3. More details about the air pollution data used in the United Kingdom Biobank are available elsewhere.6
Assessment of Other Variables
Age, gender, ethnicity, educational background, employment, smoking status, and alcohol intake were utilized as baseline data. The ethnic background of participants was divided into six categories: White, Black, Asian, Chinese, Mixed, and other. The educational background was divided into six categories: higher national diploma (HND), national vocational qualification (NVQ), higher national certificate (HNC), or equivalent; A levels or AS levels (including the higher school certificate), or equivalent; O levels (including the school certificate), general certificate of secondary educations (GCSEs), or equivalent; certificate of secondary educations (CSEs), or equivalent; college or university degree; and other professional qualification. Employment status was divided into seven categories: retired; unable to work because of sickness or disability; looking after home and/or family; unemployed; in paid employment or self-employed; student (full-time or part-time); or doing unpaid or voluntary work. Smoking status () was divided into three categories: never-smokers, current and former smokers. Alcohol consumption frequency was divided into five categories: daily or almost daily; three or four times a week; once or twice a week; occasional drinking; and never. Body mass index (BMI) was categorized as obese (BMI ≥ 30), overweight (25 ≤ BMI < 30), normal weight (18.5 ≤ BMI < 25), and underweight (BMI < 18.5). Evaluation of physical activity was conducted through the questions in the International Physical Activity Questionnaire, which graded activity into three degrees: low, moderate, and high.7 A questionnaire8 containing the usual dietary intake was completed by United Kingdom Biobank participants during the baseline assessment. The intake of fruits (fresh fruit intake and dried fruit intake), vegetables (cooked vegetable intake and salad/raw vegetable intake), fish (oily fish intake and non-oily fish intake), processed meat and unprocessed red meat (beef intake, lamb/mutton intake, and pork intake) from the United Kingdom Biobank food intake questionnaire was used to calculate the health diet scores (): fruit intake ≥ three pieces per day, vegetable intake ≥ four tablespoons per day, fish intake ≥ twice per week, processed meat intake ≤ twice per week, unprocessed red meat intake ≤ twice per week. Each favorable dietary factor gave a point, so the healthy diet scores were 0–5. The serum concentrations of glycosylated hemoglobin and total cholesterol were regarded as continuous variables. Vascular problems included angina, heart attack, stroke, and high blood pressure.
Data Analysis
All analyses were performed using R version 4.0.2. The data are summarized descriptively. Continuous variables are represented as mean (standard deviation) and comparison between the two groups was performed by independent sample t test. The classification variables are represented as percentages (%) and the rate was compared by χ2 test. The link between air pollution and hearing impairment was investigated using a multivariate logistic regression model with and without adjusting for other variables. Model 1 was unadjusted, Model 2 was adjusted for age and gender, and Model 3 was further adjusted for race, educational level, employment, smoking status and alcohol consumption frequency, BMI, physical activity, glycosylated hemoglobin, total cholesterol, and vascular diseases (heart attack, stroke, angina, and hypertension). Moreover, we evaluated the association between subgroups stratified by healthy diet scores (low: 0–2, and high: 3–5), gender (female and male) and age (≤50, 51–60, and >60). The Wald test was used to test interactions among subgroups. P < 0.05 (two-sided test) was considered statistically significant.
Results
In total, 158,811 subjects were enrolled in this study, including 18,881 (11.9%) with hearing impairment and 139,930 (88.1%) with normal hearing, 54.5% were female (n = 86,516), 91.7% were white (n = 145,633), with the mean (standard deviation) age of 56.68 (8.15) years. The distribution of baseline characteristics and air pollution in the two groups is shown in Table 1. Except for physical activity, other variables were significantly distributed in the two groups (P < 0.05). In comparison to the group of people with normal hearing, the subjects in the hearing impairment group were older on average, non-whites. In addition, they were more likely to be obese and to have cardiovascular problems. Furthermore, the hearing impairment group was exposed to higher mean annual concentrations of air pollutants than the normal hearing group (Table 1).
TABLE 1
| Normal hearing | Hearing impairment | P | |
| N | 139,930 | 18,881 | |
| Age (years), mean (SD) | 56.21 (8.13) | 60.13 (7.40) | <0.001 |
| Gender (%) | 0.004 | ||
|  Female | 76,414 (54.6) | 10,102 (53.5) | |
|  Male | 63,516 (45.4) | 8,779 (46.5) | |
| Race (%) | <0.001 | ||
|  White ethnicity | 129,996 (93.2) | 15,637 (83.3) | |
|  Mixed ethnicity | 1,053 (0.8) | 137 (0.7) | |
|  Asian ethnicity | 3,541 (2.5) | 1,328 (7.1) | |
|  Black ethnicity | 3,019 (2.2) | 1,026 (5.5) | |
|  Chinese ethnicity | 462 (0.3) | 123 (0.7) | |
|  Other ethnicity | 1,336 (1.0) | 523 (2.8) | |
| Education (%) | <0.001 | ||
|  Other professional qualification | 7,113 (5.9) | 1,191 (8.6) | |
|  College or university degree | 48,983 (40.7) | 5,098 (36.8) | |
|  O level/GCSEs or equivalent | 30,497 (25.4) | 3,580 (25.9) | |
|  CSEs or equivalent | 8,280 (6.9) | 845 (6.1) | |
|  A/AS levels or equivalent | 16,528 (13.7) | 1,670 (12.1) | |
|  NVQ or HND or HNC or equivalent | 8,876 (7.4) | 1,465 (10.6) | |
| Employment (%) | <0.001 | ||
|  Inpaid employment or self-employed | 81,816 (59.0) | 7,467 (40.1) | |
|  Retired | 44,969 (32.4) | 9,136 (49.1) | |
|  Looking after home and/or family | 4,195 (3.0) | 474 (2.5) | |
|  Unable to work because of sickness or disability | 3,575 (2.6) | 883 (4.7) | |
|  Unemployed | 2,996 (2.2) | 478 (2.6) | |
|  Doing unpaid or voluntary work | 678 (0.5) | 115 (0.6) | |
|  Full-time or part-time student | 384 (0.3) | 66 (0.4) | |
| BMI (%), kg/m2 | <0.001 | ||
|  Underweight | 721 (0.5) | 121 (0.6) | |
|  Normal weight | 46,225 (33.2) | 5,590 (30.0) | |
|  Overweight | 58,610 (42.1) | 7,817 (41.9) | |
|  Obesity | 33,548 (24.1) | 5,127 (27.5) | |
| Smoke (%) | <0.001 | ||
|  Never | 77,310 (55.4) | 10,140 (54.0) | |
|  Previous | 48,353 (34.7) | 6,572 (35.0) | |
|  Current | 13,839 (9.9) | 2,054 (10.9) | |
| Drink frequency (%) | <0.001 | ||
|  Daily or almost daily | 29,132 (20.8) | 3,327 (17.7) | |
|  Three or four times a week | 32,246 (23.1) | 3,443 (18.3) | |
|  Once or twice a week | 35,588 (25.5) | 4,367 (23.2) | |
|  Occasional drinkers | 32,275 (23.1) | 5,095 (27.0) | |
|  Never | 10,551 (7.5) | 2,609 (13.8) | |
| Physical activity (%) | 0.061 | ||
|  Low | 20,316 (17.6) | 2,658 (18.1) | |
|  Moderate | 46,945 (40.7) | 5,839 (39.7) | |
|  High | 48,013 (41.7) | 6,198 (42.2) | |
| HbA1c, mean (SD), mmol/mol | 36.02 (6.50) | 37.55 (8.10) | <0.001 |
| TC, mean (SD), mmol/L | 5.71 (1.14) | 5.60 (1.20) | <0.001 |
| Vascular problems (%) | <0.001 | ||
|  None | 10,0638 (73.8) | 11,826 (64.9) | |
|  Hypertension | 29,376 (21.5) | 4,868 (26.7) | |
|  Heart attack, angina, or stroke | 3,184 (2.3) | 703 (3.9) | |
|  High blood pressure and heart attack, angina, or stroke | 3,235 (2.4) | 813 (4.5) | |
| Air pollution | |||
|  PM2.5, mean (SD), μg/m3 | 9.88 (0.91) | 9.94 (0.95) | <0.001 |
|  PM10, mean (SD), μg/m3 | 16.28 (1.82) | 16.45 (1.85) | <0.001 |
|  PM2.5 absorbance, mean (SD), per-meter | 1.21 (0.27) | 1.24 (0.29) | <0.001 |
|  NOX, mean (SD), μg/m3 | 43.52 (14.44) | 44.92 (15.83) | <0.001 |
|  NO2, mean (SD), μg/m3 | 26.88 (7.21) | 27.72 (7.70) | <0.001 |
Characteristics of participants (N = 158,811).
Abbreviations: N, number; SD, standard deviation; GCSEs, general certificate of secondary educations; CSEs, certificate of secondary educations; NVQ, national vocational qualification; HND, higher national diploma; HNC, higher national certificate; BMI, body mass index; HbA1c, glycosylated hemoglobin; TC, total cholesterol; PM, particulate matter; NO2, nitrogen dioxides; NOX, nitrogen oxides.
Table 2 shows the risks of several air pollutants and hearing impairment. Model 1 (without adjustment for any confounders) showed significant associations between air pollutants and hearing impairment (P < 0.001) [PM2.5: odds ratio (OR) = 2.03, 95% confidence interval (95% CI) 1.73–2.40; PM10: OR = 1.64, 95% CI 1.51–1.78; PM2.5 absorbance: OR = 1.48, 95% CI 1.40–1.56; NOX: OR = 1.06, 95% CI 1.05–1.07; NO2: OR = 1.17, 95% CI 1.41–1.19]. After adjusting for age and gender, Model 2 showed that air pollutants were still significantly associated with hearing impairment (P < 0.001), and all OR values were larger than Model 1 (PM2.5: OR = 3.02, 95% CI 2.56–3.57; PM10: OR = 1.82, 95% CI 1.67–1.97; PM2.5 absorbance: OR = 1.67, 95% CI 1.59–1.77; NOX: OR = 1.09, 95% CI 1.08–1.10; NO2: OR = 1.24, 95% CI 1.21–1.26). Except for PM2.5 and PM2.5 absorbance, which showed no significant associations with hearing impairment (P = 0.970 and P = 0.063, respectively), we observed that the associations between the other pollutants and hearing impairment remained in Model 3 after further adjusting for other confounders on the basis of Model 2 (PM10: OR = 1.15, 95% CI 1.02–1.30, P = 0.023; NOx: OR = 1.02, 95% CI 1.00–1.03, P = 0.040; NO2: OR = 1.03, 95% CI 1.01–1.06, P = 0.044), even though the estimates were lower than those in Models 1 and 2.
TABLE 2
| Model 1 | Model 2 | Model 3 | ||||
| OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | |
| PM2.5 | 2.03 (1.73–2.40) | <0.001 | 3.02 (2.56–3.57) | <0.001 | 1.01 (0.79–1.29) | 0.970 |
| PM10 | 1.64 (1.51–1.78) | <0.001 | 1.82 (1.67–1.97) | <0.001 | 1.15 (1.02–1.30) | 0.023 |
| PM2.5 absorbance | 1.48 (1.40–1.56) | <0.001 | 1.67 (1.59–1.77) | <0.001 | 1.08 (0.99–1.18) | 0.063 |
| NOX | 1.06 (1.05–1.07) | <0.001 | 1.09 (1.08–1.10) | <0.001 | 1.02 (1.001–1.03) | 0.040 |
| NO2 | 1.17 (1.14–1.19) | <0.001 | 1.24 (1.21–1.26) | <0.001 | 1.03 (1.01–1.06) | 0.044 |
Association of air pollution and hearing impairment.
Abbreviations: OR, odds ratio; CI, confidence interval; PM, particulate matter; NO2, nitrogen dioxides; NOX, nitrogen oxides.
Model 1: unadjusted.
Model 2: adjusted for age and gender.
Model 3: adjusted for age, gender, race, education, employment, smoking, drink frequency, body mass index, physical activity, glycosylated hemoglobin, total cholesterol, and vascular disease (heart attack, stroke, angina, and hypertension).
Table 3 shows the associations between several air pollutants and hearing impairment, stratified by healthy diet scores. In this study, no significant associations and moderating effects were observed. After stratification by age (Table 4), we found that PM10, PM2.5 absorbance, NOX, and NO2 were associated with hearing impairment in participants up to and including 50 years of age (PM10: OR = 1.62, 95% CI 1.20–2.18, P = 0.002; PM2.5 absorbance: OR = 1.32, 95% CI 1.08–1.61, P = 0.006; NOX: OR = 1.04, 95% CI 1.01–1.08, P = 0.014; NO2: OR = 1.09, 95% CI 1.01–1.17, P = 0.031). In participants aged 51 to 60 years and above 60, there was no connection between air pollution and hearing impairment. Additionally, there was a statistically significant interaction between age and air pollution with hearing impairment (P < 0.05). Further, after stratifying by gender (Table 5), we found that NOX and NO2 were correlated with hearing impairment in men.
TABLE 3
| Low (N = 58324) | High (N = 94414) | P-interaction | |||
| OR (95% CI) | P | OR (95% CI) | P | ||
| PM2.5 | 1.05 (0.69, 1.61) | 0.823 | 0.84 (0.62, 1.14) | 0.271 | 0.403 |
| PM10 | 1.20 (0.97, 1.49) | 0.086 | 1.14 (0.98, 1.32) | 0.094 | 0.688 |
| PM2.5 absorbance | 1.10 (0.95, 1.27) | 0.191 | 1.10 (0.99, 1.22) | 0.060 | 0.902 |
| NOX | 1.02 (0.99, 1.05) | 0.147 | 1.01 (0.99, 1.03) | 0.498 | 0.300 |
| NO2 | 1.04 (0.99, 1.10) | 0.161 | 1.02 (0.98, 1.06) | 0.315 | 0.377 |
Associations of air pollution and hearing impairment in subgroups stratified by healthy diet scores.
Abbreviations: OR, odds ratio; CI, confidence interval; PM, particulate matter; NO2, nitrogen dioxides; NOX, nitrogen oxides.
All models were adjusted for age, gender, race, education, employment, smoking, drink frequency, body mass index, physical activity, glycosylated hemoglobin, total cholesterol, and vascular disease (heart attack, stroke, angina, and hypertension).
This subgroup included 152,738 participants because of the missing data of dietary information for 6,073 participants.
TABLE 4
| ≤50 (N = 40,978) | 51–60 (N = 53,844) | >60 (N = 63,989) | P-interaction | ||||
| OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | ||
| PM2.5 | 1.74 (0.96–3.14) | 0.067 | 1.02 (0.66–1.59) | 0.918 | 0.80 (0.57–1.13) | 0.198 | 0.013 |
| PM10 | 1.62 (1.20–2.18) | 0.002 | 1.15 (0.92–1.43) | 0.215 | 1.04 (0.88–1.23) | 0.647 | 0.005 |
| PM2.5 absorbance | 1.32 (1.08, 1.61) | 0.006 | 1.04 (0.89–1.21) | 0.626 | 1.05 (0.93–1.18) | 0.417 | 0.029 |
| NOX | 1.04 (1.01–1.08) | 0.014 | 1.02 (0.99–1.05) | 0.247 | 1.00 (0.98–1.02) | 0.921 | 0.005 |
| NO2 | 1.09 (1.01–1.17) | 0.031 | 1.03 (0.97–1.09) | 0.292 | 1.01 (0.97–1.06) | 0.641 | 0.017 |
Associations of air pollution and hearing impairment in subgroups stratified by age.
Abbreviations: OR, odds ratio; CI, confidence interval; PM, particulate matter; NO2, nitrogen dioxides; NOX, nitrogen oxides.
All models were adjusted for gender, race, education, employment, smoking, drink frequency, body mass index, physical activity, glycosylated hemoglobin, total cholesterol, and vascular disease (heart attack, stroke, angina, and hypertension).
TABLE 5
| Female (N = 86516) | Male (N = 72295) | P-interaction | |||
| OR (95% CI) | P | OR (95% CI) | P | ||
| PM2.5 | 0.86 (0.61–1.22) | 0.406 | 1.19 (0.83–1.69) | 0.346 | 0.191 |
| PM10 | 1.16 (0.98–1.38) | 0.085 | 1.15 (0.97–1.37) | 0.118 | 0.914 |
| PM2.5 absorbance | 1.10 (0.98–1.23) | 0.112 | 1.07 (0.95–1.21) | 0.261 | 0.920 |
| NOX | 1.01 (0.98–1.02) | 0.879 | 1.03 (1.01–1.05) | 0.011 | 0.061 |
| NO2 | 1.02 (0.97–1.06) | 0.489 | 1.05 (1.00–1.09) | 0.049 | 0.216 |
Associations of air pollution and hearing impairment in subgroups stratified by gender.
Abbreviations: OR, odds ratio; CI, confidence interval; PM, particulate matter; NO2, nitrogen dioxides; NOX, nitrogen oxides.
All models were adjusted for age, race, education, employment, smoking, drink frequency, body mass index, physical activity, glycosylated hemoglobin, total cholesterol, and vascular disease (heart attack, stroke, angina, and hypertension).
Discussion
In this cross-sectional study, we investigated the association between hearing impairment and air pollution (comprising PM2.5, PM10, PM2.5 absorbance, NOX, and NO2) using United Kingdom Biobank data. We found that exposure to PM10, NOX, and NO2 was linked to hearing impairment after adjusting for confounding factors, while PM2.5 and PM2.5 absorbance showed no similar correlations. Furthermore, there was no modification of these associations by a healthy diet. Regarding age, interaction effects were observed.
The relationship between air pollution and hearing impairment has not been fully established yet. Several studies indicated that exposure to NO2 could be related to hearing problems. found that people exposed to moderate (hazard ratio, HR = 1.40, 95% CI 1.27–1.54) and high levels of NO2 (HR = 1.63, 95% CI 1.48–1.81) were at higher risk of developing sensorineural hearing loss than those exposed to the low level. The results of were similar, finding a significantly increased risk of SSNHL in those exposed to high concentrations of NO2 (adjusted HR = 1.02, 95% CI 1.01–1.04). Likewise, discovered that SSNHL was associated with short-term exposure to NO2 (14 days) (adjusted OR = 3.12, 95% CI 2.16–4.49). Consistent with previous studies, NO2 was associated with hearing impairment in our study. Moreover, NOX, a term that contains several nitrogen compounds but is mainly composed of nitrogen oxide and NO2, showed an association with hearing impairment.
In contrast to our expectations, we found a significant association between PM10 and hearing impairment but not PM2.5. Conversely, previous studies (; ) showed no correlation between PM10 and hearing impairment. A study reported () a significantly higher risk of developing SSNHL with moderate (adjusted HR = 1.58, 95% CI 1.21–2.06) or high (adjusted HR = 1.32, 95% CI 1.00–1.74) level exposure to PM2.5 compared to those exposed to the low level. And another study discovered a slight negative association between the maximum PM2.5 concentration and the admission rate of SSNHL (). In 2017, a study () in a large national health survey reported that oxidative potential of PM2.5 rather than PM2.5, was associated with diabetes prevalence, indicating that the impact of particulate matter on diabetes might vary with the compositions. According to a study () conducted at three sites (urban roadside, central urban background, and rural) in Birmingham, United Kingdom, organics, nitrate, and sulfate accounted for a substantial amount of the overall mass for both PM10 and PM2.5. This research also showed that proportions of these three major parts and other secondary compositions like iron-rich dust and sodium chloride varied in both. Although discrepancies in associations with diseases after PM2.5 and PM10 exposure could be explained by different compositions of particulate matter, the evidence may still be limited. More research is required to clarify this issue in the future.
Oxidative stress and mitochondrial dysfunction play a crucial role in hearing impairment (). Air pollution might be involved in oxidative stress by producing or directly acting as reactive oxygen species (), which can then induce mitochondrial damage (). Dysfunctional mitochondria increase reactive oxygen species generation and accumulation, reducing the mitochondrial membrane potential, activating the apoptosis pathway, and causing the death of inner ear hair cells (). What’s more, air pollution might indirectly be associated with hearing impairment by causing cardiovascular diseases through pro-inflammatory pathways and the production of reactive oxygen species (; ). It has been demonstrated that cardiovascular diseases are risk factors for hearing impairment (; ). Nonetheless, the link between air pollution and hearing impairment was still evident after adjusting for related vascular problems in Model 3, suggesting that other mechanisms may also be involved in the link between air pollution and hearing impairment.
There was evidence that a healthy diet could protect against hearing impairment by reducing vascular damage, decreasing inflammation, and inhibiting oxidative damage (; ). Based on similar mechanistic pathways, modifying the health effects of air pollution by diet may be possible. But in our study, no effect modification of diet was observed. Studies previously showed an interaction between dietary patterns and air pollution exposure on health-related outcomes. In a birth cohort in Northeast China, animal foods pattern was found to significantly modify the association between exposure to NO2 and carbon monoxide and gestational diabetes mellitus, with higher intake related to a higher rate of gestational diabetes mellitus following exposure to air pollution (). A Mediterranean diet reduced cardiovascular disease mortality risk related to long-term exposure to air pollutants in a large prospective US cohort (). A prospective cohort study of Chinese older adults reported that a plant-based dietary pattern mitigated the adverse effects of air pollution on cognitive function ().
It seems to be accepted that hearing impairment becomes more common with increasing age (). Nevertheless, the association between air pollution and hearing impairment was only found in participants younger under or equal to 50 years of age in this study. An interaction effect between age and air pollution on hearing impairment was also observed. Age is an unmodifiable risk factor for hearing impairment, which could lead to cochlear aging (). However, modifiable risk factors play a significant part in the development of hearing impairment at a relatively young age (i.e., <85 years old), while their effects decrease in the oldest people (i.e., ≥85 years old) (). Therefore, we speculated that air pollution, a modifiable risk factor, might have a greater impact on people younger than or equal to 50 years old compared to those over 50 years old, even if our study subjects were all under 85 years old.
Our research used data from the United Kingdom Biobank, a national cohort with good quality control. Additionally, the hearing test was based on the DTT data in the United Kingdom Biobank, which represented an ecologically effective and objective hearing indicator. We also adjusted for many confounders (including demographic information, lifestyle, and related diseases affecting hearing) to reduce their potential impact. However, our research also had some limitations. Above all, the cross-sectional design of this study was inadequate to account for the cause and effect between air pollution and hearing impairment, and further longitudinal studies are needed. Second, the sample of participants in United Kingdom Biobank was suggested to be unrepresentative of the general population because of the bias toward recruiting participants who were generally healthier and had a higher socioeconomic status (). Hence, the subsample from United Kingdom Biobank and estimated hearing impairment rate in this study might not be representative of the general population. Third, like other epidemiological studies of air pollution, there might be potential misclassifications of air pollution exposure in this study because air pollution exposure was evaluated at the place of residence. Fourth, in the United Kingdom, where emissions regulations are strict and average pollution level is relatively low, it is not clear to what extent this study can be generalizable to other settings. Finally, in spite of adjusting for many confounders in our study, the potential effects of residual confounds of unmeasured variables could not be excluded, such as the use of ototoxic drugs, which was not considered due to lack of data.
Conclusion
In conclusion, we found that exposure to PM10, NOX, and NO2 was associated with hearing impairment in British adults, while PM2.5 and PM2.5 absorbance did not show similar correlations. Our findings may help researchers pay more attention to the impact of air pollution on hearing impairment and provide a basis for developing effective prevention strategies.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Statements
Data availability statement
The data supporting the results of this study can be found in the website of UK Biobank (www.ukbiobank.ac.uk) upon application.
Ethics statement
The study involving human participants was carried out with the ethical approval obtained by United Kingdom Biobank from the National Health Service National Research Ethics Service.
Author contributions
YS, YT, and LY conceived the overall project and developed the methods as well as procedures throughout the study. DL and LY managed the data collection and data entry and carried out data verification and statistical analyses. LY drafted the first version of the manuscript. All authors oversaw statistical analysis, involved in the interpretation of the results, reviewed, and approved the final manuscript.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Number: 82071058).
Acknowledgments
We are grateful to all the participants of United Kingdom Biobank. We also sincerely thank those who participated in data collection and management of United Kingdom Biobank. This study has been carried out with the use of the United Kingdom Biobank resource (application number 69741).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
2.^https://biobank.ctsu.ox.ac.UK/crystal/label.cgi?id=100049
5.^http://www.escapeproject.eu/
6.^https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/EnviroExposEst.pdf
7.^https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/ipaq_analysis.pdf
8.^https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=100052
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Summary
Keywords
hearing impairment, air pollution, digit triplet test (DTT), United Kingdom Biobank (UKB), healthy diet
Citation
Yuan L, Li D, Tian Y and Sun Y (2022) The Risk of Hearing Impairment From Ambient Air Pollution and the Moderating Effect of a Healthy Diet: Findings From the United Kingdom Biobank. Front. Cell. Neurosci. 16:856124. doi: 10.3389/fncel.2022.856124
Received
16 January 2022
Accepted
24 February 2022
Published
06 April 2022
Volume
16 - 2022
Edited by
Hai Huang, Tulane University, United States
Reviewed by
Zhiqiang Yan, Fudan University, China; Hongzhe Li, VA Loma Linda Healthcare System, United States
Updates
Copyright
© 2022 Yuan, Li, Tian and Sun.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yu Sun, sunyu@hust.edu.cn
†These authors share first authorship
This article was submitted to Cellular Neuropathology, a section of the journal Frontiers in Cellular Neuroscience
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.