Abstract
Objective:
To investigate the effects of occupational noise exposure on disease control and psychological status in patients with diabetic nephropathy.
Methods:
A cross-sectional study was conducted on 306 patients with diabetic nephropathy who visited the Second Affiliated Hospital of Fujian Medical University between January 2022 and January 2024. The patients were divided into two groups based on their occupational noise exposure level as follows: Group A (noise level ≥55 dB, 148 cases) and Group B (noise level <55 dB, 158 cases). The general information, noise exposure levels, glycaemic indicators [glycosylated haemoglobin (HbA1c), fasting plasma glucose (FPG), 2-hour postprandial plasma glucose (2hPG), glycated serum protein (GSP)], renal function indicators [blood urea nitrogen (BUN), urinary albumin excretion rate (UAER), serum creatinine (SCr), 24-hour urinary protein (24hUP)] and psychological status [Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS)] were compared between Groups A and B. Univariate analysis was performed using Pearson correlation analysis.
Results:
No significant differences were observed in age, gender and body mass index between the two groups (P > 0.05). However, glycaemic indicators such as HbA1c, FPG, PBG, GSP; renal function indicators such as BUN, UAER, SCr, 24hUP and psychological status (SAS and SDS scores) in Group A were significantly higher than those in Group B (P < 0.05). A significantly positive association was found between noise levels with SDS score (r = 0.321), FPG (r = 0.320) and UAER level (r = 0.405) (P < 0.001).
Conclusion:
This study reveals that occupational noise exposure negatively impacts disease control and psychological status in patients with diabetic nephropathy. Therefore, focusing on noise control in clinical practice is essential.
Keywords: anxiety, blood glucose, depression, diabetic nephropathies, occupational noise, renal function
KEY MESSAGES
-
(1)
Occupational noise exposure negatively affects disease control in patients with diabetic nephropathy by interfering with blood glucose and renal function indicators.
-
(2)
The noise level is significantly positively correlated with depression levels, fasting plasma glucose and urinary albumin excretion rate, indicating its adverse effects on the physical and mental health of patients with diabetic nephropathy.
-
(3)
Clinical medical staff should focus on improving the recovery environment for patients with diabetic nephropathy.
INTRODUCTION
Diabetic nephropathy, a common microvascular complication of diabetes, has become a leading cause of end-stage renal disease globally, notably threatening the lives of patients and their quality of life.[1,2,3] For patients with diabetic nephropathy, effective disease control and stable psychological status are crucial for delaying disease progression.[4] Emerging evidence indicates that specific occupational exposures may influence chronic disease management, particularly in populations with metabolic disorders.[5]
With the acceleration of modern industrialisation and urbanisation, occupational noise has emerged as a distinct environmental stressor that warrants scientific investigation.[6,7] Whilst environmental noise pollution represents a broad public health concern, occupational noise exposure has unique characteristics in terms of intensity, duration and regulatory frameworks.[8,9] Occupational noise sources are wide-ranging, including factory machine sounds and construction site noises.[6,10,11] Long-term exposure to such noise can lead to sleep disorders, blood pressure changes, endocrine imbalances and psychological issues, including anxiety and depression in humans.
Noise can impact diabetes by disturbing sleep and neuroendocrine regulation, leading to fluctuations in glucose and hindering disease control.[12] Noise may also affect kidney function through stress-induced changes in renal haemodynamics, adding to kidney burden and influencing renal function indicators.[13] Additionally, noise can negatively affect psychological status by causing restlessness, raising pressure and triggering or worsening anxiety and depression.[14] Whilst previous studies have demonstrated the harmful effects of noise on healthy people,[15,16] evidence specifically addressing its impact on patients with diabetic nephropathy is lacking, especially regarding the quantitative associations between noise, disease control and psychological status. The current study aims to fill this gap.
Therefore, we conducted a cross-sectional study to assess the relationship between noise exposure and disease indicators (disease control and psychological status). This analysis aims to provide a basis for clinical interventions that can improve the quality of life and prognosis for patients with diabetic nephropathy.
MATERIALS AND METHODS
Study Design
This cross-sectional study included 365 patients diagnosed with diabetic nephropathy at the Second Affiliated Hospital of Fujian Medical University from January 2022 to January 2024. Eleven cases were excluded due to other diseases that severely affect renal function. After applying the exclusion criteria, 59 additional cases were excluded, resulting in a final sample size of 306 cases for the study.
When investigating patients’ general information, the noise levels in their work environment were collected. Based on the noise thresholds from previous studies,[17] patients were classified into following two groups: the high noise exposure group (Group A, n = 148), with noise levels ≥55 dB and the low noise exposure group (Group B, n = 158), with noise levels <55 dB.
This study followed the guidelines of the Helsinki Declaration and received ethical approval from the Ethics Committee of the Second Affiliated Hospital of Fujian Medical University (approval number: 2023-693). Additionally, all patients provided written informed consent.
Inclusion and Exclusion Criteria
The inclusion criteria for patients were as follows: (1) meets the diagnostic criteria[18] for diabetic nephropathy; (2) disease course of diabetic nephropathy ≥6 months; (3) type 2 diabetes and (4) complete medical records, including information on occupational noise exposure, disease control measurements and psychological status.
The exclusion criteria for patients were as follows: (1) patients with severe mental disorders who cannot accurately complete the psychological scales; (2) patients with other diseases that seriously affect renal function, such as hypertension; (3) renal dysfunction before the onset of diabetes and (4) patients over 60 years old.
Treatment Methods
In this study, all patients received conventional treatment for diabetic nephropathy, which included diet control (such as regulating the intake of carbohydrates, proteins and salts); plasma glucose control (using oral hypoglycaemic drugs or insulin injections based on individual needs, regularly monitoring plasma glucose levels and adjusting drug dosages to maintain glucose levels within a reasonable range) and symptomatic treatment measures for nephropathy.
Environmental Noise Detection
An electronic decibel metre (HS5633A, Jiaxing Hangsheng Electronic Co., Ltd., China) was used to measure the noise level in patients’ workplaces. The average A-weighted equivalent sound pressure level was recorded for 15 minutes at two time periods (09:00–09:15 and 15:00–15:15) for 1 day.
Observation Indicators
General information and medical history
General information of the patients was collected from the medical record system, including details such as age, gender, body mass index, place of residence and education level. Additionally, the medical history related to diabetes and nephropathy was obtained, including the course of diabetes, the course of diabetic nephropathy, treatment methods, the stage of nephropathy and comorbidities.
Glycaemic indicators
All subjects were instructed to fast for 10 hours prior to blood collection, and 5 mL peripheral venous blood sample was collected early in the morning. Glycaemic indicators, including glycosylated haemoglobin (HbA1c), fasting plasma glucose (FPG), 2-hour postprandial plasma glucose (2hPG), glycated serum protein (GSP), were measured using an automatic biochemical analyser (Mindray Bio-Medical Electronics Co., Ltd, China; BS − 280 type). All data were collected during routine clinical follow-up visits.
Renal function indicators
Renal function indicators, including blood urea nitrogen (BUN), urinary albumin excretion rate (UAER), serum creatinine (SCr) and 24-hour urinary protein (24hUP), were assessed as follows: BUN and SCr were measured using an automatic biochemical analyser (BS-240 VET, Mindray Medical, Shenzhen, China), and 24hUP was measured by radioimmunoassay. UAER was calculated using the following formula: UAER = urinary albumin concentration (mg/dL) × urine volume of the day (dL/24 h).
Psychological status assessments
The following scales were used to assess the psychological status of the patients:
(1) The Self-rating Anxiety Scale (SAS) was used to assess the anxiety level of patients.[19] The SAS comprises 20 items, covering various feelings and symptoms related to anxiety. Each item is rated on a 4-point scale, ranging from ‘never or rarely’ (1 point) to ‘almost all or all the time’ (4 points). The total score is calculated by summing the scores of all items and then multiplying by 1.25 to obtain the standard score. Scores of 50–59 points indicate mild anxiety, 60–69 points indicate moderate anxiety and scores ≥70 points indicate severe anxiety. The SAS is widely applied and validated, with a Cronbach’s α coefficient ranging from 0.8 to 0.9.[20]
(2) The Self-rating Depression Scale (SDS) was used to assess the depression level of patients.[21] The SDS scale comprises 20 items, mainly focusing on depressive emotions and related symptoms. Each item is scored on a 4-point scale, based on the frequency of symptom occurrence, ranging from ‘never or occasionally’ (1 point) to ‘always’ (4 points). The scores from the 20 items are summed to form the crude score, which is then multiplied by 1.25 to obtain the standard score. The higher the score, the higher the level of depression. The SDS has a Cronbach’s α coefficient of approximately 0.8.[22] Patients were asked to complete the SAS and SDS scales upon admission.
Statistical Methods
SPSS26.0 software (IBM Corp, Armonk, NY, USA) was used to perform statistical analysis on the data. The tables in this study were created using Microsoft Excel 2019 (Microsoft Corporation, Seattle, USA). The figures in this article were generated using Prism (GraphPad Software, version 5.0, Boston, USA).
For continuous variables, the Shapiro–Wilk test was used to determine whether the data followed a normal distribution. If the data followed a normal distribution, then they were expressed as mean ± standard deviation (SD) and compared using the t-test. If the data did not follow a normal distribution, then the median and interquartile range [M (P25, P75)] were used for the representation and tested using Mann–Whitney U test. The Chi-square test was used for categorical variables. Pearson correlation analysis was used to evaluate the correlation between the noise level and disease control indicators (such as HbA1c, FPG and UAER) and psychological status. The correlation coefficient r > 0.3 indicates P < 0.001.
RESULTS
General Information
The noise exposure intensity of Group A was significantly higher than that of Group B (P < 0.05). Meanwhile, no significant difference between groups was found in terms of age, gender, body mass index, place of residence, education level, course of diabetic nephropathy, course of diabetes, medication and comorbidities (P > 0.05, Table 1).
Table 1.
General Information of Groups A and B.
| Characters | Group A (n = 148) | Group B (n = 158) | t/χ2 | P |
|---|---|---|---|---|
| Age (years) | 53.99 ± 4.86 | 54.20 ± 5.08 | −1.529 | 0.127 |
| Gender | ||||
| Male | 86 (58.1) | 91 (57.6) | 0.008 | 0.928 |
| Female | 62 (41.9) | 67 (42.4) | ||
| Body mass index (kg/m2) | 23.22 ± 2.49 | 22.72 ± 2.22 | 1.863 | 0.063 |
| Place of residence | 0.228 | 0.633 | ||
| Rural | 56 (37.8) | 64 (40.5) | ||
| Urban | 92 (62.2) | 94 (59.5) | ||
| Education level | 0.253 | 0.969 | ||
| Junior high school | 53 (35.8) | 55 (34.8) | ||
| High school | 67 (45.3) | 70 (44.3) | ||
| College | 22 (14.9) | 25 (15.8) | ||
| Dropout or other | 6 (4.0) | 8 (5.1) | ||
| Course of diabetic nephropathy (years) | 3.87 ± 0.34 | 4.13 ± 0.38 | 6.292 | 0.872 |
| Stage of nephropathy | 0.230 | 0.973 | ||
| I | 43 (29.1) | 44 (27.8) | ||
| II | 71 (48.0) | 75 (47.5) | ||
| III | 32 (21.6) | 36 (22.8) | ||
| IV | 2 (1.3) | 3 (1.9) | ||
| Course of diabetes (years) | 8.76 ± 1.60 | 8.87 ± 1.41 | 0.326 | 0.738 |
| Diabetes medication | 0.010 | 0.995 | ||
| Oral | 23 (15.5) | 24 (15.2) | ||
| Insulin injection | 77 (52.0) | 83 (52.5) | ||
| Oral plus insulin injection | 48 (32.5) | 51 (32.3) | ||
| Comorbidity | 0.123 | 0.726 | ||
| Cardiovascular disease | 10 (6.8) | 8 (5.1) | ||
| Haematological disease | 11 (7.4) | 14 (8.9) | ||
| Neurological disease | 6 (4.1) | 8 (5.1) | ||
| Hyperlipidaemia | 4 (2.7) | 5 (3.2) | ||
| Infection | 3 (2.0) | 4 (2.5) | ||
| Noise exposure intensity (dBA) | 61.09 ± 4.91 | 46.76 ± 4.98 | 27.077 | <0.001 |
Glycaemic indicators
As shown in Figure 1, the glycaemic indicators, such as HbA1c, FPG, PBG and GSP in Group A, were significantly higher than those in Group B (P < 0.001).
Figure 1.
Glycaemic indicators of Groups A and B.
Notes: *** indicates P<0.001. 2hPG, 2-h postprandial plasma glucose; FPG, fasting plasma glucose; GSP, glycated serum protein; HbA1c, glycosylated haemoglobin.
Renal function indicators
As shown in Table 2, the BUN, UAER, SCr and 24hUP levels in Group A were significantly higher than those in Group B (P < 0.05).
Table 2.
Renal Function Related Indicators of Groups A and B.
| Group | BUN (mmol/L) | UAER (mg/24 h) | SCr (µmol/L) | 24hUP (g/d) |
|---|---|---|---|---|
| Group A (n = 148) | 11.89 ± 2.50 | 72.23 ± 8.62 | 176.23 ± 23.26 | 197.26 (160.37, 222.54) |
| Group B (n = 158) | 9.41 ± 2.32 | 60.49 ± 6.78 | 158.03 ± 20.51 | 178.36 (155.16, 211.16) |
| t/Z | 8.989 | 13.297 | 7.267 | 6.546 |
| P | <0.001 | <0.001 | <0.001 | <0.001 |
Notes: 24hUP, 24-hour urinary protein; BUN, blood urea nitrogen; SCr, serum creatinine; UAER, urinary albumin excretion rate;.
Psychological status
As shown in Table 3, the SAS and SDS scores in Group A were significantly higher than those in Group B (P<0.05).
Table 3.
Psychological Conditions of Groups A and B (Point, x ± s).
| Group | SAS | SDS |
|---|---|---|
| Group A (n = 148) | 57.29 ± 4.85 | 59.76 ± 5.23 |
| Group B (n = 158) | 50.24 ± 4.48 | 53.61 ± 5.13 |
| t | 8.458 | 7.564 |
| P | <0.001 | <0.001 |
Notes: SAS, Self-rating Anxiety Scale; SDS, Self-rating Depression Scale.
Correlation Analysis between Noise Level and Disease Indicators
The noise level exhibited a strikingly positive correlation with SDS score, FPG and UAER (r > 0.3, P < 0.001), revealing correlation coefficients of 0.321, 0.320 and 0.405, respectively. The specific results are illustrated in Table 4.
Table 4.
Correlation Analysis between Noise Level and Disease Indicators.
| Indicator | r | P | |
|---|---|---|---|
| SAS | 0.220 | 0.746 | |
| SDS | 0.321 | <0.001 | |
| HbA1c | 0.273 | 0.358 | |
| FPG | 0.320 | <0.001 | |
| 2hPG | 0.263 | 0.773 | |
| GSP | 0.269 | 0.698 | |
| BUN | 0.253 | 0.803 | |
| UAER | 0.405 | <0.001 | |
| SCr | 0.213 | >0.894 | |
| 24hUP | 0.208 | >0.925 | |
Notes: FPG, fasting plasma glucose; SDS, Self-rating Depression Scale; UAER, urinary albumin excretion rate.
DISCUSSION
Noise can disrupt patients’ sleep and neuroendocrine regulation, leading to fluctuations in plasma glucose and blood pressure, which can negatively affect disease control. Long-term exposure to high-intensity occupational noise, such as that from factories or construction sites, activates the sympathetic nervous system, triggering the release of stress hormones such as adrenaline and cortisol.[23] Elevated cortisol levels affect glucose regulation by promoting the breakdown of liver glycogen and reducing glucose uptake by peripheral tissues, increasing plasma glucose levels.[24] For patients with diabetic nephropathy, who already have impaired glucose regulation, such fluctuations worsen the disease, supporting the observed noise–FPG correlation in this study.
The stress response induced by noise may also affect renal haemodynamics, altering the glomerular filtration rate, increasing the burden on the kidneys and affecting renal function indicators.[25] Long-term exposure to a noisy environment can lead to continuous contraction of renal blood vessels, a decrease in renal blood flow and the acceleration of glomerular sclerosis. Simultaneously, activation of the renin–angiotensin–aldosterone system may occur, further elevating blood pressure and increasing kidney strain.[26] These mechanisms align with the positive correlation between noise level and UAER observed in this study.
Long-term exposure to noise can make patients irritable and restless, increasing psychological pressure and potentially triggering or exacerbating anxiety and depression. Chronic noise exposure can cause an imbalance of neurotransmitters in the brain, such as abnormal secretion of serotonin and dopamine, which may lead to emotional regulation disorders. This phenomenon is reflected in the changes in SAS and SDS scores in this study.[27] Noise was found to be a key influencing factor for FPG and UAER, which is consistent with the previous correlation analysis. This finding indicates that noise may have a direct or indirect impact on plasma glucose regulation and renal function through various pathways. In terms of emotional state, the considerable influence of noise on the SAS and SDS scores emphasises its role in contributing to anxiety and depression in patients. These findings highlight the need to address noise exposure as a critical factor in the management of diabetic nephropathy patients. Clinicians should implement strategies to reduce patients’ exposure to noise, which may not only improve their psychological well-being but also contribute to improvements in disease control. Additionally, anxiety and depression of patients reduce patients’ compliance with treatments, such as dietary control and medication, which could, in turn, affect disease progression.
Earlier studies have shown that noise exposure negatively affects the sleep, cardiovascular system and mental health of healthy people. However, studies on its impact on disease control and psychological status in patients with diabetic nephropathy are relatively few.[28,29,30] Some studies indicate that noise may affect plasma glucose control in diabetic patients, but evidence regarding its specific impact on diabetic nephropathy is limited.[31,32,33] In contrast to these studies, the current study further explores the correlation between noise exposure and multiple disease indicators in patients with diabetic nephropathy, addressing not only plasma glucose but also renal function indicators and psychological status.However, existing studies have shown that the impact of environmental noise on chronic disease management is complex and multifaceted. For example, beyond diabetic nephropathy, which is the focus of this study, noise exposure has also been linked to an increased risk of cardiovascular disease. Such an exposure may also contribute to disease progression by affecting blood pressure regulation, vascular endothelial function and the balance of the autonomic nervous system.[34] In terms of mental and psychological aspects, noise pollution is associated with the onset and progression of mental disorders such as anxiety and depression. The underlying mechanisms may involve disturbances in neurotransmitter disorders, imbalances in stress hormones and changes in brain structure and function.[26]
However, this study has limitations. Despite the substantial correlation between noise levels and renal function indicators, the direct or indirect (e.g., via plasma glucose) effects of noise on the kidney due to design flaws remain unclear, thereby necessitating further exploration. Although most confounding factors were controlled, unmeasured or uncontrolled variables, such as patients’ noise tolerance, may affect results. In the psychological assessment, whilst the SAS and SDS scales are reliable and valid, factors such as family or financial stressors were not considered. Future studies should comprehensively assess factors that may affect patients’ psychological well-being. Noise measurement was also limited to work hours; thus, the use of portable devices for 24-hour monitoring could be considered.
Clinical staff must emphasise the importance of noise regulation for patients with diabetic nephropathy. Patients should reduce noise exposure (e.g., via sound-proofing gear or adjusting their work environments). For those unable to evade noise, personalised protection such as earplugs/headphones is advisable. In cases of high noise exposure, psychological support becomes essential. Healthcare providers should routinely gauge patients’ mental states and offer counselling, relaxation drills or referrals to specialists for those experiencing anxiety or depression. This approach will improve patients’ mental well-being, enhance their confidence in disease management and contribute to improved disease control.
CONCLUSION
This study shows that occupational noise exposure has a substantial impact on the disease control and psychological status of patients with diabetic nephropathy. The noise level is significantly positively correlated with some indicators (such as SDS, FPG and UAER), and further confirming the adverse effects of noise exposure on patients. Noise may affect patients through multiple pathways. Clinical medical staff should focus on controlling occupational noise, implement measures such as sound insulation and incorporate psychological interventions into comprehensive treatment. Additionally, occupational noise should be strictly regulated for patients with diabetic nephropathy.
Ethical Approval
This study obtained ethical approval from the Ethics Committee of the Second Affiliated Hospital of Fujian Medical University (approval number: 2023-693).
Consent to Participate
All patients furnished their signed consent after being duly informed.
Author Contributions
LL designed and conducted the research and authored the paper. XZ designed the research, oversaw the report preparation, and contributed to the data analysis. TG offered clinical counsel and supervised the report compilation.
Availability of Data and Materials
All experimental data included in this study can be obtained by contacting the first author if needed.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgment
Not applicable.
Funding Statement
This research received no external funding.
References
- 1.Samsu N. Diabetic nephropathy: challenges in pathogenesis, diagnosis, and treatment. Biomed Res Int. 2021;2021:1497449. doi: 10.1155/2021/1497449. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Wal P, Tyagi S, Pal RS, Yadav A, Jaiswal R. A strategic investigation on diabetic nephropathy; its conceptual model and clinical manifestations: a review. Curr Diabetes Rev. 2023;19:e260422204036. doi: 10.2174/1573399818666220426091238. [DOI] [PubMed] [Google Scholar]
- 3.Elendu C, John Okah M, Fiemotongha KDJ, Adeyemo BI, Bassey BN, Omeludike EK, et al. Comprehensive advancements in the prevention and treatment of diabetic nephropathy: a narrative review. Medicine (Baltimore) 2023;102:e35397. doi: 10.1097/MD.0000000000035397. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Sindhu D, Sharma GS, Kumbala D. Management of diabetic kidney disease: where do we stand?: A narrative review. Medicine (Baltimore) 2023;102:e33366. doi: 10.1097/MD.0000000000033366. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Zhang Z, Heerspink HJL, Chertow GM, Correa-Rotter R, Gasparrini A, Jongs N, et al. Ambient heat exposure and kidney function in patients with chronic kidney disease: a post-hoc analysis of the DAPA-CKD trial. Lancet Planet Health. 2024;8:e225–33. doi: 10.1016/S2542-5196(24)00026-3. [DOI] [PubMed] [Google Scholar]
- 6.Peris E, Fenech B. Associations and effect modification between transportation noise, self-reported response to noise and the wider determinants of health: a narrative synthesis of the literature. Sci Total Environ. 2020;748:141040. doi: 10.1016/j.scitotenv.2020.141040. [DOI] [PubMed] [Google Scholar]
- 7.Warner SC, Sagovac S, Godwin C, Xia T, Batterman S. Community’s perception on ambient air and noise pollution: a qualitative study in Southwest Detroit. Environ Just. 2023;16:286–96. doi: 10.1089/env.2021.0085. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Gong X, Fenech B, Blackmore C, Chen Y, Rodgers G, Gulliver J, et al. Association between noise annoyance and mental health outcomes: a systematic review and meta-analysis. Int J Environ Res Public Health. 2022;19:2696. doi: 10.3390/ijerph19052696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hahad O, Kuntic M, Al-Kindi S, Kuntic I, Gilan D, Petrowski K, et al. Noise and mental health: evidence, mechanisms, and consequences. J Expo Sci Environ Epidemiol. 2025;35:16–23. doi: 10.1038/s41370-024-00642-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Smith MG, Cordoza M, Basner M. Environmental noise and effects on sleep: an update to the WHO systematic review and meta-analysis. Environ Health Perspect. 2022;130:76001. doi: 10.1289/EHP10197. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Hahad O, Herzog J, Röösli M, Schmidt FP, Daiber A, Münzel T. Acute exposure to simulated nocturnal train noise leads to impaired sleep quality and endothelial dysfunction in young healthy men and women: a sex-specific analysis. Int J Environ Res Public Health. 2022;19:13844. doi: 10.3390/ijerph192113844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Rahmanian M, Zare Sakhvidi MJ, Mehrparvar AH, Zare Sakhvidi F, Dadvand P. Association between occupational noise exposure and diabetes: a systematic review and meta-analysis. Int J Hyg Environ Health. 2023;252:114222. doi: 10.1016/j.ijheh.2023.114222. [DOI] [PubMed] [Google Scholar]
- 13.Kim YJ, Choi WJ, Ham S, Kang SK, Lee W. Association between occupational or environmental noise exposure and renal function among middle-aged and older Korean adults: a cross-sectional study. Sci Rep. 2021;11:24127. doi: 10.1038/s41598-021-03647-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Beutel ME, Brähler E, Ernst M, Klein E, Reiner I, Wiltink J, et al. Noise annoyance predicts symptoms of depression, anxiety and sleep disturbance 5 years later. Findings from the Gutenberg Health Study. Eur J Public Health. 2020;30:516–21. doi: 10.1093/eurpub/ckaa015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Hahad O, Frenis K, Kuntic M, Daiber A, Münzel T. Accelerated aging and age-related diseases (CVD and Neurological) due to air pollution and traffic noise exposure. Int J Mol Sci. 2021;22:2419. doi: 10.3390/ijms22052419. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Schmidt FP, Basner M, Kröger G, Weck S, Schnorbus B, Muttray A, et al. Effect of nighttime aircraft noise exposure on endothelial function and stress hormone release in healthy adults. Eur Heart J. 2013;34:3508–14a. doi: 10.1093/eurheartj/eht269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Zhang L, Han X. Relationship between noise exposure and prognosis of patients with heart failure: a retrospective analysis. Noise Health. 2024;26:51–7. doi: 10.4103/nah.nah_101_23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Samsu N. Diabetic nephropathy: challenges in pathogenesis, diagnosis, and treatment. Biomed Res Int. 2021;2021:1497449. doi: 10.1155/2021/1497449. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Zung WW. A rating instrument for anxiety disorders. Psychosomatics. 1971;12:371–9. doi: 10.1016/S0033-3182(71)71479-0. [DOI] [PubMed] [Google Scholar]
- 20.Shao R, He P, Ling B, Tan L, Xu L, Hou Y, et al. Prevalence of depression and anxiety and correlations between depression, anxiety, family functioning, social support and coping styles among Chinese medical students. BMC Psychol. 2020;8:38. doi: 10.1186/s40359-020-00402-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zung WW, Richards CB, Short MJ. Self-rating depression scale in an outpatient clinic. Further validation of the SDS. Arch Gen Psychiatry. 1965;13:508–15. doi: 10.1001/archpsyc.1965.01730060026004. [DOI] [PubMed] [Google Scholar]
- 22.Cheng L, Gao W, Xu Y, Yu Z, Wang W, Zhou J, et al. Anxiety and depression in rheumatoid arthritis patients: prevalence, risk factors and consistency between the Hospital Anxiety and Depression Scale and Zung’s Self-rating Anxiety Scale/Depression Scale. Rheumatol Adv Pract. 2023;7(rkad):100. doi: 10.1093/rap/rkad100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Lucas D, Guerrero F, Jouve E, Hery S, Capellmann P, Mansourati J. Effect of occupational exposure to welding fumes and noise on heart rate variability: An exposed-unexposed study on welders and airport workers’ population. Front Public Health. 2022;10:937774. doi: 10.3389/fpubh.2022.937774. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Brosolo G, Da Porto A, Bulfone L, Vacca A, Bertin N, Catena C, et al. Cortisol secretion and abnormalities of glucose metabolism in nondiabetic patients with hypertension. J Hypertens. 2024;42:227–35. doi: 10.1097/HJH.0000000000003590. [DOI] [PubMed] [Google Scholar]
- 25.Kannenkeril D, Jung S, Ott C, Striepe K, Kolwelter J, Schmieder RE, et al. Association of noise annoyance with measured renal hemodynamic changes. Kidney Blood Press Res. 2021;46:323–30. doi: 10.1159/000515527. [DOI] [PubMed] [Google Scholar]
- 26.Dzhambov AM, Tokmakova MP, Gatseva PD, Zdravkov NG, Gencheva DG, Ivanova NG, et al. Community noise exposure and its effect on blood pressure and renal function in patients with hypertension and cardiovascular disease. Folia Med (Plovdiv) 2017;59:344–56. doi: 10.1515/folmed-2017-0045. [DOI] [PubMed] [Google Scholar]
- 27.Cheng Y, Wang X, Yu Y, Gu J, Zhao M, Fu Q, et al. Noise induced depression-like behavior, neuroinflammation and synaptic plasticity impairments: the protective effects of luteolin. Neurochem Res. 2022;47:3318–30. doi: 10.1007/s11064-022-03683-0. [DOI] [PubMed] [Google Scholar]
- 28.Krittanawong C, Qadeer YK, Hayes RB, Wang Z, Virani S, Zeller M, et al. Noise exposure and cardiovascular health. Curr Probl Cardiol. 2023;48:101938. doi: 10.1016/j.cpcardiol.2023.101938. [DOI] [PubMed] [Google Scholar]
- 29.Aydın Sayılan A, Kulakaç N, Sayılan S. The effects of noise levels on pain, anxiety, and sleep in patients. Nurs Crit Care. 2021;26:79–85. doi: 10.1111/nicc.12525. [DOI] [PubMed] [Google Scholar]
- 30.Stanovská M, Tomášková H, Šlachtová H, Potužníková D, Argalášová L. Health impact of environmental and industrial noise − a narrative review. Med Pr. 2024;75:425–31. doi: 10.13075/mp.5893.01491. [DOI] [PubMed] [Google Scholar]
- 31.Mihalikova D, Stamm P, Kvandova M, Pednekar C, Strohm L, Ubbens H, et al. Exposure to aircraft noise exacerbates cardiovascular and oxidative damage in three mouse models of diabetes. Eur J Prev Cardiol. 2024;32:zwae320. doi: 10.1093/eurjpc/zwae320. [DOI] [PubMed] [Google Scholar]
- 32.Wu S, Du W, Wu Z, Wen F, Zhong X, Huang X, et al. Effect of chronic noise exposure on glucose and lipid metabolism in mice via modulating gut microbiota and regulating CREB/CRTC2 and SREBP1/SCD pathway. Ecotoxicol Environ Saf. 2024;270:115887. doi: 10.1016/j.ecoenv.2023.115887. [DOI] [PubMed] [Google Scholar]
- 33.Sørensen M, Hvidtfeldt UA, Poulsen AH, Thygesen LC, Frohn LM, Khan J, et al. Long-term exposure to transportation noise and risk of type 2 diabetes: a cohort study. Environ Res. 2023;217:114795. doi: 10.1016/j.envres.2022.114795. [DOI] [PubMed] [Google Scholar]
- 34.Münzel T, Sørensen M, Daiber A. Transportation noise pollution and cardiovascular disease. Nat Rev Cardiol. 2021;18:619–36. doi: 10.1038/s41569-021-00532-5. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All experimental data included in this study can be obtained by contacting the first author if needed.