Objective Growing evidence suggests that alcohol consumption is a risk factor for hearing loss; however, the evidence has been inconsistent. This systematic review and meta-analysis aimed to evaluate the effect of alcohol consumption on hearing loss. Methods We searched several databases up to November 2021, for published articles using the keywords “alcohol drinking” and “hearing loss”. Two investigators independently conducted the study selection and data extraction. Based on the results of the heterogeneity analysis (Q statistic and I2 statistic), a fixed- or random-effects model was used to calculate the pooled odds ratios (ORs). Subgroup and sensitivity analyses were performed to assess the potential sources of heterogeneity and robustness of the pooled estimation. Publication bias in the literature was evaluated using Egger’s test. Results In total, 18 (9 cross-sectional, 5 case-control, and 4 cohort) observational studies were identified in this search; 27,849 participants were included. Compared with non-drinkers, the pooled OR of drinkers was 1.22 (95% confidence interval: 1.09–1.35). Conclusion Evidence suggests a positive association between alcohol consumption and hearing loss. Drinkers were at a higher risk than non-drinkers. Drinking limitations may be useful for preventing hearing loss.
ObjectiveThere is no unified standard for measuring workplace non-Gaussian noise (known as complex noise) exposure. This study aimed to develop a draft guideline for measuring workplace non-Gaussian complex noise exposure based on noise temporal structure adjustment.MethodsNoise exposure level, e.g., the A-weighted sound pressure level normalized to a nominal 8-h working day (LEX,8h), was adjusted using the temporal structure (expressed by kurtosis) of noise. Noise waveform analysis or the instrument's direct reading was used.ResultsThe framework of the draft guideline included measurement metrics, the protocol using kurtosis to adjust LEX,8h, technical requirements for measuring instruments, measurement steps, data analysis, and measurement recording.ConclusionThe draft guideline could provide a basis for accurately measuring workers' exposure to non-Gaussian noise.
Objectives: Occupational noise-induced hearing loss (NIHL) is one of the most prevalent occupational diseases worldwide. Few studies have been reported on applying kurtosis-adjusted noise energy (e.g., kurtosis-adjusted cumulative noise exposure, CNE-K) as a joint indicator for assessing NIHL. This study aimed to analyze the effectiveness of CNE-K in assessing occupational hearing loss associated with complex noise in typical manufacturing industries.Design: A cross-sectional survey of 1404 Chinese manufacturing workers from typical manufacturing industries was conducted. General demographic characteristics, noise exposure data, and noise-induced permanent threshold shifts (NIPTS) at 3, 4, and 6 kHz (NIPTS 346 ) were collected and analyzed. The role of kurtosis in high-frequency noiseinduced hearing loss (HFNIHL) was also analyzed. The degree of overlap of the two logistic curves (i.e., between complex noise CNE-K and HFNIHL%, and between Gaussian noise CNE and HFNIHL%) was used to evaluate the effectiveness of CNE-K, using a stratified analysis based on age, sex, industry, or job type. Results:The binary logistic regression analysis showed that in addition to age, sex, exposure duration, and Eight-hour Continuous Equivalent A-weighted Sound Pressure Level (L Aeq,8h ), kurtosis was a key factor influencing HFNIHL% in workers (odds ratio = 1.18, p < 0.05), and its odds ratio increased with an increase in kurtosis value. Multiple linear regression analysis demonstrated that the contribution of kurtosis to NIPTS 346 was second to L Aeq,8h . Complex noise led to a higher risk of NIHL than Gaussian noise at frequencies of 3, 4, 6, and 8 kHz after adjusting for age, sex, and CNE (p < 0.05). As kurtosis increased, the notch in the audiogram became deeper, and the frequency at which the notch began to deepen shifted from 3 to 1 kHz. The logistic curve between complex noise CNE-K and HFNIHL% nearly overlapped with that between Gaussian noise CNE and HFNIHL%, and the average difference in HFNIHL% between the two curves decreased from 8.1 to 0.4%. Moreover, the decrease of average difference in HFNIHL% between the two logistic curves was evident in several subgroups, such as male workers, aged <30 and 30 to 50 years, furniture and woodworking industries and gunning and nailing job types with relatively high kurtosis values.Conclusions: Kurtosis, as an indirect metric of noise temporal structure, was an important risk factor for occupational NIHL. Kurtosis-adjusted CNE metric could be more effective than CNE alone in assessing occupational hearing loss risk associated with complex noise.
Background: Multiple genetic and environmental factors influence the severity of NIHL. However, few studies have reported interactions among such factors in modulating the risk of NIHL. This study aimed to assess for interactions among gene polymorphisms, noise metrics, and lifestyles on the risk of NIHL.Methods: A case-control study was conducted using 307 patients with NIHL and 307 matched healthy individuals from five manufacturing industries. General demographic data, lifestyle details, and noise exposure levels were recorded. The kompetitive allele-specific polymerase chain reaction (KASP) was used to analyze the genotypes of 18 single nucleotide polymorphisms (SNPs). The generalized multifactor dimensionality reduction (GMDR) method was used to examine the effects of all possible interactions. Results: The proportion of people with complex noise exposure, high CNE, high adj-CNE, smoking, propensity to watch loud videos, or sedentary lifestyle was significantly greater in the NIHL group than in the healthy group (P < 0.05). The GMDR model demonstrated a relevant interaction between NRN1 rs3805789 and CAT rs7943316. Subjects with the SNP pair of NRN1 rs3805789-CC and CAT rs7943316-AT, NRN1 rs3805789-CT and CAT rs7943316-AA, NRN1 rs3805789-CT and CAT rs7943316-TT, NRN1 rs3805789-CT/TT and CAT rs7943316-AA, or NRN1 rs3805789-CC and CAT rs7943316-AT/TT had higher risks of NIHL than those with NRN1 rs3805789-CC and CAT rs7943316-AA (P < 0.05). There was an interaction among NRN1 rs3805789, CAT rs7943316, and kurtosis. Subjects exposed to complex noise and carrying both NRN1 rs3805789-CT and CAT rs7943316-TT or NRN1 rs3805789-CT/TT and CAT rs7943316-AA had higher risks of NIHL than those exposed to steady noise and carrying both NRN1 rs3805789-CC and CAT rs7943316-AA (P < 0.05). The best six‐locus model involving NRN1 rs3805789, CAT rs7943316, smoking, video volume, physical exercise, and working pressure for the risk of NIHL was found to be the interaction (P = 0.0010). An interaction was also found among smoking, video volume, physical exercise, working pressure, and kurtosis (P = 0.0107).Conclusions: Complex noise, high CNE, high adj-CNE, smoking, high video volume, and sedentary lifestyle are environmental risk factors for NIHL. Concurrence of NRN1 rs3805789 and CAT rs7943316 constitutes a genetic risk factor for NIHL. Complex noise exposure significantly increases the risk of NIHL in subjects with a high genetic risk score. Interactions between genes and lifestyle as well as noise metrics and lifestyle affect the risk of NIHL. These results provide a theoretical basis for screening genetic and environmental risk factors to prevent NIHL.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.