Epidemiological characteristics of hearing loss associated with noise temporal structure among manufacturing workers

Interactions of genetic variations in FAS, GJB2 and PTPRN2 are associated with noise-induced hearing loss: a case-control study in China

BACKGROUND

This study aimed to screen and validate noise-induced hearing loss (NIHL) associated single nucleotide polymorphisms (SNPs), construct genetic risk prediction models, and evaluate higher-order gene-gene, gene-environment interactions for NIHL in Chinese population.

METHODS

First, 83 cases and 83 controls were recruited and 60 candidate SNPs were genotyped. Then SNPs with promising results were validated in another case-control study (153 cases and 252 controls). NIHL-associated SNPs were identified by logistic regression analysis, and a genetic risk model was constructed based on the genetic risk score (GRS), and classification and regression tree (CART) analysis was used to evaluate interactions among gene-gene and gene-environment.

RESULTS

Six SNPs in five genes were significantly associated with NIHL risk (p < 0.05). A positive dose-response relationship was found between GRS values and NIHL risk. CART analysis indicated that strongest interaction was among subjects with age ≥ 45 years and cumulative noise exposure ≥ 95 [dB(A)·years], without personal protective equipment, and carried GJB2 rs3751385 (AA/AB) and FAS rs1468063 (AA/AB) (OR = 10.038, 95% CI = 2.770, 47.792), compared with the referent group. CDH23, FAS, GJB2, PTPRN2 and SIK3 may be NIHL susceptibility genes.

CONCLUSION

GRS values may be utilized in the evaluation of the cumulative effect of genetic risk for NIHL based on NIHL-associated SNPs. Gene-gene, gene-environment interaction patterns play an important role in the incidence of NIHL.

Investigation of critical factors influencing the underestimation of hearing loss predicted by the ISO 1999 predicting model

OBJECTIVE

To analyze factors influencing the underestimation of noise-induced permanent threshold shift (NIPTS) among manufacturing workers, providing baseline data for revising noise exposure standard.

DESIGN

A cross-sectional study was designed with 2702 noise-exposed workers from 35 enterprises from 10 industries. Personal noise exposure level(LAeq,8h) and noise kurtosis level were determined by a noise dosimeter. Questionnaires and hearing loss tests were performed for each subject. The predicted NIPTS was calculated using the ISO 1999:2013 model for each participant, and the actual measured NIPTS was corrected for age and sex. The factors influencing the underestimation of NIPTS were investigated.

RESULTS

The predicted NIPTS at each test frequency (0.5, 1, 2, 3, 4, or 6kHz) and mean NIPTS at 2, 3, 4, and 6kHz (NIPTS2346) using the ISO 1999:2013 model were significantly lower than their corresponding measured NIPTS, respectively (P < 0.001). The ISO model significantly underestimated the NIPTS2346 by 12.36 dB HL. The multiple linear regression analysis showed that noise exposure level, exposure duration, age, and kurtosis could affect the degree of underestimation of NIPTS2346. The generalized additive model (GAM) with (penalized) spline components showed nonlinear relationships between critical factors (age, exposure duration, noise level, and kurtosis) and the underestimated NIPTS2346.The underestimated NIPTS2346 decreased with an increase in exposure duration (especially over ten years). There was no apparent trend in the underestimated NIPTS2346 with age. The underestimated NIPTS2346 decreased with the increased noise level [especially > 90 dB(A)]. The underestimated NIPTS2346 increased with an increase in noise kurtosis after adjusting for the noise exposure level and exposure duration and ultimately exhibiting a linear regression relationship.

CONCLUSIONS

The ISO 1999 predicting model significantly underestimated the noise-induced hearing loss among manufacturing workers. The degree of underestimation became more significant at the noise exposure condition of fewer than ten years, less than 90 dB(A), and higher kurtosis levels. It is necessary to apply kurtosis to adjust the underestimation of hearing loss and consider the applying condition of noise energy metrics when using the ISO predicting model.

The role of kurtosis and kurtosis-adjusted energy metric in occupational noise-induced hearing loss among metal manufacturing workers

Background

Noise energy has been well-established to increase the risk of occupational noise-induced hearing loss (NIHL). However, the role of noise temporal structure (expressed by kurtosis) or its combination with energy metrics (e.g., kurtosis-adjusted cumulative noise exposure, adj-CNE) in occupational NIHL was still unclear.

Methods

A cross-sectional survey of 867 Chinese workers, including 678 metal manufacturing workers and 189 workers exposed to Gaussian noise, was conducted. Noise energy metrics, including L_Aeq,8h and CNE, kurtosis (β), and adj-CNE were used to quantify noise exposure levels. Noise-induced permanent threshold shift at frequencies 3, 4, and 6 kHz (NIPTS₃₄₆) and the prevalence of high-frequency NIHL (HFNIHL%) were calculated for each participant. The dose-response relationship between kurtosis or adj-CNE and occupational NIHL was observed.

Results

Among 867 workers, different types of work had specific and independent noise energy and kurtosis values (p > 0.05). HFNIHL% increased with an increase in exposure duration (ED), L_Aeq,8h, CNE, or kurtosis (p < 0.01), and there were strong linear relationships between HFNIHL% and ED (coefficient of determination [R²] = 0.963), CNE (R² = 0.976), or kurtosis (R² = 0.938, when CNE < 100 dB(A)∙year). The "V" shape notching extent in NIPTS became deeper with increasing kurtosis when CNE < 100 dB(A)∙year and reached the notching bottom at the frequency of 4 or 6 kHz. The workers exposed to complex noise (β ≥ 10) had a higher risk of NIHL than those exposed to Gaussian noise (β < 10) at the frequencies of 3, 4, 6, and 8 kHz (OR > 2, p < 0.01). Moreover, HFNIHL% increased with adj-CNE (p < 0.001). There were strong linear relationships between NIHL and adj-CNE or CNE when β ≥ 10 (R²_adj-CNE > R²_CNE). After CNE was adjusted by kurtosis, average differences in NIPTS₃₄₆ or HFNIHL% between the complex and Gaussian noise group were significantly reduced (p < 0.05).

Conclusion

Kurtosis was a key factor influencing occupational NIHL among metal manufacturing workers, and its combination with energy metrics could assess the risk of NIHL more effectively than CNE alone.