Noised-induced hearing loss (NIHL) is an acquired, progressive neurological damage caused by exposure to intense noise in various environments including industrial, military and entertaining settings. The prevalence of NIHL is much higher than other occupational injuries in industrialized countries. Recent studies have revealed that genetic factors, together with environmental conditions, also contribute to NIHL. A group of genes which are linked to the susceptibility of NIHL had been uncovered, involving the progression of oxidative stress, potassium ion cycling, cilia structure, heat shock protein 70 (HSP70), DNA damage repair, apoptosis, and some other genes. In this review, we briefly summarized the studies primary in population and some animal researches concerning the susceptible genes of NIHL, intending to give insights into the further exploration of NIHL prevention and individual treatment.

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The Role of Genetic Variants in the Susceptibility of Noise-Induced Hearing Loss

Objective: In order to provide a theoretical basis for the revision of the current diagnostic criteria for occupational noise-induced deafness (ONID), we evaluated the degree of ONID by analyzing different high-frequency-hearing- threshold-weighted values (HFTWVs). Methods: A retrospective study was conducted to evaluate the diagnosis of patients with ONID from January 2016 to January 2017 in Guangdong province, China. Based on 3 hearing tests (each interval between the tests was greater than 3 days), the minimum threshold value of each frequency was obtained using the 2007 edition’s diagnostic criteria for ONID. The speech frequency and the HFTWVs were analyzed based on age, noise exposure, and diagnostic classi-fication using SPSS21.0. Results: 168 patients in total were involved in this study, 154 males and 14 females, and the average age was 41.18 ± 6.07. The diagnosis rate was increased by the weighted value of the high frequencies and was more than the mean value of the pure speech frequency (MVPSF). The diagnosis rate for the weighted 4 kHz frequency level increased by 13.69% (χ2 = 9.880, P = 0.002), the weighted 6 kHz level increased by 15.47% (χ2 = 9.985, P = 0.002), and the weighted 4 kHz + 6 kHz level increased by 15.47% (χ2 = 9.985, P = 0.002). The differences were all statistically significant. The diagnostic rate of the different thresholds showed no obvious difference between the genders. The age groups were divided into less than or equal to 40 years old (group A) and 40 - 50 years old (group B). There were several groups with a high frequency: high frequency weighted 4 kHz ( group A χ2 = 3.380, P = 0.050; group B χ2 = 4.054, P = 0.032), high frequency weighted 6 kHz (group A χ2 = 6.362, P = 0.012; group B χ2 = 4.054, P = 0.032), weighted 4 kHz + 6 kHz (group A χ2 = 6.362 P = 0.012; B χ2 = 4.054, P = 0.032) than those of MVPSF in the same group on ONID diagnosis rate. The differences between the groups were statistically significant. There was no significant difference between the age groups (χ2 = 2.265, P = 0.944). The better ear’s (the smaller hearing threshold weighted value) MVPSF and the weighted values for the different high frequencies were examined in light of the number of working years; the group that was exposed to noise for more than 10 years had significantly higher values than those of the average thresholds of each frequency band in the groups with 3 - 5 years of exposure (F = 2.271, P = 0.001) and 6 - 10 years of exposure (F = 1.563, P = 0.046). The differences were statistically significant. The different HFTWVs were higher than those of the MVPSF values, and the high frequency weighted 4 kHz + 6 kHz level showed the greatest difference, with an average increase of 2.83 dB. The diagnostic rate that included the weighted high frequency values was higher for the mild, moderate, and severe cases than those patients who were only screened with the pure frequency tests. The results of the comparisons of the diagnosis rates for mild ONID were as follows: the weighted 3 kHz high frequency level (χ2 = 3.117, P = 0.077) had no significant difference, but the weighted 4 kHz level (χ2 = 10.835, P = 0.001), 6 kHz level (χ2 = 9.985, P = 0.002), 3 kHz + 4 kHz level (χ2 = 6.315, P = 0.012), 3 kHz + 6 kHz level (χ2 = 6.315, P = 0.012), 4 kHz + 6 kHz level (χ2 = 9.985, P = 0.002), and 3 kHz + 4 kHz + 6 kHz level (χ2 = 7.667, P = 0.002) were significantly higher than the diagnosis rate of the mean value of the PSF. There was no significant difference between the 2 groups for the moderate and severe grades (P > 0.05). Conclusion: Different HFTWVs increase the diagnostic rate of ONID. The weighted 4 kHz, 6 kHz, and 4 kHz + 6 kHz high frequency values greatly affected the diagnostic results, and the weighted 4 kHz + 6 kHz high frequency hearing threshold value has the maximum the effect on the ONID diagnosis results.

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The Effect of a High-Frequency-Hearing-Threshold Weighted Value on the Diagnosis of Occupational-Noise-Induced Deafness

This study intended to explore the effect of C-Jun N-terminal kinases 1 (JNK1) polymorphisms on the sensitivity of individual hearing loss. A total of 1333 subjects, including 683 NIHL workers and 650 normal-hearing workers from east China, were included in this cross-sectional study. Genotyping of three JNK1 single nucleotide polymorphisms (rs9284, rs8428, and rs11598320) was performed. The relationship between different genotypes and noise-induced hearing loss was analyzed. Results show that rs11598320 TT genotype was associated with a higher risk of NIHL (OR 1.57, 95% CI 0.91–2.70). Stratified analysis indicated that the rs11598320 AT + AA genotype was associated with a decreased risk of hearing loss in subjects exposed to noise ≤ 16 years or a noise level > 92 dB (OR 0.68, 95% CI 0.50–0.93 and OR 0.64, 95% CI 0.42–0.96, respectively). The rs8428 TT genotype was associated with an increased risk of noise-induced hearing loss when the noise level was > 92 dB (OR 1.73, 95% CI 1.11–2.70). Haplotype TCT (rs9284–rs8424–rs11598320) was associated with an increased risk of noise-induced hearing loss (OR 1.30, 95% CI 1.00–1.68). Single nucleotide polymorphisms (rs11598320 and rs8424) in JNK1 can be used as new biomarkers of susceptibility for noise-induced hearing loss in Chinese workers.

Single nucleotide polymorphisms in JNK1 are associated with susceptibility to noise-induced hearing loss in a Chinese population.

Rosa chinensis cultivars with volatile aromas are important resources in the perfume industry. The four rose cultivars introduced to Guizhou province are rich in volatile substances. In this study, volatiles from four Rosa chinensis cultivars were extracted using headspace-solid phase microextraction (HS-SPME), and analyzed with two-dimensional gas chromatography quadrupole time of flight mass spectrometry (GC × GC-QTOFMS). A total of 122 volatiles were identified; the main compounds in these samples were benzyl alcohol, phenylethyl alcohol, citronellol, beta-myrcene and limonene. A total of 68, 78, 71, and 56 volatile compounds were identified in Rosa ‘Blue River’ (RBR), Rosa ‘Crimson Glory’ (RCG), Rosa ‘Pink Panther’ (RPP), and Rosa ‘Funkuhr’ (RF) samples, respectively. The total volatile contents were in the following order: RBR > RCG > RPP > RF. Four cultivars exhibited similar volatility profiles, with alcohols, alkanes, and esters as the major chemical groups, followed by aldehydes, aromatic hydrocarbons, ketones, benzene, and other compounds. Alcohols and aldehydes were quantitatively the two most abundant chemical groups that included the highest number and highest content of compounds. Different cultivars have different aromas, and RCG had high contents of phenyl acetate, rose oxide, trans-rose oxide, phenylethyl alcohol and 1,3,5-trimethoxybenzene, characterized by floral and rose descriptors. RBR contained a high content of phenylethyl alcohol, and RF contained a high content of 3,5-dimethoxytoluene. Hierarchical cluster analysis (HCA) of all volatiles showed that the three cultivars (RCG, RPP, and RF) had similar volatile characteristics and were significantly different from RBR. Differential metabolites among cultivars were screened based on the OPLS-DA model, and there were six main enriched pathways of differential metabolites: biosynthesis of secondary metabolites, monoterpenoid biosynthesis, metabolic pathways, limonene and pinene degradation, sesquiterpenoid and triterpenoid biosynthesis, and alpha-linolenic acid metabolism. The biosynthesis of secondary metabolites is the most differential metabolic pathway.

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Characterization of volatiles in flowers from four Rosa chinensis cultivars by HS-SPME-GC × GC-QTOFMS

The Effect of PCDH15 Gene Variations on the Risk of Noise-induced Hearing Loss in a Chinese Population.

Noise exposure can lead to various kinds of disorders. Noise-induced hearing loss (NIHL) is one of the leading disorders confusing the noise-exposed workers. It is essential to identify NIHL markers for its early diagnosis and new therapeutic targets for its treatment. In this study, a total of 90 plasma samples from 60 noise-exposed steel factory male workers (the noise group) with (NIHL group, n = 30) and without NIHL (non-NIHL group, n = 30) and 30 male controls without noise exposure (control group) were collected. Untargeted human plasma metabolomic profiles were determined with HPLC-MS/MS. The levels of the metabolites in the samples were normalized to total peak intensity, and the processed data were subjected to multivariate data analysis. The Wilcoxon test and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed. With the threshold of p < 0.05 and the variable importance of projection (VIP) value >1, 469 differential plasma metabolites associated with noise exposure (DMs-NE) were identified, and their associated 58 KEGG pathways were indicated. In total, 33 differential metabolites associated with NIHL (DMs-NIHL) and their associated 12 KEGG pathways were identified. There were six common pathways associated with both noise exposure and NIHL. Through multiple comparisons, seven metabolites were shown to be dysregulated in the NIHL group compared with the other two groups. Through LASSO regression analysis, two risk models were constructed for NIHL status predication which could discriminate NIHL from non-NIHL workers with the area under the curve (AUC) values of 0.840 and 0.872, respectively, indicating their efficiency in NIHL diagnosis. To validate the results of the metabolomics, cochlear gene expression comparisons between susceptible and resistant mice in the GSE8342 dataset from Gene Expression Omnibus (GEO) were performed. The immune response and cell death-related processes were highlighted for their close relations with noise exposure, indicating their critical roles in noise-induced disorders. We concluded that there was a significant difference between the metabolite’s profiles between NIHL cases and non-NIHL individuals. Noise exposure could lead to dysregulations of a variety of biological pathways, especially immune response and cell death-related processes. Our results might provide new clues for noise exposure studies and NIHL diagnosis.

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers

Guo Shun Chen

Papers

The Effect of PCDH15 Gene Variations on the Risk of Noise-induced Hearing Loss in a Chinese Population.

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers