Noise exposure worker recruitment physical examination screening biomarker and application thereof

Abstract

The invention discloses a noise exposure worker recruitment physical examination screening biomarker and application thereof, and relates to the technical field of biology. The method comprises the following steps: extracting DNA from blood cells of a worker who is diagnosed to suffer from NIHL in a factory and a worker with normal hearing matched with the worker in the aspects of gender, age and the like, carrying out genetic typing detection on ten SNPs loci of 9 genes such as FOXM1 and the like, constructing an NIHL prediction model by using seven machine learning algorithms, and verifying the correlation between the SNPs loci and noise-induced hearing loss; meanwhile, PNN and GRNN with excellent performance indexes are selected to evaluate each SNPs site and importance of characteristic variables such as age, gender, noise exposure level and noisy working age, and rs1134648 of an AIMP1 gene and rs2304277 of an OGG1 gene are determined as SNPs sites remarkably associated with occurrence and development of NIHL in combination with genotype association analysis results; the method is easy to operate, high in screening sensitivity and specificity and suitable for screening large-scale susceptible people, and a basis is provided for screening biomarkers for noise-induced hearing loss.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and particularly to a biomarker for recruitment physical examination screening of noise-exposed workers and its application. Background Art

[0002] Noise Induced Hearing Loss (NIHL) is a common sensorineural hearing disorder caused by long-term exposure of workers to high-intensity noise. With the continuous deepening of the global industrialization process, the incidence rate of NIHL has also increased year by year and has become one of the major public health problems worldwide. Due to the lack of specific and sensitive early screening indicators for NIHL, most people have moderate to severe hearing loss when confirmed by physical examination, and it is usually irreversible permanent damage.

[0003] After the test population is exposed to the same or similar levels of noise environment, not all subjects will develop NIHL. Even if hearing loss symptoms appear, the levels of auditory threshold shift (temporary auditory threshold shift and permanent auditory threshold shift) may vary among the affected individuals. These individual response differences reveal significant susceptibility differences in NIHL among different individuals. With the continuous development of molecular biology techniques and the in-depth study of population epidemiology, some studies have shown that single nucleotide polymorphisms (SNPs) of genes such as HDAC2, GSTP1, SOD2, and STAT3 are related to the genetic susceptibility of NIHL, suggesting that genetic factors play an important role in the occurrence and development of NIHL.

[0004] Konings et al. found a significant association between the SNP locus of the CAT gene and the susceptibility to NIHL. In Shen's experiment, compared with individuals carrying the wild-type GSTM1 genotype, the risk of NIHL in individuals carrying the GSTM1 null genotype was significantly increased (OR = 1.64). Van Laer et al. found that genes involved in the potassium cycle in the inner ear (including KCNE1, KCNQ1, and KCNQ4) might explain the variability of noise susceptibility. Shen et al. also found that the hOGG1 Cys / Cys genotype might be a genetic susceptibility marker for NIHL in Chinese Han population. In addition, the study by Gao Dengfeng et al. also found that the dominant models of single nucleotide polymorphisms rs1053023 (OR = 1.516, p = 0.001) and rs1053005 (OR = 1.509, p = 0.002) of the STAT3 gene were associated with the genetic susceptibility to NIHL. Miao et al. also found that potential functional SNPs (rs2304186, rs41275750, and rs76524493) in the AKT2 gene and their interactions might be potential biomarkers for NIHL susceptibility. Summary of the Invention

[0005] An object of the present invention is to provide a biomarker for pre-employment physical examination screening of noise-exposed workers and its application, so as to solve the problems raised in the above background art.

[0006] In the present invention, DNA was extracted from the blood cells of workers diagnosed with NIHL in the factory and those with normal hearing who were matched in terms of gender, age, etc. Genotyping tests were performed on a total of 10 SNPs loci of 9 genes, namely FOXM1 (rs12582464), MTOR (rs2295080), AIMP1 (rs1134648, rs13534), SAE1 (rs309184), OGG1 (rs2304277), AKT2 (rs41275750), UBE2I (rs7204003), SIRT1 (rs12049646), and PIK3R1 gene (rs706713). These genes and SNPs loci were obtained through experimental research, next-generation sequencing, and relevant standards screening by this research group in recent years, and are candidate genetic markers that may be closely related to noise exposure and noise-induced hearing loss (NIHL). In addition, an NIHL prediction model was constructed through 7 machine learning algorithms to verify the correlation between these 10 SNPs loci and noise-induced hearing loss. By comparing the performance indicators of each model in NIHL prediction, the better-performing PNN and GRNN in terms of various performance indicators were selected to evaluate the importance of each SNPs locus and characteristic variables such as age, gender, noise exposure level, and noise exposure working years, and genotype association analysis was performed to determine the SNPs loci significantly associated with the occurrence and development of NIHL, which were used as biomarkers for screening during the recruitment physical examination of noise-exposed workers.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The present invention provides a biomarker for screening during the recruitment physical examination of noise-exposed workers, and the biomarker is the rs1134648 polymorphism locus of the AIMP1 gene and / or the rs2304277 polymorphism locus of the OGG1 gene.

[0009] The sequence of the rs1134648 polymorphism locus of the AIMP1 gene is shown as SEQ ID NO: 1-2:

[0010] ATTTTCTGTCTCAGTGAAGCAAATA[C / G]CATTTCCATCTGGTACTCCACTGC.

[0011] The sequence of the rs2304277 polymorphism locus of the OGG1 gene is shown as SEQ ID NO: 3-4:

[0012] TCCCTAAGCAGTTACTGTGTGCCCA[G / A]TGTGATGCCAGGTGCTGTGCAAGCT.

[0013] The present invention also provides an application of the above-mentioned biomarker in the preparation of a screening product for the physical examination of newly recruited workers exposed to noise. The product contains primers for specifically amplifying the rs1134648 polymorphism site of the AIMP1 gene and / or primers for specifically amplifying the rs2304277 polymorphism site of the OGG1 gene; the product includes reagents and kits.

[0014] The present invention also provides primers for detecting the above-mentioned biomarker, the primers include primers for specifically amplifying the rs1134648 polymorphism site of the AIMP1 gene and / or primers for specifically amplifying the rs2304277 polymorphism site of the OGG1 gene;

[0015] The primers for specifically amplifying the rs1134648 polymorphism site of the AIMP1 gene are shown as SEQ ID NO:5 and SEQ ID NO:6; the primers for specifically amplifying the rs2304277 polymorphism site of the OGG1 gene are shown as SEQ ID NO:7 and SEQ ID NO:8.

[0016] SEQ ID NO:5

[0017] rs1134648-F: CTCAGACAGTGGATATTTGATAGC

[0018] SEQ ID NO:6

[0019] rs1134648-R: GTTGTTACTGCTGTAGACTGTATC

[0020] SEQ ID NO:7

[0021] rs2304277-F: TGAAAGAGTGAATGAATGAAGTCC

[0022] SEQ ID NO:8

[0023] rs2304277-R: TAGATAAGAATATCCACCAGTCGG.

[0024] The present invention also provides an application of the above-mentioned primers in the preparation of a screening product for the physical examination of newly recruited workers exposed to noise. The product contains the primers as described in SEQ ID NOs:5-8 above; the product includes reagents and kits

[0025] The present invention also provides a screening reagent for the physical examination of newly recruited workers exposed to noise. The reagent contains specific amplification primers for the rs1134648 site of the above-mentioned AIMP1 gene and / or the rs2304277 site of the OGG1 gene, as shown in SEQ ID NOs:5-8.

[0026] The present invention also provides a physical examination screening kit for newly recruited workers exposed to noise, which contains reagents for detecting rs1134648 of the AIMP1 gene and / or rs2304277 of the OGG1 gene. The reagents contain specific amplification primers for the above 2 SNPs loci, as shown in SEQ ID NO:5-8.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0028] For the first time, the present invention extracts DNA from the blood cells of workers diagnosed with NIHL and age- and sex-matched normal-hearing workers. Through 7 machine learning algorithms, based on the genotype data of 10 SNPs loci of 9 genes, a NIHL prediction model is constructed to verify the association between these genetic markers and the occurrence and development of NIHL, and these are used as genetic markers for the physical examination of the blood cells of newly recruited workers exposed to noise. The operation is simple, and the screening sensitivity and specificity are high, which is suitable for large-scale screening of susceptible populations.

[0029] The kit of the present invention is easy to operate, has high screening sensitivity and specificity, is suitable for the physical examination screening of newly recruited workers in large-scale populations susceptible to noise-induced hearing loss, and provides a basis for screening biomarkers for noise-induced hearing loss. Description of the Drawings

[0030] Figure 1 The process of training 10 SNPs loci using PNN and GRNN and their corresponding accuracies;

[0031] Figure 2 The importance ranking of 10 SNPs loci and characteristic variables such as age, sex, noise exposure level, and noise exposure years in the PNN and GRNN models. Detailed Embodiments

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] Example 1

[0034] Selection of Research Subjects

[0035] This study took 1,138 noise-exposed workers with occupational noise exposure in 52 noise-exposed enterprises covered by the occupational disease hazard monitoring system in Jiangsu Province as the research objects. Demographic data were collected and the on-site noise exposure level was measured according to the national standard GBZ / T 189.8-2007 (Measurement of Physical Factors in the Workplace, Part 8: Noise). All research objects underwent pure tone audiometry (PTA), and finally those with an average hearing threshold of >25 dB(A) at high frequencies (3.0, 4.0, 6.0 kHz) in one or both ears were included in the case group. The control group was frequency-matched according to factors such as age, gender, smoking, and drinking. The inclusion criteria for all research objects were: (1) Chinese workers; (2) a noise exposure occupational history of three years or more; (3) complete occupational health surveillance data; (3) the levels of other occupational harmful factors (heavy metals, organic solvents, CO, high temperature, vibration, etc.) that may affect NIHL in the working environment except for noise were all lower than the requirements of occupational exposure limits (OELs). The research protocol of this study was reviewed and approved by the Ethics Committee of Jiangsu Provincial Center for Disease Control and Prevention. All research objects signed the relevant informed consent forms and authorized to participate in the research of this project.

[0036] 1. Questionnaire survey

[0037] During the recruitment medical examination, after the research objects gave informed consent and signed the informed consent form, the occupational health questionnaire was filled out by the way of being guided by specially trained and qualified investigators and self-filling. The content mainly included gender, age, smoking and drinking status, noise exposure situation, medical history, and drug-taking history.

[0038] 2. Measurement of noise exposure intensity

[0039] The noise exposure level in the working environment was measured according to the national standard of "Measurement of Physical Factors in the Workplace, Part 8: Noise" (GBZ / T 189.8-2007): national standard (GBZ / T 189.8-2007). In the selected workplace, the QuestNoise Pro-DL multi-functional personal noise dosimeter (Quest Company, USA) was used to conduct noise exposure measurements three times a year. Before each measurement, the equipment was calibrated, and the results were converted into 8-hour equivalent continuous A-weighted sound pressure (LEX, 8 hours) to represent the noise exposure intensity. The occupational health standard in China stipulates that LEX, 8h ≥ 80 dB(A) is a noise operation.

[0040] 3. Pure tone audiometry and definition of NIHL

[0041] According to the provisions of the "Diagnostic Criteria for Occupational Noise-induced Deafness in China" (GBZ 49-2014), all study subjects must be removed from the noise environment for at least 48 hours before pure tone audiometry. The formal test was carried out in an anechoic chamber with good sound insulation effect (background noise value < 25 dB(A)). Experienced occupational physicians used an audiometer to measure the hearing thresholds of both ears of the study subjects at a total of 6 frequencies: 0.5, 1.0, 2.0, 3.0, 4.0, and 6.0 kHz. All hearing threshold measurement results were adjusted for age and gender according to "Acoustics - Statistical Distribution of Hearing Thresholds by Age and Gender". Participants with an average hearing threshold greater than 25 dB(A) in one or both ears at high frequencies (3.0, 4.0, and 6.0 kHz) were assigned to the case group, and the control group was frequency-matched in terms of age, gender, smoking habit, alcohol consumption, and other factors.

[0042] 4. Peripheral blood collection, pretreatment, and extraction

[0043] Blood sample collection and pretreatment: 5.0 ml of fasting elbow vein blood was collected from the study subjects in the early morning and placed in a heparin sodium automatic venous blood collection tube. After thorough mixing, it was placed in an ice box and taken back to the laboratory. Centrifugation was carried out at 800×g for 10 min at room temperature to separate serum and blood cells, and the blood cells were used for genomic DNA extraction. The QIAamp 96 DNA QIAcube HT Kit was used to extract genomic DNA from peripheral blood cells according to the operation manual, and detection was carried out using a Nanodrop OneC ultra-micro ultraviolet spectrophotometer. The results showed that the absorbance (A) 260 / A280 was between 1.8 and 2.0, indicating that the extracted DNA had high purity and could be used for the next experiment.

[0044] 5. SNPs genotyping

[0045] The genotyping procedure was further carried out using an ABI 7900 real-time PCR system. The conditions for real-time PCR were as follows: 95°C, 10 min; 95°C, 15 s; 60°C, 1 minute (the last two steps were repeated 40 times).

[0046] 6. Selection and quality control of SNPs

[0047] Appropriate SNPs were screened by querying the 1000 Genomes Project database (http: / / www.1000genomes.org / ) and the National Center for Biotechnology Information (NCBI) dbSNP database (http: / / www.ncbi.nlm.nih.gov / snp / ). The screening criteria were as follows:

[0048] (1) SNP loci frequently reported in Chinese and English literature in the past decade and related to NIHL.

[0049] (2) The minor allele frequency (MAF) corresponding to the locus > 0.05.

[0050] (3) The linkage disequilibrium (LD) value r between any two loci 2 > 0.8.

[0051] The SNPs loci screened according to the above criteria were first processed by TASSEL 5.0 software, including missing data processing, genotype filtering, and data format conversion, to ensure data quality and compatibility. Then, the " --indep-pairwise " command option of pLINK v1.07 was used to further prune the SNPs loci.

[0052] 7. Statistical analysis

[0053] All data were processed and analyzed using SPSS 27.0 software. This included continuous variables (age, noise exposure level, etc.). The median and interquartile range M(P25, P75) did not meet the normal distribution, and the Mann-Whitney U test was used for comparative analysis; categorical variables (such as age, gender, smoking habit, and alcohol consumption) were compared using Pearson's χ 2 test, and the significance level α was taken as 0.05. The genotypes of 10 SNPs loci were respectively encoded as 0, 1, and 2, representing wild type, heterozygous type, and mutant type, indicating the number of alleles at each SNP locus. In addition, the goodness-of-fit chi-square test was used to verify whether the gene frequency distribution of each SNPs locus in the entire population conforms to the Hardy-Weinberg genetic equilibrium law (P value > 0.05).

[0054] 8. Construction and verification of the NIHL prediction model

[0055] Based on the data of 10 SNPs loci, the NIHL prediction model was developed and implemented using MATLAB 9.0 (R2016a), and accuracy, recall rate, precision, F-score, R 2 and AUC were selected as performance indicators to comprehensively evaluate and compare the prediction performance of 7 machine learning algorithms.

[0056] Results:

[0057] (1) Description of the basic characteristics of the research subjects

[0058] According to the inclusion and exclusion criteria of the study population and combined with the results of pure tone audiometry (PTA), a total of 1,138 workers were finally included in this study as the study population, including 753 in the case group and 585 in the control group. The case group and the control group were comparable in terms of age, gender, smoking and alcohol consumption, and the differences were not statistically significant (P>0.05). However, the differences in noise exposure years, noise exposure level and high-frequency hearing impairment between the case group and the control group were statistically significant (P<0.05). Among them, 33(28,42) in the case group was significantly higher than 15(12,19) in the control group, about 2.2 times that of the control group.

[0059] Table 1. Basic characteristics of the study subjects

[0060]

[0061]

[0062] Note: a Two-sided χ 2 test;

[0063] b Two-sided Wilcoxon signed-rank test.

[0064] (2) Construction of the NIHL prediction model

[0065] The genotype data of 10 SNPs loci and the basic characteristic data of the study subjects were imported into MATLAB 9.0 (R2016a) software to establish the NIHL prediction model. The accuracy, recall rate, precision, F-score, R 2 and AUC of seven machine learning algorithms on the training set, cross-validation set and test set are shown in Tables 2 to 4 to measure the prediction effects of each model. Based on the comprehensive tabular data, it can be concluded that PNN and GRNN perform better in terms of various performance indicators in NIHL prediction, showing better comprehensive performance. The processes of training 10 SNPs loci using PNN and GRN and their corresponding accuracies are shown in Figure 1 . Regarding the importance ranking of SNPs loci and characteristic variables such as age and gender, PNN and GRNN reached the same conclusion, as shown in Figure 2 .

[0066] Table 2. Accuracy, recall rate, precision, F-score, R 2 and AUC of seven machine learning algorithms on the training set

[0067] Number of SNP\Algorithm Accuracy R P F score <![CDATA[R 2 > AUC 88\Decision Tree(DT) 60.00％ 60.00％ 70.40％ 0.637 0.621 0.619 88\Gradient Boosting Decision Tree(GBDT) 60.00％ 60.00％ 58.10％ 0.589 0.570 0.581 88\K-Nearest Neighbor(KNN) 68.90％ 68.90％ 66.00％ 0.674 0.648 0.652 88\eXtreme Gradient Boost(XGBoost) 71.10％ 71.10％ 72.30％ 0.717 0.694 0.706 88\Genetic Algorithm-Random Forests(GA-RF) 84.40％ 84.40％ 71.30％ 0.773 0.757 0.752 88\Probabilistic Neural Network(PNN) 78.64％ 79.45％ 78.44％ 0.805 0.797 0.808 88\Generalized Regression Neural Network(GRNN) 85.36％ 85.09％ 84.60％ 0.897 0.862 0.857

[0068] Table 3. Accuracy, recall rate, precision, F-score, R 2 and AUC of seven machine learning algorithms on the cross-validation set

[0069] Number of SNP\Algorithm Accuracy R P F score <![CDATA[R 2 > AUC 8\Dec ision Tree(DT) 57.70％ 57.70％ 57.80％ 0.536 0.496 0.492 8\Gradient Boosting Decision Tree(GBDT) 52.80％ 52.80％ 52.30％ 0.521 0.474 0.487 8\K-Nearest Neighbor(KNN) 53.90％ 53.90％ 53.20％ 0.534 0.485 0.494 8\eXtreme Gradient Boost(XGBoost) 57.00％ 57.00％ 56.30％ 0.554 0.506 0.511 8\Genetic Algorithm-Random Forests(GA-RF) 57.70％ 57.70％ 58.30％ 0.538 0.528 0.524 8\Probabilistic Neural Network(PNN) 55.14％ 58.90％ 54.95％ 0.539 0.490 0.489 8\Generalized Regression Neural Network(GRNN) 57.59％ 59.09％ 58.59％ 0.562 0.518 0.513

[0070] Table 4. Accuracy, recall rate, precision, F-score, R of seven machine learning algorithms on the test set 2 and AUC

[0071] Number of SNP\Algorithm Accuracy R P F score <![CDATA[R 2 > AUC 10\Decision Tree(DT) 59.34％ 59.34％ 59.42％ 0.604 0.568 0.572 10\Gradient Boosting Decision Tree(GBDT) 54.22％ 56.42％ 56.35％ 0.554 0.542 0.540 10\K-Nearest Neighbor(KNN) 56.80％ 56.80％ 56.20％ 0.568 0.556 0.552 10\eXtreme Gradient Boost(XGBoost) 63.80％ 63.80％ 63.30％ 0.615 0.598 0.586 10\Genetic Algorithm-Random Forests(GA-RF) 64.65％ 66.82％ 65.95％ 0.659 0.639 0.628 10\Probabilistic Neural Network(PNN) 64.33％ 67.80％ 63.17％ 0.678 0.612 0.614 10\Generalized Regression Neural Network(GRNN) 66.12％ 70.46％ 68.33％ 0.690 0.658 0.647

[0072] (3) Association analysis of gene polymorphism and NIHL

[0073] The results of univariate and multivariate logistic regression analysis of 10 SNPs loci are shown in Table 5. The results of univariate analysis showed that the genotype distributions of SNPs loci of 2 genes (AIMP1(rs1134648), OGG1(rs2304277)) were significantly different between the case group and the control group (P < 0.05); after multivariate logistic correction for age, gender, smoking, and drinking, etc., it was found that the genotype distributions of these two SNPs loci were still significantly different between the case group and the control group (P < 0.05). After multiple collinearity diagnosis, the tolerance of each SNPs locus was > 0.1, and the VIF (variance inflation factor) was < 2, indicating that there was no multiple collinearity between SNPs loci.

[0074] Table 5: Results of association analysis between genotypes of 10 SNPs loci and NIHL

[0075]

[0076]

[0077] Note: a Data from the NCBI dbSNP database;

[0078] b P value of Hardy-Weinberg test;

[0079] c After two-sided χ 2 test;

[0080] d Adjusted for age, gender, smoking, and drinking status.

[0081] In the present invention, 9 genes with a total of 10 SNPs loci were selected through searching and analyzing using the Hapmap database and the NCBI database, consulting relevant literature, and according to relevant screening criteria. They are FOXM1 (rs12582464), MTOR (rs2295080), AIMP1 (rs1134648, rs13534), SAE1 (rs309184), OGG1 (rs2304277), AKT2 (rs41275750), UBE2I (rs7204003), SIRT1 (rs12049646), and PIK3R1 gene (rs706713). Based on the genotype data of these 10 SNPs loci, a NIHL prediction model was constructed by 7 machine learning algorithms, and a correlation study and analysis were carried out. Finally, the relationships between rs1134648 of the AIMP1 gene and rs2304277 of the OGG1 gene and the susceptibility of the noise-exposed population were verified.

[0082] The sequence of the rs1134648 polymorphism locus of the AIMP1 gene is shown in SEQ ID NO: 1-2:

[0083] ATTTTCTGTCTCAGTGAAGCAAATA[C / G]CATTTCCATCTGGTACTCCACTGC.

[0084] The sequence of the rs2304277 polymorphism locus of the OGG1 gene is shown in SEQ ID NO: 3-4:

[0085] TCCCTAAGCAGTTACTGTGTGCCCA[G / A]TGTGATGCCAGGTGCTGTGCAAGCT.

[0086] The kit contains reagents for detecting rs1134648 of the AIMP1 gene and rs2304277 of the OGG1 gene. The RT primers for the above 2 SNPs loci in the reagents are shown in SEQ ID NO: 5-8:

[0087] SEQ ID NO:5

[0088] rs1134648-F:CTCAGACAGTGGATATTTGATAGC

[0089] SEQ ID NO:6

[0090] rs1134648-R:GTTGTTACTGCTGTAGACTGTATC

[0091] SEQ ID NO:7

[0092] rs2304277 - F: TGAAAGAGTGAATGAATGAAGTCC

[0093] SEQ ID NO:8

[0094] rs2304277 - R: TAGATAAGAATATCCACCAGTCGG

[0095] In summary, the biomarkers rs1134648 and rs2304277 provided by the present invention can be used to predict the susceptibility risk of noise - induced hearing loss.

[0096] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non - exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0097] It should be noted that the above content only illustrates the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. For those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements all fall within the protection scope of the claims of the present invention.

Claims

1. A biomarker for physical examination screening of noise-exposed workers, characterized in that: The biomarker is the rs1134648 polymorphic site of the AIMP1 gene and / or the rs2304277 polymorphic site of the OGG1 gene. The sequence of the rs1134648 polymorphic site of the AIMP1 gene is shown in SEQ ID NOs: 1 to 2, and the sequence of the rs2304277 polymorphic site of the OGG1 gene is shown in SEQ ID NOs: 3 to 4.

2. Use of the biomarker as claimed in claim 1 in the preparation of a product for physical examination screening of noise-exposed workers.

3. The use according to claim 2, characterized in that: The product comprises primers for specifically amplifying the rs1134648 polymorphic site of the AIMP1 gene and / or primers for specifically amplifying the rs2304277 polymorphic site of the OGG1 gene; The products include reagents and test kits.

4. A primer for specifically amplifying the biomarker according to claim 1, characterized in that: The primers include primers for specifically amplifying the rs1134648 polymorphic site of the AIMP1 gene and / or primers for specifically amplifying the rs2304277 polymorphic site of the OGG1 gene; The primers for specifically amplifying the rs1134648 polymorphic site of the AIMP1 gene are shown in SEQ ID NO:5 and SEQ ID NO:6; the primers for specifically amplifying the rs2304277 polymorphic site of the OGG1 gene are shown in SEQ ID NO:7 and SEQ ID NO:

8.

5. Use of the primer according to claim 4 in preparing a product for physical examination screening of noise-exposed workers.

6. The use according to claim 5, characterized in that: The product comprises the primers described in claim 4; the product comprises reagents and a kit.

7. A reagent for physical examination screening of noise-exposed workers, characterized in that: The reagent comprises the primer according to claim 4.

8. A physical examination screening kit for noise-exposed workers, characterized in that: Comprising the primers described in claim 4.

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