Polymorphisms and NIHL: a systematic review and meta-analyses

BACKGROUND: Noise-induced hearing loss (NIHL) has always been a global public health problem, which is related to noise exposure and genetic factors. Many researchers have tried to identify the polymorphisms that cause different individuals' susceptibility to NIHL. We conducted a meta-analysis of the most frequently studied polymorphisms to identify those genes that may be associated with NIHL and may provide value in risk prevention. METHODS: PubMed, China National Knowledge Infrastructure (CNKI) database, Embase, Wang Fang, Web of Science and Cochrane library were searched, and qualified studies on the correlation between polymorphism and NIHL susceptibility were screened, and then polymorphisms cited in at least three studies were selected for meta-analysis. Fixed- or random-effects models were used to calculate odds ratios and 95% confidence intervals. Statistical I(2) tests and sensitivity analyses were used to detect interstudy heterogeneity and test the statistical stability of overall estimates, respectively. Egger's tests were applied to detect publication bias among included studies. All of the above analyses were performed using stata 17.0. RESULTS: 64 genes were initially selected and introduced in 74 papers. Among them, 10 genes (and 25 polymorphisms) have been reported in more than 3 papers. Twenty five polymorphisms participated in the meta-analysis. Of the 25 polymorphisms, only 5 were significantly associated with the risk of AR: rs611419 (GRHL2) polymorphism and rs3735715 polymorphism (GRHL2), rs208679 polymorphism (CAT), rs3813346 polymorphism (EYA4) were significantly associated with the susceptibility of NIHL, rs2227956 polymorphism (HSP70) was significantly associated with the susceptibility of white population NIHL, and the remaining 20 gene polymorphisms were not significantly associated with NIHL. CONCLUSION: We found polymorphisms that are valuable for the prevention of NIHL and polymorphisms that are not related to NIHL. This is the first step to establish an effective risk prediction system for the population, especially for high-risk groups, which may help us better identify and prevent the occurrence of NIHL. In addition, our research results contribute to the in-depth exploration of NIHL. SYSTEMATIC REVIEW REGISTRATION: https://inplasy.com/inplasy-2023-6-0003/, identifier INPLASY202360003.