Association of MMP-9 polymorphisms with diabetic nephropathy risk: A protocol for systematic review and meta-analysis

BACKGROUND: Diabetic nephropathy (DN) is a multifactorial disease with gene–environment interaction resulting in progressive renal function damage. Multiple studies have assessed the association between matrix metalloproteinase-9 (MMP-9) gene promoter polymorphism and DN susceptibility. However, the results are inconclusive. In the present study, we will conduct a meta-analysis to further examine this relationship more precisely. METHODS: Electronic databases (Pubmed, Web of Science, Embase, Google Scholar, Wanfang, China Biological Medicine and China National Knowledge Infrastructure) will be used to search clinical case–control studies about MMP-9 polymorphism and DN published until 18 August 2020. The language will be restricted to Chinese and English. Two reviewers will take charge of completing the selection of study, the extraction of data as well as the assessment of study quality independently. The Newcastle-Ottawa Scale will be used to evaluate the study quality. We will evaluate the association under 5 genetic models. Fixed-effects or random-effects models will be used to calculate the effect sizes of odds ratio and 95% confidence intervals. Afterwards, subgroup analysis will be conducted in terms of the ethnicity and genotyping method. Additionally, sensitivity analysis will be performed via sequentially omitting each of the included studies one at a time. The funnel plots, Egger regression test, and Begg rank correlation test will be used to test the potential publication bias. All the statistical analyses will be performed using Review Manager 5.3 and Stata 12.0. RESULTS: This protocol reported according to the Preferred Reporting ltems for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement. This study will provide a better understanding of the association between MMP-9 polymorphisms and DN risk. CONCLUSION: Publishing this protocol will minimize the potential bias related to data mining, thus contributing to generation of reliable evidence. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/H5FS4