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A comprehensive evaluation of single nucleotide polymorphisms associated with atrophic gastritis risk: A protocol for systematic review and network meta-analysis

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been inconsistently associated with atrophic gastritis (AG) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with AG. METHODS: To identify all associated studies of SNPs and AG published, databases had been searched...

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Detalles Bibliográficos
Autores principales: Tang, Zhen-Yu, Ye, Zhuo-Miao, Zheng, Jing-Hui, Jiang, Feng, Tang, You-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373582/
https://www.ncbi.nlm.nih.gov/pubmed/32702817
http://dx.doi.org/10.1097/MD.0000000000020677
Descripción
Sumario:BACKGROUND: Single nucleotide polymorphisms (SNPs) have been inconsistently associated with atrophic gastritis (AG) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with AG. METHODS: To identify all associated studies of SNPs and AG published, databases had been searched through January 2020 from the databases of PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, Embase, the Chinese Science and Technology Periodical Database (VIP), Cochrane Library, and Wanfang databases. With the help of network meta-analysis and Thakkinstian algorithm, the best genetic model with the strongest correlation with AG was selected, the final result – matching to the noteworthy correlation – was obtained by referring to the false positive reporting rate (false positive report probability, FPRP). Based on STREGA's stated criteria, the methodological quality of the data we collected was valued. Both Stata 14.0 and GeMTC will be used for a comprehensive review of the system and will be used in our meta-analysis. RESULTS: This study will provide a high-quality evidence to find the SNP most associated with AG susceptibility and the best genetic model. CONCLUSIONS: This study will explore which SNP is most associated with AG susceptibility. REGISTRATION: INPLASY202050016.