Kong Yixuan, Zheng Jinghui, Xu Xiangmei, Chen Xuan, Wang Jie, Lu Liying, Ye Zhuomiao
Ruikang Affiliated Hospital of Guangxi University of Chinese Medicine.
Department of Geriatrics, Ruikang Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Medicine (Baltimore). 2020 Jun 26;99(26):e20791. doi: 10.1097/MD.0000000000020791.
According to the relevant reports that single nucleotide polymorphisms (SNPs) may contribute to change of homocysteine (HCY) levels and increase the risk of hypertension (HTN). During the inconsistent results, this meta-analysis purpose is systematically review and synthesized relevant data on HCY levels and SNPs in HTN.
The systematic search database, from the following database to find out the association studies of SNPs and HTN publications up until March 2020 from the databases of PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), the Chinese Science and Technology Periodical Database (VIP) and Wan fang databases, and Chinese Biomedical Literature Database (CBM). Network meta-analysis and Thakkinstian's algorithm were used to select the most appropriate genetic model, along with false positive report probability (FPRP) for noteworthy associations. All statistical analyses were calculated with STATA software (version 14.0; StataCorp, College Station, TX).
This meta-analysis will provide high-quality evidence to the effects of SNP on HTN and levels of HCY, and find between SNPs and HTN susceptibility on in all the genetic models, and choose the best one.
This meta-analysis will research which SNP is the most correlated with HTN risk.
INPLASY202050002.
根据相关报道,单核苷酸多态性(SNP)可能导致同型半胱氨酸(HCY)水平变化并增加高血压(HTN)风险。鉴于研究结果不一致,本荟萃分析旨在系统回顾和综合有关高血压中HCY水平和SNP的相关数据。
系统检索数据库,从PubMed、Embase、Web of Science、Cochrane图书馆、中国知网(CNKI)、中国科技期刊数据库(VIP)、万方数据库以及中国生物医学文献数据库(CBM)中查找截至2020年3月的SNP与高血压相关的关联研究出版物。采用网络荟萃分析和Thakkinstian算法选择最合适的遗传模型,并对值得关注的关联进行假阳性报告概率(FPRP)分析。所有统计分析均使用STATA软件(版本14.0;StataCorp,学院站,德克萨斯州)进行计算。
本荟萃分析将为SNP对高血压和HCY水平的影响提供高质量证据,在所有遗传模型中找出SNP与高血压易感性之间的关系,并选择最佳模型。
本荟萃分析将研究哪种SNP与高血压风险最相关。
INPLASY202050002