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作 者:邱蕾[1] 曾渝[2] 刘静[2] 黑启明[2] 王大红[2]
机构地区:[1]海南医学院社会医学教研室,海口571199 [2]海南医学院健康管理教研室,海口571199
出 处:《上海交通大学学报(医学版)》2014年第4期527-532,共6页Journal of Shanghai Jiao tong University:Medical Science
摘 要:目的采用meta分析的方法评价促卵泡激素受体(FSHR)基因单核苷酸多态性(SNP)位点Asn680Ser多态性与多囊卵巢综合征(PCOS)易感性的关系。方法检索PubMed、Ovid、EMBASE、中国生物医学文献数据库(CBM)与CNKI中关于FSHR基因Asn680Ser位点多态性与PCOS易感性的病例一对照研究,检索时限均从建库起至2013年1月。由2名评价者按照纳入和排除标准独立选择文献,提取资料和评价质量后,采用Stata11.0软件进行meta分析,并用Begg漏斗图评估文献的发表偏移。结果共纳入11篇文献,累计病例1344例,对照3885例。meta分析结果显示,无论在显性、隐性以及加性遗传模式下,Asn680Ser与PCOS相关性的合并OR(95%c,)分别为1.19(1.02~1.39)、1.21(1.00~1.45)和1.27(1.02~1.58),差异均具有统计学意义(P〈0.05)。结论FSHR基因Asn680Ser位点多态性与PCOS的易感性有关,在显性、隐性及加性遗传模式中,FSHR基因Asn680Ser位点的Ser等位基因均为PCOS的危险因素。Objective To evaluate the correlation of the single-nuleotide polymorphisrn (Asn680Ser polymorphism) of follicle-stimulating hormone receptor (FSHR) and the susceptibility of polycystic ovarian syndrome (PGOS) by the rneta-analysis. Methods Databases including the Puhrned, Ovid, EMBASE, CBM, and QNKI were searched to collect the case-control studies on the correlation of the Asn680Ser polyrnorphisms of FSHR gene and PCOS susceptibility. Records before January, 2013 were retrieved. Records were screened independently by two reviewers according to the inclusion and exclusion criteria. Data were extracted and the quality was evaluated. Then the data were analyzed by the rneta-analysis using the Stata 11.0 software and the Begg's funnel plot was used to assess the publishing bias of articles. Results Totally 11 articles were selected, which involved 1 344 cases and 3 885 controls. The results of recta-analysis showed that there were significant differences between the two groups for the dominant, recessive, and addictive genetic models, and the pooled OR (95% CI) were 1.19(1.02 -1.39), 1.21(1.00 -1.45), 1.27(1.02 -1.58), respectively. The differences were statistically significant (P 〈 0.05). Conclusion The Asn680Ser polyrnorphisms of FSHR gene are associated with the susceptibility of PGOS. Ser allele of Asn680Ser is a risk factor of the PGOS for the dominant, recessive, and addictive genetic models.
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