ObjectiveTo systematically review the correlation between apolipoprotein E (ApoE) polymorphism and sporadic Alzheimer's disease (SAD) in Chinese population. MethodsThe case-control studies about the relationship between ApoE polymorphism and SAD in Chinese population were electronically retrieved in PubMed, EMbase, CBM, The Cochrane Library (Issue 8, 2013), CNKI, VIP, and WanFang Data from the date of their establishment to August 2013. Literature screening according to the inclusion and exclusion criteria, data extraction and methodological quality assessment of the included stuides were completed by two reviewers independently. Meta-analysis was then conducted using Stata 12.0 software. ResultsA total of 50 case-control studies invovling 3 396 cases and 4 917 controls were finally included. The results of meta-analysis showed that, in Chinese, the risk of SAD was 2.89 times higher in population with allele ε4 than in population with allele ε3 (OR=2.89, 95%CI 2.61 to 3.19, P < 0.001); 7.24 times higher in those with ε4/ε4 genotype than in those with ε3/ε3 genotype (OR=7.24, 95%CI 5.11 to 10.24, P < 0.001); 2.90 times higher in ε3/ε4 genotype than in ε3/ε3 genotype (OR=2.90, 95%CI 2.56 to 3.29, P < 0.001); 2.11 times higher in ε2/ε4 genotype than in ε3/ε3 genotype (OR=2.11, 95%CI 1.64 to 2.72, P < 0.001); and no statistic significance was found in the risk of SAD compared ε2/ε3, ε2/ε2 genotypes and ε2 allele with ε3/ε3 genotype and ε3 allele. ConclusionFor Chinese population, ApoE allele ε4 is significantly associated with the onset of SAD, and genotype ε4/ε4 is a high risk factor of SAD. While allele ε2 is not associated with the onset of SAD. Since a great deal of current studies failed to conduct stratified analysis, it is suggested to further conduct relevant relevant studies according to clinical classification of SAD and patients' characteristics.
ObjectiveTo systematically evaluate the association between 936C/T polymorphism in vascular endothelial growth factor (VEGF) gene and the risk of preeclampsia (PE). MethodsSuch databases as PubMed, EMbase, The Cochrane Library (Issue 11, 2014), CBM, CNKI, VIP, and WanFang Data were searched up to November 2014, to collect case-control studies of the association between 936C/T polymorphism in VEGF gene and the risk of PE. Two reveiwers independently screened studies according to the inclusion and exclusion criteria, extracted data, and assessed the risk of bias of included studies. And then, meta-analysis was conducted using RevMan 5.3 software. ResultsA total of nine case-control studies involving 904 PE patients and 1 113 controls were included. The results of meta-analysis showed that, significant association was found between VEGF gene 936C/T polymorphism and the risk of PE in the total analysis (T vs. C:OR=1.61, 95%CI 1.17 to 2.22, P=0.003; TT vs. CC:OR=2.65, 95%CI 1.37 to 5.11, P=0.004; CT vs. CC:OR=1.55, 95%CI 1.09 to 2.22, P=0.02; TT+CT vs. CC:OR=1.68, 95%CI 1.15 to 2.45, P=0.007; TT vs. CT+CC:OR=2.19, 95%CI 1.31 to 3.68, P=0.003). In the subgroup analysis, significant association of the polymorphism was found in Asians but not in Caucasians. ConclusionVEGF gene 936C/T polymorphism may be associated with PE risk in Asians. Due to limited quantity and quality of the included studies, the conclusion should be assessed in further studies.
Structural equation modeling (SEM) is a frequently used multivariate technique in social, psychology, educational, behavioral science. Meta-analytic structural equation modeling (MASEM) combines the ideas of metaanalysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. A two-stage structural equation modeling (TSSEM) approach to conducting MASEM that was based on a fixed-effects model will be introduced. To introduce the operation of metaSEM package for multivariate meta-analyst, we are adopt the fixed-effects model, assumes that the population correlation matrices based on TSSEM, to analyze the instance data.