ADIPOQ gene polymorphisms were indicated to be associated with coronary artery disease (CAD) in diabetic patients, however, published studies reported inconsistent results. We performed this meta-analysis to reach a more accurate estimation of the relationship between two common ADIPOQ genetic polymorphisms (rs2241766 and rs1501299) and CAD risk in diabetic patients. Eligible studies were retrieved by searching PubMed, Embase, Wangfang, VIP database and China National Knowledge Infrastructure databases. Included and excluded criteria were formulated. The case group was diabetic patients with CAD, and the control group was diabetic subjects without CAD. Summary odds rations (ORs) and 95% confidence intervals (CIs) were used to evaluate ADIPOQ polymorphisms associations with CAD risk in diabetic group. Heterogeneity was evaluated by Q statistic and I2 statistic. A total of twelve published articles, involving 3996 cases and 8876 controls were included in this meta-analysis. The pooled results from rs1501299 polymorphism showed decreased risk in homozygote model (TT VS GG: OR=0.67, 95%CI=0.54-0.83). Heterogeneity was detected in our study. Sensitivity analysis and subgroup analysis were conducted in the meta-analysis. For rs2241766 polymorphism, an increased risk was detected in Caucasian subgroup in heterozygote model (CT VS TT: OR=1.19, 95%CI=1.00-1.42). In genotyping method (PCR-RFLP) subgroup, an increased risk was found in recessive model (GG VS GT+TT: OR=2.05, 95%CI=1.23-3.39). In the sensitivity analysis of rs1501299, decreased risk was detected in allelic model (T VS G: OR=0.86, 95%CI=0.76-0.98) and recessive model (TT VS TG+GG: OR=0.47, 95%CI=0.33-0.67). Publication bias is not observed in our results. Our meta-analysis suggests that the rs1501299 polymorphism may play a protective role in CAD in diabetic patients. The rs2241766 polymorphism is found to be associated with a significant increase in CAD risk in Caucasian and genotyping method (PCR-RFLP) subgroups. Further studies are needed to confirm the prediagnostic effect of the two gene polymorphisms in CAD risk in diabetic patients.

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Fuzzy neural network with its own characteristics, strong ability to learn and easy to fit the system, is widely used in in practice. This article firstly put forward to the general model based on improved genetic algorithm and fuzzy neural network. Secondly introduce the four layers fuzzy neural network model. As the general fuzzy neural network often use BP algorithm to study which has the deficiency of difficult to avoid local minimum and hard to find the global optimum, therefore, this article design the fuzzy neural network based on the improved genetic algorithm. According to change the coding method crossover operators and mutation operators, it improves the optimizing capacity. And then, gives FNN-IGA programming diagram. Finally, the inverted pendulum simulation experiments show the superiority of the optimal control model.