中文摘要：

这研究被瞄准在 PPAR-γ 探索在几多型性的联合效果之间的协会；并且 RXR-α；有由背错误繁殖的新陈代谢的症候群的风险的基因和环境因素人工的神经网络(BPANN ) 。我们基于从新陈代谢的症候群病人聚集的数据建立了模型(n = 1012 ) 并且正常控制(n = 1069 ) 由 BPANN。为每个输入变量的吝啬的影响价值(MIV ) 被计算，因素的顺序根据他们的绝对 MIV 被排序。概括 multifactor 维数减小(GMDR ) 证实了 PPAR-γ 的联合效果；并且 RXR-α；基于从 BPANN 的结果。由 BPANN 分析，序列根据新陈代谢的症候群风险因素的重要性在身体团索引( BMI )的顺序，浆液 adiponectin ， rs4240711 ，性， rs4842194 ，类型 2 糖尿病的家庭历史， rs2920502 ，物理活动，酒精喝酒， rs3856806 ，高血压的家庭历史， rs1045570 ， rs6537944 ，年龄， rs17817276 ， hyperlipidemia 的家庭历史，吸烟， rs1801282 和 rs3132291 。然而，没有多型性在多重逻辑回归分析是统计上重要的。在为环境因素控制以后， ₂, B ₁ 和 B ₂(rs4240711， rs4842194， rs2920502 和 rs3856806 ) 建模的 ₁, 是最好的模型(交叉验证一致性 10/10， P = 0.0107 ) 与 GMDR 方法。在结论， PPAR-γ 的相互作用；并且 RXR-α；基因能在危险性起一个作用到新陈代谢的症候群。一个更现实主义的模型被使用 BPANN 外面屏蔽象新陈代谢的症候群一样的多重病原学的疾病的决定因素获得。

英文摘要：

This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.