| Application of the back-error propagation artificial neural network （BPANN） on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population |
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| 作者姓名： | Xu Zhao Kang Xu Hui Shi Jinluo Cheng Jianhua Ma Yanqin Gao Qian Lie Xinhua Ye Ying Lu Xiaofang Yu Juan Du Wencong Du Qing Ye Ling Zhou |
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| 作者单位： | [1]Department of Epidemiology and Biostatisties, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China [2]Department of Endocrinology, the Affiliated Changzhou Second Hospital, Nanjing Medical University, Changzhou, Jiangsu 213003, China [3]Department of Endocn＇nology, the First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu 210029, China [4]Department of Endocrinology, the Third Affiliated Hospital, Nanjing Medical University, Yizheng, Jiangsu 211400, China |
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| 基金项目： | The study was supported by the National Natural Science Founda- tion of China Grant No. 30771858, Jiangsu Provincial Natural Science Foundation Grant No. BK2007229 and Priority Academic Program Development of Jiangsu Higher Education Institutions （PAPD）. |
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| 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.
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| 关 键 词： | BP人工神经网络 代谢综合征 中国汉族人群 应用程序 误差传播 遗传变异 基因 logistic回归分析 |
| 收稿时间： | 2012-10-03 |
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