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

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 propagation 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-γ and RXR-α based on the results from BPANN.By BPANN analysis,the sequences according to the importance of metabolic syndrome risk factors 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,rsl045570,rs6537944,age,rsl7817276,family history of hyperlipidemia,smoking,rs1801282 and rs3132291.However,no polymorphism was statistically significant in multiple logistic regression analysis.After controlling for environmental factors,A_1,A_2,B_1 and B_2(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.