文章摘要
陆 莹,杜文聪,李 倩,叶新华,俞晓芳,马建华,成金罗,高燕勤,杜 娟,石 慧,曹园园,周 玲.应用BP人工神经网络探讨PPAR-γ和RXR-α基因多态性与汉族人群2型糖尿病遗传易感性的关系[J].南京医科大学学报,2011,(1):1~7
应用BP人工神经网络探讨PPAR-γ和RXR-α基因多态性与汉族人群2型糖尿病遗传易感性的关系
Application study of BPANN on genetic variants in peroxisome proliferators activated receptor-γ (PPAR-γ) and retinoid X receptor-α (RXR-α) gene and type 2 diabetes risk in a Chinese Han population
投稿时间:2010-07-29  
DOI:10.7655
中文关键词: BP神经网络  2型糖尿病  PPAR-γ基因  RXR-α基因  脂联素
英文关键词: BPANN  type 2 diabetes mellitus  peroxisome proliferators activated receptor-γ gene  retinoid X receptor-α gene  adiponectin
基金项目:国家自然科学基金(30771858),江苏省自然科学基金(BK2007229)资助
作者单位
陆 莹 南京医科大学公共卫生学院流行病与卫生统计学系, 江苏 南京 210029 
杜文聪 南京医科大学公共卫生学院流行病与卫生统计学系, 江苏 南京 210029 
李 倩 南京医科大学附属南京市第一医院内分泌科, 江苏 南京 210006 
叶新华 南京医科大学附属常州第二人民医院内分泌科, 江苏 常州 213003 
俞晓芳 南京医科大学第三附属医院内分泌科, 江苏 仪征 211400 
马建华 南京医科大学附属常州第二人民医院内分泌科, 江苏 常州 213003 
成金罗 南京医科大学附属常州第二人民医院内分泌科, 江苏 常州 213003 
高燕勤 南京医科大学第三附属医院内分泌科, 江苏 仪征 211400 
杜 娟 南京医科大学公共卫生学院流行病与卫生统计学系, 江苏 南京 210029 
石 慧 南京医科大学公共卫生学院流行病与卫生统计学系, 江苏 南京 210029 
曹园园 南京医科大学公共卫生学院流行病与卫生统计学系, 江苏 南京 210029 
周 玲 南京医科大学公共卫生学院流行病与卫生统计学系, 江苏 南京 210029 
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中文摘要:
      目的:探讨BP人工神经网络(BPANN)在研究过氧化物酶体增殖物激活受体γ(PPAR-γ)和视黄醛α受体(RXR-α)基因单核苷酸多态性(SNP)位点与中国南方地区汉族人群2型糖尿病(T2DM)易感性关系中的应用特点?方法:采用BPANN分析方法, 对591例2型糖尿病患者和724例正常对照者的基因多态性位点的分型结果?血清脂联素水平以及其他所有可能的影响因素按照平均影响值(MIV)的绝对值大小排序,并与Logistic回归模型的分析结果相比较,用多因子降维法(MDR)分析基因间的交互作用?结果:BPANN多因素分析中,2型糖尿病危险因子的顺位为血清脂联素浓度?高血压史?腰围?rs4240711?rs3132291?rs3856806?2型糖尿病家族史?饮酒?高脂血症史?吸烟?年龄?BMI指数?rs1045570?性别?rs2920502?rs6537944?rs4842194?rs17827276?rs1801282;而多因素Logistic回归分析中只有8个变量入选最终模型,因子顺位为高血压史?T2DM家族史?腰围?饮酒?吸烟?rs4240711?rs4842194?血清脂联素浓度;多因子降维法(MDR)分析结果显示模型X1X2X3(rs3856806,rs3132291,rs4240711)为最佳模型(交叉验证一致性10/10,P=0.0107)?结论:PPAR-γ和RXR-α基因多态性改变的交互作用对于中国南方汉族T2DM遗传易感性可能具有一定的作用?BPANN用于筛选T2DM等复杂多病因疾病的影响因素,可能提供更切合实际情况的模型?
英文摘要:
      Objective: To explore the applied characteristics of back-propagation artificial neural network (BPANN) on studying the genetic variants in PPAR-γ and RXR-α gene and type 2 diabetes risk in a Chinese Han population. Methods: With BPANN as fitting model based upon data gathered from type 2 diabetes patients(n=591) and normal controls (n=724), the mean impact value (MIV) for each input variables and sequencing the factors according to their absolute MIVs were calculated. The results from BPANN were compared with multiple logistic regression analysis,and multifactor dimensionality reduction (MDR) method was used to consider the joint effects of PPAR-γ and RXR-α gene. Results:By BPANN analysis, the risk factors of diabetes mellitus were serum adiponectin level, hypertension, waist, rs4240711, rs3132291, rs3856806, diabetes mellitus family history,no alcohol drinking hyperlipoproteinmia, smoking, age, body mass index, rs1045570, gender, rs2920502, rs6537944, rs4842194, rs17827276 and rs1801282. However, only 8 factors were statistically significant in multiple logistic regression analysis, arrayed according to the important valne: hypertension, diabetes mellitus family history, waist, no alcohol drinkiy, smoking, rs4240711, rs4842194 and serum adiponectin level. Model X1 X2 X3(rs3856806, rs3132291, rs4240711) was the best model (CV Consistency=10/10, P=0.0107) with MDR method. Conclusion: These results suggested that the interactions of PPAR-γ and RXR-α gene might have important role in the susceptibility of T2DM. Neural network could be used to analyze the risk factors of diseases and more complicated relationships (main effects and interactions) between inputs and outputs, better than using the traditional methods.
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