代谢酶基因多态性与环境暴露交互作用的分析方法及其应用

沈靖; 王润田; 徐希平

Abstract

沈靖,王润田,徐希平.代谢酶基因多态性与环境暴露交互作用的分析方法及其应用[J].Chinese journal of Epidemiology,2001,22(1):61-64

代谢酶基因多态性与环境暴露交互作用的分析方法及其应用

Application of the interaction models between the polymorphism(s) of metabolic gene(s) and environmental exposure

Received:June 06, 2000

DOI：

KeyWord: 代谢酶基因环境暴露交互作用

English Key Word: Metabolic gene polymorphisms Environmental exposure Interaction

FundProject:

Author Name	Affiliation
SHEN Jing	Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100083, China
WANG Runtian	Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100083, China
XU Xiping	北京大学生态遗传与生殖卫生研究中心、哈佛大学公共卫生学院群体遗传研究中心

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Abstract:

目的: 以肿瘤易感基因谷胱苷肽-S-转硫酶 (GST)M1缺失基因型为例，说明基因与环境暴露交互作用的分析方法以及应用。方法: 采用社区为基础的病例对照研究方法，代谢酶基因多态性的检测用PCR技术，资料分析用多因素logistic回归模型。研究对象为 1997年 1月至 1998年 12月经扬中市人民医院确诊，肠型胃癌病例 112例，以同期该地无上消化道肿瘤的“健康”人群为对照，共 675例。结果: 调整混杂因素后，GSTM1缺失基因型与既往吸烟史的交互作用系数为 3.38，OReg值达 8.40，有极显著意义，为 4型交互作用中的超相乘模型；GSTM 1缺失基因型与吸烟量的交互作用呈高暴露-基因效应，交互作用系数分别为 0.995、2.085和 2.157，即随着暴露剂量增加，交互作用强度也逐渐增加；与饮酒量呈低暴露-基因效应，交互作用系数分别为 1.01和 0.97，交互作用强度随暴露剂量增加而逐渐降低。结论: 基于logistic模型的分析方法，可用于评价基因-环境之间的交互作用,以及剂量反应关系的暴露基因效应。

English Abstract:

Objective:　Taking GST M1 as an example to introduce analytic method of interaction models between the polymorphism(s)of metabolic gene(s)and environmental exposure in stomach cancer susceptibility. Methods:　Using community-based case-control design, combined with molecular biological techniques (PCR) and multiple variables logistic regression models, we analyzed 112 intestinal types of stomach cancer cases with endoscopy and pathology diagnosis in the Yangzhong City Hospital during January 1997 and December 1998. A total of 675 controls were selected from persons who had no history of digestive system cancers. Results:　After adjustment of confounding variables with both GST M1 null genotype and history of ever tobacco smoking, the results showed a significant types of 4 gene-environment interaction. Interaction index (γ)value was 3.38 and OR _eg value was 8.40, suggesting that a super multiplicative interaction occured. The results also showed that GST M1 null genotype had a high exposure-gene effect interaction with tobacco smoking (pack year), while γ values were 0.995, 2.085 and 2.157 respectively. A low exposure-gene effect interaction was found in GST M1 null geno type with the amount of (kg°year) alcohol consumption while γ values were 1.01 and 0.97 respectively. Conclusion:　Logistic regression model can be used to evaluate gene-environment interaction and dose-response of exposure-gene effect.

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