基于病例-对照和队列人群研究基因-环境交互作用对血脂异常发生风险的预测

A large number of evidences showed that lipid levels and/or dyslipidemia are closely related with the development of cardiovascular diseases. Early identification those at high risk of dyslipidemia will be helpful and available for strengthening individualized intervention to decrease the morbidity and mortality of cardiovascular diseases. In recent years, our research team is engaged in the risk prediction of cardiovascular metabolic diseases, and found the present risk predictive model of dyslipidemia has the limited predictive performance based on the traditional risk factors. Therefore, it is necessary to identify and enter the novel biomarkers into the risk predictive approach to improve the predictive performance of the model. Based on the previous studies, the 1:1 matched case-control study and the prospective cohort study design will be performed, and more than fifty-one susceptibility genes polymorphism loci of dyslipidemia will be searched and tested in order to assess the genetic risk factors of dyslipidemia using to the genetic risk score. The association of gene-gene and gene-environment interaction will be clarified with lipid levels and/or dyslipidemia. In addition, the predictive performance of the genetic risk score and the gene-environment interaction will be explored by the data mining technology, and then the individual risk assessment approach will be developed and evaluated based on the genetic risk score and the gene-environment interaction. Moreover, the potential biological mechanism of the genetic variants will be also explored for explaining etiology and pathogenesis of the dyslipidemia. The findings of this study not only could help to improve the predictive and identifiable performance of risk assessment model for lipid levels and/or dyslipidemia, but also to provide evidence for early screening the high risk subjects and establishing individualized intervention strategy of dyslipidemia and cardiovascular diseases.

血脂异常与心血管病的发生发展密切相关，早期发现血脂异常高危人群并加强个体化干预能有效降低心血管病的发病率和病死率。本课题组近年来致力于心血管代谢性疾病风险评估，发现基于传统危险因素构建的血脂异常风险评估模型预测能力有限，需要纳入新的生物标志物提高模型的鉴别能力。本课题拟在前期研究基础上采用病例-对照和队列研究相结合的设计方案，对目前发现的51个血脂异常易感基因多态性位点进行基因型检测，运用遗传风险评分综合评价血脂异常的遗传危险因素，阐述基因-基因、基因-环境交互作用与血脂异常的关系，结合数据挖掘技术探讨遗传风险评分及基因-环境交互作用对血脂异常发生风险的预测作用，建立个体化的血脂异常风险评估模型；同时对血脂异常易感基因的生物学功能进行研究。相关研究结果有助于提高血脂异常风险评估模型的预测鉴别能力，对血脂异常高危人群早期筛检、个体化干预方案制定及心血管病防治关口前移具有重要的理论和应用价值。

血脂异常是遗传与环境共同作用的结果，但作用机制尚未完全明确，深入研究基因-环境交互作用将为血脂异常病因和发病机制研究提供科学依据。本研究以河南省禹州市、遂平县、通许县、新乡县、义马市18-79岁农村居民为研究对象，开展了以下研究工作，①根据GWAS报道结果，筛选血脂异常易感基因多态性位点并进行分型，进行关联性分析后筛选位点；②通过病例-对照研究，建立基于基因-环境交互作用的血脂异常发生风险预测模型；③通过前瞻性队列研究，验证基因-环境交互作用的血脂异常风险评估模型，建立不同血脂指标的预测模型；④采用荧光素酶报告基因重组质粒实验检测SNPs突变对ZPR1基因转录水平的影响，探讨SNPs在血脂异常发生中的可能生物学机制。课题组采用1:1匹配的方法建立了本研究专用的血脂异常病例-对照流行病学背景数据库和生物标本库，完成了35个易感基因SNPs位点的筛选和基因型检测工作；调整相关混杂因素后，22个位点多态性与血脂异常存在统计学关联。课题组首先建立了传统血脂异常风险评估模型，然后在传统模型中加入遗传因素后，模型的鉴别性能有所提高。其中，遗传因素以多基因遗传风险评分（PGGRS）的方式进入模型时，遗传因素对模型预测能力的贡献最大。在前瞻性数据的基础上，验证了遗传因素对血脂异常发生的作用，加入遗传因素后，血脂异常预测模型的曲线下面积（AUC）、净重分类改善指数（NRI）,综合判别改善指数（IDI）均有提高 （P <0.05）。同时建立了高总胆固醇（TC）血症，高甘油三酯血（TG）症，低高密度脂蛋白胆固醇（HDLC）血症，高低密度脂蛋白胆固醇（LDLC）血症的基因-环境交互作用的发病风险预测模型。通过双荧光素酶报告基因实验研究发现，rs964184-G等位基因的荧光素酶活性明显高于携带rs964184-C的荧光素酶活性。总之，血脂异常受环境和基因的共同作用，遗传因素可用于血脂异常预测。本研究建立的基因-环境交互的血脂异常风险预测模型能够在一定程度上预测血脂异常的发生，为全面认识基因在血脂异常发生中的作用提供了科学依据。