【作者】 王慧；

【导师】 张薇；

【作者基本信息】 山东大学 ， 内科学（心血管病）， 2017， 博士

【摘要】 1研究背景代谢综合征(metabolic syndrome,MetS)在全球的患病率普遍上升,2000-2001年期间,MetS的患病率在中国男性为9.8%,女性为17.8%。MetS及其组分增加了心血管疾病(cardiovascular disease,CVD)及 2 型糖尿病(type Ⅱ diabetes,T2DM)的风险,导致了沉重的经济负担。目前认为MetS的始动环节为胰岛素抵抗,主要表现为糖脂代谢紊乱。MetS病因复杂,机制至今不清,由多个基因与环境因素参与其中并相互作用。影响糖脂代谢及胰岛素敏感性的因素可能参与MetS的发生、发展及影响对MetS组分的治疗。细胞死亡诱导的 DFF45 样效应子 3(cell-death-inducing DFF45-like effector C,CIDEC)是CIDE分子家族的一员 研究发现其对脂肪组织完整性及脂质代谢有重要的调节作用,且参与调控葡萄糖代谢及胰岛素靶器官的胰岛素敏感性。CIDEC参与抑制脂质水解及介导脂肪组织分化。在不 同来源的人体细胞中过表达CIDEC可促进细胞中大脂滴的形成及甘油三酯(triglyceride,TG)的积累。CIDEC全身性或脂肪特异性敲除促进了肝脏脂肪沉积及高甘油二酯血症,减少了白色脂肪组织(white adipose tissue,WAT)脂质含量,及引起全身性胰岛素抵抗。因此CIDEC可能参与调节与控制全身的代谢状态。CIDEC位于染色体3p区,既往报道3p 区域某些基因与MetS及T2DM的发生有关。根据以上研究结果,可推断CIDEC可能是MetS的候选基因之一。人群研究显示肥胖人群脂肪组织中CIDEC的表达水平较非肥胖人群降低,且体重减轻使CIDEC在WAT的表达的改变在不同人群中并不一致。并且CIDEC 在WAT的表达与胰岛素抵抗状态的关系亦不一致,表现为无关或负相关。CIDEC E186X突变引起CIDEC分子羧基末端缺失,从而导致了某一新型部分性脂肪代谢障碍综合征的发生。但是目前研究缺乏CIDEC基因遗传变异与人类疾病特点的相关性研究,因而本研究通过病例-对照研究及纵向随访研究探讨了CIDEC基因多态性与MetS及其组分风险的关联。不同遗传背景的个体对某类药物的反应大不相同,而个体的遗传背景很大程度上造成了药物效果的差别。遗传学分支药物遗传学旨在研究遗传变异与个体对药物反应之间的联系。寻找可能的药物遗传学标记,有望于用药前预测患者对药物的反应,从而辅助用药方案决策,为快速有效的个体化治疗提供了可能性。目前基因导向的药物治疗方案选择在临床主要应用于抗肿瘤治疗。心血管系统药物中,除氯吡格雷药效、华法令剂量及他汀不良反应相关标记外,已确定的药物遗传学标记较少。MetS的病理生理机制涉及多个分子通路中基因产物间的相互作用,而这些分子或通路蛋白可能正是纠正MetS组分的药物发挥作用的靶点。CIDEC可能参与到MetS复杂的发病机制中,所以CIDEC基因多态性是否可能影响药物对MetS组分的治疗效果值得探讨。并且在评价药效时,应同时进行药物成本-效果分析,对比特定条件下不同药物治疗方案的优劣,以权衡某药物治疗方案成本与效果间的平衡,促进用药方案经济有效及医疗资源合理分配,然而目前鲜有将药物经济学研究应用于药物遗传学标记的筛选与评估的报道。综上,CIDEC基因在中国汉族人群中是否中存在多态性?CIDEC特定单核苷酸多态性(single nucleotide polymorphism,SNP)位点基因多态性是否与MetS及其各组分的风险、降压药物效果及成本-效果存在关联?CIDEC特定SNP位点基因多态性是否影响CIDEC基因及下游蛋白ATGL表达?鉴于以上问题,构成本研究的目的。2研究目的(1)研究CIDEC基因SNP位点rs1053239、rs2479多态性在中国山东汉族人群的分布情况;(2)探讨CIDEC基因多态性与MetS及其各组分之间的关联及性别分层分析、MetS人群亚组分析两者关联;(3)探讨CIDEC基因多态性对降压药物效果的影响;(4)探讨CIDEC基因多态性对随访期间医疗花费及降压药物成本-效果的影响;(5)初步研究CIDEC基因SNP位点rs2479基因多态性对CIDEC及ATGL基因表达的影响。3研究方法3.1研究人群研究人群来自山东省城乡社区中国汉族居民,包括MetS患者1064例(病例组)及健康人1099例(对照组)。根据2009年IDF/NHLBI/AHA/WHF/IAS/IASO联合声明制定MetS诊断标准。共1359名受试者参与随访,5年随访末时可随访到1184名受试者。分别获取基线水平及随访末研究人群的信息。通过调查问卷收集一般情况,吸烟、饮酒习惯,既往史,随访期间接受药物治疗、医疗检查内容及花费及其他医疗花费信息。测量受试者的血压、腰围,根据体重与身高计算体质指数(body mass index,BMI)。抽取空腹静脉血并测定血清TG、总胆固醇(total cholesterol,TC)、高密度脂蛋白胆固醇(high-density lipoprotein cholesterol,HDL-C)、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol,LDL-C)、空腹血糖(fasting plasma glucose,FPG)水平。3.2 SNP位点选择与检测选择CIDFC基因在中国北京汉族人群(Han Chinese in Beijing,CHB)中非内含子区域最小等位基因频率(minor allele frequency,MAF)大于5%的标签SNP位点。SNP位点基因分型在深圳华大基因科技服务有限公司进行。受试者空腹静脉全血用于提取DNA,采用Sequenom MassARRAYY(?)基因分型系统检测基因多态性。3.3细胞培养与质粒转染利用 pHB-CMVIE-ZsGreen-Puro 基因载体构建 CIDEC 基因 SNP 位点 rs2479 G等位基因(野生型)与A等位基因(突变型)重组过表达质粒,CIDEC基因由CMVIE启动子调控表达。以高糖培养基培养HEK293T细胞,分为4组:对照组、空载体组、野生型质粒过表达组、突变型质粒过表达组。分别以1.6μg/mL浓度将空载体、野生型质粒、突变型质粒转染入细胞培养板中HEK293T细胞内并培养细胞。3.4 实时定量聚合酶链反应(real-time quantitative polymerase chain reaction,RT-qPCR)提取细胞总RNA,以逆转录反应、实时定量荧光PCR检测CIDEC mRNA水平。3.5免疫印迹检测通过提取细胞总蛋白,SDS-PAGE凝胶电泳,电转膜,蛋白封闭,一抗、二抗结合反应,显色反应检测目的蛋白的表达水平。3.6统计学分析应用SPSS 17.0软件进行统计学分析。采用卡方检验比较分类变量资料组间差异。采用单因素方差分析、配对t检验或两独立样本t检验比较连续型数值变量资料组间差异。采用二元Logistic回归分析MetS及其各组分的独立危险因素及保护因素。采用拟和优度卡方检验计算基因型分布是否符合Hardy-Weinberg遗传平衡定律。采用多因素线性回归分析SNP位点基因多态性对MetS各组分5年随访变化量的独立影响,以及对降压药单药降压效果及对随访期间各项平均医疗花费的影响。医疗花费根据消费期间消费者价格指数进行通货膨胀转换,以美元(U.S.dollar,USD)作为单位。计算不同基因型间成本-效果比(cost-effectiveness ratio,CER)及增量成本-效果比(incremental cost-effectiveness ratio,ICER)。双侧截尾P值小于 0.05时判定差异有统计学意义。4结果4.1受试者基线性别及年龄构成情况共2163名受试者纳入研究,其中男性占42.0%,女性占58.0%。人群年龄主要集中于45-54岁,其中男性、女性年龄分别主要集中于55-64、45-54岁。女性与男性比例随年龄增长而逐步下降。对照组与MetS组性别及年龄构成与整体人群相似。4.2对照组与MetS组基线临床特征比较平均年龄与性别比例在对照组与MetS组中的差别无统计学意义。与对照组相比,MetS 组 BMI、腰围、收缩压(systolic blood pressure,SBP)、舒张压(diastolic blood pressure,DBP)、TG、TC、LDL-C、FPG 及饮酒率均显著升高,HDL-C、吸烟率显著降低(P均<0.001)。4.3 SNP位点检测SNP位点rsl053239、rs2479的次要等位基因分别为C、A,MAF分别为0.409、0.261。两个SNP位点在整体人群、对照组及MetS组的基因型频率分布均符合Hardy-Weinberg遗传平衡定律。4.4对照组与MetS组不同基因型间基线临床特征比较对于SNP位点rsl053239,MetS组中,与CG基因型携带者相比,CC基因型携带者腰围增加,与GG基因型携带者相比,CG基因型携带者SBP升高(P均<0.01)。对于SNP位点rs2479,对照组中,与GG基因型携带者相比,AG基因型携带者SBP、HDL-C降低(P均<0.05);MetS组中,与AG及AA基因型携带者相比,GG基因型携带者FPG降低(P均<0.01)。4.5对照组与MetS组SNP位点基因型频率与等位基因频率比较SNP位点rs1053239、rs2479基因型及等位基因频率在对照组与MetS组间的分布均无统计学差异。4.6 MetS各组分分组后SNP位点基因型频率与等位基因频率比较根据MetS各组分诊断标准将分别将整体人群按腰围、血压、TG、HDL-C、FPG分为正常组与异常组。SNP位点rs1053239、rs2479基因型及等位基因频率在腰围、血压、TG、HDL-C正常组与异常组间的分布均无统计学差异。SNP位点rs1053239 GG、CG、CC 3种基因型频率在FPG正常组与升高组间的分布有统计学差异(P = 0.027);与FPG正常组相比,FPG升高组G等位基因频率降低,C等位基因频率升高(P= 0.014)。SNP位点rs2479 GG、AG、AA 3种基因型频率在FPG正常组与升高组间的分布有统计学差异(P<0.001);与FPG正常组相比,FPG升高组G等位基因频率降低,A等位基因频率升高(P<0.001)。4.7 SNP 位点 rs1053239、rs2479 与 MetS 风险的关系以性别、年龄、吸烟、饮酒、SNP位点基因型为自变量,MetS为因变量,进行逐步Logistic回归,在共显性、显性、隐性遗传模型下,SNP位点rs1053239基因型、rs2479基因型均未进入回归模型。4.8 MetS各组分危险因素的筛选分别以腰围增加、血压升高、TG升高、HDL-C降低、FPG升高为因变量,以性别、年龄、吸烟、饮酒、MetS其他组分、SNP位点rs1053239基因型或rs2479基因型为自变量进行逐步Logistic回归。SNP位点rs1053239基因型在共显性、显性、隐性遗传模型下均未进入以腰围、血压、TG、HDL-C、FPG为因变量的回归模型。SNP位点rs2479基因型在共显性、显性、隐性遗传模型下均未进入以腰围、血压、TG、HDL-C为因变量的回归模型,在共显性(OR1.258[95%C.I.1.073-1.476],P=0.005)、显性(1.353[1.098-1.666],P= 0.004)遗传模型下增加 FPG升高的风险。4.9性别分层分析SNP位点rs1053239、rs2479对MetS及其各组分风险的影响男性或女性受试者对照组与MetS组基线临床特征、对照组与MetS组不同基因型间基线临床特征、对照组与MetS组SNP位点基因型频率与等位基因频率、MetS各组分分组后SNP位点基因型频率与等位基因频率、SNP位点与MetS及其各组分风险的关系均与整体人群相似,其中SNP位点rs2479基因型在共显性(1.332[1.037-1.712],P=0.025)遗传模型下增加男性FPG升高的风险,在共显性(1.353[1.095-1.672],P=0.005)、显性(1.496[1.134-1.973],P= 0.004)遗传模型下增加女性FPG升高的风险。4.10 MetS人群SNP位点rs1053239、rs2479对MetS各组分风险的影响MetS人群MetS各组分分组后SNP位点基因型频率与等位基因频率、SNP位点与MetS各组分风险的关系均与整体人群相似,其中SNP位点rs1053239基因型在共显性(1.322[1.102-1.585],P=0.003)、显。性(1.505[1.150-1.970],P= 0.003)遗传模型下增加MetS人群FPG升高的风险,SNP位点rs2479基因型在共显性(1.671[1.360-2.052],P<0.001)、显性(1.905[1.472-2.465],P<0.001)、隐性(1.893[1.157-3.095],P=0.011)遗传模型下增加MetS人群FPG升高的风险。4.11随访期间未应用降压、降脂、降糖药的受试者基线与5年随访末临床特征比较随访期间未应用降压、降脂、降糖药的受试者共238例,与2007年基线相比,5年随访末时BMI、腰围、SBP、DBP、TG、FPG均显著升高(P均<0.001),TC亦升高(P<0.05),HDL-C显著降低(P<0.001),LDL-C亦降低(P<0.05)。4.12随访期间未应用降压、降脂、降糖药的受试者不同基因型间临床特征5年随访变化量比较对于SNP位点rsl053239,与CG基因型携带者相比,CC基因型携带者TC增加量较大,与CG基因型携带者相比,GG基因型携带者FPG增加量较大(P均<0.05)。对于SNP位点rs2479,与GG或AG 基因型携带者相比,AA基因型携带者TC增加量较大,LDL-C增加(P均<0.01)。4.13 SNP位点与MetS各组分5年随访变化量的关系分别以腰围、SBP、DBP、TG、HDL-C、FPG变化量为因变量,以性别、年龄、因变量基线水平、MetS其他组分变化量、SNP位点rs1053239基因型或rs2479基因型为自变量进行多元逐步线性回归。SNP位点rsl053239基因型在共显性、显性、隐性遗传模型下均未进入以腰围、TG、HDL-C、FPG为因变量的回归模型,在共显性(β = 0.256,P<0.001)、显性(β = 0.244,P<0.001)遗传模型下增加SBP进展风险,在共显性(β = 0.199,P<0.001)、显性(β = 0.209,P=0.001)、隐性(β = 0.129,P= 0.019)遗传模型下增加DBP进展风险。SNP位点rs2479基因型在共显性、显性、隐性遗传模型下下均未进入以腰围、SBP、HDL-C、FPG为因变量的回归模型,在共显性(β = 0.176,P=0.002)、显性(β = 0.188,P=0.003)、隐性(β = 0.112,P= 0.038)遗传模型下增加DBP进展风险,在共显性(β = 0.187,P=0.002)、显性(β = 0.216,P<0.001)遗传模型下增加TG进展风险。4.14随访期间应用降压药单药的受试者基线与5年随访末临床特征比较随访期间应用降压药单药的受试者共362例,与2007年基线相比,5年随访末时SBP、DBP、TG、HDL-C、LDL-C均显著降低(P均<0.001)。4.15随访期间应用降压药单药的受试者不同基因型间降压效果比较随访期间应用针对血管紧张素Ⅱ(angiotensin Ⅱ,AngⅡ)的降压药单药的受试者共109例。对于SNP位点rs1053239,用药后GG基因型携带者SBP、DBP升高,C等位基因携带者SBP、DBP均显著降低(P均<0.001):对于 SNP位点rs2479,用药后GG基因型携带者SBP降低,DBP升高,与GG基因型携带者相比,A等位基因携带者SBP、DBP均显著降低(P均<0.001)。随访期间应用钙通道阻断剂(calcium channel blockers,CCB)单药的受试者共114 例。对于SNP 位点rs1053239,与GG基因型携带者相比,C等位基因携带者用药后SBP、DBP均降低(P均<0.05);对于SNP位点rs2479,用药后SBP、DBP的变化景在GG基因型携带者与A等位基因携带者的差异无统计学意义。随访期间应用利尿剂单药的受试者共139例。对于SNP位点rs1053239,用药后SBP、DBP的变化量在GG基因型携带者与C等位基因携带者的差异无统计学意义;对于SNP位点rs2479,与GG基因型携带者相比,A等位基因携带者用药后SBP、DBP的减少量均较大(P均<0.05)。4.16 SNP位点与针对AngⅡ的降压药降压效果的关系在随访期间应用针对AngⅡ的降压药单药的人群中,分别以SBP、DBP变化量为因变量,以性别、年龄、吸烟、饮洒、MetS各组分基线水平、SNP位点rs1053239基因型或rs2479基因型为自变量进行多元逐步线性回归。SNP位点rs1053239 基因型在共显性(β =-0.401,P<0.001)、显性(β =-0.400,P<0.001)、隐性(β =-0.361,P<0.001)遗传模型下下促进针对AngⅡⅡ的降压药降低SBP 的程度,在共显性((β =-0.545,P<0.001)、显性(β =-0.331,,P=0.001)、隐性(β =-0.229,P = 0.015)遗传模型下促进针对AngⅡ的降压药降低DBP的程度。SNP 位点 rs2479 基因型在共显性(β =-0.459,P<0.001)、显性(β =-0.453,P<0.001)遗传模型下促进针对AngⅡ的降压药降低SBP的程度,在共显性(β =-0.454,,P<0.001)、显性(β =-0.445,P<0.001)遗传模型下促进针对AngⅡ的降压药降低DBP的程度。4.17 SNP位点与CCB降压效果的关系在随访期间应用CCB单药的人群中,分别以SBP、DBP变化量为因变量,以性别、年龄、吸烟、饮酒、MetS各组分基线水平、SNP位点rs1053239基因型或rs2479基因型为自变量进行多元逐步线性回归。SNP位点rs1053239基因型在共显性(β = 0.241,P= 0.003)、显性(β = 0.274,P= 0.003)遗传模型下抑制CCB降低 SBP 的程度,在共显性(β = 0.282,P<0.001)、隐性(β = 0.245,P<0.001)遗传模型下抑制CCB降低DBP的程度。SNP位点rs2479基因型在共显性、显性、隐性遗传模型下均未进入以SBP为因变量的回归方程,在共显性(β = 0.170,P =0.004)、隐性((β = 0.197,P<0.001)遗传模型下抑制CCB降低DBP的程度。4.18 SNP位点与利尿剂降压效果的关系在随访期间应用利尿剂单药的人群中,分别以SBP、DBP变化量为因变量,以性别、年龄、吸烟、饮酒、MetS各组分基线水平、SNP位点rs1053239基因型或rs2479基因型为自变量进行多元逐步线性回归。SNP位点rs1053239基因型在共显性、显性、隐性遗传模型下均未进入以SBP、DBP为因变量的回归方程。SNP位点rs2479基因型在共显性、显性、隐性遗传模型下均未进入以SBP为因变量的回归方程,在显性(β =-0.146,P=0.048)遗传模型下促进利尿剂降低DBP的程度。4.19随访期间受试者不同基因型间各项平均医疗花费比较对于SNP位点rs1053239及rs2479,平均医疗总花费,检查平均花费,药物平均花费,药物花费中属于降压、降脂、降糖药所用平均花费在各基因型间的差别均无统计学意义。4.20 SNP位点与5年随访期间各项平均医疗花费的关系分别以药物平均花费,检查平均花费,降压药、降脂药、降糖药平均花费为因变量,以性别、年龄、MetS各组分基线水平、SNP位点rs1053239基因型或rs2479基因型为自变量进行多元逐步线性回归。SNP位点rs1053239基因型在隐性遗传模型下减少药物平均花费(β =-0.057,P=0.038)及增加检查平均花费(β = 0.049,P=0.034),在共显性、显性、隐性遗传模型下均未进入以降压药、降脂药、降糖药平均花费为因变量的回归方程。SNP位点rs2479基因型在共显性、显性、隐性遗传模型下均未进入以药物平均花费,检查平均花费,降压药、降糖药平均花费为因变量的回归方程,在共显性((β = 0.141,P=0.006)、显性((β = 0.124,P=0.019)、隐性((β = 0.097,P = 0.024)遗传模型下增加降脂药平均花费。4.21 SNP位点对应用降压药单药CER的影响对于SNP位点rs1053239,高血压患者应用针对AngⅡ的降压药降低SBP、DBP的CER在GG基因型携带者中为121USD/mmHg、296USD/mmHg,在C等位基因携带者中为-65USD/mmHg、-119USD/mmHg;应用CCB剂降低SBP、DBP的CER在GG基因型携带者中为-97USD/mmHg、-165USD/mmHg,在C等位基因携带者中为-26USD/mmHg、-39USD/mmHg;应用利尿剂降低SBP、DBP的CER在GG基因型携带者中为-58USD/mmHg、-97USD/mmHg,在C等位基因携带者中为-56USD/mmHg、-71USD/mmHg。对于SNP位点rs2479,高血压患者应用针对AngⅡ的降压药降低SBP、DBP的CER在GG基因型携带者中为-9676USD/mmHg、1668USD/mmHg,在 A 等位基因携带者中为-77USD/mmHg、-119USD/mmHg;应用CCB降低SBP、DBP的CER在GG基因型携带者中为-26USD/mmHg、-46USD/mmHg,在A等位基因携带者中为-53USD/mmHg、-60USD/mmHg;应用利尿剂降低SBP、DBP的CER在GG基因型携带者中为-93USD/mmHg、-122USD/mmHg,在 A 等位基因携带者可为-35USD/mmHg、-51USD/mmHg。4.22 SNP位点对应用降压药单药ICER的影响对于SNP位点rs1053239,较随访期间未应用降压药,高血压患者应用针对AngⅡ的降压药降低SBP、DBP的ICER在GG基因型携带者中为-47USD/mmHg、-140USD/mmHg,在 C 等位基因携带者中为-21USD/mmHg、-47USD/mmHg;应用CCB降低SBP、DBP的ICER在GG基因型携带者中为-12USD/mmHg、-31USD/mmHg,在C等位基因携带者可为-9USD/mmHg、-17USD/mmHg;应用利尿剂降低SBP、DBP的ICER在GG基因型携带者中为-15USD/mmHg、-35USD/mmHg,在 C 等位基因携带者中为-14USD/mmHg、-28USD/mmHg。对于SNP位点rs2479,较随访期间未应用降压药,高血压患者应用针对AngⅡ的降压药降低 SBP、DBP 的 ICER 在 GG 基因型携带者可为-30USD/mmHg、-91USD/mmHg,在A等位基因携带者中为-24USD/mmHg、-47USD/mmHg;应用CCB降低SBP、DBP的ICER在GG基因型携带者中为-9USD/mmHg、-22USD/mmHg,在A等位基因携带者中为-10USD/mmHg、-19USD/mmHg;应用利尿剂降低SBP、DBP的ICER在GG基因型携带者中为-17USD/mmHg、-40USD/mmHg,在A等位基因携带者中为-12USD/mmHg、-23USD/mmHg。4.23 SNP位点rs2479对CIDEC及ATGL基因表达的影响向HEK293T细胞转染CIDEC rs2479多态性位点野生型与突变型过表达质粒,CIDEC mRNA水平在野生型与突变型过表达组无明显差异,与野生型过表达组相比,突变型过表达组的CIDEC蛋白白水平降低,ATGL蛋白水平升高。5结论(1)中国汉族人群中存在CIDEC基因SNP位点rs1053239、rs2479多态性;(2)SNP位点rs1053239多态性与MetS及其各组分风险无关,rs2479 A等位基因是FPG升高的独立危险因素,且此关联在男性与女性中及MetS人群中相似,rs2479多态性与MetS及其其他组分风险无关;(3)SNP位点rs1053239 C等位基因与高血压进展独立相关,rs2479 A等位基因与DBP升高及高甘油三酯血症进展独立相关;(4)SNP位点rs1053239 C等位基因及rs2479 A等位基因预示针对AngⅡ的降压药良好的降压效果及成本-效果;(5)SNP位点rs1053239 C等位基因预示较低的药物花费及较高的检查花费,rs2479 A等位基因预示较高的降脂药花费;(6)SNP位点rs2479 A等位基因使CIDEC蛋白表达水平降低,并可促进ATGL表达。更多还原

【Abstract】 1 IntroductionThe metabolic syndrome(MetS)is increasingly prevalent worldwide.In 2000-2001,the prevalence of MetS was 9.8%in Chinese men and 17.8%in Chinese women.MetS and its components are independent risk factors of cardiovascular disease(CVD)and type II diabetes(T2DM).And the financial pressure caused by MetS and its components on society is immense.It is currently believed that insulin resistance triggers the pathogenesis of MetS,and the subsequent glucose and lipid metabolism dysfunction is manifested as the main feature.Numerous genes and environmental factors interaction is involved in the pathogenesis of MetS,which has not been completely clarified.It is supposed that genes that take parts in the regulation of glucose and lipid metabolism might integrate to the MetS development and progression,and possibly might affect the efficacy of therapies on MetS components.Cell death-inducing DFF45-like effector C(CIDEC)belongs to the family of CIDE molecules.CIDEC has been recognized to play an important role in the regulation of adipose tissue integrity and lipid homeostasis.It was also proved to tune glucose metabolism and the sensitivity of insulin-targeted organs.CIDEC was indicated to inhibit lipid hydrolysis and to promote adipose tissue differentiation.Overexpression of CIDEC in human cells of various origins promoted large lipid droplets formation and triglyceride(TG)accumulation.Knockdown of murine CIDEC in whole-body or in adipose tissue caused hepatic steatosis,hypertriglyceridemia,white adipose tissue(WAT)amounts reduction,and whole-body insulin resistance.CIDEC was thus supposed to adjust and control systemic metabolic homeostasis.On this basis,together with its chromosome location of 3p,loci previously implicated to predispose to MetS and T2DM,CIDEC was hence speculated to be one of MetS candidate genes.CIDEC expression in adipose tissue was suppressed in obese population,and its change upon weight loss was inconsistent in different population.CIDEC expression in WAT was negatively correlated with insulin resistance indices in some reports,and unrelated in others.Moreover,loss of C-terminal of CIDEC caused by E186X mutation led to a new class of partial lipodystrophy.Nevertheless,research exploring CIDEC genetic variation with disease features has not yet been conducted in human population.And this study investigated the association of CIDEC gene polymorphism with MetS and its components,both in case-control and follow-up studies.The responses of individuals to particular class of drugs differ in population largely due to genetic background varieties.As a branch of genetics,pharmacogenetics deals with genetic link with different drug responses.Recognition of pharmacogenetic markers would facilitate the prediction of patients’ response to drugs.In this sense,pharmacogenetic markers may assist medical therapy choices selection and possibly provide patients a rapid,efficient,and individualized treatment.Currently genotype-guided drug therapy is mostly applied in anti-tumor treatment.And there are few confirmed pharmacogenetic predictors for cardiovascular agents,except for that for efficacy of clopidogrel,dosage of warfarin,and adverse effects of statins.Clusters of gene products in multiple signaling networks interact to take parts in the etiology of MetS.And such groups of proteins may be involved in the molecular targets of pharmacological agents for MetS components.As CIDEC was speculated to function in MetS pathogenesis.the possibility is worth lucubrating of CIDEC gene variation influencing the effectiveness of drugs for the individual components of MetS.Additionally,cost-effectiveness analysis should also be performed when assessing medicine efficacy to compare medication regimens under specific conditions.It would contribute to the balance of the cost and effectiveness of a therapeutic regimen,promote sensible medical resource allocation,and facilitate economical and effective medical decision.However,few studies have yet applied pharmacoeconomic exploration to the screening and evaluation of genetic predictors of drug efficacy.To sum up,it is not clear whether there is polymorphism of CIDEC gene in Chinese Han population,and whether specific single nucleotide polymorphism(SNP)in CIDEC gene is associated with the risk of MetS and its components,and with the efficacy and cost-effectiveness of antihypertensive drugs.And what is the effect of CIDEC polymorphism on the expression of CIDEC and its downstream gene ATGL?In view of these questions,we set the aim of this study.2 Objectives(1)To investigate the distribution of SNP rs1053239 and rs2479 polymorphism of CIDEC gene in Chinese Han population of Shandong District.(2)To examine the association of CIDEC gene polymorphism with the risk of MetS and its components in pooled population analysis,gender stratification analysis,and MetS subgroup analysis.(3)To assess the influence of CIDEC gene polymorphism on the efficacy of antihypertensive drugs.(4)To analyze the effects of CIDEC gene polymorphism on follow-up medical costs and cost-effectiveness of antihypertensive drugs.(5)To preliminarily study whether SNP rs2479 polymorphism of CIDEC gene influences CIDEC and ATGL gene expression.3 Methods3.1 Study populationParticipants were Han Chinese recruited from urban and rural communities of Shandong Province.Totally 1064 MetS patients(case group)and 1099 healthy subjects(control group)were recruited.MetS was defined on the basis of the recommendation of IDF/NHLB1/AHA/WHF/IAS/IASO.Totally 1359 participants were enrolled in the 5-year follow-up study.At the last follow-up.1184 subjects were available.Information of subjects at baseline and at the end of 5-year follow-up was acquired.Questionnaires were conducted to collected information on general conditions,smoking and alcohol-taking habits,previous history,drug therapy and medical examination during follow-up.and various types of medical costs.Blood pressure and waist circumference of all participants were measured.Body mass index(BMI)was calculated according to the weight and height.Fasting blood were collected to determine serum TG,total cholesterol(TC),high-density lipoprotein cholesterol(HDL-C).low-density lipoprotein cholesterol(LDL-C),and fasting plasma glucose(FPG).3.2 SNP selection and genotypingNon-intron tag SNPs located within CIDEC gene with a minor allele frequency(MAF)of>5%in Han Chinese in Beijing(CHB)were selected in this study.SNP genotyping was conducted in BGI Tech,Shenzhen.Venous fasting blood samples of subjects were used to extract DNA.Sequenom MassArray system(?)were applied in SNP genotyping.3.3 Cell culture and plasmid transfectionThe pHB-CMVIE-ZsGreen-Puro gene vector was applied to construct recombinant expression plasmids containing SNP rs2479 G(wild-type)or A(mutant)allele of CIDEC gene.Expression of CIDEC gene was initiated by the promoter CMVIE.HEK293T cells were cultured with high glucose medium and categorized into 4 groups,i.e.control,vehicle,wild-type and mutant group.The vehicle,wild-type and mutant plasmids were transfected into HEK293T cells in cell culture plates in a concentration of 1.6μg/mL.3.4 Real-time quantitative polymerase chain reaction(RT-qPCR)Total cell RNA was extracted and reverse transcribed.The expression of CIDEC was detected by real-time fluorescent quantitative PCR.3.5 Immunoblotting assayTotal cell protein was extracted.The interested protein levels were detected by SDS-PAGE gel electrophoresis,protein membrane transfer,epitopes blocking,conjugation reaction with primary antibody and secondary antibody,and chromogenic reaction.3.6 Statistical analysisSPSS Ver.17.0 was applied in statistical analysis.Categorical variables were tested via chi-square test.Continuous variables were compared by analysis of variance,paired t test,or Student’s t test.Regressions of binary logistic were conducted to screen the independent risk and protective factors for MetS and its individual components.Hardy-Weinberg equilibrium for genotype distribution of SNP was examined by goodness-of-fit chi-square test.Multiple linear regressions(MLR)were performed to evaluate the independent effect of genetic variation on the development of MetS components during 5-year follow-up.The influence of SNP on drug efficacy and various average medical costs were also assessed using MLR.Medical costs were inflated based on Chinese consumer price index during consumption,and denominated in U.S.dollar(USD).Cost-effectiveness ratio(CER)and incremental cost-effectiveness ratio(ICER)were calculated among different genotypes.A two-tailed P value of<0.5 was determined to be statistically significant.4 Results4.1 Gender and age structure of subjects at baselineA total of 2163 subjects were enrolled in the study,with 42.0 percent males and 58.0 percent females.Subjects were mainly ranged 45-54 years old,with 55-64 for males and 45-54 for females.Ratio of female to male gradually decreased with increase of age.Gender and age structure of subjects in control and MetS were similar to the overall subjects,respectively.4.2 Comparison of baseline clinical characteristics between control and MetSMean age and sex ratio were not significantly different between control and MetS group.Participants in MetS group possessed an increased BMI,waist circumference,systolic blood pressure(SBP),diastolic blood pressure(DBP),TG,TC,LDL-C,FPG.and alcohol intake rate and a decreased HDL-C and smoking rate compared with control group(all P<0.001).4.3 SNP genotypingThe minor allele of SNP rs1053239 and rs2479 were C and A,with a MAF of 0.409 and 0.261,respectively.Genotype distribution at both SNPs conformed to Hardy-Weinberg equilibrium in control group,MetS group,and pooled participants,respectively.4.4 Comparison of baseline clinical characteristics among SNP genotypes in control and MetSIn MetS group stratified by rs1053239 genotype,an increased waist circumference was observed in CC compared with CG genotype,and an elevated SBP was observed in CG compared with GG genotype(both P<0.01).In control group stratified by rs2479 genotype,AG genotype carriers possessed a reduced SBP and HDL-C compared with GG genotype carriers(both P<0.05);and in MetS group,participants harboring GG genotype had a decreased FPG compared with AG and AA genotype carriers(both P<0.01).respectively.4.5 Comparison of genotype and allele frequency of SNP in control and MetSThe difference of genotype and allele frequencies of SNP rs1053239 and rs2479 were not statistically significant between control and MetS group.4.6 Comparison of genotype and allele frequency of SNP in dichotomized MetS componentsWaist circumference,blood pressure,TG.HDL-C.and FPG were dichotomized according to diagnostic criteria of MetS,respectively.Genotype and allele frequencies of SNP rs1053239 and rs2479 did not significantly differ between waist circumference,blood pressure.TG or HDL-C normal and abnormal groups.GG,CG and CC genotype frequencies of SNP rs1053239 significantly differed between FPG normal and abnormal groups(P = 0.027);G allele frequency was decreased and C allele frequency increased in FPG abnormal group(P = 0.014).GG,AG and AA genotype frequencies of SNP rs2479 significantly differed between FPG normal and abnormal groups(P<0.001);G allele frequency was decreased and A allele frequency increased in FPG abnormal group(P<0.001).4.7 Association of SNP rs1053239 and rs2479 with MetS riskStepwise Logistic regression was conducted with MetS as dependent variable and gender,age,smoking,alcohol intake and genotype of SNP as independent variables.SNP rs1053239 or rs2479 genotype did not enter the regression model in co-dominant,dominant or recessive genetic models,respectively.4.8 Screening of risk factors of MetS componentsStepwise Logistic regression was conducted with elevated waist circumference,elevated blood pressure,elevated TG,reduced HDL-C and elevated FPG as dependent variable and gender,age,smoking,alcohol intake,other MetS components levels and genotype of SNP as independent variables.SNP rs1053239 genotype did not enter the regression model with waist circumference,blood pressure.TG.HDL-C or FPG as dependent variable in co-dominant,dominant or recessive genetic models,respectively.SNP rs2479 genotype did not enter the regression model with waist circumference,blood pressure,TG or HDL-C as dependent variable in co-dominant,dominant or recessive genetic models,respectively,but increased the risk of elevated FPG in co-dominant(OR 1.258[95%C.I.1.073-1.476],P＝0.005)and dominant(1.353[1.098-1.666].P= 0.004)genetic models,respectively.4.9 Gender stratification analysis of effects of SNP rs1053239 and rs2479 on risks of MetS and its componentsThe baseline clinical characteristics among genotypes between control and MetS and among genotypes in control and MetS,the genotype and allele frequencies of SNP in control and MetS and in dichotomized MetS components,and the association of SNP with risks of MetS and its components were similar in males,females and overall participants.SNP rs2479 genotype increased the risk of elevated FPG in co-dominant(1.332[1.037-1.712].P = 0.025)genetic models for males,and in co-dominant(1.353[1.095-1.672],P=0.005)and dominant(1.496[1.134-1.973],P = 0.004)genetic models for females,respectively.4.10 Effects of SNP rs1053239 and rs2479 on risks of MetS components in MetS groupThe genotype and allele frequencies of SNP in dichotomized MetS components and the association of SNP with risks of MetS components were similar to total subjects.For MetS patients.SNP rs1053239 genotype increased the risk of elevated FPG in co-dominant(1.322[1.102-1.585],P=0.003)and dominant(1.505[1.150-1.970].P = 0.003)genetic models,and SNP rs2479 genotype in co-dominant(1.671[1.360-2.052].P<0.001).dominant(1.905[1.472-2.465],P<0.001)and recessive(1.893[1.157-3.095],P ＝0.011)genetic models.4.11 Comparison of clinical characteristics of subjects without antihypertensive,lipid-modulating and hypoglycemic treatment during follow-up at baseline and at the end of 5-year follow-upA total of 238 subjects were not treated with antihypertensive,lipid-modulating and hypoglycemic agents during follow-up.Compared with baseline in 2007,increased BMI.waist circumference,SBP,DBP,TG,FPG(all P<0.001).and TC(P<0.05).and decrease HDL.-C(P<0.001)and LDL-C(P<0.05)were observed.4.12 Comparison of 5-year changes of clinical characteristics among SNP genoty pes in participants without antihypertensive,lipid-modulating,or hypoglycemic medication treatment during follow-upFor rs1053239,TC rose more greatly in CC than in CG genotype carriers,and FPG rose more greatly in GG than in CG genotype carriers(both P<0.05).For rs2479.a greater increase in TC and LDL-C were observed in subjects harboring AA genotype compared with GG or AG genotype carriers(all P<0.01).4.13 Association of SNP with 5-year changes of MetS componentsStepwise linear regression was conducted with 5-year changes of waist circumference,SBP,DBP,TG,HDL-C and FPG as dependent variable and gender,age,baseline level of dependent variable,changes of other MetS components,and genotype of SNP as independent variables.SNP rs1053239 genotype did not enter the regression model with waist circumference,TG,HDL-C or FPG as dependent variable in co-dominant,dominant or recessive genetic models,respectively,but contributed to SBP progression in co-dominant(β 0.256,P<0.001)and dominant(P= 0.244,P<0.001)genetic models,and to DBP progression in co-dominant(β=0.199,P<0.001),dominant(β= 0.209,P=0.00])and recessive(β=0.129,P=0.019)genetic models,respectively.SNP rs2479 genotype did not enter the regression model with waist circumference,SBP,HDL-C or FPG as dependent variable in co-dominant,dominant or recessive genetic models,respectively but contributed to DBP progression in co-dominant(β=0.176,P=0.002),dominant(β= 0.188,P=0.003)and recessive(β=0.112,P=0.038)genetic models,and to TG progression in co-dominant(β=0.187,P=0.002)and dominant(β＝0.216,P<0.001)genetic models,respectively.4.14 Comparison of clinical characteristics of subjects treated with antihypertensive monotherapy during follow-up at baseline and at the end of 5-year follow-upA total of 362 subjects were treated with antihypertensive monotherapy during follow-up.Compared with baseline in 2007,decreased SBP,DBP,TG,HDL-C and LDL-C were observed(all P<0.001).4.15 Comparison of efficacy of antihypertensive monotherapy among SNP genotypes in subjects treated with antihypertensive monotherapy during follow-upTotally 109 subjects were treated with angiotensin Ⅱ(Ang Ⅱ)-targeted monotherapy during follow-up.For SNP rs1053239.SBP and DBP were increased upon treatment for GG genotype,and decreased for C allele carriers(both P<0.001).For rs2479,SBP was decreased while DBP increased upon treatment for GG genotype,and both SBP and DBP were decreased more significantly in A allele carriers than in GG genotype(both P<0.001).Totally 114 subjects were treated with calcium channel blockers(CCB)monotherapy during follow-up.For SNP rs1053239.both SBP and DBP were decreased more significantly upon treatment in C allele carriers than in GG genotype(both P<0.05).For rs2479,changes of SBP or DBP in A allele carriers did not differ significantly from that in GG genotype,respectively.Totally 139 subjects were treated with diuretics monotherapy during follow-up.For rs1053239,changes of SBP or DBP in C allele carriers upon treatment did not differ significantly from that in GG genotype,respectively.For rs2479,both SBP and DBP were decreased more significantly upon treatment in A allele carriers than in GG genotype(both P<0.05).4.16 Association of SNP with efficacy of Ang Ⅱ-targeted agentsIn participants treated with Ang Ⅱ-targeted monotherapy during follow-up,stepwise linear regression was conducted with 5-year changes of SBP and DBP as dependent variable and gender,age,smoking,alcohol intake,baseline level of MetS components,and genotype of SNP as independent variables.SNP rs 1053239 genotype contributed to SBP lowering effect of Ang Ⅱ-targeted agents in co-dominant(β=-0.401.P<0.001).dominant(β=-0.400,P<0.001)and recessive(β=-0.361.P<0.001)genetic models,and to DBP lowering effect in co-dominant(β=-0.545,P<0.001).dominant(β=-0.331,P＝0.001)and recessive(β=-0.229,P=0.015)genetic models,respectively.SNP rs2479 genotype contributed to SBP lowering effect of Ang Ⅱ-targeted agents in co-dominant(β=-0.459,P<0.001)and dominant(β=-0.453,P<0.001)genetic models,and to DBP lowering effect in co-dominant(β=-0.454.P<0.001)and dominant(β=-0.445.P<0.001)genetic models,respectively.4.17 Association of SNP with efficacy of CCBIn participants treated with CCB monotherapy during follow-up.stepwise linear regression was conducted with 5-year changes of SBP and DBP as dependent variable and gender,age,smoking,alcohol intake,baseline level of’ MetS components,and genotype of SNP as independent variables.SNP rs 1053239 genotype suppressed SBP lowering effect of CCB in co-dominant(β= 0.241,P＝0.003)and dominant(β=0.274,P=0.003)genetic models,and suppressed DBP lowering effect in co-dominant(β=0.282,P<0.001)and recessive(β=0.245,P<0.001)genetic models,respectively.SNP rs2479 genotype did not enter the regression model with SBP as dependent variable in co-dominant,dominant or recessive genetic models,respectively,but suppressed DBP lowering effect of CCB in co-dominant(β=0.170,P=0.004)and recessive(β=0.197,P<0.001)genetic models,respectively.4.18 Association of SNP with efficacy of diureticsIn participants treated with diuretics monotherapy during follow-up,stepwise linear regression was conducted with 5-year changes of SBP and DBP as dependent variable and gender,age,smoking,alcohol intake,baseline level of MetS components,and genotype of SNP as independent variables.SNP rs1053239 genotype did not enter the regression model with SBP or DBP as dependent variable in co-dominant,dominant or recessive genetic models,respectively.SNP rs2479 genotype did not enter the regression model with SBP as dependent variable in co-dominant,dominant or recessive genetic models,respectively,but contributed to DBP lowering effect of diuretics in dominant(β=-0.146,P=0.048)genetic models.4.19 Comparison of average medical costs among SNP genotypes during follow-upAverage total medical costs,examination costs,medicine costs,and antihypertensive,lipid-modulating and hypoglycemic medicine costs did not differ significantly among SNP rs1053239 or rs2479 genotypes.4.20 Association of SNP with average medical costs during follow-upStepwise linear regression was conducted with average examination costs,medicine costs,and antihypertensive,lipid-modulating and hypoglycemic medicine costs as dependent variable and gender,age,baseline level of MetS components,and genotype of SNP as independent variables.SNP rs1053239 decreased average medicine costs(β=-0.057,P=0.038)and increased average examination costs(β=0.049,P=0.034)in recessive genetic model,respectively,but did not enter the regression model with average antihypertensive,lipid-modulating or hypoglycemic medicine costs as dependent variable in co-dominant,dominant or recessive genetic models,respectively.SNP rs2479 did not enter the regression model with average examination costs,medicine costs,or antihypertensive or hypoglycemic medicine costs as dependent variable in co-dominant,dominant or recessive genetic models,respectively,but increased average lipid-modulating medicine costs in co-dominant(β= 0.141,P= 0.006),dominant(β=0.124,P＝0.019)and recessive(β=0.097,P = 0.024)genetic model,respectively.4.21 Effects of SNP on CER of antihypertensive drugsFor rs1053239.the CER of Ang Ⅱ-targeted agents to lower SBP and DBP were 121USD/mmHg and 296USD/mmHg for GG genotype and-65USD/mmHg and-119USD/mmHg for C allele carriers;and for CCB,-97USD/mmHg and-165USD/mmHg for GG genotype and-26USD/mmHg and-39USD/mmHg for C allele carriers;and for diuretics,-58USD/mmHg and-97USD/mmHg for GG genotype and-56USD/mmHg and-71USD/mmHg for C allele carriers,respectively.For rs2479,the CER of Ang Ⅱ-targeted agents to lower SBP and DBP were-9676USD/mmHg and 1668USD/mmHg for GG genotype and-77USD/mmHg and-119USD/mmHg for A allele carriers;and for CCB,-26USD/mmHg and-46USD/mmHg for GG genotype and-53USD/mmHg and-60USD/mmHg for A allele carriers;and for diuretics,-93USD/mmHg and-122USD/mmHg for GG genotype and-35USD/mmHg and-51USD/mmHg for A allele carriers,respectively.4.22 Effects of SNP on ICER of antihypertensive drugsFor rs1053239,compared with participants without antihypertensive treatment,the ICER of’ Ang Ⅱ-targeted agents to lower SBP and DBP were-47USD/mmHg and-140USD/mmHg for GG genotype and-21USD/mmHg and-47USD/mmHg for C allele carriers;and for CCB,-12USD/mmHg and-31USD/mmHg for GG genotype and-9USD/mmHg and-17USD/mmHg for C allele carriers;and for diuretics,-15USD/mmHg and-35USD/mmHg for GG genotype and-14USD/mmHg and-28USD/mmHg for C allele carriers,respectively.For rs2479.compared with participants without antihypertensive treatment,the CER of Ang Ⅱ-targeted agents to lower SBP and DBP were-30USD/mmHg.-911USD/mmHg for CG genotype and-24USD/mmHg,-47USD/mmHg for A allele carriers;and for CCB,-9USD/mmHg,-22USD/mmHg for GG genotype and-10USD/mmHg,-19USD/mmHg for A allele carriers;and for diuretics,-17USD/mmHg,-40USD/mmHg for GG genotype and-12USD/mmHg,-23USD/mmHg for A allele carriers,respectively.4.23 Effects of SNP rs2479 polymorphism on CIDEC gene expressionHEK293T cells were transfected with recombinant expression plasmids containing CIDEC SNP rs2479 G(wild-type)or A(mutant)allele.The amounts of CIDEC mRNA did not differ significantly between wild-type and mutant group,but CIDEC protein level was decreased and ATGL protein level increased in mutant than in wild-type group.5 Conclusions(1)SNP rs1053239 and rs2479 polymorphism of CIDEC gene existed in Chinese Han population.(2)SNP rs1053239 polymorphism was not associated with the risks of MetS or its components.Rs2479 A allele was an independent risk factor of elevated FPG,and the association was similar to that of males and females,and of MetS subgroup;rs2479 was not associated with the risks of MetS or its other components.(3)SNP rs1053239 C allele was independently associated with hypertension progression,and rs2479 A allele with DBP and hypertriglyceridemia exacerbation.(4)SNP rs1053239 C allele and rs2479 A allele predicted higher efficacy and cost-effectiveness of Ang II-targeted antihypertensive agents.(5)SNP rs1053239 C allele indicated lower medication costs and higher examination costs,and rs2479 A allele indicated a higher lipid-modulating medicine costs.(6)SNP rs2479 A allele decreased protein expression of CIDEC and increased protein expression of ATGL.更多还原