摘要
一、研究背景和目的:
随着社会经济的发展,肥胖(Obesity)在全球的发病率正在逐年上升。肥胖是2型糖尿病、高血压、高血脂、冠心病等疾病的主要危险因素,已成为威胁人类健康的常见疾病。单纯性肥胖是一个多因素疾病,遗传因素在肥胖的发生中起着重要的作用。ZAG是近年来发现的一种新的脂肪动员因子。在体外实验及动物实验中均证实ZAG能促进脂肪分解、减轻体重,但是在人类其基因多态性,如单核苷酸多态性(single nucleotide polymorphism, SNP)与肥胖症相关性方面的大样本研究目前国内外尚无报道。本研究的主要目的是研究中国北京地区汉族人群中ZAG基因的SNP与肥胖症的相关性。
二、研究方法:
1、临床样本和临床资料的采集:
研究对象来自北京平谷地区和协和医院体检中心。采用“病例-对照”的研究方法,建立肥胖/超重组和健康对照组两组研究人群,收集其临床资料和生化指标,包括患者的一般资料、既往病史、疾病家族史、人体测量指数和生化指标,并按照以下的纳入和排除标准,收集573名超重或肥胖患者和304名健康人。
1)超重/肥胖组
·纳入标准:
①查体:BMI≥25.0kg/m2,
②生化指标:FBG<5.6mmol/L, ALT<100U/L(2.5倍正常值),AST<92U/L(2.5倍正常值),Cr<132umol/L、BUN<7.14mmol/L。
·排除标准:
①既往肝病史及慢性及恶性肿瘤病史;
②任何一点不符合纳入标准。
2)健康对照组
·纳入标准:
①查体:BMI<25.0kg/m2,
②生化指标:FBG<5.6mmol/L, ALT<100U/L(2.5倍正常值),AST<92U/L(2.5倍正常值),Cr<132umol/L、BUN<7.14mmol/L, TC<5.69mmol/L、TG<1.69mmol/L、LDL-C<3.62mmol/L、HDL-C>0.92mmol/L
·排除标准:
①既往肝病史及慢性及恶性肿瘤病史;
②任何一点不符合纳入标准。
2、ZAG基因SNP的选择
本研究SNP的选择基于以下3个方面:①Hapmap上提供的中国北京汉族人群(China Bejing Han, CBH)的关于ZAG基因的遗传多态性位点的信息,在ZAG基因区域上选择出基于中国汉族人群的标签SNP。②随机挑选30名正常人,对ZAG基因的4个外显子及5’启动密码子(strat codon)上游1.5kb和3’终止密码子(stop codon)下游0.9kb的DNA序列进行扩增、测序,寻找待测定SNP位点,要求MAF≥5%。③同时兼顾国外在ZAG基因多态性与肥胖症上研究筛选到的阳性位点。通过以上方法我们从中选择5个待检测的SNP位点:rs2247607(A>T)、rs4727442(G>T)、rs4215(A>G)、rs2527923(C>T)和rs2527882(C>T)。
3、ZAG基因SNP的检测
1)rs2247607位点碱基为A的序列可被限制性内切酶Spe Ⅰ的识别并切割,在碱基A→T的变化后,则失去了酶切位点,因此采用限制性内切酶酶切法测定SNP位点的基因型。
2)用TaqMan探针技术对rs4727442、rs4215、rs2527923、rs2527882进行SNP检测。
三、研究结果
1、一般资料:超重/肥胖组和健康对照组间年龄、身高、体重、BMI、腰围、体脂、血压、空腹血糖、UA、TC、TG、HDL-C、LDL-C的比较发现:与健康对照组相比,超重/肥胖组的年龄、体重、BMI、腰围、体脂、血压、空腹血糖、UA、TC、TG、HDL-C、LDL-C均较高,P值均小于0.01,差异呈极显著性,符合本研究分组的结果。
2、总样本、超重/肥胖组以及健康对照组中,ZAG基因各SNP位点Hardy-Weinberg平衡检验的P值均>0.05,均符合Hardy-Weinberg平衡,说明本研究的研究对象能代表总体人群。
3、等位基因的“病例-对照”关联分析:ZAG基因的rs2247607、rs4727442、rs4215、 rs2527923、rs2527882位点等位基因分布频率在超重/肥胖组和健康对照组之间比较,P值分别为0.303、0.906、0.763、0.795、0.778,均无统计学差异。
4、基因型的“病例-对照”关联分析:超重/肥胖组和健康对照组之间ZAG基因各SNP位点基因型分布频率比较,P值分别为0.582、0.217、0.369、0.938、0.942,均无统计学差异。
5、显隐性模型中基因型的“病例-对照”关联分析:在显性模型中,ZAG基因各SNP位点的基因型频率在超重/肥胖组和健康对照组两组之间比较,P值分别为0.727、0.390、0.387、0.892、0.860,均无统计学差异。而在隐性模型中,两组之间ZAG基因各SNP位点的基因型频率比较,P值分别为0.299、0.244、0.440、0.721、0.737,均无统计学差异。
6、单倍体型的“病例-对照”关联分析:用Haploview4.1软件对实验数据分析,rs2527882与rs2527923在同一个连锁不平衡区内,而rs4215、rs4727442和rs2247607在另一个连锁不平衡区内,从而可构建两组单倍体CC、TT和ATT、 GGT、GGA。超重/肥胖组和健康对照组间单倍体型CC、TT和ATT、GGT、GGA分布频率比较,P值分别为0.749、0.714、0.934、0.443、0.365,均无统计学差异。
7、各基因型间临床生化指标比较:ZAG基因各SNP的基因型间年龄、性别、身高、BMI、腰围、体脂、舒张压、空腹血糖、UA、TC、TG、HDL-C、LDL-C比较均无统计学差异。rs4215、rs2527923、rs2527882的基因型间收缩压比较,P值分别为0.013、0.004、0.003,差异呈显著性。体重在rs4215的GG、AG+AA基因型间以及rs4727442的GG、GT+TT基因型间比较,P值分别为0.026、0.028,差异呈显著性。
四、结论
1、中国北京地区汉族人群中ZAG基因的rs2247607、rs4727442、rs4215、rs2527923和rs2527882与肥胖症进行关联研究,发现ZAG基因的各SNP与肥胖症可能无明显相关性。
2、ZAG基因的rs4215、rs2527923、rs2527882与收缩压可能有一定的相关性。而rs4215、rs4727442可能与体重有一定的相关性,具有rs4215的GG基因型和rs4727442的GG基因型的群体可能增加高体重风险。
1. Background and Objective
Nowadays the global incidence of obesity is increasing yearly with the social-economic development. Obesity that has been a common disease to threaten human health is the main risk factor of type2diabetes mellitus, hypertension, hyperlipidemia, coronary heart disease and other diseases. Obesity is a multifactorial disease, in which genetic factors play an important role in pathogenicity. ZAG has been found to be a new kind of lipid mobilizing factor recently. It has been confirmed both in experiments in vitro and animal experiments that ZAG can promote lipolysis and help to loss weight. However there were no large human sample reports on the relationship between single nucleotide polymorphism and obesity currently. Therefore the main objective of present study is to investigate the ZAG genetic polymorphisms association with obesity in China Beijing Han Race.
2. Methods
2.1Collection of clinical samples and data
A case-control design was applied, and two groups including obesity/overweight group and healthy control group were established. Collection of clinical data and biochemical Indexes including general information, past medical history, familial disease history, anthropometric indexes and biochemical indexes. According to admitting and excluding standards stated in the following, a total of573overweight or obese and304healthy persons were recruited in our study.
Overweight/obesity Group
Admitting Standard:
Examination:BMI≥25.0kg/m2
Biochemical indexes:FBG<5.6mmol/L, ALT<100U/L, AST<92U/L, Cr<132umol/L, BUN<7.14mmol/L.
Excluding Standards:
Liver disease and malignant tumor history;
Anyone that not to comply with admitting standard.
Healthy Control Group
Admitting standard:
Examination:BMI<25.0kg/m2, BP<140/90mmHg
Biochemical indexes:FBG<5.6mmol/L, ALT<100U/L, AST<92U/L, Cr<132umol/L, BUN<7.14mmol/L, TC<5.69mmol/L, TG<1.69mmol/L, LDL-C<362mmol/L、HDL-C>0.92mmol/L.
Excluding Standards:
Liver disease and malignant tumor history;
Anyone that not to comply with admitting standard.
2.2Selection of SNP
Selection Criterias of SNPs was based on3aspects following:①select the ZAG genetic tag SNP of China Bejing Han provided by Hapmap.②30DNA samples were amplified and sequenced the4exons of1.5kb upstream of start codon and0.9kb downstream of stop codon of ZAG gene to search SNPs.③The positive SNP site from abroad study. Five SNPs were found:rs2247607(A>T), rs4727442(G>T), rs4215(A>G),rs2527923(C>T) and rs2527882(C>T)
2.3SNP genotyping
1) The sequence including rs2247607with adenine can be identified and cut by restriction endonuclease. When adenine is replaced by thymine, it will loss the restriction site, therefore restriction enzyme digestion of restriction endonuclease is applied to determine the genotype of SNP.
2) SNP genotypings of rs4727442, rs4215, rs2527923and rs2527882are operated by TaqMan probe technology.
3. Results
3.1Result of testing of sample mean to age, body weight, BMI, waist circumference, body fat, blood pressure, FBG, UA, TC, TG, HDL-C, LDL-C between overweight/obesity group and healthy control group shows that P<0.01. The difference is significant.
3.2Hardy-Weinberg equilibrium was calculated using the chi-squard test. All SNPs were in Hardy-Weinberg equilibrium either in the cases or control.
3.3Compared overweight/obesity group with healthy control group, the P vaules of allele frequencies of each SNP such as rs2247607, rs4727442, rs4215, rs2527923and rs2527882, was0.303、0.906、0.763、0.795、0.778respectively. All P vaules were over0.05, which had no statistical significance, and suggested that all above SNPs have no obvious correlation with obesity.
3.4Compared overweight/obesity group with healthy control group, the P vaules of genotype frequencies of each SNP such as rs2247607, rs4727442, rs4215, rs2527923and rs2527882, was0.582、0.217、0.369、0.938、0.942respectively. All over0.05, which had no statistical significance, and suggested that all above SNPs have no obvious correlation with obesity.
3.5In dominance and recessive models, there was no obvious difference of the genotype frequency of all above SNPs between the overweight/obesity group and healthy control group, which indicated that all above genotype of SNPs had no obvious correlation with obesity.
3.6Data analysis results calculated by Haploview4.1software show that rs2527882and rs2527923are in the same LD blok, meanwhile rs4215, rs4727442and rs2247607are in another LD blok, so two groups of haplotype can be constructed. The P vaules of all above haplotypes frequency between overweight/obesity group and healthy control group are all over0.05, which has no statistical significance.
3.7Clinic data analysis showed that the difference of systolic blood pressures between the genotypes of rs4215, rs2527923and rs2527882is significant (P<0.05). The differences of body weights between rs4215's genotype of GG and AG+AA, and between rs4727442's genotype of GG and GT+TT were significant, which suggested that body weight may be related to rs4215and rs4727442.
4. Conclution
4.1Five SNPs of ZAG gene (rs2247607, rs4727442, rs4215, rs2527923, rs2527882) had no association in Beijing Han population with obesity using case-control study.
4.2The three SNPs of ZAG gene (rs4215, rs2527923, rs2527882) may have some association with SBP. The SNPs of ZAG gene (rs4215, rs4727442) may have some association with weight. The GG genotype of rs4215and rs4727442may increase risk of overweight.
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