2型糖尿病及相关代谢性疾病的分子遗传学研究
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摘要
目的了解2型糖尿病(T2DM)家系中T2DM患者和非糖尿病(DM)一级亲属代谢综合征(MS)及主要组分的患病率，并采用遗传度分析，了解各种代谢指标受遗传因素影响的程度。
方法(1)选取重庆地区汉族人群中符合入选条件的T2DM家系。共纳入247个家系，1312名研究对象，其中男587名，女725名。家系组共1178名，配偶组134名。研究对象测定人体测量学参数、血压、血脂谱、超敏C-反应蛋白(hsCRP)、非酯化脂肪酸(NEFA)等，行口服葡萄糖耐量试验(OGTT)。分别采用IDF和NCEP-ATPⅢ(根据亚洲人群腰围切点修正)标准诊断MS。(2)去除家系组中的二级亲、配偶组中有DM家族史和患有T2DM者，将剩余成员分为T2DM组、一级亲属(FDR)组和对照组三组。比较三组人体测量学参数、血压、血糖、血脂、胰岛素、hsCRP、NEFA等指标的差别；比较三组MS及主要成分的患病率。(3)采用S．A．G．E．软件，计算各项代谢指标在T2DM家系的遗传度。
结果在校正年龄因素后，FDR组腰围(waist)和腰臀比(WHR)显著高于对照组，差异有统计学意义(P＜0.05和P＜0.01)。在校正年龄、BMI后，与对照组比较，FDR组SBP、OGTT 2h血糖、空腹胰岛素和HomaIR也显著增高(P＜0.01或P＜0.05)，HDL-C显著降低(P＜0.05)。TG、空腹血糖、OGTT2h胰岛素、超敏C反应蛋白(hsCRP)等指标FDR组较对照组有增高趋势，但差异无统计学意义(P＞0.05)。FDR组糖调节受损(IGR)、肥胖、腹型肥胖、高血压、高TG血症、低HDL-C血症、MS(IDF标准)和MS(NCEP-ATPⅢ标准)的患病率分别为44.9％、33.3％、34.1％、21.1％、28.2％、30.1％、20.8％和19.2％。在校正年龄、性别影响后，FDR组患IGR的风险是对照组的13.2倍(95％CI=5.91～29.46)，肥胖、腹型肥胖和低HDL-C血症的风险分别是对照组的2.13倍(95％CI=1.23～3.68)、1.92倍(95％CI=1.14～3.21)和1.73倍(95％CI=1.01～2.98)，MS患病风险是对照组的2.57倍(IDF标准，95％CI=1.14～3.21)或2.35倍(NCEP-ATPⅢ标准，95％CI=1.17～4.71)。T2DM患者各种代谢紊乱的发生更为明显。肥胖、腹型肥胖、高血压风险是对照组的3.81倍(95％CI=2.22～6.56)、3.91倍(95％CI=2.34～6.51)和3.82倍(95％CI=2.17～6.74)，MS风险是对照组的7.96倍(IDF标准，95％CI=4.03～15.74)或12.13倍(NCEP-ATPⅢ标准，95％CI=6.14～23.96)，高TG血症和低HDL-C血症的患病风险分别是2.51倍(95％CI=1.53～4.11)和2.31倍(95％CI=1.38～3.89)。遗传度分析显示空腹血糖、空腹胰岛素和Homaβ的遗传度最高，分别为0.71、0.68和0.68；Homaβ的遗传度(0.68)高于Homa IR(0.54)；BMI、腰围和hsCRP的遗传度0.45左右；血脂谱中TC、HDL-C遗传度较高，分别为0.51和0.55，TG稍低，为0.32；所有指标中，SBP和DBP的遗传度最低，分别为0.25和0.28。
结论T2DM及相关的代谢代谢疾病如肥胖、高血压、脂代谢紊乱等有明显的家族聚集性。T2DM家系中非DM一级亲属已表现出程度不同的代谢紊乱。各种代谢指标在T2DM家系中具有中等程度的遗传度，提示相关代谢指标受遗传因素和环境因素的共同作用。
目的研究过氧化物酶体增殖活化受体γ共活化因子-1β(PGC-1β)基因单核苷酸多态性(SNPs)与T2DM和相关代谢疾病的关联。
方法(1)选择无亲缘关系的18例正常人，39例T2DM患者。PCR扩增PGC-1β基因12个外显子及侧翼序列和启动子上游1.6kb，经变性高效液相色谱(DHPLC)结合测序技术筛查SNPs位点，估计变异位点之间的连锁不平衡(LD)。(2)选择重庆地区474例T2DM患者，313例对照，对筛查到的编码区SNPs行病例-对照分析。
结果在PGC-1β基因共发现9个SNPs，5个位于编码区，其中4个为错义突变(Ala203Pro、Arg265Gln、Val279Ile、Arg292Ser)、1个为同义突变(Leu42Leu)，启动子区2个(-1263G＞A、-985C＞T)，其余2个位于内含子区域(IVS2-132 G＞A和IVS9-31G＞C)。4个错义突变呈强LD，位于同一单倍域内，其中Ala203Pro和Val279Ile完全相关。4个错义突变相距仅268bp，采用直接测序技术进行基因型鉴定。对Ala203Pro、Arg265Gln和Arg292Ser位点的病例—对照研究提示这3个SNPs均与T2DM无显著关联(P＞0.05)。病例—对照研究提示Arg265Gln与肥胖的发生有关，与GG基因型个体相比，GA或AA基因型的个体对肥胖的易感性显著增加(additive模式：OR=1.436，95％CI=1.079～1.921，P=0.013；dominant模式：OR=1.424，95％CI=1.029～1.970，P=0.033；recessive模式：OR=2.648，95％CI=1.008～6.961，P=0.048)。Arg265Gln与肥胖的关联受性别影响。在男性，与GG型比较，GA／AA型的BMI、腰围、腰臀比和DBP均显著增加(P＜0.05)。女性则无上述指标的差别。Ala203Pro和Arg292Ser位点与肥胖无显著相关(P＞0.05)。4个错义突变共构成3种常见单倍型，占所有单倍型的95.3％。3种单倍型与T2DM均无显著关联(P＞0.05)。肥胖者H3单倍型的频率高于对照组(16.6％vs12.8％，P=0.039)。
结论PGC-1β基因SNPs与中国重庆地区T2DM无明显关联。但Arg265Gln及一个常见单倍型可能与肥胖的发生有关，具有A等位基因的265Gln可能作为危险因子参与肥胖发生的病理过程。Arg265Gln与肥胖的关联在男性更明显。
目的研究过氧化物酶体增殖活化受体γ共活化因子-1α(PGC-1α)和过氧化物酶体增殖活化受体γ(PPARγ)基因SNPs与T2DM和肥胖的关系。并研究PGC-1α、PGC-1β和PPARγ三个基因SNPs交互作用对T2DM和肥胖的影响。
方法RFLP技术鉴定基因型。分析PGC-1α基因Gly482Ser和Thr394Thr位点、PPARγ基因Pr012Ala和C161T位点与T2DM和肥胖的关联。采用多因素维度降低法(MDR)法和Logistic回归，研究PPARγ、PGC-1α和PGC-1β基因多个位点之间的交互作用对T2DM和相关疾病的影响。
结果PPARγ基因Pro12Ala和C161T位点，PGC-1α基因Gly482Ser和Thr394Thr位点均与T2DM无显著相关。除dominant模式下PGC-1αThr394Thr与肥胖关联外(OR=0.697，95％CI=0.497～0.977，P=0.036)，其余三个SNPs与肥胖均无显著关联。PPARγ基因C161T位点可能和IR的发生有关，CT／TT基因型与CC基因型比较，空腹胰岛素和HomaIR显著增加(分别为11.13±5.36 mU／L vs 9.83±5.50 mU／L，P=0.030和2.474±1.23 vs 2.18±1.30，P=0.033)。本组人群中，PPARγ、PGC-1α和PGC-1β3个基因交互作用与T2DM的发病无关。通过MDR法，在3个基因5个位点中建立了与肥胖关联的2位点交互模型，PGC-1α基因Gly482Ser和PGC-1β基因Arg265Gln对肥胖的发生有交互作用。该模型的交叉确认一致性最高(10／10)，检验正确率为0.5432(P=0.0010)。基因型组合高危组较低危组发生肥胖的风险增加，OR为1.6034(95％CI 0.5493～4.6804，X~2=7.7275，P=0.0054)，经Logistic回归验证的结果与MDR法一致。
结论PPARγ基因Pro12Ala和C161T位点，PGC-1α基因Gly482Ser和Thr394Thr位点与重庆地区T2DM不相关，但PPARγCl61T位点可能参与IR的发生，PGC-1αThr394Thr位点可能与肥胖有关。MDR法建立了与肥胖关联的2位点交互模式，PGC-1α基因Gly482Ser和PGC-1β基因Arg265Gln对肥胖的发生有交互作用。MDR法可作为研究多基因交互关系的有力工具。
Objeetive To estimate the prevalence of the metabolic syndrome (MS) and its main components in diabetic patients and non-diabetic first degree relatives(FDR) in families with type 2 diabetes mellitus(T2DM), and estimate the heritabilities of MS and its related features.
Methods (1) 1312 subjects(587male and 725 female) from 247 Chongqing Han families were enrolled. 1178 of them came from T2DM family, and 134 of them were their spouses. Anthropometry, blood pressure, oral glucose tolerance test (OGTT), lipid levels、high-sensitivity C-reactive protein (hsCRP)、non-esterified fatty acid (NEFA) were examined. MS was defied according to International Diabetes Federation(IDF) definition and National Cholesterol Education Program Adult Treatment PanelⅢ(NCEP-ATPⅢ) guideline with Asian criteria for obesity. (2)Except second degree relatives and spouses who had diabetic relatives or who per se were diabetic, all the subjects were divided into three groups: T2DM, first degree relatives (FDR)and control group. The clinical and metabolic characteristics and the prevalence of MS and its main components were compared among three groups. (3) The Statistical Analysis for Genetic Epidemiology (S.A.G.E.) program was applied to calculate heritability of the metabolic traits.
Results After adjusted for age, waist circumferences and WHR in FDR group were higher than those in controls(P＜0.05and P＜0.01). After adjusted for age and BMI, the levels of SBP, OGTT 2h glucose, fasting insulin, and HomaIR in FDR group were higher than those in controls(P＜0.05 or P＜0.01), and HDL-C lower than that in controls(P＜0.05). Although there were higher TG, fasting glucose, OGTT 2h insulin, hsCRP in FDR group than in control group, there were no statistic difference between two groups(P＞0.05). In FDR group, the prevalence of IGR, obesity, central obesity, hypertension, hypertriglyceridemia, decreased blood HDL-C, MS(IDF criterion) and MS (NCEP-ATPⅢcriterion) were 44.9%, 33.3%, 34.1%, 21.1%, 28.2%, 30.1%, 20.8% and 19.2% respectively. After adjusted for age and sex, the risk of MS and its main components increased in FDR than in controls: IGR (OR 13.2, 95%CI=5.91～29.46), obesity(OR 2.13, 95% CI=1.23～3.68), central obesity(OR 1.92, 95 %CI=1.14～3.21), decreased blood HDL-C (OR 1.73, 95%CI=1.01～2.98), MS (IDF OR 2.57, 95%CI=1.14～3.21; NCEP-ATPⅢOR 2.35, 95%CI=1.17～4.71). There were higher prevalence of MS in T2DM than in controls, the risk of obesity was 3.81 (95% CI=2.22～6.56), obesity(OR 3.91, 95% CI=2.34～6.51), central obesity(OR 3.91, 95% CI=2.34～6.51), hypertension (OR 3.82, 95% CI=2.17～6.74), MS (IDF OR 7.96, 95%CI=4.03～15.74, NCEP-ATPⅢOR 12.13, 95%CI=6.14～23.96 ), hyper- triglyceridemia(OR 2.51, 95%CI=1.53～4.11), decreased blood HDL-C (OR 2.31, 95%CI=1.38～3.89). In T2DM family, there were moderate heritability estimates of metabolic phenotypes. Fasting glucose concentration, fasting insulin and Homaβhad the highest heritability estimates of 0.71, 0.68 and 0.68 respectively. The heritability estimate of Homaβwas higher than HomaIR(0.68 vs 0.54). Heritability estimates for the features of BMI, waist, hsCRP, TG and HDL-C were also high (0.32～0.55). Among all of the metabolic traits, SBP and DBP had the lowest heritability(0.25 and 0.28 respectively).
Conclusion There is significant familial aggregation of T2DM and related phenotypes including obesity, hypertension and dyslipidaemia There are more or less metabolic abnormalities in the non-diabetic FDR. The moderate heritability of metabolic traits in T2DM family is due to both genetic and environmental factors.
Objective To study the association of single nucleotide polymorphisms (SNPs) in peroxisome proliferators- activated receptorγcoactivator-1β(PGC- 1β) with T2DM and with related metabolic disease.
Methods (1) The DNA samples of 18 controls and 39 T2DM patients were selected to identify SNPs in PGC- 1βgene. all of 12 exons, including exon-intron boundaries and promoter region (□31.6 kb) were amplificated, and the PCR products were examined by denaturing high performance liquid chromatography(DHPLC) followed by sequencing to search SNPs, then pairwise linkage disequilibrium (LD) test and haplotype were examined. (2)The missense variants were genotyped in 474 T2DM patients and in 313 controls in Chongqqing area to investigate their genetic association with T2DM and obesity.
Results A total of 9 SNPs were identified in PGC- 1βgene, including four missense variants(Ala203Pro,Arg265Gln,Val279Ile, Arg292Ser)in exon 5, one silent variant (Leu42Leu), two SNPs in promoter region (-1263G＞A, -985C＞T) and two SNPs in intron (IVS2-132 G＞A, IVS9-31G＞C). The four missense variants were in LD and in a haplotype block. Because the four missense SNPs were 268bp apart, sequencing was used for genotyping. There was no association between Ala203Pro, Arg265Gln, rg292Ser and T2DM (P＞0.05). The SNP of Arg265Gln showed an increased risk with obesity (additive model: OR=1.436, 95% CI=1.079～1.921, P=0.013; dominant model: OR=1.424, 95%CI=1.029～1.970, P=0.033: recessive model: OR=2.648, 95%CI=1.008～6.961, P= 0.048). In male, subjects with Arg265Gln GA/AA genotype had higher BMI, waist and Waist-to-hip ratio(WHR) than those with GG genotype(P＜0.05). Among four missense SNPs genotyped, three common haplo- types (frequency＞0.05) accounted for 95.3% of the observed haplotypes. There were no association between the three haplotypes and T2DM (P＞0.05). The frequency of haplotype H3 was higher in obesity group than in controls(16.6%vs12.8%, P=0.039).
Conclusion There is no association between PGC- 1βSNPs and T2DM in Chongqing area. Arg265Gln and a common haplotypes in PGC-1βgene may contribute to the pathogenesis of obesity, especially in male.
Objective To investigate the impact of peroxisome proliferators-activated receptorγcoactivator-1α(PGC- 1α) and of Peroxisome proliferators- activated receptorγ(PPARγ) polymorphisms on T2DM and on obesity. To explore the effects of gene to gene interactions among PGC- 1α、PGC- 1βand PPARyon the risk of T2DM and obesity.
Methods Genotyping was performed by means of RFLE Case-control analysis were studied to investigate genetic association of SNPs (PGC-1αGly482Ser and Thr394Thr,PPARγPro 12Ala and C161T) with T2DM and obesity. Multifactor dimensionality reduction (MDR) and logistic regression were used to analyze gene-gene interactions of PGC-1α、PGC- 1βand PPARy.
Results There was no association between the four SNPs and T2DM (P＞0.05). The PGC-1αThr394Thr showed an increased risk of obesity with an dominant model (OR=0.697, 95%CI=0.497～0.977, P=0.036). There was no relationship between Gly482Ser,Pro 12Ala, C161T polymorphisms and obesity (P＞0.05). Subjects with PPARγC161T CT/TT genotype had higher fasting insulin and higher HomaIR than those with CC genotype (11.13±5.36 mU/L vs 9.83±5.50 mU/L, P=0.030 and 2.47±1.23 vs 2.18±1.30, P=0.033 respectively). No interaction among the three candidate genes associated with T2DM. Using the MDR method, a significant two-locus interaction between PGC-1αGly482Ser and PGC-1βArg265Gln was found among 5 loci in three genes on the risk of obesity. This model showed a cross-validation consistency of 10 of 10 and a testing accuracy of 0.5432 (P =0.0010). The odds ratio of the high-risk to low-risk group was 1.6034 (95 %CI0.5493～4.6804, X~2=7.7275, P=0.0054) .The gene-gene interactions could be validated by Logistic regression analysis.
Conclusion There is no association between the four SNPs of PGC- 1α, PPARγ, and T2DM, but PGC-1αThr394Thr could be related to obesity ,and PPARγC161T may be contribute to insulin resistance. Using the MDR method, a significant interaction between PGC-1αGly482Ser and PGC-1β,Arg265Gln for obesity has been shown. MDR analysis could be a useful method to study polygenetic disease.
方法(1)选取重庆地区汉族人群中符合入选条件的T2DM家系。共纳入247个家系，1312名研究对象，其中男587名，女725名。家系组共1178名，配偶组134名。研究对象测定人体测量学参数、血压、血脂谱、超敏C-反应蛋白(hsCRP)、非酯化脂肪酸(NEFA)等，行口服葡萄糖耐量试验(OGTT)。分别采用IDF和NCEP-ATPⅢ(根据亚洲人群腰围切点修正)标准诊断MS。(2)去除家系组中的二级亲、配偶组中有DM家族史和患有T2DM者，将剩余成员分为T2DM组、一级亲属(FDR)组和对照组三组。比较三组人体测量学参数、血压、血糖、血脂、胰岛素、hsCRP、NEFA等指标的差别；比较三组MS及主要成分的患病率。(3)采用S．A．G．E．软件，计算各项代谢指标在T2DM家系的遗传度。
结果在校正年龄因素后，FDR组腰围(waist)和腰臀比(WHR)显著高于对照组，差异有统计学意义(P＜0.05和P＜0.01)。在校正年龄、BMI后，与对照组比较，FDR组SBP、OGTT 2h血糖、空腹胰岛素和HomaIR也显著增高(P＜0.01或P＜0.05)，HDL-C显著降低(P＜0.05)。TG、空腹血糖、OGTT2h胰岛素、超敏C反应蛋白(hsCRP)等指标FDR组较对照组有增高趋势，但差异无统计学意义(P＞0.05)。FDR组糖调节受损(IGR)、肥胖、腹型肥胖、高血压、高TG血症、低HDL-C血症、MS(IDF标准)和MS(NCEP-ATPⅢ标准)的患病率分别为44.9％、33.3％、34.1％、21.1％、28.2％、30.1％、20.8％和19.2％。在校正年龄、性别影响后，FDR组患IGR的风险是对照组的13.2倍(95％CI=5.91～29.46)，肥胖、腹型肥胖和低HDL-C血症的风险分别是对照组的2.13倍(95％CI=1.23～3.68)、1.92倍(95％CI=1.14～3.21)和1.73倍(95％CI=1.01～2.98)，MS患病风险是对照组的2.57倍(IDF标准，95％CI=1.14～3.21)或2.35倍(NCEP-ATPⅢ标准，95％CI=1.17～4.71)。T2DM患者各种代谢紊乱的发生更为明显。肥胖、腹型肥胖、高血压风险是对照组的3.81倍(95％CI=2.22～6.56)、3.91倍(95％CI=2.34～6.51)和3.82倍(95％CI=2.17～6.74)，MS风险是对照组的7.96倍(IDF标准，95％CI=4.03～15.74)或12.13倍(NCEP-ATPⅢ标准，95％CI=6.14～23.96)，高TG血症和低HDL-C血症的患病风险分别是2.51倍(95％CI=1.53～4.11)和2.31倍(95％CI=1.38～3.89)。遗传度分析显示空腹血糖、空腹胰岛素和Homaβ的遗传度最高，分别为0.71、0.68和0.68；Homaβ的遗传度(0.68)高于Homa IR(0.54)；BMI、腰围和hsCRP的遗传度0.45左右；血脂谱中TC、HDL-C遗传度较高，分别为0.51和0.55，TG稍低，为0.32；所有指标中，SBP和DBP的遗传度最低，分别为0.25和0.28。
结论T2DM及相关的代谢代谢疾病如肥胖、高血压、脂代谢紊乱等有明显的家族聚集性。T2DM家系中非DM一级亲属已表现出程度不同的代谢紊乱。各种代谢指标在T2DM家系中具有中等程度的遗传度，提示相关代谢指标受遗传因素和环境因素的共同作用。
目的研究过氧化物酶体增殖活化受体γ共活化因子-1β(PGC-1β)基因单核苷酸多态性(SNPs)与T2DM和相关代谢疾病的关联。
方法(1)选择无亲缘关系的18例正常人，39例T2DM患者。PCR扩增PGC-1β基因12个外显子及侧翼序列和启动子上游1.6kb，经变性高效液相色谱(DHPLC)结合测序技术筛查SNPs位点，估计变异位点之间的连锁不平衡(LD)。(2)选择重庆地区474例T2DM患者，313例对照，对筛查到的编码区SNPs行病例-对照分析。
结果在PGC-1β基因共发现9个SNPs，5个位于编码区，其中4个为错义突变(Ala203Pro、Arg265Gln、Val279Ile、Arg292Ser)、1个为同义突变(Leu42Leu)，启动子区2个(-1263G＞A、-985C＞T)，其余2个位于内含子区域(IVS2-132 G＞A和IVS9-31G＞C)。4个错义突变呈强LD，位于同一单倍域内，其中Ala203Pro和Val279Ile完全相关。4个错义突变相距仅268bp，采用直接测序技术进行基因型鉴定。对Ala203Pro、Arg265Gln和Arg292Ser位点的病例—对照研究提示这3个SNPs均与T2DM无显著关联(P＞0.05)。病例—对照研究提示Arg265Gln与肥胖的发生有关，与GG基因型个体相比，GA或AA基因型的个体对肥胖的易感性显著增加(additive模式：OR=1.436，95％CI=1.079～1.921，P=0.013；dominant模式：OR=1.424，95％CI=1.029～1.970，P=0.033；recessive模式：OR=2.648，95％CI=1.008～6.961，P=0.048)。Arg265Gln与肥胖的关联受性别影响。在男性，与GG型比较，GA／AA型的BMI、腰围、腰臀比和DBP均显著增加(P＜0.05)。女性则无上述指标的差别。Ala203Pro和Arg292Ser位点与肥胖无显著相关(P＞0.05)。4个错义突变共构成3种常见单倍型，占所有单倍型的95.3％。3种单倍型与T2DM均无显著关联(P＞0.05)。肥胖者H3单倍型的频率高于对照组(16.6％vs12.8％，P=0.039)。
结论PGC-1β基因SNPs与中国重庆地区T2DM无明显关联。但Arg265Gln及一个常见单倍型可能与肥胖的发生有关，具有A等位基因的265Gln可能作为危险因子参与肥胖发生的病理过程。Arg265Gln与肥胖的关联在男性更明显。
目的研究过氧化物酶体增殖活化受体γ共活化因子-1α(PGC-1α)和过氧化物酶体增殖活化受体γ(PPARγ)基因SNPs与T2DM和肥胖的关系。并研究PGC-1α、PGC-1β和PPARγ三个基因SNPs交互作用对T2DM和肥胖的影响。
方法RFLP技术鉴定基因型。分析PGC-1α基因Gly482Ser和Thr394Thr位点、PPARγ基因Pr012Ala和C161T位点与T2DM和肥胖的关联。采用多因素维度降低法(MDR)法和Logistic回归，研究PPARγ、PGC-1α和PGC-1β基因多个位点之间的交互作用对T2DM和相关疾病的影响。
结果PPARγ基因Pro12Ala和C161T位点，PGC-1α基因Gly482Ser和Thr394Thr位点均与T2DM无显著相关。除dominant模式下PGC-1αThr394Thr与肥胖关联外(OR=0.697，95％CI=0.497～0.977，P=0.036)，其余三个SNPs与肥胖均无显著关联。PPARγ基因C161T位点可能和IR的发生有关，CT／TT基因型与CC基因型比较，空腹胰岛素和HomaIR显著增加(分别为11.13±5.36 mU／L vs 9.83±5.50 mU／L，P=0.030和2.474±1.23 vs 2.18±1.30，P=0.033)。本组人群中，PPARγ、PGC-1α和PGC-1β3个基因交互作用与T2DM的发病无关。通过MDR法，在3个基因5个位点中建立了与肥胖关联的2位点交互模型，PGC-1α基因Gly482Ser和PGC-1β基因Arg265Gln对肥胖的发生有交互作用。该模型的交叉确认一致性最高(10／10)，检验正确率为0.5432(P=0.0010)。基因型组合高危组较低危组发生肥胖的风险增加，OR为1.6034(95％CI 0.5493～4.6804，X~2=7.7275，P=0.0054)，经Logistic回归验证的结果与MDR法一致。
结论PPARγ基因Pro12Ala和C161T位点，PGC-1α基因Gly482Ser和Thr394Thr位点与重庆地区T2DM不相关，但PPARγCl61T位点可能参与IR的发生，PGC-1αThr394Thr位点可能与肥胖有关。MDR法建立了与肥胖关联的2位点交互模式，PGC-1α基因Gly482Ser和PGC-1β基因Arg265Gln对肥胖的发生有交互作用。MDR法可作为研究多基因交互关系的有力工具。
Objeetive To estimate the prevalence of the metabolic syndrome (MS) and its main components in diabetic patients and non-diabetic first degree relatives(FDR) in families with type 2 diabetes mellitus(T2DM), and estimate the heritabilities of MS and its related features.
Methods (1) 1312 subjects(587male and 725 female) from 247 Chongqing Han families were enrolled. 1178 of them came from T2DM family, and 134 of them were their spouses. Anthropometry, blood pressure, oral glucose tolerance test (OGTT), lipid levels、high-sensitivity C-reactive protein (hsCRP)、non-esterified fatty acid (NEFA) were examined. MS was defied according to International Diabetes Federation(IDF) definition and National Cholesterol Education Program Adult Treatment PanelⅢ(NCEP-ATPⅢ) guideline with Asian criteria for obesity. (2)Except second degree relatives and spouses who had diabetic relatives or who per se were diabetic, all the subjects were divided into three groups: T2DM, first degree relatives (FDR)and control group. The clinical and metabolic characteristics and the prevalence of MS and its main components were compared among three groups. (3) The Statistical Analysis for Genetic Epidemiology (S.A.G.E.) program was applied to calculate heritability of the metabolic traits.
Results After adjusted for age, waist circumferences and WHR in FDR group were higher than those in controls(P＜0.05and P＜0.01). After adjusted for age and BMI, the levels of SBP, OGTT 2h glucose, fasting insulin, and HomaIR in FDR group were higher than those in controls(P＜0.05 or P＜0.01), and HDL-C lower than that in controls(P＜0.05). Although there were higher TG, fasting glucose, OGTT 2h insulin, hsCRP in FDR group than in control group, there were no statistic difference between two groups(P＞0.05). In FDR group, the prevalence of IGR, obesity, central obesity, hypertension, hypertriglyceridemia, decreased blood HDL-C, MS(IDF criterion) and MS (NCEP-ATPⅢcriterion) were 44.9%, 33.3%, 34.1%, 21.1%, 28.2%, 30.1%, 20.8% and 19.2% respectively. After adjusted for age and sex, the risk of MS and its main components increased in FDR than in controls: IGR (OR 13.2, 95%CI=5.91～29.46), obesity(OR 2.13, 95% CI=1.23～3.68), central obesity(OR 1.92, 95 %CI=1.14～3.21), decreased blood HDL-C (OR 1.73, 95%CI=1.01～2.98), MS (IDF OR 2.57, 95%CI=1.14～3.21; NCEP-ATPⅢOR 2.35, 95%CI=1.17～4.71). There were higher prevalence of MS in T2DM than in controls, the risk of obesity was 3.81 (95% CI=2.22～6.56), obesity(OR 3.91, 95% CI=2.34～6.51), central obesity(OR 3.91, 95% CI=2.34～6.51), hypertension (OR 3.82, 95% CI=2.17～6.74), MS (IDF OR 7.96, 95%CI=4.03～15.74, NCEP-ATPⅢOR 12.13, 95%CI=6.14～23.96 ), hyper- triglyceridemia(OR 2.51, 95%CI=1.53～4.11), decreased blood HDL-C (OR 2.31, 95%CI=1.38～3.89). In T2DM family, there were moderate heritability estimates of metabolic phenotypes. Fasting glucose concentration, fasting insulin and Homaβhad the highest heritability estimates of 0.71, 0.68 and 0.68 respectively. The heritability estimate of Homaβwas higher than HomaIR(0.68 vs 0.54). Heritability estimates for the features of BMI, waist, hsCRP, TG and HDL-C were also high (0.32～0.55). Among all of the metabolic traits, SBP and DBP had the lowest heritability(0.25 and 0.28 respectively).
Conclusion There is significant familial aggregation of T2DM and related phenotypes including obesity, hypertension and dyslipidaemia There are more or less metabolic abnormalities in the non-diabetic FDR. The moderate heritability of metabolic traits in T2DM family is due to both genetic and environmental factors.
Objective To study the association of single nucleotide polymorphisms (SNPs) in peroxisome proliferators- activated receptorγcoactivator-1β(PGC- 1β) with T2DM and with related metabolic disease.
Methods (1) The DNA samples of 18 controls and 39 T2DM patients were selected to identify SNPs in PGC- 1βgene. all of 12 exons, including exon-intron boundaries and promoter region (□31.6 kb) were amplificated, and the PCR products were examined by denaturing high performance liquid chromatography(DHPLC) followed by sequencing to search SNPs, then pairwise linkage disequilibrium (LD) test and haplotype were examined. (2)The missense variants were genotyped in 474 T2DM patients and in 313 controls in Chongqqing area to investigate their genetic association with T2DM and obesity.
Results A total of 9 SNPs were identified in PGC- 1βgene, including four missense variants(Ala203Pro,Arg265Gln,Val279Ile, Arg292Ser)in exon 5, one silent variant (Leu42Leu), two SNPs in promoter region (-1263G＞A, -985C＞T) and two SNPs in intron (IVS2-132 G＞A, IVS9-31G＞C). The four missense variants were in LD and in a haplotype block. Because the four missense SNPs were 268bp apart, sequencing was used for genotyping. There was no association between Ala203Pro, Arg265Gln, rg292Ser and T2DM (P＞0.05). The SNP of Arg265Gln showed an increased risk with obesity (additive model: OR=1.436, 95% CI=1.079～1.921, P=0.013; dominant model: OR=1.424, 95%CI=1.029～1.970, P=0.033: recessive model: OR=2.648, 95%CI=1.008～6.961, P= 0.048). In male, subjects with Arg265Gln GA/AA genotype had higher BMI, waist and Waist-to-hip ratio(WHR) than those with GG genotype(P＜0.05). Among four missense SNPs genotyped, three common haplo- types (frequency＞0.05) accounted for 95.3% of the observed haplotypes. There were no association between the three haplotypes and T2DM (P＞0.05). The frequency of haplotype H3 was higher in obesity group than in controls(16.6%vs12.8%, P=0.039).
Conclusion There is no association between PGC- 1βSNPs and T2DM in Chongqing area. Arg265Gln and a common haplotypes in PGC-1βgene may contribute to the pathogenesis of obesity, especially in male.
Objective To investigate the impact of peroxisome proliferators-activated receptorγcoactivator-1α(PGC- 1α) and of Peroxisome proliferators- activated receptorγ(PPARγ) polymorphisms on T2DM and on obesity. To explore the effects of gene to gene interactions among PGC- 1α、PGC- 1βand PPARyon the risk of T2DM and obesity.
Methods Genotyping was performed by means of RFLE Case-control analysis were studied to investigate genetic association of SNPs (PGC-1αGly482Ser and Thr394Thr,PPARγPro 12Ala and C161T) with T2DM and obesity. Multifactor dimensionality reduction (MDR) and logistic regression were used to analyze gene-gene interactions of PGC-1α、PGC- 1βand PPARy.
Results There was no association between the four SNPs and T2DM (P＞0.05). The PGC-1αThr394Thr showed an increased risk of obesity with an dominant model (OR=0.697, 95%CI=0.497～0.977, P=0.036). There was no relationship between Gly482Ser,Pro 12Ala, C161T polymorphisms and obesity (P＞0.05). Subjects with PPARγC161T CT/TT genotype had higher fasting insulin and higher HomaIR than those with CC genotype (11.13±5.36 mU/L vs 9.83±5.50 mU/L, P=0.030 and 2.47±1.23 vs 2.18±1.30, P=0.033 respectively). No interaction among the three candidate genes associated with T2DM. Using the MDR method, a significant two-locus interaction between PGC-1αGly482Ser and PGC-1βArg265Gln was found among 5 loci in three genes on the risk of obesity. This model showed a cross-validation consistency of 10 of 10 and a testing accuracy of 0.5432 (P =0.0010). The odds ratio of the high-risk to low-risk group was 1.6034 (95 %CI0.5493～4.6804, X~2=7.7275, P=0.0054) .The gene-gene interactions could be validated by Logistic regression analysis.
Conclusion There is no association between the four SNPs of PGC- 1α, PPARγ, and T2DM, but PGC-1αThr394Thr could be related to obesity ,and PPARγC161T may be contribute to insulin resistance. Using the MDR method, a significant interaction between PGC-1αGly482Ser and PGC-1β,Arg265Gln for obesity has been shown. MDR analysis could be a useful method to study polygenetic disease.
引文
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