| Background and aimsCoronary heart disease (CHD) is a major threat to human health. The latest statistics shows that, in the developed countries, the mortality rate of CHD has declined, but in developing countries, including China, the mortality rate of CHD still shows increasing tendency. According to WHO estimates, China is expected to meet an epidemic peak of CHD by 2020. Therefore, it is an immediate task to improve the strategies of CHD control and prevention for Chinese health authorities.There are many pathogenesises for CHD, these include lipid infiltration theory, neuroendocrine theory, homocysteine metabolism disorder theory and inflammatory induced vascular intima injury theory. These pathophysiological processes may eventually cause coronary atherosclerosis which leads to the deficiency in blood and oxygen supply and endangers the pump function of the heart. Along with the development of biomedicine, great progresses in the diagnosis, treatment and prevention on CHD have made. In predicting strategies for CHD, the Framingham Heart Study, the women’s health study, the Prospective cardiovascular Munster study and the systematic coronary risk evaluation project have been performed and provided multiple versions of the 10-year CHD Risk Prediction Scores for clinicians. Then, scientists want to reduce the morbidity and mortality of CHD by combining the preventive medicine means. Recently, the applicability of these risk assessment strategies has been doubted by worldwide experts, the objections mainly include:1) the representation of human race; 2) the integrity of CHD risk factors, etc. Of the representation of human race, the above prediction strategies are mainly derived from the European and American white but not for other races; of the integrity, the above strategies included the age, sex, hypertension and smoking, while, the serum lipids, glycosylated hemoglobin, diabetes, family history and body mass index have not reached a consensus in these available CHD Risk Prediction Scores. In other words, it is a good idea to reduce the morbidity and mortality of CHD by combining the preventive medicine, but the existing CHD Risk Prediction Scores need to be improved.Of CHD risk factors, age, gender, family history and race are classic unmodifiable risk factors; smoking, hypertension, dyslipidemia and diabetes are the classic modifiable risk factors. To perfect and improve the prediction strategy of CHD, we must integrate these known risk factors comprehensively; on the other hand, we also need to explore new risk factors including genetic susceptibility.From a genetic point of view, the genetic basis of CHD may represent the accumulation of multiple DNA variants. Genome wide association studies (GWAS) have identified more than 100 genes associated with CHD in the human genome. Single nucleotide polymorphism (SNP) show individual differences and racial differences in allele distribution in many diseases.The role of stromal cell-derived factor 1 (SDF-1) in atherosclerotic was gradually paid more attention. Studies detected high expression of SDF-1 in human coronary atherosclerotic plaque, mainly expressed in endothelial cells, smooth muscle cells, monocytes/macrophages and lymphocytes, while, no expression of SDF-1 was found in normal arterial wall. SDF-1 may activate T-lymphocytes and is induced by proinflammatory stimuli such as lipopolysaccharide, tumour necrosis factor (TNF), or interleukin-1 (IL-1). SDF-1 activates C-X-C chemokine receptor type 4 (CXCR4) to induce a rapid and transient rise in the level of intracellular calcium ions and chemotaxis. Thus, SDF-1 plays an important role in host inflammatory responses. An SNP at position 801 (rs1801157) in the 3’-untranslated region (3’-UTR), whose anallele is regarded as a target of cis-acting factors, has been shown to up-regulate the expression of CXCL12. Studies have shown that rs 1801157 is associated with susceptibility to blast invasionin acute myelogenous leukemia, sporadic prostate cancer, and breast cancer. Since inflammation is believed to participate in the local, myocardial, and systemic complications of atherosclerosis, it is necessary to explore whether there is a correlation between SDF-1 and CHD.Recently, inhibition of telomerase and marked telomere shortening were found to be closely associated with the increasing severity of atherosclerosis. A telomere is a region of repetitive nucleotide sequences at the end of each chromatid of most eukaryotic organisms that protects the end of the chromosome from deterioration or from fusing with neighboring chromosomes. Nevertheless, telomeres will be consumed during cell division. Telomerase reverse transcriptase (TERT) is a catalytic subunit of the telomerase, which together with the telomerase RNA component (TERC), are the most important components of the telomerase complex. Telomerase is a ribonucleo protein polymerase that maintains telomere ends by addition of the telomere repeat TTAGGG. The enzyme consists of a protein component with reverse transcriptase activity, encoded by the gene, and an RNA component that serves as a template for the telomere repeat. Human TERT (hTERT) is located in 5pl5.33, and rs2736100 is located in the second intron of hTERT. Reports have shown that rs2736100 acts as a critical factor in hTERT synthesis, in charge of hTERT activation. A SNP of rs2736100 was identified to be associated with susceptibility to idiopathic pulmonary fibrosis, lung cancer, glioma, testicular germ cell cancer, and bladder cancer. Abnormal vascular smooth muscle cells (VSMCs) proliferation is thought to contribute to the pathogenesis of vascular occlusive lesions, including atherosclerosis. Vigorous division and remodeling of VSMCs might consume telomeres. Because hTERT is the key molecular complex that maintains telomere stabilization, genetic polymorphisms in hTERT might be associated with atherosclerosis.Different from CHD associated SNPs studies, serum biochemical indices reflect dynamic physiological and pathophysiological processes within the body. Recently, awareness of the importance of blood biochemical markers is growing. Classical biochemical markers, including uric acid (UA), total bilirubin(TBIL), andγ-glutamyltransferase (y-GT), are considered to change in CHD patients as well. However, these risk factors had not applied in CHD predicating strategies. The main reasons are:1) sporadic, small sample size studies on the new risk factors, such as UA and gamma-glutamyl transpeptidase (y-GT) need to be confirmed in a well-designed large-scale population study; 2) of the classic risk factors, such as blood lipids, more studies are need to reveal the real association pattern between these risk factors and CHD. As research on CHD has progressed, scientists have tried to combine the identified markers in order to produce an index with high diagnostic performance. Several studies have reported that the value can been improved markedly through integration of multiple methods such as LDL-C/(HDL-C+TBIL) or LDL-C/HDL-C. These explorings provided an expectable progress for CHD prediction.How to improve the performance of the blood biochemical indicators in the CHD prediction strategy using an appropriate population size; how to find new gene markers for CHD, and how to combine the gene risk factors with age, gender and classic biochemical indicators; these questions are essential to provide a Chinese Han population-based CHD prediction strategy.China is a multi-ethnic country with a large population, the pathophysiological process may differ between nationalities, mainly for:1) different nationalities have different genetic background, even with the Han, the genetic background may also complex because population migration, interracial marriages and other factors; 2) living habits and food culture differed between different nationalities and urban and rural. Therefore, it is very challenging to explore a CHD prediction strategy suitable for Chinese, thus, it is not a topic that can be completed in a region of a single study. To this end, this study will be the mainly focused on the Han population in Yuxi. We plan to carry out some exploratory research and validation firstly and then expand our study gradually. At the same time, due to a large number of ethnic minorities living in the Yunnan Yuxi, the Yi, Hani and Dai are ranked the first three minorities, so, we also plan to know the genetic differences between these minorities.Therefor, the follo wings are the purposes of this study:Firstly, to verify the associations between new biochemical indexes and precence of CHD; to reveal the real association pattern between classic risk factors and CHD; to establish one or two mathematic model for CHD predicting with high diagnosis performance.Secondly, to evaluate whether the SNP, rs1801157 in the 3’-UTR of the SDF-1 gene, associated with the presence of CHD using a case-control study; to compare the differences of genotype and allele distributions in ethnic Han, Yi, Hani and Dai; to learn the ethnic or racial differences in the gene locus by comparing our results with domestic and foreign researches.Thirdly, to evaluate whetherthe SNP, rs2736100 in the hTERT gene, associated with the presence of CHD using another case-control study; to learn the ethnic differences in the gene locus by comparing the genotype and allele distribution differences in ethnic Han, Yi, Hani and Dai.Fourthly, through the application of the predicating models derived from first chapter in the subjects of the 2nd and the 3rd chapter, we could conform the model validity and repeatability again, in addition, we also plan to observe the relevance between genetic susceptibility and traditional risk factors and attempt to explore the possibility of the combination of genetic susceptibility with traditional risk factors based predicating models.MethodsChapter I:The establishment and clinical assessment of the CHD predicting models of Han living in Yuxi CityThe samples for model development:1049 CHD (664 male, age range,27-87 years, median age,62.2 years; 385 female, age range,38-86 years, median age,64.9 years) and 627 non-CHD subjects (400 male, age range,24-84 years, median age, 45.5 years; 227 female, age range,23-71 years; median age,43.3 years), visited People’s Hospital of Yuxi City between October 2010 and March 2013, were enrolled consecutively in this case-control study.The samples for model validation:431 CHD (312 male, age range,27-82 years, median age,61.7 years; 119 female, age range,45-82 years, median age,65.5 years) and 412 non-CHD subjects (198 male, age range,26-76 years, median age,49.4 years; 214 female, age range,22-77 years, median age,46.0 years), visited People’s Hospital of Yuxi City between November 2013 and December 2014, were enrolled consecutively in this case-control study.Cardiometabolic risk factors, include total cholesterol(TC), LDL-C, HDL-C, triglycerides(TG), homocysteine(HCY), UA, apolipoprotein A-1(APOA1), apolipoprotein B-100(APOB100), lipoprotein (a) [Lp(a)], TBIL, direct bilirubin(DBIL), y-GT and etc were measured by routine laboratory methods in Roche cobas c 701The method of model developement:the distribution tendency of all variables was assessed; variables were not normally distributed were tranfered by logarithmic transformation. The associations between variables were evaluated by Pearson correlation coefficient analysis. Univariate analysis was performed to learn the associations between each independent variable and CHD, the independent variables with significance< 0.1 were introduced into the starting model and then eliminated manually using the backward step-by-step approach, depending on the largest p value. All items showing statistical significance at P<0.05 were retained in the final screening model. Internal cross-validation was used to minimise overfitting. The performance of discrimination was evaluated by an area under receiver operating characteristic curve (AUC). The screening model was externally validated in the validation population, with AUC calculated. In view of the discrepancies between males and females in previous risk score models, we developed and validated our models by sex. All analyses were performed using SPSS for Windows (V.20).Continuous variables that were normally or approximately normally distributed are presented as the means+standard deviation (SD) (X±s), while those with a skewed distribution are presented as the median (1st and 3rd quantiles).Chapter II:Study on the association between SNP of stromal cell-derived factor-1 gene and the presence of CHD in Han living in Yuxi CityTo assess genetic polymorphisms related to CHD, we recruited 84 ethnic Han patients (64 males and 20 females; age range,36-79 years; median age,62.4 years) with CHD who were unrelated consecutive inpatients at People’s Hospital of Yuxi City between April and October 2013. To obtain an estimate of the genetic distribution of the reference allele in the general population, we also randomly obtained DNA samples from 253 healthy individuals with no history of CHD who visited the People’s Hospital of Yuxi City (152 males and 101 females; age range, 26-60 years; median age,33.9 years). The 253 healthy controls did not have a history of chronic disease, autoimmune disease, or cardiovascular disease. All above subjects were local Hans living in the Yuxi area for more than three generations, there is no marriage or close relatives of other minorities.The study also included the minority population living in the Yuanjiang Hani, Yi and Dai Autonomous County. Samples were collected in July 2014, specimens of Yi collected from Wadi village (169,75 males,94 females, age range from 22 to 84 with an average of 43.8), specimens of Hani were collected from Yinyuan town (128,56 males,72 females, age range from 20 to 90 with an average of 50.1), specimens of Dai were collected from Ganzhuang residential district (215,68 males,147 females, age range from 21 to 82 with an average of 47.8). All above regions were for the national natural communities, there is no marriage or close relatives of other minorities.Patients were diagnosed with CHD according to American Heart Association guidelines. All patients were confirmed by the obstruction of at least 1 large epicardial coronary artery by atheromatous plaque using coronary angiography. Patients who met the exclusion criteria will be excluded from this study:alcohol abuse, diabetes, a history of smoking, chronic lung disease, xanthelasma, and evidence of noncoronary atherosclerotic disease. Hypertension was defined as a systolic pressure>140 mmHg or a diastolic pressure>90 mmHg.3ml peripheral venous blood was sampled from the objects, which was then put in 100 microliter 0.5 mol/1 EDTA (pH8.0). After being placed for 15-30 minutes with room temperature, the blood sample was centrifiiged for 10 minutes with 3000rmp. Blood cells were then separated and placed in-20℃ freezer. Biochemical indexes, like TC, LDL-C, HDL-C, TG, HCY, UA and etc were measured by routine laboratory methods. The method and devices were the same as the Chaptert Ⅰ.Genetic polymorphisms in SDF-1 were identified using the Mass Array(?) system (Sequenom, San Diego, CA, USA) according to the manufacturer’s user guide. A 293-bp SDF-1 fragment was PCR-amplified using the extracted genomic DNA as template. PCR was performed using HiFiFast DNA polymerase (Biovisualab, Shanghai, China). The upstream primer was 5 ACGTTGGATGTCACACTGCTGC CTCAGCTC3’and the downstream primer was 5’ACGTTGGATGACCCCCT TCTCCATCCACAT3’. The extension primer sequence was 5’GCCCTCCCAGA AGAGGCAGACC3’.The amplification reaction system was 25uL reaction system containing Nanopure H2O 1.85uL, PCR Buffer with MgCl2(10×) 0.625 uL, MgCl2(25mM) 0.325uL, dNTP mix(25mM) 0.1 uL, Primer mix(500nM each) 1.0uL, Genomic DNA(5-10ng/uL)1.0uL, Hotstar Taq(?)(5U/uL) 0.1 uL.2uL upstream and downstream primers respectively,4uL DNA template. Double distilled water was added to make the total volume of 25uL. The thermocycling conditions were as follows:Reactions were run in 5 uL volumes using an amplification protocol of 94℃ for 15 minutes, followed by 45 cycles of 94℃ for 20 seconds,56℃ for 30 seconds, then 72℃ for 60 seconds.To verify the size of the PCR product, amplicons were visualized on 6.0% polyacrylamide gels with appropriate size markers.The Mass Array(?) system is based on single base primer extension technology. Mass Array(?) technology uses matrix-assisted laser desorption ionization time-of-flight mass spectrometry to measure the mass of the extension product(s) directly and to correlate the detected mass with a specific genotype. For details on the protocol, please refer to "SNP Genotyping Using the Sequenom Mass Array(?) iPLEX Platform (http://www.sequenom.com/)".Statistical analysis was performed by SPSS version 20.0 statistical software. Data were presented as X ±or as percentages for discrete variables. Differences in quantitative traits between the two groups were analyzed by the independent sample t-test. Differences in discrete variables were analyzed using X2 test. Frequency distribution of genotypes and alleles were consistent with the Hardy-Weinberg equilibrium evaluating with X2 test. Statistical significance was taken at a P value<0.05 for all comparisons.Chapter III:Study on the association between SNP of telomerase reverse transcriptase gene and the presence of CHD in Han living in Yuxi CityCases in the study group were the same as the Chaptert Ⅱ. The controls included 257 (153 males and 104 females, aged 18-58, mean age:33.9) subjects whose allele was successfully identified. The inclusion criteria, exclusion criteria and the collection and processing of the samples are the same as Chaptert Ⅱ.Biochemical indexes, like TC, LDL-C, HDL-C, TG, HCY, UA and etc were measured by routine laboratory methods. The method and devices were the same as the Chaptert Ⅰ.Genetic polymorphisms in hTERT were determined by Mass Array(?) System (San Diego, CA) according to the manufacturer’s user guide. We used the upstream primers 5’ACGTTGGATGTGACACCCCCACAAGCTAAG3’and the downstream primer 5’ACGTTGGATGACAAAGGAGGAAAAGCAGGG3 to Ⅰ. amplify the specific hTERT fragment that covers rs2736100. A 90-bp hTERT fragment was PCR amplified with extracted genomic DNA as the template. The extension primer sequence was 5’TCCGTG TTGAGTGTTTCT3’. The thermocycling conditions were as follows:94℃ for 15 min, followed by 45 cycles of 94℃ for 20 s,56℃ for 30 s, and 72℃ for 60 s. To verify the size of the PCR product, amplicons were visualized on 6.0% polyacrylamide gels with appropriatesize markers. For further details on the protocol, please refer to "SNP Genotyping Using the Sequenom Mass Array(?)(?) iPLEX Platform (http://www.sequenom.com/)".The Statistical analysis is Similar to Chapter Ⅱ.Chapter Ⅳ:Exploration on the combination of genetic susceptibility and mathematical modelsTo explore the possibility of the combination of genetic susceptibility with traditional risk factors based predicating models. The subjects known alleles or genotypes of SDF-1 rs1801157 and hTERT rs1801157 were divided into groups according to their alleles or genotypes, the probabilities of each subject were calculated using formulas:Probability(CHDmale final)=1/{1+exp-(-6.846+0.102age-3.316HDL-C-0.6771nTG+0.4811nLp(a)+2.1951nHCY-0.7251nTBIL)}. Probability (CHDfemaiefinal)=1/{l+exp-(-15.356+0.180age-3.105HDL-C+2.9381nHCY+0.904 lny-GT)}.The average probabilities and standard deviation of each genotype group were calculated by gender, the differences between groups were tested by one-way ANOVA or the independent sample t-test. Statistical analysis was performed by SPSS version 20.0 statistical software. Statistical significance was taken at a P value<0.05 for all comparisons.Subjects in Chapter II known SDF-1 rs1801157 genotype were 84 Han CHD patients and 253 healthy Han controls; subjects in Chapter III known hTERT rs1801157 genotype were 84 Han patients with CHD and 257 healthy Han controls. The populations in Chapter II and III were pooled together respectively. The probabilities of each subject were calculated and the average probabilities were compared by gender and genotype using above formulas.ResultsChaptert I:Age, HCY, and HDL were common risk factors of CHD both in males and females. TG, Lp(a) and TBil were male-specific risk factors. y-GT was female-specific risk factor. Males and females CHD predicting models were: Probability(CHDmale fmal)= 1/{1+exp-(-6.846+0.102age-3.316HDL-0.6771nTG+ 0.4811nLp(a)+2.1951nHCY-0.7251nTBIL)},Probability(CHDfemaie fmal)=1/{1+exp-(-15.356+0.180age-3.105HDL+2.9381nHCY+0.9041ny-GT)}.The above models were validated in a validation population with high performance.Chaptert II:Of the rs1801157 polymorphism, the genotype frequencies in the CHD were:G/G (n=50,59.5%), G/A (n=30,35.7%) and A/A (n=4,4.8%), respectively; the genotype frequencies in the controls were:G/G (n=120,47.4%), G/A (n=111,43.9%) and A/A (n=22,8.7%), respectively.there were no significant differences existed between CHD patients and controls regarding all genotypes. The allele frequencies of A and G in CHD patients and controls were 77.4% and 22.6% and 69.4% and 30.6%, respectively. The G allele frequency in CHD patients was significantly higher than that in controls(P value=0.047). Significant differences of genotype and allele distributions in local ethnic Han (control), Yi, Hani and Dai were observed. The allele distribution in Yuxi Han showed significant difference comparing with foreign researches with different races.Chaptert III:Of the rs2736100 polymorphism, the genotype frequencies in the CHD were:G/G (n=15,17.9%), G/T (n=48,57.1%) and T/T (n=21,25.0%), respectively; the genotype frequencies in the controls were:G/G (n=25,9.7%), G/T (n=171,66.5%) and T/T (n=61,23.7%), respectively. The frequency of G/G genotype in CHD patients was significantly higher than that in controls (P value=0.044). The allele frequencies of G and T in CHD patients and controls were 46.4% and 53.6% and 43.0% and 57.0%, respectively. There were no significant differences existed between CHD patients and controls regarding allele frequencies. Significant differences of genotype and allele distributions in local ethnic Han (control), Yi, Hani and Dai were observed.Chaptert IV:Of females and males, at the two loci, rs1801157 and rs2736100, the mean probabilities of CHD were significantly higher in patients groups than that in control groups. At loci rs1801157, the mean probability of CHD in males with G allele was higher than that in males with A allele, conversely, the mean probability of CHD in females with G allele was lower than that in females with A allele. At loci rs2736100, the highest mean probabilities were subjects with G/G in both male and female.Conclusions1. Clinical biochemical indexes based CHD predicting models with high performance were established through strict statistical quality control and variables screening.2. The rs1801157 G Alleles is a correlative risk factor of CHD in our population, the gene locus has the ethnic and racial differences.3. The rs2736100 GG genotype is a correlative risk factor of CHD in our population, there are differences in the genotype distribution among different ethnic.4. There is a certain correlation between genetic susceptibility and risk assessment mathematical model, the combination of genetic susceptibility factors and traditional risk factors may improve the performance of CHD predicting strategy. |
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