Correlation between SCAP Genetic Polymorphism and Coronary Artery Disease in a Han population in Xinjiang, China

Correlation between SCAP Genetic Polymorphism and Coronary Artery Disease in a Han population in Xinjiang, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlation between SCAP Genetic Polymorphism and Coronary Artery Disease in a Han population in Xinjiang, China Xiao-Dong He, Zhen-Yan Fu, Dilare Adi, Yi-Tong Ma, Ying-Hong Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-147277/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract SREBP cleavage-activating protein (SCAP) plays a vital role in the modulation of cholesterol homeostasis, and cholesterol dysregulation is tightly associated with coronary artery disease (CAD). To investigate the correlation of the genetic polymorphism of SCAP with CAD, we conducted a case-control study of 528 CAD patients (case group) and 483 age- and sex- matched subjects from whom CAD was excluded (control group). Three tagSNPs (rs147215799, rs17079634 and rs59586735) in SCAP gene were genotyped in all participants, the genotype and allele frequencies of which were compared between two groups to determine their associations with CAD. We found rs17079634 showed significant difference in genotype distribution between the case and control group ( P =0.016). The difference was most prominent in a dominant model (TT vs. CT + CC, P =0.004). After adjustment for confounding factors, the difference remained statistically significant (OR =1.363, 95% confidence interval [CI]:1.022~1.818, P =0.035). Whereas no significant associations of the other two SNPs with CAD were observed ( P =0.393 for rs147215799 and 0.303 for rs59586735, respectively）. We drew conclusion that the SCAP genetic polymorphism rs17079634 was associated with CAD. Cardiac & Cardiovascular Systems Medical Genetics SREBP cleavage-activating protein (SCAP) single nucleotide polymorphism (SNP) coronary artery disease Introduction Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, and its incidence is still rapidly increasing, especially in China, meanwhile the burden arising from it in terms of mortality and financial cost is increasingly huge [ 1 , 2 ] . The etiology of CAD is extremely complex and there are still abundant problems awaiting resolution. Overall, numerous studies have indicated that CAD is a complicated polygenic disease and a result of interaction between an individual’s genetic composition and a variety of environmental risk factors [ 3 , 4 ] . Some traditional risk factors for CAD such as hypercholesterolemia and so on have been well-established. Besides these, many genetic alterations have been demonstrated to be associated with CAD and may contribute to CAD susceptibility [ 5 , 6 ] . However, most of the heritability of CAD remain unexplained, indicating that additional susceptibility loci await identification [ 7 , 8 ] . As an important macromolecule involved in the development and progression of atherosclerosis which acts as major pathophysiologic events, cholesterol level in vivo is modulated by genetic and environmental factors [ 9 ] , among them the sterol regulatory element binding protein (SREBP)- SREBP cleavage-activating protein (SCAP) pathway plays a crucial role. Being a major component in this pathway, SCAP binds with SREBPs through its carboxyl-terminal domain to forge SCAP-SREBP complex [ 10 ] . Working with insulin induced gene proteins (INSIGs), the complex transfers SREBPs from the endoplasmic reticulum to the Golgi apparatus via feedback regulation of cholesterol levels, finally affecting the synthesis of lipid [ 11 – 13 ] . Given the central role of the SREBP-SCAP pathway in the regulation of cholesterol, the variations in the SCAP locus might affect the development and progression of atherosclerosis, thereby contributing to the susceptibility of CAD. Thus far, several studies has investigated the association of genetic variants in SCAP gene with coronary heart disease [ 14 – 16 ] . However, the SNPs involved in these studies were mainly limited to rs12487736 and the results were at odds with each other. To further systematically evaluate the association of genetic polymorphism in SCAP with CAD, here we chose three tagSNPs in SCAP and conducted a case-control study in a Han population in Xinjiang, China. Materials And Methods Ethical approval of the study protocol The study was approved by the Ethics Committee of the of the First Affiliated Hospital of Xinjiang Medical University (Xinjiang, China) and was conducted according to the standards of the Declaration of Helsinki. All of the participants provided written informed consents for this study. Subjects This study was designed in a case - control study. In total, 1011 subjects were recruited in this study. Of them, 528 patients served as case group who was diagnosed with CAD at the First Affiliated Hospital of Xinjiang Medical University from January 2018 to December 2019, and 483 sex-and age-matched CAD-free individuals served as control group. CAD diagnosis was established by the presence of clinical symptoms such as chest pain and at least one significant coronary artery stenosis of ≥ 50% luminal diameter on coronary angiography,. All of subjects in control group underwent coronary angiography, with no coronary artery stenosis found in them. Patients with congenital or rheumatic heart disease, malignant tumor, multiple organ failure syndrome or other severe illness limiting life expectancy and drug addicts were excluded from this study. Most patients in the case group had received standardized treatment for coronary heart disease, including lipid-lowering drugs. All of subjects in the control group had not taken lipid-lowering drugs. The following information was collected: age, gender, smoking, diabetes mellitus and hypertension history, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein AⅠ (ApoAⅠ), apolipoprotein B (ApoB), lipoprotein (a) (Lp(a)) and fasting plasma glucose (FPG). Biochemical analysis Serum concentrations of TC, TG, HDL-C, LDL-C, apoAⅠ, apoB, Lp(a), FPG were measured using standard methods in the Department of Clinical Laboratory of the First Affiliated Hospital, Xinjiang Medical University (Xinjiang, China). Hypertension was defined as SBP ≧ 140mmHg and /or DBP ≧ 90mmHg. Smoking was defined as currently smoking cigarettes. Diabetes mellitus was defined as: classic symptoms of hyperglycemia or hyperglycemic crisis plus elevated plasma glucose, includes FPG ≧ 7.0mmol/L, or 2h PG ≧ 11.1mmol/L during OGTT, or a random plasma glucose ≧ 11.1mmol/L; In the absence of unequivocal symptoms of hyperglycemia, diagnosis requires repeating the glucose measurement on another day. SNP selection and Genotyping Blood samples were taken by using anticoagulant ethylene diamine tetraacetic acid (EDTA) tube, and standardized phenol-chloroform method was used to extract genomic DNA from peripheral leukocytes. Three tagSNPs in SCAP, rs147215799, rs59586735, and rs17079634, were selected using Haploview 4.2 software and 1000 Genomes Project ( https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/ ) with minor allele frequency (MAF) ≥ 0.05 and linkage disequilibrium patterns with r2 ≥ 0.8 as a cutoff [ 17 ] . SNP genotyping was performed using an improved multiplex ligase detection reaction method (iMLDR, Genesky Bio-Tech Cod., Ltd., Shanghai, China). We genotyped the selected SNP loci in one ligation reaction. Two multiplex PCR reactions were designed to amplify fragments covering all SNP loci. The PCR programme for both reactions was 95°C, 2 min; 11 cycles × (94°C, 20 s; 65°C– 0.5°C/cycle, 40 s; 72°C, 1 min 30 s); 24 cycles × (94°C, 20 s; 59°C, 30 s; 72°C, 1 min 30 s); 72°C, 2 min; hold at 4°C. The ligation cycling programme was 95°C, 2 min; 38 cycles × (94°C, 1 min; 56°C, 4 min); hold at 4°C. Half a microlitre of ligation product was loaded into the ABI 3730XL and the raw data were analyzed by GeneMapper 4.1. All primers, probes and labelling oligos were designed by and ordered from Genesky Biotechnologies Inc [ 18 ] . Statistical analyses Statistical analyses were carried out using SPSS version 22.0 (SPSS, Chicago, IL). The Hardy-Weinberg equilibrium was assessed by Chi-square test [ 19 ] . Continuous variables are expressed as means ± SD in case of normal distribution and as the median (interquartile range) in case of non-normal distribution. Differences in the categorical variables, such as the frequencies of smoking, hypertension, diabetes, and genotypes were analyzed using the chi-square test. After adjusting confounding variables, general linear model analysis was undertaken to test the association between SCAP genotypes and CAD. In addition, a two-tailed P -value less than 0.05 was considered to be statistically significant. Results Characteristics of study participants Table 1 showed the clinical characteristics of CAD patients (n = 528) and control subjects(n = 483). Between the two groups, there existed significant differences in following variables: hypertension, diabetes, cigarette smoking, as well as the serum concentrations of TC, HDL-C, LDL-C, ApoAⅠand FPG (all P 0.05). It was worthwhile noting that serum LDL-C and TC level were significantly higher in control group than in CAD group, the reason for which was the result from the prevalent use of lipid-lowering drugs in CAD patients Table 1 Clinical and metabolic characteristics of subjects. Statistically significant values are in italics. Data are presented as number of subjects (%) or mean standard ± deviation. Risk factors Case Control χ 2 or t P value Age (years) 56.84 ± 8.58 56.06 ± 1.15 1.234 0.217 Male, n (%) 287(54.4%) 263(54.5%) 0.001 0.974 Smoking, n (%) 227(43.0%) 176(36.4%) 4.151 0.042 Hypertension, n(%) 304(57.6%) 225 (46.6%) 11.178 0.001 Diabetes, n(%) 143 (27.1%) 69 (14.3%) 23.131 < 0.001 TG (mmol/L) 1.95 ± 1.20 1.91 ± 1.03 0.390 0.697 TC (mmol/L) 4.04 ± 1.04 4.30 ± 1.07 -3.980 < 0.001 HDL-C (mmol/L) 1.07 ± 0.29 1.13 ± 0.32 -2.862 0.004 LDL-C (mmol/L) 2.50 ± 0.87 2.64 ± 0.83 -2.488 0.013 ApoAⅠ (mmol/L) 1.20 ± 0.25 1.25 ± 0.25 -3.193 0.001 ApoB (mmol/L) 0.83 ± 0.26 0.86 ± 0.28 -1.568 0.117 Lp(a) (mmol/L) 210.33 ± 187.68 192.23 ± 159.55 1.605 0.109 FPG (mmol/L) 6.16 ± 2.59 5.44 ± 1.67 5.168 < 0.001 SCAP genotypes and alleles distributions between CAD group and control subjects The genotypes and allelse distributions of three selected SNPs in SCAP gene were listed in Table 2 . The distributions of SCAP genotypes for CAD group and control group were all in accordance with predicted Hardy-Weinberg equilibrium (H-WE) values. Of the three SNPs, rs17079634 was shown to be significantly associated with CAD, for its three genotypes, T/T, C/T and C/C, distributed significantly differently between CAD and control groups ( P = 0.016)(shown in Table 2 ). If analyzed by specific genetic models (indicated in Table 3 ), the differences were most prominent in dominant model (TT vs CC + CT, P = 0.008), followed by additional model (CT vs TT + CC, P = 0.02), whereas not present in recessive model (CC vs CT + TT, P = 0.27). Meanwhile, there also existed significant difference in the distributions of the two alleles (T and C) between the two groups ( P = 0.008). However, genotype distributions of the remaining two SNPs didn’t show different between the two groups ( P = 0.393 for rs147215799 and 0.303 for rs59586735, respectively) (shown in Table 2 ). Table 2 Distributions of genotypes and alleles of SNPs in subjects. Statistically significant values are in italics. SNP rs17079634 rs147215799 rs59586735 Genotype T/T C/T C/C T/T C/T C/C T/T C/T C/C CAD 356 157 15 461 66 1 148 274 106 Control 365 109 9 432 51 0 116 271 96 χ2 8.287 1.866 2.386 P value 0.016 0.393 0.303 Table 3 Distributions of rs17079634 genotypes according to different genetic model and alleles in two groups. Statistically significant values are in italics. Dominant model Recessive model Additional model Alleles TT CT + CC CC CT + TT CT CC + TT T C CAD 356 172 15 513 157 371 869 187 Control 365 118 9 474 109 374 839 127 χ2 8.116 0.004 1.041 0.308 6.688 0.010 8.002 0.005 P value The association of SCAP rs17079634 with serum lipid profile in control subjects To explore whether this polymorphism rs17079634 affects serum lipid profile, thereby leading to its association with CAD, we compared the serum lipid profile among different genotypes of this SNP only in the control subjects given CAD patients had prevalently taken lipid-lowering medications such as statins. The three genotypes didn’t show significant differences in serum lipid levels ( P = 0.934 for TG, 0.910 for TC, 0.284 for HDL-C, 0.992 for LDL-C, 0.805 for ApoAⅠ, 0.468 for apoB and 0.353 for Lp(a), respectively) (data not shown), which suggested that other underlying mechanisms contributed to the association. The association of SCAP rs17079634 with CAD after adjustment for confounding factors To further investigate whether the association of rs17079634 with CAD resulted from the confounding factors, we performed multivariable logistic regression analysis, with the results showed in Table 4 . After adjustment for confounding factors such as hypertension, cigarette smoking as well as the serum concentrations of glucose and apoAⅠ, the association remained significant in a dominant model (OR = 1.363, 95%CI:1.022 ~ 1.818, P = 0.035). Table 4 The association of SCAP rs17079634 with CAD after adjustment for confounding factors. Statistically significant values are in italics. Factors B S.E. Wald P Value OR 95% CI Smoking .250 .136 3.374 .066 1.284 0.983 ~ 1.677 HP .310 .147 6.328 . 012 1.400 1.077 ~ 1.820 DM .704 .170 17.053 . 000 2.022 1.448 ~ 2.824 Dominant Model .310 .147 4.442 . 035 1.363 1.022 ~ 1.818 Constant − .699 .216 10.435 . 001 .497 Discussion In this observational, candidate gene association study among 1011 participants, we examined the association between genetic variants in the SCAP locus and CAD susceptibility, and found rs17079634 in SCAP gene was significantly correlated with CAD, specifically, the frequencies of the CT genotype and C allele of SCAP rs17079634 were significantly higher in CAD patients than in control subjects, and still significant after the confounding factors were adjusted. This indicated that carriers of SCAP rs17079634 CT genotype or C allele have significantly increased risk of CAD. Abundant studies have demonstrated that, as a complicated polygenic disease, CAD is a consequence of interaction between an individual’s genetic composition and a variety of environmental risk factors. As to the former, the foundation for putative causative genes that may be involved in CAD is based on a candidate gene approach. Among the numerous impacts disposing CAD, lipid metabolism dysregulation is believed to play a crucial role, including abnomality in serum lipid profile and excessive cholesterol accumulation in coronary artery contributing to atherosclerosis, etc. Hence, genes involved in cholesterol metabolism are reasonable candidates for CAD. Actually, much attention has been focused on the association of genetic polymorphism in related genes with CAD. Serving as SREBP chaperone, SCAP is the sterol sensing receptor directly interacting with and controlling SREBP transcription factor activation, thereby playing a pivotal role in the modulation of cholesterol and other lipids synthesis. Accordingly, abnormality in SCAP gene and its expression may lead to disturbed cellular cholesterol homeostasis. [ 20 – 22 ] There has been some studies investigating the correlation of SCAP genetic polymorphism with CAD, although the polymorphism involved in these studies is mainly rs12487736 variant and the results were inconsistent. Fan et al investigated the expression of SCAP in human atheroma and the association of its allelic variants with sudden cardiac death(SCD), concluded that SCAP rs12487736 may contribute to SCD in early middle-aged men [ 16 ] , but Chen et al didn’t found rs12487736 was associated with premature coronary artery disease in a Chinese population [ 15 ] . In fact, as of 2020, neither the largest common variant association studies, nor the largest exome-sequencing-based rare, coding variant association studies has nominated SCAP as a genome-wide significant risk locus for CAD [ 23 – 24 ] . In our study, We drew positive conclusion, that is, rs17079634 in SCAP gene was significantly associated with CAD. We speculated that these existing studies were primarily based on European ancestry populations, and future studies in East Asian populations may find relevant in them. Given the crucial role of SCAP in homeostasis modulation of cholesterol and other lipids, it’s natural for researchers to consider if serum lipid levels mediate the correlation between SCAP rs17079634 and CAD, in other words, rs17079634 was associated with serum lipid, thereby leading to its correlation with CAD. To make out this, we performed association analysis between rs17079634 and serum lipid profile only in control subjects (for in case group, lipid-lowering agents such as statins have been used prevalently and their serum lipid levels have been affected). We came to a negative conclusion, that is, SCAP rs17079634 was not associated with blood lipid levels. Another explanation is, the genetic alterations in SCAP may involve in pathogenesis of atherosclerosis through disturbed cholesterol metabolism and accumulation locally at coronary arteries, thereby participating in the development and progression of CAD. Further functional analysis is warranted to verify the hypothesis. Taken together, we found SCAP rs17079634 was strongly associated with CAD, and the carriers of CT genotype or C allele may be at greater risk for CAD. However, several limitations of this study should be mentioned. First and foremost, our study enrolled solely Chinese Han subjects and can not draw ubiquitous conclusion. Second, sample size of the present study was relatively small, which may influence statistical significance and power. Third, the conclusions drew in this work was based only on observational study. Overall, it is reasonable to conduct further association studies with larger sample, rational design and involvement of diverse ethnics with different genetic backgrounds to validate our results, moreover, functional tests should also be put on the agenda to elucidate its underlying molecular mechanisms. Abbreviations SCAP SREBP cleavage-activating protein SREBP Sterol regulatory element binding protein CAD Coronary artery disease TG Triglyceride TC Total cholesterol LDL-C Low-density lipoprotein cholesterol HDL-C High-density lipoprotein cholesterol ApoAⅠ Apolipoprotein AⅠ ApoB Apolipoprotein B Lp(a) Lipoprotein (a) FPG Fasting plasma glucose Declarations Acknowledgements We thank all patients for participating in the present study. Authors’contributions X.D.H., Z.Y.F. and Y.T.M. conceived and designed the study. X.D.H., D.A., Y.H.W. and Y.T.W. performed the study. A.A., B.D.C. and F.L. analyzed the data. X.D.H. wrote the paper. All authors read and approved the final manuscript. Conflicts of interest The author(s) declare no competing interests. Funding This work was supported by the Open Project of Key Laboratory from Science and Technology Department of Xinjiang Uygur Autonomous Region [grant number 2020D04008]; The National Natural Science Foundation of China [grant numbers, 81970380]. References Benjamin, E. J. et al. Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation. 139 (10): e56-e528. 10.1161/CIR.0000000000000659 (2019) Roth, G. A. et al. Global, Regional, and National Burden of Cardiovascular Diseases for 10 Causes, 1990 to 2015. J. Am. Coll. Cardiol. 70: 1-25. 10.1016/j.jacc.2017.04.052 (2017) Marenberg, M. E., Risch, N., Berkman, L.F., Floderus, B., de Faire, U. Genetic susceptibility to death from coronary heart disease in a study of twins. N. Engl. J. Med. 330: 1041-1046. 10.1056/nejm199404143301503 (1994) Khera, A. V. et al. Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease. N. Engl. J. 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Cell. 177(1):132-145. 10.1016/j.cell.2019.02.015 (2019) Klarin, D. et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat. Genet. 49 (9): 1392-1397.10.1038/ng.3914 (2017) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-147277","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":9103342,"identity":"eb4bc1aa-3610-45ae-b679-ac3078a19bac","order_by":0,"name":"Xiao-Dong He","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiao-Dong","middleName":"","lastName":"He","suffix":""},{"id":9103343,"identity":"57ff7aab-583f-47a1-9cda-b65dcba516ed","order_by":1,"name":"Zhen-Yan Fu","email":"","orcid":"","institution":"First Affiliated 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University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying-Hong","middleName":"","lastName":"Wang","suffix":""},{"id":9103347,"identity":"52bef6fb-e299-4ea5-ba0d-bab175a8d5a2","order_by":5,"name":"Yong-Tao Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yong-Tao","middleName":"","lastName":"Wang","suffix":""},{"id":9103348,"identity":"c648b108-91c7-4e5d-a45e-76c6c22af77a","order_by":6,"name":"Asiya Abudesimu","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Asiya","middleName":"","lastName":"Abudesimu","suffix":""},{"id":9103349,"identity":"1df9fcf4-8104-4533-9b52-4e5ac5ff52e5","order_by":7,"name":"Bang-Dang Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bang-Dang","middleName":"","lastName":"Chen","suffix":""},{"id":9103350,"identity":"38f28d86-b802-4208-a40c-4813de1adef0","order_by":8,"name":"Fen Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fen","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2021-01-14 06:14:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-147277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-147277/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13654158,"identity":"05dbab4b-ea43-4d2b-98fc-9dee650de618","added_by":"auto","created_at":"2021-09-17 09:56:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":361883,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-147277/v1/c8d5cf91-f9ad-495b-8697-fa936df20f45.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCorrelation between SCAP Genetic Polymorphism and Coronary Artery Disease in a Han population in Xinjiang, China\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, and its incidence is still rapidly increasing, especially in China, meanwhile the burden arising from it in terms of mortality and financial cost is increasingly huge\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The etiology of CAD is extremely complex and there are still abundant problems awaiting resolution. Overall, numerous studies have indicated that CAD is a complicated polygenic disease and a result of interaction between an individual\u0026rsquo;s genetic composition and a variety of environmental risk factors\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Some traditional risk factors for CAD such as hypercholesterolemia and so on have been well-established. Besides these, many genetic alterations have been demonstrated to be associated with CAD and may contribute to CAD susceptibility\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. However, most of the heritability of CAD remain unexplained, indicating that additional susceptibility loci await identification\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAs an important macromolecule involved in the development and progression of atherosclerosis which acts as major pathophysiologic events, cholesterol level in vivo is modulated by genetic and environmental factors\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, among them the sterol regulatory element binding protein (SREBP)- SREBP cleavage-activating protein (SCAP) pathway plays a crucial role. Being a major component in this pathway, SCAP binds with SREBPs through its carboxyl-terminal domain to forge SCAP-SREBP complex\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Working with insulin induced gene proteins (INSIGs), the complex transfers SREBPs from the endoplasmic reticulum to the Golgi apparatus via feedback regulation of cholesterol levels, finally affecting the synthesis of lipid\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGiven the central role of the SREBP-SCAP pathway in the regulation of cholesterol, the variations in the \u003cem\u003eSCAP\u003c/em\u003e locus might affect the development and progression of atherosclerosis, thereby contributing to the susceptibility of CAD. Thus far, several studies has investigated the association of genetic variants in \u003cem\u003eSCAP\u003c/em\u003e gene with coronary heart disease\u003csup\u003e[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, the SNPs involved in these studies were mainly limited to rs12487736 and the results were at odds with each other. To further systematically evaluate the association of genetic polymorphism in \u003cem\u003eSCAP\u003c/em\u003e with CAD, here we chose three tagSNPs in \u003cem\u003eSCAP\u003c/em\u003e and conducted a case-control study in a Han population in Xinjiang, China.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eEthical approval of the study protocol\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the of the First Affiliated Hospital of Xinjiang Medical University (Xinjiang, China) and was conducted according to the standards of the Declaration of Helsinki. All of the participants provided written informed consents for this study.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eSubjects\u003c/h2\u003e\n\u003cp\u003eThis study was designed in a case - control study. In total, 1011 subjects were recruited in this study. Of them, 528 patients served as case group who was diagnosed with CAD at the First Affiliated Hospital of Xinjiang Medical University from January 2018 to December 2019, and 483 sex-and age-matched CAD-free individuals served as control group. CAD diagnosis was established by the presence of clinical symptoms such as chest pain and at least one significant coronary artery stenosis of \u0026ge;\u0026thinsp;50% luminal diameter on coronary angiography,. All of subjects in control group underwent coronary angiography, with no coronary artery stenosis found in them. Patients with congenital or rheumatic heart disease, malignant tumor, multiple organ failure syndrome or other severe illness limiting life expectancy and drug addicts were excluded from this study.\u003c/p\u003e\n\u003cp\u003eMost patients in the case group had received standardized treatment for coronary heart disease, including lipid-lowering drugs. All of subjects in the control group had not taken lipid-lowering drugs.\u003c/p\u003e\n\u003cp\u003eThe following information was collected: age, gender, smoking, diabetes mellitus and hypertension history, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein AⅠ (ApoAⅠ), apolipoprotein B (ApoB), lipoprotein (a) (Lp(a)) and fasting plasma glucose (FPG).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eBiochemical analysis\u003c/h2\u003e\n\u003cp\u003eSerum concentrations of TC, TG, HDL-C, LDL-C, apoAⅠ, apoB, Lp(a), FPG were measured using standard methods in the Department of Clinical Laboratory of the First Affiliated Hospital, Xinjiang Medical University (Xinjiang, China). Hypertension was defined as SBP\u0026thinsp;≧\u0026thinsp;140mmHg and /or DBP\u0026thinsp;≧\u0026thinsp;90mmHg. Smoking was defined as currently smoking cigarettes. Diabetes mellitus was defined as: classic symptoms of hyperglycemia or hyperglycemic crisis plus elevated plasma glucose, includes FPG\u0026thinsp;≧\u0026thinsp;7.0mmol/L, or 2h PG\u0026thinsp;≧\u0026thinsp;11.1mmol/L during OGTT, or a random plasma glucose\u0026thinsp;≧\u0026thinsp;11.1mmol/L; In the absence of unequivocal symptoms of hyperglycemia, diagnosis requires repeating the glucose measurement on another day.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eSNP selection and Genotyping\u003c/h2\u003e\n\u003cp\u003eBlood samples were taken by using anticoagulant ethylene diamine tetraacetic acid (EDTA) tube, and standardized phenol-chloroform method was used to extract genomic DNA from peripheral leukocytes. Three tagSNPs in SCAP, rs147215799, rs59586735, and rs17079634, were selected using Haploview 4.2 software and 1000 Genomes Project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/\u003c/span\u003e\u003c/span\u003e) with minor allele frequency (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;0.05 and linkage disequilibrium patterns with r2\u0026thinsp;\u0026ge;\u0026thinsp;0.8 as a cutoff\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSNP genotyping was performed using an improved multiplex ligase detection reaction method (iMLDR, Genesky Bio-Tech Cod., Ltd., Shanghai, China). We genotyped the selected SNP loci in one ligation reaction. Two multiplex PCR reactions were designed to amplify fragments covering all SNP loci. The PCR programme for both reactions was 95\u0026deg;C, 2 min; 11 cycles \u0026times; (94\u0026deg;C, 20 s; 65\u0026deg;C\u0026ndash; 0.5\u0026deg;C/cycle, 40 s; 72\u0026deg;C, 1 min 30 s); 24 cycles \u0026times; (94\u0026deg;C, 20 s; 59\u0026deg;C, 30 s; 72\u0026deg;C, 1 min 30 s); 72\u0026deg;C, 2 min; hold at 4\u0026deg;C. The ligation cycling programme was 95\u0026deg;C, 2 min; 38 cycles \u0026times; (94\u0026deg;C, 1 min; 56\u0026deg;C, 4 min); hold at 4\u0026deg;C. Half a microlitre of ligation product was loaded into the ABI 3730XL and the raw data were analyzed by GeneMapper 4.1. All primers, probes and labelling oligos were designed by and ordered from Genesky Biotechnologies Inc\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analyses\u003c/h2\u003e\n\u003cp\u003eStatistical analyses were carried out using SPSS version 22.0 (SPSS, Chicago, IL). The Hardy-Weinberg equilibrium was assessed by Chi-square test\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Continuous variables are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD in case of normal distribution and as the median (interquartile range) in case of non-normal distribution. Differences in the categorical variables, such as the frequencies of smoking, hypertension, diabetes, and genotypes were analyzed using the chi-square test. After adjusting confounding variables, general linear model analysis was undertaken to test the association between SCAP genotypes and CAD. In addition, a two-tailed \u003cem\u003eP\u003c/em\u003e-value less than 0.05 was considered to be statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1 showed the clinical characteristics of CAD patients (n\u0026thinsp;=\u0026thinsp;528) and control subjects(n\u0026thinsp;=\u0026thinsp;483). Between the two groups, there existed significant differences in following variables: hypertension, diabetes, cigarette smoking, as well as the serum concentrations of TC, HDL-C, LDL-C, ApoAⅠand FPG (all\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were present in following variables between the two groups: ages, sex, serum concentrations of TG, ApoB and Lp(a) (all\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). It was worthwhile noting that serum LDL-C and TC level were significantly higher in control group than in CAD group, the reason for which was the result from the prevalent use of lipid-lowering drugs in CAD patients\u003c/div\u003e\n\u003cdiv class=\"CaptionNumber\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" style=\"width: 410px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClinical and metabolic characteristics of subjects. Statistically significant values are in italics. Data are presented as number of subjects (%) or mean standard\u0026thinsp;\u0026plusmn;\u0026thinsp;deviation.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eRisk factors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003eCase\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 39.0852px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e or t\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 45.9148px;\" align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e56.84\u0026thinsp;\u0026plusmn;\u0026thinsp;8.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e56.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e0.217\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eMale, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e287(54.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e263(54.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e0.974\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eSmoking, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e227(43.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e176(36.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e0.042\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eHypertension, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e304(57.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e225 (46.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eDiabetes, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e143 (27.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e69 (14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eTG (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.390\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.697\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eTC (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.980\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.862\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eApoAⅠ (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eApoB (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eLp(a) (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e210.33\u0026thinsp;\u0026plusmn;\u0026thinsp;187.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e192.23\u0026thinsp;\u0026plusmn;\u0026thinsp;159.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.605\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.109\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 110px;\" align=\"left\"\u003e\n\u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e6.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 91px;\" align=\"left\"\u003e\n\u003cp\u003e5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 39.0852px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 45.9148px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cspan class=\"BoldItalic\"\u003eSCAP\u003c/span\u003egenotypes and alleles distributions between CAD group and control subjects\u003c/h2\u003e\n\u003cp\u003eThe genotypes and allelse distributions of three selected SNPs in \u003cem\u003eSCAP\u003c/em\u003e gene were listed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The distributions of \u003cem\u003eSCAP\u003c/em\u003e genotypes for CAD group and control group were all in accordance with predicted Hardy-Weinberg equilibrium (H-WE) values. Of the three SNPs, rs17079634 was shown to be significantly associated with CAD, for its three genotypes, T/T, C/T and C/C, distributed significantly differently between CAD and control groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016)(shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). If analyzed by specific genetic models (indicated in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), the differences were most prominent in dominant model (TT vs CC\u0026thinsp;+\u0026thinsp;CT, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), followed by additional model (CT vs TT\u0026thinsp;+\u0026thinsp;CC, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), whereas not present in recessive model (CC vs CT\u0026thinsp;+\u0026thinsp;TT, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27). Meanwhile, there also existed significant difference in the distributions of the two alleles (T and C) between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). However, genotype distributions of the remaining two SNPs didn\u0026rsquo;t show different between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.393 for rs147215799 and 0.303 for rs59586735, respectively) (shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDistributions of genotypes and alleles of SNPs in subjects. Statistically significant values are in italics.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSNP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ers17079634\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ers147215799\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ers59586735\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGenotype\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT/T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC/T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC/C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT/T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC/T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC/C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT/T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC/T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC/C\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e461\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e274\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e432\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e8.287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.866\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.386\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e0.016\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.303\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDistributions of rs17079634 genotypes according to different genetic model and alleles in two groups. Statistically significant values are in italics.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDominant model\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRecessive model\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAdditional model\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAlleles\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e869\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e187\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e474\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e109\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e839\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8.116\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.041\u003c/p\u003e\n\u003cp\u003e0.308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e6.688\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e0.010\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8.002\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eThe association of\u003cspan class=\"BoldItalic\"\u003eSCAP\u003c/span\u003ers17079634 with serum lipid profile in control subjects\u003c/h2\u003e\n\u003cp\u003eTo explore whether this polymorphism rs17079634 affects serum lipid profile, thereby leading to its association with CAD, we compared the serum lipid profile among different genotypes of this SNP only in the control subjects given CAD patients had prevalently taken lipid-lowering medications such as statins. The three genotypes didn\u0026rsquo;t show significant differences in serum lipid levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.934 for TG, 0.910 for TC, 0.284 for HDL-C, 0.992 for LDL-C, 0.805 for ApoAⅠ, 0.468 for apoB and 0.353 for Lp(a), respectively) (data not shown), which suggested that other underlying mechanisms contributed to the association.\u003c/p\u003e\n\u003ch2\u003eThe association of\u003cspan class=\"BoldItalic\"\u003eSCAP\u003c/span\u003ers17079634 with CAD after adjustment for confounding factors\u003c/h2\u003e\n\u003cp\u003eTo further investigate whether the association of rs17079634 with CAD resulted from the confounding factors, we performed multivariable logistic regression analysis, with the results showed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. After adjustment for confounding factors such as hypertension, cigarette smoking as well as the serum concentrations of glucose and apoAⅠ, the association remained significant in a dominant model (OR\u0026thinsp;=\u0026thinsp;1.363, 95%CI:1.022\u0026thinsp;~\u0026thinsp;1.818, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe association of \u003cem\u003eSCAP\u003c/em\u003e rs17079634 with CAD after adjustment for confounding factors. Statistically significant values are in italics.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS.E.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWald\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.250\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.983\u0026thinsp;~\u0026thinsp;1.677\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.328\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003cem\u003e012\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.077\u0026thinsp;~\u0026thinsp;1.820\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.704\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003cem\u003e000\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.448\u0026thinsp;~\u0026thinsp;2.824\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDominant Model\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003cem\u003e035\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.363\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.022\u0026thinsp;~\u0026thinsp;1.818\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.699\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003cem\u003e001\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this observational, candidate gene association study among 1011 participants, we examined the association between genetic variants in the SCAP locus and CAD susceptibility, and found rs17079634 in SCAP gene was significantly correlated with CAD, specifically, the frequencies of the CT genotype and C allele of \u003cem\u003eSCAP\u003c/em\u003e rs17079634 were significantly higher in CAD patients than in control subjects, and still significant after the confounding factors were adjusted. This indicated that carriers of \u003cem\u003eSCAP\u003c/em\u003e rs17079634 CT genotype or C allele have significantly increased risk of CAD.\u003c/p\u003e\u003cp\u003eAbundant studies have demonstrated that, as a complicated polygenic disease, CAD is a consequence of interaction between an individual\u0026rsquo;s genetic composition and a variety of environmental risk factors. As to the former, the foundation for putative causative genes that may be involved in CAD is based on a candidate gene approach. Among the numerous impacts disposing CAD, lipid metabolism dysregulation is believed to play a crucial role, including abnomality in serum lipid profile and excessive cholesterol accumulation in coronary artery contributing to atherosclerosis, etc. Hence, genes involved in cholesterol metabolism are reasonable candidates for CAD. Actually, much attention has been focused on the association of genetic polymorphism in related genes with CAD. Serving as SREBP chaperone, SCAP is the sterol sensing receptor directly interacting with and controlling SREBP transcription factor activation, thereby playing a pivotal role in the modulation of cholesterol and other lipids synthesis. Accordingly, abnormality in \u003cem\u003eSCAP\u003c/em\u003e gene and its expression may lead to disturbed cellular cholesterol homeostasis. \u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThere has been some studies investigating the correlation of \u003cem\u003eSCAP\u003c/em\u003e genetic polymorphism with CAD, although the polymorphism involved in these studies is mainly rs12487736 variant and the results were inconsistent. Fan et al investigated the expression of \u003cem\u003eSCAP\u003c/em\u003e in human atheroma and the association of its allelic variants with sudden cardiac death(SCD), concluded that \u003cem\u003eSCAP\u003c/em\u003e rs12487736 may contribute to SCD in early middle-aged men\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, but Chen et al didn\u0026rsquo;t found rs12487736 was associated with premature coronary artery disease in a Chinese population\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. In fact, as of 2020, neither the largest common variant association studies, nor the largest exome-sequencing-based rare, coding variant association studies has nominated SCAP as a genome-wide significant risk locus for CAD \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In our study, We drew positive conclusion, that is, rs17079634 in SCAP gene was significantly associated with CAD. We speculated that these existing studies were primarily based on European ancestry populations, and future studies in East Asian populations may find relevant in them.\u003c/p\u003e\u003cp\u003eGiven the crucial role of SCAP in homeostasis modulation of cholesterol and other lipids, it\u0026rsquo;s natural for researchers to consider if serum lipid levels mediate the correlation between \u003cem\u003eSCAP\u003c/em\u003e rs17079634 and CAD, in other words, rs17079634 was associated with serum lipid, thereby leading to its correlation with CAD. To make out this, we performed association analysis between rs17079634 and serum lipid profile only in control subjects (for in case group, lipid-lowering agents such as statins have been used prevalently and their serum lipid levels have been affected). We came to a negative conclusion, that is, \u003cem\u003eSCAP\u003c/em\u003e rs17079634 was not associated with blood lipid levels. Another explanation is, the genetic alterations in \u003cem\u003eSCAP\u003c/em\u003e may involve in pathogenesis of atherosclerosis through disturbed cholesterol metabolism and accumulation locally at coronary arteries, thereby participating in the development and progression of CAD. Further functional analysis is warranted to verify the hypothesis.\u003c/p\u003e\u003cp\u003eTaken together, we found \u003cem\u003eSCAP\u003c/em\u003e rs17079634 was strongly associated with CAD, and the carriers of CT genotype or C allele may be at greater risk for CAD. However, several limitations of this study should be mentioned. First and foremost, our study enrolled solely Chinese Han subjects and can not draw ubiquitous conclusion. Second, sample size of the present study was relatively small, which may influence statistical significance and power. Third, the conclusions drew in this work was based only on observational study. Overall, it is reasonable to conduct further association studies with larger sample, rational design and involvement of diverse ethnics with different genetic backgrounds to validate our results, moreover, functional tests should also be put on the agenda to elucidate its underlying molecular mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eSCAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eSREBP cleavage-activating protein\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eSREBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eSterol regulatory element binding protein\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eCAD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eCoronary artery disease\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eTG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eTriglyceride\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eTotal cholesterol\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eLDL-C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eHDL-C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eApoAⅠ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eApolipoprotein AⅠ\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eApoB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eApolipoprotein B\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eLp(a)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eLipoprotein (a)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eFPG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eFasting plasma glucose\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe thank all patients for participating in the present study.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo;contributions\u003c/h2\u003e\n\u003cp\u003eX.D.H., Z.Y.F. and Y.T.M. conceived and designed the study. X.D.H., D.A., Y.H.W. and Y.T.W. performed the study. A.A., B.D.C. and F.L. analyzed the data. X.D.H. wrote the paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Open Project of Key Laboratory from Science and Technology Department of Xinjiang Uygur Autonomous Region [grant number 2020D04008]; The National Natural Science Foundation of China [grant numbers, 81970380].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBenjamin, E. J. et al. Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation. 139 (10): e56-e528. 10.1161/CIR.0000000000000659 (2019)\u003c/li\u003e\n\u003cli\u003eRoth, G. A. et al. 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Point mutation in luminal loop 7 of Scap protein blocks interaction with loop 1 and abolishes movement to Golgi. J. Biol. Chem. 288: 14059-14067. 10.1074/jbc.M113.469528 (2013)\u003c/li\u003e\n\u003cli\u003eMusunuru, K., Kathiresan, S. Genetics of Common, Complex Coronary Artery Disease. Cell. 177(1):132-145. 10.1016/j.cell.2019.02.015 (2019)\u003c/li\u003e\n\u003cli\u003eKlarin, D. et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat. Genet. 49 (9): 1392-1397.10.1038/ng.3914 (2017)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SREBP cleavage-activating protein (SCAP), single nucleotide polymorphism (SNP), coronary artery disease ","lastPublishedDoi":"10.21203/rs.3.rs-147277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-147277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\tSREBP cleavage-activating protein (SCAP) plays a vital role in the modulation of cholesterol homeostasis, and cholesterol dysregulation is tightly associated with coronary artery disease (CAD). To investigate the correlation of the genetic polymorphism of \u003cem\u003eSCAP \u003c/em\u003ewith CAD,\u003cstrong\u003e \u003c/strong\u003ewe conducted a case-control study of 528 CAD patients (case group) and 483 age- and sex- matched subjects from whom CAD was excluded (control group). Three tagSNPs (rs147215799, rs17079634 and rs59586735) in \u003cem\u003eSCAP\u003c/em\u003e gene were genotyped in all participants, the genotype and allele frequencies of which were compared between two groups to determine their associations with CAD. We found rs17079634 showed significant difference in genotype distribution between the case and control group (\u003cem\u003eP\u003c/em\u003e=0.016). The difference was most prominent in a dominant model (TT vs. CT + CC, \u003cem\u003eP\u003c/em\u003e=0.004). After adjustment for confounding factors, the difference remained statistically significant (OR =1.363, 95% confidence interval [CI]:1.022~1.818, \u003cem\u003eP\u003c/em\u003e=0.035). Whereas no significant associations of the other two SNPs with CAD were observed (\u003cem\u003eP\u003c/em\u003e=0.393 for rs147215799 and 0.303 for rs59586735, respectively）. We drew conclusion that the \u003cem\u003eSCAP\u003c/em\u003e genetic polymorphism rs17079634 was associated with CAD.\u003c/p\u003e","manuscriptTitle":"Correlation between SCAP Genetic Polymorphism and Coronary Artery Disease in a Han population in Xinjiang, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-01-29 16:36:33","doi":"10.21203/rs.3.rs-147277/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a0c7318-ff67-465d-9898-b306f46ca425","owner":[],"postedDate":"January 29th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2147707,"name":"Cardiac \u0026 Cardiovascular Systems"},{"id":2147708,"name":"Medical Genetics"}],"tags":[],"updatedAt":"2021-02-11T13:44:11+00:00","versionOfRecord":[],"versionCreatedAt":"2021-01-29 16:36:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-147277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-147277","identity":"rs-147277","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}