冠状动脉粥样硬化血液生物标志谱与CT影像特征的相关性研究
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摘要
背景和目的
研究表明血液中代谢紊乱的血脂及炎性因子可致血管内膜损伤,并逐步发展为动脉粥样硬化。目前临床上常用影像学征像评价冠状动脉管腔狭窄程度及粥样斑块的形状特征,但难以区分斑块性质及成分。本课题开展相关研究并提出”血管内斑块性质的血液印记学假说”,即通过血液相关基因表达谱、炎性因子谱与血脂代谢谱的分析,间接反映冠脉斑块性质及成分差异,以预警心血管疾病风险度。
对象与方法
通过双源CT分析健康对照组,无临床症状组与有临床症状组共205例冠状动脉影像特征,根据斑块性质分为A组(未见斑块对照),B组(钙化斑块),C组(非钙化斑块和混合斑块患者)。利用GeXP高通量技术分析外周血IL-1β,IL-6, IL-8, IFN-γ, MCP-1, VWF, MTHFR, L-Selectin, TNFα,Ubiquitin, MCSF, ICAM-1, ID2, HMOX-1, LDL-R等15种与心血管疾病相关基因的表达；液体芯片技术检测IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IFN-y, MCP-1, TNFα, GM-CSF等10种细胞因子的水平；日立全自动生化分析仪检测血脂水平及超敏C-反应蛋白(hsCRP)等生化指标；气相色谱法检测外周血n-3多不饱和脂肪酸。
结果
一、C组与对照组比较,收缩压和GLU、TC、TG、APOB、APOC2及hsCRP共7项指标升高,差异具有统计学意义。利用SPSS16.0软件进行两类判别分析,判断C组的正确率为85.7%。ROC (receiver operating characteristic)分析显示外周血生化指标联合检测鉴别C组和对照组的曲线下面积(area under curve, AUC)为0.720,敏感性和特异性分别为60.5%和76.8%。钙化斑块组与对照组之间未见有统计学差异指标。
二、利用GeXP技术建立和优化了同时检测17个基因的多重基因表达体系。批内(CV%)为3.695-12.537%,批间(CV%)为4.405-13.405%。用该方法对斑块组和对照组外周血基因表达水平进行检测,结果显示C组(排除糖尿病)IL-1β、IL-6、IL-8和MCP1基因表达增高。
三、液体芯片技术检测外周血细胞因子的结果显示C组与对照组相比IL-6水平显著增高(P<0.05)。ROC分析显示外周血IL-6检测鉴别两组间的曲线下面积为0.592,敏感性和特异性分别为78.0%和39.7%。
四、建立了用气相色谱检测n-3多不饱和脂肪酸(n-3 polyunsaturated fatty acids, n-3)的方法,初步分析了n-3在不同动脉粥样硬化斑块中的含量以及n-6与n-3比值,结果显示C组和钙化斑块组n-6/n-3有升高趋势。
五、IL-6联合外周血生化指标建立了区分C组与对照组的诊断模型,ROC曲线分析显示该模型对两组间的鉴别效能较高(AUC为0.746),敏感性和特异性分别为78.0%和65.1%。
结论
本研究建立了同时检测外周血17个基因的多重基因表达和气相色谱检测n-3的方法。联合外周血生化、基因表达、细胞因子水平等多指标检测,建立了区分冠状动脉粥样硬化非钙化斑块和混合斑块组的诊断模型,利用该模型将风险度较高的冠状动脉粥样硬化患者筛选出来,为早期发现和预警高风险心血管疾病提供实验室依据。
Background and Objective:Studies showed that the metabolic disorder of lipid and inflammatory factor in blood might lead to the injury of endangium, and then generate artherosclerosis gradually. The narrow degree of coronary artery and atheromatous plaque texture are evaluated by imageology characteristics. However, it is difficult to distinguish plaque characteristics and composition. Our team plans to carry out related studies and raise the blood imprinting theory that reflects the plaque characteristics. We want to evaluate the risk degree of cardiovascular disease by analyzing blood related gene expressing profile, cytokines profile and lipid metabolism profile.
Material and Methods:Up to 205 imageology characteristics from health control group, having no clinical symptom group and having clinical symptom group are analyzed by Dual source CT. Subjects were divided into group A (control group without plaque), group B (calcification group) and group C (none calcification group, and combination group). Using GeXP technology to analyze cardiovascular disease related gene expression profile in peripheral blood including IL-β, IL-6, IL-8, IFN-γ, MCP-1, VWF, MTHFR, L-Selectin, TNFa, Ubiquitin, MCSF, ICAM-1, ID2, HMOX-1 and LDL-R.10 items of cytokines expression profile were detected by liquid chip method including IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IFN-γ, MCP-1, TNFa and GM-CSF. The variations of blood lipid and hsCRP level were evaluated on Hitachi automatic analyzer. The n-3 index from peripheral blood was detected by Gas Chromatography method.
Results:Compared with control group, the level of systolic blood pressure, GLU, TC, TG, APOB, APOC2 and hsCRP increased in group C and it has significant statistic difference. Discriminant analysis showed that 85.7% of group C cases were correctly classifiled. These 7 items yield an AUC (area under curve) of 0.720 in discriminating group C patients from control group with sensitivity of 60.5% and specificity of 76.8%. There was no significant different item between calcification group and control group.
Multi-PCR systems for analyzing 17 genes were set up and optimized using GeXP technic. The within-run and between-run CV values were 3.695%-12.537% and 4.405%-13.405% respectively. We detected 15-gene expression profile in plaque group and control group with the method described as above, and found expression of IL-1β,IL-6, IL-8, MCP1 were significantly different between group C (without diabetes) and control group.
Peripheral blood cytokine levels showed that IL-6 of group C increased significantly compared with control group. IL-6 yields an AUC of 0.592 in discriminating group C patients from control group with sensitivity of 78.0% and specificity of 39.7%.
n-3 index and n-6/n-3 from peripheral blood of different plaque group were analyzed and results showed an increasing trend in plaque group.
A diagnostic model was set up by IL-6 combined with biochemical items, which yield an AUC of 0.746 in discriminating group C from control group with sensitivity of 78.0% and specificity of 65.1%.
Conclusion:In conclusion, Multi-PCR systems for analyzing 17 genes and Gas Chromatography for detecting n-3 index from peripheral blood were set up. Diagnostic model was established to distinguish group C from control group with detecting the level of blood lipid, peripheral blood gene expression profile and cytokines, which could provide useful laboratory information for risk assessment of cardiovascular disease patients.
研究表明血液中代谢紊乱的血脂及炎性因子可致血管内膜损伤,并逐步发展为动脉粥样硬化。目前临床上常用影像学征像评价冠状动脉管腔狭窄程度及粥样斑块的形状特征,但难以区分斑块性质及成分。本课题开展相关研究并提出”血管内斑块性质的血液印记学假说”,即通过血液相关基因表达谱、炎性因子谱与血脂代谢谱的分析,间接反映冠脉斑块性质及成分差异,以预警心血管疾病风险度。
对象与方法
通过双源CT分析健康对照组,无临床症状组与有临床症状组共205例冠状动脉影像特征,根据斑块性质分为A组(未见斑块对照),B组(钙化斑块),C组(非钙化斑块和混合斑块患者)。利用GeXP高通量技术分析外周血IL-1β,IL-6, IL-8, IFN-γ, MCP-1, VWF, MTHFR, L-Selectin, TNFα,Ubiquitin, MCSF, ICAM-1, ID2, HMOX-1, LDL-R等15种与心血管疾病相关基因的表达；液体芯片技术检测IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IFN-y, MCP-1, TNFα, GM-CSF等10种细胞因子的水平；日立全自动生化分析仪检测血脂水平及超敏C-反应蛋白(hsCRP)等生化指标；气相色谱法检测外周血n-3多不饱和脂肪酸。
结果
一、C组与对照组比较,收缩压和GLU、TC、TG、APOB、APOC2及hsCRP共7项指标升高,差异具有统计学意义。利用SPSS16.0软件进行两类判别分析,判断C组的正确率为85.7%。ROC (receiver operating characteristic)分析显示外周血生化指标联合检测鉴别C组和对照组的曲线下面积(area under curve, AUC)为0.720,敏感性和特异性分别为60.5%和76.8%。钙化斑块组与对照组之间未见有统计学差异指标。
二、利用GeXP技术建立和优化了同时检测17个基因的多重基因表达体系。批内(CV%)为3.695-12.537%,批间(CV%)为4.405-13.405%。用该方法对斑块组和对照组外周血基因表达水平进行检测,结果显示C组(排除糖尿病)IL-1β、IL-6、IL-8和MCP1基因表达增高。
三、液体芯片技术检测外周血细胞因子的结果显示C组与对照组相比IL-6水平显著增高(P<0.05)。ROC分析显示外周血IL-6检测鉴别两组间的曲线下面积为0.592,敏感性和特异性分别为78.0%和39.7%。
四、建立了用气相色谱检测n-3多不饱和脂肪酸(n-3 polyunsaturated fatty acids, n-3)的方法,初步分析了n-3在不同动脉粥样硬化斑块中的含量以及n-6与n-3比值,结果显示C组和钙化斑块组n-6/n-3有升高趋势。
五、IL-6联合外周血生化指标建立了区分C组与对照组的诊断模型,ROC曲线分析显示该模型对两组间的鉴别效能较高(AUC为0.746),敏感性和特异性分别为78.0%和65.1%。
结论
本研究建立了同时检测外周血17个基因的多重基因表达和气相色谱检测n-3的方法。联合外周血生化、基因表达、细胞因子水平等多指标检测,建立了区分冠状动脉粥样硬化非钙化斑块和混合斑块组的诊断模型,利用该模型将风险度较高的冠状动脉粥样硬化患者筛选出来,为早期发现和预警高风险心血管疾病提供实验室依据。
Background and Objective:Studies showed that the metabolic disorder of lipid and inflammatory factor in blood might lead to the injury of endangium, and then generate artherosclerosis gradually. The narrow degree of coronary artery and atheromatous plaque texture are evaluated by imageology characteristics. However, it is difficult to distinguish plaque characteristics and composition. Our team plans to carry out related studies and raise the blood imprinting theory that reflects the plaque characteristics. We want to evaluate the risk degree of cardiovascular disease by analyzing blood related gene expressing profile, cytokines profile and lipid metabolism profile.
Material and Methods:Up to 205 imageology characteristics from health control group, having no clinical symptom group and having clinical symptom group are analyzed by Dual source CT. Subjects were divided into group A (control group without plaque), group B (calcification group) and group C (none calcification group, and combination group). Using GeXP technology to analyze cardiovascular disease related gene expression profile in peripheral blood including IL-β, IL-6, IL-8, IFN-γ, MCP-1, VWF, MTHFR, L-Selectin, TNFa, Ubiquitin, MCSF, ICAM-1, ID2, HMOX-1 and LDL-R.10 items of cytokines expression profile were detected by liquid chip method including IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IFN-γ, MCP-1, TNFa and GM-CSF. The variations of blood lipid and hsCRP level were evaluated on Hitachi automatic analyzer. The n-3 index from peripheral blood was detected by Gas Chromatography method.
Results:Compared with control group, the level of systolic blood pressure, GLU, TC, TG, APOB, APOC2 and hsCRP increased in group C and it has significant statistic difference. Discriminant analysis showed that 85.7% of group C cases were correctly classifiled. These 7 items yield an AUC (area under curve) of 0.720 in discriminating group C patients from control group with sensitivity of 60.5% and specificity of 76.8%. There was no significant different item between calcification group and control group.
Multi-PCR systems for analyzing 17 genes were set up and optimized using GeXP technic. The within-run and between-run CV values were 3.695%-12.537% and 4.405%-13.405% respectively. We detected 15-gene expression profile in plaque group and control group with the method described as above, and found expression of IL-1β,IL-6, IL-8, MCP1 were significantly different between group C (without diabetes) and control group.
Peripheral blood cytokine levels showed that IL-6 of group C increased significantly compared with control group. IL-6 yields an AUC of 0.592 in discriminating group C patients from control group with sensitivity of 78.0% and specificity of 39.7%.
n-3 index and n-6/n-3 from peripheral blood of different plaque group were analyzed and results showed an increasing trend in plaque group.
A diagnostic model was set up by IL-6 combined with biochemical items, which yield an AUC of 0.746 in discriminating group C from control group with sensitivity of 78.0% and specificity of 65.1%.
Conclusion:In conclusion, Multi-PCR systems for analyzing 17 genes and Gas Chromatography for detecting n-3 index from peripheral blood were set up. Diagnostic model was established to distinguish group C from control group with detecting the level of blood lipid, peripheral blood gene expression profile and cytokines, which could provide useful laboratory information for risk assessment of cardiovascular disease patients.
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73. Hojo Y, Ikeda U, Katsuki T, et al. Interleukin6 expression in coronary circulation after coronary angioplasty as a risk factor for restenosis. Heart.2000,84:83-87.
74. Boutros C, Somasundar P, Razzak A, et al. Omega-3 fatty acids:investigations from cytokine regulation to pancreatic cancer gene suppression. Arch Surg.2010, 145(6):515-520.
75. Zwart SR, Pierson D, Mehta S, et al. Capacity of omega-3 fatty acids or eicosapentaenoic acid to counteract weightlessness-induced bone loss by inhibiting NF-kappaB activation:from cells to bed rest to astronauts. J Bone Miner Res.2010,25(5):1049-1057.
76. Kalogeropoulos N, Panagiotakos DB, Pitsavos C, et al. Unsaturated fatty acids are inversely associated and n-6/n-3 ratios are positively related to inflammation and coagulation markers in plasma of apparently healthy adults. Clin Chim Acta. 2010,411(7-8):584-591.
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81. Abuissa H, O'Keefe JH Jr, Harris W, et al. Autonomic function, omega-3, and cardiovascular risk. Chest.2005,127:1088-91.
82. Stone NJ. Fish consumption, fish oil, lipids, and coronary heart disease. Circulation.1996,94:2337-2340.
83. Kromhout D, Bosschieter EB, de Lezenne Coulander C. The inverse relation between fish consumption and 20-year mortality from coronary heart disease. N Engl J Med.1985,312:1205-1209.
84. Kromhout D, Feskens EJ, Bowles CH. The protective effect of a small amount of fish on coronary heart disease mortality in an elderly population. Int J Epidemiol.1995,24:340-345.
85.陆海波,赵长宏,马玉彦,等.血清蛋白质指纹图谱诊断早期胃癌临床意义.现代生物医学进展.2006,6(8)：12-14.
86.卢纹岱,朱一力.SPSS forWindows统计分析.北京：电子工业出版社.2002,374.
87.赵一俏,刘北,付强,等.基于判别分析的多生化标志物策略在冠心病诊断中的意义.南方医科大学学报.2010,30(11)：2583-2585.
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61. Dunbar SA, Vander Zee CA, Oliver KG, et al. Quantitative, multiplexedDetection of bacterial pathogens:DNA and protein application of the Luminex LabMAP system. J Microbiol Methods.2003,53:245.
62. Kaul R,Chen P,Binder SR. Detection of immunoglobulin M antibodies specific for toxoplasma gondii with increased selectivity for recently acquired infections. J ClinMicrobiol.2004,42:5705.
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65.64.Wallace J, Zhou Y, Usmani GN, etal. BARCODE-ALL:Accelerated and cost effective genetic risk-stratification in acute leukemia using spectrally addressable liquid bead microarrays. Leukemia.2003,17:1404-1410.
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73. Hojo Y, Ikeda U, Katsuki T, et al. Interleukin6 expression in coronary circulation after coronary angioplasty as a risk factor for restenosis. Heart.2000,84:83-87.
74. Boutros C, Somasundar P, Razzak A, et al. Omega-3 fatty acids:investigations from cytokine regulation to pancreatic cancer gene suppression. Arch Surg.2010, 145(6):515-520.
75. Zwart SR, Pierson D, Mehta S, et al. Capacity of omega-3 fatty acids or eicosapentaenoic acid to counteract weightlessness-induced bone loss by inhibiting NF-kappaB activation:from cells to bed rest to astronauts. J Bone Miner Res.2010,25(5):1049-1057.
76. Kalogeropoulos N, Panagiotakos DB, Pitsavos C, et al. Unsaturated fatty acids are inversely associated and n-6/n-3 ratios are positively related to inflammation and coagulation markers in plasma of apparently healthy adults. Clin Chim Acta. 2010,411(7-8):584-591.
77. Hans-Anton Lehr, Christoph Hiibner, Barbara Finckh, et al. Dietary Fish Oil Reduces Leukocyte/Endothelium Interaction Following Systemic Administra-tion of Oxidatively Modified Low Density Lipoprotein. Circulation.1991, 84(4):1725-1731.
78. Tull SP, Yates CM, Maskrey BH, et al. Omega-3 Fatty acids and inflammation: novel interactions reveal a new step in neutrophil recruitment. PLOS Biol.2009 Aug;7(8):e1000177.
79. Jing X. Kang, Karsten H. Weylandt. Modulation of Inflammatory Cytokines by Omega-3 Fatty Acids. Lipids in Health and Disease, (?) Springer Science+Business Media B.Y.2008,133-143.
80. Simopoulos AP. The importance of the ratio of omega-6/omega-3 essential Fatty acids. Biomed Pharmacother.2002,56:365-79.
81. Abuissa H, O'Keefe JH Jr, Harris W, et al. Autonomic function, omega-3, and cardiovascular risk. Chest.2005,127:1088-91.
82. Stone NJ. Fish consumption, fish oil, lipids, and coronary heart disease. Circulation.1996,94:2337-2340.
83. Kromhout D, Bosschieter EB, de Lezenne Coulander C. The inverse relation between fish consumption and 20-year mortality from coronary heart disease. N Engl J Med.1985,312:1205-1209.
84. Kromhout D, Feskens EJ, Bowles CH. The protective effect of a small amount of fish on coronary heart disease mortality in an elderly population. Int J Epidemiol.1995,24:340-345.
85.陆海波,赵长宏,马玉彦,等.血清蛋白质指纹图谱诊断早期胃癌临床意义.现代生物医学进展.2006,6(8)：12-14.
86.卢纹岱,朱一力.SPSS forWindows统计分析.北京：电子工业出版社.2002,374.
87.赵一俏,刘北,付强,等.基于判别分析的多生化标志物策略在冠心病诊断中的意义.南方医科大学学报.2010,30(11)：2583-2585.
1. Libby P. Coronary artery injury and the biology of atherosclerosis:inflammation, thrombosis, and stabilization [J]. American Journal of Cardiology,2000,86 (8): 3-8.
2. Nissen SE, Tuzcu EM, Schoenhagen P, et al. Effect of intensive compared with moderate lipid-lowering therapy on progression of coronary atherosclerosis:a randomized controlled trial [J]. JAMA 2004,291(9):1071-1080.
3. Rieber J, Meissner O, Babaryka G, et al. Diagnostic accuracy of optical coherence tomography and intravascular ultrasound for the detection and characterization of atherosclerotic plaque composition in ex-vivo coronary specimens:a comparison with histology[J]. Coronary Artery Disease 2006; 17(5): 425-430.
4. Sato Y, Matsumoto N, Inoue F, et al. Computed tomography assessment of the regression of an atherosclerotic coronary artery plaque [J]. Circ J 2005,69(9): 1141-1143.
5. Schroeder S, Kopp AF, Baumbach A, et al. Noninvasive detection and evaluation of atherosclerotic coronary plaques with multislice computed tomography [J]. J Am Coll Cardiol 2001,37(5):1430-1435.
6. Wang YN, Che SM, Ma AQ, et al. Clinical significance of serum cytokines IL-lbeta, sIL-2R, IL-6, TNF-alpha, and IFN-v in acute coronary syndrome [J]. Chin Med Sci J.2004,19(2):120-124.
7. Hojo Y, Ikeda U, Katsuki T, et al.interleukin6 expression in coronary circulation after coronary angioplasty as a risk factor for restenosis [J]. Heart,2000, 84:83-87.
8. Marz W, Winkelmann BR.HMG-reductase inhibition:anti-inflammatory effects beyond lipid lowering [J]. Journal fur Kardiologie-Austrian Journal of Cardiology,2002; 9(7-8),284-294.
9. Aukrust P, Berge R K, Ueland T, et al. Interaction between chemokines and oxidative stress:possible pathogenic role in acute coronary syndromes [J]. J Am Coll Cardiol,2001,37 (2):485-491.
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